{
    "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\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        flags, names = zip(*[i.split(cls.HEADER1_FLAG_SEP, 1)\n                             if cls.HEADER1_FLAG_SEP in i 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 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: 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\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        flags, names = zip(*[i.split(cls.HEADER1_FLAG_SEP, 1)\n                             if cls.HEADER1_FLAG_SEP in i 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: 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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                            "file_name": "Orange/data/io_base.py",
                            "type": "method",
                            "name": "_FileWriter.formatter",
                            "content": "class _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 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: 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))])",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 650,
                            "end_lineno": 663,
                            "parent": {
                                "file_name": "Orange/data/io_base.py",
                                "type": "class",
                                "name": "_FileWriter",
                                "content": "class _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 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: 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))])",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 577,
                                "end_lineno": 679,
                                "parent": null
                            }
                        },
                        {
                            "file_name": "Orange/data/io_base.py",
                            "type": "class",
                            "name": "_FileWriter",
                            "content": "class _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 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: 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))])",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 577,
                            "end_lineno": 679,
                            "parent": null
                        }
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                    "deleted_entities": []
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        },
        {
            "old_file_content": "import os\nimport unittest\nfrom tempfile import NamedTemporaryFile\n\nimport numpy as np\n\nfrom Orange.data import ContinuousVariable, DiscreteVariable, StringVariable, \\\n    TimeVariable, Domain, Table\nfrom Orange.data.io import TabReader, ExcelReader\nfrom Orange.data.io_util import guess_data_type\nfrom Orange.misc.collections import natural_sorted\n\n\nclass TestTableFilters(unittest.TestCase):\n    def test_guess_data_type_continuous(self):\n        # should be ContinuousVariable\n        valuemap, values, coltype = guess_data_type(list(range(1, 100)))\n        self.assertEqual(ContinuousVariable, coltype)\n        self.assertIsNone(valuemap)\n        np.testing.assert_array_equal(np.array(list(range(1, 100))), values)\n\n        valuemap, values, coltype = guess_data_type([1, 2, 3, 1, 2, 3])\n        self.assertEqual(ContinuousVariable, coltype)\n        self.assertIsNone(valuemap)\n        np.testing.assert_array_equal([1, 2, 3, 1, 2, 3], values)\n\n        valuemap, values, coltype = guess_data_type(\n            [\"1\", \"2\", \"3\", \"1\", \"2\", \"3\"])\n        self.assertEqual(ContinuousVariable, coltype)\n        self.assertIsNone(valuemap)\n        np.testing.assert_array_equal([1, 2, 3, 1, 2, 3], values)\n\n    def test_guess_data_type_discrete(self):\n        # should be DiscreteVariable\n        valuemap, values, coltype = guess_data_type([1, 2, 1, 2])\n        self.assertEqual(DiscreteVariable, coltype)\n        self.assertEqual([1, 2], valuemap)\n        np.testing.assert_array_equal([1, 2, 1, 2], values)\n\n        valuemap, values, coltype = guess_data_type([\"1\", \"2\", \"1\", \"2\", \"a\"])\n        self.assertEqual(DiscreteVariable, coltype)\n        self.assertEqual([\"1\", \"2\", \"a\"], valuemap)\n        np.testing.assert_array_equal(['1', '2', '1', '2', 'a'], values)\n\n        # just below the threshold for string variable\n        in_values = list(map(lambda x: str(x) + \"a\", range(24))) + [\"a\"] * 76\n        valuemap, values, coltype = guess_data_type(in_values)\n        self.assertEqual(DiscreteVariable, coltype)\n        self.assertEqual(natural_sorted(set(in_values)), valuemap)\n        np.testing.assert_array_equal(in_values, values)\n\n    def test_guess_data_type_string(self):\n        # should be StringVariable\n        # too many different values for discrete\n        in_values = list(map(lambda x: str(x) + \"a\", range(90)))\n        valuemap, values, coltype = guess_data_type(in_values)\n        self.assertEqual(StringVariable, coltype)\n        self.assertIsNone(valuemap)\n        np.testing.assert_array_equal(in_values, values)\n\n        # more than len(values)**0.7\n        in_values = list(map(lambda x: str(x) + \"a\", range(25))) + [\"a\"] * 75\n        valuemap, values, coltype = guess_data_type(in_values)\n        self.assertEqual(StringVariable, coltype)\n        self.assertIsNone(valuemap)\n        np.testing.assert_array_equal(in_values, values)\n\n        # more than 100 different values - exactly 101\n        # this is the case when len(values)**0.7 rule would vote for the\n        # DiscreteVariable\n        in_values = list(map(lambda x: str(x) + \"a\", range(100))) + [\"a\"] * 999\n        valuemap, values, coltype = guess_data_type(in_values)\n        self.assertEqual(StringVariable, coltype)\n        self.assertIsNone(valuemap)\n        np.testing.assert_array_equal(in_values, values)\n\n    def test_guess_data_type_time(self):\n        in_values = [\"2019-10-10\", \"2019-10-10\", \"2019-10-10\", \"2019-10-01\"]\n        valuemap, _, coltype = guess_data_type(in_values)\n        self.assertEqual(TimeVariable, coltype)\n        self.assertIsNone(valuemap)\n\n        in_values = [\"2019-10-10T12:08:51\", \"2019-10-10T12:08:51\",\n                     \"2019-10-10T12:08:51\", \"2019-10-01T12:08:51\"]\n        valuemap, _, coltype = guess_data_type(in_values)\n        self.assertEqual(TimeVariable, coltype)\n        self.assertIsNone(valuemap)\n\n        in_values = [\"2019-10-10 12:08:51\", \"2019-10-10 12:08:51\",\n                     \"2019-10-10 12:08:51\", \"2019-10-01 12:08:51\"]\n        valuemap, _, coltype = guess_data_type(in_values)\n        self.assertEqual(TimeVariable, coltype)\n        self.assertIsNone(valuemap)\n\n        in_values = [\"2019-10-10 12:08\", \"2019-10-10 12:08\",\n                     \"2019-10-10 12:08\", \"2019-10-01 12:08\"]\n        valuemap, _, coltype = guess_data_type(in_values)\n        self.assertEqual(TimeVariable, coltype)\n        self.assertIsNone(valuemap)\n\n    def test_guess_data_type_values_order(self):\n        \"\"\"\n        Test if values are ordered naturally\n        \"\"\"\n        in_values = [\n            \"something1\", \"something12\", \"something2\", \"something1\",\n            \"something20\", \"something1\", \"something2\", \"something12\",\n            \"something1\", \"something12\"\n        ]\n        res = [\"something1\", \"something2\", \"something12\", \"something20\"]\n        valuemap, _, coltype = guess_data_type(in_values)\n        self.assertEqual(DiscreteVariable, coltype)\n        self.assertListEqual(res, valuemap)\n\nclass TestWriters(unittest.TestCase):\n    def setUp(self):\n        self.domain = Domain([DiscreteVariable(\"a\", values=tuple(\"xyz\")),\n                         ContinuousVariable(\"b\", number_of_decimals=3)],\n                        ContinuousVariable(\"c\", number_of_decimals=0),\n                        [StringVariable(\"d\")])\n        self.data = Table.from_numpy(\n            self.domain,\n            np.array([[1, 0.5], [2, np.nan], [np.nan, 1.0625]]),\n            np.array([3, 1, 7]),\n            np.array([\"foo bar baz\".split()]).T\n        )\n\n    def test_write_tab(self):\n        with NamedTemporaryFile(suffix=\".tab\", delete=False) as f:\n            fname = f.name\n        try:\n            TabReader.write(fname, self.data)\n            with open(fname, encoding=\"utf-8\") as f:\n                self.assertEqual(f.read().strip(), \"\"\"\nc\\td\\ta\\tb\ncontinuous\\tstring\\tx y z\\tcontinuous\nclass\\tmeta\\t\\t\n3.0\\tfoo\\ty\\t0.5\n1.0\\tbar\\tz\\t\n7.0\\tbaz\\t\\t1.0625\"\"\".strip())\n        finally:\n            os.remove(fname)\n\n    def test_roundtrip_xlsx(self):\n        with NamedTemporaryFile(suffix=\".xlsx\", delete=False) as f:\n            fname = f.name\n        try:\n            ExcelReader.write(fname, self.data)\n            data = ExcelReader(fname).read()\n            np.testing.assert_equal(data.X, self.data.X)\n            np.testing.assert_equal(data.Y, self.data.Y)\n            np.testing.assert_equal(data.metas, self.data.metas)\n            np.testing.assert_equal(data.domain, self.data.domain)\n        finally:\n            os.remove(fname)\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n",
            "new_file_content": "import os\nimport unittest\nfrom tempfile import NamedTemporaryFile\n\nimport numpy as np\n\nfrom Orange.data import ContinuousVariable, DiscreteVariable, StringVariable, \\\n    TimeVariable, Domain, Table\nfrom Orange.data.io import TabReader, ExcelReader\nfrom Orange.data.io_util import guess_data_type\nfrom Orange.misc.collections import natural_sorted\n\n\nclass TestTableFilters(unittest.TestCase):\n    def test_guess_data_type_continuous(self):\n        # should be ContinuousVariable\n        valuemap, values, coltype = guess_data_type(list(range(1, 100)))\n        self.assertEqual(ContinuousVariable, coltype)\n        self.assertIsNone(valuemap)\n        np.testing.assert_array_equal(np.array(list(range(1, 100))), values)\n\n        valuemap, values, coltype = guess_data_type([1, 2, 3, 1, 2, 3])\n        self.assertEqual(ContinuousVariable, coltype)\n        self.assertIsNone(valuemap)\n        np.testing.assert_array_equal([1, 2, 3, 1, 2, 3], values)\n\n        valuemap, values, coltype = guess_data_type(\n            [\"1\", \"2\", \"3\", \"1\", \"2\", \"3\"])\n        self.assertEqual(ContinuousVariable, coltype)\n        self.assertIsNone(valuemap)\n        np.testing.assert_array_equal([1, 2, 3, 1, 2, 3], values)\n\n    def test_guess_data_type_discrete(self):\n        # should be DiscreteVariable\n        valuemap, values, coltype = guess_data_type([1, 2, 1, 2])\n        self.assertEqual(DiscreteVariable, coltype)\n        self.assertEqual([1, 2], valuemap)\n        np.testing.assert_array_equal([1, 2, 1, 2], values)\n\n        valuemap, values, coltype = guess_data_type([\"1\", \"2\", \"1\", \"2\", \"a\"])\n        self.assertEqual(DiscreteVariable, coltype)\n        self.assertEqual([\"1\", \"2\", \"a\"], valuemap)\n        np.testing.assert_array_equal(['1', '2', '1', '2', 'a'], values)\n\n        # just below the threshold for string variable\n        in_values = list(map(lambda x: str(x) + \"a\", range(24))) + [\"a\"] * 76\n        valuemap, values, coltype = guess_data_type(in_values)\n        self.assertEqual(DiscreteVariable, coltype)\n        self.assertEqual(natural_sorted(set(in_values)), valuemap)\n        np.testing.assert_array_equal(in_values, values)\n\n    def test_guess_data_type_string(self):\n        # should be StringVariable\n        # too many different values for discrete\n        in_values = list(map(lambda x: str(x) + \"a\", range(90)))\n        valuemap, values, coltype = guess_data_type(in_values)\n        self.assertEqual(StringVariable, coltype)\n        self.assertIsNone(valuemap)\n        np.testing.assert_array_equal(in_values, values)\n\n        # more than len(values)**0.7\n        in_values = list(map(lambda x: str(x) + \"a\", range(25))) + [\"a\"] * 75\n        valuemap, values, coltype = guess_data_type(in_values)\n        self.assertEqual(StringVariable, coltype)\n        self.assertIsNone(valuemap)\n        np.testing.assert_array_equal(in_values, values)\n\n        # more than 100 different values - exactly 101\n        # this is the case when len(values)**0.7 rule would vote for the\n        # DiscreteVariable\n        in_values = list(map(lambda x: str(x) + \"a\", range(100))) + [\"a\"] * 999\n        valuemap, values, coltype = guess_data_type(in_values)\n        self.assertEqual(StringVariable, coltype)\n        self.assertIsNone(valuemap)\n        np.testing.assert_array_equal(in_values, values)\n\n    def test_guess_data_type_time(self):\n        in_values = [\"2019-10-10\", \"2019-10-10\", \"2019-10-10\", \"2019-10-01\"]\n        valuemap, _, coltype = guess_data_type(in_values)\n        self.assertEqual(TimeVariable, coltype)\n        self.assertIsNone(valuemap)\n\n        in_values = [\"2019-10-10T12:08:51\", \"2019-10-10T12:08:51\",\n                     \"2019-10-10T12:08:51\", \"2019-10-01T12:08:51\"]\n        valuemap, _, coltype = guess_data_type(in_values)\n        self.assertEqual(TimeVariable, coltype)\n        self.assertIsNone(valuemap)\n\n        in_values = [\"2019-10-10 12:08:51\", \"2019-10-10 12:08:51\",\n                     \"2019-10-10 12:08:51\", \"2019-10-01 12:08:51\"]\n        valuemap, _, coltype = guess_data_type(in_values)\n        self.assertEqual(TimeVariable, coltype)\n        self.assertIsNone(valuemap)\n\n        in_values = [\"2019-10-10 12:08\", \"2019-10-10 12:08\",\n                     \"2019-10-10 12:08\", \"2019-10-01 12:08\"]\n        valuemap, _, coltype = guess_data_type(in_values)\n        self.assertEqual(TimeVariable, coltype)\n        self.assertIsNone(valuemap)\n\n    def test_guess_data_type_values_order(self):\n        \"\"\"\n        Test if values are ordered naturally\n        \"\"\"\n        in_values = [\n            \"something1\", \"something12\", \"something2\", \"something1\",\n            \"something20\", \"something1\", \"something2\", \"something12\",\n            \"something1\", \"something12\"\n        ]\n        res = [\"something1\", \"something2\", \"something12\", \"something20\"]\n        valuemap, _, coltype = guess_data_type(in_values)\n        self.assertEqual(DiscreteVariable, coltype)\n        self.assertListEqual(res, valuemap)\n\nclass TestWriters(unittest.TestCase):\n    def setUp(self):\n        self.domain = Domain([DiscreteVariable(\"a\", values=tuple(\"xyz\")),\n                         ContinuousVariable(\"b\", number_of_decimals=3)],\n                        ContinuousVariable(\"c\", number_of_decimals=0),\n                        [StringVariable(\"d\")])\n        self.data = Table.from_numpy(\n            self.domain,\n            np.array([[1, 0.5], [2, np.nan], [np.nan, 1.0625]]),\n            np.array([3, 1, 7]),\n            np.array([\"foo bar baz\".split()]).T\n        )\n\n    def test_write_tab(self):\n        with NamedTemporaryFile(suffix=\".tab\", delete=False) as f:\n            fname = f.name\n        try:\n            TabReader.write(fname, self.data)\n            with open(fname, encoding=\"utf-8\") as f:\n                self.assertEqual(f.read().strip(), \"\"\"\nc\\td\\ta\\tb\ncontinuous\\tstring\\tx y z\\tcontinuous\nclass\\tmeta\\t\\t\n3\\tfoo\\ty\\t0.500\n1\\tbar\\tz\\t\n7\\tbaz\\t\\t1.06250\"\"\".strip())\n        finally:\n            os.remove(fname)\n\n    def test_roundtrip_xlsx(self):\n        with NamedTemporaryFile(suffix=\".xlsx\", delete=False) as f:\n            fname = f.name\n        try:\n            ExcelReader.write(fname, self.data)\n            data = ExcelReader(fname).read()\n            np.testing.assert_equal(data.X, self.data.X)\n            np.testing.assert_equal(data.Y, self.data.Y)\n            np.testing.assert_equal(data.metas, self.data.metas)\n            np.testing.assert_equal(data.domain, self.data.domain)\n        finally:\n            os.remove(fname)\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n",
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                    "path": "Orange/data/tests/test_io.py"
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                "new_commit_hash": "061d97661",
                "mode": "100644"
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            "binary_line": null,
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                            {
                                "content": "        finally:",
                                "type": "context"
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                            {
                                "content": "            os.remove(fname)",
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                        {
                            "file_name": "Orange/data/tests/test_io.py",
                            "type": "class",
                            "name": "TestWriters",
                            "content": "class TestWriters(unittest.TestCase):\n    def setUp(self):\n        self.domain = Domain([DiscreteVariable(\"a\", values=tuple(\"xyz\")),\n                         ContinuousVariable(\"b\", number_of_decimals=3)],\n                        ContinuousVariable(\"c\", number_of_decimals=0),\n                        [StringVariable(\"d\")])\n        self.data = Table.from_numpy(\n            self.domain,\n            np.array([[1, 0.5], [2, np.nan], [np.nan, 1.0625]]),\n            np.array([3, 1, 7]),\n            np.array([\"foo bar baz\".split()]).T\n        )\n\n    def test_write_tab(self):\n        with NamedTemporaryFile(suffix=\".tab\", delete=False) as f:\n            fname = f.name\n        try:\n            TabReader.write(fname, self.data)\n            with open(fname, encoding=\"utf-8\") as f:\n                self.assertEqual(f.read().strip(), \"\"\"\nc\\td\\ta\\tb\ncontinuous\\tstring\\tx y z\\tcontinuous\nclass\\tmeta\\t\\t\n3.0\\tfoo\\ty\\t0.5\n1.0\\tbar\\tz\\t\n7.0\\tbaz\\t\\t1.0625\"\"\".strip())\n        finally:\n            os.remove(fname)\n\n    def test_roundtrip_xlsx(self):\n        with NamedTemporaryFile(suffix=\".xlsx\", delete=False) as f:\n            fname = f.name\n        try:\n            ExcelReader.write(fname, self.data)\n            data = ExcelReader(fname).read()\n            np.testing.assert_equal(data.X, self.data.X)\n            np.testing.assert_equal(data.Y, self.data.Y)\n            np.testing.assert_equal(data.metas, self.data.metas)\n            np.testing.assert_equal(data.domain, self.data.domain)\n        finally:\n            os.remove(fname)",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 115,
                            "end_lineno": 155,
                            "parent": null
                        },
                        {
                            "file_name": "Orange/data/tests/test_io.py",
                            "type": "method",
                            "name": "TestWriters.test_write_tab",
                            "content": "class TestWriters(unittest.TestCase):\n    def setUp(self):\n        self.domain = Domain([DiscreteVariable(\"a\", values=tuple(\"xyz\")),\n                         ContinuousVariable(\"b\", number_of_decimals=3)],\n                        ContinuousVariable(\"c\", number_of_decimals=0),\n                        [StringVariable(\"d\")])\n        self.data = Table.from_numpy(\n            self.domain,\n            np.array([[1, 0.5], [2, np.nan], [np.nan, 1.0625]]),\n            np.array([3, 1, 7]),\n            np.array([\"foo bar baz\".split()]).T\n        )\n\n    def test_write_tab(self):\n        with NamedTemporaryFile(suffix=\".tab\", delete=False) as f:\n            fname = f.name\n        try:\n            TabReader.write(fname, self.data)\n            with open(fname, encoding=\"utf-8\") as f:\n                self.assertEqual(f.read().strip(), \"\"\"\nc\\td\\ta\\tb\ncontinuous\\tstring\\tx y z\\tcontinuous\nclass\\tmeta\\t\\t\n3.0\\tfoo\\ty\\t0.5\n1.0\\tbar\\tz\\t\n7.0\\tbaz\\t\\t1.0625\"\"\".strip())\n        finally:\n            os.remove(fname)\n\n    def test_roundtrip_xlsx(self):\n        with NamedTemporaryFile(suffix=\".xlsx\", delete=False) as f:\n            fname = f.name\n        try:\n            ExcelReader.write(fname, self.data)\n            data = ExcelReader(fname).read()\n            np.testing.assert_equal(data.X, self.data.X)\n            np.testing.assert_equal(data.Y, self.data.Y)\n            np.testing.assert_equal(data.metas, self.data.metas)\n            np.testing.assert_equal(data.domain, self.data.domain)\n        finally:\n            os.remove(fname)",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 128,
                            "end_lineno": 142,
                            "parent": {
                                "file_name": "Orange/data/tests/test_io.py",
                                "type": "class",
                                "name": "TestWriters",
                                "content": "class TestWriters(unittest.TestCase):\n    def setUp(self):\n        self.domain = Domain([DiscreteVariable(\"a\", values=tuple(\"xyz\")),\n                         ContinuousVariable(\"b\", number_of_decimals=3)],\n                        ContinuousVariable(\"c\", number_of_decimals=0),\n                        [StringVariable(\"d\")])\n        self.data = Table.from_numpy(\n            self.domain,\n            np.array([[1, 0.5], [2, np.nan], [np.nan, 1.0625]]),\n            np.array([3, 1, 7]),\n            np.array([\"foo bar baz\".split()]).T\n        )\n\n    def test_write_tab(self):\n        with NamedTemporaryFile(suffix=\".tab\", delete=False) as f:\n            fname = f.name\n        try:\n            TabReader.write(fname, self.data)\n            with open(fname, encoding=\"utf-8\") as f:\n                self.assertEqual(f.read().strip(), \"\"\"\nc\\td\\ta\\tb\ncontinuous\\tstring\\tx y z\\tcontinuous\nclass\\tmeta\\t\\t\n3.0\\tfoo\\ty\\t0.5\n1.0\\tbar\\tz\\t\n7.0\\tbaz\\t\\t1.0625\"\"\".strip())\n        finally:\n            os.remove(fname)\n\n    def test_roundtrip_xlsx(self):\n        with NamedTemporaryFile(suffix=\".xlsx\", delete=False) as f:\n            fname = f.name\n        try:\n            ExcelReader.write(fname, self.data)\n            data = ExcelReader(fname).read()\n            np.testing.assert_equal(data.X, self.data.X)\n            np.testing.assert_equal(data.Y, self.data.Y)\n            np.testing.assert_equal(data.metas, self.data.metas)\n            np.testing.assert_equal(data.domain, self.data.domain)\n        finally:\n            os.remove(fname)",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 115,
                                "end_lineno": 155,
                                "parent": null
                            }
                        }
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    "new_commit_hash": "a2c89569162ee199dde35751214d960270182db8",
    "commit_message": "Merge pull request #6457 from janezd/save-continuous-format\n\nCSVWriter: Write numeric variables via repr_val",
    "commit_date": "2023-06-02T13:13:34+02:00",
    "metadata": {}
}
{
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    "new_commit_hash": "a2c89569162ee199dde35751214d960270182db8",
    "test_file_codes": [
        "import os\nimport unittest\nfrom tempfile import NamedTemporaryFile\n\nimport numpy as np\n\nfrom Orange.data import ContinuousVariable, DiscreteVariable, StringVariable, \\\n    TimeVariable, Domain, Table\nfrom Orange.data.io import TabReader, ExcelReader\nfrom Orange.data.io_util import guess_data_type\nfrom Orange.misc.collections import natural_sorted\n\n\nclass TestTableFilters(unittest.TestCase):\n    def test_guess_data_type_continuous(self):\n        # should be ContinuousVariable\n        valuemap, values, coltype = guess_data_type(list(range(1, 100)))\n        self.assertEqual(ContinuousVariable, coltype)\n        self.assertIsNone(valuemap)\n        np.testing.assert_array_equal(np.array(list(range(1, 100))), values)\n\n        valuemap, values, coltype = guess_data_type([1, 2, 3, 1, 2, 3])\n        self.assertEqual(ContinuousVariable, coltype)\n        self.assertIsNone(valuemap)\n        np.testing.assert_array_equal([1, 2, 3, 1, 2, 3], values)\n\n        valuemap, values, coltype = guess_data_type(\n            [\"1\", \"2\", \"3\", \"1\", \"2\", \"3\"])\n        self.assertEqual(ContinuousVariable, coltype)\n        self.assertIsNone(valuemap)\n        np.testing.assert_array_equal([1, 2, 3, 1, 2, 3], values)\n\n    def test_guess_data_type_discrete(self):\n        # should be DiscreteVariable\n        valuemap, values, coltype = guess_data_type([1, 2, 1, 2])\n        self.assertEqual(DiscreteVariable, coltype)\n        self.assertEqual([1, 2], valuemap)\n        np.testing.assert_array_equal([1, 2, 1, 2], values)\n\n        valuemap, values, coltype = guess_data_type([\"1\", \"2\", \"1\", \"2\", \"a\"])\n        self.assertEqual(DiscreteVariable, coltype)\n        self.assertEqual([\"1\", \"2\", \"a\"], valuemap)\n        np.testing.assert_array_equal(['1', '2', '1', '2', 'a'], values)\n\n        # just below the threshold for string variable\n        in_values = list(map(lambda x: str(x) + \"a\", range(24))) + [\"a\"] * 76\n        valuemap, values, coltype = guess_data_type(in_values)\n        self.assertEqual(DiscreteVariable, coltype)\n        self.assertEqual(natural_sorted(set(in_values)), valuemap)\n        np.testing.assert_array_equal(in_values, values)\n\n    def test_guess_data_type_string(self):\n        # should be StringVariable\n        # too many different values for discrete\n        in_values = list(map(lambda x: str(x) + \"a\", range(90)))\n        valuemap, values, coltype = guess_data_type(in_values)\n        self.assertEqual(StringVariable, coltype)\n        self.assertIsNone(valuemap)\n        np.testing.assert_array_equal(in_values, values)\n\n        # more than len(values)**0.7\n        in_values = list(map(lambda x: str(x) + \"a\", range(25))) + [\"a\"] * 75\n        valuemap, values, coltype = guess_data_type(in_values)\n        self.assertEqual(StringVariable, coltype)\n        self.assertIsNone(valuemap)\n        np.testing.assert_array_equal(in_values, values)\n\n        # more than 100 different values - exactly 101\n        # this is the case when len(values)**0.7 rule would vote for the\n        # DiscreteVariable\n        in_values = list(map(lambda x: str(x) + \"a\", range(100))) + [\"a\"] * 999\n        valuemap, values, coltype = guess_data_type(in_values)\n        self.assertEqual(StringVariable, coltype)\n        self.assertIsNone(valuemap)\n        np.testing.assert_array_equal(in_values, values)\n\n    def test_guess_data_type_time(self):\n        in_values = [\"2019-10-10\", \"2019-10-10\", \"2019-10-10\", \"2019-10-01\"]\n        valuemap, _, coltype = guess_data_type(in_values)\n        self.assertEqual(TimeVariable, coltype)\n        self.assertIsNone(valuemap)\n\n        in_values = [\"2019-10-10T12:08:51\", \"2019-10-10T12:08:51\",\n                     \"2019-10-10T12:08:51\", \"2019-10-01T12:08:51\"]\n        valuemap, _, coltype = guess_data_type(in_values)\n        self.assertEqual(TimeVariable, coltype)\n        self.assertIsNone(valuemap)\n\n        in_values = [\"2019-10-10 12:08:51\", \"2019-10-10 12:08:51\",\n                     \"2019-10-10 12:08:51\", \"2019-10-01 12:08:51\"]\n        valuemap, _, coltype = guess_data_type(in_values)\n        self.assertEqual(TimeVariable, coltype)\n        self.assertIsNone(valuemap)\n\n        in_values = [\"2019-10-10 12:08\", \"2019-10-10 12:08\",\n                     \"2019-10-10 12:08\", \"2019-10-01 12:08\"]\n        valuemap, _, coltype = guess_data_type(in_values)\n        self.assertEqual(TimeVariable, coltype)\n        self.assertIsNone(valuemap)\n\n    def test_guess_data_type_values_order(self):\n        \"\"\"\n        Test if values are ordered naturally\n        \"\"\"\n        in_values = [\n            \"something1\", \"something12\", \"something2\", \"something1\",\n            \"something20\", \"something1\", \"something2\", \"something12\",\n            \"something1\", \"something12\"\n        ]\n        res = [\"something1\", \"something2\", \"something12\", \"something20\"]\n        valuemap, _, coltype = guess_data_type(in_values)\n        self.assertEqual(DiscreteVariable, coltype)\n        self.assertListEqual(res, valuemap)\n\nclass TestWriters(unittest.TestCase):\n    def setUp(self):\n        self.domain = Domain([DiscreteVariable(\"a\", values=tuple(\"xyz\")),\n                         ContinuousVariable(\"b\", number_of_decimals=3)],\n                        ContinuousVariable(\"c\", number_of_decimals=0),\n                        [StringVariable(\"d\")])\n        self.data = Table.from_numpy(\n            self.domain,\n            np.array([[1, 0.5], [2, np.nan], [np.nan, 1.0625]]),\n            np.array([3, 1, 7]),\n            np.array([\"foo bar baz\".split()]).T\n        )\n\n    def test_write_tab(self):\n        with NamedTemporaryFile(suffix=\".tab\", delete=False) as f:\n            fname = f.name\n        try:\n            TabReader.write(fname, self.data)\n            with open(fname, encoding=\"utf-8\") as f:\n                self.assertEqual(f.read().strip(), \"\"\"\nc\\td\\ta\\tb\ncontinuous\\tstring\\tx y z\\tcontinuous\nclass\\tmeta\\t\\t\n3\\tfoo\\ty\\t0.500\n1\\tbar\\tz\\t\n7\\tbaz\\t\\t1.06250\"\"\".strip())\n        finally:\n            os.remove(fname)\n\n    def test_roundtrip_xlsx(self):\n        with NamedTemporaryFile(suffix=\".xlsx\", delete=False) as f:\n            fname = f.name\n        try:\n            ExcelReader.write(fname, self.data)\n            data = ExcelReader(fname).read()\n            np.testing.assert_equal(data.X, self.data.X)\n            np.testing.assert_equal(data.Y, self.data.Y)\n            np.testing.assert_equal(data.metas, self.data.metas)\n            np.testing.assert_equal(data.domain, self.data.domain)\n        finally:\n            os.remove(fname)\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...\nrunning build_ext\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\nbuilding 'Orange.classification._tree_scorers' 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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.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\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/_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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.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\nbuilding 'Orange.classification._simple_tree' 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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.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\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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.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\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\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\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/_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\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\nCreating /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.7/site-packages/Orange3.egg-link (link to .)\nAdding Orange3 3.36.0.dev0+a2c8956 to easy-install.pth file\nInstalling orange-canvas script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8\nProcessing dependencies for Orange3==3.36.0.dev0+a2c8956\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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.7/site-packages/xlrd-2.0.1-py3.7.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-3avwcuj1/serverfiles-0.3.1/setup.cfg\nRunning serverfiles-0.3.1/setup.py -q bdist_egg --dist-dir /tmp/easy_install-3avwcuj1/serverfiles-0.3.1/egg-dist-tmp-dxjb_ge8\ncreating /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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._tree_scorers' 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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.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\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/_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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.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\nbuilding 'Orange.classification._simple_tree' 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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.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\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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.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\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\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\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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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/_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\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\nCreating /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages/Orange3.egg-link (link to .)\nAdding Orange3 3.36.0.dev0+a2c8956 to easy-install.pth file\nInstalling orange-canvas script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8\nProcessing dependencies for Orange3==3.36.0.dev0+a2c8956\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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages\nSearching for setuptools==75.3.0\nBest match: setuptools 75.3.0\nAdding setuptools 75.3.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages\nSearching for scipy==1.10.1\nBest match: scipy 1.10.1\nAdding scipy 1.10.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages\nSearching for scikit-learn==1.1.3\nBest match: scikit-learn 1.1.3\nAdding scikit-learn 1.1.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages\nSearching for pyqtgraph==0.13.3\nBest match: pyqtgraph 0.13.3\nAdding pyqtgraph 0.13.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/bin\nInstalling pip3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages\nSearching for pandas==2.0.3\nBest match: pandas 2.0.3\nAdding pandas 2.0.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages\nSearching for orange-widget-base==4.22.0\nBest match: orange-widget-base 4.22.0\nAdding orange-widget-base 4.22.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages\nSearching for orange-canvas-core==0.1.35\nBest match: orange-canvas-core 0.1.35\nAdding orange-canvas-core 0.1.35 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages\nSearching for openTSNE==1.0.1\nBest match: openTSNE 1.0.1\nAdding openTSNE 1.0.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages\nSearching for numpy==1.24.4\nBest match: numpy 1.24.4\nAdding numpy 1.24.4 to easy-install.pth file\nInstalling f2py script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/bin\nInstalling f2py3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/bin\nInstalling f2py3.8 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages\nSearching for networkx==3.1\nBest match: networkx 3.1\nAdding networkx 3.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages\nSearching for matplotlib==3.7.5\nBest match: matplotlib 3.7.5\nAdding matplotlib 3.7.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages\nSearching for keyring==25.5.0\nBest match: keyring 25.5.0\nAdding keyring 25.5.0 to easy-install.pth file\nInstalling keyring script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages\nSearching for Bottleneck==1.4.0\nBest match: Bottleneck 1.4.0\nAdding Bottleneck 1.4.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages\nSearching for urllib3==2.2.3\nBest match: urllib3 2.2.3\nAdding urllib3 2.2.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/bin\nInstalling jupyter-kernelspec script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/bin\nInstalling jupyter-run script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/bin\nInstalling jupyter-migrate script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/bin\nInstalling jupyter-troubleshoot script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages\nSearching for typing-extensions==4.12.2\nBest match: typing-extensions 4.12.2\nAdding typing-extensions 4.12.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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\ndetected new path './setuptools/_vendor'\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages\nSearching for docutils==0.20.1\nBest match: docutils 0.20.1\nAdding docutils 0.20.1 to easy-install.pth file\nInstalling docutils script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages\nSearching for importlib-resources==6.4.5\nBest match: importlib-resources 6.4.5\nAdding importlib-resources 6.4.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages\nSearching for pyparsing==3.1.4\nBest match: pyparsing 3.1.4\nAdding pyparsing 3.1.4 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages\nSearching for pillow==10.4.0\nBest match: pillow 10.4.0\nAdding pillow 10.4.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages\nSearching for kiwisolver==1.4.7\nBest match: kiwisolver 1.4.7\nAdding kiwisolver 1.4.7 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/bin\nInstalling pyftmerge script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/bin\nInstalling pyftsubset script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/bin\nInstalling ttx script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages\nSearching for contourpy==1.1.1\nBest match: contourpy 1.1.1\nAdding contourpy 1.1.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages\nSearching for anyio==4.5.2\nBest match: anyio 4.5.2\nAdding anyio 4.5.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages\nSearching for ipython==8.12.3\nBest match: ipython 8.12.3\nAdding ipython 8.12.3 to easy-install.pth file\nInstalling ipython script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/bin\nInstalling ipython3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages\nSearching for zipp==3.20.2\nBest match: zipp 3.20.2\nAdding zipp 3.20.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages\nSearching for pickleshare==0.7.5\nBest match: pickleshare 0.7.5\nAdding pickleshare 0.7.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages\nSearching for backcall==0.2.0\nBest match: backcall 0.2.0\nAdding backcall 0.2.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages\nFinished processing dependencies for Orange3==3.36.0.dev0+a2c8956\nVerifying Orange installation...\n✅ Orange installation successful!\nSuccessfully installed orange using Python 3.8\n",
    "setup_res_stderr": "Using CPython 3.7.9\nCreating virtual environment at: .venv\nActivate with: source .venv/bin/activate\nResolved 18 packages in 167ms\nPrepared 18 packages in 2ms\nInstalled 18 packages in 127ms\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          scipy>=1.9.0,<=1.9.1\n          scipy>=1.10.0,<=1.10.1\n      depend on Python>=3.8,<3.12, we can conclude that all of:\n          scipy>=1.9.0,<=1.9.1\n          scipy>=1.10.0,<=1.10.1\n       cannot be used.\n      And because only the following versions of scipy are available:\n          scipy<=1.9.0\n          scipy==1.9.1\n          scipy==1.9.2\n          scipy==1.9.3\n          scipy==1.10.0\n          scipy==1.10.1\n          scipy==1.11.0\n          scipy==1.11.1\n          scipy==1.11.2\n          scipy==1.11.3\n          scipy==1.11.4\n          scipy==1.12.0\n          scipy==1.13.0\n          scipy==1.13.1\n          scipy==1.14.0\n          scipy==1.14.1\n          scipy==1.15.0\n      we can conclude that all of:\n          scipy>=1.9.0,<1.9.2\n          scipy>1.9.3,<1.11.0\n       cannot be used. (1)\n\n      Because the current Python version (3.7.9) does not satisfy Python>=3.8 and scipy>=1.9.2,<=1.9.3 depends on Python>=3.8,\n      we can conclude that scipy>=1.9.2,<=1.9.3 cannot be used.\n      And because we know from (1) that all of:\n          scipy>=1.9.0,<1.9.2\n          scipy>1.9.3,<1.11.0\n       cannot be used, we can conclude that scipy>=1.9.0,<1.11.0 cannot be used.\n      And because scipy==1.11.0 was yanked (reason: License Violation), we can conclude that scipy>=1.9.0,<1.11.1 cannot be\n      used. (2)\n\n      Because the current Python version (3.7.9) does not satisfy Python>=3.8 and scipy>=1.11.1,<=1.11.3 depends on\n      Python>=3.9,<3.13, we can conclude that scipy>=1.11.1,<=1.11.3 cannot be used.\n      And because we know from (2) that scipy>=1.9.0,<1.11.1 cannot be used, we can conclude that scipy>=1.9.0,<1.11.4 cannot\n      be used. (3)\n\n      Because the current Python version (3.7.9) does not satisfy Python>=3.8 and scipy>=1.11.4,<=1.13.1 depends on\n      Python>=3.9, we can conclude that scipy>=1.11.4,<=1.13.1 cannot be used.\n      And because we know from (3) that scipy>=1.9.0,<1.11.4 cannot be used, we can conclude that scipy>=1.9.0,<1.14.0 cannot\n      be used. (4)\n\n      Because the current Python version (3.7.9) does not satisfy Python>=3.8 and scipy>=1.14.0 depends on Python>=3.10, we can\n      conclude that scipy>=1.14.0 cannot be used.\n      And because we know from (4) that scipy>=1.9.0,<1.14.0 cannot be used, we can conclude that scipy>=1.9.0 cannot be used.\n      And because you require scipy>=1.9, we can conclude that your requirements are unsatisfiable.\n\n      hint: Pre-releases are available for `scipy` in the requested range (e.g., 1.15.0rc2), 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 3ms\nInstalled 2 packages in 26ms\n + psycopg2==2.9.9\n + pymssql==2.3.2\nResolved 19 packages in 147ms\nInstalled 16 packages in 2.67s\n + catboost==1.2.7\n + cycler==0.11.0\n + fonttools==4.38.0\n + graphviz==0.20.1\n + kiwisolver==1.4.5\n + matplotlib==3.5.3\n + pandas==1.3.5\n + pillow==9.5.0\n + plotly==5.18.0\n + pyparsing==3.1.4\n + python-dateutil==2.9.0.post0\n + pytz==2024.2\n + scipy==1.7.3\n + six==1.17.0\n + tenacity==8.2.3\n + xgboost==1.6.2\nResolved 5 packages in 11ms\nInstalled 3 packages in 102ms\n + joblib==1.3.2\n + scikit-learn==1.0.2\n + threadpoolctl==3.1.0\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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:3427:27: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 3427 |   __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:4883:27: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 4883 |   __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:5629:43: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5629 |           for (__pyx_t_19 = 0; __pyx_t_19 < __pyx_t_18; __pyx_t_19+=1) {\n      |                                           ^\nOrange/classification/_tree_scorers.c:5750:39: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5750 |           __pyx_t_20 = ((__pyx_v_left >= __pyx_v_min_leaf) != 0);\n      |                                       ^~\nOrange/classification/_tree_scorers.c:5756:53: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5756 |           __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:7217:41: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 7217 |         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:7874:41: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 7874 |         for (__pyx_t_12 = 0; __pyx_t_12 < __pyx_t_11; __pyx_t_12+=1) {\n      |                                         ^\nOrange/classification/_tree_scorers.c:7934:150: warning: comparison of integer expressions of different signedness: ‘__pyx_t_5numpy_int32_t’ {aka ‘int’} and ‘unsigned int’ [-Wsign-compare]\n 7934 |               __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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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:3863:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3863 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:3862:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3862 |   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:4934:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4934 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:4933:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4933 |   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:4732:223: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4732 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_26, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_27, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_y;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:4307:7: note: ‘__pyx_v_win_y’ was declared here\n 4307 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:4721:223: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4721 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_27, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_26, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_x;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:4306:7: note: ‘__pyx_v_win_x’ was declared here\n 4306 |   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:5682:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5682 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:5681:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5681 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_1get_winners’:\nOrange/projection/_som.c:3184:223: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3184 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_22, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_23, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_y;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:2887:7: note: ‘__pyx_v_win_y’ was declared here\n 2887 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:3173:223: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3173 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_23, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_22, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_x;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:2886:7: note: ‘__pyx_v_win_x’ was declared here\n 2886 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_3update’:\nOrange/projection/_som.c:3357:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3357 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/classification/_simple_tree.c: In function ‘gain_ratio_c’:\nOrange/classification/_simple_tree.c:63:9: warning: implicit declaration of function ‘qsort_r’; did you mean ‘qsort’? [-Wimplicit-function-declaration]\n   63 |         qsort_r(base, nel, size, compar, thunk)\n      |         ^~~~~~~\nOrange/classification/_simple_tree.c:147:9: note: in expansion of macro ‘QSORT_R_FUNC’\n  147 |         QSORT_R_FUNC(examples, size, sizeof(struct Example), (void*) &compar_attr, compar_examples);\n      |         ^~~~~~~~~~~~\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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-nf51f4_9/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 85ms\nPrepared 15 packages in 1ms\nInstalled 15 packages in 69ms\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\nResolved 59 packages in 110ms\nInstalled 55 packages in 134ms\n + anyio==4.5.2\n + backports-tarfile==1.2.0\n + baycomp==1.0.3\n + bottleneck==1.4.0\n + certifi==2024.12.14\n + cffi==1.17.1\n + chardet==5.2.0\n + charset-normalizer==3.4.1\n + contourpy==1.1.1\n + cryptography==44.0.0\n + cycler==0.12.1\n + et-xmlfile==2.0.0\n + fonttools==4.55.3\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 + importlib-resources==6.4.5\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==25.5.0\n + keyrings-alt==5.0.2\n + kiwisolver==1.4.7\n + matplotlib==3.7.5\n + more-itertools==10.5.0\n + networkx==3.1\n + openpyxl==3.1.5\n + opentsne==1.0.1\n + pandas==2.0.3\n + pillow==10.4.0\n + pip==24.3.1\n + pycparser==2.22\n + pyparsing==3.1.4\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==1.1.3\n + scipy==1.10.1\n + secretstorage==3.3.3\n + serverfiles==0.3.1\n + six==1.17.0\n + sniffio==1.3.1\n + threadpoolctl==3.5.0\n + typing-extensions==4.12.2\n + tzdata==2024.2\n + urllib3==2.2.3\n + xlrd==2.0.1\n + xlsxwriter==3.2.0\n + zipp==3.20.2\nResolved 64 packages in 70ms\nInstalled 41 packages in 105ms\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 + 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==0.20.1\n + executing==2.1.0\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 + matplotlib-inline==0.1.7\n + nest-asyncio==1.6.0\n + orange-canvas-core==0.1.35\n + orange-widget-base==4.22.0\n + parso==0.8.4\n + pexpect==4.9.0\n + pickleshare==0.7.5\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==0.13.3\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 2 packages in 2ms\nInstalled 2 packages in 5ms\n + psycopg2==2.9.10\n + pymssql==2.3.2\nResolved 23 packages in 14ms\nInstalled 6 packages in 515ms\n + catboost==1.2.7\n + graphviz==0.20.3\n + nvidia-nccl-cu12==2.24.3\n + plotly==5.24.1\n + tenacity==9.0.0\n + xgboost==2.1.3\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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:3427:27: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 3427 |   __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:4883:27: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 4883 |   __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:5629:43: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5629 |           for (__pyx_t_19 = 0; __pyx_t_19 < __pyx_t_18; __pyx_t_19+=1) {\n      |                                           ^\nOrange/classification/_tree_scorers.c:5750:39: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5750 |           __pyx_t_20 = ((__pyx_v_left >= __pyx_v_min_leaf) != 0);\n      |                                       ^~\nOrange/classification/_tree_scorers.c:5756:53: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5756 |           __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:7217:41: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 7217 |         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:7874:41: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 7874 |         for (__pyx_t_12 = 0; __pyx_t_12 < __pyx_t_11; __pyx_t_12+=1) {\n      |                                         ^\nOrange/classification/_tree_scorers.c:7934:150: warning: comparison of integer expressions of different signedness: ‘__pyx_t_5numpy_int32_t’ {aka ‘int’} and ‘unsigned int’ [-Wsign-compare]\n 7934 |               __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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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:3863:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3863 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:3862:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3862 |   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:4934:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4934 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:4933:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4933 |   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:5682:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5682 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:5681:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5681 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_1get_winners’:\nOrange/projection/_som.c:3184:223: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3184 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_22, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_23, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_y;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:2887:7: note: ‘__pyx_v_win_y’ was declared here\n 2887 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:3173:223: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3173 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_23, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_22, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_x;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:2886:7: note: ‘__pyx_v_win_x’ was declared here\n 2886 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_3update’:\nOrange/projection/_som.c:3357:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3357 |   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:4732:223: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4732 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_26, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_27, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_y;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:4307:7: note: ‘__pyx_v_win_y’ was declared here\n 4307 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:4721:223: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4721 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_27, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_26, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_x;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:4306:7: note: ‘__pyx_v_win_x’ was declared here\n 4306 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/classification/_simple_tree.c: In function ‘gain_ratio_c’:\nOrange/classification/_simple_tree.c:63:9: warning: implicit declaration of function ‘qsort_r’; did you mean ‘qsort’? [-Wimplicit-function-declaration]\n   63 |         qsort_r(base, nel, size, compar, thunk)\n      |         ^~~~~~~\nOrange/classification/_simple_tree.c:147:9: note: in expansion of macro ‘QSORT_R_FUNC’\n  147 |         QSORT_R_FUNC(examples, size, sizeof(struct Example), (void*) &compar_attr, compar_examples);\n      |         ^~~~~~~~~~~~\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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_a2c89569162ee199dde35751214d960270182db8/.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",
    "new_commit_res_code": 0,
    "new_commit_res_stdout": "============================= test session starts ==============================\nplatform linux -- Python 3.8.20, pytest-8.3.4, pluggy-1.5.0\nrootdir: /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8\nconfigfile: pyproject.toml\nplugins: anyio-4.5.2, typeguard-4.3.0\ncollected 7 items\n\nr2e_tests/test_1.py .......                                              [100%]\n\n==================================== PASSES ====================================\n=========================== short test summary info ============================\nPASSED r2e_tests/test_1.py::TestTableFilters::test_guess_data_type_continuous\nPASSED r2e_tests/test_1.py::TestTableFilters::test_guess_data_type_discrete\nPASSED r2e_tests/test_1.py::TestTableFilters::test_guess_data_type_string\nPASSED r2e_tests/test_1.py::TestTableFilters::test_guess_data_type_time\nPASSED r2e_tests/test_1.py::TestTableFilters::test_guess_data_type_values_order\nPASSED r2e_tests/test_1.py::TestWriters::test_roundtrip_xlsx\nPASSED r2e_tests/test_1.py::TestWriters::test_write_tab\n============================== 7 passed in 1.56s ===============================\n",
    "new_commit_res_stderr": "",
    "old_commit_res_code": 1,
    "old_commit_res_stdout": "============================= test session starts ==============================\nplatform linux -- Python 3.8.20, pytest-8.3.4, pluggy-1.5.0\nrootdir: /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a2c89569162ee199dde35751214d960270182db8\nconfigfile: pyproject.toml\nplugins: anyio-4.5.2, typeguard-4.3.0\ncollected 7 items\n\nr2e_tests/test_1.py ......F                                              [100%]\n\n=================================== FAILURES ===================================\n__________________________ TestWriters.test_write_tab __________________________\n\nself = <r2e_tests.test_1.TestWriters testMethod=test_write_tab>\n\n        def test_write_tab(self):\n            with NamedTemporaryFile(suffix=\".tab\", delete=False) as f:\n                fname = f.name\n            try:\n                TabReader.write(fname, self.data)\n                with open(fname, encoding=\"utf-8\") as f:\n>                   self.assertEqual(f.read().strip(), \"\"\"\n    c\\td\\ta\\tb\n    continuous\\tstring\\tx y z\\tcontinuous\n    class\\tmeta\\t\\t\n    3\\tfoo\\ty\\t0.500\n    1\\tbar\\tz\\t\n    7\\tbaz\\t\\t1.06250\"\"\".strip())\nE   AssertionError: 'c\\td[53 chars]tmeta\\t\\t\\n3.0\\tfoo\\ty\\t0.5\\n1.0\\tbar\\tz\\t\\n7.0\\tbaz\\t\\t1.0625' != 'c\\td[53 chars]tmeta\\t\\t\\n3\\tfoo\\ty\\t0.500\\n1\\tbar\\tz\\t\\n7\\tbaz\\t\\t1.06250'\nE     c\td\ta\tb\nE     continuous\tstring\tx y z\tcontinuous\nE     class\tmeta\t\t\nE   - 3.0\tfoo\ty\t0.5\nE   ?  --\nE   + 3\tfoo\ty\t0.500\nE   ?  \t   \t \t   ++\nE   - 1.0\tbar\tz\t\nE   ?  --\nE   + 1\tbar\tz\t\nE   - 7.0\tbaz\t\t1.0625?  --\nE   + 7\tbaz\t\t1.06250?  \t   \t\t      +\n\nr2e_tests/test_1.py:134: AssertionError\n==================================== PASSES ====================================\n=========================== short test summary info ============================\nPASSED r2e_tests/test_1.py::TestTableFilters::test_guess_data_type_continuous\nPASSED r2e_tests/test_1.py::TestTableFilters::test_guess_data_type_discrete\nPASSED r2e_tests/test_1.py::TestTableFilters::test_guess_data_type_string\nPASSED r2e_tests/test_1.py::TestTableFilters::test_guess_data_type_time\nPASSED r2e_tests/test_1.py::TestTableFilters::test_guess_data_type_values_order\nPASSED r2e_tests/test_1.py::TestWriters::test_roundtrip_xlsx\nFAILED r2e_tests/test_1.py::TestWriters::test_write_tab - AssertionError: 'c\\...\n========================= 1 failed, 6 passed in 1.55s ==========================\n",
    "old_commit_res_stderr": ""
}
Orange/data/io_base.py Orange/data/tests/test_io.py
We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
[ISSUE]
**Title:** CSVWriter Outputs Numeric Continuous Variables Without Proper Formatting

**Description:**
When writing continuous numeric variables using `CSVWriter`, the numeric values are not formatted according to their specified representation. This results in inconsistent decimal places in the output, causing discrepancies between the actual and expected data formats.

**Example Buggy Code:**
```python
from Orange.data import Domain, ContinuousVariable, StringVariable, Table
from Orange.data.io import TabWriter

domain = Domain(
    [ContinuousVariable('a', number_of_decimals=0)],
    [ContinuousVariable('c', number_of_decimals=3)],
    [StringVariable('d')]
)
data = Table.from_numpy(
    domain,
    np.array([[3.0], [1.0], [7.0]]),
    np.array([0.5, np.nan, 1.0625]),
    np.array([['foo'], ['bar'], ['baz']])
)

with NamedTemporaryFile(suffix='.tab', delete=False) as f:
    fname = f.name
try:
    TabWriter.write(fname, data)
    with open(fname, 'r') as file:
        content = file.read().strip()
        print(content)
finally:
    os.remove(fname)
```

**Expected Behavior:**
Numeric continuous variables should be written using their `repr_val` methods, ensuring consistent formatting with the specified number of decimal places. For example, values should appear as `3`, `1`, `7`, `0.500`, and `1.06250` in the output file.

**Actual Behavior:**
Numeric values are written directly without applying `repr_val`, resulting in inconsistent formatting such as `3.0`, `1.0`, `7.0`, `0.5`, and `1.0625`. This causes mismatches when comparing the output with expected results, leading to test failures.

**Impact:**
This formatting issue can lead to incorrect data representation in CSV files, making it difficult to maintain consistency and reliability in data processing workflows.

[/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

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
        """
        flags, names = zip(*[i.split(cls.HEADER1_FLAG_SEP, 1)
                             if cls.HEADER1_FLAG_SEP in i 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 value
        elif var.is_discrete:
            return lambda value: "" if isnan(value) else var.values[int(value)]
        elif var.is_string:
            return lambda value: 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:** CSVWriter Outputs Numeric Continuous Variables Without Proper Formatting

**Description:**
When writing continuous numeric variables using `CSVWriter`, the numeric values are not formatted according to their specified representation. This results in inconsistent decimal places in the output, causing discrepancies between the actual and expected data formats.

**Example Buggy Code:**
```python
from Orange.data import Domain, ContinuousVariable, StringVariable, Table
from Orange.data.io import TabWriter

domain = Domain(
    [ContinuousVariable('a', number_of_decimals=0)],
    [ContinuousVariable('c', number_of_decimals=3)],
    [StringVariable('d')]
)
data = Table.from_numpy(
    domain,
    np.array([[3.0], [1.0], [7.0]]),
    np.array([0.5, np.nan, 1.0625]),
    np.array([['foo'], ['bar'], ['baz']])
)

with NamedTemporaryFile(suffix='.tab', delete=False) as f:
    fname = f.name
try:
    TabWriter.write(fname, data)
    with open(fname, 'r') as file:
        content = file.read().strip()
        print(content)
finally:
    os.remove(fname)
```

**Expected Behavior:**
Numeric continuous variables should be written using their `repr_val` methods, ensuring consistent formatting with the specified number of decimal places. For example, values should appear as `3`, `1`, `7`, `0.500`, and `1.06250` in the output file.

**Actual Behavior:**
Numeric values are written directly without applying `repr_val`, resulting in inconsistent formatting such as `3.0`, `1.0`, `7.0`, `0.5`, and `1.0625`. This causes mismatches when comparing the output with expected results, leading to test failures.

**Impact:**
This formatting issue can lead to incorrect data representation in CSV files, making it difficult to maintain consistency and reliability in data processing workflows.

[/ISSUE]
{
    "TestTableFilters.test_guess_data_type_continuous": "PASSED",
    "TestTableFilters.test_guess_data_type_discrete": "PASSED",
    "TestTableFilters.test_guess_data_type_string": "PASSED",
    "TestTableFilters.test_guess_data_type_time": "PASSED",
    "TestTableFilters.test_guess_data_type_values_order": "PASSED",
    "TestWriters.test_roundtrip_xlsx": "PASSED",
    "TestWriters.test_write_tab": "PASSED"
}
diff --git a/Orange/data/io_base.py b/Orange/data/io_base.py
index 677220995..629fce528 100644
--- a/Orange/data/io_base.py
+++ b/Orange/data/io_base.py
@@ -654,7 +654,7 @@ class _FileWriter:
         if var.is_time:
             return var.repr_val
         elif var.is_continuous:
-            return lambda value: "" if isnan(value) else value
+            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:

{"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\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        flags, names = zip(*[i.split(cls.HEADER1_FLAG_SEP, 1)\n                             if cls.HEADER1_FLAG_SEP in i 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 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: 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:** CSVWriter Outputs Numeric Continuous Variables Without Proper Formatting

**Description:**
When writing continuous numeric variables using `CSVWriter`, the numeric values are not formatted according to their specified representation. This results in inconsistent decimal places in the output, causing discrepancies between the actual and expected data formats.

**Example Buggy Code:**
```python
from Orange.data import Domain, ContinuousVariable, StringVariable, Table
from Orange.data.io import TabWriter

domain = Domain(
    [ContinuousVariable('a', number_of_decimals=0)],
    [ContinuousVariable('c', number_of_decimals=3)],
    [StringVariable('d')]
)
data = Table.from_numpy(
    domain,
    np.array([[3.0], [1.0], [7.0]]),
    np.array([0.5, np.nan, 1.0625]),
    np.array([['foo'], ['bar'], ['baz']])
)

with NamedTemporaryFile(suffix='.tab', delete=False) as f:
    fname = f.name
try:
    TabWriter.write(fname, data)
    with open(fname, 'r') as file:
        content = file.read().strip()
        print(content)
finally:
    os.remove(fname)
```

**Expected Behavior:**
Numeric continuous variables should be written using their `repr_val` methods, ensuring consistent formatting with the specified number of decimal places. For example, values should appear as `3`, `1`, `7`, `0.500`, and `1.06250` in the output file.

**Actual Behavior:**
Numeric values are written directly without applying `repr_val`, resulting in inconsistent formatting such as `3.0`, `1.0`, `7.0`, `0.5`, and `1.0625`. This causes mismatches when comparing the output with expected results, leading to test failures.

**Impact:**
This formatting issue can lead to incorrect data representation in CSV files, making it difficult to maintain consistency and reliability in data processing workflows.

[/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

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
        """
        flags, names = zip(*[i.split(cls.HEADER1_FLAG_SEP, 1)
                             if cls.HEADER1_FLAG_SEP in i 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 value
        elif var.is_discrete:
            return lambda value: "" if isnan(value) else var.values[int(value)]
        elif var.is_string:
            return lambda value: 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