# Copyright (c) 2006, 2008-2014 LOGILAB S.A. (Paris, FRANCE) # Copyright (c) 2012 Ry4an Brase # Copyright (c) 2012 Google, Inc. # Copyright (c) 2012 Anthony VEREZ # Copyright (c) 2014-2020 Claudiu Popa # Copyright (c) 2014 Brett Cannon # Copyright (c) 2014 Arun Persaud # Copyright (c) 2015 Ionel Cristian Maries # Copyright (c) 2017, 2020 Anthony Sottile # Copyright (c) 2017 Mikhail Fesenko # Copyright (c) 2018 Scott Worley # Copyright (c) 2018 ssolanki # Copyright (c) 2019, 2021 Pierre Sassoulas # Copyright (c) 2019 Hugo van Kemenade # Copyright (c) 2019 Taewon D. Kim # Copyright (c) 2020-2021 hippo91 # Copyright (c) 2020 Frank Harrison # Copyright (c) 2020 Eli Fine # Copyright (c) 2020 Shiv Venkatasubrahmanyam # Copyright (c) 2021 Daniël van Noord <13665637+DanielNoord@users.noreply.github.com> # Copyright (c) 2021 Marc Mueller <30130371+cdce8p@users.noreply.github.com> # Copyright (c) 2021 Maksym Humetskyi # Copyright (c) 2021 bot # Copyright (c) 2021 Aditya Gupta # Licensed under the GPL: https://www.gnu.org/licenses/old-licenses/gpl-2.0.html # For details: https://github.com/PyCQA/pylint/blob/main/LICENSE # pylint: disable=redefined-builtin """a similarities / code duplication command line tool and pylint checker The algorithm is based on comparing the hash value of n successive lines of a file. First the files are read and any line that doesn't fullfill requirement are removed (comments, docstrings...) Those stripped lines are stored in the LineSet class which gives access to them. Then each index of the stripped lines collection is associated with the hash of n successive entries of the stripped lines starting at the current index (n is the minimum common lines option). The common hashes between both linesets are then looked for. If there are matches, then the match indices in both linesets are stored and associated with the corresponding couples (start line number/end line number) in both files. This association is then postprocessed to handle the case of successive matches. For example if the minimum common lines setting is set to four, then the hashes are computed with four lines. If one of match indices couple (12, 34) is the successor of another one (11, 33) then it means that there are in fact five lines wich are common. Once postprocessed the values of association table are the result looked for, i.e start and end lines numbers of common lines in both files. """ import copy import functools import itertools import operator import re import sys from collections import defaultdict from getopt import getopt from io import BufferedIOBase, BufferedReader, BytesIO from itertools import chain, groupby from typing import ( Any, Dict, FrozenSet, Generator, Iterable, List, NamedTuple, NewType, Optional, Set, TextIO, Tuple, Union, ) import astroid from astroid import nodes from pylint.checkers import BaseChecker, MapReduceMixin, table_lines_from_stats from pylint.interfaces import IRawChecker from pylint.reporters.ureports.nodes import Table from pylint.typing import CheckerStats from pylint.utils import decoding_stream DEFAULT_MIN_SIMILARITY_LINE = 4 REGEX_FOR_LINES_WITH_CONTENT = re.compile(r".*\w+") # Index defines a location in a LineSet stripped lines collection Index = NewType("Index", int) # LineNumber defines a location in a LinesSet real lines collection (the whole file lines) LineNumber = NewType("LineNumber", int) # LineSpecifs holds characteristics of a line in a file class LineSpecifs(NamedTuple): line_number: LineNumber text: str # Links LinesChunk object to the starting indices (in lineset's stripped lines) # of the different chunk of lines that are used to compute the hash HashToIndex_T = Dict["LinesChunk", List[Index]] # Links index in the lineset's stripped lines to the real lines in the file IndexToLines_T = Dict[Index, "SuccessiveLinesLimits"] # The types the streams read by pylint can take. Originating from astroid.nodes.Module.stream() and open() STREAM_TYPES = Union[TextIO, BufferedReader, BytesIO] class CplSuccessiveLinesLimits: """ This class holds a couple of SuccessiveLinesLimits objects, one for each file compared, and a counter on the number of common lines between both stripped lines collections extracted from both files """ __slots__ = ("first_file", "second_file", "effective_cmn_lines_nb") def __init__( self, first_file: "SuccessiveLinesLimits", second_file: "SuccessiveLinesLimits", effective_cmn_lines_nb: int, ) -> None: self.first_file = first_file self.second_file = second_file self.effective_cmn_lines_nb = effective_cmn_lines_nb # Links the indices ot the starting line in both lineset's stripped lines to # the start and end lines in both files CplIndexToCplLines_T = Dict["LineSetStartCouple", CplSuccessiveLinesLimits] class LinesChunk: """ The LinesChunk object computes and stores the hash of some consecutive stripped lines of a lineset. """ __slots__ = ("_fileid", "_index", "_hash") def __init__(self, fileid: str, num_line: int, *lines: Iterable[str]) -> None: self._fileid: str = fileid """The name of the file from which the LinesChunk object is generated """ self._index: Index = Index(num_line) """The index in the stripped lines that is the starting of consecutive lines""" self._hash: int = sum(hash(lin) for lin in lines) """The hash of some consecutive lines""" def __eq__(self, o: Any) -> bool: if not isinstance(o, LinesChunk): return NotImplemented return self._hash == o._hash def __hash__(self) -> int: return self._hash def __repr__(self) -> str: return ( f"" ) def __str__(self) -> str: return ( f"LinesChunk object for file {self._fileid}, starting at line {self._index} \n" f"Hash is {self._hash}" ) class SuccessiveLinesLimits: """ A class to handle the numbering of begin and end of successive lines. :note: Only the end line number can be updated. """ __slots__ = ("_start", "_end") def __init__(self, start: LineNumber, end: LineNumber) -> None: self._start: LineNumber = start self._end: LineNumber = end @property def start(self) -> LineNumber: return self._start @property def end(self) -> LineNumber: return self._end @end.setter def end(self, value: LineNumber) -> None: self._end = value def __repr__(self) -> str: return f">" class LineSetStartCouple(NamedTuple): """ Indices in both linesets that mark the beginning of successive lines """ fst_lineset_index: Index snd_lineset_index: Index def __repr__(self) -> str: return ( f">" ) def __eq__(self, other) -> bool: if not isinstance(other, LineSetStartCouple): return NotImplemented return ( self.fst_lineset_index == other.fst_lineset_index and self.snd_lineset_index == other.snd_lineset_index ) def __hash__(self) -> int: return hash(self.fst_lineset_index) + hash(self.snd_lineset_index) def increment(self, value: Index) -> "LineSetStartCouple": return LineSetStartCouple( Index(self.fst_lineset_index + value), Index(self.snd_lineset_index + value), ) LinesChunkLimits_T = Tuple["LineSet", LineNumber, LineNumber] def hash_lineset( lineset: "LineSet", min_common_lines: int = DEFAULT_MIN_SIMILARITY_LINE ) -> Tuple[HashToIndex_T, IndexToLines_T]: """ Return two dicts. The first associates the hash of successive stripped lines of a lineset to the indices of the starting lines. The second dict, associates the index of the starting line in the lineset's stripped lines to the couple [start, end] lines number in the corresponding file. :param lineset: lineset object (i.e the lines in a file) :param min_common_lines: number of successive lines that are used to compute the hash :return: a dict linking hashes to corresponding start index and a dict that links this index to the start and end lines in the file """ hash2index = defaultdict(list) index2lines = {} # Comments, docstring and other specific patterns maybe excluded -> call to stripped_lines # to get only what is desired lines = tuple(x.text for x in lineset.stripped_lines) # Need different iterators on same lines but each one is shifted 1 from the precedent shifted_lines = [iter(lines[i:]) for i in range(min_common_lines)] for index_i, *succ_lines in enumerate(zip(*shifted_lines)): start_linenumber = lineset.stripped_lines[index_i].line_number try: end_linenumber = lineset.stripped_lines[ index_i + min_common_lines ].line_number except IndexError: end_linenumber = lineset.stripped_lines[-1].line_number + 1 index = Index(index_i) index2lines[index] = SuccessiveLinesLimits( start=LineNumber(start_linenumber), end=LineNumber(end_linenumber) ) l_c = LinesChunk(lineset.name, index, *succ_lines) hash2index[l_c].append(index) return hash2index, index2lines def remove_successives(all_couples: CplIndexToCplLines_T) -> None: """ Removes all successive entries in the dictionary in argument :param all_couples: collection that has to be cleaned up from successives entries. The keys are couples of indices that mark the beginning of common entries in both linesets. The values have two parts. The first one is the couple of starting and ending line numbers of common successives lines in the first file. The second part is the same for the second file. For example consider the following dict: >>> all_couples {(11, 34): ([5, 9], [27, 31]), (23, 79): ([15, 19], [45, 49]), (12, 35): ([6, 10], [28, 32])} There are two successives keys (11, 34) and (12, 35). It means there are two consecutive similar chunks of lines in both files. Thus remove last entry and update the last line numbers in the first entry >>> remove_successives(all_couples) >>> all_couples {(11, 34): ([5, 10], [27, 32]), (23, 79): ([15, 19], [45, 49])} """ couple: LineSetStartCouple for couple in tuple(all_couples.keys()): to_remove = [] test = couple.increment(Index(1)) while test in all_couples: all_couples[couple].first_file.end = all_couples[test].first_file.end all_couples[couple].second_file.end = all_couples[test].second_file.end all_couples[couple].effective_cmn_lines_nb += 1 to_remove.append(test) test = test.increment(Index(1)) for target in to_remove: try: all_couples.pop(target) except KeyError: pass def filter_noncode_lines( ls_1: "LineSet", stindex_1: Index, ls_2: "LineSet", stindex_2: Index, common_lines_nb: int, ) -> int: """ Return the effective number of common lines between lineset1 and lineset2 filtered from non code lines, that is to say the number of common successive stripped lines except those that do not contain code (for example a ligne with only an ending parathensis) :param ls_1: first lineset :param stindex_1: first lineset starting index :param ls_2: second lineset :param stindex_2: second lineset starting index :param common_lines_nb: number of common successive stripped lines before being filtered from non code lines :return: the number of common successives stripped lines that contain code """ stripped_l1 = [ lspecif.text for lspecif in ls_1.stripped_lines[stindex_1 : stindex_1 + common_lines_nb] if REGEX_FOR_LINES_WITH_CONTENT.match(lspecif.text) ] stripped_l2 = [ lspecif.text for lspecif in ls_2.stripped_lines[stindex_2 : stindex_2 + common_lines_nb] if REGEX_FOR_LINES_WITH_CONTENT.match(lspecif.text) ] return sum(sline_1 == sline_2 for sline_1, sline_2 in zip(stripped_l1, stripped_l2)) class Commonality(NamedTuple): cmn_lines_nb: int fst_lset: "LineSet" fst_file_start: LineNumber fst_file_end: LineNumber snd_lset: "LineSet" snd_file_start: LineNumber snd_file_end: LineNumber class Similar: """finds copy-pasted lines of code in a project""" def __init__( self, min_lines: int = DEFAULT_MIN_SIMILARITY_LINE, ignore_comments: bool = False, ignore_docstrings: bool = False, ignore_imports: bool = False, ignore_signatures: bool = False, ) -> None: self.min_lines = min_lines self.ignore_comments = ignore_comments self.ignore_docstrings = ignore_docstrings self.ignore_imports = ignore_imports self.ignore_signatures = ignore_signatures self.linesets: List["LineSet"] = [] def append_stream( self, streamid: str, stream: STREAM_TYPES, encoding: Optional[str] = None ) -> None: """append a file to search for similarities""" if isinstance(stream, BufferedIOBase): if encoding is None: raise ValueError readlines = decoding_stream(stream, encoding).readlines else: readlines = stream.readlines # type: ignore # hint parameter is incorrectly typed as non-optional try: self.linesets.append( LineSet( streamid, readlines(), self.ignore_comments, self.ignore_docstrings, self.ignore_imports, self.ignore_signatures, ) ) except UnicodeDecodeError: pass def run(self) -> None: """start looking for similarities and display results on stdout""" if self.min_lines == 0: return self._display_sims(self._compute_sims()) def _compute_sims(self) -> List[Tuple[int, Set[LinesChunkLimits_T]]]: """compute similarities in appended files""" no_duplicates: Dict[int, List[Set[LinesChunkLimits_T]]] = defaultdict(list) for commonality in self._iter_sims(): num = commonality.cmn_lines_nb lineset1 = commonality.fst_lset start_line_1 = commonality.fst_file_start end_line_1 = commonality.fst_file_end lineset2 = commonality.snd_lset start_line_2 = commonality.snd_file_start end_line_2 = commonality.snd_file_end duplicate = no_duplicates[num] couples: Set[LinesChunkLimits_T] for couples in duplicate: if (lineset1, start_line_1, end_line_1) in couples or ( lineset2, start_line_2, end_line_2, ) in couples: break else: duplicate.append( { (lineset1, start_line_1, end_line_1), (lineset2, start_line_2, end_line_2), } ) sims: List[Tuple[int, Set[LinesChunkLimits_T]]] = [] ensembles: List[Set[LinesChunkLimits_T]] for num, ensembles in no_duplicates.items(): cpls: Set[LinesChunkLimits_T] for cpls in ensembles: sims.append((num, cpls)) sims.sort() sims.reverse() return sims def _display_sims( self, similarities: List[Tuple[int, Set[LinesChunkLimits_T]]] ) -> None: """Display computed similarities on stdout""" report = self._get_similarity_report(similarities) print(report) def _get_similarity_report( self, similarities: List[Tuple[int, Set[LinesChunkLimits_T]]] ) -> str: """Create a report from similarities""" report: str = "" duplicated_line_number: int = 0 for number, couples in similarities: report += f"\n{number} similar lines in {len(couples)} files\n" couples_l = sorted(couples) line_set = start_line = end_line = None for line_set, start_line, end_line in couples_l: report += f"=={line_set.name}:[{start_line}:{end_line}]\n" if line_set: for line in line_set._real_lines[start_line:end_line]: report += f" {line.rstrip()}\n" if line.rstrip() else "\n" duplicated_line_number += number * (len(couples_l) - 1) total_line_number: int = sum(len(lineset) for lineset in self.linesets) report += f"TOTAL lines={total_line_number} duplicates={duplicated_line_number} percent={duplicated_line_number * 100.0 / total_line_number:.2f}\n" return report def _find_common( self, lineset1: "LineSet", lineset2: "LineSet" ) -> Generator[Commonality, None, None]: """ Find similarities in the two given linesets. This the core of the algorithm. The idea is to compute the hashes of a minimal number of successive lines of each lineset and then compare the hashes. Every match of such comparison is stored in a dict that links the couple of starting indices in both linesets to the couple of corresponding starting and ending lines in both files. Last regroups all successive couples in a bigger one. It allows to take into account common chunk of lines that have more than the minimal number of successive lines required. """ hash_to_index_1: HashToIndex_T hash_to_index_2: HashToIndex_T index_to_lines_1: IndexToLines_T index_to_lines_2: IndexToLines_T hash_to_index_1, index_to_lines_1 = hash_lineset(lineset1, self.min_lines) hash_to_index_2, index_to_lines_2 = hash_lineset(lineset2, self.min_lines) hash_1: FrozenSet[LinesChunk] = frozenset(hash_to_index_1.keys()) hash_2: FrozenSet[LinesChunk] = frozenset(hash_to_index_2.keys()) common_hashes: Iterable[LinesChunk] = sorted( hash_1 & hash_2, key=lambda m: hash_to_index_1[m][0] ) # all_couples is a dict that links the couple of indices in both linesets that mark the beginning of # successive common lines, to the corresponding starting and ending number lines in both files all_couples: CplIndexToCplLines_T = {} for c_hash in sorted(common_hashes, key=operator.attrgetter("_index")): for indices_in_linesets in itertools.product( hash_to_index_1[c_hash], hash_to_index_2[c_hash] ): index_1 = indices_in_linesets[0] index_2 = indices_in_linesets[1] all_couples[ LineSetStartCouple(index_1, index_2) ] = CplSuccessiveLinesLimits( copy.copy(index_to_lines_1[index_1]), copy.copy(index_to_lines_2[index_2]), effective_cmn_lines_nb=self.min_lines, ) remove_successives(all_couples) for cml_stripped_l, cmn_l in all_couples.items(): start_index_1 = cml_stripped_l.fst_lineset_index start_index_2 = cml_stripped_l.snd_lineset_index nb_common_lines = cmn_l.effective_cmn_lines_nb com = Commonality( cmn_lines_nb=nb_common_lines, fst_lset=lineset1, fst_file_start=cmn_l.first_file.start, fst_file_end=cmn_l.first_file.end, snd_lset=lineset2, snd_file_start=cmn_l.second_file.start, snd_file_end=cmn_l.second_file.end, ) eff_cmn_nb = filter_noncode_lines( lineset1, start_index_1, lineset2, start_index_2, nb_common_lines ) if eff_cmn_nb > self.min_lines: yield com def _iter_sims(self) -> Generator[Commonality, None, None]: """iterate on similarities among all files, by making a cartesian product """ for idx, lineset in enumerate(self.linesets[:-1]): for lineset2 in self.linesets[idx + 1 :]: yield from self._find_common(lineset, lineset2) def get_map_data(self): """Returns the data we can use for a map/reduce process In this case we are returning this instance's Linesets, that is all file information that will later be used for vectorisation. """ return self.linesets def combine_mapreduce_data(self, linesets_collection): """Reduces and recombines data into a format that we can report on The partner function of get_map_data()""" self.linesets = [line for lineset in linesets_collection for line in lineset] def stripped_lines( lines: Iterable[str], ignore_comments: bool, ignore_docstrings: bool, ignore_imports: bool, ignore_signatures: bool, ) -> List[LineSpecifs]: """ Return tuples of line/line number/line type with leading/trailing whitespace and any ignored code features removed :param lines: a collection of lines :param ignore_comments: if true, any comment in the lines collection is removed from the result :param ignore_docstrings: if true, any line that is a docstring is removed from the result :param ignore_imports: if true, any line that is an import is removed from the result :param ignore_signatures: if true, any line that is part of a function signature is removed from the result :return: the collection of line/line number/line type tuples """ if ignore_imports or ignore_signatures: tree = astroid.parse("".join(lines)) if ignore_imports: node_is_import_by_lineno = ( (node.lineno, isinstance(node, (nodes.Import, nodes.ImportFrom))) for node in tree.body ) line_begins_import = { lineno: all(is_import for _, is_import in node_is_import_group) for lineno, node_is_import_group in groupby( node_is_import_by_lineno, key=lambda x: x[0] ) } current_line_is_import = False if ignore_signatures: def _get_functions( functions: List[nodes.NodeNG], tree: nodes.NodeNG ) -> List[nodes.NodeNG]: """Recursively get all functions including nested in the classes from the tree.""" for node in tree.body: if isinstance(node, (nodes.FunctionDef, nodes.AsyncFunctionDef)): functions.append(node) if isinstance( node, (nodes.ClassDef, nodes.FunctionDef, nodes.AsyncFunctionDef), ): _get_functions(functions, node) return functions functions = _get_functions([], tree) signature_lines = set( chain( *( range( func.lineno, func.body[0].lineno if func.body else func.tolineno + 1, ) for func in functions ) ) ) strippedlines = [] docstring = None for lineno, line in enumerate(lines, start=1): line = line.strip() if ignore_docstrings: if not docstring: if line.startswith('"""') or line.startswith("'''"): docstring = line[:3] line = line[3:] elif line.startswith('r"""') or line.startswith("r'''"): docstring = line[1:4] line = line[4:] if docstring: if line.endswith(docstring): docstring = None line = "" if ignore_imports: current_line_is_import = line_begins_import.get( lineno, current_line_is_import ) if current_line_is_import: line = "" if ignore_comments: line = line.split("#", 1)[0].strip() if ignore_signatures and lineno in signature_lines: line = "" if line: strippedlines.append( LineSpecifs(text=line, line_number=LineNumber(lineno - 1)) ) return strippedlines @functools.total_ordering class LineSet: """ Holds and indexes all the lines of a single source file. Allows for correspondance between real lines of the source file and stripped ones, which are the real ones from which undesired patterns have been removed. """ def __init__( self, name: str, lines: List[str], ignore_comments: bool = False, ignore_docstrings: bool = False, ignore_imports: bool = False, ignore_signatures: bool = False, ) -> None: self.name = name self._real_lines = lines self._stripped_lines = stripped_lines( lines, ignore_comments, ignore_docstrings, ignore_imports, ignore_signatures ) def __str__(self): return f"" def __len__(self): return len(self._real_lines) def __getitem__(self, index): return self._stripped_lines[index] def __lt__(self, other): return self.name < other.name def __hash__(self): return id(self) def __eq__(self, other): if not isinstance(other, LineSet): return False return self.__dict__ == other.__dict__ @property def stripped_lines(self): return self._stripped_lines @property def real_lines(self): return self._real_lines MSGS = { "R0801": ( "Similar lines in %s files\n%s", "duplicate-code", "Indicates that a set of similar lines has been detected " "among multiple file. This usually means that the code should " "be refactored to avoid this duplication.", ) } def report_similarities( sect, stats: CheckerStats, old_stats: CheckerStats, ): """make a layout with some stats about duplication""" lines = ["", "now", "previous", "difference"] lines += table_lines_from_stats( stats, old_stats, ("nb_duplicated_lines", "percent_duplicated_lines") ) sect.append(Table(children=lines, cols=4, rheaders=1, cheaders=1)) # wrapper to get a pylint checker from the similar class class SimilarChecker(BaseChecker, Similar, MapReduceMixin): """checks for similarities and duplicated code. This computation may be memory / CPU intensive, so you should disable it if you experiment some problems. """ __implements__ = (IRawChecker,) # configuration section name name = "similarities" # messages msgs = MSGS # configuration options # for available dict keys/values see the optik parser 'add_option' method options = ( ( "min-similarity-lines", { "default": DEFAULT_MIN_SIMILARITY_LINE, "type": "int", "metavar": "", "help": "Minimum lines number of a similarity.", }, ), ( "ignore-comments", { "default": True, "type": "yn", "metavar": "", "help": "Comments are removed from the similarity computation", }, ), ( "ignore-docstrings", { "default": True, "type": "yn", "metavar": "", "help": "Docstrings are removed from the similarity computation", }, ), ( "ignore-imports", { "default": False, "type": "yn", "metavar": "", "help": "Imports are removed from the similarity computation", }, ), ( "ignore-signatures", { "default": False, "type": "yn", "metavar": "", "help": "Signatures are removed from the similarity computation", }, ), ) # reports reports = (("RP0801", "Duplication", report_similarities),) def __init__(self, linter=None) -> None: BaseChecker.__init__(self, linter) Similar.__init__( self, min_lines=self.config.min_similarity_lines, ignore_comments=self.config.ignore_comments, ignore_docstrings=self.config.ignore_docstrings, ignore_imports=self.config.ignore_imports, ignore_signatures=self.config.ignore_signatures, ) self.stats: CheckerStats = {} def set_option(self, optname, value, action=None, optdict=None): """method called to set an option (registered in the options list) Overridden to report options setting to Similar """ BaseChecker.set_option(self, optname, value, action, optdict) if optname == "min-similarity-lines": self.min_lines = self.config.min_similarity_lines elif optname == "ignore-comments": self.ignore_comments = self.config.ignore_comments elif optname == "ignore-docstrings": self.ignore_docstrings = self.config.ignore_docstrings elif optname == "ignore-imports": self.ignore_imports = self.config.ignore_imports elif optname == "ignore-signatures": self.ignore_signatures = self.config.ignore_signatures def open(self): """init the checkers: reset linesets and statistics information""" self.linesets = [] self.stats = self.linter.add_stats( nb_duplicated_lines=0, percent_duplicated_lines=0 ) def process_module(self, node: nodes.Module) -> None: """process a module the module's content is accessible via the stream object stream must implement the readlines method """ with node.stream() as stream: self.append_stream(self.linter.current_name, stream, node.file_encoding) def close(self): """compute and display similarities on closing (i.e. end of parsing)""" total = sum(len(lineset) for lineset in self.linesets) duplicated = 0 stats = self.stats for num, couples in self._compute_sims(): msg = [] lineset = start_line = end_line = None for lineset, start_line, end_line in couples: msg.append(f"=={lineset.name}:[{start_line}:{end_line}]") msg.sort() if lineset: for line in lineset.real_lines[start_line:end_line]: msg.append(line.rstrip()) self.add_message("R0801", args=(len(couples), "\n".join(msg))) duplicated += num * (len(couples) - 1) stats["nb_duplicated_lines"] = duplicated stats["percent_duplicated_lines"] = total and duplicated * 100.0 / total def get_map_data(self): """Passthru override""" return Similar.get_map_data(self) def reduce_map_data(self, linter, data): """Reduces and recombines data into a format that we can report on The partner function of get_map_data()""" recombined = SimilarChecker(linter) recombined.min_lines = self.min_lines recombined.ignore_comments = self.ignore_comments recombined.ignore_docstrings = self.ignore_docstrings recombined.ignore_imports = self.ignore_imports recombined.ignore_signatures = self.ignore_signatures recombined.open() Similar.combine_mapreduce_data(recombined, linesets_collection=data) recombined.close() def register(linter): """required method to auto register this checker""" linter.register_checker(SimilarChecker(linter)) def usage(status=0): """display command line usage information""" print("finds copy pasted blocks in a set of files") print() print( "Usage: symilar [-d|--duplicates min_duplicated_lines] \ [-i|--ignore-comments] [--ignore-docstrings] [--ignore-imports] [--ignore-signatures] file1..." ) sys.exit(status) def Run(argv=None): """standalone command line access point""" if argv is None: argv = sys.argv[1:] s_opts = "hdi" l_opts = ( "help", "duplicates=", "ignore-comments", "ignore-imports", "ignore-docstrings", "ignore-signatures", ) min_lines = DEFAULT_MIN_SIMILARITY_LINE ignore_comments = False ignore_docstrings = False ignore_imports = False ignore_signatures = False opts, args = getopt(argv, s_opts, l_opts) for opt, val in opts: if opt in ("-d", "--duplicates"): min_lines = int(val) elif opt in ("-h", "--help"): usage() elif opt in ("-i", "--ignore-comments"): ignore_comments = True elif opt in ("--ignore-docstrings",): ignore_docstrings = True elif opt in ("--ignore-imports",): ignore_imports = True elif opt in ("--ignore-signatures",): ignore_signatures = True if not args: usage(1) sim = Similar( min_lines, ignore_comments, ignore_docstrings, ignore_imports, ignore_signatures ) for filename in args: with open(filename, encoding="utf-8") as stream: sim.append_stream(filename, stream) sim.run() sys.exit(0) if __name__ == "__main__": Run()