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How to use get_path_ method in autotest

Best Python code snippet using autotest_python

urlmatcher.py

Source:urlmatcher.py

...69        for _, u in enumerate(matching_urls):70            parsed_u = urlparse(u)71            if parsed_u.scheme != parsed_url_to_match.scheme:72                continue73            url_path = get_path_(parsed_url_to_match)74            u_path = get_path_(parsed_u)75            if not should_use_matched_(url, u):76                continue77            score = compute_levenshtein_(url_path, u_path)78            # Levenshtein edit distance is the number of edits between the two79            # string. Thus, the smaller the better.80            if score < best_match_score:81                best_match = u82                best_match_score = score83        # Do a final sanity check that the match make sense.84        if best_match == NO_MATCH:85            return best_match, best_match_score86        return best_match, best_match_score87    def get_all_sift4_scores_(self, url, matching_urls):88        '''Get all matching scores for the given URL.'''89        parsed_url_to_match = urlparse(url)90        result = {}91        for _, u in enumerate(matching_urls):92            parsed_u = urlparse(u)93            if parsed_u.scheme != parsed_url_to_match.scheme:94                result[u] = DEFAULT_EDIT_DISTANCE95                continue96            if not should_use_matched_(url, u):97                result[u] = DEFAULT_EDIT_DISTANCE98                continue99            url_path = get_path_(parsed_url_to_match)100            u_path = get_path_(parsed_u)101            # Search for 500 tokens with a maximum of 200 distance.102            score = compute_sift4_(url_path, u_path, 5)103            result[u] = score104        return result105    def sift4_(self, url, matching_urls):106        '''This is based on Sift4 matching algorithm.'''107        best_match = NO_MATCH108        best_match_score = DEFAULT_EDIT_DISTANCE109        all_sift4_scores = self.get_all_sift4_scores_(url, matching_urls)110        for matching_url, score in all_sift4_scores.items():111            # Levenshtein edit distance is the number of edits between the two112            # string. Thus, the smaller the better.113            if not should_use_matched_(url, matching_url):114                continue115            parsed_matching_url = urlparse(matching_url)116            matching_url_path = get_path_(parsed_matching_url)117            if score < best_match_score:118                best_match = matching_url119                best_match_score = score120        return best_match, best_match_score121    def mahimahi_(self, url, matching_urls):122        '''This is based on Mahimahi's matching function.'''123        parsed_url_to_match = urlparse(url)124        url_to_match_without_query = '{scheme}://{netloc}{path}'.format(125                scheme=parsed_url_to_match.scheme,126                netloc=parsed_url_to_match.netloc,127                path=parsed_url_to_match.path)128        best_match = NO_MATCH129        best_match_score = -1130        for _, u in enumerate(matching_urls):131            parsed_u = urlparse(u)132            if parsed_u.scheme != parsed_url_to_match.scheme:133                continue134            if remove_query_(parsed_url_to_match) != remove_query_(parsed_u):135                continue136            score = min(len(url), len(u))137            for i in range(score):138                if url[i] != u[i]:139                    if i > best_match_score:140                        best_match = u141                        best_match_score = i142                        break143            if score > best_match_score:144                best_match = u145                best_match_score = score146        return best_match, best_match_score147def compute_levenshtein_(seq1, seq2):148    size_x = len(seq1) + 1149    size_y = len(seq2) + 1150    matrix = np.zeros ((size_x, size_y))151    for x in range(size_x):152        matrix [x, 0] = x153    for y in range(size_y):154        matrix [0, y] = y155    for x in range(1, size_x):156        for y in range(1, size_y):157            if seq1[x-1] == seq2[y-1]:158                matrix [x,y] = min(159                    matrix[x-1, y] + 1,160                    matrix[x-1, y-1],161                    matrix[x, y-1] + 1162                )163            else:164                matrix [x,y] = min(165                    matrix[x-1,y] + 1,166                    matrix[x-1,y-1] + 1,167                    matrix[x,y-1] + 1168                )169    return (matrix[size_x - 1, size_y - 1])170def compute_sift4_(seq1, seq2, max_offset, max_distance=0):171    '''172    Computes the distance between seq1 and seq2.173    Params:174        seq1: first string to compare175        seq2: second string to compare176        max_offset: number of positions of search for matching tokens177        max_distance: maximum distance between the two strings before giving up178    '''179    # Handle cases where seq1 or seq2 are invalid (None or empty)180    if seq1 is None or len(seq1) == 0:181        if seq2 is None:182            return 0183        return len(seq2)184    if seq2 is None or len(seq2) == 0:185        return len(seq1)186    # General case: both strings are valid and comparable.187    # var l1=s1.length;188    # var l2=s2.length;189    len_seq1 = len(seq1)190    len_seq2 = len(seq2)191    c_1 = 0192    c_2 = 0193    lcss = 0  # largest common subsequence194    local_cs = 0  # local common substring195    trans = 0  # number of transpositions ('ab' vs 'ba')196    offset_arr = []  # offset object: { "c1": c1, "c2": c2, "trans": trans }197    while (c_1 < len_seq1) and (c_2 < len_seq2):198        if seq1[c_1] == seq2[c_2]:199            local_cs += 1200            is_trans = False201            i = 0202            while i < len(offset_arr):  # see if current match is a transposition203                offset = offset_arr[i]204                if c_1 <= offset['c1'] or c_2 <= offset['c2']:205                    # when two matches cross, the one considered a transposition is the one with the largest difference in offsets206                    is_trans = abs(c_2 - c_1) >= abs(offset['c2'] - offset['c1'])207                    if is_trans:208                        trans += 1209                    else:210                        if not offset['trans']:211                            offset['trans'] = True212                            trans += 1;213                    break214                else:215                    if c_1 > offset['c2'] and c_2 > offset['c1']:216                        offset_arr.pop(0)217                    else:218                        i += 1219            offset_arr.append({220                'c1': c_1,221                'c2': c_2,222                'trans': is_trans,223            })224        else:225            lcss += local_cs226            local_cs = 0227            if c_1 != c_2:228                c_1 = min(c_1,c_2)229                c_2 = min(c_1,c_2)  # using min allows the computation of transposition230            # if matching characters are found, remove 1 from both cursors (they get incremented at the end of the loop)231            # so that we can have only one code block handling matches232            while i < max_offset and (c_1 + i < len_seq1 or c_2 + i < len_seq2):233                if (c_1 + i < len_seq1) and (seq1[c_1 + i] == seq2[c_2]):234                    c_1 += i - 1235                    c_2 -= 1236                    break237                if (c_2 + i < len_seq2) and (seq1[c_1] == seq2[c_2 + i]):238                    c_1 -= 1239                    c_2 += i - 1240                    break241                i += 1242        c_1 += 1243        c_2 += 1244        if max_distance > 0:245            temporary_dist = max(c_1,c_2) - (lcss - trans)246            if temporary_dist >= max_distance:247                return round(temporary_dist)248        # this covers the case where the last match is on the last token in list, so that it can compute transpositions correctly249        if (c_1 >= len_seq1) or (c_2 >= len_seq2):250            lcss += local_cs251            local_cs = 0252            c_1 = min(c_1, c_2)253            c_2 = min(c_1,c_2)254    lcss += local_cs255    return round(max(len_seq1, len_seq2) - (lcss - trans)) # remove half the number of transpositions from the lcss256def should_use_matched_(url1, url2):257    '''Checks if the given URLs have the same netloc.'''258    parsed_url1 = urlparse(url1)259    parsed_url2 = urlparse(url2)260    url1_path = parsed_url1.path261    url1_tokens = url1_path.split('/')262    num_url1_tokens = len(url1_tokens)263    url2_path = parsed_url2.path264    url2_tokens = url2_path.split('/')265    num_url2_tokens = len(url2_tokens)266    # return parsed_url1.netloc == parsed_url2.netloc and \267    #         num_url1_tokens == num_url2_tokens and \268    #         abs(len(url1_tokens[-1]) - len(url2_tokens[-1])) <= 2269    return parsed_url1.netloc == parsed_url2.netloc and \270            num_url1_tokens == num_url2_tokens271def remove_query_(parsed_url):272    '''Removes the query from the URL.'''273    retval = '{scheme}://{netloc}{path}'.format(274            scheme=parsed_url.scheme,275            netloc=parsed_url.netloc,276            path=get_path_(parsed_url))277    return retval278def get_path_(parsed_url):279    '''Returns the path and parameter.'''280    retval = parsed_url.path281    if parsed_url.params != '':282        retval += ';' + parsed_url.params283    return retval284def last_path_token_match_(parsed_u, parsed_url_to_match):285    '''Checks whether the last token of the path (ignoring the params) match.'''286    parsed_url_to_match_last_path_token = parsed_url_to_match.path.split('/')[-1]287    parsed_u_last_path_token = parsed_u.path.split('/')[-1]...

file_manager.py

Source:file_manager.py

...27    28    29    def delete(self):30        for file_name in self.get_delete_candidates_():31            file_path = self.get_path_(file_name, self.dst_dir, self.dst_extension)32            try:33                file_path.unlink()34                self.dst_file_names.remove(file_name)35            except FileNotFoundError as error:36                print(f'Error: file "{file_path}" does not found')37            finally:38                continue39    40    def get_path_(self, file_name, root_dir, extension):41        return root_dir / (file_name + '.' + extension)42    43    # def get_delete_candidates_(self):44    #     '''45    #     Doesnot work when src and dst is the same.46    #     '''47    #     for file_name in self.dst_file_names:48    #         if file_name not in self.src_file_names:49    #             yield file_name50    51    def get_delete_candidates_(self):52        candidates = []53        for file_name in self.dst_file_names:54            if file_name not in self.src_file_names:55                candidates.append(file_name)56        return candidates57                58                59    def random_copy(self, annot_dir, image_dir, ratio=0.2):60        file_names = list(self.src_file_names)61        62        count = math.ceil(ratio * len(file_names))63        indexes = np.random.permutation(len(file_names))64        indexes = indexes[:count]65        for i in indexes:66            annot_file_path = self.get_path_(file_names[i], self.src_dir, self.src_extension)67            img_file_path = self.get_path_(file_names[i], self.dst_dir, self.dst_extension)68            69            copyfile(annot_file_path, annot_dir / annot_file_path.name)...

test_com.py

Source:test_com.py

...3    file_name = 'test.csv'4    sub_dir = 'test_data'5    path = os.path.join(pathlib.Path(__file__).parent,sub_dir,file_name)6    return path7def get_path_(base_path:str,file_name:str='',sub_dir:str=''):8    import pathlib,os9    file_name = 'test.csv'10    sub_dir = 'test_data'11    if sub_dir != '':12        path = os.path.join(pathlib.Path(base_path).parent,sub_dir,file_name)13    else:14        path = os.path.join(pathlib.Path(base_path).parent,file_name)...

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