{
    "file_diffs": [
        {
            "old_file_content": "import pickle\nimport re\nimport sys\nimport warnings\nfrom typing import Iterable, Optional, Tuple, List, Generator, Callable, Any\n\nfrom ast import literal_eval\nfrom collections import OrderedDict\nfrom functools import lru_cache\nfrom itertools import chain, repeat\nfrom math import isnan\n\nfrom os import path, remove\nfrom fnmatch import fnmatch\nfrom glob import glob\n\nimport numpy as np\nimport pandas\n\nfrom Orange.data import Table, Domain, Variable, DiscreteVariable, \\\n    StringVariable, ContinuousVariable, TimeVariable\nfrom Orange.data.io_util import Compression, open_compressed, \\\n    isnastr, guess_data_type, sanitize_variable\nfrom Orange.data.util import get_unique_names_duplicates\nfrom Orange.data.variable import VariableMeta\nfrom Orange.misc.collections import natural_sorted\nfrom Orange.util import Registry, flatten, namegen\n\n__all__ = [\"FileFormatBase\", \"Flags\", \"DataTableMixin\", \"PICKLE_PROTOCOL\"]\n\n\nPICKLE_PROTOCOL = 4\n\n\nclass MissingReaderException(IOError):\n    # subclasses IOError for backward compatibility\n    pass\n\n\nclass Flags:\n    \"\"\"Parser for column flags (i.e. third header row)\"\"\"\n    DELIMITER = ' '\n    _RE_SPLIT = re.compile(r'(?<!\\\\)' + DELIMITER).split\n    _RE_ATTR_UNQUOTED_STR = re.compile(r'^[a-zA-Z_]').match\n    ALL = OrderedDict((\n        ('class', 'c'),\n        ('ignore', 'i'),\n        ('meta', 'm'),\n        ('weight', 'w'),\n        ('.+?=.*?', ''),  # general key=value attributes\n    ))\n    RE_ALL = re.compile(r'^({})$'.format('|'.join(\n        filter(None, flatten(ALL.items())))))\n\n    def __init__(self, flags):\n        for v in filter(None, self.ALL.values()):\n            setattr(self, v, False)\n        self.attributes = {}\n        for flag in flags or []:\n            flag = flag.strip()\n            if self.RE_ALL.match(flag):\n                if '=' in flag:\n                    k, v = flag.split('=', 1)\n                    if not Flags._RE_ATTR_UNQUOTED_STR(v):\n                        try:\n                            v = literal_eval(v)\n                        except SyntaxError:\n                            # If parsing failed, treat value as string\n                            pass\n                    # map True and False to booleans\n                    if v in (\"True\", \"False\"):\n                        v = {\"True\": True, \"False\": False}[v]\n                    self.attributes[k] = v\n                else:\n                    setattr(self, flag, True)\n                    setattr(self, self.ALL.get(flag, ''), True)\n            elif flag:\n                warnings.warn('Invalid attribute flag \\'{}\\''.format(flag))\n\n    @staticmethod\n    def join(iterable, *args):\n        return Flags.DELIMITER.join(i.strip().replace(Flags.DELIMITER,\n                                                      '\\\\' + Flags.DELIMITER)\n                                    for i in chain(iterable, args)).lstrip()\n\n    @staticmethod\n    def split(s):\n        return [i.replace('\\\\' + Flags.DELIMITER, Flags.DELIMITER)\n                for i in Flags._RE_SPLIT(s)]\n\n\n# Matches discrete specification where all the values are listed, space-separated\n_RE_DISCRETE_LIST = re.compile(r'^\\s*[^\\s]+(\\s[^\\s]+)+\\s*$')\n_RE_TYPES = re.compile(r'^\\s*({}|{}|)\\s*$'.format(\n    _RE_DISCRETE_LIST.pattern,\n    '|'.join(flatten(getattr(vartype, 'TYPE_HEADERS')\n                     for vartype in Variable.registry.values()))\n))\n_RE_FLAGS = re.compile(r'^\\s*( |{}|)*\\s*$'.format(\n    '|'.join(flatten(filter(None, i) for i in Flags.ALL.items()))\n))\n\n\nclass _ColumnProperties:\n    def __init__(self, valuemap=None, values=None, orig_values=None,\n                 coltype=None, coltype_kwargs=None):\n        self.valuemap = valuemap\n        self.values = values\n        self.orig_values = orig_values\n        self.coltype = coltype\n        if coltype_kwargs is None:\n            self.coltype_kwargs = {}\n        else:\n            self.coltype_kwargs = dict(coltype_kwargs)\n\n\nclass _TableHeader:\n    \"\"\"\n    Contains functions for table header construction (and its data).\n    \"\"\"\n    HEADER1_FLAG_SEP = '#'\n\n    def __init__(self, headers: List):\n        \"\"\"\n        Parameters\n        ----------\n        headers: List\n            Header rows, to be used for constructing domain.\n        \"\"\"\n        names, types, flags = self.create_header_data(headers)\n        self.names = get_unique_names_duplicates(names)\n        self.types = types\n        self.flags = flags\n\n    @classmethod\n    def create_header_data(cls, headers: List) -> Tuple[List, List, List]:\n        \"\"\"\n        Consider various header types (single-row, two-row, three-row, none).\n\n        Parameters\n        ----------\n        headers: List\n            Header rows, to be used for constructing domain.\n\n        Returns\n        -------\n        names: List\n            List of variable names.\n        types: List\n            List of variable types.\n        flags: List\n            List of meta info (i.e. class, meta, ignore, weights).\n        \"\"\"\n        return {3: lambda x: x,\n                2: cls._header2,\n                1: cls._header1}.get(len(headers), cls._header0)(headers)\n\n    @classmethod\n    def _header2(cls, headers: List[List[str]]) -> Tuple[List, List, List]:\n        names, flags = headers\n        return names, cls._type_from_flag(flags), cls._flag_from_flag(flags)\n\n    @classmethod\n    def _header1(cls, headers: List[List[str]]) -> Tuple[List, List, List]:\n        \"\"\"\n        First row format either:\n          1) delimited column names\n          2) -||- with type and flags prepended, separated by #,\n             e.g. d#sex,c#age,cC#IQ\n        \"\"\"\n\n        def is_flag(x):\n            return bool(Flags.RE_ALL.match(cls._type_from_flag([x])[0]) or\n                        Flags.RE_ALL.match(cls._flag_from_flag([x])[0]))\n\n        flags, names = zip(*[i.split(cls.HEADER1_FLAG_SEP, 1)\n                             if cls.HEADER1_FLAG_SEP in i and\n                             is_flag(i.split(cls.HEADER1_FLAG_SEP)[0])\n                             else ('', i)\n                             for i in headers[0]])\n        names = list(names)\n        return names, cls._type_from_flag(flags), cls._flag_from_flag(flags)\n\n    @classmethod\n    def _header0(cls, _) -> Tuple[List, List, List]:\n        # Use heuristics for everything\n        return [], [], []\n\n    @staticmethod\n    def _type_from_flag(flags: List[str]) -> List[str]:\n        return [''.join(filter(str.isupper, flag)).lower() for flag in flags]\n\n    @staticmethod\n    def _flag_from_flag(flags: List[str]) -> List[str]:\n        return [Flags.join(filter(str.islower, flag)) for flag in flags]\n\n\nclass _TableBuilder:\n    X_ARR, Y_ARR, M_ARR, W_ARR = range(4)\n    DATA_IND, DOMAIN_IND, TYPE_IND = range(3)\n\n    def __init__(self, data: np.ndarray, ncols: int,\n                 header: _TableHeader, offset: int):\n        self.data = data\n        self.ncols = ncols\n        self.header = header\n        self.offset = offset\n        self.namegen: Generator[str] = namegen('Feature ', 1)\n\n        self.cols_X: List[np.ndarray] = []\n        self.cols_Y: List[np.ndarray] = []\n        self.cols_M: List[np.ndarray] = []\n        self.cols_W: List[np.ndarray] = []\n        self.attrs: List[Variable] = []\n        self.clses: List[Variable] = []\n        self.metas: List[Variable] = []\n\n    def create_table(self) -> Table:\n        self.create_columns()\n        if not self.data.size:\n            return Table.from_domain(self.get_domain(), 0)\n        else:\n            return Table.from_numpy(self.get_domain(), *self.get_arrays())\n\n    def create_columns(self):\n        names = self.header.names\n        types = self.header.types\n\n        for col in range(self.ncols):\n            flag = Flags(Flags.split(self.header.flags[col]))\n            if flag.i:\n                continue\n\n            type_ = types and types[col].strip()\n            creator = self._get_column_creator(type_)\n            column = creator(self.data, col, values=type_, offset=self.offset)\n            self._take_column(names and names[col], column, flag)\n            self._reclaim_memory(self.data, col)\n\n    @classmethod\n    def _get_column_creator(cls, type_: str) -> Callable:\n        if type_ in StringVariable.TYPE_HEADERS:\n            return cls._string_column\n        elif type_ in ContinuousVariable.TYPE_HEADERS:\n            return cls._cont_column\n        elif type_ in TimeVariable.TYPE_HEADERS:\n            return cls._time_column\n        elif _RE_DISCRETE_LIST.match(type_):\n            return cls._disc_with_vals_column\n        elif type_ in DiscreteVariable.TYPE_HEADERS:\n            return cls._disc_no_vals_column\n        else:\n            return cls._unknown_column\n\n    @staticmethod\n    def _string_column(data: np.ndarray, col: int, **_) -> _ColumnProperties:\n        vals, _ = _TableBuilder._values_mask(data, col)\n        return _ColumnProperties(values=vals, coltype=StringVariable,\n                                 orig_values=vals)\n\n    @staticmethod\n    def _cont_column(data: np.ndarray, col: int,\n                     offset=0, **_) -> _ColumnProperties:\n        orig_vals, namask = _TableBuilder._values_mask(data, col)\n        values = np.empty(data.shape[0], dtype=float)\n        try:\n            np.copyto(values, orig_vals, casting=\"unsafe\", where=~namask)\n            values[namask] = np.nan\n        except ValueError:\n            row = 0\n            for row, num in enumerate(orig_vals):\n                if not isnastr(num):\n                    try:\n                        float(num)\n                    except ValueError:\n                        break\n            raise ValueError(f'Non-continuous value in (1-based) '\n                             f'line {row + offset + 1}, column {col + 1}')\n        return _ColumnProperties(values=values, coltype=ContinuousVariable,\n                                 orig_values=orig_vals)\n\n    @staticmethod\n    def _time_column(data: np.ndarray, col: int, **_) -> _ColumnProperties:\n        vals, namask = _TableBuilder._values_mask(data, col)\n        return _ColumnProperties(values=np.where(namask, \"\", vals),\n                                 coltype=TimeVariable, orig_values=vals)\n\n    @staticmethod\n    def _disc_column(data: np.ndarray, col: int) -> \\\n            Tuple[np.ndarray, VariableMeta]:\n        vals, namask = _TableBuilder._values_mask(data, col)\n        return np.where(namask, \"\", vals), DiscreteVariable\n\n    @staticmethod\n    def _disc_no_vals_column(data: np.ndarray, col: int, **_) -> \\\n            _ColumnProperties:\n        vals, coltype = _TableBuilder._disc_column(data, col)\n        return _ColumnProperties(valuemap=natural_sorted(set(vals) - {\"\"}),\n                                 values=vals, coltype=coltype,\n                                 orig_values=vals)\n\n    @staticmethod\n    def _disc_with_vals_column(data: np.ndarray, col: int,\n                               values=\"\", **_) -> _ColumnProperties:\n        vals, coltype = _TableBuilder._disc_column(data, col)\n        return _ColumnProperties(valuemap=Flags.split(values), values=vals,\n                                 coltype=coltype, orig_values=vals)\n\n    @staticmethod\n    def _unknown_column(data: np.ndarray, col: int, **_) -> _ColumnProperties:\n        orig_vals, namask = _TableBuilder._values_mask(data, col)\n        valuemap, values, coltype = guess_data_type(orig_vals, namask)\n        return _ColumnProperties(valuemap=valuemap, values=values,\n                                 coltype=coltype, orig_values=orig_vals)\n\n    @staticmethod\n    def _values_mask(data: np.ndarray, col: int) -> \\\n            Tuple[np.ndarray, np.ndarray]:\n        try:\n            values = data[:, col]\n        except IndexError:\n            values = np.array([], dtype=object)\n        return values, isnastr(values)\n\n    def _take_column(self, name: Optional[str], column: _ColumnProperties,\n                     flag: Flags):\n        cols, dom_vars = self._lists_from_flag(flag, column.coltype)\n        values = column.values\n        if dom_vars is not None:\n            if not name:\n                name = next(self.namegen)\n\n            values, var = sanitize_variable(\n                column.valuemap, values, column.orig_values,\n                column.coltype, column.coltype_kwargs, name=name)\n            var.attributes.update(flag.attributes)\n            dom_vars.append(var)\n\n        if isinstance(values, np.ndarray) and not values.flags.owndata:\n            values = values.copy()  # might view `data` (string columns)\n        cols.append(values)\n\n    def _lists_from_flag(self, flag: Flags, coltype: VariableMeta) -> \\\n            Tuple[List, Optional[List]]:\n        if flag.m or coltype is StringVariable:\n            return self.cols_M, self.metas\n        elif flag.w:\n            return self.cols_W, None\n        elif flag.c:\n            return self.cols_Y, self.clses\n        else:\n            return self.cols_X, self.attrs\n\n    @staticmethod\n    def _reclaim_memory(data: np.ndarray, col: int):\n        # allow gc to reclaim memory used by string values\n        try:\n            data[:, col] = None\n        except IndexError:\n            pass\n\n    def get_domain(self) -> Domain:\n        return Domain(self.attrs, self.clses, self.metas)\n\n    def get_arrays(self) -> Tuple[np.ndarray, np.ndarray,\n                                  np.ndarray, np.ndarray]:\n        lists = ((self.cols_X, None),\n                 (self.cols_Y, None),\n                 (self.cols_M, object),\n                 (self.cols_W, float))\n        X, Y, M, W = [self._list_into_ndarray(lst, dt) for lst, dt in lists]\n        if X is None:\n            X = np.empty((self.data.shape[0], 0), dtype=np.float_)\n        return X, Y, M, W\n\n    @staticmethod\n    def _list_into_ndarray(lst: List, dtype=None) -> Optional[np.ndarray]:\n        if not lst:\n            return None\n\n        array = np.c_[tuple(lst)]\n        if dtype is not None:\n            array.astype(dtype)\n        else:\n            assert array.dtype == np.float_\n        return array\n\n\nclass DataTableMixin:\n    @classmethod\n    def data_table(cls, data: Iterable[List[str]],\n                   headers: Optional[List] = None) -> Table:\n        \"\"\"\n        Return Orange.data.Table given rows of `headers` (iterable of iterable)\n        and rows of `data` (iterable of iterable).\n\n        Basically, the idea of subclasses is to produce those two iterables,\n        however they might.\n\n        If `headers` is not provided, the header rows are extracted from `data`,\n        assuming they precede it.\n\n        Parameters\n        ----------\n        data: Iterable\n            File content.\n        headers: List (Optional)\n            Header rows, to be used for constructing domain.\n\n        Returns\n        -------\n        table: Table\n            Data as Orange.data.Table.\n        \"\"\"\n        if not headers:\n            headers, data = cls.parse_headers(data)\n\n        header = _TableHeader(headers)\n        # adjusting data may change header properties\n        array, n_columns = cls.adjust_data_width(data, header)\n        builder = _TableBuilder(array, n_columns, header, len(headers))\n        return builder.create_table()\n\n    @classmethod\n    def parse_headers(cls, data: Iterable[List[str]]) -> Tuple[List, Iterable]:\n        \"\"\"\n        Return (header rows, rest of data) as discerned from `data`.\n\n        Parameters\n        ----------\n        data: Iterable\n            File content.\n\n        Returns\n        -------\n        header_rows: List\n            Header rows, to be used for constructing domain.\n        data: Iterable\n            File content without header rows.\n        \"\"\"\n        data = iter(data)\n        header_rows = []\n\n        # Try to parse a three-line header\n        lines = []\n        try:\n            lines.append(list(next(data)))\n            lines.append(list(next(data)))\n            lines.append(list(next(data)))\n        except StopIteration:\n            lines, data = [], chain(lines, data)\n        if lines:\n            l1, l2, l3 = lines\n            # Three-line header if line 2 & 3 match (1st line can be anything)\n            if cls.__header_test2(l2) and cls.__header_test3(l3):\n                header_rows = [l1, l2, l3]\n            else:\n                lines, data = [], chain((l1, l2, l3), data)\n\n        # Try to parse a single-line header\n        if not header_rows:\n            try:\n                lines.append(list(next(data)))\n            except StopIteration:\n                pass\n            if lines:\n                # Header if none of the values in line 1 parses as a number\n                if not all(cls.__is_number(i) for i in lines[0]):\n                    header_rows = [lines[0]]\n                else:\n                    data = chain(lines, data)\n\n        return header_rows, data\n\n    @staticmethod\n    def __is_number(item: str) -> bool:\n        try:\n            float(item)\n        except ValueError:\n            return False\n        return True\n\n    @staticmethod\n    def __header_test2(items: List) -> bool:\n        # Second row items are type identifiers\n        return all(map(_RE_TYPES.match, items))\n\n    @staticmethod\n    def __header_test3(items: List) -> bool:\n        # Third row items are flags and column attributes (attr=value)\n        return all(map(_RE_FLAGS.match, items))\n\n    @classmethod\n    def adjust_data_width(cls, data: Iterable, header: _TableHeader) -> \\\n            Tuple[np.ndarray, int]:\n        \"\"\"\n        Determine maximum row length.\n        Return data as an array, with width dependent on header size.\n        Append `names`, `types` and `flags` if shorter than row length.\n\n        Parameters\n        ----------\n        data: Iterable\n            File content without header rows.\n        header: _TableHeader\n            Header lists converted into _TableHeader.\n\n        Returns\n        -------\n        data: np.ndarray\n            File content without header rows.\n        rowlen: int\n            Number of columns in data.\n        \"\"\"\n\n        def equal_len(lst):\n            nonlocal strip\n            if len(lst) > rowlen > 0:\n                lst = lst[:rowlen]\n                strip = True\n            elif len(lst) < rowlen:\n                lst.extend([''] * (rowlen - len(lst)))\n            return lst\n\n        rowlen = max(map(len, (header.names, header.types, header.flags)))\n        strip = False\n\n        # Ensure all data is of equal width in a column-contiguous array\n        data = [equal_len([s.strip() for s in row])\n                for row in data if any(row)]\n        array = np.array(data, dtype=object, order='F')\n\n        if strip:\n            warnings.warn(\"Columns with no headers were removed.\")\n\n        # Data may actually be longer than headers were\n        try:\n            rowlen = array.shape[1]\n        except IndexError:\n            pass\n        else:\n            for lst in (header.names, header.types, header.flags):\n                equal_len(lst)\n        return array, rowlen\n\n\nclass _FileReader:\n    @classmethod\n    def get_reader(cls, filename):\n        \"\"\"Return reader instance that can be used to read the file\n\n        Parameters\n        ----------\n        filename : str\n\n        Returns\n        -------\n        FileFormat\n        \"\"\"\n        for ext, reader in cls.readers.items():\n            # Skip ambiguous, invalid compression-only extensions added on OSX\n            if ext in Compression.all:\n                continue\n            if fnmatch(path.basename(filename), '*' + ext):\n                return reader(filename)\n\n        raise MissingReaderException('No readers for file \"{}\"'.format(filename))\n\n    @classmethod\n    def set_table_metadata(cls, filename, table):\n        # pylint: disable=bare-except\n        if isinstance(filename, str) and path.exists(filename + '.metadata'):\n            try:\n                with open(filename + '.metadata', 'rb') as f:\n                    table.attributes = pickle.load(f)\n            # Unpickling throws different exceptions, not just UnpickleError\n            except:\n                with open(filename + '.metadata', encoding='utf-8') as f:\n                    table.attributes = OrderedDict(\n                        (k.strip(), v.strip())\n                        for k, v in (line.split(\":\", 1)\n                                     for line in f.readlines()))\n\n\nclass _FileWriter:\n    @classmethod\n    def write(cls, filename, data, with_annotations=True):\n        if cls.OPTIONAL_TYPE_ANNOTATIONS:\n            return cls.write_file(filename, data, with_annotations)\n        else:\n            return cls.write_file(filename, data)\n\n    @classmethod\n    def write_table_metadata(cls, filename, data):\n        def write_file(fn):\n            if all(isinstance(key, str) and isinstance(value, str)\n                   for key, value in data.attributes.items()):\n                with open(fn, 'w', encoding='utf-8') as f:\n                    f.write(\"\\n\".join(\"{}: {}\".format(*kv)\n                                      for kv in data.attributes.items()))\n            else:\n                with open(fn, 'wb') as f:\n                    pickle.dump(data.attributes, f, protocol=PICKLE_PROTOCOL)\n\n        if isinstance(filename, str):\n            metafile = filename + '.metadata'\n            if getattr(data, 'attributes', None):\n                write_file(metafile)\n            elif path.exists(metafile):\n                remove(metafile)\n\n    @staticmethod\n    def header_names(data):\n        return ['weights'] * data.has_weights() + \\\n               [v.name for v in chain(data.domain.class_vars,\n                                      data.domain.metas,\n                                      data.domain.attributes)]\n\n    @staticmethod\n    def header_types(data):\n        def _vartype(var):\n            if var.is_continuous or var.is_string:\n                return var.TYPE_HEADERS[0]\n            elif var.is_discrete:\n                # if number of values is 1 order is not important if more\n                # values write order in file\n                return (\n                    Flags.join(var.values) if len(var.values) >= 2\n                    else var.TYPE_HEADERS[0]\n                )\n            raise NotImplementedError\n\n        return ['continuous'] * data.has_weights() + \\\n               [_vartype(v) for v in chain(data.domain.class_vars,\n                                           data.domain.metas,\n                                           data.domain.attributes)]\n\n    @staticmethod\n    def header_flags(data):\n        return list(chain(\n            ['weight'] * data.has_weights(),\n            (Flags.join([flag], *('{}={}'.format(*a) for a in\n                                  sorted(var.attributes.items())))\n             for flag, var in chain(zip(repeat('class'),\n                                        data.domain.class_vars),\n                                    zip(repeat('meta'), data.domain.metas),\n                                    zip(repeat(''), data.domain.attributes)))))\n\n    @classmethod\n    def write_headers(cls, write, data, with_annotations=True):\n        \"\"\"`write` is a callback that accepts an iterable\"\"\"\n        write(cls.header_names(data))\n        if with_annotations:\n            write(cls.header_types(data))\n            write(cls.header_flags(data))\n\n    @classmethod\n    def formatter(cls, var):\n        # type: (Variable) -> Callable[[Variable], Any]\n        # Return a column 'formatter' function. The function must return\n        # something that `write` knows how to write\n        if var.is_time:\n            return var.repr_val\n        elif var.is_continuous:\n            return lambda value: \"\" if isnan(value) else var.repr_val(value)\n        elif var.is_discrete:\n            return lambda value: \"\" if isnan(value) else var.values[int(value)]\n        elif var.is_string:\n            return lambda value: \"\" if pandas.isnull(value) else value\n        else:\n            return var.repr_val\n\n    @classmethod\n    def write_data(cls, write, data):\n        \"\"\"`write` is a callback that accepts an iterable\"\"\"\n        vars_ = list(\n            chain((ContinuousVariable('_w'),) if data.has_weights() else (),\n                  data.domain.class_vars,\n                  data.domain.metas,\n                  data.domain.attributes))\n\n        formatters = [cls.formatter(v) for v in vars_]\n        for row in zip(data.W if data.W.ndim > 1 else data.W[:, np.newaxis],\n                       data.Y if data.Y.ndim > 1 else data.Y[:, np.newaxis],\n                       data.metas,\n                       data.X):\n            write([fmt(v) for fmt, v in zip(formatters, flatten(row))])\n\n\nclass _FileFormatMeta(Registry):\n    def __new__(mcs, name, bases, attrs):\n        newcls = super().__new__(mcs, name, bases, attrs)\n\n        # Optionally add compressed versions of extensions as supported\n        if getattr(newcls, 'SUPPORT_COMPRESSED', False):\n            new_extensions = list(getattr(newcls, 'EXTENSIONS', ()))\n            for compression in Compression.all:\n                for ext in newcls.EXTENSIONS:\n                    new_extensions.append(ext + compression)\n                if sys.platform in ('darwin', 'win32'):\n                    # OSX file dialog doesn't support filtering on double\n                    # extensions (e.g. .csv.gz)\n                    # https://bugreports.qt.io/browse/QTBUG-38303\n                    # This is just here for OWFile that gets QFileDialog\n                    # filters from FileFormat.readers.keys()\n                    # EDIT: Windows exhibit similar problems:\n                    # while .tab.gz works, .tab.xz and .tab.bz2 do not!\n                    new_extensions.append(compression)\n            newcls.EXTENSIONS = tuple(new_extensions)\n\n        return newcls\n\n    @property\n    def formats(cls):\n        return cls.registry.values()\n\n    @lru_cache(5)\n    def _ext_to_attr_if_attr2(cls, attr, attr2):\n        \"\"\"\n        Return ``{ext: `attr`, ...}`` dict if ``cls`` has `attr2`.\n        If `attr` is '', return ``{ext: cls, ...}`` instead.\n\n        If there are multiple formats for an extension, return a format\n        with the lowest priority.\n        \"\"\"\n        formats = OrderedDict()\n        for format_ in sorted(cls.registry.values(), key=lambda x: x.PRIORITY):\n            if not hasattr(format_, attr2):\n                continue\n            for ext in getattr(format_, 'EXTENSIONS', []):\n                # Only adds if not yet registered\n                formats.setdefault(ext, getattr(format_, attr, format_))\n        return formats\n\n    @property\n    def names(cls):\n        return cls._ext_to_attr_if_attr2('DESCRIPTION', '__class__')\n\n    @property\n    def writers(cls):\n        return cls._ext_to_attr_if_attr2('', 'write_file')\n\n    @property\n    def readers(cls):\n        return cls._ext_to_attr_if_attr2('', 'read')\n\n    @property\n    def img_writers(cls):\n        warnings.warn(\n            f\"'{__name__}.FileFormat.img_writers' is no longer used and \"\n            \"will be removed. Please use \"\n            \"'Orange.widgets.io.FileFormat.img_writers' instead.\",\n            DeprecationWarning, stacklevel=2\n        )\n        return cls._ext_to_attr_if_attr2('', 'write_image')\n\n    @property\n    def graph_writers(cls):\n        return cls._ext_to_attr_if_attr2('', 'write_graph')\n\n\nclass FileFormatBase(_FileReader, _FileWriter, metaclass=_FileFormatMeta):\n    # Priority when multiple formats support the same extension. Also\n    # the sort order in file open/save combo boxes. Lower is better.\n    PRIORITY = 10000\n    OPTIONAL_TYPE_ANNOTATIONS = False\n\n    @classmethod\n    def locate(cls, filename, search_dirs=('.',)):\n        \"\"\"Locate a file with given filename that can be opened by one\n        of the available readers.\n\n        Parameters\n        ----------\n        filename : str\n        search_dirs : Iterable[str]\n\n        Returns\n        -------\n        str\n            Absolute path to the file\n        \"\"\"\n        if path.exists(filename):\n            return filename\n\n        for directory in search_dirs:\n            absolute_filename = path.join(directory, filename)\n            if path.exists(absolute_filename):\n                break\n            for ext in cls.readers:\n                if fnmatch(path.basename(filename), '*' + ext):\n                    break\n                # glob uses fnmatch internally\n                matching_files = glob(absolute_filename + ext)\n                if matching_files:\n                    absolute_filename = matching_files[0]\n                    break\n            if path.exists(absolute_filename):\n                break\n        else:\n            absolute_filename = \"\"\n\n        if not path.exists(absolute_filename):\n            raise IOError('File \"{}\" was not found.'.format(filename))\n\n        return absolute_filename\n\n    @staticmethod\n    def open(filename, *args, **kwargs):\n        \"\"\"\n        Format handlers can use this method instead of the builtin ``open()``\n        to transparently (de)compress files if requested (according to\n        `filename` extension). Set ``SUPPORT_COMPRESSED=True`` if you use this.\n        \"\"\"\n        return open_compressed(filename, *args, **kwargs)\n\n    @classmethod\n    def qualified_name(cls):\n        return cls.__module__ + '.' + cls.__name__\n",
            "new_file_content": "import pickle\nimport re\nimport sys\nimport warnings\nfrom typing import Iterable, Optional, Tuple, List, Generator, Callable, Any\n\nfrom ast import literal_eval\nfrom collections import OrderedDict\nfrom functools import lru_cache\nfrom itertools import chain, repeat\nfrom math import isnan\n\nfrom os import path, remove\nfrom fnmatch import fnmatch\nfrom glob import glob\n\nimport numpy as np\nimport pandas\n\nfrom Orange.data import Table, Domain, Variable, DiscreteVariable, \\\n    StringVariable, ContinuousVariable, TimeVariable\nfrom Orange.data.io_util import Compression, open_compressed, \\\n    isnastr, guess_data_type, sanitize_variable\nfrom Orange.data.util import get_unique_names_duplicates\nfrom Orange.data.variable import VariableMeta\nfrom Orange.misc.collections import natural_sorted\nfrom Orange.util import Registry, flatten, namegen\n\n__all__ = [\"FileFormatBase\", \"Flags\", \"DataTableMixin\", \"PICKLE_PROTOCOL\"]\n\n\nPICKLE_PROTOCOL = 4\n\n\nclass MissingReaderException(IOError):\n    # subclasses IOError for backward compatibility\n    pass\n\n\nclass Flags:\n    \"\"\"Parser for column flags (i.e. third header row)\"\"\"\n    DELIMITER = ' '\n    _RE_SPLIT = re.compile(r'(?<!\\\\)' + DELIMITER).split\n    _RE_ATTR_UNQUOTED_STR = re.compile(r'^[a-zA-Z_]').match\n    ALL = OrderedDict((\n        ('class', 'c'),\n        ('ignore', 'i'),\n        ('meta', 'm'),\n        ('weight', 'w'),\n        ('.+?=.*?', ''),  # general key=value attributes\n    ))\n    RE_ALL = re.compile(r'^({})$'.format('|'.join(\n        filter(None, flatten(ALL.items())))))\n\n    def __init__(self, flags):\n        for v in filter(None, self.ALL.values()):\n            setattr(self, v, False)\n        self.attributes = {}\n        for flag in flags or []:\n            flag = flag.strip()\n            if self.RE_ALL.match(flag):\n                if '=' in flag:\n                    k, v = flag.split('=', 1)\n                    if not Flags._RE_ATTR_UNQUOTED_STR(v):\n                        try:\n                            v = literal_eval(v)\n                        except SyntaxError:\n                            # If parsing failed, treat value as string\n                            pass\n                    # map True and False to booleans\n                    if v in (\"True\", \"False\"):\n                        v = {\"True\": True, \"False\": False}[v]\n                    self.attributes[k] = v\n                else:\n                    setattr(self, flag, True)\n                    setattr(self, self.ALL.get(flag, ''), True)\n            elif flag:\n                warnings.warn('Invalid attribute flag \\'{}\\''.format(flag))\n\n    @staticmethod\n    def join(iterable, *args):\n        return Flags.DELIMITER.join(i.strip().replace(Flags.DELIMITER,\n                                                      '\\\\' + Flags.DELIMITER)\n                                    for i in chain(iterable, args)).lstrip()\n\n    @staticmethod\n    def split(s):\n        return [i.replace('\\\\' + Flags.DELIMITER, Flags.DELIMITER)\n                for i in Flags._RE_SPLIT(s)]\n\n\n# Matches discrete specification where all the values are listed, space-separated\n_RE_DISCRETE_LIST = re.compile(r'^\\s*[^\\s]+(\\s[^\\s]+)+\\s*$')\n_RE_TYPES = re.compile(r'^\\s*({}|{}|)\\s*$'.format(\n    _RE_DISCRETE_LIST.pattern,\n    '|'.join(flatten(getattr(vartype, 'TYPE_HEADERS')\n                     for vartype in Variable.registry.values()))\n))\n_RE_FLAGS = re.compile(r'^\\s*( |{}|)*\\s*$'.format(\n    '|'.join(flatten(filter(None, i) for i in Flags.ALL.items()))\n))\n\n\nclass _ColumnProperties:\n    def __init__(self, valuemap=None, values=None, orig_values=None,\n                 coltype=None, coltype_kwargs=None):\n        self.valuemap = valuemap\n        self.values = values\n        self.orig_values = orig_values\n        self.coltype = coltype\n        if coltype_kwargs is None:\n            self.coltype_kwargs = {}\n        else:\n            self.coltype_kwargs = dict(coltype_kwargs)\n\n\nclass _TableHeader:\n    \"\"\"\n    Contains functions for table header construction (and its data).\n    \"\"\"\n    HEADER1_FLAG_SEP = '#'\n\n    def __init__(self, headers: List):\n        \"\"\"\n        Parameters\n        ----------\n        headers: List\n            Header rows, to be used for constructing domain.\n        \"\"\"\n        names, types, flags = self.create_header_data(headers)\n        self.names = get_unique_names_duplicates(names)\n        self.types = types\n        self.flags = flags\n\n    @classmethod\n    def create_header_data(cls, headers: List) -> Tuple[List, List, List]:\n        \"\"\"\n        Consider various header types (single-row, two-row, three-row, none).\n\n        Parameters\n        ----------\n        headers: List\n            Header rows, to be used for constructing domain.\n\n        Returns\n        -------\n        names: List\n            List of variable names.\n        types: List\n            List of variable types.\n        flags: List\n            List of meta info (i.e. class, meta, ignore, weights).\n        \"\"\"\n        return {3: lambda x: x,\n                2: cls._header2,\n                1: cls._header1}.get(len(headers), cls._header0)(headers)\n\n    @classmethod\n    def _header2(cls, headers: List[List[str]]) -> Tuple[List, List, List]:\n        names, flags = headers\n        return names, cls._type_from_flag(flags), cls._flag_from_flag(flags)\n\n    @classmethod\n    def _header1(cls, headers: List[List[str]]) -> Tuple[List, List, List]:\n        \"\"\"\n        First row format either:\n          1) delimited column names\n          2) -||- with type and flags prepended, separated by #,\n             e.g. d#sex,c#age,cC#IQ\n        \"\"\"\n\n        def is_flag(x):\n            return bool(cls._type_from_flag([x])[0] and\n                        _RE_TYPES.match(cls._type_from_flag([x])[0]) or\n                        Flags.RE_ALL.match(cls._flag_from_flag([x])[0]))\n\n        flags, names = zip(*[i.split(cls.HEADER1_FLAG_SEP, 1)\n                             if cls.HEADER1_FLAG_SEP in i and\n                             is_flag(i.split(cls.HEADER1_FLAG_SEP)[0])\n                             else ('', i)\n                             for i in headers[0]])\n        names = list(names)\n        return names, cls._type_from_flag(flags), cls._flag_from_flag(flags)\n\n    @classmethod\n    def _header0(cls, _) -> Tuple[List, List, List]:\n        # Use heuristics for everything\n        return [], [], []\n\n    @staticmethod\n    def _type_from_flag(flags: List[str]) -> List[str]:\n        return [''.join(filter(str.isupper, flag)).lower() for flag in flags]\n\n    @staticmethod\n    def _flag_from_flag(flags: List[str]) -> List[str]:\n        return [Flags.join(filter(str.islower, flag)) for flag in flags]\n\n\nclass _TableBuilder:\n    X_ARR, Y_ARR, M_ARR, W_ARR = range(4)\n    DATA_IND, DOMAIN_IND, TYPE_IND = range(3)\n\n    def __init__(self, data: np.ndarray, ncols: int,\n                 header: _TableHeader, offset: int):\n        self.data = data\n        self.ncols = ncols\n        self.header = header\n        self.offset = offset\n        self.namegen: Generator[str] = namegen('Feature ', 1)\n\n        self.cols_X: List[np.ndarray] = []\n        self.cols_Y: List[np.ndarray] = []\n        self.cols_M: List[np.ndarray] = []\n        self.cols_W: List[np.ndarray] = []\n        self.attrs: List[Variable] = []\n        self.clses: List[Variable] = []\n        self.metas: List[Variable] = []\n\n    def create_table(self) -> Table:\n        self.create_columns()\n        if not self.data.size:\n            return Table.from_domain(self.get_domain(), 0)\n        else:\n            return Table.from_numpy(self.get_domain(), *self.get_arrays())\n\n    def create_columns(self):\n        names = self.header.names\n        types = self.header.types\n\n        for col in range(self.ncols):\n            flag = Flags(Flags.split(self.header.flags[col]))\n            if flag.i:\n                continue\n\n            type_ = types and types[col].strip()\n            creator = self._get_column_creator(type_)\n            column = creator(self.data, col, values=type_, offset=self.offset)\n            self._take_column(names and names[col], column, flag)\n            self._reclaim_memory(self.data, col)\n\n    @classmethod\n    def _get_column_creator(cls, type_: str) -> Callable:\n        if type_ in StringVariable.TYPE_HEADERS:\n            return cls._string_column\n        elif type_ in ContinuousVariable.TYPE_HEADERS:\n            return cls._cont_column\n        elif type_ in TimeVariable.TYPE_HEADERS:\n            return cls._time_column\n        elif _RE_DISCRETE_LIST.match(type_):\n            return cls._disc_with_vals_column\n        elif type_ in DiscreteVariable.TYPE_HEADERS:\n            return cls._disc_no_vals_column\n        else:\n            return cls._unknown_column\n\n    @staticmethod\n    def _string_column(data: np.ndarray, col: int, **_) -> _ColumnProperties:\n        vals, _ = _TableBuilder._values_mask(data, col)\n        return _ColumnProperties(values=vals, coltype=StringVariable,\n                                 orig_values=vals)\n\n    @staticmethod\n    def _cont_column(data: np.ndarray, col: int,\n                     offset=0, **_) -> _ColumnProperties:\n        orig_vals, namask = _TableBuilder._values_mask(data, col)\n        values = np.empty(data.shape[0], dtype=float)\n        try:\n            np.copyto(values, orig_vals, casting=\"unsafe\", where=~namask)\n            values[namask] = np.nan\n        except ValueError:\n            row = 0\n            for row, num in enumerate(orig_vals):\n                if not isnastr(num):\n                    try:\n                        float(num)\n                    except ValueError:\n                        break\n            raise ValueError(f'Non-continuous value in (1-based) '\n                             f'line {row + offset + 1}, column {col + 1}')\n        return _ColumnProperties(values=values, coltype=ContinuousVariable,\n                                 orig_values=orig_vals)\n\n    @staticmethod\n    def _time_column(data: np.ndarray, col: int, **_) -> _ColumnProperties:\n        vals, namask = _TableBuilder._values_mask(data, col)\n        return _ColumnProperties(values=np.where(namask, \"\", vals),\n                                 coltype=TimeVariable, orig_values=vals)\n\n    @staticmethod\n    def _disc_column(data: np.ndarray, col: int) -> \\\n            Tuple[np.ndarray, VariableMeta]:\n        vals, namask = _TableBuilder._values_mask(data, col)\n        return np.where(namask, \"\", vals), DiscreteVariable\n\n    @staticmethod\n    def _disc_no_vals_column(data: np.ndarray, col: int, **_) -> \\\n            _ColumnProperties:\n        vals, coltype = _TableBuilder._disc_column(data, col)\n        return _ColumnProperties(valuemap=natural_sorted(set(vals) - {\"\"}),\n                                 values=vals, coltype=coltype,\n                                 orig_values=vals)\n\n    @staticmethod\n    def _disc_with_vals_column(data: np.ndarray, col: int,\n                               values=\"\", **_) -> _ColumnProperties:\n        vals, coltype = _TableBuilder._disc_column(data, col)\n        return _ColumnProperties(valuemap=Flags.split(values), values=vals,\n                                 coltype=coltype, orig_values=vals)\n\n    @staticmethod\n    def _unknown_column(data: np.ndarray, col: int, **_) -> _ColumnProperties:\n        orig_vals, namask = _TableBuilder._values_mask(data, col)\n        valuemap, values, coltype = guess_data_type(orig_vals, namask)\n        return _ColumnProperties(valuemap=valuemap, values=values,\n                                 coltype=coltype, orig_values=orig_vals)\n\n    @staticmethod\n    def _values_mask(data: np.ndarray, col: int) -> \\\n            Tuple[np.ndarray, np.ndarray]:\n        try:\n            values = data[:, col]\n        except IndexError:\n            values = np.array([], dtype=object)\n        return values, isnastr(values)\n\n    def _take_column(self, name: Optional[str], column: _ColumnProperties,\n                     flag: Flags):\n        cols, dom_vars = self._lists_from_flag(flag, column.coltype)\n        values = column.values\n        if dom_vars is not None:\n            if not name:\n                name = next(self.namegen)\n\n            values, var = sanitize_variable(\n                column.valuemap, values, column.orig_values,\n                column.coltype, column.coltype_kwargs, name=name)\n            var.attributes.update(flag.attributes)\n            dom_vars.append(var)\n\n        if isinstance(values, np.ndarray) and not values.flags.owndata:\n            values = values.copy()  # might view `data` (string columns)\n        cols.append(values)\n\n    def _lists_from_flag(self, flag: Flags, coltype: VariableMeta) -> \\\n            Tuple[List, Optional[List]]:\n        if flag.m or coltype is StringVariable:\n            return self.cols_M, self.metas\n        elif flag.w:\n            return self.cols_W, None\n        elif flag.c:\n            return self.cols_Y, self.clses\n        else:\n            return self.cols_X, self.attrs\n\n    @staticmethod\n    def _reclaim_memory(data: np.ndarray, col: int):\n        # allow gc to reclaim memory used by string values\n        try:\n            data[:, col] = None\n        except IndexError:\n            pass\n\n    def get_domain(self) -> Domain:\n        return Domain(self.attrs, self.clses, self.metas)\n\n    def get_arrays(self) -> Tuple[np.ndarray, np.ndarray,\n                                  np.ndarray, np.ndarray]:\n        lists = ((self.cols_X, None),\n                 (self.cols_Y, None),\n                 (self.cols_M, object),\n                 (self.cols_W, float))\n        X, Y, M, W = [self._list_into_ndarray(lst, dt) for lst, dt in lists]\n        if X is None:\n            X = np.empty((self.data.shape[0], 0), dtype=np.float_)\n        return X, Y, M, W\n\n    @staticmethod\n    def _list_into_ndarray(lst: List, dtype=None) -> Optional[np.ndarray]:\n        if not lst:\n            return None\n\n        array = np.c_[tuple(lst)]\n        if dtype is not None:\n            array.astype(dtype)\n        else:\n            assert array.dtype == np.float_\n        return array\n\n\nclass DataTableMixin:\n    @classmethod\n    def data_table(cls, data: Iterable[List[str]],\n                   headers: Optional[List] = None) -> Table:\n        \"\"\"\n        Return Orange.data.Table given rows of `headers` (iterable of iterable)\n        and rows of `data` (iterable of iterable).\n\n        Basically, the idea of subclasses is to produce those two iterables,\n        however they might.\n\n        If `headers` is not provided, the header rows are extracted from `data`,\n        assuming they precede it.\n\n        Parameters\n        ----------\n        data: Iterable\n            File content.\n        headers: List (Optional)\n            Header rows, to be used for constructing domain.\n\n        Returns\n        -------\n        table: Table\n            Data as Orange.data.Table.\n        \"\"\"\n        if not headers:\n            headers, data = cls.parse_headers(data)\n\n        header = _TableHeader(headers)\n        # adjusting data may change header properties\n        array, n_columns = cls.adjust_data_width(data, header)\n        builder = _TableBuilder(array, n_columns, header, len(headers))\n        return builder.create_table()\n\n    @classmethod\n    def parse_headers(cls, data: Iterable[List[str]]) -> Tuple[List, Iterable]:\n        \"\"\"\n        Return (header rows, rest of data) as discerned from `data`.\n\n        Parameters\n        ----------\n        data: Iterable\n            File content.\n\n        Returns\n        -------\n        header_rows: List\n            Header rows, to be used for constructing domain.\n        data: Iterable\n            File content without header rows.\n        \"\"\"\n        data = iter(data)\n        header_rows = []\n\n        # Try to parse a three-line header\n        lines = []\n        try:\n            lines.append(list(next(data)))\n            lines.append(list(next(data)))\n            lines.append(list(next(data)))\n        except StopIteration:\n            lines, data = [], chain(lines, data)\n        if lines:\n            l1, l2, l3 = lines\n            # Three-line header if line 2 & 3 match (1st line can be anything)\n            if cls.__header_test2(l2) and cls.__header_test3(l3):\n                header_rows = [l1, l2, l3]\n            else:\n                lines, data = [], chain((l1, l2, l3), data)\n\n        # Try to parse a single-line header\n        if not header_rows:\n            try:\n                lines.append(list(next(data)))\n            except StopIteration:\n                pass\n            if lines:\n                # Header if none of the values in line 1 parses as a number\n                if not all(cls.__is_number(i) for i in lines[0]):\n                    header_rows = [lines[0]]\n                else:\n                    data = chain(lines, data)\n\n        return header_rows, data\n\n    @staticmethod\n    def __is_number(item: str) -> bool:\n        try:\n            float(item)\n        except ValueError:\n            return False\n        return True\n\n    @staticmethod\n    def __header_test2(items: List) -> bool:\n        # Second row items are type identifiers\n        return all(map(_RE_TYPES.match, items))\n\n    @staticmethod\n    def __header_test3(items: List) -> bool:\n        # Third row items are flags and column attributes (attr=value)\n        return all(map(_RE_FLAGS.match, items))\n\n    @classmethod\n    def adjust_data_width(cls, data: Iterable, header: _TableHeader) -> \\\n            Tuple[np.ndarray, int]:\n        \"\"\"\n        Determine maximum row length.\n        Return data as an array, with width dependent on header size.\n        Append `names`, `types` and `flags` if shorter than row length.\n\n        Parameters\n        ----------\n        data: Iterable\n            File content without header rows.\n        header: _TableHeader\n            Header lists converted into _TableHeader.\n\n        Returns\n        -------\n        data: np.ndarray\n            File content without header rows.\n        rowlen: int\n            Number of columns in data.\n        \"\"\"\n\n        def equal_len(lst):\n            nonlocal strip\n            if len(lst) > rowlen > 0:\n                lst = lst[:rowlen]\n                strip = True\n            elif len(lst) < rowlen:\n                lst.extend([''] * (rowlen - len(lst)))\n            return lst\n\n        rowlen = max(map(len, (header.names, header.types, header.flags)))\n        strip = False\n\n        # Ensure all data is of equal width in a column-contiguous array\n        data = [equal_len([s.strip() for s in row])\n                for row in data if any(row)]\n        array = np.array(data, dtype=object, order='F')\n\n        if strip:\n            warnings.warn(\"Columns with no headers were removed.\")\n\n        # Data may actually be longer than headers were\n        try:\n            rowlen = array.shape[1]\n        except IndexError:\n            pass\n        else:\n            for lst in (header.names, header.types, header.flags):\n                equal_len(lst)\n        return array, rowlen\n\n\nclass _FileReader:\n    @classmethod\n    def get_reader(cls, filename):\n        \"\"\"Return reader instance that can be used to read the file\n\n        Parameters\n        ----------\n        filename : str\n\n        Returns\n        -------\n        FileFormat\n        \"\"\"\n        for ext, reader in cls.readers.items():\n            # Skip ambiguous, invalid compression-only extensions added on OSX\n            if ext in Compression.all:\n                continue\n            if fnmatch(path.basename(filename), '*' + ext):\n                return reader(filename)\n\n        raise MissingReaderException('No readers for file \"{}\"'.format(filename))\n\n    @classmethod\n    def set_table_metadata(cls, filename, table):\n        # pylint: disable=bare-except\n        if isinstance(filename, str) and path.exists(filename + '.metadata'):\n            try:\n                with open(filename + '.metadata', 'rb') as f:\n                    table.attributes = pickle.load(f)\n            # Unpickling throws different exceptions, not just UnpickleError\n            except:\n                with open(filename + '.metadata', encoding='utf-8') as f:\n                    table.attributes = OrderedDict(\n                        (k.strip(), v.strip())\n                        for k, v in (line.split(\":\", 1)\n                                     for line in f.readlines()))\n\n\nclass _FileWriter:\n    @classmethod\n    def write(cls, filename, data, with_annotations=True):\n        if cls.OPTIONAL_TYPE_ANNOTATIONS:\n            return cls.write_file(filename, data, with_annotations)\n        else:\n            return cls.write_file(filename, data)\n\n    @classmethod\n    def write_table_metadata(cls, filename, data):\n        def write_file(fn):\n            if all(isinstance(key, str) and isinstance(value, str)\n                   for key, value in data.attributes.items()):\n                with open(fn, 'w', encoding='utf-8') as f:\n                    f.write(\"\\n\".join(\"{}: {}\".format(*kv)\n                                      for kv in data.attributes.items()))\n            else:\n                with open(fn, 'wb') as f:\n                    pickle.dump(data.attributes, f, protocol=PICKLE_PROTOCOL)\n\n        if isinstance(filename, str):\n            metafile = filename + '.metadata'\n            if getattr(data, 'attributes', None):\n                write_file(metafile)\n            elif path.exists(metafile):\n                remove(metafile)\n\n    @staticmethod\n    def header_names(data):\n        return ['weights'] * data.has_weights() + \\\n               [v.name for v in chain(data.domain.class_vars,\n                                      data.domain.metas,\n                                      data.domain.attributes)]\n\n    @staticmethod\n    def header_types(data):\n        def _vartype(var):\n            if var.is_continuous or var.is_string:\n                return var.TYPE_HEADERS[0]\n            elif var.is_discrete:\n                # if number of values is 1 order is not important if more\n                # values write order in file\n                return (\n                    Flags.join(var.values) if len(var.values) >= 2\n                    else var.TYPE_HEADERS[0]\n                )\n            raise NotImplementedError\n\n        return ['continuous'] * data.has_weights() + \\\n               [_vartype(v) for v in chain(data.domain.class_vars,\n                                           data.domain.metas,\n                                           data.domain.attributes)]\n\n    @staticmethod\n    def header_flags(data):\n        return list(chain(\n            ['weight'] * data.has_weights(),\n            (Flags.join([flag], *('{}={}'.format(*a) for a in\n                                  sorted(var.attributes.items())))\n             for flag, var in chain(zip(repeat('class'),\n                                        data.domain.class_vars),\n                                    zip(repeat('meta'), data.domain.metas),\n                                    zip(repeat(''), data.domain.attributes)))))\n\n    @classmethod\n    def write_headers(cls, write, data, with_annotations=True):\n        \"\"\"`write` is a callback that accepts an iterable\"\"\"\n        write(cls.header_names(data))\n        if with_annotations:\n            write(cls.header_types(data))\n            write(cls.header_flags(data))\n\n    @classmethod\n    def formatter(cls, var):\n        # type: (Variable) -> Callable[[Variable], Any]\n        # Return a column 'formatter' function. The function must return\n        # something that `write` knows how to write\n        if var.is_time:\n            return var.repr_val\n        elif var.is_continuous:\n            return lambda value: \"\" if isnan(value) else var.repr_val(value)\n        elif var.is_discrete:\n            return lambda value: \"\" if isnan(value) else var.values[int(value)]\n        elif var.is_string:\n            return lambda value: \"\" if pandas.isnull(value) else value\n        else:\n            return var.repr_val\n\n    @classmethod\n    def write_data(cls, write, data):\n        \"\"\"`write` is a callback that accepts an iterable\"\"\"\n        vars_ = list(\n            chain((ContinuousVariable('_w'),) if data.has_weights() else (),\n                  data.domain.class_vars,\n                  data.domain.metas,\n                  data.domain.attributes))\n\n        formatters = [cls.formatter(v) for v in vars_]\n        for row in zip(data.W if data.W.ndim > 1 else data.W[:, np.newaxis],\n                       data.Y if data.Y.ndim > 1 else data.Y[:, np.newaxis],\n                       data.metas,\n                       data.X):\n            write([fmt(v) for fmt, v in zip(formatters, flatten(row))])\n\n\nclass _FileFormatMeta(Registry):\n    def __new__(mcs, name, bases, attrs):\n        newcls = super().__new__(mcs, name, bases, attrs)\n\n        # Optionally add compressed versions of extensions as supported\n        if getattr(newcls, 'SUPPORT_COMPRESSED', False):\n            new_extensions = list(getattr(newcls, 'EXTENSIONS', ()))\n            for compression in Compression.all:\n                for ext in newcls.EXTENSIONS:\n                    new_extensions.append(ext + compression)\n                if sys.platform in ('darwin', 'win32'):\n                    # OSX file dialog doesn't support filtering on double\n                    # extensions (e.g. .csv.gz)\n                    # https://bugreports.qt.io/browse/QTBUG-38303\n                    # This is just here for OWFile that gets QFileDialog\n                    # filters from FileFormat.readers.keys()\n                    # EDIT: Windows exhibit similar problems:\n                    # while .tab.gz works, .tab.xz and .tab.bz2 do not!\n                    new_extensions.append(compression)\n            newcls.EXTENSIONS = tuple(new_extensions)\n\n        return newcls\n\n    @property\n    def formats(cls):\n        return cls.registry.values()\n\n    @lru_cache(5)\n    def _ext_to_attr_if_attr2(cls, attr, attr2):\n        \"\"\"\n        Return ``{ext: `attr`, ...}`` dict if ``cls`` has `attr2`.\n        If `attr` is '', return ``{ext: cls, ...}`` instead.\n\n        If there are multiple formats for an extension, return a format\n        with the lowest priority.\n        \"\"\"\n        formats = OrderedDict()\n        for format_ in sorted(cls.registry.values(), key=lambda x: x.PRIORITY):\n            if not hasattr(format_, attr2):\n                continue\n            for ext in getattr(format_, 'EXTENSIONS', []):\n                # Only adds if not yet registered\n                formats.setdefault(ext, getattr(format_, attr, format_))\n        return formats\n\n    @property\n    def names(cls):\n        return cls._ext_to_attr_if_attr2('DESCRIPTION', '__class__')\n\n    @property\n    def writers(cls):\n        return cls._ext_to_attr_if_attr2('', 'write_file')\n\n    @property\n    def readers(cls):\n        return cls._ext_to_attr_if_attr2('', 'read')\n\n    @property\n    def img_writers(cls):\n        warnings.warn(\n            f\"'{__name__}.FileFormat.img_writers' is no longer used and \"\n            \"will be removed. Please use \"\n            \"'Orange.widgets.io.FileFormat.img_writers' instead.\",\n            DeprecationWarning, stacklevel=2\n        )\n        return cls._ext_to_attr_if_attr2('', 'write_image')\n\n    @property\n    def graph_writers(cls):\n        return cls._ext_to_attr_if_attr2('', 'write_graph')\n\n\nclass FileFormatBase(_FileReader, _FileWriter, metaclass=_FileFormatMeta):\n    # Priority when multiple formats support the same extension. Also\n    # the sort order in file open/save combo boxes. Lower is better.\n    PRIORITY = 10000\n    OPTIONAL_TYPE_ANNOTATIONS = False\n\n    @classmethod\n    def locate(cls, filename, search_dirs=('.',)):\n        \"\"\"Locate a file with given filename that can be opened by one\n        of the available readers.\n\n        Parameters\n        ----------\n        filename : str\n        search_dirs : Iterable[str]\n\n        Returns\n        -------\n        str\n            Absolute path to the file\n        \"\"\"\n        if path.exists(filename):\n            return filename\n\n        for directory in search_dirs:\n            absolute_filename = path.join(directory, filename)\n            if path.exists(absolute_filename):\n                break\n            for ext in cls.readers:\n                if fnmatch(path.basename(filename), '*' + ext):\n                    break\n                # glob uses fnmatch internally\n                matching_files = glob(absolute_filename + ext)\n                if matching_files:\n                    absolute_filename = matching_files[0]\n                    break\n            if path.exists(absolute_filename):\n                break\n        else:\n            absolute_filename = \"\"\n\n        if not path.exists(absolute_filename):\n            raise IOError('File \"{}\" was not found.'.format(filename))\n\n        return absolute_filename\n\n    @staticmethod\n    def open(filename, *args, **kwargs):\n        \"\"\"\n        Format handlers can use this method instead of the builtin ``open()``\n        to transparently (de)compress files if requested (according to\n        `filename` extension). Set ``SUPPORT_COMPRESSED=True`` if you use this.\n        \"\"\"\n        return open_compressed(filename, *args, **kwargs)\n\n    @classmethod\n    def qualified_name(cls):\n        return cls.__module__ + '.' + cls.__name__\n",
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                                "content": "        def is_flag(x):",
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                                "content": "            return bool(Flags.RE_ALL.match(cls._type_from_flag([x])[0]) or",
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                                "content": "            return bool(cls._type_from_flag([x])[0] and",
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                                "content": "                        Flags.RE_ALL.match(cls._flag_from_flag([x])[0]))",
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                        {
                            "file_name": "Orange/data/io_base.py",
                            "type": "class",
                            "name": "_TableHeader",
                            "content": "class _TableHeader:\n    \"\"\"\n    Contains functions for table header construction (and its data).\n    \"\"\"\n    HEADER1_FLAG_SEP = '#'\n\n    def __init__(self, headers: List):\n        \"\"\"\n        Parameters\n        ----------\n        headers: List\n            Header rows, to be used for constructing domain.\n        \"\"\"\n        names, types, flags = self.create_header_data(headers)\n        self.names = get_unique_names_duplicates(names)\n        self.types = types\n        self.flags = flags\n\n    @classmethod\n    def create_header_data(cls, headers: List) -> Tuple[List, List, List]:\n        \"\"\"\n        Consider various header types (single-row, two-row, three-row, none).\n\n        Parameters\n        ----------\n        headers: List\n            Header rows, to be used for constructing domain.\n\n        Returns\n        -------\n        names: List\n            List of variable names.\n        types: List\n            List of variable types.\n        flags: List\n            List of meta info (i.e. class, meta, ignore, weights).\n        \"\"\"\n        return {3: lambda x: x,\n                2: cls._header2,\n                1: cls._header1}.get(len(headers), cls._header0)(headers)\n\n    @classmethod\n    def _header2(cls, headers: List[List[str]]) -> Tuple[List, List, List]:\n        names, flags = headers\n        return names, cls._type_from_flag(flags), cls._flag_from_flag(flags)\n\n    @classmethod\n    def _header1(cls, headers: List[List[str]]) -> Tuple[List, List, List]:\n        \"\"\"\n        First row format either:\n          1) delimited column names\n          2) -||- with type and flags prepended, separated by #,\n             e.g. d#sex,c#age,cC#IQ\n        \"\"\"\n\n        def is_flag(x):\n            return bool(Flags.RE_ALL.match(cls._type_from_flag([x])[0]) or\n                        Flags.RE_ALL.match(cls._flag_from_flag([x])[0]))\n\n        flags, names = zip(*[i.split(cls.HEADER1_FLAG_SEP, 1)\n                             if cls.HEADER1_FLAG_SEP in i and\n                             is_flag(i.split(cls.HEADER1_FLAG_SEP)[0])\n                             else ('', i)\n                             for i in headers[0]])\n        names = list(names)\n        return names, cls._type_from_flag(flags), cls._flag_from_flag(flags)\n\n    @classmethod\n    def _header0(cls, _) -> Tuple[List, List, List]:\n        # Use heuristics for everything\n        return [], [], []\n\n    @staticmethod\n    def _type_from_flag(flags: List[str]) -> List[str]:\n        return [''.join(filter(str.isupper, flag)).lower() for flag in flags]\n\n    @staticmethod\n    def _flag_from_flag(flags: List[str]) -> List[str]:\n        return [Flags.join(filter(str.islower, flag)) for flag in flags]",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 117,
                            "end_lineno": 195,
                            "parent": null
                        },
                        {
                            "file_name": "Orange/data/io_base.py",
                            "type": "method",
                            "name": "_TableHeader._header1",
                            "content": "class _TableHeader:\n    \"\"\"\n    Contains functions for table header construction (and its data).\n    \"\"\"\n    HEADER1_FLAG_SEP = '#'\n\n    def __init__(self, headers: List):\n        \"\"\"\n        Parameters\n        ----------\n        headers: List\n            Header rows, to be used for constructing domain.\n        \"\"\"\n        names, types, flags = self.create_header_data(headers)\n        self.names = get_unique_names_duplicates(names)\n        self.types = types\n        self.flags = flags\n\n    @classmethod\n    def create_header_data(cls, headers: List) -> Tuple[List, List, List]:\n        \"\"\"\n        Consider various header types (single-row, two-row, three-row, none).\n\n        Parameters\n        ----------\n        headers: List\n            Header rows, to be used for constructing domain.\n\n        Returns\n        -------\n        names: List\n            List of variable names.\n        types: List\n            List of variable types.\n        flags: List\n            List of meta info (i.e. class, meta, ignore, weights).\n        \"\"\"\n        return {3: lambda x: x,\n                2: cls._header2,\n                1: cls._header1}.get(len(headers), cls._header0)(headers)\n\n    @classmethod\n    def _header2(cls, headers: List[List[str]]) -> Tuple[List, List, List]:\n        names, flags = headers\n        return names, cls._type_from_flag(flags), cls._flag_from_flag(flags)\n\n    @classmethod\n    def _header1(cls, headers: List[List[str]]) -> Tuple[List, List, List]:\n        \"\"\"\n        First row format either:\n          1) delimited column names\n          2) -||- with type and flags prepended, separated by #,\n             e.g. d#sex,c#age,cC#IQ\n        \"\"\"\n\n        def is_flag(x):\n            return bool(Flags.RE_ALL.match(cls._type_from_flag([x])[0]) or\n                        Flags.RE_ALL.match(cls._flag_from_flag([x])[0]))\n\n        flags, names = zip(*[i.split(cls.HEADER1_FLAG_SEP, 1)\n                             if cls.HEADER1_FLAG_SEP in i and\n                             is_flag(i.split(cls.HEADER1_FLAG_SEP)[0])\n                             else ('', i)\n                             for i in headers[0]])\n        names = list(names)\n        return names, cls._type_from_flag(flags), cls._flag_from_flag(flags)\n\n    @classmethod\n    def _header0(cls, _) -> Tuple[List, List, List]:\n        # Use heuristics for everything\n        return [], [], []\n\n    @staticmethod\n    def _type_from_flag(flags: List[str]) -> List[str]:\n        return [''.join(filter(str.isupper, flag)).lower() for flag in flags]\n\n    @staticmethod\n    def _flag_from_flag(flags: List[str]) -> List[str]:\n        return [Flags.join(filter(str.islower, flag)) for flag in flags]",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 164,
                            "end_lineno": 182,
                            "parent": {
                                "file_name": "Orange/data/io_base.py",
                                "type": "class",
                                "name": "_TableHeader",
                                "content": "class _TableHeader:\n    \"\"\"\n    Contains functions for table header construction (and its data).\n    \"\"\"\n    HEADER1_FLAG_SEP = '#'\n\n    def __init__(self, headers: List):\n        \"\"\"\n        Parameters\n        ----------\n        headers: List\n            Header rows, to be used for constructing domain.\n        \"\"\"\n        names, types, flags = self.create_header_data(headers)\n        self.names = get_unique_names_duplicates(names)\n        self.types = types\n        self.flags = flags\n\n    @classmethod\n    def create_header_data(cls, headers: List) -> Tuple[List, List, List]:\n        \"\"\"\n        Consider various header types (single-row, two-row, three-row, none).\n\n        Parameters\n        ----------\n        headers: List\n            Header rows, to be used for constructing domain.\n\n        Returns\n        -------\n        names: List\n            List of variable names.\n        types: List\n            List of variable types.\n        flags: List\n            List of meta info (i.e. class, meta, ignore, weights).\n        \"\"\"\n        return {3: lambda x: x,\n                2: cls._header2,\n                1: cls._header1}.get(len(headers), cls._header0)(headers)\n\n    @classmethod\n    def _header2(cls, headers: List[List[str]]) -> Tuple[List, List, List]:\n        names, flags = headers\n        return names, cls._type_from_flag(flags), cls._flag_from_flag(flags)\n\n    @classmethod\n    def _header1(cls, headers: List[List[str]]) -> Tuple[List, List, List]:\n        \"\"\"\n        First row format either:\n          1) delimited column names\n          2) -||- with type and flags prepended, separated by #,\n             e.g. d#sex,c#age,cC#IQ\n        \"\"\"\n\n        def is_flag(x):\n            return bool(Flags.RE_ALL.match(cls._type_from_flag([x])[0]) or\n                        Flags.RE_ALL.match(cls._flag_from_flag([x])[0]))\n\n        flags, names = zip(*[i.split(cls.HEADER1_FLAG_SEP, 1)\n                             if cls.HEADER1_FLAG_SEP in i and\n                             is_flag(i.split(cls.HEADER1_FLAG_SEP)[0])\n                             else ('', i)\n                             for i in headers[0]])\n        names = list(names)\n        return names, cls._type_from_flag(flags), cls._flag_from_flag(flags)\n\n    @classmethod\n    def _header0(cls, _) -> Tuple[List, List, List]:\n        # Use heuristics for everything\n        return [], [], []\n\n    @staticmethod\n    def _type_from_flag(flags: List[str]) -> List[str]:\n        return [''.join(filter(str.isupper, flag)).lower() for flag in flags]\n\n    @staticmethod\n    def _flag_from_flag(flags: List[str]) -> List[str]:\n        return [Flags.join(filter(str.islower, flag)) for flag in flags]",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 117,
                                "end_lineno": 195,
                                "parent": null
                            }
                        }
                    ],
                    "added_entities": [],
                    "deleted_entities": []
                }
            ]
        },
        {
            "old_file_content": "# pylint: disable=protected-access\nimport unittest\nfrom unittest.mock import Mock\n\nimport numpy as np\n\nfrom Orange.data import ContinuousVariable, TimeVariable, \\\n    DiscreteVariable, StringVariable, Table\nfrom Orange.data.io_base import _TableHeader, _TableBuilder, DataTableMixin\n\n\nclass InitTestData(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        cls.header0 = [[\"0.1\", \"0.5\", \"0.1\", \"21.0\"],\n                       [\"0.2\", \"0.1\", \"2.5\", \"123.0\"],\n                       [\"0.0\", \"0.0\", \"0.0\", \"0.0\"]]\n        cls.header1 = [[\"a\", \"b\", \"c\", \"d\"],\n                       [\"red\", \"2019-10-10\", \"0.0\", \"21.0\"],\n                       [\"red\", \"2019-10-12\", \"0.0\", \"123.0\"],\n                       [\"green\", \"2019-10-11\", \"\", \"0.0\"]]\n        cls.header1_flags = [[\"m#a\", \"cC#b\", \"m#c\", \"d\", \"i#e\", \"f\"],\n                             [\"red\", \"0.5\", \"0.0\", \"0.0\", \"aa\", \"a\"],\n                             [\"red\", \"0.1\", \"1.0\", \"1.0\", \"b\", \"b\"],\n                             [\"green\", \"0.0\", \"2.0\", \"2.0\", \"c\", \"c\"]]\n        cls.header3 = [[\"a\", \"b\", \"c\", \"d\", \"w\", \"e\", \"f\", \"g\"],\n                       [\"d\", \"c\", \"c\", \"c\", \"c\", \"d\", \"s\", \"yes no\"],\n                       [\"meta\", \"class\", \"meta\", \"\", \"weight\", \"i\", \"\", \"\"],\n                       [\"red\", \"0.5\", \"0.0\", \"0.0\", \"0.5\", \"aa\", \"a\", \"no\"]]\n\n\nclass TestTableHeader(InitTestData):\n    def test_rename_variables(self):\n        th = _TableHeader([[\"a\", \"\", \"b\", \"\", \"a\"]])\n        self.assertListEqual(th.names, [\"a (1)\", \"\", \"b\", \"\", \"a (2)\"])\n\n    def test_get_header_data_0(self):\n        names, types, flags = _TableHeader.create_header_data([])\n        self.assertListEqual(names, [])\n        self.assertListEqual(types, [])\n        self.assertListEqual(flags, [])\n\n    def test_get_header_data_1(self):\n        names, types, flags = _TableHeader.create_header_data(self.header1[:1])\n        self.assertListEqual(names, [\"a\", \"b\", \"c\", \"d\"])\n        self.assertListEqual(types, [\"\", \"\", \"\", \"\"])\n        self.assertListEqual(flags, [\"\", \"\", \"\", \"\"])\n\n    def test_get_header_data_1_flags(self):\n        names, types, flags = _TableHeader.create_header_data(\n            self.header1_flags[:1])\n        self.assertListEqual(names, [\"a\", \"b\", \"c\", \"d\", \"e\", \"f\"])\n        self.assertListEqual(types, [\"\", \"c\", \"\", \"\", \"\", \"\"])\n        self.assertListEqual(flags, [\"m\", \"c\", \"m\", \"\", \"i\", \"\"])\n\n    def test_get_header_data_3(self):\n        names, types, flags = _TableHeader.create_header_data(self.header3[:3])\n        self.assertListEqual(names, [\"a\", \"b\", \"c\", \"d\", \"w\", \"e\", \"f\", \"g\"])\n        self.assertListEqual(\n            types, [\"d\", \"c\", \"c\", \"c\", \"c\", \"d\", \"s\", \"yes no\"])\n        self.assertListEqual(\n            flags, [\"meta\", \"class\", \"meta\", \"\", \"weight\", \"i\", \"\", \"\"])\n\n\nclass TestTableBuilder(InitTestData):\n    def test_string_column(self):\n        data = np.array(self.header1[1:])\n        creator = _TableBuilder._get_column_creator(\"s\")\n        column = creator(data, 0)\n\n        self.assertIsNone(column.valuemap)\n        np.testing.assert_array_equal(column.values, [\"red\", \"red\", \"green\"])\n        np.testing.assert_array_equal(column.orig_values,\n                                      [\"red\", \"red\", \"green\"])\n        self.assertEqual(column.coltype, StringVariable)\n        self.assertDictEqual(column.coltype_kwargs, {})\n\n    def test_continuous_column(self):\n        data = np.array(self.header0)\n        creator = _TableBuilder._get_column_creator(\"c\")\n        column = creator(data, 0)\n\n        self.assertIsNone(column.valuemap)\n        np.testing.assert_array_equal(column.values, [0.1, 0.2, 0])\n        np.testing.assert_array_equal(column.orig_values,\n                                      [\"0.1\", \"0.2\", \"0.0\"])\n        self.assertEqual(column.coltype, ContinuousVariable)\n        self.assertDictEqual(column.coltype_kwargs, {})\n\n    def test_continuous_column_raises(self):\n        data = np.array([[\"a\", \"2\"], [\"3\", \"4\"]])\n        creator = _TableBuilder._get_column_creator(\"c\")\n        self.assertRaises(ValueError, creator, data, 0)\n\n    def test_time_column(self):\n        data = np.array(self.header1[1:])\n        creator = _TableBuilder._get_column_creator(\"t\")\n        column = creator(data, 1)\n\n        self.assertIsNone(column.valuemap)\n        np.testing.assert_array_equal(\n            column.values, ['2019-10-10', '2019-10-12', '2019-10-11'])\n        np.testing.assert_array_equal(\n            column.orig_values, ['2019-10-10', '2019-10-12', '2019-10-11'])\n        self.assertEqual(column.coltype, TimeVariable)\n        self.assertDictEqual(column.coltype_kwargs, {})\n\n    def test_discrete_column(self):\n        data = np.array(self.header1[1:])\n        creator = _TableBuilder._get_column_creator(\"d\")\n        columns = creator(data, 0)\n\n        self.assertListEqual(columns.valuemap, ['green', 'red'])\n        np.testing.assert_array_equal(columns.values, [\"red\", \"red\", \"green\"])\n        np.testing.assert_array_equal(columns.orig_values,\n                                      [\"red\", \"red\", \"green\"])\n        self.assertEqual(columns.coltype, DiscreteVariable)\n        self.assertDictEqual(columns.coltype_kwargs, {})\n\n    def test_column_parts_discrete_values(self):\n        data = np.array(self.header1[1:])\n        vals = \"green red\"\n        creator = _TableBuilder._get_column_creator(vals)\n        column = creator(data, 0, vals)\n\n        self.assertListEqual(column.valuemap, ['green', 'red'])\n        np.testing.assert_array_equal(column.values, [\"red\", \"red\", \"green\"])\n        np.testing.assert_array_equal(column.orig_values,\n                                      [\"red\", \"red\", \"green\"])\n        self.assertEqual(column.coltype, DiscreteVariable)\n        self.assertDictEqual(column.coltype_kwargs, {})\n\n    def test_unknown_type_column(self):\n        data = np.array(self.header0)\n        creator = _TableBuilder._get_column_creator(\"\")\n        column = creator(data, 0)\n\n        self.assertIsNone(column.valuemap)\n        np.testing.assert_array_equal(column.values, [0.1, 0.2, 0])\n        np.testing.assert_array_equal(column.orig_values, [\"0.1\", \"0.2\", \"0.0\"])\n        self.assertEqual(column.coltype, ContinuousVariable)\n        self.assertDictEqual(column.coltype_kwargs, {})\n\n\nclass TestDataTableMixin(InitTestData):\n    def test_data_table_empty(self):\n        self.assertIsInstance(DataTableMixin.data_table([]), Table)\n\n    def test_data_table_0(self):\n        self.assertIsInstance(DataTableMixin.data_table(self.header0), Table)\n\n    def test_data_table_1(self):\n        self.assertIsInstance(DataTableMixin.data_table(self.header1), Table)\n\n    def test_data_table_1_flags(self):\n        self.assertIsInstance(DataTableMixin.data_table(\n            self.header1_flags), Table)\n\n    def test_data_table_3(self):\n        self.assertIsInstance(DataTableMixin.data_table(self.header3), Table)\n\n    def test_parse_headers_empty(self):\n        headers, data = DataTableMixin.parse_headers([])\n        self.assertListEqual(headers, [])\n        self.assertListEqual(list(data), [])\n\n    def test_parse_headers_0(self):\n        hdata = self.header0\n        headers, data = DataTableMixin.parse_headers(hdata)\n        self.assertListEqual(headers, [])\n        self.assertListEqual(list(data), hdata)\n\n    def test_parse_headers_1(self):\n        hdata = self.header1\n        headers, data = DataTableMixin.parse_headers(hdata)\n        self.assertListEqual(headers, [[\"a\", \"b\", \"c\", \"d\"]])\n        self.assertListEqual(list(data), hdata[1:])\n\n    def test_parse_headers_1_flags(self):\n        hdata = self.header1_flags\n        headers, data = DataTableMixin.parse_headers(hdata)\n        self.assertListEqual(\n            headers, [[\"m#a\", \"cC#b\", \"m#c\", \"d\", \"i#e\", \"f\"]])\n        self.assertListEqual(list(data), hdata[1:])\n\n    def test_parse_headers_3(self):\n        hdata = self.header3\n        headers, data = DataTableMixin.parse_headers(hdata)\n        self.assertListEqual(\n            headers, [[\"a\", \"b\", \"c\", \"d\", \"w\", \"e\", \"f\", \"g\"],\n                      [\"d\", \"c\", \"c\", \"c\", \"c\", \"d\", \"s\", \"yes no\"],\n                      [\"meta\", \"class\", \"meta\", \"\", \"weight\", \"i\", \"\", \"\"]])\n        self.assertListEqual(list(data), hdata[3:])\n\n    def test_adjust_data_width_lengthen(self):\n        names, types, flags = [\"a\", \"b\", \"c\", \"d\", \"e\"], [\"\", \"c\"], [\"m\", \"c\"]\n        header = Mock()\n        header.names = names\n        header.types = types\n        header.flags = flags\n        adjusted, n = DataTableMixin.adjust_data_width(self.header0, header)\n        _data = np.hstack((np.array(self.header0, dtype=object),\n                           np.array([[\"\"]] * 3, dtype=object)))\n        np.testing.assert_array_equal(_data, adjusted)\n        self.assertEqual(adjusted.shape, (len(self.header0), 5))\n        self.assertEqual(n, 5)\n        self.assertListEqual(names, [\"a\", \"b\", \"c\", \"d\", \"e\"])\n        self.assertListEqual(types, [\"\", \"c\", \"\", \"\", \"\"])\n        self.assertListEqual(flags, [\"m\", \"c\", \"\", \"\", \"\"])\n\n    def test_adjust_data_width_shorten(self):\n        names, types, flags = [\"a\", \"b\", \"c\"], [\"\", \"c\"], [\"m\", \"c\"]\n        header = Mock()\n        header.names = names\n        header.types = types\n        header.flags = flags\n        with self.assertWarns(UserWarning):\n            adjusted, n = DataTableMixin.adjust_data_width(self.header0, header)\n        np.testing.assert_array_equal(\n            adjusted, np.array(self.header0, dtype=object)[:, :3])\n        self.assertEqual(adjusted.shape, (len(self.header0), 3))\n        self.assertEqual(n, 3)\n        self.assertListEqual(names, [\"a\", \"b\", \"c\"])\n        self.assertListEqual(types, [\"\", \"c\", \"\"])\n        self.assertListEqual(flags, [\"m\", \"c\", \"\"])\n\n    def test_adjust_data_width_empty(self):\n        names, types, flags = [\"a\", \"b\"], [], []\n        header = Mock()\n        header.names = names\n        header.types = types\n        header.flags = flags\n        data = [[\"\", \"\"], [\"\", \"\"]]\n        adjusted, n = DataTableMixin.adjust_data_width(data, header)\n        np.testing.assert_array_equal(adjusted, [])\n        self.assertEqual(n, 2)\n        self.assertListEqual(names, [\"a\", \"b\"])\n        self.assertListEqual(types, [])\n        self.assertListEqual(flags, [])\n\n\nif __name__ == '__main__':\n    unittest.main()\n",
            "new_file_content": "# pylint: disable=protected-access\nimport unittest\nfrom unittest.mock import Mock\n\nimport numpy as np\n\nfrom Orange.data import ContinuousVariable, TimeVariable, \\\n    DiscreteVariable, StringVariable, Table\nfrom Orange.data.io_base import _TableHeader, _TableBuilder, DataTableMixin\n\n\nclass InitTestData(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        cls.header0 = [[\"0.1\", \"0.5\", \"0.1\", \"21.0\"],\n                       [\"0.2\", \"0.1\", \"2.5\", \"123.0\"],\n                       [\"0.0\", \"0.0\", \"0.0\", \"0.0\"]]\n        cls.header1 = [[\"a\", \"b\", \"c\", \"d\"],\n                       [\"red\", \"2019-10-10\", \"0.0\", \"21.0\"],\n                       [\"red\", \"2019-10-12\", \"0.0\", \"123.0\"],\n                       [\"green\", \"2019-10-11\", \"\", \"0.0\"]]\n        cls.header1_flags = [[\"m#a\", \"cC#b\", \"m#c\", \"d\", \"i#e\", \"f\"],\n                             [\"red\", \"0.5\", \"0.0\", \"0.0\", \"aa\", \"a\"],\n                             [\"red\", \"0.1\", \"1.0\", \"1.0\", \"b\", \"b\"],\n                             [\"green\", \"0.0\", \"2.0\", \"2.0\", \"c\", \"c\"]]\n        cls.header1_flags2 = [[\"D#a1\", \"D#a2\", \"cD#a3\", \"C#a4\", \"S#a5\",\n                               \"mS#a6\", \"T#a7\", \"mT#a8\", \"T#a9\"],\n                              [\"\", \"0\", \"\", \"0\", \"a\", \"a\",\n                               \"2024-01-01\", \"2024-01-01\", \"\"],\n                              [\"\", \"1\", \"\", \"1\", \"b\", \"b\",\n                               \"2024-01-01\", \"2024-01-01\", \"\"],\n                              [\"green\", \"0.0\", \"2.0\", \"2.0\", \"c\", \"c\"]]\n        cls.header3 = [[\"a\", \"b\", \"c\", \"d\", \"w\", \"e\", \"f\", \"g\"],\n                       [\"d\", \"c\", \"c\", \"c\", \"c\", \"d\", \"s\", \"yes no\"],\n                       [\"meta\", \"class\", \"meta\", \"\", \"weight\", \"i\", \"\", \"\"],\n                       [\"red\", \"0.5\", \"0.0\", \"0.0\", \"0.5\", \"aa\", \"a\", \"no\"]]\n\n\nclass TestTableHeader(InitTestData):\n    def test_rename_variables(self):\n        th = _TableHeader([[\"a\", \"\", \"b\", \"\", \"a\"]])\n        self.assertListEqual(th.names, [\"a (1)\", \"\", \"b\", \"\", \"a (2)\"])\n\n    def test_get_header_data_0(self):\n        names, types, flags = _TableHeader.create_header_data([])\n        self.assertListEqual(names, [])\n        self.assertListEqual(types, [])\n        self.assertListEqual(flags, [])\n\n    def test_get_header_data_1(self):\n        names, types, flags = _TableHeader.create_header_data(self.header1[:1])\n        self.assertListEqual(names, [\"a\", \"b\", \"c\", \"d\"])\n        self.assertListEqual(types, [\"\", \"\", \"\", \"\"])\n        self.assertListEqual(flags, [\"\", \"\", \"\", \"\"])\n\n    def test_get_header_data_1_flags(self):\n        names, types, flags = _TableHeader.create_header_data(\n            self.header1_flags[:1])\n        self.assertListEqual(names, [\"a\", \"b\", \"c\", \"d\", \"e\", \"f\"])\n        self.assertListEqual(types, [\"\", \"c\", \"\", \"\", \"\", \"\"])\n        self.assertListEqual(flags, [\"m\", \"c\", \"m\", \"\", \"i\", \"\"])\n\n    def test_get_header_data_1_flags2(self):\n        names, types, flags = _TableHeader.create_header_data(\n            self.header1_flags2[:1])\n        names_ = [\"a1\", \"a2\", \"a3\", \"a4\", \"a5\", \"a6\", \"a7\", \"a8\", \"a9\"]\n        types_ = [\"d\", \"d\", \"d\", \"c\", \"s\", \"s\", \"t\", \"t\", \"t\"]\n        flags_ = [\"\", \"\", \"c\", \"\", \"\", \"m\", \"\", \"m\", \"\"]\n        self.assertListEqual(names, names_)\n        self.assertListEqual(types, types_)\n        self.assertListEqual(flags, flags_)\n\n    def test_get_header_data_3(self):\n        names, types, flags = _TableHeader.create_header_data(self.header3[:3])\n        self.assertListEqual(names, [\"a\", \"b\", \"c\", \"d\", \"w\", \"e\", \"f\", \"g\"])\n        self.assertListEqual(\n            types, [\"d\", \"c\", \"c\", \"c\", \"c\", \"d\", \"s\", \"yes no\"])\n        self.assertListEqual(\n            flags, [\"meta\", \"class\", \"meta\", \"\", \"weight\", \"i\", \"\", \"\"])\n\n\nclass TestTableBuilder(InitTestData):\n    def test_string_column(self):\n        data = np.array(self.header1[1:])\n        creator = _TableBuilder._get_column_creator(\"s\")\n        column = creator(data, 0)\n\n        self.assertIsNone(column.valuemap)\n        np.testing.assert_array_equal(column.values, [\"red\", \"red\", \"green\"])\n        np.testing.assert_array_equal(column.orig_values,\n                                      [\"red\", \"red\", \"green\"])\n        self.assertEqual(column.coltype, StringVariable)\n        self.assertDictEqual(column.coltype_kwargs, {})\n\n    def test_continuous_column(self):\n        data = np.array(self.header0)\n        creator = _TableBuilder._get_column_creator(\"c\")\n        column = creator(data, 0)\n\n        self.assertIsNone(column.valuemap)\n        np.testing.assert_array_equal(column.values, [0.1, 0.2, 0])\n        np.testing.assert_array_equal(column.orig_values,\n                                      [\"0.1\", \"0.2\", \"0.0\"])\n        self.assertEqual(column.coltype, ContinuousVariable)\n        self.assertDictEqual(column.coltype_kwargs, {})\n\n    def test_continuous_column_raises(self):\n        data = np.array([[\"a\", \"2\"], [\"3\", \"4\"]])\n        creator = _TableBuilder._get_column_creator(\"c\")\n        self.assertRaises(ValueError, creator, data, 0)\n\n    def test_time_column(self):\n        data = np.array(self.header1[1:])\n        creator = _TableBuilder._get_column_creator(\"t\")\n        column = creator(data, 1)\n\n        self.assertIsNone(column.valuemap)\n        np.testing.assert_array_equal(\n            column.values, ['2019-10-10', '2019-10-12', '2019-10-11'])\n        np.testing.assert_array_equal(\n            column.orig_values, ['2019-10-10', '2019-10-12', '2019-10-11'])\n        self.assertEqual(column.coltype, TimeVariable)\n        self.assertDictEqual(column.coltype_kwargs, {})\n\n    def test_discrete_column(self):\n        data = np.array(self.header1[1:])\n        creator = _TableBuilder._get_column_creator(\"d\")\n        columns = creator(data, 0)\n\n        self.assertListEqual(columns.valuemap, ['green', 'red'])\n        np.testing.assert_array_equal(columns.values, [\"red\", \"red\", \"green\"])\n        np.testing.assert_array_equal(columns.orig_values,\n                                      [\"red\", \"red\", \"green\"])\n        self.assertEqual(columns.coltype, DiscreteVariable)\n        self.assertDictEqual(columns.coltype_kwargs, {})\n\n    def test_column_parts_discrete_values(self):\n        data = np.array(self.header1[1:])\n        vals = \"green red\"\n        creator = _TableBuilder._get_column_creator(vals)\n        column = creator(data, 0, vals)\n\n        self.assertListEqual(column.valuemap, ['green', 'red'])\n        np.testing.assert_array_equal(column.values, [\"red\", \"red\", \"green\"])\n        np.testing.assert_array_equal(column.orig_values,\n                                      [\"red\", \"red\", \"green\"])\n        self.assertEqual(column.coltype, DiscreteVariable)\n        self.assertDictEqual(column.coltype_kwargs, {})\n\n    def test_unknown_type_column(self):\n        data = np.array(self.header0)\n        creator = _TableBuilder._get_column_creator(\"\")\n        column = creator(data, 0)\n\n        self.assertIsNone(column.valuemap)\n        np.testing.assert_array_equal(column.values, [0.1, 0.2, 0])\n        np.testing.assert_array_equal(column.orig_values, [\"0.1\", \"0.2\", \"0.0\"])\n        self.assertEqual(column.coltype, ContinuousVariable)\n        self.assertDictEqual(column.coltype_kwargs, {})\n\n\nclass TestDataTableMixin(InitTestData):\n    def test_data_table_empty(self):\n        self.assertIsInstance(DataTableMixin.data_table([]), Table)\n\n    def test_data_table_0(self):\n        self.assertIsInstance(DataTableMixin.data_table(self.header0), Table)\n\n    def test_data_table_1(self):\n        self.assertIsInstance(DataTableMixin.data_table(self.header1), Table)\n\n    def test_data_table_1_flags(self):\n        self.assertIsInstance(DataTableMixin.data_table(\n            self.header1_flags), Table)\n\n    def test_data_table_3(self):\n        self.assertIsInstance(DataTableMixin.data_table(self.header3), Table)\n\n    def test_parse_headers_empty(self):\n        headers, data = DataTableMixin.parse_headers([])\n        self.assertListEqual(headers, [])\n        self.assertListEqual(list(data), [])\n\n    def test_parse_headers_0(self):\n        hdata = self.header0\n        headers, data = DataTableMixin.parse_headers(hdata)\n        self.assertListEqual(headers, [])\n        self.assertListEqual(list(data), hdata)\n\n    def test_parse_headers_1(self):\n        hdata = self.header1\n        headers, data = DataTableMixin.parse_headers(hdata)\n        self.assertListEqual(headers, [[\"a\", \"b\", \"c\", \"d\"]])\n        self.assertListEqual(list(data), hdata[1:])\n\n    def test_parse_headers_1_flags(self):\n        hdata = self.header1_flags\n        headers, data = DataTableMixin.parse_headers(hdata)\n        self.assertListEqual(\n            headers, [[\"m#a\", \"cC#b\", \"m#c\", \"d\", \"i#e\", \"f\"]])\n        self.assertListEqual(list(data), hdata[1:])\n\n    def test_parse_headers_3(self):\n        hdata = self.header3\n        headers, data = DataTableMixin.parse_headers(hdata)\n        self.assertListEqual(\n            headers, [[\"a\", \"b\", \"c\", \"d\", \"w\", \"e\", \"f\", \"g\"],\n                      [\"d\", \"c\", \"c\", \"c\", \"c\", \"d\", \"s\", \"yes no\"],\n                      [\"meta\", \"class\", \"meta\", \"\", \"weight\", \"i\", \"\", \"\"]])\n        self.assertListEqual(list(data), hdata[3:])\n\n    def test_adjust_data_width_lengthen(self):\n        names, types, flags = [\"a\", \"b\", \"c\", \"d\", \"e\"], [\"\", \"c\"], [\"m\", \"c\"]\n        header = Mock()\n        header.names = names\n        header.types = types\n        header.flags = flags\n        adjusted, n = DataTableMixin.adjust_data_width(self.header0, header)\n        _data = np.hstack((np.array(self.header0, dtype=object),\n                           np.array([[\"\"]] * 3, dtype=object)))\n        np.testing.assert_array_equal(_data, adjusted)\n        self.assertEqual(adjusted.shape, (len(self.header0), 5))\n        self.assertEqual(n, 5)\n        self.assertListEqual(names, [\"a\", \"b\", \"c\", \"d\", \"e\"])\n        self.assertListEqual(types, [\"\", \"c\", \"\", \"\", \"\"])\n        self.assertListEqual(flags, [\"m\", \"c\", \"\", \"\", \"\"])\n\n    def test_adjust_data_width_shorten(self):\n        names, types, flags = [\"a\", \"b\", \"c\"], [\"\", \"c\"], [\"m\", \"c\"]\n        header = Mock()\n        header.names = names\n        header.types = types\n        header.flags = flags\n        with self.assertWarns(UserWarning):\n            adjusted, n = DataTableMixin.adjust_data_width(self.header0, header)\n        np.testing.assert_array_equal(\n            adjusted, np.array(self.header0, dtype=object)[:, :3])\n        self.assertEqual(adjusted.shape, (len(self.header0), 3))\n        self.assertEqual(n, 3)\n        self.assertListEqual(names, [\"a\", \"b\", \"c\"])\n        self.assertListEqual(types, [\"\", \"c\", \"\"])\n        self.assertListEqual(flags, [\"m\", \"c\", \"\"])\n\n    def test_adjust_data_width_empty(self):\n        names, types, flags = [\"a\", \"b\"], [], []\n        header = Mock()\n        header.names = names\n        header.types = types\n        header.flags = flags\n        data = [[\"\", \"\"], [\"\", \"\"]]\n        adjusted, n = DataTableMixin.adjust_data_width(data, header)\n        np.testing.assert_array_equal(adjusted, [])\n        self.assertEqual(n, 2)\n        self.assertListEqual(names, [\"a\", \"b\"])\n        self.assertListEqual(types, [])\n        self.assertListEqual(flags, [])\n\n\nif __name__ == '__main__':\n    unittest.main()\n",
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                            {
                                "content": "                             [\"red\", \"0.5\", \"0.0\", \"0.0\", \"aa\", \"a\"],",
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                            {
                                "content": "                             [\"red\", \"0.1\", \"1.0\", \"1.0\", \"b\", \"b\"],",
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                            {
                                "content": "        cls.header1_flags2 = [[\"D#a1\", \"D#a2\", \"cD#a3\", \"C#a4\", \"S#a5\",",
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                            {
                                "content": "                               \"mS#a6\", \"T#a7\", \"mT#a8\", \"T#a9\"],",
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                                "content": "                       [\"d\", \"c\", \"c\", \"c\", \"c\", \"d\", \"s\", \"yes no\"],",
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                            {
                                "content": "                       [\"meta\", \"class\", \"meta\", \"\", \"weight\", \"i\", \"\", \"\"],",
                                "type": "context"
                            }
                        ]
                    },
                    "modified_entities": [
                        {
                            "file_name": "Orange/data/tests/test_io_base.py",
                            "type": "method",
                            "name": "InitTestData.setUpClass",
                            "content": "class InitTestData(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        cls.header0 = [[\"0.1\", \"0.5\", \"0.1\", \"21.0\"],\n                       [\"0.2\", \"0.1\", \"2.5\", \"123.0\"],\n                       [\"0.0\", \"0.0\", \"0.0\", \"0.0\"]]\n        cls.header1 = [[\"a\", \"b\", \"c\", \"d\"],\n                       [\"red\", \"2019-10-10\", \"0.0\", \"21.0\"],\n                       [\"red\", \"2019-10-12\", \"0.0\", \"123.0\"],\n                       [\"green\", \"2019-10-11\", \"\", \"0.0\"]]\n        cls.header1_flags = [[\"m#a\", \"cC#b\", \"m#c\", \"d\", \"i#e\", \"f\"],\n                             [\"red\", \"0.5\", \"0.0\", \"0.0\", \"aa\", \"a\"],\n                             [\"red\", \"0.1\", \"1.0\", \"1.0\", \"b\", \"b\"],\n                             [\"green\", \"0.0\", \"2.0\", \"2.0\", \"c\", \"c\"]]\n        cls.header1_flags2 = [[\"D#a1\", \"D#a2\", \"cD#a3\", \"C#a4\", \"S#a5\",\n                               \"mS#a6\", \"T#a7\", \"mT#a8\", \"T#a9\"],\n                              [\"\", \"0\", \"\", \"0\", \"a\", \"a\",\n                               \"2024-01-01\", \"2024-01-01\", \"\"],\n                              [\"\", \"1\", \"\", \"1\", \"b\", \"b\",\n                               \"2024-01-01\", \"2024-01-01\", \"\"],\n                              [\"green\", \"0.0\", \"2.0\", \"2.0\", \"c\", \"c\"]]\n        cls.header3 = [[\"a\", \"b\", \"c\", \"d\", \"w\", \"e\", \"f\", \"g\"],\n                       [\"d\", \"c\", \"c\", \"c\", \"c\", \"d\", \"s\", \"yes no\"],\n                       [\"meta\", \"class\", \"meta\", \"\", \"weight\", \"i\", \"\", \"\"],\n                       [\"red\", \"0.5\", \"0.0\", \"0.0\", \"0.5\", \"aa\", \"a\", \"no\"]]",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 14,
                            "end_lineno": 36,
                            "parent": {
                                "file_name": "Orange/data/tests/test_io_base.py",
                                "type": "class",
                                "name": "InitTestData",
                                "content": "class InitTestData(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        cls.header0 = [[\"0.1\", \"0.5\", \"0.1\", \"21.0\"],\n                       [\"0.2\", \"0.1\", \"2.5\", \"123.0\"],\n                       [\"0.0\", \"0.0\", \"0.0\", \"0.0\"]]\n        cls.header1 = [[\"a\", \"b\", \"c\", \"d\"],\n                       [\"red\", \"2019-10-10\", \"0.0\", \"21.0\"],\n                       [\"red\", \"2019-10-12\", \"0.0\", \"123.0\"],\n                       [\"green\", \"2019-10-11\", \"\", \"0.0\"]]\n        cls.header1_flags = [[\"m#a\", \"cC#b\", \"m#c\", \"d\", \"i#e\", \"f\"],\n                             [\"red\", \"0.5\", \"0.0\", \"0.0\", \"aa\", \"a\"],\n                             [\"red\", \"0.1\", \"1.0\", \"1.0\", \"b\", \"b\"],\n                             [\"green\", \"0.0\", \"2.0\", \"2.0\", \"c\", \"c\"]]\n        cls.header1_flags2 = [[\"D#a1\", \"D#a2\", \"cD#a3\", \"C#a4\", \"S#a5\",\n                               \"mS#a6\", \"T#a7\", \"mT#a8\", \"T#a9\"],\n                              [\"\", \"0\", \"\", \"0\", \"a\", \"a\",\n                               \"2024-01-01\", \"2024-01-01\", \"\"],\n                              [\"\", \"1\", \"\", \"1\", \"b\", \"b\",\n                               \"2024-01-01\", \"2024-01-01\", \"\"],\n                              [\"green\", \"0.0\", \"2.0\", \"2.0\", \"c\", \"c\"]]\n        cls.header3 = [[\"a\", \"b\", \"c\", \"d\", \"w\", \"e\", \"f\", \"g\"],\n                       [\"d\", \"c\", \"c\", \"c\", \"c\", \"d\", \"s\", \"yes no\"],\n                       [\"meta\", \"class\", \"meta\", \"\", \"weight\", \"i\", \"\", \"\"],\n                       [\"red\", \"0.5\", \"0.0\", \"0.0\", \"0.5\", \"aa\", \"a\", \"no\"]]",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 12,
                                "end_lineno": 36,
                                "parent": null
                            }
                        },
                        {
                            "file_name": "Orange/data/tests/test_io_base.py",
                            "type": "class",
                            "name": "InitTestData",
                            "content": "class InitTestData(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        cls.header0 = [[\"0.1\", \"0.5\", \"0.1\", \"21.0\"],\n                       [\"0.2\", \"0.1\", \"2.5\", \"123.0\"],\n                       [\"0.0\", \"0.0\", \"0.0\", \"0.0\"]]\n        cls.header1 = [[\"a\", \"b\", \"c\", \"d\"],\n                       [\"red\", \"2019-10-10\", \"0.0\", \"21.0\"],\n                       [\"red\", \"2019-10-12\", \"0.0\", \"123.0\"],\n                       [\"green\", \"2019-10-11\", \"\", \"0.0\"]]\n        cls.header1_flags = [[\"m#a\", \"cC#b\", \"m#c\", \"d\", \"i#e\", \"f\"],\n                             [\"red\", \"0.5\", \"0.0\", \"0.0\", \"aa\", \"a\"],\n                             [\"red\", \"0.1\", \"1.0\", \"1.0\", \"b\", \"b\"],\n                             [\"green\", \"0.0\", \"2.0\", \"2.0\", \"c\", \"c\"]]\n        cls.header1_flags2 = [[\"D#a1\", \"D#a2\", \"cD#a3\", \"C#a4\", \"S#a5\",\n                               \"mS#a6\", \"T#a7\", \"mT#a8\", \"T#a9\"],\n                              [\"\", \"0\", \"\", \"0\", \"a\", \"a\",\n                               \"2024-01-01\", \"2024-01-01\", \"\"],\n                              [\"\", \"1\", \"\", \"1\", \"b\", \"b\",\n                               \"2024-01-01\", \"2024-01-01\", \"\"],\n                              [\"green\", \"0.0\", \"2.0\", \"2.0\", \"c\", \"c\"]]\n        cls.header3 = [[\"a\", \"b\", \"c\", \"d\", \"w\", \"e\", \"f\", \"g\"],\n                       [\"d\", \"c\", \"c\", \"c\", \"c\", \"d\", \"s\", \"yes no\"],\n                       [\"meta\", \"class\", \"meta\", \"\", \"weight\", \"i\", \"\", \"\"],\n                       [\"red\", \"0.5\", \"0.0\", \"0.0\", \"0.5\", \"aa\", \"a\", \"no\"]]",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 12,
                            "end_lineno": 36,
                            "parent": null
                        }
                    ],
                    "added_entities": [],
                    "deleted_entities": []
                },
                {
                    "descriptor": {
                        "old_range": {
                            "start": 53,
                            "length": 6
                        },
                        "new_range": {
                            "start": 60,
                            "length": 16
                        },
                        "section": "class TestTableHeader(InitTestData):"
                    },
                    "line_group": {
                        "all_lines": [
                            {
                                "content": "        self.assertListEqual(types, [\"\", \"c\", \"\", \"\", \"\", \"\"])",
                                "type": "context"
                            },
                            {
                                "content": "        self.assertListEqual(flags, [\"m\", \"c\", \"m\", \"\", \"i\", \"\"])",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "    def test_get_header_data_1_flags2(self):",
                                "type": "added"
                            },
                            {
                                "content": "        names, types, flags = _TableHeader.create_header_data(",
                                "type": "added"
                            },
                            {
                                "content": "            self.header1_flags2[:1])",
                                "type": "added"
                            },
                            {
                                "content": "        names_ = [\"a1\", \"a2\", \"a3\", \"a4\", \"a5\", \"a6\", \"a7\", \"a8\", \"a9\"]",
                                "type": "added"
                            },
                            {
                                "content": "        types_ = [\"d\", \"d\", \"d\", \"c\", \"s\", \"s\", \"t\", \"t\", \"t\"]",
                                "type": "added"
                            },
                            {
                                "content": "        flags_ = [\"\", \"\", \"c\", \"\", \"\", \"m\", \"\", \"m\", \"\"]",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertListEqual(names, names_)",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertListEqual(types, types_)",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertListEqual(flags, flags_)",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "    def test_get_header_data_3(self):",
                                "type": "context"
                            },
                            {
                                "content": "        names, types, flags = _TableHeader.create_header_data(self.header3[:3])",
                                "type": "context"
                            },
                            {
                                "content": "        self.assertListEqual(names, [\"a\", \"b\", \"c\", \"d\", \"w\", \"e\", \"f\", \"g\"])",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            }
                        ]
                    },
                    "modified_entities": [
                        {
                            "file_name": "Orange/data/tests/test_io_base.py",
                            "type": "class",
                            "name": "TestTableHeader",
                            "content": "class TestTableHeader(InitTestData):\n    def test_rename_variables(self):\n        th = _TableHeader([[\"a\", \"\", \"b\", \"\", \"a\"]])\n        self.assertListEqual(th.names, [\"a (1)\", \"\", \"b\", \"\", \"a (2)\"])\n\n    def test_get_header_data_0(self):\n        names, types, flags = _TableHeader.create_header_data([])\n        self.assertListEqual(names, [])\n        self.assertListEqual(types, [])\n        self.assertListEqual(flags, [])\n\n    def test_get_header_data_1(self):\n        names, types, flags = _TableHeader.create_header_data(self.header1[:1])\n        self.assertListEqual(names, [\"a\", \"b\", \"c\", \"d\"])\n        self.assertListEqual(types, [\"\", \"\", \"\", \"\"])\n        self.assertListEqual(flags, [\"\", \"\", \"\", \"\"])\n\n    def test_get_header_data_1_flags(self):\n        names, types, flags = _TableHeader.create_header_data(\n            self.header1_flags[:1])\n        self.assertListEqual(names, [\"a\", \"b\", \"c\", \"d\", \"e\", \"f\"])\n        self.assertListEqual(types, [\"\", \"c\", \"\", \"\", \"\", \"\"])\n        self.assertListEqual(flags, [\"m\", \"c\", \"m\", \"\", \"i\", \"\"])\n\n    def test_get_header_data_1_flags2(self):\n        names, types, flags = _TableHeader.create_header_data(\n            self.header1_flags2[:1])\n        names_ = [\"a1\", \"a2\", \"a3\", \"a4\", \"a5\", \"a6\", \"a7\", \"a8\", \"a9\"]\n        types_ = [\"d\", \"d\", \"d\", \"c\", \"s\", \"s\", \"t\", \"t\", \"t\"]\n        flags_ = [\"\", \"\", \"c\", \"\", \"\", \"m\", \"\", \"m\", \"\"]\n        self.assertListEqual(names, names_)\n        self.assertListEqual(types, types_)\n        self.assertListEqual(flags, flags_)\n\n    def test_get_header_data_3(self):\n        names, types, flags = _TableHeader.create_header_data(self.header3[:3])\n        self.assertListEqual(names, [\"a\", \"b\", \"c\", \"d\", \"w\", \"e\", \"f\", \"g\"])\n        self.assertListEqual(\n            types, [\"d\", \"c\", \"c\", \"c\", \"c\", \"d\", \"s\", \"yes no\"])\n        self.assertListEqual(\n            flags, [\"meta\", \"class\", \"meta\", \"\", \"weight\", \"i\", \"\", \"\"])",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 39,
                            "end_lineno": 79,
                            "parent": null
                        }
                    ],
                    "added_entities": [
                        {
                            "file_name": "Orange/data/tests/test_io_base.py",
                            "type": "method",
                            "name": "TestTableHeader.test_get_header_data_1_flags2",
                            "content": "class TestTableHeader(InitTestData):\n    def test_rename_variables(self):\n        th = _TableHeader([[\"a\", \"\", \"b\", \"\", \"a\"]])\n        self.assertListEqual(th.names, [\"a (1)\", \"\", \"b\", \"\", \"a (2)\"])\n\n    def test_get_header_data_0(self):\n        names, types, flags = _TableHeader.create_header_data([])\n        self.assertListEqual(names, [])\n        self.assertListEqual(types, [])\n        self.assertListEqual(flags, [])\n\n    def test_get_header_data_1(self):\n        names, types, flags = _TableHeader.create_header_data(self.header1[:1])\n        self.assertListEqual(names, [\"a\", \"b\", \"c\", \"d\"])\n        self.assertListEqual(types, [\"\", \"\", \"\", \"\"])\n        self.assertListEqual(flags, [\"\", \"\", \"\", \"\"])\n\n    def test_get_header_data_1_flags(self):\n        names, types, flags = _TableHeader.create_header_data(\n            self.header1_flags[:1])\n        self.assertListEqual(names, [\"a\", \"b\", \"c\", \"d\", \"e\", \"f\"])\n        self.assertListEqual(types, [\"\", \"c\", \"\", \"\", \"\", \"\"])\n        self.assertListEqual(flags, [\"m\", \"c\", \"m\", \"\", \"i\", \"\"])\n\n    def test_get_header_data_1_flags2(self):\n        names, types, flags = _TableHeader.create_header_data(\n            self.header1_flags2[:1])\n        names_ = [\"a1\", \"a2\", \"a3\", \"a4\", \"a5\", \"a6\", \"a7\", \"a8\", \"a9\"]\n        types_ = [\"d\", \"d\", \"d\", \"c\", \"s\", \"s\", \"t\", \"t\", \"t\"]\n        flags_ = [\"\", \"\", \"c\", \"\", \"\", \"m\", \"\", \"m\", \"\"]\n        self.assertListEqual(names, names_)\n        self.assertListEqual(types, types_)\n        self.assertListEqual(flags, flags_)\n\n    def test_get_header_data_3(self):\n        names, types, flags = _TableHeader.create_header_data(self.header3[:3])\n        self.assertListEqual(names, [\"a\", \"b\", \"c\", \"d\", \"w\", \"e\", \"f\", \"g\"])\n        self.assertListEqual(\n            types, [\"d\", \"c\", \"c\", \"c\", \"c\", \"d\", \"s\", \"yes no\"])\n        self.assertListEqual(\n            flags, [\"meta\", \"class\", \"meta\", \"\", \"weight\", \"i\", \"\", \"\"])",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 63,
                            "end_lineno": 71,
                            "parent": {
                                "file_name": "Orange/data/tests/test_io_base.py",
                                "type": "class",
                                "name": "TestTableHeader",
                                "content": "class TestTableHeader(InitTestData):\n    def test_rename_variables(self):\n        th = _TableHeader([[\"a\", \"\", \"b\", \"\", \"a\"]])\n        self.assertListEqual(th.names, [\"a (1)\", \"\", \"b\", \"\", \"a (2)\"])\n\n    def test_get_header_data_0(self):\n        names, types, flags = _TableHeader.create_header_data([])\n        self.assertListEqual(names, [])\n        self.assertListEqual(types, [])\n        self.assertListEqual(flags, [])\n\n    def test_get_header_data_1(self):\n        names, types, flags = _TableHeader.create_header_data(self.header1[:1])\n        self.assertListEqual(names, [\"a\", \"b\", \"c\", \"d\"])\n        self.assertListEqual(types, [\"\", \"\", \"\", \"\"])\n        self.assertListEqual(flags, [\"\", \"\", \"\", \"\"])\n\n    def test_get_header_data_1_flags(self):\n        names, types, flags = _TableHeader.create_header_data(\n            self.header1_flags[:1])\n        self.assertListEqual(names, [\"a\", \"b\", \"c\", \"d\", \"e\", \"f\"])\n        self.assertListEqual(types, [\"\", \"c\", \"\", \"\", \"\", \"\"])\n        self.assertListEqual(flags, [\"m\", \"c\", \"m\", \"\", \"i\", \"\"])\n\n    def test_get_header_data_1_flags2(self):\n        names, types, flags = _TableHeader.create_header_data(\n            self.header1_flags2[:1])\n        names_ = [\"a1\", \"a2\", \"a3\", \"a4\", \"a5\", \"a6\", \"a7\", \"a8\", \"a9\"]\n        types_ = [\"d\", \"d\", \"d\", \"c\", \"s\", \"s\", \"t\", \"t\", \"t\"]\n        flags_ = [\"\", \"\", \"c\", \"\", \"\", \"m\", \"\", \"m\", \"\"]\n        self.assertListEqual(names, names_)\n        self.assertListEqual(types, types_)\n        self.assertListEqual(flags, flags_)\n\n    def test_get_header_data_3(self):\n        names, types, flags = _TableHeader.create_header_data(self.header3[:3])\n        self.assertListEqual(names, [\"a\", \"b\", \"c\", \"d\", \"w\", \"e\", \"f\", \"g\"])\n        self.assertListEqual(\n            types, [\"d\", \"c\", \"c\", \"c\", \"c\", \"d\", \"s\", \"yes no\"])\n        self.assertListEqual(\n            flags, [\"meta\", \"class\", \"meta\", \"\", \"weight\", \"i\", \"\", \"\"])",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 39,
                                "end_lineno": 79,
                                "parent": null
                            }
                        }
                    ],
                    "deleted_entities": []
                }
            ]
        }
    ],
    "old_commit_hash": "1d5c28890a282f962a1ba22b7ff2450d2388b7a6^",
    "new_commit_hash": "1d5c28890a282f962a1ba22b7ff2450d2388b7a6",
    "commit_message": "CSVReader, ExcelReader: Consider attribute type in header flags",
    "commit_date": "2024-07-29T14:08:39+02:00",
    "metadata": {}
}
{
    "repo_name": "orange3",
    "new_commit_hash": "1d5c28890a282f962a1ba22b7ff2450d2388b7a6",
    "test_file_codes": [
        "# pylint: disable=protected-access\nimport unittest\nfrom unittest.mock import Mock\n\nimport numpy as np\n\nfrom Orange.data import ContinuousVariable, TimeVariable, \\\n    DiscreteVariable, StringVariable, Table\nfrom Orange.data.io_base import _TableHeader, _TableBuilder, DataTableMixin\n\n\nclass InitTestData(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        cls.header0 = [[\"0.1\", \"0.5\", \"0.1\", \"21.0\"],\n                       [\"0.2\", \"0.1\", \"2.5\", \"123.0\"],\n                       [\"0.0\", \"0.0\", \"0.0\", \"0.0\"]]\n        cls.header1 = [[\"a\", \"b\", \"c\", \"d\"],\n                       [\"red\", \"2019-10-10\", \"0.0\", \"21.0\"],\n                       [\"red\", \"2019-10-12\", \"0.0\", \"123.0\"],\n                       [\"green\", \"2019-10-11\", \"\", \"0.0\"]]\n        cls.header1_flags = [[\"m#a\", \"cC#b\", \"m#c\", \"d\", \"i#e\", \"f\"],\n                             [\"red\", \"0.5\", \"0.0\", \"0.0\", \"aa\", \"a\"],\n                             [\"red\", \"0.1\", \"1.0\", \"1.0\", \"b\", \"b\"],\n                             [\"green\", \"0.0\", \"2.0\", \"2.0\", \"c\", \"c\"]]\n        cls.header1_flags2 = [[\"D#a1\", \"D#a2\", \"cD#a3\", \"C#a4\", \"S#a5\",\n                               \"mS#a6\", \"T#a7\", \"mT#a8\", \"T#a9\"],\n                              [\"\", \"0\", \"\", \"0\", \"a\", \"a\",\n                               \"2024-01-01\", \"2024-01-01\", \"\"],\n                              [\"\", \"1\", \"\", \"1\", \"b\", \"b\",\n                               \"2024-01-01\", \"2024-01-01\", \"\"],\n                              [\"green\", \"0.0\", \"2.0\", \"2.0\", \"c\", \"c\"]]\n        cls.header3 = [[\"a\", \"b\", \"c\", \"d\", \"w\", \"e\", \"f\", \"g\"],\n                       [\"d\", \"c\", \"c\", \"c\", \"c\", \"d\", \"s\", \"yes no\"],\n                       [\"meta\", \"class\", \"meta\", \"\", \"weight\", \"i\", \"\", \"\"],\n                       [\"red\", \"0.5\", \"0.0\", \"0.0\", \"0.5\", \"aa\", \"a\", \"no\"]]\n\n\nclass TestTableHeader(InitTestData):\n    def test_rename_variables(self):\n        th = _TableHeader([[\"a\", \"\", \"b\", \"\", \"a\"]])\n        self.assertListEqual(th.names, [\"a (1)\", \"\", \"b\", \"\", \"a (2)\"])\n\n    def test_get_header_data_0(self):\n        names, types, flags = _TableHeader.create_header_data([])\n        self.assertListEqual(names, [])\n        self.assertListEqual(types, [])\n        self.assertListEqual(flags, [])\n\n    def test_get_header_data_1(self):\n        names, types, flags = _TableHeader.create_header_data(self.header1[:1])\n        self.assertListEqual(names, [\"a\", \"b\", \"c\", \"d\"])\n        self.assertListEqual(types, [\"\", \"\", \"\", \"\"])\n        self.assertListEqual(flags, [\"\", \"\", \"\", \"\"])\n\n    def test_get_header_data_1_flags(self):\n        names, types, flags = _TableHeader.create_header_data(\n            self.header1_flags[:1])\n        self.assertListEqual(names, [\"a\", \"b\", \"c\", \"d\", \"e\", \"f\"])\n        self.assertListEqual(types, [\"\", \"c\", \"\", \"\", \"\", \"\"])\n        self.assertListEqual(flags, [\"m\", \"c\", \"m\", \"\", \"i\", \"\"])\n\n    def test_get_header_data_1_flags2(self):\n        names, types, flags = _TableHeader.create_header_data(\n            self.header1_flags2[:1])\n        names_ = [\"a1\", \"a2\", \"a3\", \"a4\", \"a5\", \"a6\", \"a7\", \"a8\", \"a9\"]\n        types_ = [\"d\", \"d\", \"d\", \"c\", \"s\", \"s\", \"t\", \"t\", \"t\"]\n        flags_ = [\"\", \"\", \"c\", \"\", \"\", \"m\", \"\", \"m\", \"\"]\n        self.assertListEqual(names, names_)\n        self.assertListEqual(types, types_)\n        self.assertListEqual(flags, flags_)\n\n    def test_get_header_data_3(self):\n        names, types, flags = _TableHeader.create_header_data(self.header3[:3])\n        self.assertListEqual(names, [\"a\", \"b\", \"c\", \"d\", \"w\", \"e\", \"f\", \"g\"])\n        self.assertListEqual(\n            types, [\"d\", \"c\", \"c\", \"c\", \"c\", \"d\", \"s\", \"yes no\"])\n        self.assertListEqual(\n            flags, [\"meta\", \"class\", \"meta\", \"\", \"weight\", \"i\", \"\", \"\"])\n\n\nclass TestTableBuilder(InitTestData):\n    def test_string_column(self):\n        data = np.array(self.header1[1:])\n        creator = _TableBuilder._get_column_creator(\"s\")\n        column = creator(data, 0)\n\n        self.assertIsNone(column.valuemap)\n        np.testing.assert_array_equal(column.values, [\"red\", \"red\", \"green\"])\n        np.testing.assert_array_equal(column.orig_values,\n                                      [\"red\", \"red\", \"green\"])\n        self.assertEqual(column.coltype, StringVariable)\n        self.assertDictEqual(column.coltype_kwargs, {})\n\n    def test_continuous_column(self):\n        data = np.array(self.header0)\n        creator = _TableBuilder._get_column_creator(\"c\")\n        column = creator(data, 0)\n\n        self.assertIsNone(column.valuemap)\n        np.testing.assert_array_equal(column.values, [0.1, 0.2, 0])\n        np.testing.assert_array_equal(column.orig_values,\n                                      [\"0.1\", \"0.2\", \"0.0\"])\n        self.assertEqual(column.coltype, ContinuousVariable)\n        self.assertDictEqual(column.coltype_kwargs, {})\n\n    def test_continuous_column_raises(self):\n        data = np.array([[\"a\", \"2\"], [\"3\", \"4\"]])\n        creator = _TableBuilder._get_column_creator(\"c\")\n        self.assertRaises(ValueError, creator, data, 0)\n\n    def test_time_column(self):\n        data = np.array(self.header1[1:])\n        creator = _TableBuilder._get_column_creator(\"t\")\n        column = creator(data, 1)\n\n        self.assertIsNone(column.valuemap)\n        np.testing.assert_array_equal(\n            column.values, ['2019-10-10', '2019-10-12', '2019-10-11'])\n        np.testing.assert_array_equal(\n            column.orig_values, ['2019-10-10', '2019-10-12', '2019-10-11'])\n        self.assertEqual(column.coltype, TimeVariable)\n        self.assertDictEqual(column.coltype_kwargs, {})\n\n    def test_discrete_column(self):\n        data = np.array(self.header1[1:])\n        creator = _TableBuilder._get_column_creator(\"d\")\n        columns = creator(data, 0)\n\n        self.assertListEqual(columns.valuemap, ['green', 'red'])\n        np.testing.assert_array_equal(columns.values, [\"red\", \"red\", \"green\"])\n        np.testing.assert_array_equal(columns.orig_values,\n                                      [\"red\", \"red\", \"green\"])\n        self.assertEqual(columns.coltype, DiscreteVariable)\n        self.assertDictEqual(columns.coltype_kwargs, {})\n\n    def test_column_parts_discrete_values(self):\n        data = np.array(self.header1[1:])\n        vals = \"green red\"\n        creator = _TableBuilder._get_column_creator(vals)\n        column = creator(data, 0, vals)\n\n        self.assertListEqual(column.valuemap, ['green', 'red'])\n        np.testing.assert_array_equal(column.values, [\"red\", \"red\", \"green\"])\n        np.testing.assert_array_equal(column.orig_values,\n                                      [\"red\", \"red\", \"green\"])\n        self.assertEqual(column.coltype, DiscreteVariable)\n        self.assertDictEqual(column.coltype_kwargs, {})\n\n    def test_unknown_type_column(self):\n        data = np.array(self.header0)\n        creator = _TableBuilder._get_column_creator(\"\")\n        column = creator(data, 0)\n\n        self.assertIsNone(column.valuemap)\n        np.testing.assert_array_equal(column.values, [0.1, 0.2, 0])\n        np.testing.assert_array_equal(column.orig_values, [\"0.1\", \"0.2\", \"0.0\"])\n        self.assertEqual(column.coltype, ContinuousVariable)\n        self.assertDictEqual(column.coltype_kwargs, {})\n\n\nclass TestDataTableMixin(InitTestData):\n    def test_data_table_empty(self):\n        self.assertIsInstance(DataTableMixin.data_table([]), Table)\n\n    def test_data_table_0(self):\n        self.assertIsInstance(DataTableMixin.data_table(self.header0), Table)\n\n    def test_data_table_1(self):\n        self.assertIsInstance(DataTableMixin.data_table(self.header1), Table)\n\n    def test_data_table_1_flags(self):\n        self.assertIsInstance(DataTableMixin.data_table(\n            self.header1_flags), Table)\n\n    def test_data_table_3(self):\n        self.assertIsInstance(DataTableMixin.data_table(self.header3), Table)\n\n    def test_parse_headers_empty(self):\n        headers, data = DataTableMixin.parse_headers([])\n        self.assertListEqual(headers, [])\n        self.assertListEqual(list(data), [])\n\n    def test_parse_headers_0(self):\n        hdata = self.header0\n        headers, data = DataTableMixin.parse_headers(hdata)\n        self.assertListEqual(headers, [])\n        self.assertListEqual(list(data), hdata)\n\n    def test_parse_headers_1(self):\n        hdata = self.header1\n        headers, data = DataTableMixin.parse_headers(hdata)\n        self.assertListEqual(headers, [[\"a\", \"b\", \"c\", \"d\"]])\n        self.assertListEqual(list(data), hdata[1:])\n\n    def test_parse_headers_1_flags(self):\n        hdata = self.header1_flags\n        headers, data = DataTableMixin.parse_headers(hdata)\n        self.assertListEqual(\n            headers, [[\"m#a\", \"cC#b\", \"m#c\", \"d\", \"i#e\", \"f\"]])\n        self.assertListEqual(list(data), hdata[1:])\n\n    def test_parse_headers_3(self):\n        hdata = self.header3\n        headers, data = DataTableMixin.parse_headers(hdata)\n        self.assertListEqual(\n            headers, [[\"a\", \"b\", \"c\", \"d\", \"w\", \"e\", \"f\", \"g\"],\n                      [\"d\", \"c\", \"c\", \"c\", \"c\", \"d\", \"s\", \"yes no\"],\n                      [\"meta\", \"class\", \"meta\", \"\", \"weight\", \"i\", \"\", \"\"]])\n        self.assertListEqual(list(data), hdata[3:])\n\n    def test_adjust_data_width_lengthen(self):\n        names, types, flags = [\"a\", \"b\", \"c\", \"d\", \"e\"], [\"\", \"c\"], [\"m\", \"c\"]\n        header = Mock()\n        header.names = names\n        header.types = types\n        header.flags = flags\n        adjusted, n = DataTableMixin.adjust_data_width(self.header0, header)\n        _data = np.hstack((np.array(self.header0, dtype=object),\n                           np.array([[\"\"]] * 3, dtype=object)))\n        np.testing.assert_array_equal(_data, adjusted)\n        self.assertEqual(adjusted.shape, (len(self.header0), 5))\n        self.assertEqual(n, 5)\n        self.assertListEqual(names, [\"a\", \"b\", \"c\", \"d\", \"e\"])\n        self.assertListEqual(types, [\"\", \"c\", \"\", \"\", \"\"])\n        self.assertListEqual(flags, [\"m\", \"c\", \"\", \"\", \"\"])\n\n    def test_adjust_data_width_shorten(self):\n        names, types, flags = [\"a\", \"b\", \"c\"], [\"\", \"c\"], [\"m\", \"c\"]\n        header = Mock()\n        header.names = names\n        header.types = types\n        header.flags = flags\n        with self.assertWarns(UserWarning):\n            adjusted, n = DataTableMixin.adjust_data_width(self.header0, header)\n        np.testing.assert_array_equal(\n            adjusted, np.array(self.header0, dtype=object)[:, :3])\n        self.assertEqual(adjusted.shape, (len(self.header0), 3))\n        self.assertEqual(n, 3)\n        self.assertListEqual(names, [\"a\", \"b\", \"c\"])\n        self.assertListEqual(types, [\"\", \"c\", \"\"])\n        self.assertListEqual(flags, [\"m\", \"c\", \"\"])\n\n    def test_adjust_data_width_empty(self):\n        names, types, flags = [\"a\", \"b\"], [], []\n        header = Mock()\n        header.names = names\n        header.types = types\n        header.flags = flags\n        data = [[\"\", \"\"], [\"\", \"\"]]\n        adjusted, n = DataTableMixin.adjust_data_width(data, header)\n        np.testing.assert_array_equal(adjusted, [])\n        self.assertEqual(n, 2)\n        self.assertListEqual(names, [\"a\", \"b\"])\n        self.assertListEqual(types, [])\n        self.assertListEqual(flags, [])\n\n\nif __name__ == '__main__':\n    unittest.main()\n"
    ],
    "test_file_names": [
        "test_1.py"
    ],
    "setup_res_code": 0,
    "setup_res_stdout": "Starting Orange installation attempts...\nAttempting installation with Python 3.7...\nCompiling Orange/classification/_tree_scorers.pyx because it changed.\nCompiling Orange/data/_contingency.pyx because it changed.\nCompiling Orange/data/_io.pyx because it changed.\nCompiling Orange/data/_valuecount.pyx because it changed.\nCompiling Orange/data/_variable.pyx because it changed.\nCompiling Orange/distance/_distance.pyx because it changed.\nCompiling Orange/preprocess/_discretize.pyx because it changed.\nCompiling Orange/preprocess/_relieff.pyx because it changed.\nCompiling Orange/projection/_som.pyx because it changed.\n[1/9] Cythonizing Orange/classification/_tree_scorers.pyx\n[2/9] Cythonizing Orange/data/_contingency.pyx\n[3/9] Cythonizing Orange/data/_io.pyx\n[4/9] Cythonizing Orange/data/_valuecount.pyx\n[5/9] Cythonizing Orange/data/_variable.pyx\n[6/9] Cythonizing Orange/distance/_distance.pyx\n[7/9] Cythonizing Orange/preprocess/_discretize.pyx\n[8/9] Cythonizing Orange/preprocess/_relieff.pyx\n[9/9] Cythonizing Orange/projection/_som.pyx\nrunning build_ext\nbuilding 'Orange.classification._simple_tree' extension\ncreating build\ncreating build/temp.linux-x86_64-3.7\ncreating build/temp.linux-x86_64-3.7/Orange\ncreating build/temp.linux-x86_64-3.7/Orange/classification\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/classification/_simple_tree.c -o build/temp.linux-x86_64-3.7/Orange/classification/_simple_tree.o\ncreating build/lib.linux-x86_64-3.7\ncreating build/lib.linux-x86_64-3.7/Orange\ncreating build/lib.linux-x86_64-3.7/Orange/classification\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/classification/_simple_tree.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/classification/_simple_tree.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.widgets.utils._grid_density' extension\ncreating build/temp.linux-x86_64-3.7/Orange/widgets\ncreating build/temp.linux-x86_64-3.7/Orange/widgets/utils\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/widgets/utils/_grid_density.cpp -o build/temp.linux-x86_64-3.7/Orange/widgets/utils/_grid_density.o\ncreating build/lib.linux-x86_64-3.7/Orange/widgets\ncreating build/lib.linux-x86_64-3.7/Orange/widgets/utils\nc++ -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/widgets/utils/_grid_density.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/widgets/utils/_grid_density.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.classification._tree_scorers' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/classification/_tree_scorers.c -o build/temp.linux-x86_64-3.7/Orange/classification/_tree_scorers.o\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/classification/_tree_scorers.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/classification/_tree_scorers.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.data._contingency' extension\ncreating build/temp.linux-x86_64-3.7/Orange/data\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/data/_contingency.c -o build/temp.linux-x86_64-3.7/Orange/data/_contingency.o\ncreating build/lib.linux-x86_64-3.7/Orange/data\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/data/_contingency.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/data/_contingency.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.data._io' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/data/_io.c -o build/temp.linux-x86_64-3.7/Orange/data/_io.o\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/data/_io.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/data/_io.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.data._valuecount' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/data/_valuecount.c -o build/temp.linux-x86_64-3.7/Orange/data/_valuecount.o\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/data/_valuecount.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/data/_valuecount.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.data._variable' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/data/_variable.c -o build/temp.linux-x86_64-3.7/Orange/data/_variable.o\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/data/_variable.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/data/_variable.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.distance._distance' extension\ncreating build/temp.linux-x86_64-3.7/Orange/distance\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/distance/_distance.c -o build/temp.linux-x86_64-3.7/Orange/distance/_distance.o\ncreating build/lib.linux-x86_64-3.7/Orange/distance\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/distance/_distance.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/distance/_distance.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.preprocess._discretize' extension\ncreating build/temp.linux-x86_64-3.7/Orange/preprocess\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/preprocess/_discretize.c -o build/temp.linux-x86_64-3.7/Orange/preprocess/_discretize.o\ncreating build/lib.linux-x86_64-3.7/Orange/preprocess\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/preprocess/_discretize.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/preprocess/_discretize.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.preprocess._relieff' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/preprocess/_relieff.cpp -o build/temp.linux-x86_64-3.7/Orange/preprocess/_relieff.o\nc++ -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/preprocess/_relieff.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/preprocess/_relieff.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.projection._som' extension\ncreating build/temp.linux-x86_64-3.7/Orange/projection\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/projection/_som.c -o build/temp.linux-x86_64-3.7/Orange/projection/_som.o\ncreating build/lib.linux-x86_64-3.7/Orange/projection\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/projection/_som.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/projection/_som.cpython-37m-x86_64-linux-gnu.so\ncopying build/lib.linux-x86_64-3.7/Orange/classification/_simple_tree.cpython-37m-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-3.7/Orange/widgets/utils/_grid_density.cpython-37m-x86_64-linux-gnu.so -> Orange/widgets/utils\ncopying build/lib.linux-x86_64-3.7/Orange/classification/_tree_scorers.cpython-37m-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-3.7/Orange/data/_contingency.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/data/_io.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/data/_valuecount.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/data/_variable.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/distance/_distance.cpython-37m-x86_64-linux-gnu.so -> Orange/distance\ncopying build/lib.linux-x86_64-3.7/Orange/preprocess/_discretize.cpython-37m-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-3.7/Orange/preprocess/_relieff.cpython-37m-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-3.7/Orange/projection/_som.cpython-37m-x86_64-linux-gnu.so -> Orange/projection\nrunning develop\nrunning egg_info\ncreating Orange3.egg-info\nwriting Orange3.egg-info/PKG-INFO\nwriting dependency_links to Orange3.egg-info/dependency_links.txt\nwriting entry points to Orange3.egg-info/entry_points.txt\nwriting requirements to Orange3.egg-info/requires.txt\nwriting top-level names to Orange3.egg-info/top_level.txt\nwriting manifest file 'Orange3.egg-info/SOURCES.txt'\nreading manifest file 'Orange3.egg-info/SOURCES.txt'\nreading manifest template 'MANIFEST.in'\nadding license file 'LICENSE'\nwriting manifest file 'Orange3.egg-info/SOURCES.txt'\nrunning build_ext\ncopying build/lib.linux-x86_64-3.7/Orange/classification/_simple_tree.cpython-37m-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-3.7/Orange/widgets/utils/_grid_density.cpython-37m-x86_64-linux-gnu.so -> Orange/widgets/utils\ncopying build/lib.linux-x86_64-3.7/Orange/classification/_tree_scorers.cpython-37m-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-3.7/Orange/data/_contingency.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/data/_io.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/data/_valuecount.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/data/_variable.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/distance/_distance.cpython-37m-x86_64-linux-gnu.so -> Orange/distance\ncopying build/lib.linux-x86_64-3.7/Orange/preprocess/_discretize.cpython-37m-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-3.7/Orange/preprocess/_relieff.cpython-37m-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-3.7/Orange/projection/_som.cpython-37m-x86_64-linux-gnu.so -> Orange/projection\nCreating /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/Orange3.egg-link (link to .)\nAdding Orange3 3.38.0.dev0+1d5c288 to easy-install.pth file\nInstalling orange-canvas script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6\nProcessing dependencies for Orange3==3.38.0.dev0+1d5c288\nSearching for xlsxwriter\nReading https://pypi.org/simple/xlsxwriter/\nDownloading https://files.pythonhosted.org/packages/a7/ea/53d1fe468e63e092cf16e2c18d16f50c29851242f9dd12d6a66e0d7f0d02/XlsxWriter-3.2.0-py3-none-any.whl#sha256=ecfd5405b3e0e228219bcaf24c2ca0915e012ca9464a14048021d21a995d490e\nBest match: XlsxWriter 3.2.0\nProcessing XlsxWriter-3.2.0-py3-none-any.whl\nInstalling XlsxWriter-3.2.0-py3-none-any.whl to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages\nAdding XlsxWriter 3.2.0 to easy-install.pth file\nInstalling vba_extract.py script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/XlsxWriter-3.2.0-py3.7.egg\nSearching for xlrd>=1.2.0\nReading https://pypi.org/simple/xlrd/\nDownloading https://files.pythonhosted.org/packages/a6/0c/c2a72d51fe56e08a08acc85d13013558a2d793028ae7385448a6ccdfae64/xlrd-2.0.1-py2.py3-none-any.whl#sha256=6a33ee89877bd9abc1158129f6e94be74e2679636b8a205b43b85206c3f0bbdd\nBest match: xlrd 2.0.1\nProcessing xlrd-2.0.1-py2.py3-none-any.whl\nInstalling xlrd-2.0.1-py2.py3-none-any.whl to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages\nAdding xlrd 2.0.1 to easy-install.pth file\nInstalling runxlrd.py script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/xlrd-2.0.1-py3.7.egg\nSearching for xgboost<2.1,>=1.7.4\nReading https://pypi.org/simple/xgboost/\nDownloading https://files.pythonhosted.org/packages/c3/eb/496aa2f5d356af4185f770bc76055307f8d1870e11016b10fd779b21769c/xgboost-2.0.3-py3-none-manylinux2014_x86_64.whl#sha256=30bd5f789fad467fd49e04e5d19e04238b931682c3951a514da5c2410b3bf59c\nBest match: xgboost 2.0.3\nProcessing xgboost-2.0.3-py3-none-manylinux2014_x86_64.whl\nInstalling xgboost-2.0.3-py3-none-manylinux2014_x86_64.whl to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages\nAdding xgboost 2.0.3 to easy-install.pth file\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/xgboost-2.0.3-py3.7-linux-x86_64.egg\nSearching for serverfiles\nReading https://pypi.org/simple/serverfiles/\nDownloading https://files.pythonhosted.org/packages/33/36/794fe1c4ed29b0f56a26d72e956baf0285d8fe9e29faf36797a28a8ad45b/serverfiles-0.3.1.tar.gz#sha256=5e10fc32e758791e3735bc08bce2ed4702a83b1e45ab96c5d59cc8aeecfbebe1\nBest match: serverfiles 0.3.1\nProcessing serverfiles-0.3.1.tar.gz\nWriting /tmp/easy_install-nfp64f5j/serverfiles-0.3.1/setup.cfg\nRunning serverfiles-0.3.1/setup.py -q bdist_egg --dist-dir /tmp/easy_install-nfp64f5j/serverfiles-0.3.1/egg-dist-tmp-_6as4_5g\ncreating /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/serverfiles-0.3.1-py3.7.egg\nExtracting serverfiles-0.3.1-py3.7.egg to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages\nAdding serverfiles 0.3.1 to easy-install.pth file\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/serverfiles-0.3.1-py3.7.egg\nSearching for scipy>=1.9\nReading https://pypi.org/simple/scipy/\nDownloading https://files.pythonhosted.org/packages/d9/7b/2b8ac283cf32465ed08bc20a83d559fe7b174a484781702ba8accea001d6/scipy-1.15.0.tar.gz#sha256=300742e2cc94e36a2880ebe464a1c8b4352a7b0f3e36ec3d2ac006cdbe0219ac\nBest match: scipy 1.15.0\nProcessing scipy-1.15.0.tar.gz\nVerifying Orange installation...\n❌ Orange verification failed\nPython 3.7 installation failed, trying Python 3.8...\nAttempting installation with Python 3.8...\nrunning build_ext\nbuilding 'Orange.classification._simple_tree' extension\ncreating build/temp.linux-x86_64-cpython-38/Orange/classification\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/classification/_simple_tree.c -o build/temp.linux-x86_64-cpython-38/Orange/classification/_simple_tree.o\ncreating build/lib.linux-x86_64-cpython-38/Orange/classification\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/classification/_simple_tree.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/classification/_simple_tree.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.widgets.utils._grid_density' extension\ncreating build/temp.linux-x86_64-cpython-38/Orange/widgets/utils\nc++ -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/widgets/utils/_grid_density.cpp -o build/temp.linux-x86_64-cpython-38/Orange/widgets/utils/_grid_density.o\ncreating build/lib.linux-x86_64-cpython-38/Orange/widgets/utils\nc++ -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -pthread -shared build/temp.linux-x86_64-cpython-38/Orange/widgets/utils/_grid_density.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/widgets/utils/_grid_density.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.classification._tree_scorers' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/classification/_tree_scorers.c -o build/temp.linux-x86_64-cpython-38/Orange/classification/_tree_scorers.o\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/classification/_tree_scorers.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/classification/_tree_scorers.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.data._contingency' extension\ncreating build/temp.linux-x86_64-cpython-38/Orange/data\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/data/_contingency.c -o build/temp.linux-x86_64-cpython-38/Orange/data/_contingency.o\ncreating build/lib.linux-x86_64-cpython-38/Orange/data\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/data/_contingency.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/data/_contingency.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.data._io' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/data/_io.c -o build/temp.linux-x86_64-cpython-38/Orange/data/_io.o\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/data/_io.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/data/_io.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.data._valuecount' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/data/_valuecount.c -o build/temp.linux-x86_64-cpython-38/Orange/data/_valuecount.o\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/data/_valuecount.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/data/_valuecount.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.data._variable' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/data/_variable.c -o build/temp.linux-x86_64-cpython-38/Orange/data/_variable.o\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/data/_variable.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/data/_variable.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.distance._distance' extension\ncreating build/temp.linux-x86_64-cpython-38/Orange/distance\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/distance/_distance.c -o build/temp.linux-x86_64-cpython-38/Orange/distance/_distance.o\ncreating build/lib.linux-x86_64-cpython-38/Orange/distance\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/distance/_distance.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/distance/_distance.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.preprocess._discretize' extension\ncreating build/temp.linux-x86_64-cpython-38/Orange/preprocess\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/preprocess/_discretize.c -o build/temp.linux-x86_64-cpython-38/Orange/preprocess/_discretize.o\ncreating build/lib.linux-x86_64-cpython-38/Orange/preprocess\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/preprocess/_discretize.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/preprocess/_discretize.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.preprocess._relieff' extension\nc++ -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/preprocess/_relieff.cpp -o build/temp.linux-x86_64-cpython-38/Orange/preprocess/_relieff.o\nc++ -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -pthread -shared build/temp.linux-x86_64-cpython-38/Orange/preprocess/_relieff.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/preprocess/_relieff.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.projection._som' extension\ncreating build/temp.linux-x86_64-cpython-38/Orange/projection\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/projection/_som.c -o build/temp.linux-x86_64-cpython-38/Orange/projection/_som.o\ncreating build/lib.linux-x86_64-cpython-38/Orange/projection\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/projection/_som.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/projection/_som.cpython-38-x86_64-linux-gnu.so\ncopying build/lib.linux-x86_64-cpython-38/Orange/classification/_simple_tree.cpython-38-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-cpython-38/Orange/widgets/utils/_grid_density.cpython-38-x86_64-linux-gnu.so -> Orange/widgets/utils\ncopying build/lib.linux-x86_64-cpython-38/Orange/classification/_tree_scorers.cpython-38-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_contingency.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_io.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_valuecount.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_variable.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/distance/_distance.cpython-38-x86_64-linux-gnu.so -> Orange/distance\ncopying build/lib.linux-x86_64-cpython-38/Orange/preprocess/_discretize.cpython-38-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-cpython-38/Orange/preprocess/_relieff.cpython-38-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-cpython-38/Orange/projection/_som.cpython-38-x86_64-linux-gnu.so -> Orange/projection\nrunning develop\nrunning egg_info\nwriting Orange3.egg-info/PKG-INFO\nwriting dependency_links to Orange3.egg-info/dependency_links.txt\nwriting entry points to Orange3.egg-info/entry_points.txt\nwriting requirements to Orange3.egg-info/requires.txt\nwriting top-level names to Orange3.egg-info/top_level.txt\ndependency 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manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_math.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayscalars.h won't 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/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_math.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_math.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_math.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative\nreading manifest file 'Orange3.egg-info/SOURCES.txt'\nreading manifest template 'MANIFEST.in'\nadding license file 'LICENSE'\nwriting manifest file 'Orange3.egg-info/SOURCES.txt'\nrunning build_ext\ncopying build/lib.linux-x86_64-cpython-38/Orange/classification/_simple_tree.cpython-38-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-cpython-38/Orange/widgets/utils/_grid_density.cpython-38-x86_64-linux-gnu.so -> Orange/widgets/utils\ncopying build/lib.linux-x86_64-cpython-38/Orange/classification/_tree_scorers.cpython-38-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_contingency.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_io.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_valuecount.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_variable.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/distance/_distance.cpython-38-x86_64-linux-gnu.so -> Orange/distance\ncopying build/lib.linux-x86_64-cpython-38/Orange/preprocess/_discretize.cpython-38-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-cpython-38/Orange/preprocess/_relieff.cpython-38-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-cpython-38/Orange/projection/_som.cpython-38-x86_64-linux-gnu.so -> Orange/projection\nCreating /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/Orange3.egg-link (link to .)\nAdding Orange3 3.38.0.dev0+1d5c288 to easy-install.pth file\nInstalling orange-canvas script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6\nProcessing dependencies for Orange3==3.38.0.dev0+1d5c288\nSearching for xlsxwriter\nReading https://pypi.org/simple/xlsxwriter/\nDownloading https://files.pythonhosted.org/packages/a7/ea/53d1fe468e63e092cf16e2c18d16f50c29851242f9dd12d6a66e0d7f0d02/XlsxWriter-3.2.0-py3-none-any.whl#sha256=ecfd5405b3e0e228219bcaf24c2ca0915e012ca9464a14048021d21a995d490e\nBest match: XlsxWriter 3.2.0\nProcessing XlsxWriter-3.2.0-py3-none-any.whl\nInstalling XlsxWriter-3.2.0-py3-none-any.whl to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages\nAdding XlsxWriter 3.2.0 to easy-install.pth file\nInstalling vba_extract.py script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/XlsxWriter-3.2.0-py3.8.egg\nSearching for xlrd>=1.2.0\nReading https://pypi.org/simple/xlrd/\nDownloading https://files.pythonhosted.org/packages/a6/0c/c2a72d51fe56e08a08acc85d13013558a2d793028ae7385448a6ccdfae64/xlrd-2.0.1-py2.py3-none-any.whl#sha256=6a33ee89877bd9abc1158129f6e94be74e2679636b8a205b43b85206c3f0bbdd\nBest match: xlrd 2.0.1\nProcessing xlrd-2.0.1-py2.py3-none-any.whl\nInstalling xlrd-2.0.1-py2.py3-none-any.whl to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages\nAdding xlrd 2.0.1 to easy-install.pth file\ndetected new path './XlsxWriter-3.2.0-py3.8.egg'\nInstalling runxlrd.py script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/xlrd-2.0.1-py3.8.egg\nSearching for xgboost<2.1,>=1.7.4\nReading https://pypi.org/simple/xgboost/\nDownloading https://files.pythonhosted.org/packages/c3/eb/496aa2f5d356af4185f770bc76055307f8d1870e11016b10fd779b21769c/xgboost-2.0.3-py3-none-manylinux2014_x86_64.whl#sha256=30bd5f789fad467fd49e04e5d19e04238b931682c3951a514da5c2410b3bf59c\nBest match: xgboost 2.0.3\nProcessing xgboost-2.0.3-py3-none-manylinux2014_x86_64.whl\nInstalling xgboost-2.0.3-py3-none-manylinux2014_x86_64.whl to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages\nAdding xgboost 2.0.3 to easy-install.pth file\ndetected new path './xlrd-2.0.1-py3.8.egg'\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/xgboost-2.0.3-py3.8-linux-x86_64.egg\nSearching for serverfiles\nReading https://pypi.org/simple/serverfiles/\nDownloading https://files.pythonhosted.org/packages/33/36/794fe1c4ed29b0f56a26d72e956baf0285d8fe9e29faf36797a28a8ad45b/serverfiles-0.3.1.tar.gz#sha256=5e10fc32e758791e3735bc08bce2ed4702a83b1e45ab96c5d59cc8aeecfbebe1\nBest match: serverfiles 0.3.1\nProcessing serverfiles-0.3.1.tar.gz\nWriting /tmp/easy_install-m15ukm_5/serverfiles-0.3.1/setup.cfg\nRunning serverfiles-0.3.1/setup.py -q bdist_egg --dist-dir /tmp/easy_install-m15ukm_5/serverfiles-0.3.1/egg-dist-tmp-upfk_v75\nAdding serverfiles 0.3.1 to easy-install.pth file\ndetected new path './xgboost-2.0.3-py3.8-linux-x86_64.egg'\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/serverfiles-0.3.1-py3.8.egg\nSearching for scipy>=1.9\nReading https://pypi.org/simple/scipy/\nDownloading https://files.pythonhosted.org/packages/d9/7b/2b8ac283cf32465ed08bc20a83d559fe7b174a484781702ba8accea001d6/scipy-1.15.0.tar.gz#sha256=300742e2cc94e36a2880ebe464a1c8b4352a7b0f3e36ec3d2ac006cdbe0219ac\nBest match: scipy 1.15.0\nProcessing scipy-1.15.0.tar.gz\nVerifying Orange installation...\n❌ Orange verification failed\nPython 3.7,3.8 installation failed, trying Python 3.8...\nAttempting installation with Python 3.10...\nCompiling Orange/classification/_tree_scorers.pyx because it changed.\nCompiling Orange/data/_contingency.pyx because it changed.\nCompiling Orange/data/_io.pyx because it changed.\nCompiling Orange/data/_valuecount.pyx because it changed.\nCompiling Orange/data/_variable.pyx because it changed.\nCompiling Orange/distance/_distance.pyx because it changed.\nCompiling Orange/preprocess/_discretize.pyx because it changed.\nCompiling Orange/preprocess/_relieff.pyx because it changed.\nCompiling Orange/projection/_som.pyx because it changed.\n[1/9] Cythonizing Orange/classification/_tree_scorers.pyx\n[2/9] Cythonizing Orange/data/_contingency.pyx\n[3/9] Cythonizing Orange/data/_io.pyx\n[4/9] Cythonizing Orange/data/_valuecount.pyx\n[5/9] Cythonizing Orange/data/_variable.pyx\n[6/9] Cythonizing Orange/distance/_distance.pyx\n[7/9] Cythonizing Orange/preprocess/_discretize.pyx\n[8/9] Cythonizing Orange/preprocess/_relieff.pyx\n[9/9] Cythonizing Orange/projection/_som.pyx\nrunning build_ext\nbuilding 'Orange.classification._simple_tree' extension\ncreating build/temp.linux-x86_64-cpython-310/Orange/classification\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/include/python3.10 -c Orange/classification/_simple_tree.c -o build/temp.linux-x86_64-cpython-310/Orange/classification/_simple_tree.o\ncreating build/lib.linux-x86_64-cpython-310/Orange/classification\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-310/Orange/classification/_simple_tree.o -L/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-310/Orange/classification/_simple_tree.cpython-310-x86_64-linux-gnu.so\nbuilding 'Orange.widgets.utils._grid_density' extension\ncreating build/temp.linux-x86_64-cpython-310/Orange/widgets/utils\nc++ -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/include/python3.10 -c Orange/widgets/utils/_grid_density.cpp -o build/temp.linux-x86_64-cpython-310/Orange/widgets/utils/_grid_density.o\ncreating build/lib.linux-x86_64-cpython-310/Orange/widgets/utils\nc++ -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -pthread -shared build/temp.linux-x86_64-cpython-310/Orange/widgets/utils/_grid_density.o -L/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-310/Orange/widgets/utils/_grid_density.cpython-310-x86_64-linux-gnu.so\nbuilding 'Orange.classification._tree_scorers' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/include/python3.10 -c Orange/classification/_tree_scorers.c -o build/temp.linux-x86_64-cpython-310/Orange/classification/_tree_scorers.o\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-310/Orange/classification/_tree_scorers.o -L/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-310/Orange/classification/_tree_scorers.cpython-310-x86_64-linux-gnu.so\nbuilding 'Orange.data._contingency' extension\ncreating build/temp.linux-x86_64-cpython-310/Orange/data\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/include/python3.10 -c Orange/data/_contingency.c -o build/temp.linux-x86_64-cpython-310/Orange/data/_contingency.o\ncreating build/lib.linux-x86_64-cpython-310/Orange/data\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-310/Orange/data/_contingency.o -L/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-310/Orange/data/_contingency.cpython-310-x86_64-linux-gnu.so\nbuilding 'Orange.data._io' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/include/python3.10 -c Orange/data/_io.c -o build/temp.linux-x86_64-cpython-310/Orange/data/_io.o\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-310/Orange/data/_io.o -L/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-310/Orange/data/_io.cpython-310-x86_64-linux-gnu.so\nbuilding 'Orange.data._valuecount' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/include/python3.10 -c Orange/data/_valuecount.c -o build/temp.linux-x86_64-cpython-310/Orange/data/_valuecount.o\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-310/Orange/data/_valuecount.o -L/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-310/Orange/data/_valuecount.cpython-310-x86_64-linux-gnu.so\nbuilding 'Orange.data._variable' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/include/python3.10 -c Orange/data/_variable.c -o build/temp.linux-x86_64-cpython-310/Orange/data/_variable.o\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-310/Orange/data/_variable.o -L/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-310/Orange/data/_variable.cpython-310-x86_64-linux-gnu.so\nbuilding 'Orange.distance._distance' extension\ncreating build/temp.linux-x86_64-cpython-310/Orange/distance\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/include/python3.10 -c Orange/distance/_distance.c -o build/temp.linux-x86_64-cpython-310/Orange/distance/_distance.o\ncreating build/lib.linux-x86_64-cpython-310/Orange/distance\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-310/Orange/distance/_distance.o -L/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-310/Orange/distance/_distance.cpython-310-x86_64-linux-gnu.so\nbuilding 'Orange.preprocess._discretize' extension\ncreating build/temp.linux-x86_64-cpython-310/Orange/preprocess\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/include/python3.10 -c Orange/preprocess/_discretize.c -o build/temp.linux-x86_64-cpython-310/Orange/preprocess/_discretize.o\ncreating build/lib.linux-x86_64-cpython-310/Orange/preprocess\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-310/Orange/preprocess/_discretize.o -L/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-310/Orange/preprocess/_discretize.cpython-310-x86_64-linux-gnu.so\nbuilding 'Orange.preprocess._relieff' extension\nc++ -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/include/python3.10 -c Orange/preprocess/_relieff.cpp -o build/temp.linux-x86_64-cpython-310/Orange/preprocess/_relieff.o\nc++ -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -pthread -shared build/temp.linux-x86_64-cpython-310/Orange/preprocess/_relieff.o -L/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-310/Orange/preprocess/_relieff.cpython-310-x86_64-linux-gnu.so\nbuilding 'Orange.projection._som' extension\ncreating build/temp.linux-x86_64-cpython-310/Orange/projection\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/include/python3.10 -c Orange/projection/_som.c -o build/temp.linux-x86_64-cpython-310/Orange/projection/_som.o\ncreating build/lib.linux-x86_64-cpython-310/Orange/projection\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-310/Orange/projection/_som.o -L/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-310/Orange/projection/_som.cpython-310-x86_64-linux-gnu.so\ncopying build/lib.linux-x86_64-cpython-310/Orange/classification/_simple_tree.cpython-310-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-cpython-310/Orange/widgets/utils/_grid_density.cpython-310-x86_64-linux-gnu.so -> Orange/widgets/utils\ncopying build/lib.linux-x86_64-cpython-310/Orange/classification/_tree_scorers.cpython-310-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-cpython-310/Orange/data/_contingency.cpython-310-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-310/Orange/data/_io.cpython-310-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-310/Orange/data/_valuecount.cpython-310-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-310/Orange/data/_variable.cpython-310-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-310/Orange/distance/_distance.cpython-310-x86_64-linux-gnu.so -> Orange/distance\ncopying build/lib.linux-x86_64-cpython-310/Orange/preprocess/_discretize.cpython-310-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-cpython-310/Orange/preprocess/_relieff.cpython-310-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-cpython-310/Orange/projection/_som.cpython-310-x86_64-linux-gnu.so -> Orange/projection\nrunning develop\nrunning egg_info\nwriting Orange3.egg-info/PKG-INFO\nwriting dependency_links to Orange3.egg-info/dependency_links.txt\nwriting entry points to Orange3.egg-info/entry_points.txt\nwriting requirements to Orange3.egg-info/requires.txt\nwriting top-level names to Orange3.egg-info/top_level.txt\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/npy_math.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/npy_math.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/npy_math.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/npy_math.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/npy_math.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/npy_math.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative\nreading manifest file 'Orange3.egg-info/SOURCES.txt'\nreading manifest template 'MANIFEST.in'\nadding license file 'LICENSE'\nwriting manifest file 'Orange3.egg-info/SOURCES.txt'\nrunning build_ext\ncopying build/lib.linux-x86_64-cpython-310/Orange/classification/_simple_tree.cpython-310-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-cpython-310/Orange/widgets/utils/_grid_density.cpython-310-x86_64-linux-gnu.so -> Orange/widgets/utils\ncopying build/lib.linux-x86_64-cpython-310/Orange/classification/_tree_scorers.cpython-310-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-cpython-310/Orange/data/_contingency.cpython-310-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-310/Orange/data/_io.cpython-310-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-310/Orange/data/_valuecount.cpython-310-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-310/Orange/data/_variable.cpython-310-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-310/Orange/distance/_distance.cpython-310-x86_64-linux-gnu.so -> Orange/distance\ncopying build/lib.linux-x86_64-cpython-310/Orange/preprocess/_discretize.cpython-310-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-cpython-310/Orange/preprocess/_relieff.cpython-310-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-cpython-310/Orange/projection/_som.cpython-310-x86_64-linux-gnu.so -> Orange/projection\nCreating /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/Orange3.egg-link (link to .)\nAdding Orange3 3.38.0.dev0+1d5c288 to easy-install.pth file\nInstalling orange-canvas script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6\nProcessing dependencies for Orange3==3.38.0.dev0+1d5c288\nSearching for XlsxWriter==3.2.0\nBest match: XlsxWriter 3.2.0\nAdding XlsxWriter 3.2.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for xlrd==2.0.1\nBest match: xlrd 2.0.1\nAdding xlrd 2.0.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for xgboost==2.0.3\nBest match: xgboost 2.0.3\nAdding xgboost 2.0.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for serverfiles==0.3.1\nBest match: serverfiles 0.3.1\nAdding serverfiles 0.3.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for scipy==1.15.0\nBest match: scipy 1.15.0\nAdding scipy 1.15.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for scikit-learn==1.6.0\nBest match: scikit-learn 1.6.0\nAdding scikit-learn 1.6.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for requests==2.32.3\nBest match: requests 2.32.3\nAdding requests 2.32.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for qtconsole==5.6.1\nBest match: qtconsole 5.6.1\nAdding qtconsole 5.6.1 to easy-install.pth file\nInstalling jupyter-qtconsole script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for PyYAML==6.0.2\nBest match: PyYAML 6.0.2\nAdding PyYAML 6.0.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for python-louvain==0.16\nBest match: python-louvain 0.16\nAdding python-louvain 0.16 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for pyqtgraph==0.13.7\nBest match: pyqtgraph 0.13.7\nAdding pyqtgraph 0.13.7 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for pygments==2.19.1\nBest match: pygments 2.19.1\nAdding pygments 2.19.1 to easy-install.pth file\nInstalling pygmentize script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for pip==24.3.1\nBest match: pip 24.3.1\nAdding pip 24.3.1 to easy-install.pth file\nInstalling pip script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/bin\nInstalling pip3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for pandas==2.2.3\nBest match: pandas 2.2.3\nAdding pandas 2.2.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for packaging==24.2\nBest match: packaging 24.2\nAdding packaging 24.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/setuptools/_vendor\nSearching for orange-widget-base==4.25.1\nBest match: orange-widget-base 4.25.1\nAdding orange-widget-base 4.25.1 to easy-install.pth file\ndetected new path './setuptools/_vendor'\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for orange-canvas-core==0.2.5\nBest match: orange-canvas-core 0.2.5\nAdding orange-canvas-core 0.2.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for openpyxl==3.1.5\nBest match: openpyxl 3.1.5\nAdding openpyxl 3.1.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for openTSNE==1.0.2\nBest match: openTSNE 1.0.2\nAdding openTSNE 1.0.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for numpy==1.26.4\nBest match: numpy 1.26.4\nAdding numpy 1.26.4 to easy-install.pth file\nInstalling f2py script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for networkx==3.4.2\nBest match: networkx 3.4.2\nAdding networkx 3.4.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for matplotlib==3.10.0\nBest match: matplotlib 3.10.0\nAdding matplotlib 3.10.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for keyrings.alt==5.0.2\nBest match: keyrings.alt 5.0.2\nAdding keyrings.alt 5.0.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for keyring==25.6.0\nBest match: keyring 25.6.0\nAdding keyring 25.6.0 to easy-install.pth file\nInstalling keyring script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for joblib==1.4.2\nBest match: joblib 1.4.2\nAdding joblib 1.4.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for httpx==0.28.1\nBest match: httpx 0.28.1\nAdding httpx 0.28.1 to easy-install.pth file\nInstalling httpx script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for chardet==5.2.0\nBest match: chardet 5.2.0\nAdding chardet 5.2.0 to easy-install.pth file\nInstalling chardetect script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for catboost==1.2.7\nBest match: catboost 1.2.7\nAdding catboost 1.2.7 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for Bottleneck==1.4.2\nBest match: Bottleneck 1.4.2\nAdding Bottleneck 1.4.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for baycomp==1.0.3\nBest match: baycomp 1.0.3\nAdding baycomp 1.0.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for AnyQt==0.2.0\nBest match: AnyQt 0.2.0\nAdding AnyQt 0.2.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for threadpoolctl==3.5.0\nBest match: threadpoolctl 3.5.0\nAdding threadpoolctl 3.5.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for certifi==2024.12.14\nBest match: certifi 2024.12.14\nAdding certifi 2024.12.14 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for urllib3==2.3.0\nBest match: urllib3 2.3.0\nAdding urllib3 2.3.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for idna==3.10\nBest match: idna 3.10\nAdding idna 3.10 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for charset-normalizer==3.4.1\nBest match: charset-normalizer 3.4.1\nAdding charset-normalizer 3.4.1 to easy-install.pth file\nInstalling normalizer script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for QtPy==2.4.2\nBest match: QtPy 2.4.2\nAdding QtPy 2.4.2 to easy-install.pth file\nInstalling qtpy script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for ipykernel==6.29.5\nBest match: ipykernel 6.29.5\nAdding ipykernel 6.29.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for jupyter-client==8.6.3\nBest match: jupyter-client 8.6.3\nAdding jupyter-client 8.6.3 to easy-install.pth file\nInstalling jupyter-kernel script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/bin\nInstalling jupyter-kernelspec script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/bin\nInstalling jupyter-run script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for jupyter-core==5.7.2\nBest match: jupyter-core 5.7.2\nAdding jupyter-core 5.7.2 to easy-install.pth file\nInstalling jupyter script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/bin\nInstalling jupyter-migrate script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/bin\nInstalling jupyter-troubleshoot script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for traitlets==5.14.3\nBest match: traitlets 5.14.3\nAdding traitlets 5.14.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for tzdata==2024.2\nBest match: tzdata 2024.2\nAdding tzdata 2024.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for pytz==2024.2\nBest match: pytz 2024.2\nAdding pytz 2024.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for python-dateutil==2.9.0.post0\nBest match: python-dateutil 2.9.0.post0\nAdding python-dateutil 2.9.0.post0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for typing-extensions==4.12.2\nBest match: typing-extensions 4.12.2\ntyping-extensions 4.12.2 is already the active version in easy-install.pth\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/setuptools/_vendor\nSearching for qasync==0.27.1\nBest match: qasync 0.27.1\nAdding qasync 0.27.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for dictdiffer==0.9.0\nBest match: dictdiffer 0.9.0\nAdding dictdiffer 0.9.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for requests-cache==1.2.1\nBest match: requests-cache 1.2.1\nAdding requests-cache 1.2.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for commonmark==0.9.1\nBest match: commonmark 0.9.1\nAdding commonmark 0.9.1 to easy-install.pth file\nInstalling cmark script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for docutils==0.21.2\nBest match: docutils 0.21.2\nAdding docutils 0.21.2 to easy-install.pth file\nInstalling docutils script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/bin\nInstalling rst2html script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/bin\nInstalling rst2html4 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/bin\nInstalling rst2html5 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/bin\nInstalling rst2latex script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/bin\nInstalling rst2man script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/bin\nInstalling rst2odt script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/bin\nInstalling rst2pseudoxml script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/bin\nInstalling rst2s5 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/bin\nInstalling rst2xetex script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/bin\nInstalling rst2xml script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for et-xmlfile==2.0.0\nBest match: et-xmlfile 2.0.0\nAdding et-xmlfile 2.0.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for pyparsing==3.2.1\nBest match: pyparsing 3.2.1\nAdding pyparsing 3.2.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for pillow==11.1.0\nBest match: pillow 11.1.0\nAdding pillow 11.1.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for kiwisolver==1.4.8\nBest match: kiwisolver 1.4.8\nAdding kiwisolver 1.4.8 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for fonttools==4.55.3\nBest match: fonttools 4.55.3\nAdding fonttools 4.55.3 to easy-install.pth file\nInstalling fonttools script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/bin\nInstalling pyftmerge script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/bin\nInstalling pyftsubset script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/bin\nInstalling ttx script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for cycler==0.12.1\nBest match: cycler 0.12.1\nAdding cycler 0.12.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for contourpy==1.3.1\nBest match: contourpy 1.3.1\nAdding contourpy 1.3.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for jaraco.context==6.0.1\nBest match: jaraco.context 6.0.1\nAdding jaraco.context 6.0.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for jaraco.classes==3.4.0\nBest match: jaraco.classes 3.4.0\nAdding jaraco.classes 3.4.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for jaraco.functools==4.1.0\nBest match: jaraco.functools 4.1.0\nAdding jaraco.functools 4.1.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for importlib-metadata==8.5.0\nBest match: importlib-metadata 8.5.0\nAdding importlib-metadata 8.5.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for jeepney==0.8.0\nBest match: jeepney 0.8.0\nAdding jeepney 0.8.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for SecretStorage==3.3.3\nBest match: SecretStorage 3.3.3\nAdding SecretStorage 3.3.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for httpcore==1.0.7\nBest match: httpcore 1.0.7\nAdding httpcore 1.0.7 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for anyio==4.8.0\nBest match: anyio 4.8.0\nAdding anyio 4.8.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for six==1.17.0\nBest match: six 1.17.0\nAdding six 1.17.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for plotly==5.24.1\nBest match: plotly 5.24.1\nAdding plotly 5.24.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for graphviz==0.20.3\nBest match: graphviz 0.20.3\nAdding graphviz 0.20.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for tornado==6.4.2\nBest match: tornado 6.4.2\nAdding tornado 6.4.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for pyzmq==26.2.0\nBest match: pyzmq 26.2.0\nAdding pyzmq 26.2.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for psutil==6.1.1\nBest match: psutil 6.1.1\nAdding psutil 6.1.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for nest-asyncio==1.6.0\nBest match: nest-asyncio 1.6.0\nAdding nest-asyncio 1.6.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for matplotlib-inline==0.1.7\nBest match: matplotlib-inline 0.1.7\nAdding matplotlib-inline 0.1.7 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for ipython==8.31.0\nBest match: ipython 8.31.0\nAdding ipython 8.31.0 to easy-install.pth file\nInstalling ipython script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/bin\nInstalling ipython3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for debugpy==1.8.11\nBest match: debugpy 1.8.11\nAdding debugpy 1.8.11 to easy-install.pth file\nInstalling debugpy script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for comm==0.2.2\nBest match: comm 0.2.2\nAdding comm 0.2.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for platformdirs==4.3.6\nBest match: platformdirs 4.3.6\nAdding platformdirs 4.3.6 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for url-normalize==1.4.3\nBest match: url-normalize 1.4.3\nAdding url-normalize 1.4.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for cattrs==24.1.2\nBest match: cattrs 24.1.2\nAdding cattrs 24.1.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for attrs==24.3.0\nBest match: attrs 24.3.0\nAdding attrs 24.3.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for backports.tarfile==1.2.0\nBest match: backports.tarfile 1.2.0\nbackports.tarfile 1.2.0 is already the active version in easy-install.pth\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/setuptools/_vendor\nSearching for more-itertools==10.5.0\nBest match: more-itertools 10.5.0\nAdding more-itertools 10.5.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for zipp==3.21.0\nBest match: zipp 3.21.0\nAdding zipp 3.21.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for cryptography==44.0.0\nBest match: cryptography 44.0.0\nAdding cryptography 44.0.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for h11==0.14.0\nBest match: h11 0.14.0\nAdding h11 0.14.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for sniffio==1.3.1\nBest match: sniffio 1.3.1\nAdding sniffio 1.3.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for exceptiongroup==1.2.2\nBest match: exceptiongroup 1.2.2\nAdding exceptiongroup 1.2.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for tenacity==9.0.0\nBest match: tenacity 9.0.0\nAdding tenacity 9.0.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for stack-data==0.6.3\nBest match: stack-data 0.6.3\nAdding stack-data 0.6.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for prompt-toolkit==3.0.48\nBest match: prompt-toolkit 3.0.48\nAdding prompt-toolkit 3.0.48 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for pexpect==4.9.0\nBest match: pexpect 4.9.0\nAdding pexpect 4.9.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for jedi==0.19.2\nBest match: jedi 0.19.2\nAdding jedi 0.19.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for decorator==5.1.1\nBest match: decorator 5.1.1\nAdding decorator 5.1.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for cffi==1.17.1\nBest match: cffi 1.17.1\nAdding cffi 1.17.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for pure-eval==0.2.3\nBest match: pure-eval 0.2.3\nAdding pure-eval 0.2.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for asttokens==3.0.0\nBest match: asttokens 3.0.0\nAdding asttokens 3.0.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for executing==2.1.0\nBest match: executing 2.1.0\nAdding executing 2.1.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for wcwidth==0.2.13\nBest match: wcwidth 0.2.13\nAdding wcwidth 0.2.13 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for ptyprocess==0.7.0\nBest match: ptyprocess 0.7.0\nAdding ptyprocess 0.7.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for parso==0.8.4\nBest match: parso 0.8.4\nAdding parso 0.8.4 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nSearching for pycparser==2.22\nBest match: pycparser 2.22\nAdding pycparser 2.22 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages\nFinished processing dependencies for Orange3==3.38.0.dev0+1d5c288\nVerifying Orange installation...\n✅ Orange installation successful!\nSuccessfully installed orange using Python 3.10\n",
    "setup_res_stderr": "Using CPython 3.7.9\nCreating virtual environment at: .venv\nActivate with: source .venv/bin/activate\nResolved 18 packages in 122ms\nPrepared 18 packages in 2ms\nInstalled 18 packages in 168ms\n + cython==0.29.37\n + exceptiongroup==1.2.2\n + importlib-metadata==6.7.0\n + iniconfig==2.0.0\n + numpy==1.17.5\n + packaging==24.0\n + pluggy==1.2.0\n + pyqt5==5.15.10\n + pyqt5-qt5==5.15.16\n + pyqt5-sip==12.13.0\n + pyqtwebengine==5.15.6\n + pyqtwebengine-qt5==5.15.16\n + pytest==7.4.4\n + setuptools==59.8.0\n + tomli==2.0.1\n + typing-extensions==4.7.1\n + wheel==0.42.0\n + zipp==3.15.0\n  × No solution found when resolving dependencies:\n  ╰─▶ Because the current Python version (3.7.9) does not satisfy Python>=3.8 and all of:\n          pandas>=1.4.0,<=1.4.4\n          pandas>=1.5.1,<=1.5.3\n          pandas>=2.0.1,<=2.0.3\n      depend on Python>=3.8, we can conclude that all of:\n          pandas>=1.4.0,<=1.4.4\n          pandas>=1.5.1,<=1.5.3\n          pandas>=2.0.1,<=2.0.3\n       cannot be used.\n      And because only the following versions of pandas are available:\n          pandas<=1.4.0\n          pandas==1.4.1\n          pandas==1.4.2\n          pandas==1.4.3\n          pandas==1.4.4\n          pandas==1.5.0\n          pandas==1.5.1\n          pandas==1.5.2\n          pandas==1.5.3\n          pandas==2.0.0\n          pandas==2.0.1\n          pandas==2.0.2\n          pandas==2.0.3\n          pandas==2.1.0\n          pandas==2.1.1\n          pandas==2.1.2\n          pandas==2.1.3\n          pandas==2.1.4\n          pandas==2.2.0\n          pandas==2.2.1\n          pandas==2.2.2\n          pandas==2.2.3\n      we can conclude that all of:\n          pandas>=1.4.0,<1.5.0\n          pandas>1.5.0,<2.0.0\n          pandas>2.0.0,<2.1.0\n       cannot be used. (1)\n\n      Because the current Python version (3.7.9) does not satisfy Python>=3.9 and pandas>=2.1.0 depends on Python>=3.9, we can\n      conclude that pandas>=2.1.0 cannot be used.\n      And because we know from (1) that all of:\n          pandas>=1.4.0,<1.5.0\n          pandas>1.5.0,<2.0.0\n          pandas>2.0.0,<2.1.0\n       cannot be used, we can conclude that all of:\n          pandas>=1.4.0,<1.5.0\n          pandas>1.5.0,<2.0.0\n          pandas>2.0.0\n       cannot be used.\n      And because you require one of:\n          pandas>=1.4.0,<1.5.0\n          pandas>1.5.0,<2.0.0\n          pandas>2.0.0\n      we can conclude that your requirements are unsatisfiable.\n\n      hint: Pre-releases are available for `pandas` in the requested range (e.g., 2.2.0rc0), but pre-releases weren't enabled\n      (try: `--prerelease=allow`)\n  × No solution found when resolving dependencies:\n  ╰─▶ Because the current Python version (3.7.9) does not satisfy Python>=3.8 and pyqtgraph>=0.13.1,<=0.13.3 depends on\n      Python>=3.8, we can conclude that pyqtgraph>=0.13.1,<=0.13.3 cannot be used.\n      And because only the following versions of pyqtgraph are available:\n          pyqtgraph<=0.13.1\n          pyqtgraph==0.13.2\n          pyqtgraph==0.13.3\n          pyqtgraph==0.13.4\n          pyqtgraph==0.13.5\n          pyqtgraph==0.13.6\n          pyqtgraph==0.13.7\n      we can conclude that pyqtgraph>=0.13.1,<0.13.4 cannot be used. (1)\n\n      Because the current Python version (3.7.9) does not satisfy Python>=3.9 and pyqtgraph>=0.13.4 depends on Python>=3.9, we\n      can conclude that pyqtgraph>=0.13.4 cannot be used.\n      And because we know from (1) that pyqtgraph>=0.13.1,<0.13.4 cannot be used, we can conclude that pyqtgraph>=0.13.1 cannot\n      be used.\n      And because you require pyqtgraph>=0.13.1, we can conclude that your requirements are unsatisfiable.\nResolved 2 packages in 50ms\nInstalled 2 packages in 39ms\n + psycopg2==2.9.9\n + pymssql==2.3.2\nResolved 5 packages in 20ms\nInstalled 4 packages in 495ms\n + joblib==1.3.2\n + scikit-learn==1.0.2\n + scipy==1.7.3\n + threadpoolctl==3.1.0\nIn file included from /home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m/Python.h:8,\n                 from Orange/classification/_simple_tree.c:781:\n/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m/pyconfig.h:1504: warning: \"_GNU_SOURCE\" redefined\n 1504 | #define _GNU_SOURCE 1\n      | \nOrange/classification/_simple_tree.c:2: note: this is the location of the previous definition\n    2 | #define _GNU_SOURCE\n      | \nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/classification/_tree_scorers.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_2find_threshold_entropy’:\nOrange/classification/_tree_scorers.c:3426:27: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 3426 |   __pyx_t_7 = ((__pyx_v_N <= __pyx_v_min_leaf) != 0);\n      |                           ^~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_6find_threshold_MSE’:\nOrange/classification/_tree_scorers.c:4872:27: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 4872 |   __pyx_t_1 = ((__pyx_v_N <= __pyx_v_min_leaf) != 0);\n      |                           ^~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_8find_binarization_MSE’:\nOrange/classification/_tree_scorers.c:5618:43: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5618 |           for (__pyx_t_19 = 0; __pyx_t_19 < __pyx_t_18; __pyx_t_19+=1) {\n      |                                           ^\nOrange/classification/_tree_scorers.c:5739:39: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5739 |           __pyx_t_20 = ((__pyx_v_left >= __pyx_v_min_leaf) != 0);\n      |                                       ^~\nOrange/classification/_tree_scorers.c:5745:53: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5745 |           __pyx_t_20 = (((__pyx_v_N - __pyx_v_left) >= __pyx_v_min_leaf) != 0);\n      |                                                     ^~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_14compute_predictions_csr’:\nOrange/classification/_tree_scorers.c:7206:41: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 7206 |         for (__pyx_t_12 = 0; __pyx_t_12 < __pyx_t_11; __pyx_t_12+=1) {\n      |                                         ^\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_16compute_predictions_csc’:\nOrange/classification/_tree_scorers.c:7863:41: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 7863 |         for (__pyx_t_12 = 0; __pyx_t_12 < __pyx_t_11; __pyx_t_12+=1) {\n      |                                         ^\nOrange/classification/_tree_scorers.c:7923:150: warning: comparison of integer expressions of different signedness: ‘__pyx_t_5numpy_int32_t’ {aka ‘int’} and ‘unsigned int’ [-Wsign-compare]\n 7923 |               __pyx_t_16 = (((*((__pyx_t_5numpy_int32_t const  *) ( /* dim=0 */ (__pyx_v_indices.data + __pyx_t_15 * __pyx_v_indices.strides[0]) ))) != __pyx_v_i) != 0);\n      |                                                                                                                                                      ^~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/data/_contingency.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nOrange/data/_io.c: In function ‘__pyx_pf_6Orange_4data_3_io_sparse_read_float.constprop’:\nOrange/data/_io.c:4120:43: warning: ‘__pyx_v_decs’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4120 |           __pyx_v_value = ((__pyx_v_value * __pyx_v_decs) + (__pyx_v_c & 0xf));\n      |                            ~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/data/_io.c:4318:24: warning: ‘__pyx_v_value’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4318 |           __pyx_t_10 = PyFloat_FromDouble(__pyx_v_value); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 223, __pyx_L5_error)\n      |                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOrange/data/_io.c:683:40: warning: ‘__pyx_v_attr_index’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n  683 |   #define PyInt_FromLong               PyLong_FromLong\n      |                                        ^~~~~~~~~~~~~~~\nOrange/data/_io.c:1590:7: note: ‘__pyx_v_attr_index’ was declared here\n 1590 |   int __pyx_v_attr_index;\n      |       ^~~~~~~~~~~~~~~~~~\nOrange/data/_io.c:3183:25: warning: ‘__pyx_v_atomp’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3183 |           __pyx_v_atomp = (__pyx_v_atomp + 1);\n      |           ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/data/_valuecount.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/distance/_distance.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/preprocess/_discretize.c:774:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/preprocess/_relieff.cpp:790:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/projection/_som.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_5update_hex’:\nOrange/projection/_som.c:3944:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3944 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:3943:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3943 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pf_6Orange_10projection_4_som_8update_sparse.constprop’:\nOrange/projection/_som.c:5094:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5094 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:5093:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5093 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_1get_winners’:\nOrange/projection/_som.c:3244:223: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3244 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_23, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_24, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_y;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:2892:7: note: ‘__pyx_v_win_y’ was declared here\n 2892 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:3233:223: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3233 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_24, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_23, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_x;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:2891:7: note: ‘__pyx_v_win_x’ was declared here\n 2891 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pf_6Orange_10projection_4_som_10update_sparse_hex.constprop’:\nOrange/projection/_som.c:5842:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5842 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:5841:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5841 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_7get_winners_sparse’:\nOrange/projection/_som.c:4871:223: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4871 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_27, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_28, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_y;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:4389:7: note: ‘__pyx_v_win_y’ was declared here\n 4389 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:4860:223: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4860 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_28, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_27, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_x;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:4388:7: note: ‘__pyx_v_win_x’ was declared here\n 4388 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_3update’:\nOrange/projection/_som.c:3438:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3438 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/setuptools/command/easy_install.py:159: EasyInstallDeprecationWarning: easy_install command is deprecated. Use build and pip and other standards-based tools.\n  EasyInstallDeprecationWarning,\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/setuptools/command/install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.\n  setuptools.SetuptoolsDeprecationWarning,\nwarning: no files found matching 'README*' under directory 'Orange'\nwarning: no files found matching 'LICENSE' under directory 'Orange'\nwarning: no files found matching '*.basket' under directory 'Orange/datasets'\nwarning: no files found matching '*.svg' under directory 'Orange/canvas'\nwarning: no files found matching '*.js' under directory 'Orange/widgets'\nwarning: no files found matching '*.css' under directory 'Orange/widgets'\nwarning: no files found matching '*.html' under directory 'Orange/widgets'\nwarning: no files found matching '*.svg' under directory 'distribute'\nwarning: no files found matching 'COPYING'\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/pkg_resources/__init__.py:119: PkgResourcesDeprecationWarning:  is an invalid version and will not be supported in a future release\n  PkgResourcesDeprecationWarning,\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/setuptools/command/install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.\n  setuptools.SetuptoolsDeprecationWarning,\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.7/site-packages/pkg_resources/__init__.py:119: PkgResourcesDeprecationWarning:  is an invalid version and will not be supported in a future release\n  PkgResourcesDeprecationWarning,\nerror: Couldn't find a setup script in /tmp/easy_install-a7uvysi8/scipy-1.15.0.tar.gz\nUsing CPython 3.8.20\nCreating virtual environment at: .venv\nActivate with: source .venv/bin/activate\nResolved 15 packages in 80ms\nPrepared 15 packages in 0.95ms\nInstalled 15 packages in 33ms\n + cython==0.29.37\n + exceptiongroup==1.2.2\n + iniconfig==2.0.0\n + numpy==1.24.4\n + packaging==24.2\n + pluggy==1.5.0\n + pyqt5==5.15.11\n + pyqt5-qt5==5.15.16\n + pyqt5-sip==12.15.0\n + pyqtwebengine==5.15.7\n + pyqtwebengine-qt5==5.15.16\n + pytest==8.3.4\n + setuptools==75.3.0\n + tomli==2.2.1\n + wheel==0.45.1\n  × No solution found when resolving dependencies:\n  ╰─▶ Because the current Python version (3.8.20) does not satisfy Python>=3.9 and scikit-learn>=1.4.0 depends on Python>=3.9,\n      we can conclude that scikit-learn>=1.4.0 cannot be used.\n      And because only the following versions of scikit-learn are available:\n          scikit-learn<=1.4.0\n          scikit-learn==1.4.1.post1\n          scikit-learn==1.4.2\n          scikit-learn==1.5.0\n          scikit-learn==1.5.1\n          scikit-learn==1.5.2\n          scikit-learn==1.6.0\n      and you require scikit-learn>=1.4.0, we can conclude that your requirements are unsatisfiable.\n\n      hint: Pre-releases are available for `scikit-learn` in the requested range (e.g., 1.6.0rc1), but pre-releases weren't\n      enabled (try: `--prerelease=allow`)\nResolved 63 packages in 116ms\nInstalled 60 packages in 76ms\n + anyqt==0.2.0\n + asttokens==3.0.0\n + attrs==24.3.0\n + backcall==0.2.0\n + cattrs==24.1.2\n + certifi==2024.12.14\n + charset-normalizer==3.4.1\n + comm==0.2.2\n + commonmark==0.9.1\n + contourpy==1.1.1\n + cycler==0.12.1\n + debugpy==1.8.11\n + decorator==5.1.1\n + dictdiffer==0.9.0\n + docutils==0.20.1\n + executing==2.1.0\n + fonttools==4.55.3\n + idna==3.10\n + importlib-metadata==8.5.0\n + importlib-resources==6.4.5\n + ipykernel==6.29.5\n + ipython==8.12.3\n + jedi==0.19.2\n + jupyter-client==8.6.3\n + jupyter-core==5.7.2\n + kiwisolver==1.4.7\n + matplotlib==3.7.5\n + matplotlib-inline==0.1.7\n + nest-asyncio==1.6.0\n + orange-canvas-core==0.2.2\n + orange-widget-base==4.24.0\n + parso==0.8.4\n + pexpect==4.9.0\n + pickleshare==0.7.5\n + pillow==10.4.0\n + pip==24.3.1\n + platformdirs==4.3.6\n + prompt-toolkit==3.0.48\n + psutil==6.1.1\n + ptyprocess==0.7.0\n + pure-eval==0.2.3\n + pygments==2.19.1\n + pyparsing==3.1.4\n + pyqtgraph==0.13.3\n + python-dateutil==2.9.0.post0\n + pyzmq==26.2.0\n + qasync==0.27.1\n + qtconsole==5.6.1\n + qtpy==2.4.2\n + requests==2.32.3\n + requests-cache==1.2.1\n + six==1.17.0\n + stack-data==0.6.3\n + tornado==6.4.2\n + traitlets==5.14.3\n + typing-extensions==4.12.2\n + url-normalize==1.4.3\n + urllib3==2.2.3\n + wcwidth==0.2.13\n + zipp==3.20.2\nResolved 2 packages in 2ms\nInstalled 2 packages in 4ms\n + psycopg2==2.9.10\n + pymssql==2.3.2\nIn file included from /home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8/Python.h:8,\n                 from Orange/classification/_simple_tree.c:781:\n/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8/pyconfig.h:1578: warning: \"_GNU_SOURCE\" redefined\n 1578 | #define _GNU_SOURCE 1\n      | \nOrange/classification/_simple_tree.c:2: note: this is the location of the previous definition\n    2 | #define _GNU_SOURCE\n      | \nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/classification/_tree_scorers.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_2find_threshold_entropy’:\nOrange/classification/_tree_scorers.c:3426:27: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 3426 |   __pyx_t_7 = ((__pyx_v_N <= __pyx_v_min_leaf) != 0);\n      |                           ^~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_6find_threshold_MSE’:\nOrange/classification/_tree_scorers.c:4872:27: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 4872 |   __pyx_t_1 = ((__pyx_v_N <= __pyx_v_min_leaf) != 0);\n      |                           ^~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_8find_binarization_MSE’:\nOrange/classification/_tree_scorers.c:5618:43: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5618 |           for (__pyx_t_19 = 0; __pyx_t_19 < __pyx_t_18; __pyx_t_19+=1) {\n      |                                           ^\nOrange/classification/_tree_scorers.c:5739:39: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5739 |           __pyx_t_20 = ((__pyx_v_left >= __pyx_v_min_leaf) != 0);\n      |                                       ^~\nOrange/classification/_tree_scorers.c:5745:53: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5745 |           __pyx_t_20 = (((__pyx_v_N - __pyx_v_left) >= __pyx_v_min_leaf) != 0);\n      |                                                     ^~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_14compute_predictions_csr’:\nOrange/classification/_tree_scorers.c:7206:41: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 7206 |         for (__pyx_t_12 = 0; __pyx_t_12 < __pyx_t_11; __pyx_t_12+=1) {\n      |                                         ^\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_16compute_predictions_csc’:\nOrange/classification/_tree_scorers.c:7863:41: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 7863 |         for (__pyx_t_12 = 0; __pyx_t_12 < __pyx_t_11; __pyx_t_12+=1) {\n      |                                         ^\nOrange/classification/_tree_scorers.c:7923:150: warning: comparison of integer expressions of different signedness: ‘__pyx_t_5numpy_int32_t’ {aka ‘int’} and ‘unsigned int’ [-Wsign-compare]\n 7923 |               __pyx_t_16 = (((*((__pyx_t_5numpy_int32_t const  *) ( /* dim=0 */ (__pyx_v_indices.data + __pyx_t_15 * __pyx_v_indices.strides[0]) ))) != __pyx_v_i) != 0);\n      |                                                                                                                                                      ^~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/data/_contingency.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nOrange/data/_io.c: In function ‘__pyx_pf_6Orange_4data_3_io_sparse_read_float.constprop’:\nOrange/data/_io.c:4120:43: warning: ‘__pyx_v_decs’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4120 |           __pyx_v_value = ((__pyx_v_value * __pyx_v_decs) + (__pyx_v_c & 0xf));\n      |                            ~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/data/_io.c:4318:24: warning: ‘__pyx_v_value’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4318 |           __pyx_t_10 = PyFloat_FromDouble(__pyx_v_value); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 223, __pyx_L5_error)\n      |                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOrange/data/_io.c:683:40: warning: ‘__pyx_v_attr_index’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n  683 |   #define PyInt_FromLong               PyLong_FromLong\n      |                                        ^~~~~~~~~~~~~~~\nOrange/data/_io.c:1590:7: note: ‘__pyx_v_attr_index’ was declared here\n 1590 |   int __pyx_v_attr_index;\n      |       ^~~~~~~~~~~~~~~~~~\nOrange/data/_io.c:3183:25: warning: ‘__pyx_v_atomp’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3183 |           __pyx_v_atomp = (__pyx_v_atomp + 1);\n      |           ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/data/_valuecount.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/distance/_distance.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/preprocess/_discretize.c:774:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/preprocess/_relieff.cpp:790:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/projection/_som.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_5update_hex’:\nOrange/projection/_som.c:3944:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3944 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:3943:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3943 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pf_6Orange_10projection_4_som_8update_sparse.constprop’:\nOrange/projection/_som.c:5094:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5094 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:5093:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5093 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_3update’:\nOrange/projection/_som.c:3438:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3438 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_1get_winners’:\nOrange/projection/_som.c:3244:223: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3244 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_23, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_24, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_y;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:2892:7: note: ‘__pyx_v_win_y’ was declared here\n 2892 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:3233:223: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3233 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_24, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_23, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_x;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:2891:7: note: ‘__pyx_v_win_x’ was declared here\n 2891 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pf_6Orange_10projection_4_som_10update_sparse_hex.constprop’:\nOrange/projection/_som.c:5842:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5842 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:5841:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5841 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_7get_winners_sparse’:\nOrange/projection/_som.c:4871:223: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4871 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_27, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_28, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_y;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:4389:7: note: ‘__pyx_v_win_y’ was declared here\n 4389 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:4860:223: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4860 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_28, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_27, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_x;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:4388:7: note: ‘__pyx_v_win_x’ was declared here\n 4388 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/setuptools/_distutils/dist.py:261: UserWarning: Unknown distribution option: 'test_suite'\n  warnings.warn(msg)\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/setuptools/_distutils/dist.py:261: UserWarning: Unknown distribution option: 'test_suite'\n  warnings.warn(msg)\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/setuptools/command/develop.py:41: EasyInstallDeprecationWarning: easy_install command is deprecated.\n!!\n\n        ********************************************************************************\n        Please avoid running ``setup.py`` and ``easy_install``.\n        Instead, use pypa/build, pypa/installer or other\n        standards-based tools.\n\n        See https://github.com/pypa/setuptools/issues/917 for details.\n        ********************************************************************************\n\n!!\n  easy_install.initialize_options(self)\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated.\n!!\n\n        ********************************************************************************\n        Please avoid running ``setup.py`` directly.\n        Instead, use pypa/build, pypa/installer or other\n        standards-based tools.\n\n        See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.\n        ********************************************************************************\n\n!!\n  self.initialize_options()\nwarning: no files found matching 'README*' under directory 'Orange'\nwarning: no files found matching 'LICENSE' under directory 'Orange'\nwarning: no files found matching '*.basket' under directory 'Orange/datasets'\nwarning: no files found matching '*.svg' under directory 'Orange/canvas'\nwarning: no files found matching '*.js' under directory 'Orange/widgets'\nwarning: no files found matching '*.css' under directory 'Orange/widgets'\nwarning: no files found matching '*.html' under directory 'Orange/widgets'\nwarning: no files found matching '*.svg' under directory 'distribute'\nwarning: no files found matching 'COPYING'\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/setuptools/_distutils/dist.py:261: UserWarning: Unknown distribution option: 'test_suite'\n  warnings.warn(msg)\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.8/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated.\n!!\n\n        ********************************************************************************\n        Please avoid running ``setup.py`` directly.\n        Instead, use pypa/build, pypa/installer or other\n        standards-based tools.\n\n        See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.\n        ********************************************************************************\n\n!!\n  self.initialize_options()\nerror: Couldn't find a setup script in /tmp/easy_install-gw70f5mi/scipy-1.15.0.tar.gz\nUsing CPython 3.10.16\nCreating virtual environment at: .venv\nActivate with: source .venv/bin/activate\nResolved 15 packages in 75ms\nPrepared 15 packages in 1ms\nInstalled 15 packages in 30ms\n + cython==3.0.11\n + exceptiongroup==1.2.2\n + iniconfig==2.0.0\n + numpy==2.2.1\n + packaging==24.2\n + pluggy==1.5.0\n + pyqt5==5.15.11\n + pyqt5-qt5==5.15.16\n + pyqt5-sip==12.16.1\n + pyqtwebengine==5.15.7\n + pyqtwebengine-qt5==5.15.16\n + pytest==8.3.4\n + setuptools==75.8.0\n + tomli==2.2.1\n + wheel==0.45.1\nResolved 62 packages in 26ms\nUninstalled 1 package in 11ms\nInstalled 60 packages in 350ms\n + anyio==4.8.0\n + backports-tarfile==1.2.0\n + baycomp==1.0.3\n + bottleneck==1.4.2\n + catboost==1.2.7\n + certifi==2024.12.14\n + cffi==1.17.1\n + chardet==5.2.0\n + charset-normalizer==3.4.1\n + contourpy==1.3.1\n + cryptography==44.0.0\n + cycler==0.12.1\n + et-xmlfile==2.0.0\n + fonttools==4.55.3\n + graphviz==0.20.3\n + h11==0.14.0\n + httpcore==1.0.7\n + httpx==0.28.1\n + idna==3.10\n + importlib-metadata==8.5.0\n + jaraco-classes==3.4.0\n + jaraco-context==6.0.1\n + jaraco-functools==4.1.0\n + jeepney==0.8.0\n + joblib==1.4.2\n + keyring==25.6.0\n + keyrings-alt==5.0.2\n + kiwisolver==1.4.8\n + matplotlib==3.10.0\n + more-itertools==10.5.0\n + networkx==3.4.2\n - numpy==2.2.1\n + numpy==1.26.4\n + openpyxl==3.1.5\n + opentsne==1.0.2\n + pandas==2.2.3\n + pillow==11.1.0\n + pip==24.3.1\n + plotly==5.24.1\n + pycparser==2.22\n + pyparsing==3.2.1\n + python-dateutil==2.9.0.post0\n + python-louvain==0.16\n + pytz==2024.2\n + pyyaml==6.0.2\n + requests==2.32.3\n + scikit-learn==1.6.0\n + scipy==1.15.0\n + secretstorage==3.3.3\n + serverfiles==0.3.1\n + six==1.17.0\n + sniffio==1.3.1\n + tenacity==9.0.0\n + threadpoolctl==3.5.0\n + typing-extensions==4.12.2\n + tzdata==2024.2\n + urllib3==2.3.0\n + xgboost==2.0.3\n + xlrd==2.0.1\n + xlsxwriter==3.2.0\n + zipp==3.21.0\nResolved 58 packages in 23ms\nInstalled 39 packages in 75ms\n + anyqt==0.2.0\n + asttokens==3.0.0\n + attrs==24.3.0\n + cattrs==24.1.2\n + comm==0.2.2\n + commonmark==0.9.1\n + debugpy==1.8.11\n + decorator==5.1.1\n + dictdiffer==0.9.0\n + docutils==0.21.2\n + executing==2.1.0\n + ipykernel==6.29.5\n + ipython==8.31.0\n + jedi==0.19.2\n + jupyter-client==8.6.3\n + jupyter-core==5.7.2\n + matplotlib-inline==0.1.7\n + nest-asyncio==1.6.0\n + orange-canvas-core==0.2.5\n + orange-widget-base==4.25.1\n + parso==0.8.4\n + pexpect==4.9.0\n + platformdirs==4.3.6\n + prompt-toolkit==3.0.48\n + psutil==6.1.1\n + ptyprocess==0.7.0\n + pure-eval==0.2.3\n + pygments==2.19.1\n + pyqtgraph==0.13.7\n + pyzmq==26.2.0\n + qasync==0.27.1\n + qtconsole==5.6.1\n + qtpy==2.4.2\n + requests-cache==1.2.1\n + stack-data==0.6.3\n + tornado==6.4.2\n + traitlets==5.14.3\n + url-normalize==1.4.3\n + wcwidth==0.2.13\nResolved 39 packages in 81ms\nInstalled 25 packages in 30ms\n + alabaster==1.0.0\n + astroid==3.3.8\n + babel==2.16.0\n + colorama==0.4.6\n + dill==0.3.9\n + imagesize==1.4.1\n + isort==5.13.2\n + jinja2==3.1.5\n + libcst==1.5.1\n + mando==0.7.1\n + markupsafe==3.0.2\n + mccabe==0.7.0\n + pylint==3.3.3\n + radon==6.0.1\n + recommonmark==0.7.1\n + snowballstemmer==2.2.0\n + sphinx==8.1.3\n + sphinxcontrib-applehelp==2.0.0\n + sphinxcontrib-devhelp==2.0.0\n + sphinxcontrib-htmlhelp==2.1.0\n + sphinxcontrib-jsmath==1.0.1\n + sphinxcontrib-qthelp==2.0.0\n + sphinxcontrib-serializinghtml==2.0.0\n + tomlkit==0.13.2\n + trubar==0.3.4\nResolved 2 packages in 2ms\nInstalled 2 packages in 4ms\n + psycopg2==2.9.10\n + pymssql==2.3.2\nperformance hint: Orange/preprocess/_relieff.pyx:63:5: Exception check on 'calc_difference' will always require the GIL to be acquired.\nPossible solutions:\n\t1. Declare 'calc_difference' as 'noexcept' if you control the definition and you're sure you don't want the function to raise exceptions.\n\t2. Use an 'int' return type on 'calc_difference' to allow an error code to be returned.\nperformance hint: Orange/preprocess/_relieff.pyx:119:5: Exception check on 'k_nearest_reg' will always require the GIL to be acquired.\nPossible solutions:\n\t1. Declare 'k_nearest_reg' as 'noexcept' if you control the definition and you're sure you don't want the function to raise exceptions.\n\t2. Use an 'int' return type on 'k_nearest_reg' to allow an error code to be returned.\nperformance hint: Orange/preprocess/_relieff.pyx:183:5: Exception check on 'k_nearest_per_class' will always require the GIL to be acquired.\nPossible solutions:\n\t1. Declare 'k_nearest_per_class' as 'noexcept' if you control the definition and you're sure you don't want the function to raise exceptions.\n\t2. Use an 'int' return type on 'k_nearest_per_class' to allow an error code to be returned.\nperformance hint: Orange/preprocess/_relieff.pyx:161:23: Exception check after calling 'calc_difference' will always require the GIL to be acquired.\nPossible solutions:\n\t1. Declare 'calc_difference' as 'noexcept' if you control the definition and you're sure you don't want the function to raise exceptions.\n\t2. Use an 'int' return type on 'calc_difference' to allow an error code to be returned.\nperformance hint: Orange/preprocess/_relieff.pyx:177:23: Exception check after calling 'calc_difference' will always require the GIL to be acquired.\nPossible solutions:\n\t1. Declare 'calc_difference' as 'noexcept' if you control the definition and you're sure you don't want the function to raise exceptions.\n\t2. Use an 'int' return type on 'calc_difference' to allow an error code to be returned.\nperformance hint: Orange/preprocess/_relieff.pyx:201:23: Exception check after calling 'calc_difference' will always require the GIL to be acquired.\nPossible solutions:\n\t1. Declare 'calc_difference' as 'noexcept' if you control the definition and you're sure you don't want the function to raise exceptions.\n\t2. Use an 'int' return type on 'calc_difference' to allow an error code to be returned.\nperformance hint: Orange/preprocess/_relieff.pyx:217:27: Exception check after calling 'calc_difference' will always require the GIL to be acquired.\nPossible solutions:\n\t1. Declare 'calc_difference' as 'noexcept' if you control the definition and you're sure you don't want the function to raise exceptions.\n\t2. Use an 'int' return type on 'calc_difference' to allow an error code to be returned.\nperformance hint: Orange/preprocess/_relieff.pyx:250:25: Exception check after calling 'k_nearest_reg' will always require the GIL to be acquired.\nPossible solutions:\n\t1. Declare 'k_nearest_reg' as 'noexcept' if you control the definition and you're sure you don't want the function to raise exceptions.\n\t2. Use an 'int' return type on 'k_nearest_reg' to allow an error code to be returned.\nperformance hint: Orange/preprocess/_relieff.pyx:287:31: Exception check after calling 'k_nearest_per_class' will always require the GIL to be acquired.\nPossible solutions:\n\t1. Declare 'k_nearest_per_class' as 'noexcept' if you control the definition and you're sure you don't want the function to raise exceptions.\n\t2. Use an 'int' return type on 'k_nearest_per_class' to allow an error code to be returned.\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/setuptools/_distutils/dist.py:270: UserWarning: Unknown distribution option: 'test_suite'\n  warnings.warn(msg)\nIn file included from /home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/include/python3.10/Python.h:8,\n                 from Orange/classification/_simple_tree.c:781:\n/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/include/python3.10/pyconfig.h:1621: warning: \"_GNU_SOURCE\" redefined\n 1621 | #define _GNU_SOURCE 1\n      | \nOrange/classification/_simple_tree.c:2: note: this is the location of the previous definition\n    2 | #define _GNU_SOURCE\n      | \nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarraytypes.h:1929,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/classification/_tree_scorers.c:1253:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_2find_threshold_entropy’:\nOrange/classification/_tree_scorers.c:20278:26: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n20278 |   __pyx_t_7 = (__pyx_v_N <= __pyx_v_min_leaf);\n      |                          ^~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_6find_threshold_MSE’:\nOrange/classification/_tree_scorers.c:21847:26: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n21847 |   __pyx_t_1 = (__pyx_v_N <= __pyx_v_min_leaf);\n      |                          ^~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_8find_binarization_MSE’:\nOrange/classification/_tree_scorers.c:22657:43: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n22657 |           for (__pyx_t_19 = 0; __pyx_t_19 < __pyx_t_18; __pyx_t_19+=1) {\n      |                                           ^\nOrange/classification/_tree_scorers.c:22778:38: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n22778 |           __pyx_t_20 = (__pyx_v_left >= __pyx_v_min_leaf);\n      |                                      ^~\nOrange/classification/_tree_scorers.c:22784:52: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n22784 |           __pyx_t_20 = ((__pyx_v_N - __pyx_v_left) >= __pyx_v_min_leaf);\n      |                                                    ^~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_14compute_predictions_csr’:\nOrange/classification/_tree_scorers.c:24434:41: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n24434 |         for (__pyx_t_12 = 0; __pyx_t_12 < __pyx_t_11; __pyx_t_12+=1) {\n      |                                         ^\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_16compute_predictions_csc’:\nOrange/classification/_tree_scorers.c:25156:41: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n25156 |         for (__pyx_t_12 = 0; __pyx_t_12 < __pyx_t_11; __pyx_t_12+=1) {\n      |                                         ^\nOrange/classification/_tree_scorers.c:25216:149: warning: comparison of integer expressions of different signedness: ‘__pyx_t_5numpy_int32_t’ {aka ‘int’} and ‘unsigned int’ [-Wsign-compare]\n25216 |               __pyx_t_16 = ((*((__pyx_t_5numpy_int32_t const  *) ( /* dim=0 */ (__pyx_v_indices.data + __pyx_t_15 * __pyx_v_indices.strides[0]) ))) != __pyx_v_i);\n      |                                                                                                                                                     ^~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarraytypes.h:1929,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/data/_contingency.c:1253:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nOrange/data/_io.c: In function ‘__pyx_pf_6Orange_4data_3_io_sparse_read_float.constprop’:\nOrange/data/_io.c:5091:43: warning: ‘__pyx_v_decs’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5091 |           __pyx_v_value = ((__pyx_v_value * __pyx_v_decs) + (__pyx_v_c & 0xf));\n      |                            ~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/data/_io.c:5261:23: warning: ‘__pyx_v_value’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5261 |           __pyx_t_5 = PyFloat_FromDouble(__pyx_v_value); if (unlikely(!__pyx_t_5)) __PYX_ERR(0, 223, __pyx_L5_error)\n      |                       ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOrange/data/_io.c:1148:40: warning: ‘__pyx_v_attr_index’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 1148 |   #define PyInt_FromLong               PyLong_FromLong\n      |                                        ^~~~~~~~~~~~~~~\nOrange/data/_io.c:2863:7: note: ‘__pyx_v_attr_index’ was declared here\n 2863 |   int __pyx_v_attr_index;\n      |       ^~~~~~~~~~~~~~~~~~\nOrange/data/_io.c:4691:30: warning: ‘__pyx_v_atomp’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4691 |           (__pyx_v_atomp[0]) = 0;\n      |           ~~~~~~~~~~~~~~~~~~~^~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarraytypes.h:1929,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/data/_valuecount.c:1253:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarraytypes.h:1929,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/distance/_distance.c:1253:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarraytypes.h:1929,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/preprocess/_discretize.c:1252:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarraytypes.h:1929,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/preprocess/_relieff.cpp:1266:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarraytypes.h:1929,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/projection/_som.c:1253:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_5update_hex’:\nOrange/projection/_som.c:20732:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n20732 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:20731:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n20731 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pf_6Orange_10projection_4_som_10update_sparse_hex.constprop’:\nOrange/projection/_som.c:22819:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n22819 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:22818:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n22818 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pf_6Orange_10projection_4_som_8update_sparse.constprop’:\nOrange/projection/_som.c:21996:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n21996 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:21995:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n21995 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_1get_winners’:\nOrange/projection/_som.c:19911:223: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n19911 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_23, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_24, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_y;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:19558:7: note: ‘__pyx_v_win_y’ was declared here\n19558 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:19900:223: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n19900 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_24, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_23, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_x;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:19557:7: note: ‘__pyx_v_win_x’ was declared here\n19557 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_3update’:\nOrange/projection/_som.c:20161:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n20161 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_7get_winners_sparse’:\nOrange/projection/_som.c:21717:223: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n21717 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_27, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_28, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_y;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:21234:7: note: ‘__pyx_v_win_y’ was declared here\n21234 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:21706:223: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n21706 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_28, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_27, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_x;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:21233:7: note: ‘__pyx_v_win_x’ was declared here\n21233 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/setuptools/_distutils/dist.py:270: UserWarning: Unknown distribution option: 'test_suite'\n  warnings.warn(msg)\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/setuptools/command/develop.py:41: EasyInstallDeprecationWarning: easy_install command is deprecated.\n!!\n\n        ********************************************************************************\n        Please avoid running ``setup.py`` and ``easy_install``.\n        Instead, use pypa/build, pypa/installer or other\n        standards-based tools.\n\n        See https://github.com/pypa/setuptools/issues/917 for details.\n        ********************************************************************************\n\n!!\n  easy_install.initialize_options(self)\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6/.venv/lib/python3.10/site-packages/setuptools/_distutils/cmd.py:79: SetuptoolsDeprecationWarning: setup.py install is deprecated.\n!!\n\n        ********************************************************************************\n        Please avoid running ``setup.py`` directly.\n        Instead, use pypa/build, pypa/installer or other\n        standards-based tools.\n\n        See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.\n        ********************************************************************************\n\n!!\n  self.initialize_options()\nwarning: no files found matching 'README*' under directory 'Orange'\nwarning: no files found matching 'LICENSE' under directory 'Orange'\nwarning: no files found matching '*.basket' under directory 'Orange/datasets'\nwarning: no files found matching '*.svg' under directory 'Orange/canvas'\nwarning: no files found matching '*.js' under directory 'Orange/widgets'\nwarning: no files found matching '*.css' under directory 'Orange/widgets'\nwarning: no files found matching '*.html' under directory 'Orange/widgets'\nwarning: no files found matching '*.svg' under directory 'distribute'\nwarning: no files found matching 'COPYING'\nResolved 102 packages in 2.24s\nPrepared 1 package in 46.07s\nUninstalled 1 package in 0.27ms\nInstalled 1 package in 0.99ms\n - orange3==3.38.0.dev0+1d5c288\n + orange3==3.38.0.dev0+1d5c288 (from file:///home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6)\n",
    "new_commit_res_code": 0,
    "new_commit_res_stdout": "============================= test session starts ==============================\nplatform linux -- Python 3.10.16, pytest-8.3.4, pluggy-1.5.0\nrootdir: /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6\nconfigfile: pyproject.toml\nplugins: anyio-4.8.0, typeguard-4.3.0\ncollected 26 items\n\nr2e_tests/test_1.py ..........................                           [100%]\n\n==================================== PASSES ====================================\n=========================== short test summary info ============================\nPASSED r2e_tests/test_1.py::TestTableHeader::test_get_header_data_0\nPASSED r2e_tests/test_1.py::TestTableHeader::test_get_header_data_1\nPASSED r2e_tests/test_1.py::TestTableHeader::test_get_header_data_1_flags\nPASSED r2e_tests/test_1.py::TestTableHeader::test_get_header_data_1_flags2\nPASSED r2e_tests/test_1.py::TestTableHeader::test_get_header_data_3\nPASSED r2e_tests/test_1.py::TestTableHeader::test_rename_variables\nPASSED r2e_tests/test_1.py::TestTableBuilder::test_column_parts_discrete_values\nPASSED r2e_tests/test_1.py::TestTableBuilder::test_continuous_column\nPASSED r2e_tests/test_1.py::TestTableBuilder::test_continuous_column_raises\nPASSED r2e_tests/test_1.py::TestTableBuilder::test_discrete_column\nPASSED r2e_tests/test_1.py::TestTableBuilder::test_string_column\nPASSED r2e_tests/test_1.py::TestTableBuilder::test_time_column\nPASSED r2e_tests/test_1.py::TestTableBuilder::test_unknown_type_column\nPASSED r2e_tests/test_1.py::TestDataTableMixin::test_adjust_data_width_empty\nPASSED r2e_tests/test_1.py::TestDataTableMixin::test_adjust_data_width_lengthen\nPASSED r2e_tests/test_1.py::TestDataTableMixin::test_adjust_data_width_shorten\nPASSED r2e_tests/test_1.py::TestDataTableMixin::test_data_table_0\nPASSED r2e_tests/test_1.py::TestDataTableMixin::test_data_table_1\nPASSED r2e_tests/test_1.py::TestDataTableMixin::test_data_table_1_flags\nPASSED r2e_tests/test_1.py::TestDataTableMixin::test_data_table_3\nPASSED r2e_tests/test_1.py::TestDataTableMixin::test_data_table_empty\nPASSED r2e_tests/test_1.py::TestDataTableMixin::test_parse_headers_0\nPASSED r2e_tests/test_1.py::TestDataTableMixin::test_parse_headers_1\nPASSED r2e_tests/test_1.py::TestDataTableMixin::test_parse_headers_1_flags\nPASSED r2e_tests/test_1.py::TestDataTableMixin::test_parse_headers_3\nPASSED r2e_tests/test_1.py::TestDataTableMixin::test_parse_headers_empty\n============================== 26 passed in 1.20s ==============================\n",
    "new_commit_res_stderr": "",
    "old_commit_res_code": 1,
    "old_commit_res_stdout": "============================= test session starts ==============================\nplatform linux -- Python 3.10.16, pytest-8.3.4, pluggy-1.5.0\nrootdir: /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1d5c28890a282f962a1ba22b7ff2450d2388b7a6\nconfigfile: pyproject.toml\nplugins: anyio-4.8.0, typeguard-4.3.0\ncollected 26 items\n\nr2e_tests/test_1.py ...F......................                           [100%]\n\n=================================== FAILURES ===================================\n________________ TestTableHeader.test_get_header_data_1_flags2 _________________\n\nself = <r2e_tests.test_1.TestTableHeader testMethod=test_get_header_data_1_flags2>\n\n    def test_get_header_data_1_flags2(self):\n        names, types, flags = _TableHeader.create_header_data(\n            self.header1_flags2[:1])\n        names_ = [\"a1\", \"a2\", \"a3\", \"a4\", \"a5\", \"a6\", \"a7\", \"a8\", \"a9\"]\n        types_ = [\"d\", \"d\", \"d\", \"c\", \"s\", \"s\", \"t\", \"t\", \"t\"]\n        flags_ = [\"\", \"\", \"c\", \"\", \"\", \"m\", \"\", \"m\", \"\"]\n>       self.assertListEqual(names, names_)\nE       AssertionError: Lists differ: ['D#a1', 'D#a2', 'a3', 'a4', 'S#a5', 'a6', 'T#a7', 'a8', 'T#a9'] != ['a1', 'a2', 'a3', 'a4', 'a5', 'a6', 'a7', 'a8', 'a9']\nE       \nE       First differing element 0:\nE       'D#a1'\nE       'a1'\nE       \nE       - ['D#a1', 'D#a2', 'a3', 'a4', 'S#a5', 'a6', 'T#a7', 'a8', 'T#a9']\nE       ?   --      --                  --            --            --\nE       \nE       + ['a1', 'a2', 'a3', 'a4', 'a5', 'a6', 'a7', 'a8', 'a9']\n\nr2e_tests/test_1.py:69: AssertionError\n==================================== PASSES ====================================\n=========================== short test summary info ============================\nPASSED r2e_tests/test_1.py::TestTableHeader::test_get_header_data_0\nPASSED r2e_tests/test_1.py::TestTableHeader::test_get_header_data_1\nPASSED r2e_tests/test_1.py::TestTableHeader::test_get_header_data_1_flags\nPASSED r2e_tests/test_1.py::TestTableHeader::test_get_header_data_3\nPASSED r2e_tests/test_1.py::TestTableHeader::test_rename_variables\nPASSED r2e_tests/test_1.py::TestTableBuilder::test_column_parts_discrete_values\nPASSED r2e_tests/test_1.py::TestTableBuilder::test_continuous_column\nPASSED r2e_tests/test_1.py::TestTableBuilder::test_continuous_column_raises\nPASSED r2e_tests/test_1.py::TestTableBuilder::test_discrete_column\nPASSED r2e_tests/test_1.py::TestTableBuilder::test_string_column\nPASSED r2e_tests/test_1.py::TestTableBuilder::test_time_column\nPASSED r2e_tests/test_1.py::TestTableBuilder::test_unknown_type_column\nPASSED r2e_tests/test_1.py::TestDataTableMixin::test_adjust_data_width_empty\nPASSED r2e_tests/test_1.py::TestDataTableMixin::test_adjust_data_width_lengthen\nPASSED r2e_tests/test_1.py::TestDataTableMixin::test_adjust_data_width_shorten\nPASSED r2e_tests/test_1.py::TestDataTableMixin::test_data_table_0\nPASSED r2e_tests/test_1.py::TestDataTableMixin::test_data_table_1\nPASSED r2e_tests/test_1.py::TestDataTableMixin::test_data_table_1_flags\nPASSED r2e_tests/test_1.py::TestDataTableMixin::test_data_table_3\nPASSED r2e_tests/test_1.py::TestDataTableMixin::test_data_table_empty\nPASSED r2e_tests/test_1.py::TestDataTableMixin::test_parse_headers_0\nPASSED r2e_tests/test_1.py::TestDataTableMixin::test_parse_headers_1\nPASSED r2e_tests/test_1.py::TestDataTableMixin::test_parse_headers_1_flags\nPASSED r2e_tests/test_1.py::TestDataTableMixin::test_parse_headers_3\nPASSED r2e_tests/test_1.py::TestDataTableMixin::test_parse_headers_empty\nFAILED r2e_tests/test_1.py::TestTableHeader::test_get_header_data_1_flags2 - ...\n========================= 1 failed, 25 passed in 1.31s =========================\n",
    "old_commit_res_stderr": ""
}
Orange/data/io_base.py Orange/data/tests/test_io_base.py
We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
[ISSUE]
**Title:** Header Flags Prefix Attribute Names, Causing Parsing Issues

**Description:**
When parsing headers in CSV or Excel files, the attribute type flags are incorrectly prefixed to the attribute names. This results in names like `"D#a1"` instead of the expected `"a1"`, leading to failures in processes that rely on clean attribute names.

**Example Code:**
```python
header1_flags2 = [
    ["D#a1", "D#a2", "cD#a3", "C#a4", "S#a5", "mS#a6", "T#a7", "mT#a8", "T#a9"],
    ["", "0", "", "0", "a", "a", "2024-01-01", "2024-01-01", ""],
    ["", "1", "", "1", "b", "b", "2024-01-01", "2024-01-01", ""],
    ["green", "0.0", "2.0", "2.0", "c", "c"]
]

names, types, flags = _TableHeader.create_header_data(header1_flags2[:1])
print(names)
```

**Expected Behavior:**
The `names` list should contain attribute names without any flag prefixes:
```
["a1", "a2", "a3", "a4", "a5", "a6", "a7", "a8", "a9"]
```

**Actual Behavior:**
The `names` list incorrectly includes flag prefixes:
```
["D#a1", "D#a2", "cD#a3", "C#a4", "S#a5", "mS#a6", "T#a7", "mT#a8", "T#a9"]
```

This unintended prefixing causes tests and functionalities that expect clean attribute names to fail or behave unexpectedly.
[/ISSUE]
--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.

--- BEGIN FILE ---
```
### Orange/data/io_base.py
import pickle
import re
import sys
import warnings
from typing import Iterable, Optional, Tuple, List, Generator, Callable, Any

from ast import literal_eval
from collections import OrderedDict
from functools import lru_cache
from itertools import chain, repeat
from math import isnan

from os import path, remove
from fnmatch import fnmatch
from glob import glob

import numpy as np
import pandas

from Orange.data import Table, Domain, Variable, DiscreteVariable, \
    StringVariable, ContinuousVariable, TimeVariable
from Orange.data.io_util import Compression, open_compressed, \
    isnastr, guess_data_type, sanitize_variable
from Orange.data.util import get_unique_names_duplicates
from Orange.data.variable import VariableMeta
from Orange.misc.collections import natural_sorted
from Orange.util import Registry, flatten, namegen

__all__ = ["FileFormatBase", "Flags", "DataTableMixin", "PICKLE_PROTOCOL"]


PICKLE_PROTOCOL = 4


class MissingReaderException(IOError):
    # subclasses IOError for backward compatibility
    pass


class Flags:
    """Parser for column flags (i.e. third header row)"""
    DELIMITER = ' '
    _RE_SPLIT = re.compile(r'(?<!\\)' + DELIMITER).split
    _RE_ATTR_UNQUOTED_STR = re.compile(r'^[a-zA-Z_]').match
    ALL = OrderedDict((
        ('class', 'c'),
        ('ignore', 'i'),
        ('meta', 'm'),
        ('weight', 'w'),
        ('.+?=.*?', ''),  # general key=value attributes
    ))
    RE_ALL = re.compile(r'^({})$'.format('|'.join(
        filter(None, flatten(ALL.items())))))

    def __init__(self, flags):
        for v in filter(None, self.ALL.values()):
            setattr(self, v, False)
        self.attributes = {}
        for flag in flags or []:
            flag = flag.strip()
            if self.RE_ALL.match(flag):
                if '=' in flag:
                    k, v = flag.split('=', 1)
                    if not Flags._RE_ATTR_UNQUOTED_STR(v):
                        try:
                            v = literal_eval(v)
                        except SyntaxError:
                            # If parsing failed, treat value as string
                            pass
                    # map True and False to booleans
                    if v in ("True", "False"):
                        v = {"True": True, "False": False}[v]
                    self.attributes[k] = v
                else:
                    setattr(self, flag, True)
                    setattr(self, self.ALL.get(flag, ''), True)
            elif flag:
                warnings.warn('Invalid attribute flag \'{}\''.format(flag))

    @staticmethod
    def join(iterable, *args):
        return Flags.DELIMITER.join(i.strip().replace(Flags.DELIMITER,
                                                      '\\' + Flags.DELIMITER)
                                    for i in chain(iterable, args)).lstrip()

    @staticmethod
    def split(s):
        return [i.replace('\\' + Flags.DELIMITER, Flags.DELIMITER)
                for i in Flags._RE_SPLIT(s)]


# Matches discrete specification where all the values are listed, space-separated
_RE_DISCRETE_LIST = re.compile(r'^\s*[^\s]+(\s[^\s]+)+\s*$')
_RE_TYPES = re.compile(r'^\s*({}|{}|)\s*$'.format(
    _RE_DISCRETE_LIST.pattern,
    '|'.join(flatten(getattr(vartype, 'TYPE_HEADERS')
                     for vartype in Variable.registry.values()))
))
_RE_FLAGS = re.compile(r'^\s*( |{}|)*\s*$'.format(
    '|'.join(flatten(filter(None, i) for i in Flags.ALL.items()))
))


class _ColumnProperties:
    def __init__(self, valuemap=None, values=None, orig_values=None,
                 coltype=None, coltype_kwargs=None):
        self.valuemap = valuemap
        self.values = values
        self.orig_values = orig_values
        self.coltype = coltype
        if coltype_kwargs is None:
            self.coltype_kwargs = {}
        else:
            self.coltype_kwargs = dict(coltype_kwargs)


class _TableHeader:
    """
    Contains functions for table header construction (and its data).
    """
    HEADER1_FLAG_SEP = '#'

    def __init__(self, headers: List):
        """
        Parameters
        ----------
        headers: List
            Header rows, to be used for constructing domain.
        """
        names, types, flags = self.create_header_data(headers)
        self.names = get_unique_names_duplicates(names)
        self.types = types
        self.flags = flags

    @classmethod
    def create_header_data(cls, headers: List) -> Tuple[List, List, List]:
        """
        Consider various header types (single-row, two-row, three-row, none).

        Parameters
        ----------
        headers: List
            Header rows, to be used for constructing domain.

        Returns
        -------
        names: List
            List of variable names.
        types: List
            List of variable types.
        flags: List
            List of meta info (i.e. class, meta, ignore, weights).
        """
        return {3: lambda x: x,
                2: cls._header2,
                1: cls._header1}.get(len(headers), cls._header0)(headers)

    @classmethod
    def _header2(cls, headers: List[List[str]]) -> Tuple[List, List, List]:
        names, flags = headers
        return names, cls._type_from_flag(flags), cls._flag_from_flag(flags)

    @classmethod
    def _header1(cls, headers: List[List[str]]) -> Tuple[List, List, List]:
        """
        First row format either:
          1) delimited column names
          2) -||- with type and flags prepended, separated by #,
             e.g. d#sex,c#age,cC#IQ
        """

        def is_flag(x):
            return bool(Flags.RE_ALL.match(cls._type_from_flag([x])[0]) or
                        Flags.RE_ALL.match(cls._flag_from_flag([x])[0]))

        flags, names = zip(*[i.split(cls.HEADER1_FLAG_SEP, 1)
                             if cls.HEADER1_FLAG_SEP in i and
                             is_flag(i.split(cls.HEADER1_FLAG_SEP)[0])
                             else ('', i)
                             for i in headers[0]])
        names = list(names)
        return names, cls._type_from_flag(flags), cls._flag_from_flag(flags)

    @classmethod
    def _header0(cls, _) -> Tuple[List, List, List]:
        # Use heuristics for everything
        return [], [], []

    @staticmethod
    def _type_from_flag(flags: List[str]) -> List[str]:
        return [''.join(filter(str.isupper, flag)).lower() for flag in flags]

    @staticmethod
    def _flag_from_flag(flags: List[str]) -> List[str]:
        return [Flags.join(filter(str.islower, flag)) for flag in flags]


class _TableBuilder:
    X_ARR, Y_ARR, M_ARR, W_ARR = range(4)
    DATA_IND, DOMAIN_IND, TYPE_IND = range(3)

    def __init__(self, data: np.ndarray, ncols: int,
                 header: _TableHeader, offset: int):
        self.data = data
        self.ncols = ncols
        self.header = header
        self.offset = offset
        self.namegen: Generator[str] = namegen('Feature ', 1)

        self.cols_X: List[np.ndarray] = []
        self.cols_Y: List[np.ndarray] = []
        self.cols_M: List[np.ndarray] = []
        self.cols_W: List[np.ndarray] = []
        self.attrs: List[Variable] = []
        self.clses: List[Variable] = []
        self.metas: List[Variable] = []

    def create_table(self) -> Table:
        self.create_columns()
        if not self.data.size:
            return Table.from_domain(self.get_domain(), 0)
        else:
            return Table.from_numpy(self.get_domain(), *self.get_arrays())

    def create_columns(self):
        names = self.header.names
        types = self.header.types

        for col in range(self.ncols):
            flag = Flags(Flags.split(self.header.flags[col]))
            if flag.i:
                continue

            type_ = types and types[col].strip()
            creator = self._get_column_creator(type_)
            column = creator(self.data, col, values=type_, offset=self.offset)
            self._take_column(names and names[col], column, flag)
            self._reclaim_memory(self.data, col)

    @classmethod
    def _get_column_creator(cls, type_: str) -> Callable:
        if type_ in StringVariable.TYPE_HEADERS:
            return cls._string_column
        elif type_ in ContinuousVariable.TYPE_HEADERS:
            return cls._cont_column
        elif type_ in TimeVariable.TYPE_HEADERS:
            return cls._time_column
        elif _RE_DISCRETE_LIST.match(type_):
            return cls._disc_with_vals_column
        elif type_ in DiscreteVariable.TYPE_HEADERS:
            return cls._disc_no_vals_column
        else:
            return cls._unknown_column

    @staticmethod
    def _string_column(data: np.ndarray, col: int, **_) -> _ColumnProperties:
        vals, _ = _TableBuilder._values_mask(data, col)
        return _ColumnProperties(values=vals, coltype=StringVariable,
                                 orig_values=vals)

    @staticmethod
    def _cont_column(data: np.ndarray, col: int,
                     offset=0, **_) -> _ColumnProperties:
        orig_vals, namask = _TableBuilder._values_mask(data, col)
        values = np.empty(data.shape[0], dtype=float)
        try:
            np.copyto(values, orig_vals, casting="unsafe", where=~namask)
            values[namask] = np.nan
        except ValueError:
            row = 0
            for row, num in enumerate(orig_vals):
                if not isnastr(num):
                    try:
                        float(num)
                    except ValueError:
                        break
            raise ValueError(f'Non-continuous value in (1-based) '
                             f'line {row + offset + 1}, column {col + 1}')
        return _ColumnProperties(values=values, coltype=ContinuousVariable,
                                 orig_values=orig_vals)

    @staticmethod
    def _time_column(data: np.ndarray, col: int, **_) -> _ColumnProperties:
        vals, namask = _TableBuilder._values_mask(data, col)
        return _ColumnProperties(values=np.where(namask, "", vals),
                                 coltype=TimeVariable, orig_values=vals)

    @staticmethod
    def _disc_column(data: np.ndarray, col: int) -> \
            Tuple[np.ndarray, VariableMeta]:
        vals, namask = _TableBuilder._values_mask(data, col)
        return np.where(namask, "", vals), DiscreteVariable

    @staticmethod
    def _disc_no_vals_column(data: np.ndarray, col: int, **_) -> \
            _ColumnProperties:
        vals, coltype = _TableBuilder._disc_column(data, col)
        return _ColumnProperties(valuemap=natural_sorted(set(vals) - {""}),
                                 values=vals, coltype=coltype,
                                 orig_values=vals)

    @staticmethod
    def _disc_with_vals_column(data: np.ndarray, col: int,
                               values="", **_) -> _ColumnProperties:
        vals, coltype = _TableBuilder._disc_column(data, col)
        return _ColumnProperties(valuemap=Flags.split(values), values=vals,
                                 coltype=coltype, orig_values=vals)

    @staticmethod
    def _unknown_column(data: np.ndarray, col: int, **_) -> _ColumnProperties:
        orig_vals, namask = _TableBuilder._values_mask(data, col)
        valuemap, values, coltype = guess_data_type(orig_vals, namask)
        return _ColumnProperties(valuemap=valuemap, values=values,
                                 coltype=coltype, orig_values=orig_vals)

    @staticmethod
    def _values_mask(data: np.ndarray, col: int) -> \
            Tuple[np.ndarray, np.ndarray]:
        try:
            values = data[:, col]
        except IndexError:
            values = np.array([], dtype=object)
        return values, isnastr(values)

    def _take_column(self, name: Optional[str], column: _ColumnProperties,
                     flag: Flags):
        cols, dom_vars = self._lists_from_flag(flag, column.coltype)
        values = column.values
        if dom_vars is not None:
            if not name:
                name = next(self.namegen)

            values, var = sanitize_variable(
                column.valuemap, values, column.orig_values,
                column.coltype, column.coltype_kwargs, name=name)
            var.attributes.update(flag.attributes)
            dom_vars.append(var)

        if isinstance(values, np.ndarray) and not values.flags.owndata:
            values = values.copy()  # might view `data` (string columns)
        cols.append(values)

    def _lists_from_flag(self, flag: Flags, coltype: VariableMeta) -> \
            Tuple[List, Optional[List]]:
        if flag.m or coltype is StringVariable:
            return self.cols_M, self.metas
        elif flag.w:
            return self.cols_W, None
        elif flag.c:
            return self.cols_Y, self.clses
        else:
            return self.cols_X, self.attrs

    @staticmethod
    def _reclaim_memory(data: np.ndarray, col: int):
        # allow gc to reclaim memory used by string values
        try:
            data[:, col] = None
        except IndexError:
            pass

    def get_domain(self) -> Domain:
        return Domain(self.attrs, self.clses, self.metas)

    def get_arrays(self) -> Tuple[np.ndarray, np.ndarray,
                                  np.ndarray, np.ndarray]:
        lists = ((self.cols_X, None),
                 (self.cols_Y, None),
                 (self.cols_M, object),
                 (self.cols_W, float))
        X, Y, M, W = [self._list_into_ndarray(lst, dt) for lst, dt in lists]
        if X is None:
            X = np.empty((self.data.shape[0], 0), dtype=np.float_)
        return X, Y, M, W

    @staticmethod
    def _list_into_ndarray(lst: List, dtype=None) -> Optional[np.ndarray]:
        if not lst:
            return None

        array = np.c_[tuple(lst)]
        if dtype is not None:
            array.astype(dtype)
        else:
            assert array.dtype == np.float_
        return array


class DataTableMixin:
    @classmethod
    def data_table(cls, data: Iterable[List[str]],
                   headers: Optional[List] = None) -> Table:
        """
        Return Orange.data.Table given rows of `headers` (iterable of iterable)
        and rows of `data` (iterable of iterable).

        Basically, the idea of subclasses is to produce those two iterables,
        however they might.

        If `headers` is not provided, the header rows are extracted from `data`,
        assuming they precede it.

        Parameters
        ----------
        data: Iterable
            File content.
        headers: List (Optional)
            Header rows, to be used for constructing domain.

        Returns
        -------
        table: Table
            Data as Orange.data.Table.
        """
        if not headers:
            headers, data = cls.parse_headers(data)

        header = _TableHeader(headers)
        # adjusting data may change header properties
        array, n_columns = cls.adjust_data_width(data, header)
        builder = _TableBuilder(array, n_columns, header, len(headers))
        return builder.create_table()

    @classmethod
    def parse_headers(cls, data: Iterable[List[str]]) -> Tuple[List, Iterable]:
        """
        Return (header rows, rest of data) as discerned from `data`.

        Parameters
        ----------
        data: Iterable
            File content.

        Returns
        -------
        header_rows: List
            Header rows, to be used for constructing domain.
        data: Iterable
            File content without header rows.
        """
        data = iter(data)
        header_rows = []

        # Try to parse a three-line header
        lines = []
        try:
            lines.append(list(next(data)))
            lines.append(list(next(data)))
            lines.append(list(next(data)))
        except StopIteration:
            lines, data = [], chain(lines, data)
        if lines:
            l1, l2, l3 = lines
            # Three-line header if line 2 & 3 match (1st line can be anything)
            if cls.__header_test2(l2) and cls.__header_test3(l3):
                header_rows = [l1, l2, l3]
            else:
                lines, data = [], chain((l1, l2, l3), data)

        # Try to parse a single-line header
        if not header_rows:
            try:
                lines.append(list(next(data)))
            except StopIteration:
                pass
            if lines:
                # Header if none of the values in line 1 parses as a number
                if not all(cls.__is_number(i) for i in lines[0]):
                    header_rows = [lines[0]]
                else:
                    data = chain(lines, data)

        return header_rows, data

    @staticmethod
    def __is_number(item: str) -> bool:
        try:
            float(item)
        except ValueError:
            return False
        return True

    @staticmethod
    def __header_test2(items: List) -> bool:
        # Second row items are type identifiers
        return all(map(_RE_TYPES.match, items))

    @staticmethod
    def __header_test3(items: List) -> bool:
        # Third row items are flags and column attributes (attr=value)
        return all(map(_RE_FLAGS.match, items))

    @classmethod
    def adjust_data_width(cls, data: Iterable, header: _TableHeader) -> \
            Tuple[np.ndarray, int]:
        """
        Determine maximum row length.
        Return data as an array, with width dependent on header size.
        Append `names`, `types` and `flags` if shorter than row length.

        Parameters
        ----------
        data: Iterable
            File content without header rows.
        header: _TableHeader
            Header lists converted into _TableHeader.

        Returns
        -------
        data: np.ndarray
            File content without header rows.
        rowlen: int
            Number of columns in data.
        """

        def equal_len(lst):
            nonlocal strip
            if len(lst) > rowlen > 0:
                lst = lst[:rowlen]
                strip = True
            elif len(lst) < rowlen:
                lst.extend([''] * (rowlen - len(lst)))
            return lst

        rowlen = max(map(len, (header.names, header.types, header.flags)))
        strip = False

        # Ensure all data is of equal width in a column-contiguous array
        data = [equal_len([s.strip() for s in row])
                for row in data if any(row)]
        array = np.array(data, dtype=object, order='F')

        if strip:
            warnings.warn("Columns with no headers were removed.")

        # Data may actually be longer than headers were
        try:
            rowlen = array.shape[1]
        except IndexError:
            pass
        else:
            for lst in (header.names, header.types, header.flags):
                equal_len(lst)
        return array, rowlen


class _FileReader:
    @classmethod
    def get_reader(cls, filename):
        """Return reader instance that can be used to read the file

        Parameters
        ----------
        filename : str

        Returns
        -------
        FileFormat
        """
        for ext, reader in cls.readers.items():
            # Skip ambiguous, invalid compression-only extensions added on OSX
            if ext in Compression.all:
                continue
            if fnmatch(path.basename(filename), '*' + ext):
                return reader(filename)

        raise MissingReaderException('No readers for file "{}"'.format(filename))

    @classmethod
    def set_table_metadata(cls, filename, table):
        # pylint: disable=bare-except
        if isinstance(filename, str) and path.exists(filename + '.metadata'):
            try:
                with open(filename + '.metadata', 'rb') as f:
                    table.attributes = pickle.load(f)
            # Unpickling throws different exceptions, not just UnpickleError
            except:
                with open(filename + '.metadata', encoding='utf-8') as f:
                    table.attributes = OrderedDict(
                        (k.strip(), v.strip())
                        for k, v in (line.split(":", 1)
                                     for line in f.readlines()))


class _FileWriter:
    @classmethod
    def write(cls, filename, data, with_annotations=True):
        if cls.OPTIONAL_TYPE_ANNOTATIONS:
            return cls.write_file(filename, data, with_annotations)
        else:
            return cls.write_file(filename, data)

    @classmethod
    def write_table_metadata(cls, filename, data):
        def write_file(fn):
            if all(isinstance(key, str) and isinstance(value, str)
                   for key, value in data.attributes.items()):
                with open(fn, 'w', encoding='utf-8') as f:
                    f.write("\n".join("{}: {}".format(*kv)
                                      for kv in data.attributes.items()))
            else:
                with open(fn, 'wb') as f:
                    pickle.dump(data.attributes, f, protocol=PICKLE_PROTOCOL)

        if isinstance(filename, str):
            metafile = filename + '.metadata'
            if getattr(data, 'attributes', None):
                write_file(metafile)
            elif path.exists(metafile):
                remove(metafile)

    @staticmethod
    def header_names(data):
        return ['weights'] * data.has_weights() + \
               [v.name for v in chain(data.domain.class_vars,
                                      data.domain.metas,
                                      data.domain.attributes)]

    @staticmethod
    def header_types(data):
        def _vartype(var):
            if var.is_continuous or var.is_string:
                return var.TYPE_HEADERS[0]
            elif var.is_discrete:
                # if number of values is 1 order is not important if more
                # values write order in file
                return (
                    Flags.join(var.values) if len(var.values) >= 2
                    else var.TYPE_HEADERS[0]
                )
            raise NotImplementedError

        return ['continuous'] * data.has_weights() + \
               [_vartype(v) for v in chain(data.domain.class_vars,
                                           data.domain.metas,
                                           data.domain.attributes)]

    @staticmethod
    def header_flags(data):
        return list(chain(
            ['weight'] * data.has_weights(),
            (Flags.join([flag], *('{}={}'.format(*a) for a in
                                  sorted(var.attributes.items())))
             for flag, var in chain(zip(repeat('class'),
                                        data.domain.class_vars),
                                    zip(repeat('meta'), data.domain.metas),
                                    zip(repeat(''), data.domain.attributes)))))

    @classmethod
    def write_headers(cls, write, data, with_annotations=True):
        """`write` is a callback that accepts an iterable"""
        write(cls.header_names(data))
        if with_annotations:
            write(cls.header_types(data))
            write(cls.header_flags(data))

    @classmethod
    def formatter(cls, var):
        # type: (Variable) -> Callable[[Variable], Any]
        # Return a column 'formatter' function. The function must return
        # something that `write` knows how to write
        if var.is_time:
            return var.repr_val
        elif var.is_continuous:
            return lambda value: "" if isnan(value) else var.repr_val(value)
        elif var.is_discrete:
            return lambda value: "" if isnan(value) else var.values[int(value)]
        elif var.is_string:
            return lambda value: "" if pandas.isnull(value) else value
        else:
            return var.repr_val

    @classmethod
    def write_data(cls, write, data):
        """`write` is a callback that accepts an iterable"""
        vars_ = list(
            chain((ContinuousVariable('_w'),) if data.has_weights() else (),
                  data.domain.class_vars,
                  data.domain.metas,
                  data.domain.attributes))

        formatters = [cls.formatter(v) for v in vars_]
        for row in zip(data.W if data.W.ndim > 1 else data.W[:, np.newaxis],
                       data.Y if data.Y.ndim > 1 else data.Y[:, np.newaxis],
                       data.metas,
                       data.X):
            write([fmt(v) for fmt, v in zip(formatters, flatten(row))])


class _FileFormatMeta(Registry):
    def __new__(mcs, name, bases, attrs):
        newcls = super().__new__(mcs, name, bases, attrs)

        # Optionally add compressed versions of extensions as supported
        if getattr(newcls, 'SUPPORT_COMPRESSED', False):
            new_extensions = list(getattr(newcls, 'EXTENSIONS', ()))
            for compression in Compression.all:
                for ext in newcls.EXTENSIONS:
                    new_extensions.append(ext + compression)
                if sys.platform in ('darwin', 'win32'):
                    # OSX file dialog doesn't support filtering on double
                    # extensions (e.g. .csv.gz)
                    # https://bugreports.qt.io/browse/QTBUG-38303
                    # This is just here for OWFile that gets QFileDialog
                    # filters from FileFormat.readers.keys()
                    # EDIT: Windows exhibit similar problems:
                    # while .tab.gz works, .tab.xz and .tab.bz2 do not!
                    new_extensions.append(compression)
            newcls.EXTENSIONS = tuple(new_extensions)

        return newcls

    @property
    def formats(cls):
        return cls.registry.values()

    @lru_cache(5)
    def _ext_to_attr_if_attr2(cls, attr, attr2):
        """
        Return ``{ext: `attr`, ...}`` dict if ``cls`` has `attr2`.
        If `attr` is '', return ``{ext: cls, ...}`` instead.

        If there are multiple formats for an extension, return a format
        with the lowest priority.
        """
        formats = OrderedDict()
        for format_ in sorted(cls.registry.values(), key=lambda x: x.PRIORITY):
            if not hasattr(format_, attr2):
                continue
            for ext in getattr(format_, 'EXTENSIONS', []):
                # Only adds if not yet registered
                formats.setdefault(ext, getattr(format_, attr, format_))
        return formats

    @property
    def names(cls):
        return cls._ext_to_attr_if_attr2('DESCRIPTION', '__class__')

    @property
    def writers(cls):
        return cls._ext_to_attr_if_attr2('', 'write_file')

    @property
    def readers(cls):
        return cls._ext_to_attr_if_attr2('', 'read')

    @property
    def img_writers(cls):
        warnings.warn(
            f"'{__name__}.FileFormat.img_writers' is no longer used and "
            "will be removed. Please use "
            "'Orange.widgets.io.FileFormat.img_writers' instead.",
            DeprecationWarning, stacklevel=2
        )
        return cls._ext_to_attr_if_attr2('', 'write_image')

    @property
    def graph_writers(cls):
        return cls._ext_to_attr_if_attr2('', 'write_graph')


class FileFormatBase(_FileReader, _FileWriter, metaclass=_FileFormatMeta):
    # Priority when multiple formats support the same extension. Also
    # the sort order in file open/save combo boxes. Lower is better.
    PRIORITY = 10000
    OPTIONAL_TYPE_ANNOTATIONS = False

    @classmethod
    def locate(cls, filename, search_dirs=('.',)):
        """Locate a file with given filename that can be opened by one
        of the available readers.

        Parameters
        ----------
        filename : str
        search_dirs : Iterable[str]

        Returns
        -------
        str
            Absolute path to the file
        """
        if path.exists(filename):
            return filename

        for directory in search_dirs:
            absolute_filename = path.join(directory, filename)
            if path.exists(absolute_filename):
                break
            for ext in cls.readers:
                if fnmatch(path.basename(filename), '*' + ext):
                    break
                # glob uses fnmatch internally
                matching_files = glob(absolute_filename + ext)
                if matching_files:
                    absolute_filename = matching_files[0]
                    break
            if path.exists(absolute_filename):
                break
        else:
            absolute_filename = ""

        if not path.exists(absolute_filename):
            raise IOError('File "{}" was not found.'.format(filename))

        return absolute_filename

    @staticmethod
    def open(filename, *args, **kwargs):
        """
        Format handlers can use this method instead of the builtin ``open()``
        to transparently (de)compress files if requested (according to
        `filename` extension). Set ``SUPPORT_COMPRESSED=True`` if you use this.
        """
        return open_compressed(filename, *args, **kwargs)

    @classmethod
    def qualified_name(cls):
        return cls.__module__ + '.' + cls.__name__

```
--- END FILE ---

Please first localize the bug based on the issue statement, and then generate *SEARCH/REPLACE* edits to fix the issue.

Every *SEARCH/REPLACE* edit must use this format:
1. The file path
2. The start of search block: <<<<<<< SEARCH
3. A contiguous chunk of lines to search for in the existing source code
4. The dividing line: =======
5. The lines to replace into the source code
6. The end of the replace block: >>>>>>> REPLACE

Here is an example:

```python
### mathweb/flask/app.py
<<<<<<< SEARCH
from flask import Flask
=======
import math
from flask import Flask
>>>>>>> REPLACE
```
</solution>

Please note that the *SEARCH/REPLACE* edit REQUIRES PROPER INDENTATION. If you would like to add the line '        print(x)', you must fully write that out, with all those spaces before the code!
Wrap each *SEARCH/REPLACE* edit in a code block as shown in the example above. If you have multiple *SEARCH/REPLACE* edits, use a separate code block for each one.
[ISSUE]
**Title:** Header Flags Prefix Attribute Names, Causing Parsing Issues

**Description:**
When parsing headers in CSV or Excel files, the attribute type flags are incorrectly prefixed to the attribute names. This results in names like `"D#a1"` instead of the expected `"a1"`, leading to failures in processes that rely on clean attribute names.

**Example Code:**
```python
header1_flags2 = [
    ["D#a1", "D#a2", "cD#a3", "C#a4", "S#a5", "mS#a6", "T#a7", "mT#a8", "T#a9"],
    ["", "0", "", "0", "a", "a", "2024-01-01", "2024-01-01", ""],
    ["", "1", "", "1", "b", "b", "2024-01-01", "2024-01-01", ""],
    ["green", "0.0", "2.0", "2.0", "c", "c"]
]

names, types, flags = _TableHeader.create_header_data(header1_flags2[:1])
print(names)
```

**Expected Behavior:**
The `names` list should contain attribute names without any flag prefixes:
```
["a1", "a2", "a3", "a4", "a5", "a6", "a7", "a8", "a9"]
```

**Actual Behavior:**
The `names` list incorrectly includes flag prefixes:
```
["D#a1", "D#a2", "cD#a3", "C#a4", "S#a5", "mS#a6", "T#a7", "mT#a8", "T#a9"]
```

This unintended prefixing causes tests and functionalities that expect clean attribute names to fail or behave unexpectedly.
[/ISSUE]
{
    "TestTableHeader.test_get_header_data_0": "PASSED",
    "TestTableHeader.test_get_header_data_1": "PASSED",
    "TestTableHeader.test_get_header_data_1_flags": "PASSED",
    "TestTableHeader.test_get_header_data_1_flags2": "PASSED",
    "TestTableHeader.test_get_header_data_3": "PASSED",
    "TestTableHeader.test_rename_variables": "PASSED",
    "TestTableBuilder.test_column_parts_discrete_values": "PASSED",
    "TestTableBuilder.test_continuous_column": "PASSED",
    "TestTableBuilder.test_continuous_column_raises": "PASSED",
    "TestTableBuilder.test_discrete_column": "PASSED",
    "TestTableBuilder.test_string_column": "PASSED",
    "TestTableBuilder.test_time_column": "PASSED",
    "TestTableBuilder.test_unknown_type_column": "PASSED",
    "TestDataTableMixin.test_adjust_data_width_empty": "PASSED",
    "TestDataTableMixin.test_adjust_data_width_lengthen": "PASSED",
    "TestDataTableMixin.test_adjust_data_width_shorten": "PASSED",
    "TestDataTableMixin.test_data_table_0": "PASSED",
    "TestDataTableMixin.test_data_table_1": "PASSED",
    "TestDataTableMixin.test_data_table_1_flags": "PASSED",
    "TestDataTableMixin.test_data_table_3": "PASSED",
    "TestDataTableMixin.test_data_table_empty": "PASSED",
    "TestDataTableMixin.test_parse_headers_0": "PASSED",
    "TestDataTableMixin.test_parse_headers_1": "PASSED",
    "TestDataTableMixin.test_parse_headers_1_flags": "PASSED",
    "TestDataTableMixin.test_parse_headers_3": "PASSED",
    "TestDataTableMixin.test_parse_headers_empty": "PASSED"
}
diff --git a/Orange/data/io_base.py b/Orange/data/io_base.py
index 914d1d337..6b74e72a9 100644
--- a/Orange/data/io_base.py
+++ b/Orange/data/io_base.py
@@ -170,7 +170,8 @@ class _TableHeader:
         """
 
         def is_flag(x):
-            return bool(Flags.RE_ALL.match(cls._type_from_flag([x])[0]) or
+            return bool(cls._type_from_flag([x])[0] and
+                        _RE_TYPES.match(cls._type_from_flag([x])[0]) or
                         Flags.RE_ALL.match(cls._flag_from_flag([x])[0]))
 
         flags, names = zip(*[i.split(cls.HEADER1_FLAG_SEP, 1)

{"Orange/data/io_base.py": "import pickle\nimport re\nimport sys\nimport warnings\nfrom typing import Iterable, Optional, Tuple, List, Generator, Callable, Any\n\nfrom ast import literal_eval\nfrom collections import OrderedDict\nfrom functools import lru_cache\nfrom itertools import chain, repeat\nfrom math import isnan\n\nfrom os import path, remove\nfrom fnmatch import fnmatch\nfrom glob import glob\n\nimport numpy as np\nimport pandas\n\nfrom Orange.data import Table, Domain, Variable, DiscreteVariable, \\\n    StringVariable, ContinuousVariable, TimeVariable\nfrom Orange.data.io_util import Compression, open_compressed, \\\n    isnastr, guess_data_type, sanitize_variable\nfrom Orange.data.util import get_unique_names_duplicates\nfrom Orange.data.variable import VariableMeta\nfrom Orange.misc.collections import natural_sorted\nfrom Orange.util import Registry, flatten, namegen\n\n__all__ = [\"FileFormatBase\", \"Flags\", \"DataTableMixin\", \"PICKLE_PROTOCOL\"]\n\n\nPICKLE_PROTOCOL = 4\n\n\nclass MissingReaderException(IOError):\n    # subclasses IOError for backward compatibility\n    pass\n\n\nclass Flags:\n    \"\"\"Parser for column flags (i.e. third header row)\"\"\"\n    DELIMITER = ' '\n    _RE_SPLIT = re.compile(r'(?<!\\\\)' + DELIMITER).split\n    _RE_ATTR_UNQUOTED_STR = re.compile(r'^[a-zA-Z_]').match\n    ALL = OrderedDict((\n        ('class', 'c'),\n        ('ignore', 'i'),\n        ('meta', 'm'),\n        ('weight', 'w'),\n        ('.+?=.*?', ''),  # general key=value attributes\n    ))\n    RE_ALL = re.compile(r'^({})$'.format('|'.join(\n        filter(None, flatten(ALL.items())))))\n\n    def __init__(self, flags):\n        for v in filter(None, self.ALL.values()):\n            setattr(self, v, False)\n        self.attributes = {}\n        for flag in flags or []:\n            flag = flag.strip()\n            if self.RE_ALL.match(flag):\n                if '=' in flag:\n                    k, v = flag.split('=', 1)\n                    if not Flags._RE_ATTR_UNQUOTED_STR(v):\n                        try:\n                            v = literal_eval(v)\n                        except SyntaxError:\n                            # If parsing failed, treat value as string\n                            pass\n                    # map True and False to booleans\n                    if v in (\"True\", \"False\"):\n                        v = {\"True\": True, \"False\": False}[v]\n                    self.attributes[k] = v\n                else:\n                    setattr(self, flag, True)\n                    setattr(self, self.ALL.get(flag, ''), True)\n            elif flag:\n                warnings.warn('Invalid attribute flag \\'{}\\''.format(flag))\n\n    @staticmethod\n    def join(iterable, *args):\n        return Flags.DELIMITER.join(i.strip().replace(Flags.DELIMITER,\n                                                      '\\\\' + Flags.DELIMITER)\n                                    for i in chain(iterable, args)).lstrip()\n\n    @staticmethod\n    def split(s):\n        return [i.replace('\\\\' + Flags.DELIMITER, Flags.DELIMITER)\n                for i in Flags._RE_SPLIT(s)]\n\n\n# Matches discrete specification where all the values are listed, space-separated\n_RE_DISCRETE_LIST = re.compile(r'^\\s*[^\\s]+(\\s[^\\s]+)+\\s*$')\n_RE_TYPES = re.compile(r'^\\s*({}|{}|)\\s*$'.format(\n    _RE_DISCRETE_LIST.pattern,\n    '|'.join(flatten(getattr(vartype, 'TYPE_HEADERS')\n                     for vartype in Variable.registry.values()))\n))\n_RE_FLAGS = re.compile(r'^\\s*( |{}|)*\\s*$'.format(\n    '|'.join(flatten(filter(None, i) for i in Flags.ALL.items()))\n))\n\n\nclass _ColumnProperties:\n    def __init__(self, valuemap=None, values=None, orig_values=None,\n                 coltype=None, coltype_kwargs=None):\n        self.valuemap = valuemap\n        self.values = values\n        self.orig_values = orig_values\n        self.coltype = coltype\n        if coltype_kwargs is None:\n            self.coltype_kwargs = {}\n        else:\n            self.coltype_kwargs = dict(coltype_kwargs)\n\n\nclass _TableHeader:\n    \"\"\"\n    Contains functions for table header construction (and its data).\n    \"\"\"\n    HEADER1_FLAG_SEP = '#'\n\n    def __init__(self, headers: List):\n        \"\"\"\n        Parameters\n        ----------\n        headers: List\n            Header rows, to be used for constructing domain.\n        \"\"\"\n        names, types, flags = self.create_header_data(headers)\n        self.names = get_unique_names_duplicates(names)\n        self.types = types\n        self.flags = flags\n\n    @classmethod\n    def create_header_data(cls, headers: List) -> Tuple[List, List, List]:\n        \"\"\"\n        Consider various header types (single-row, two-row, three-row, none).\n\n        Parameters\n        ----------\n        headers: List\n            Header rows, to be used for constructing domain.\n\n        Returns\n        -------\n        names: List\n            List of variable names.\n        types: List\n            List of variable types.\n        flags: List\n            List of meta info (i.e. class, meta, ignore, weights).\n        \"\"\"\n        return {3: lambda x: x,\n                2: cls._header2,\n                1: cls._header1}.get(len(headers), cls._header0)(headers)\n\n    @classmethod\n    def _header2(cls, headers: List[List[str]]) -> Tuple[List, List, List]:\n        names, flags = headers\n        return names, cls._type_from_flag(flags), cls._flag_from_flag(flags)\n\n    @classmethod\n    def _header1(cls, headers: List[List[str]]) -> Tuple[List, List, List]:\n        \"\"\"\n        First row format either:\n          1) delimited column names\n          2) -||- with type and flags prepended, separated by #,\n             e.g. d#sex,c#age,cC#IQ\n        \"\"\"\n\n        def is_flag(x):\n            return bool(Flags.RE_ALL.match(cls._type_from_flag([x])[0]) or\n                        Flags.RE_ALL.match(cls._flag_from_flag([x])[0]))\n\n        flags, names = zip(*[i.split(cls.HEADER1_FLAG_SEP, 1)\n                             if cls.HEADER1_FLAG_SEP in i and\n                             is_flag(i.split(cls.HEADER1_FLAG_SEP)[0])\n                             else ('', i)\n                             for i in headers[0]])\n        names = list(names)\n        return names, cls._type_from_flag(flags), cls._flag_from_flag(flags)\n\n    @classmethod\n    def _header0(cls, _) -> Tuple[List, List, List]:\n        # Use heuristics for everything\n        return [], [], []\n\n    @staticmethod\n    def _type_from_flag(flags: List[str]) -> List[str]:\n        return [''.join(filter(str.isupper, flag)).lower() for flag in flags]\n\n    @staticmethod\n    def _flag_from_flag(flags: List[str]) -> List[str]:\n        return [Flags.join(filter(str.islower, flag)) for flag in flags]\n\n\nclass _TableBuilder:\n    X_ARR, Y_ARR, M_ARR, W_ARR = range(4)\n    DATA_IND, DOMAIN_IND, TYPE_IND = range(3)\n\n    def __init__(self, data: np.ndarray, ncols: int,\n                 header: _TableHeader, offset: int):\n        self.data = data\n        self.ncols = ncols\n        self.header = header\n        self.offset = offset\n        self.namegen: Generator[str] = namegen('Feature ', 1)\n\n        self.cols_X: List[np.ndarray] = []\n        self.cols_Y: List[np.ndarray] = []\n        self.cols_M: List[np.ndarray] = []\n        self.cols_W: List[np.ndarray] = []\n        self.attrs: List[Variable] = []\n        self.clses: List[Variable] = []\n        self.metas: List[Variable] = []\n\n    def create_table(self) -> Table:\n        self.create_columns()\n        if not self.data.size:\n            return Table.from_domain(self.get_domain(), 0)\n        else:\n            return Table.from_numpy(self.get_domain(), *self.get_arrays())\n\n    def create_columns(self):\n        names = self.header.names\n        types = self.header.types\n\n        for col in range(self.ncols):\n            flag = Flags(Flags.split(self.header.flags[col]))\n            if flag.i:\n                continue\n\n            type_ = types and types[col].strip()\n            creator = self._get_column_creator(type_)\n            column = creator(self.data, col, values=type_, offset=self.offset)\n            self._take_column(names and names[col], column, flag)\n            self._reclaim_memory(self.data, col)\n\n    @classmethod\n    def _get_column_creator(cls, type_: str) -> Callable:\n        if type_ in StringVariable.TYPE_HEADERS:\n            return cls._string_column\n        elif type_ in ContinuousVariable.TYPE_HEADERS:\n            return cls._cont_column\n        elif type_ in TimeVariable.TYPE_HEADERS:\n            return cls._time_column\n        elif _RE_DISCRETE_LIST.match(type_):\n            return cls._disc_with_vals_column\n        elif type_ in DiscreteVariable.TYPE_HEADERS:\n            return cls._disc_no_vals_column\n        else:\n            return cls._unknown_column\n\n    @staticmethod\n    def _string_column(data: np.ndarray, col: int, **_) -> _ColumnProperties:\n        vals, _ = _TableBuilder._values_mask(data, col)\n        return _ColumnProperties(values=vals, coltype=StringVariable,\n                                 orig_values=vals)\n\n    @staticmethod\n    def _cont_column(data: np.ndarray, col: int,\n                     offset=0, **_) -> _ColumnProperties:\n        orig_vals, namask = _TableBuilder._values_mask(data, col)\n        values = np.empty(data.shape[0], dtype=float)\n        try:\n            np.copyto(values, orig_vals, casting=\"unsafe\", where=~namask)\n            values[namask] = np.nan\n        except ValueError:\n            row = 0\n            for row, num in enumerate(orig_vals):\n                if not isnastr(num):\n                    try:\n                        float(num)\n                    except ValueError:\n                        break\n            raise ValueError(f'Non-continuous value in (1-based) '\n                             f'line {row + offset + 1}, column {col + 1}')\n        return _ColumnProperties(values=values, coltype=ContinuousVariable,\n                                 orig_values=orig_vals)\n\n    @staticmethod\n    def _time_column(data: np.ndarray, col: int, **_) -> _ColumnProperties:\n        vals, namask = _TableBuilder._values_mask(data, col)\n        return _ColumnProperties(values=np.where(namask, \"\", vals),\n                                 coltype=TimeVariable, orig_values=vals)\n\n    @staticmethod\n    def _disc_column(data: np.ndarray, col: int) -> \\\n            Tuple[np.ndarray, VariableMeta]:\n        vals, namask = _TableBuilder._values_mask(data, col)\n        return np.where(namask, \"\", vals), DiscreteVariable\n\n    @staticmethod\n    def _disc_no_vals_column(data: np.ndarray, col: int, **_) -> \\\n            _ColumnProperties:\n        vals, coltype = _TableBuilder._disc_column(data, col)\n        return _ColumnProperties(valuemap=natural_sorted(set(vals) - {\"\"}),\n                                 values=vals, coltype=coltype,\n                                 orig_values=vals)\n\n    @staticmethod\n    def _disc_with_vals_column(data: np.ndarray, col: int,\n                               values=\"\", **_) -> _ColumnProperties:\n        vals, coltype = _TableBuilder._disc_column(data, col)\n        return _ColumnProperties(valuemap=Flags.split(values), values=vals,\n                                 coltype=coltype, orig_values=vals)\n\n    @staticmethod\n    def _unknown_column(data: np.ndarray, col: int, **_) -> _ColumnProperties:\n        orig_vals, namask = _TableBuilder._values_mask(data, col)\n        valuemap, values, coltype = guess_data_type(orig_vals, namask)\n        return _ColumnProperties(valuemap=valuemap, values=values,\n                                 coltype=coltype, orig_values=orig_vals)\n\n    @staticmethod\n    def _values_mask(data: np.ndarray, col: int) -> \\\n            Tuple[np.ndarray, np.ndarray]:\n        try:\n            values = data[:, col]\n        except IndexError:\n            values = np.array([], dtype=object)\n        return values, isnastr(values)\n\n    def _take_column(self, name: Optional[str], column: _ColumnProperties,\n                     flag: Flags):\n        cols, dom_vars = self._lists_from_flag(flag, column.coltype)\n        values = column.values\n        if dom_vars is not None:\n            if not name:\n                name = next(self.namegen)\n\n            values, var = sanitize_variable(\n                column.valuemap, values, column.orig_values,\n                column.coltype, column.coltype_kwargs, name=name)\n            var.attributes.update(flag.attributes)\n            dom_vars.append(var)\n\n        if isinstance(values, np.ndarray) and not values.flags.owndata:\n            values = values.copy()  # might view `data` (string columns)\n        cols.append(values)\n\n    def _lists_from_flag(self, flag: Flags, coltype: VariableMeta) -> \\\n            Tuple[List, Optional[List]]:\n        if flag.m or coltype is StringVariable:\n            return self.cols_M, self.metas\n        elif flag.w:\n            return self.cols_W, None\n        elif flag.c:\n            return self.cols_Y, self.clses\n        else:\n            return self.cols_X, self.attrs\n\n    @staticmethod\n    def _reclaim_memory(data: np.ndarray, col: int):\n        # allow gc to reclaim memory used by string values\n        try:\n            data[:, col] = None\n        except IndexError:\n            pass\n\n    def get_domain(self) -> Domain:\n        return Domain(self.attrs, self.clses, self.metas)\n\n    def get_arrays(self) -> Tuple[np.ndarray, np.ndarray,\n                                  np.ndarray, np.ndarray]:\n        lists = ((self.cols_X, None),\n                 (self.cols_Y, None),\n                 (self.cols_M, object),\n                 (self.cols_W, float))\n        X, Y, M, W = [self._list_into_ndarray(lst, dt) for lst, dt in lists]\n        if X is None:\n            X = np.empty((self.data.shape[0], 0), dtype=np.float_)\n        return X, Y, M, W\n\n    @staticmethod\n    def _list_into_ndarray(lst: List, dtype=None) -> Optional[np.ndarray]:\n        if not lst:\n            return None\n\n        array = np.c_[tuple(lst)]\n        if dtype is not None:\n            array.astype(dtype)\n        else:\n            assert array.dtype == np.float_\n        return array\n\n\nclass DataTableMixin:\n    @classmethod\n    def data_table(cls, data: Iterable[List[str]],\n                   headers: Optional[List] = None) -> Table:\n        \"\"\"\n        Return Orange.data.Table given rows of `headers` (iterable of iterable)\n        and rows of `data` (iterable of iterable).\n\n        Basically, the idea of subclasses is to produce those two iterables,\n        however they might.\n\n        If `headers` is not provided, the header rows are extracted from `data`,\n        assuming they precede it.\n\n        Parameters\n        ----------\n        data: Iterable\n            File content.\n        headers: List (Optional)\n            Header rows, to be used for constructing domain.\n\n        Returns\n        -------\n        table: Table\n            Data as Orange.data.Table.\n        \"\"\"\n        if not headers:\n            headers, data = cls.parse_headers(data)\n\n        header = _TableHeader(headers)\n        # adjusting data may change header properties\n        array, n_columns = cls.adjust_data_width(data, header)\n        builder = _TableBuilder(array, n_columns, header, len(headers))\n        return builder.create_table()\n\n    @classmethod\n    def parse_headers(cls, data: Iterable[List[str]]) -> Tuple[List, Iterable]:\n        \"\"\"\n        Return (header rows, rest of data) as discerned from `data`.\n\n        Parameters\n        ----------\n        data: Iterable\n            File content.\n\n        Returns\n        -------\n        header_rows: List\n            Header rows, to be used for constructing domain.\n        data: Iterable\n            File content without header rows.\n        \"\"\"\n        data = iter(data)\n        header_rows = []\n\n        # Try to parse a three-line header\n        lines = []\n        try:\n            lines.append(list(next(data)))\n            lines.append(list(next(data)))\n            lines.append(list(next(data)))\n        except StopIteration:\n            lines, data = [], chain(lines, data)\n        if lines:\n            l1, l2, l3 = lines\n            # Three-line header if line 2 & 3 match (1st line can be anything)\n            if cls.__header_test2(l2) and cls.__header_test3(l3):\n                header_rows = [l1, l2, l3]\n            else:\n                lines, data = [], chain((l1, l2, l3), data)\n\n        # Try to parse a single-line header\n        if not header_rows:\n            try:\n                lines.append(list(next(data)))\n            except StopIteration:\n                pass\n            if lines:\n                # Header if none of the values in line 1 parses as a number\n                if not all(cls.__is_number(i) for i in lines[0]):\n                    header_rows = [lines[0]]\n                else:\n                    data = chain(lines, data)\n\n        return header_rows, data\n\n    @staticmethod\n    def __is_number(item: str) -> bool:\n        try:\n            float(item)\n        except ValueError:\n            return False\n        return True\n\n    @staticmethod\n    def __header_test2(items: List) -> bool:\n        # Second row items are type identifiers\n        return all(map(_RE_TYPES.match, items))\n\n    @staticmethod\n    def __header_test3(items: List) -> bool:\n        # Third row items are flags and column attributes (attr=value)\n        return all(map(_RE_FLAGS.match, items))\n\n    @classmethod\n    def adjust_data_width(cls, data: Iterable, header: _TableHeader) -> \\\n            Tuple[np.ndarray, int]:\n        \"\"\"\n        Determine maximum row length.\n        Return data as an array, with width dependent on header size.\n        Append `names`, `types` and `flags` if shorter than row length.\n\n        Parameters\n        ----------\n        data: Iterable\n            File content without header rows.\n        header: _TableHeader\n            Header lists converted into _TableHeader.\n\n        Returns\n        -------\n        data: np.ndarray\n            File content without header rows.\n        rowlen: int\n            Number of columns in data.\n        \"\"\"\n\n        def equal_len(lst):\n            nonlocal strip\n            if len(lst) > rowlen > 0:\n                lst = lst[:rowlen]\n                strip = True\n            elif len(lst) < rowlen:\n                lst.extend([''] * (rowlen - len(lst)))\n            return lst\n\n        rowlen = max(map(len, (header.names, header.types, header.flags)))\n        strip = False\n\n        # Ensure all data is of equal width in a column-contiguous array\n        data = [equal_len([s.strip() for s in row])\n                for row in data if any(row)]\n        array = np.array(data, dtype=object, order='F')\n\n        if strip:\n            warnings.warn(\"Columns with no headers were removed.\")\n\n        # Data may actually be longer than headers were\n        try:\n            rowlen = array.shape[1]\n        except IndexError:\n            pass\n        else:\n            for lst in (header.names, header.types, header.flags):\n                equal_len(lst)\n        return array, rowlen\n\n\nclass _FileReader:\n    @classmethod\n    def get_reader(cls, filename):\n        \"\"\"Return reader instance that can be used to read the file\n\n        Parameters\n        ----------\n        filename : str\n\n        Returns\n        -------\n        FileFormat\n        \"\"\"\n        for ext, reader in cls.readers.items():\n            # Skip ambiguous, invalid compression-only extensions added on OSX\n            if ext in Compression.all:\n                continue\n            if fnmatch(path.basename(filename), '*' + ext):\n                return reader(filename)\n\n        raise MissingReaderException('No readers for file \"{}\"'.format(filename))\n\n    @classmethod\n    def set_table_metadata(cls, filename, table):\n        # pylint: disable=bare-except\n        if isinstance(filename, str) and path.exists(filename + '.metadata'):\n            try:\n                with open(filename + '.metadata', 'rb') as f:\n                    table.attributes = pickle.load(f)\n            # Unpickling throws different exceptions, not just UnpickleError\n            except:\n                with open(filename + '.metadata', encoding='utf-8') as f:\n                    table.attributes = OrderedDict(\n                        (k.strip(), v.strip())\n                        for k, v in (line.split(\":\", 1)\n                                     for line in f.readlines()))\n\n\nclass _FileWriter:\n    @classmethod\n    def write(cls, filename, data, with_annotations=True):\n        if cls.OPTIONAL_TYPE_ANNOTATIONS:\n            return cls.write_file(filename, data, with_annotations)\n        else:\n            return cls.write_file(filename, data)\n\n    @classmethod\n    def write_table_metadata(cls, filename, data):\n        def write_file(fn):\n            if all(isinstance(key, str) and isinstance(value, str)\n                   for key, value in data.attributes.items()):\n                with open(fn, 'w', encoding='utf-8') as f:\n                    f.write(\"\\n\".join(\"{}: {}\".format(*kv)\n                                      for kv in data.attributes.items()))\n            else:\n                with open(fn, 'wb') as f:\n                    pickle.dump(data.attributes, f, protocol=PICKLE_PROTOCOL)\n\n        if isinstance(filename, str):\n            metafile = filename + '.metadata'\n            if getattr(data, 'attributes', None):\n                write_file(metafile)\n            elif path.exists(metafile):\n                remove(metafile)\n\n    @staticmethod\n    def header_names(data):\n        return ['weights'] * data.has_weights() + \\\n               [v.name for v in chain(data.domain.class_vars,\n                                      data.domain.metas,\n                                      data.domain.attributes)]\n\n    @staticmethod\n    def header_types(data):\n        def _vartype(var):\n            if var.is_continuous or var.is_string:\n                return var.TYPE_HEADERS[0]\n            elif var.is_discrete:\n                # if number of values is 1 order is not important if more\n                # values write order in file\n                return (\n                    Flags.join(var.values) if len(var.values) >= 2\n                    else var.TYPE_HEADERS[0]\n                )\n            raise NotImplementedError\n\n        return ['continuous'] * data.has_weights() + \\\n               [_vartype(v) for v in chain(data.domain.class_vars,\n                                           data.domain.metas,\n                                           data.domain.attributes)]\n\n    @staticmethod\n    def header_flags(data):\n        return list(chain(\n            ['weight'] * data.has_weights(),\n            (Flags.join([flag], *('{}={}'.format(*a) for a in\n                                  sorted(var.attributes.items())))\n             for flag, var in chain(zip(repeat('class'),\n                                        data.domain.class_vars),\n                                    zip(repeat('meta'), data.domain.metas),\n                                    zip(repeat(''), data.domain.attributes)))))\n\n    @classmethod\n    def write_headers(cls, write, data, with_annotations=True):\n        \"\"\"`write` is a callback that accepts an iterable\"\"\"\n        write(cls.header_names(data))\n        if with_annotations:\n            write(cls.header_types(data))\n            write(cls.header_flags(data))\n\n    @classmethod\n    def formatter(cls, var):\n        # type: (Variable) -> Callable[[Variable], Any]\n        # Return a column 'formatter' function. The function must return\n        # something that `write` knows how to write\n        if var.is_time:\n            return var.repr_val\n        elif var.is_continuous:\n            return lambda value: \"\" if isnan(value) else var.repr_val(value)\n        elif var.is_discrete:\n            return lambda value: \"\" if isnan(value) else var.values[int(value)]\n        elif var.is_string:\n            return lambda value: \"\" if pandas.isnull(value) else value\n        else:\n            return var.repr_val\n\n    @classmethod\n    def write_data(cls, write, data):\n        \"\"\"`write` is a callback that accepts an iterable\"\"\"\n        vars_ = list(\n            chain((ContinuousVariable('_w'),) if data.has_weights() else (),\n                  data.domain.class_vars,\n                  data.domain.metas,\n                  data.domain.attributes))\n\n        formatters = [cls.formatter(v) for v in vars_]\n        for row in zip(data.W if data.W.ndim > 1 else data.W[:, np.newaxis],\n                       data.Y if data.Y.ndim > 1 else data.Y[:, np.newaxis],\n                       data.metas,\n                       data.X):\n            write([fmt(v) for fmt, v in zip(formatters, flatten(row))])\n\n\nclass _FileFormatMeta(Registry):\n    def __new__(mcs, name, bases, attrs):\n        newcls = super().__new__(mcs, name, bases, attrs)\n\n        # Optionally add compressed versions of extensions as supported\n        if getattr(newcls, 'SUPPORT_COMPRESSED', False):\n            new_extensions = list(getattr(newcls, 'EXTENSIONS', ()))\n            for compression in Compression.all:\n                for ext in newcls.EXTENSIONS:\n                    new_extensions.append(ext + compression)\n                if sys.platform in ('darwin', 'win32'):\n                    # OSX file dialog doesn't support filtering on double\n                    # extensions (e.g. .csv.gz)\n                    # https://bugreports.qt.io/browse/QTBUG-38303\n                    # This is just here for OWFile that gets QFileDialog\n                    # filters from FileFormat.readers.keys()\n                    # EDIT: Windows exhibit similar problems:\n                    # while .tab.gz works, .tab.xz and .tab.bz2 do not!\n                    new_extensions.append(compression)\n            newcls.EXTENSIONS = tuple(new_extensions)\n\n        return newcls\n\n    @property\n    def formats(cls):\n        return cls.registry.values()\n\n    @lru_cache(5)\n    def _ext_to_attr_if_attr2(cls, attr, attr2):\n        \"\"\"\n        Return ``{ext: `attr`, ...}`` dict if ``cls`` has `attr2`.\n        If `attr` is '', return ``{ext: cls, ...}`` instead.\n\n        If there are multiple formats for an extension, return a format\n        with the lowest priority.\n        \"\"\"\n        formats = OrderedDict()\n        for format_ in sorted(cls.registry.values(), key=lambda x: x.PRIORITY):\n            if not hasattr(format_, attr2):\n                continue\n            for ext in getattr(format_, 'EXTENSIONS', []):\n                # Only adds if not yet registered\n                formats.setdefault(ext, getattr(format_, attr, format_))\n        return formats\n\n    @property\n    def names(cls):\n        return cls._ext_to_attr_if_attr2('DESCRIPTION', '__class__')\n\n    @property\n    def writers(cls):\n        return cls._ext_to_attr_if_attr2('', 'write_file')\n\n    @property\n    def readers(cls):\n        return cls._ext_to_attr_if_attr2('', 'read')\n\n    @property\n    def img_writers(cls):\n        warnings.warn(\n            f\"'{__name__}.FileFormat.img_writers' is no longer used and \"\n            \"will be removed. Please use \"\n            \"'Orange.widgets.io.FileFormat.img_writers' instead.\",\n            DeprecationWarning, stacklevel=2\n        )\n        return cls._ext_to_attr_if_attr2('', 'write_image')\n\n    @property\n    def graph_writers(cls):\n        return cls._ext_to_attr_if_attr2('', 'write_graph')\n\n\nclass FileFormatBase(_FileReader, _FileWriter, metaclass=_FileFormatMeta):\n    # Priority when multiple formats support the same extension. Also\n    # the sort order in file open/save combo boxes. Lower is better.\n    PRIORITY = 10000\n    OPTIONAL_TYPE_ANNOTATIONS = False\n\n    @classmethod\n    def locate(cls, filename, search_dirs=('.',)):\n        \"\"\"Locate a file with given filename that can be opened by one\n        of the available readers.\n\n        Parameters\n        ----------\n        filename : str\n        search_dirs : Iterable[str]\n\n        Returns\n        -------\n        str\n            Absolute path to the file\n        \"\"\"\n        if path.exists(filename):\n            return filename\n\n        for directory in search_dirs:\n            absolute_filename = path.join(directory, filename)\n            if path.exists(absolute_filename):\n                break\n            for ext in cls.readers:\n                if fnmatch(path.basename(filename), '*' + ext):\n                    break\n                # glob uses fnmatch internally\n                matching_files = glob(absolute_filename + ext)\n                if matching_files:\n                    absolute_filename = matching_files[0]\n                    break\n            if path.exists(absolute_filename):\n                break\n        else:\n            absolute_filename = \"\"\n\n        if not path.exists(absolute_filename):\n            raise IOError('File \"{}\" was not found.'.format(filename))\n\n        return absolute_filename\n\n    @staticmethod\n    def open(filename, *args, **kwargs):\n        \"\"\"\n        Format handlers can use this method instead of the builtin ``open()``\n        to transparently (de)compress files if requested (according to\n        `filename` extension). Set ``SUPPORT_COMPRESSED=True`` if you use this.\n        \"\"\"\n        return open_compressed(filename, *args, **kwargs)\n\n    @classmethod\n    def qualified_name(cls):\n        return cls.__module__ + '.' + cls.__name__\n"}
A user will ask you to solve a task. You should first draft your thinking process (inner monologue). Then, generate the solution.

Your response format must follow the template below:
<think>
Your thoughts or/and draft, like working through an exercise on scratch paper. Be as casual and as long as you want until you are confident to generate a correct solution.
</think>
<solution>
Final solution presented to the user.
</solution>
system
We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
[ISSUE]
**Title:** Header Flags Prefix Attribute Names, Causing Parsing Issues

**Description:**
When parsing headers in CSV or Excel files, the attribute type flags are incorrectly prefixed to the attribute names. This results in names like `"D#a1"` instead of the expected `"a1"`, leading to failures in processes that rely on clean attribute names.

**Example Code:**
```python
header1_flags2 = [
    ["D#a1", "D#a2", "cD#a3", "C#a4", "S#a5", "mS#a6", "T#a7", "mT#a8", "T#a9"],
    ["", "0", "", "0", "a", "a", "2024-01-01", "2024-01-01", ""],
    ["", "1", "", "1", "b", "b", "2024-01-01", "2024-01-01", ""],
    ["green", "0.0", "2.0", "2.0", "c", "c"]
]

names, types, flags = _TableHeader.create_header_data(header1_flags2[:1])
print(names)
```

**Expected Behavior:**
The `names` list should contain attribute names without any flag prefixes:
```
["a1", "a2", "a3", "a4", "a5", "a6", "a7", "a8", "a9"]
```

**Actual Behavior:**
The `names` list incorrectly includes flag prefixes:
```
["D#a1", "D#a2", "cD#a3", "C#a4", "S#a5", "mS#a6", "T#a7", "mT#a8", "T#a9"]
```

This unintended prefixing causes tests and functionalities that expect clean attribute names to fail or behave unexpectedly.
[/ISSUE]
--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.

--- BEGIN FILE ---
```
### Orange/data/io_base.py
import pickle
import re
import sys
import warnings
from typing import Iterable, Optional, Tuple, List, Generator, Callable, Any

from ast import literal_eval
from collections import OrderedDict
from functools import lru_cache
from itertools import chain, repeat
from math import isnan

from os import path, remove
from fnmatch import fnmatch
from glob import glob

import numpy as np
import pandas

from Orange.data import Table, Domain, Variable, DiscreteVariable, \
    StringVariable, ContinuousVariable, TimeVariable
from Orange.data.io_util import Compression, open_compressed, \
    isnastr, guess_data_type, sanitize_variable
from Orange.data.util import get_unique_names_duplicates
from Orange.data.variable import VariableMeta
from Orange.misc.collections import natural_sorted
from Orange.util import Registry, flatten, namegen

__all__ = ["FileFormatBase", "Flags", "DataTableMixin", "PICKLE_PROTOCOL"]


PICKLE_PROTOCOL = 4


class MissingReaderException(IOError):
    # subclasses IOError for backward compatibility
    pass


class Flags:
    """Parser for column flags (i.e. third header row)"""
    DELIMITER = ' '
    _RE_SPLIT = re.compile(r'(?<!\\)' + DELIMITER).split
    _RE_ATTR_UNQUOTED_STR = re.compile(r'^[a-zA-Z_]').match
    ALL = OrderedDict((
        ('class', 'c'),
        ('ignore', 'i'),
        ('meta', 'm'),
        ('weight', 'w'),
        ('.+?=.*?', ''),  # general key=value attributes
    ))
    RE_ALL = re.compile(r'^({})$'.format('|'.join(
        filter(None, flatten(ALL.items())))))

    def __init__(self, flags):
        for v in filter(None, self.ALL.values()):
            setattr(self, v, False)
        self.attributes = {}
        for flag in flags or []:
            flag = flag.strip()
            if self.RE_ALL.match(flag):
                if '=' in flag:
                    k, v = flag.split('=', 1)
                    if not Flags._RE_ATTR_UNQUOTED_STR(v):
                        try:
                            v = literal_eval(v)
                        except SyntaxError:
                            # If parsing failed, treat value as string
                            pass
                    # map True and False to booleans
                    if v in ("True", "False"):
                        v = {"True": True, "False": False}[v]
                    self.attributes[k] = v
                else:
                    setattr(self, flag, True)
                    setattr(self, self.ALL.get(flag, ''), True)
            elif flag:
                warnings.warn('Invalid attribute flag \'{}\''.format(flag))

    @staticmethod
    def join(iterable, *args):
        return Flags.DELIMITER.join(i.strip().replace(Flags.DELIMITER,
                                                      '\\' + Flags.DELIMITER)
                                    for i in chain(iterable, args)).lstrip()

    @staticmethod
    def split(s):
        return [i.replace('\\' + Flags.DELIMITER, Flags.DELIMITER)
                for i in Flags._RE_SPLIT(s)]


# Matches discrete specification where all the values are listed, space-separated
_RE_DISCRETE_LIST = re.compile(r'^\s*[^\s]+(\s[^\s]+)+\s*$')
_RE_TYPES = re.compile(r'^\s*({}|{}|)\s*$'.format(
    _RE_DISCRETE_LIST.pattern,
    '|'.join(flatten(getattr(vartype, 'TYPE_HEADERS')
                     for vartype in Variable.registry.values()))
))
_RE_FLAGS = re.compile(r'^\s*( |{}|)*\s*$'.format(
    '|'.join(flatten(filter(None, i) for i in Flags.ALL.items()))
))


class _ColumnProperties:
    def __init__(self, valuemap=None, values=None, orig_values=None,
                 coltype=None, coltype_kwargs=None):
        self.valuemap = valuemap
        self.values = values
        self.orig_values = orig_values
        self.coltype = coltype
        if coltype_kwargs is None:
            self.coltype_kwargs = {}
        else:
            self.coltype_kwargs = dict(coltype_kwargs)


class _TableHeader:
    """
    Contains functions for table header construction (and its data).
    """
    HEADER1_FLAG_SEP = '#'

    def __init__(self, headers: List):
        """
        Parameters
        ----------
        headers: List
            Header rows, to be used for constructing domain.
        """
        names, types, flags = self.create_header_data(headers)
        self.names = get_unique_names_duplicates(names)
        self.types = types
        self.flags = flags

    @classmethod
    def create_header_data(cls, headers: List) -> Tuple[List, List, List]:
        """
        Consider various header types (single-row, two-row, three-row, none).

        Parameters
        ----------
        headers: List
            Header rows, to be used for constructing domain.

        Returns
        -------
        names: List
            List of variable names.
        types: List
            List of variable types.
        flags: List
            List of meta info (i.e. class, meta, ignore, weights).
        """
        return {3: lambda x: x,
                2: cls._header2,
                1: cls._header1}.get(len(headers), cls._header0)(headers)

    @classmethod
    def _header2(cls, headers: List[List[str]]) -> Tuple[List, List, List]:
        names, flags = headers
        return names, cls._type_from_flag(flags), cls._flag_from_flag(flags)

    @classmethod
    def _header1(cls, headers: List[List[str]]) -> Tuple[List, List, List]:
        """
        First row format either:
          1) delimited column names
          2) -||- with type and flags prepended, separated by #,
             e.g. d#sex,c#age,cC#IQ
        """

        def is_flag(x):
            return bool(Flags.RE_ALL.match(cls._type_from_flag([x])[0]) or
                        Flags.RE_ALL.match(cls._flag_from_flag([x])[0]))

        flags, names = zip(*[i.split(cls.HEADER1_FLAG_SEP, 1)
                             if cls.HEADER1_FLAG_SEP in i and
                             is_flag(i.split(cls.HEADER1_FLAG_SEP)[0])
                             else ('', i)
                             for i in headers[0]])
        names = list(names)
        return names, cls._type_from_flag(flags), cls._flag_from_flag(flags)

    @classmethod
    def _header0(cls, _) -> Tuple[List, List, List]:
        # Use heuristics for everything
        return [], [], []

    @staticmethod
    def _type_from_flag(flags: List[str]) -> List[str]:
        return [''.join(filter(str.isupper, flag)).lower() for flag in flags]

    @staticmethod
    def _flag_from_flag(flags: List[str]) -> List[str]:
        return [Flags.join(filter(str.islower, flag)) for flag in flags]


class _TableBuilder:
    X_ARR, Y_ARR, M_ARR, W_ARR = range(4)
    DATA_IND, DOMAIN_IND, TYPE_IND = range(3)

    def __init__(self, data: np.ndarray, ncols: int,
                 header: _TableHeader, offset: int):
        self.data = data
        self.ncols = ncols
        self.header = header
        self.offset = offset
        self.namegen: Generator[str] = namegen('Feature ', 1)

        self.cols_X: List[np.ndarray] = []
        self.cols_Y: List[np.ndarray] = []
        self.cols_M: List[np.ndarray] = []
        self.cols_W: List[np.ndarray] = []
        self.attrs: List[Variable] = []
        self.clses: List[Variable] = []
        self.metas: List[Variable] = []

    def create_table(self) -> Table:
        self.create_columns()
        if not self.data.size:
            return Table.from_domain(self.get_domain(), 0)
        else:
            return Table.from_numpy(self.get_domain(), *self.get_arrays())

    def create_columns(self):
        names = self.header.names
        types = self.header.types

        for col in range(self.ncols):
            flag = Flags(Flags.split(self.header.flags[col]))
            if flag.i:
                continue

            type_ = types and types[col].strip()
            creator = self._get_column_creator(type_)
            column = creator(self.data, col, values=type_, offset=self.offset)
            self._take_column(names and names[col], column, flag)
            self._reclaim_memory(self.data, col)

    @classmethod
    def _get_column_creator(cls, type_: str) -> Callable:
        if type_ in StringVariable.TYPE_HEADERS:
            return cls._string_column
        elif type_ in ContinuousVariable.TYPE_HEADERS:
            return cls._cont_column
        elif type_ in TimeVariable.TYPE_HEADERS:
            return cls._time_column
        elif _RE_DISCRETE_LIST.match(type_):
            return cls._disc_with_vals_column
        elif type_ in DiscreteVariable.TYPE_HEADERS:
            return cls._disc_no_vals_column
        else:
            return cls._unknown_column

    @staticmethod
    def _string_column(data: np.ndarray, col: int, **_) -> _ColumnProperties:
        vals, _ = _TableBuilder._values_mask(data, col)
        return _ColumnProperties(values=vals, coltype=StringVariable,
                                 orig_values=vals)

    @staticmethod
    def _cont_column(data: np.ndarray, col: int,
                     offset=0, **_) -> _ColumnProperties:
        orig_vals, namask = _TableBuilder._values_mask(data, col)
        values = np.empty(data.shape[0], dtype=float)
        try:
            np.copyto(values, orig_vals, casting="unsafe", where=~namask)
            values[namask] = np.nan
        except ValueError:
            row = 0
            for row, num in enumerate(orig_vals):
                if not isnastr(num):
                    try:
                        float(num)
                    except ValueError:
                        break
            raise ValueError(f'Non-continuous value in (1-based) '
                             f'line {row + offset + 1}, column {col + 1}')
        return _ColumnProperties(values=values, coltype=ContinuousVariable,
                                 orig_values=orig_vals)

    @staticmethod
    def _time_column(data: np.ndarray, col: int, **_) -> _ColumnProperties:
        vals, namask = _TableBuilder._values_mask(data, col)
        return _ColumnProperties(values=np.where(namask, "", vals),
                                 coltype=TimeVariable, orig_values=vals)

    @staticmethod
    def _disc_column(data: np.ndarray, col: int) -> \
            Tuple[np.ndarray, VariableMeta]:
        vals, namask = _TableBuilder._values_mask(data, col)
        return np.where(namask, "", vals), DiscreteVariable

    @staticmethod
    def _disc_no_vals_column(data: np.ndarray, col: int, **_) -> \
            _ColumnProperties:
        vals, coltype = _TableBuilder._disc_column(data, col)
        return _ColumnProperties(valuemap=natural_sorted(set(vals) - {""}),
                                 values=vals, coltype=coltype,
                                 orig_values=vals)

    @staticmethod
    def _disc_with_vals_column(data: np.ndarray, col: int,
                               values="", **_) -> _ColumnProperties:
        vals, coltype = _TableBuilder._disc_column(data, col)
        return _ColumnProperties(valuemap=Flags.split(values), values=vals,
                                 coltype=coltype, orig_values=vals)

    @staticmethod
    def _unknown_column(data: np.ndarray, col: int, **_) -> _ColumnProperties:
        orig_vals, namask = _TableBuilder._values_mask(data, col)
        valuemap, values, coltype = guess_data_type(orig_vals, namask)
        return _ColumnProperties(valuemap=valuemap, values=values,
                                 coltype=coltype, orig_values=orig_vals)

    @staticmethod
    def _values_mask(data: np.ndarray, col: int) -> \
            Tuple[np.ndarray, np.ndarray]:
        try:
            values = data[:, col]
        except IndexError:
            values = np.array([], dtype=object)
        return values, isnastr(values)

    def _take_column(self, name: Optional[str], column: _ColumnProperties,
                     flag: Flags):
        cols, dom_vars = self._lists_from_flag(flag, column.coltype)
        values = column.values
        if dom_vars is not None:
            if not name:
                name = next(self.namegen)

            values, var = sanitize_variable(
                column.valuemap, values, column.orig_values,
                column.coltype, column.coltype_kwargs, name=name)
            var.attributes.update(flag.attributes)
            dom_vars.append(var)

        if isinstance(values, np.ndarray) and not values.flags.owndata:
            values = values.copy()  # might view `data` (string columns)
        cols.append(values)

    def _lists_from_flag(self, flag: Flags, coltype: VariableMeta) -> \
            Tuple[List, Optional[List]]:
        if flag.m or coltype is StringVariable:
            return self.cols_M, self.metas
        elif flag.w:
            return self.cols_W, None
        elif flag.c:
            return self.cols_Y, self.clses
        else:
            return self.cols_X, self.attrs

    @staticmethod
    def _reclaim_memory(data: np.ndarray, col: int):
        # allow gc to reclaim memory used by string values
        try:
            data[:, col] = None
        except IndexError:
            pass

    def get_domain(self) -> Domain:
        return Domain(self.attrs, self.clses, self.metas)

    def get_arrays(self) -> Tuple[np.ndarray, np.ndarray,
                                  np.ndarray, np.ndarray]:
        lists = ((self.cols_X, None),
                 (self.cols_Y, None),
                 (self.cols_M, object),
                 (self.cols_W, float))
        X, Y, M, W = [self._list_into_ndarray(lst, dt) for lst, dt in lists]
        if X is None:
            X = np.empty((self.data.shape[0], 0), dtype=np.float_)
        return X, Y, M, W

    @staticmethod
    def _list_into_ndarray(lst: List, dtype=None) -> Optional[np.ndarray]:
        if not lst:
            return None

        array = np.c_[tuple(lst)]
        if dtype is not None:
            array.astype(dtype)
        else:
            assert array.dtype == np.float_
        return array


class DataTableMixin:
    @classmethod
    def data_table(cls, data: Iterable[List[str]],
                   headers: Optional[List] = None) -> Table:
        """
        Return Orange.data.Table given rows of `headers` (iterable of iterable)
        and rows of `data` (iterable of iterable).

        Basically, the idea of subclasses is to produce those two iterables,
        however they might.

        If `headers` is not provided, the header rows are extracted from `data`,
        assuming they precede it.

        Parameters
        ----------
        data: Iterable
            File content.
        headers: List (Optional)
            Header rows, to be used for constructing domain.

        Returns
        -------
        table: Table
            Data as Orange.data.Table.
        """
        if not headers:
            headers, data = cls.parse_headers(data)

        header = _TableHeader(headers)
        # adjusting data may change header properties
        array, n_columns = cls.adjust_data_width(data, header)
        builder = _TableBuilder(array, n_columns, header, len(headers))
        return builder.create_table()

    @classmethod
    def parse_headers(cls, data: Iterable[List[str]]) -> Tuple[List, Iterable]:
        """
        Return (header rows, rest of data) as discerned from `data`.

        Parameters
        ----------
        data: Iterable
            File content.

        Returns
        -------
        header_rows: List
            Header rows, to be used for constructing domain.
        data: Iterable
            File content without header rows.
        """
        data = iter(data)
        header_rows = []

        # Try to parse a three-line header
        lines = []
        try:
            lines.append(list(next(data)))
            lines.append(list(next(data)))
            lines.append(list(next(data)))
        except StopIteration:
            lines, data = [], chain(lines, data)
        if lines:
            l1, l2, l3 = lines
            # Three-line header if line 2 & 3 match (1st line can be anything)
            if cls.__header_test2(l2) and cls.__header_test3(l3):
                header_rows = [l1, l2, l3]
            else:
                lines, data = [], chain((l1, l2, l3), data)

        # Try to parse a single-line header
        if not header_rows:
            try:
                lines.append(list(next(data)))
            except StopIteration:
                pass
            if lines:
                # Header if none of the values in line 1 parses as a number
                if not all(cls.__is_number(i) for i in lines[0]):
                    header_rows = [lines[0]]
                else:
                    data = chain(lines, data)

        return header_rows, data

    @staticmethod
    def __is_number(item: str) -> bool:
        try:
            float(item)
        except ValueError:
            return False
        return True

    @staticmethod
    def __header_test2(items: List) -> bool:
        # Second row items are type identifiers
        return all(map(_RE_TYPES.match, items))

    @staticmethod
    def __header_test3(items: List) -> bool:
        # Third row items are flags and column attributes (attr=value)
        return all(map(_RE_FLAGS.match, items))

    @classmethod
    def adjust_data_width(cls, data: Iterable, header: _TableHeader) -> \
            Tuple[np.ndarray, int]:
        """
        Determine maximum row length.
        Return data as an array, with width dependent on header size.
        Append `names`, `types` and `flags` if shorter than row length.

        Parameters
        ----------
        data: Iterable
            File content without header rows.
        header: _TableHeader
            Header lists converted into _TableHeader.

        Returns
        -------
        data: np.ndarray
            File content without header rows.
        rowlen: int
            Number of columns in data.
        """

        def equal_len(lst):
            nonlocal strip
            if len(lst) > rowlen > 0:
                lst = lst[:rowlen]
                strip = True
            elif len(lst) < rowlen:
                lst.extend([''] * (rowlen - len(lst)))
            return lst

        rowlen = max(map(len, (header.names, header.types, header.flags)))
        strip = False

        # Ensure all data is of equal width in a column-contiguous array
        data = [equal_len([s.strip() for s in row])
                for row in data if any(row)]
        array = np.array(data, dtype=object, order='F')

        if strip:
            warnings.warn("Columns with no headers were removed.")

        # Data may actually be longer than headers were
        try:
            rowlen = array.shape[1]
        except IndexError:
            pass
        else:
            for lst in (header.names, header.types, header.flags):
                equal_len(lst)
        return array, rowlen


class _FileReader:
    @classmethod
    def get_reader(cls, filename):
        """Return reader instance that can be used to read the file

        Parameters
        ----------
        filename : str

        Returns
        -------
        FileFormat
        """
        for ext, reader in cls.readers.items():
            # Skip ambiguous, invalid compression-only extensions added on OSX
            if ext in Compression.all:
                continue
            if fnmatch(path.basename(filename), '*' + ext):
                return reader(filename)

        raise MissingReaderException('No readers for file "{}"'.format(filename))

    @classmethod
    def set_table_metadata(cls, filename, table):
        # pylint: disable=bare-except
        if isinstance(filename, str) and path.exists(filename + '.metadata'):
            try:
                with open(filename + '.metadata', 'rb') as f:
                    table.attributes = pickle.load(f)
            # Unpickling throws different exceptions, not just UnpickleError
            except:
                with open(filename + '.metadata', encoding='utf-8') as f:
                    table.attributes = OrderedDict(
                        (k.strip(), v.strip())
                        for k, v in (line.split(":", 1)
                                     for line in f.readlines()))


class _FileWriter:
    @classmethod
    def write(cls, filename, data, with_annotations=True):
        if cls.OPTIONAL_TYPE_ANNOTATIONS:
            return cls.write_file(filename, data, with_annotations)
        else:
            return cls.write_file(filename, data)

    @classmethod
    def write_table_metadata(cls, filename, data):
        def write_file(fn):
            if all(isinstance(key, str) and isinstance(value, str)
                   for key, value in data.attributes.items()):
                with open(fn, 'w', encoding='utf-8') as f:
                    f.write("\n".join("{}: {}".format(*kv)
                                      for kv in data.attributes.items()))
            else:
                with open(fn, 'wb') as f:
                    pickle.dump(data.attributes, f, protocol=PICKLE_PROTOCOL)

        if isinstance(filename, str):
            metafile = filename + '.metadata'
            if getattr(data, 'attributes', None):
                write_file(metafile)
            elif path.exists(metafile):
                remove(metafile)

    @staticmethod
    def header_names(data):
        return ['weights'] * data.has_weights() + \
               [v.name for v in chain(data.domain.class_vars,
                                      data.domain.metas,
                                      data.domain.attributes)]

    @staticmethod
    def header_types(data):
        def _vartype(var):
            if var.is_continuous or var.is_string:
                return var.TYPE_HEADERS[0]
            elif var.is_discrete:
                # if number of values is 1 order is not important if more
                # values write order in file
                return (
                    Flags.join(var.values) if len(var.values) >= 2
                    else var.TYPE_HEADERS[0]
                )
            raise NotImplementedError

        return ['continuous'] * data.has_weights() + \
               [_vartype(v) for v in chain(data.domain.class_vars,
                                           data.domain.metas,
                                           data.domain.attributes)]

    @staticmethod
    def header_flags(data):
        return list(chain(
            ['weight'] * data.has_weights(),
            (Flags.join([flag], *('{}={}'.format(*a) for a in
                                  sorted(var.attributes.items())))
             for flag, var in chain(zip(repeat('class'),
                                        data.domain.class_vars),
                                    zip(repeat('meta'), data.domain.metas),
                                    zip(repeat(''), data.domain.attributes)))))

    @classmethod
    def write_headers(cls, write, data, with_annotations=True):
        """`write` is a callback that accepts an iterable"""
        write(cls.header_names(data))
        if with_annotations:
            write(cls.header_types(data))
            write(cls.header_flags(data))

    @classmethod
    def formatter(cls, var):
        # type: (Variable) -> Callable[[Variable], Any]
        # Return a column 'formatter' function. The function must return
        # something that `write` knows how to write
        if var.is_time:
            return var.repr_val
        elif var.is_continuous:
            return lambda value: "" if isnan(value) else var.repr_val(value)
        elif var.is_discrete:
            return lambda value: "" if isnan(value) else var.values[int(value)]
        elif var.is_string:
            return lambda value: "" if pandas.isnull(value) else value
        else:
            return var.repr_val

    @classmethod
    def write_data(cls, write, data):
        """`write` is a callback that accepts an iterable"""
        vars_ = list(
            chain((ContinuousVariable('_w'),) if data.has_weights() else (),
                  data.domain.class_vars,
                  data.domain.metas,
                  data.domain.attributes))

        formatters = [cls.formatter(v) for v in vars_]
        for row in zip(data.W if data.W.ndim > 1 else data.W[:, np.newaxis],
                       data.Y if data.Y.ndim > 1 else data.Y[:, np.newaxis],
                       data.metas,
                       data.X):
            write([fmt(v) for fmt, v in zip(formatters, flatten(row))])


class _FileFormatMeta(Registry):
    def __new__(mcs, name, bases, attrs):
        newcls = super().__new__(mcs, name, bases, attrs)

        # Optionally add compressed versions of extensions as supported
        if getattr(newcls, 'SUPPORT_COMPRESSED', False):
            new_extensions = list(getattr(newcls, 'EXTENSIONS', ()))
            for compression in Compression.all:
                for ext in newcls.EXTENSIONS:
                    new_extensions.append(ext + compression)
                if sys.platform in ('darwin', 'win32'):
                    # OSX file dialog doesn't support filtering on double
                    # extensions (e.g. .csv.gz)
                    # https://bugreports.qt.io/browse/QTBUG-38303
                    # This is just here for OWFile that gets QFileDialog
                    # filters from FileFormat.readers.keys()
                    # EDIT: Windows exhibit similar problems:
                    # while .tab.gz works, .tab.xz and .tab.bz2 do not!
                    new_extensions.append(compression)
            newcls.EXTENSIONS = tuple(new_extensions)

        return newcls

    @property
    def formats(cls):
        return cls.registry.values()

    @lru_cache(5)
    def _ext_to_attr_if_attr2(cls, attr, attr2):
        """
        Return ``{ext: `attr`, ...}`` dict if ``cls`` has `attr2`.
        If `attr` is '', return ``{ext: cls, ...}`` instead.

        If there are multiple formats for an extension, return a format
        with the lowest priority.
        """
        formats = OrderedDict()
        for format_ in sorted(cls.registry.values(), key=lambda x: x.PRIORITY):
            if not hasattr(format_, attr2):
                continue
            for ext in getattr(format_, 'EXTENSIONS', []):
                # Only adds if not yet registered
                formats.setdefault(ext, getattr(format_, attr, format_))
        return formats

    @property
    def names(cls):
        return cls._ext_to_attr_if_attr2('DESCRIPTION', '__class__')

    @property
    def writers(cls):
        return cls._ext_to_attr_if_attr2('', 'write_file')

    @property
    def readers(cls):
        return cls._ext_to_attr_if_attr2('', 'read')

    @property
    def img_writers(cls):
        warnings.warn(
            f"'{__name__}.FileFormat.img_writers' is no longer used and "
            "will be removed. Please use "
            "'Orange.widgets.io.FileFormat.img_writers' instead.",
            DeprecationWarning, stacklevel=2
        )
        return cls._ext_to_attr_if_attr2('', 'write_image')

    @property
    def graph_writers(cls):
        return cls._ext_to_attr_if_attr2('', 'write_graph')


class FileFormatBase(_FileReader, _FileWriter, metaclass=_FileFormatMeta):
    # Priority when multiple formats support the same extension. Also
    # the sort order in file open/save combo boxes. Lower is better.
    PRIORITY = 10000
    OPTIONAL_TYPE_ANNOTATIONS = False

    @classmethod
    def locate(cls, filename, search_dirs=('.',)):
        """Locate a file with given filename that can be opened by one
        of the available readers.

        Parameters
        ----------
        filename : str
        search_dirs : Iterable[str]

        Returns
        -------
        str
            Absolute path to the file
        """
        if path.exists(filename):
            return filename

        for directory in search_dirs:
            absolute_filename = path.join(directory, filename)
            if path.exists(absolute_filename):
                break
            for ext in cls.readers:
                if fnmatch(path.basename(filename), '*' + ext):
                    break
                # glob uses fnmatch internally
                matching_files = glob(absolute_filename + ext)
                if matching_files:
                    absolute_filename = matching_files[0]
                    break
            if path.exists(absolute_filename):
                break
        else:
            absolute_filename = ""

        if not path.exists(absolute_filename):
            raise IOError('File "{}" was not found.'.format(filename))

        return absolute_filename

    @staticmethod
    def open(filename, *args, **kwargs):
        """
        Format handlers can use this method instead of the builtin ``open()``
        to transparently (de)compress files if requested (according to
        `filename` extension). Set ``SUPPORT_COMPRESSED=True`` if you use this.
        """
        return open_compressed(filename, *args, **kwargs)

    @classmethod
    def qualified_name(cls):
        return cls.__module__ + '.' + cls.__name__

```
--- END FILE ---

Please first localize the bug based on the issue statement, and then generate *SEARCH/REPLACE* edits to fix the issue.

Every *SEARCH/REPLACE* edit must use this format:
1. The file path
2. The start of search block: <<<<<<< SEARCH
3. A contiguous chunk of lines to search for in the existing source code
4. The dividing line: =======
5. The lines to replace into the source code
6. The end of the replace block: >>>>>>> REPLACE

Here is an example:

```python
### mathweb/flask/app.py
<<<<<<< SEARCH
from flask import Flask
=======
import math
from flask import Flask
>>>>>>> REPLACE
```
</solution>

Please note that the *SEARCH/REPLACE* edit REQUIRES PROPER INDENTATION. If you would like to add the line '        print(x)', you must fully write that out, with all those spaces before the code!
Wrap each *SEARCH/REPLACE* edit in a code block as shown in the example above. If you have multiple *SEARCH/REPLACE* edits, use a separate code block for each one.
user