{
    "file_diffs": [
        {
            "old_file_content": "import os.path\n\nimport numpy as np\n\nfrom Orange.misc import _distmatrix_xlsx\nfrom Orange.util import deprecated\n\n\nclass DistMatrix(np.ndarray):\n    \"\"\"\n    Distance matrix. Extends ``numpy.ndarray``.\n\n    .. attribute:: row_items\n\n        Items corresponding to matrix rows.\n\n    .. attribute:: col_items\n\n        Items corresponding to matrix columns.\n\n    .. attribute:: axis\n\n        If axis=1 we calculate distances between rows,\n        if axis=0 we calculate distances between columns.\n    \"\"\"\n    def __new__(cls, data, row_items=None, col_items=None, axis=1):\n        \"\"\"Construct a new distance matrix containing the given data.\n\n        :param data: Distance matrix\n        :type data: numpy array\n        :param row_items: Items in matrix rows\n        :type row_items: `Orange.data.Table` or `Orange.data.Instance`\n        :param col_items: Items in matrix columns\n        :type col_items: `Orange.data.Table` or `Orange.data.Instance`\n        :param axis: The axis along which the distances are calculated\n        :type axis: int\n\n        \"\"\"\n        obj = np.asarray(data).view(cls)\n        obj.row_items = row_items\n        obj.col_items = col_items\n        obj.axis = axis\n        return obj\n\n    def __array_finalize__(self, obj):\n        \"\"\"See http://docs.scipy.org/doc/numpy/user/basics.subclassing.html\"\"\"\n        if obj is None:\n            return\n        self.row_items = getattr(obj, 'row_items', None)\n        self.col_items = getattr(obj, 'col_items', None)\n        self.axis = getattr(obj, 'axis', 1)\n\n    def __array_wrap__(self, out_arr, context=None):\n        if out_arr.ndim == 0:  # a single scalar\n            return out_arr[()]\n        return np.ndarray.__array_wrap__(self, out_arr, context)\n\n    # __reduce__() and __setstate__() ensure DistMatrix is picklable.\n    def __reduce__(self):\n        state = super().__reduce__()\n        newstate = state[2] + (self.row_items, self.col_items, self.axis)\n        return state[0], state[1], newstate\n\n    # noinspection PyMethodOverriding,PyArgumentList\n    def __setstate__(self, state):\n        self.row_items = state[-3]\n        self.col_items = state[-2]\n        self.axis = state[-1]\n        super().__setstate__(state[0:-3])\n\n    @property\n    @deprecated\n    def dim(self):\n        \"\"\"Returns the single dimension of the symmetric square matrix.\"\"\"\n        return self.shape[0]\n\n    # noinspection PyPep8Naming\n    @property\n    @deprecated\n    def X(self):\n        return self\n\n    @property\n    def flat(self):\n        return self[np.triu_indices(self.shape[0], 1)]\n\n    def submatrix(self, row_items, col_items=None):\n        \"\"\"\n        Return a submatrix\n\n        Args:\n            row_items: indices of rows\n            col_items: incides of columns\n        \"\"\"\n        if not col_items:\n            col_items = row_items\n        obj = self[np.ix_(row_items, col_items)]\n        if self.row_items is not None:\n            obj.row_items = self.row_items[row_items]\n        if self.col_items is not None:\n            if self.col_items is self.row_items and row_items is col_items:\n                obj.col_items = obj.row_items\n            else:\n                obj.col_items = self.col_items[col_items]\n        return obj\n\n    @classmethod\n    def from_file(cls, filename, sheet=None):\n        \"\"\"\n        Load distance matrix from a file\n\n        The file should be preferrably encoded in ascii/utf-8. White space at\n        the beginning and end of lines is ignored.\n\n        The first line of the file starts with the matrix dimension. It\n        can be followed by a list flags\n\n        - *axis=<number>*: the axis number\n        - *symmetric*: the matrix is symmetric; when reading the element (i, j)\n          it's value is also assigned to (j, i)\n        - *asymmetric*: the matrix is asymmetric\n        - *row_labels*: the file contains row labels\n        - *col_labels*: the file contains column labels\n\n        By default, matrices are symmetric, have axis 1 and no labels are given.\n        Flags *labeled* and *labelled* are obsolete aliases for *row_labels*.\n\n        If the file has column labels, they follow in the second line.\n        Row labels appear at the beginning of each row.\n        Labels are arbitrary strings that cannot contain newlines and\n        tabulators. Labels are stored as instances of `Table` with a single\n        meta attribute named \"label\".\n\n        The remaining lines contain tab-separated numbers, preceded with labels,\n        if present. Lines are padded with zeros if necessary. If the matrix is\n        symmetric, the file contains the lower triangle; any data above the\n        diagonal is ignored.\n\n        Args:\n            filename: file name\n        \"\"\"\n        _, ext = os.path.splitext(filename)\n        if ext == \".xlsx\":\n            matrix, row_labels, col_labels, axis \\\n                = _distmatrix_xlsx.read_matrix(filename, sheet)\n        else:\n            assert sheet is None\n            matrix, row_labels, col_labels, axis = cls._from_dst(filename)\n        return cls(matrix,\n                   cls._labels_to_tables(row_labels),\n                   cls._labels_to_tables(col_labels),\n                   axis)\n\n    @staticmethod\n    def _labels_to_tables(labels):\n        # prevent circular imports, pylint: disable=import-outside-toplevel\n        from Orange.data import Table, StringVariable, Domain\n\n        if labels is None or isinstance(labels, Table):\n            return labels\n        return Table.from_numpy(\n            Domain([], metas=[StringVariable(\"label\")]),\n            np.empty((len(labels), 0)), None, np.array(labels)[:, None])\n\n    @classmethod\n    def _from_dst(cls, filename):\n        # prevent circular imports, pylint: disable=import-outside-toplevel\n        from Orange.data.io import detect_encoding\n\n        with open(filename, encoding=detect_encoding(filename)) as fle:\n            line = fle.readline()\n            if not line:\n                raise ValueError(\"empty file\")\n            data = line.strip().split()\n            if not data[0].strip().isdigit():\n                raise ValueError(\"distance file must begin with dimension\")\n            n = int(data.pop(0))\n            symmetric = True\n            axis = 1\n            col_labels = row_labels = None\n            for flag in data:\n                if flag in (\"labelled\", \"labeled\", \"row_labels\"):\n                    row_labels = []\n                elif flag == \"col_labels\":\n                    col_labels = []\n                elif flag == \"symmetric\":\n                    symmetric = True\n                elif flag == \"asymmetric\":\n                    symmetric = False\n                else:\n                    flag_data = flag.split(\"=\")\n                    if len(flag_data) == 2:\n                        name, value = map(str.strip, flag_data)\n                    else:\n                        name, value = \"\", None\n                    if name == \"axis\" and value.isdigit():\n                        axis = int(value)\n                    else:\n                        raise ValueError(f\"invalid flag '{flag}'\")\n            if col_labels is not None:\n                col_labels = [x.strip()\n                              for x in fle.readline().strip().split(\"\\t\")]\n                if len(col_labels) != n:\n                    raise ValueError(\"mismatching number of column labels, \"\n                                     f\"{len(col_labels)} != {n}\")\n\n            def num_or_lab(n, labels):\n                return f\"'{labels[n]}'\" if labels else str(n + 1)\n\n            matrix = np.zeros((n, n))\n            for i, line in enumerate(fle):\n                if i >= n:\n                    raise ValueError(\"too many rows\")\n                line = line.strip().split(\"\\t\")\n                if row_labels is not None:\n                    row_labels.append(line.pop(0).strip())\n                if len(line) > n:\n                    raise ValueError(\n                        f\"too many columns in matrix row \"\n                        f\"{num_or_lab(i, row_labels)}\")\n                for j, e in enumerate(line[:i + 1 if symmetric else n]):\n                    try:\n                        matrix[i, j] = float(e)\n                    except ValueError as exc:\n                        raise ValueError(\n                            \"invalid element at \"\n                            f\"row {num_or_lab(i, row_labels)}, \"\n                            f\"column {num_or_lab(j, col_labels)}\") from exc\n                    if symmetric:\n                        matrix[j, i] = matrix[i, j]\n            return matrix, row_labels, col_labels, axis\n\n    def auto_symmetricized(self, copy=False):\n        def self_or_copy():\n            return self.copy() if copy else self\n\n        def get_labels(labels):\n            return np.array(labels) if isinstance(labels, list) \\\n                else labels.metas[:, 0] if self._trivial_labels(labels) \\\n                else object()\n\n        h, w = self.shape\n        m = max(w, h)\n        if (abs(h - w) > 1\n                or self.row_items and self.col_items\n                   and np.any(get_labels(self.row_items)\n                              != get_labels(self.col_items))\n                or self.row_items and len(self.row_items) != m\n                or self.col_items and len(self.col_items) != m):\n            return self_or_copy()\n\n        nans = np.isnan(self)\n        low_indices = np.tril_indices(h, -1)\n        low_empty = np.all(nans[low_indices])\n        high_indices = np.triu_indices(w, 1)\n        high_empty = np.all(nans[high_indices])\n        if low_empty is high_empty:  # both non-empty, or both empty (only diagonal)\n            return self_or_copy()\n\n        indices = low_indices if low_empty else high_indices\n        if w == h:\n            matrix = np.array(self)\n        else:\n            if low_empty:\n                row = np.vstack((self[:, -1, None], [[0]])).T\n                matrix = np.vstack((self, row))\n            else:\n                col = np.hstack((self[-1, None], [[0]])).T\n                matrix = np.hstack((self, col))\n            diag_indices = np.diag_indices(len(matrix))\n            matrix[diag_indices] = np.nan_to_num(matrix[diag_indices])\n        matrix[indices] = self.T[indices]\n        return type(self)(matrix,\n                          self.row_items or self.col_items,\n                          self.col_items or self.row_items)\n\n    @staticmethod\n    def _trivial_labels(items):\n        # prevent circular imports, pylint: disable=import-outside-toplevel\n        from Orange.data import Table, StringVariable\n\n        return items and \\\n               isinstance(items, Table) and \\\n               len(items.domain.metas) == 1 and \\\n               isinstance(items.domain.metas[0], StringVariable)\n\n    def is_symmetric(self):\n        # prevent circular imports, pylint: disable=import-outside-toplevel\n        from Orange.data import Table\n\n        if self.shape[0] != self.shape[1] or not np.allclose(self, self.T):\n            return False\n        if self.row_items is None or self.col_items is None:\n            return True\n        if isinstance(self.row_items, Table):\n            return (isinstance(self.col_items, Table)\n                and self.col_items.domain == self.row_items.domain\n                and np.array_equal(self.col_items.X, self.row_items.X)\n                and np.array_equal(self.col_items.Y, self.row_items.Y)\n                and np.array_equal(self.col_items.metas, self.row_items.metas))\n        else:\n            return (not isinstance(self.col_items, Table)\n                and np.array_equal(self.row_items, self.col_items))\n\n    def has_row_labels(self):\n        \"\"\"\n        Returns `True` if row labels can be automatically determined from data\n\n        For this, the `row_items` must be an instance of `Orange.data.Table`\n        whose domain contains a single meta attribute, which has to be a string.\n        The domain may contain other variables, but not meta attributes.\n        \"\"\"\n        return self._trivial_labels(self.row_items)\n\n    def has_col_labels(self):\n        \"\"\"\n        Returns `True` if column labels can be automatically determined from\n        data\n\n        For this, the `col_items` must be an instance of `Orange.data.Table`\n        whose domain contains a single meta attribute, which has to be a string.\n        The domain may contain other variables, but not meta attributes.\n        \"\"\"\n        return self._trivial_labels(self.col_items)\n\n    def get_labels(self, items):\n        if self._trivial_labels(items):\n            return items.get_column(items.domain.metas[0])\n        elif isinstance(items, (list, tuple)) \\\n                and all(isinstance(x, str) for x in items):\n            return items\n        else:\n            return None\n\n    def save(self, filename):\n        if os.path.splitext(filename)[1] == \".xlsx\":\n            _distmatrix_xlsx.write_matrix(self, filename)\n        else:\n            self._save_dst(filename)\n\n    def _save_dst(self, filename):\n        \"\"\"\n        Save the distance matrix to a file in the file format described at\n        :obj:`~Orange.misc.distmatrix.DistMatrix.from_file`.\n\n        Args:\n            filename: file name\n        \"\"\"\n        n = len(self)\n        data = \"{}\\taxis={}\".format(n, self.axis)\n        row_labels = col_labels = None\n        if self.has_col_labels():\n            data += \"\\tcol_labels\"\n            col_labels = self.col_items\n        if self.has_row_labels():\n            data += \"\\trow_labels\"\n            row_labels = self.row_items\n        symmetric = self.is_symmetric()\n        if not symmetric:\n            data += \"\\tasymmetric\"\n        with open(filename, \"wt\") as fle:\n            fle.write(data + \"\\n\")\n            if col_labels is not None:\n                fle.write(\"\\t\".join(str(e.metas[0]) for e in col_labels) + \"\\n\")\n            for i, row in enumerate(self):\n                if row_labels is not None:\n                    fle.write(str(row_labels[i].metas[0]) + \"\\t\")\n                if symmetric:\n                    fle.write(\"\\t\".join(map(str, row[:i + 1])) + \"\\n\")\n                else:\n                    fle.write(\"\\t\".join(map(str, row)) + \"\\n\")\n",
            "new_file_content": "import os.path\n\nimport numpy as np\n\nfrom Orange.misc import _distmatrix_xlsx\nfrom Orange.util import deprecated\n\n\nclass DistMatrix(np.ndarray):\n    \"\"\"\n    Distance matrix. Extends ``numpy.ndarray``.\n\n    .. attribute:: row_items\n\n        Items corresponding to matrix rows.\n\n    .. attribute:: col_items\n\n        Items corresponding to matrix columns.\n\n    .. attribute:: axis\n\n        If axis=1 we calculate distances between rows,\n        if axis=0 we calculate distances between columns.\n    \"\"\"\n    def __new__(cls, data, row_items=None, col_items=None, axis=1):\n        \"\"\"Construct a new distance matrix containing the given data.\n\n        :param data: Distance matrix\n        :type data: numpy array\n        :param row_items: Items in matrix rows\n        :type row_items: `Orange.data.Table` or `Orange.data.Instance`\n        :param col_items: Items in matrix columns\n        :type col_items: `Orange.data.Table` or `Orange.data.Instance`\n        :param axis: The axis along which the distances are calculated\n        :type axis: int\n\n        \"\"\"\n        obj = np.asarray(data).view(cls)\n        obj.row_items = row_items\n        obj.col_items = col_items\n        obj.axis = axis\n        return obj\n\n    def __array_finalize__(self, obj):\n        \"\"\"See http://docs.scipy.org/doc/numpy/user/basics.subclassing.html\"\"\"\n        if obj is None:\n            return\n        self.row_items = getattr(obj, 'row_items', None)\n        self.col_items = getattr(obj, 'col_items', None)\n        self.axis = getattr(obj, 'axis', 1)\n\n    def __array_wrap__(self, out_arr, context=None):\n        if out_arr.ndim == 0:  # a single scalar\n            return out_arr[()]\n        return np.ndarray.__array_wrap__(self, out_arr, context)\n\n    # __reduce__() and __setstate__() ensure DistMatrix is picklable.\n    def __reduce__(self):\n        state = super().__reduce__()\n        newstate = state[2] + (self.row_items, self.col_items, self.axis)\n        return state[0], state[1], newstate\n\n    # noinspection PyMethodOverriding,PyArgumentList\n    def __setstate__(self, state):\n        self.row_items = state[-3]\n        self.col_items = state[-2]\n        self.axis = state[-1]\n        super().__setstate__(state[0:-3])\n\n    @property\n    @deprecated\n    def dim(self):\n        \"\"\"Returns the single dimension of the symmetric square matrix.\"\"\"\n        return self.shape[0]\n\n    # noinspection PyPep8Naming\n    @property\n    @deprecated\n    def X(self):\n        return self\n\n    @property\n    def flat(self):\n        return self[np.triu_indices(self.shape[0], 1)]\n\n    def submatrix(self, row_items, col_items=None):\n        \"\"\"\n        Return a submatrix\n\n        Args:\n            row_items: indices of rows\n            col_items: incides of columns\n        \"\"\"\n        if not col_items:\n            col_items = row_items\n        obj = self[np.ix_(row_items, col_items)]\n        if self.row_items is not None:\n            obj.row_items = self.row_items[row_items]\n        if self.col_items is not None:\n            if self.col_items is self.row_items and row_items is col_items:\n                obj.col_items = obj.row_items\n            else:\n                obj.col_items = self.col_items[col_items]\n        return obj\n\n    @classmethod\n    def from_file(cls, filename, sheet=None):\n        \"\"\"\n        Load distance matrix from a file\n\n        The file should be preferrably encoded in ascii/utf-8. White space at\n        the beginning and end of lines is ignored.\n\n        The first line of the file starts with the matrix dimension. It\n        can be followed by a list flags\n\n        - *axis=<number>*: the axis number\n        - *symmetric*: the matrix is symmetric; when reading the element (i, j)\n          it's value is also assigned to (j, i)\n        - *asymmetric*: the matrix is asymmetric\n        - *row_labels*: the file contains row labels\n        - *col_labels*: the file contains column labels\n\n        By default, matrices are symmetric, have axis 1 and no labels are given.\n        Flags *labeled* and *labelled* are obsolete aliases for *row_labels*.\n\n        If the file has column labels, they follow in the second line.\n        Row labels appear at the beginning of each row.\n        Labels are arbitrary strings that cannot contain newlines and\n        tabulators. Labels are stored as instances of `Table` with a single\n        meta attribute named \"label\".\n\n        The remaining lines contain tab-separated numbers, preceded with labels,\n        if present. Lines are padded with zeros if necessary. If the matrix is\n        symmetric, the file contains the lower triangle; any data above the\n        diagonal is ignored.\n\n        Args:\n            filename: file name\n        \"\"\"\n        _, ext = os.path.splitext(filename)\n        if ext == \".xlsx\":\n            matrix, row_labels, col_labels, axis \\\n                = _distmatrix_xlsx.read_matrix(filename, sheet)\n        else:\n            assert sheet is None\n            matrix, row_labels, col_labels, axis = cls._from_dst(filename)\n        return cls(matrix,\n                   cls._labels_to_tables(row_labels),\n                   cls._labels_to_tables(col_labels),\n                   axis)\n\n    @staticmethod\n    def _labels_to_tables(labels):\n        # prevent circular imports, pylint: disable=import-outside-toplevel\n        from Orange.data import Table, StringVariable, Domain\n\n        if labels is None or isinstance(labels, Table):\n            return labels\n        return Table.from_numpy(\n            Domain([], metas=[StringVariable(\"label\")]),\n            np.empty((len(labels), 0)), None, np.array(labels)[:, None])\n\n    @classmethod\n    def _from_dst(cls, filename):\n        # prevent circular imports, pylint: disable=import-outside-toplevel\n        from Orange.data.io import detect_encoding\n\n        with open(filename, encoding=detect_encoding(filename)) as fle:\n            line = fle.readline()\n            if not line:\n                raise ValueError(\"empty file\")\n            data = line.strip().split()\n            if not data[0].strip().isdigit():\n                raise ValueError(\"distance file must begin with dimension\")\n            n = int(data.pop(0))\n            symmetric = True\n            axis = 1\n            col_labels = row_labels = None\n            for flag in data:\n                if flag in (\"labelled\", \"labeled\", \"row_labels\"):\n                    row_labels = []\n                elif flag == \"col_labels\":\n                    col_labels = []\n                elif flag == \"symmetric\":\n                    symmetric = True\n                elif flag == \"asymmetric\":\n                    symmetric = False\n                else:\n                    flag_data = flag.split(\"=\")\n                    if len(flag_data) == 2:\n                        name, value = map(str.strip, flag_data)\n                    else:\n                        name, value = \"\", None\n                    if name == \"axis\" and value.isdigit():\n                        axis = int(value)\n                    else:\n                        raise ValueError(f\"invalid flag '{flag}'\")\n            if col_labels is not None:\n                col_labels = [x.strip()\n                              for x in fle.readline().strip().split(\"\\t\")]\n                if len(col_labels) != n:\n                    raise ValueError(\"mismatching number of column labels, \"\n                                     f\"{len(col_labels)} != {n}\")\n\n            def num_or_lab(n, labels):\n                return f\"'{labels[n]}'\" if labels else str(n + 1)\n\n            matrix = np.zeros((n, n))\n            for i, line in enumerate(fle):\n                if i >= n:\n                    raise ValueError(\"too many rows\")\n                line = line.strip().split(\"\\t\")\n                if row_labels is not None:\n                    row_labels.append(line.pop(0).strip())\n                if len(line) > n:\n                    raise ValueError(\n                        f\"too many columns in matrix row \"\n                        f\"{num_or_lab(i, row_labels)}\")\n                for j, e in enumerate(line[:i + 1 if symmetric else n]):\n                    try:\n                        matrix[i, j] = float(e)\n                    except ValueError as exc:\n                        raise ValueError(\n                            \"invalid element at \"\n                            f\"row {num_or_lab(i, row_labels)}, \"\n                            f\"column {num_or_lab(j, col_labels)}\") from exc\n                    if symmetric:\n                        matrix[j, i] = matrix[i, j]\n            return matrix, row_labels, col_labels, axis\n\n    def auto_symmetricized(self, copy=False):\n        def self_or_copy():\n            return self.copy() if copy else self\n\n        def get_labels(labels):\n            return np.array(labels) if isinstance(labels, list) \\\n                else labels.metas[:, 0] if self._trivial_labels(labels) \\\n                else object()\n\n        h, w = self.shape\n        m = max(w, h)\n        if (abs(h - w) > 1\n                or self.row_items and self.col_items\n                   and np.any(get_labels(self.row_items)\n                              != get_labels(self.col_items))\n                or self.row_items and len(self.row_items) != m\n                or self.col_items and len(self.col_items) != m):\n            return self_or_copy()\n\n        nans = np.isnan(self)\n        low_indices = np.tril_indices(h, -1)\n        low_empty = np.all(nans[low_indices])\n        high_indices = np.triu_indices(w, 1)\n        high_empty = np.all(nans[high_indices])\n        if low_empty is high_empty:  # both non-empty, or both empty (only diagonal)\n            return self_or_copy()\n\n        indices = low_indices if low_empty else high_indices\n        if w == h:\n            matrix = np.array(self)\n        else:\n            if low_empty:\n                row = np.vstack((self[:, -1, None], [[0]])).T\n                matrix = np.vstack((self, row))\n            else:\n                col = np.hstack((self[-1, None], [[0]])).T\n                matrix = np.hstack((self, col))\n            diag_indices = np.diag_indices(len(matrix))\n            matrix[diag_indices] = np.nan_to_num(matrix[diag_indices])\n        matrix[indices] = self.T[indices]\n        return type(self)(matrix,\n                          self.row_items or self.col_items,\n                          self.col_items or self.row_items)\n\n    def _trivial_labels(self, items):\n        # prevent circular imports, pylint: disable=import-outside-toplevel\n        from Orange.data import Table, StringVariable\n\n        return (isinstance(items, (list, tuple))\n                and all(isinstance(item, str) for item in items)\n                or\n                isinstance(items, Table)\n                and (self.axis == 0 or\n                     sum(isinstance(meta, StringVariable)\n                         for meta in items.domain.metas) == 1\n                     )\n                )\n\n    def is_symmetric(self):\n        # prevent circular imports, pylint: disable=import-outside-toplevel\n        from Orange.data import Table\n\n        if self.shape[0] != self.shape[1] or not np.allclose(self, self.T):\n            return False\n        if self.row_items is None or self.col_items is None:\n            return True\n        if isinstance(self.row_items, Table):\n            return (isinstance(self.col_items, Table)\n                and self.col_items.domain == self.row_items.domain\n                and np.array_equal(self.col_items.X, self.row_items.X)\n                and np.array_equal(self.col_items.Y, self.row_items.Y)\n                and np.array_equal(self.col_items.metas, self.row_items.metas))\n        else:\n            return (not isinstance(self.col_items, Table)\n                and np.array_equal(self.row_items, self.col_items))\n\n    def has_row_labels(self):\n        \"\"\"\n        Returns `True` if row labels can be automatically determined from data\n\n        For this, the `row_items` must be an instance of `Orange.data.Table`\n        whose domain contains a single meta attribute, which has to be a string.\n        The domain may contain other variables, but not meta attributes.\n        \"\"\"\n        return self._trivial_labels(self.row_items)\n\n    def has_col_labels(self):\n        \"\"\"\n        Returns `True` if column labels can be automatically determined from\n        data\n\n        For this, the `col_items` must be an instance of `Orange.data.Table`\n        whose domain contains a single meta attribute, which has to be a string.\n        The domain may contain other variables, but not meta attributes.\n        \"\"\"\n        return self._trivial_labels(self.col_items)\n\n    def get_labels(self, items):\n        # prevent circular imports, pylint: disable=import-outside-toplevel\n        from Orange.data import StringVariable\n\n        if not self._trivial_labels(items):\n            return None\n        if isinstance(items, (list, tuple)) \\\n                and all(isinstance(x, str) for x in items):\n            return items\n        if self.axis == 0:\n            return [attr.name for attr in items.domain.attributes]\n        else:\n            string_var = next(var for var in items.domain.metas\n                              if isinstance(var, StringVariable))\n            return items.get_column(string_var)\n\n    def save(self, filename):\n        if os.path.splitext(filename)[1] == \".xlsx\":\n            _distmatrix_xlsx.write_matrix(self, filename)\n        else:\n            self._save_dst(filename)\n\n    def _save_dst(self, filename):\n        \"\"\"\n        Save the distance matrix to a file in the file format described at\n        :obj:`~Orange.misc.distmatrix.DistMatrix.from_file`.\n\n        Args:\n            filename: file name\n        \"\"\"\n        n = len(self)\n        data = \"{}\\taxis={}\".format(n, self.axis)\n        row_labels = col_labels = None\n        if self.has_col_labels():\n            data += \"\\tcol_labels\"\n            col_labels = self.col_items\n        if self.has_row_labels():\n            data += \"\\trow_labels\"\n            row_labels = self.row_items\n        symmetric = self.is_symmetric()\n        if not symmetric:\n            data += \"\\tasymmetric\"\n        with open(filename, \"wt\") as fle:\n            fle.write(data + \"\\n\")\n            if col_labels is not None:\n                fle.write(\"\\t\".join(str(e.metas[0]) for e in col_labels) + \"\\n\")\n            for i, row in enumerate(self):\n                if row_labels is not None:\n                    fle.write(str(row_labels[i].metas[0]) + \"\\t\")\n                if symmetric:\n                    fle.write(\"\\t\".join(map(str, row[:i + 1])) + \"\\n\")\n                else:\n                    fle.write(\"\\t\".join(map(str, row)) + \"\\n\")\n",
            "header": {
                "file": {
                    "path": "Orange/misc/distmatrix.py"
                },
                "misc_line": null
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            "index_line": {
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                "new_commit_hash": "c56fa169c",
                "mode": "100644"
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            "is_binary_file": false,
            "binary_line": null,
            "minus_file": {
                "path": "a/Orange/misc/distmatrix.py"
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                        "section": "class DistMatrix(np.ndarray):"
                    },
                    "line_group": {
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                            {
                                "content": "                          self.row_items or self.col_items,",
                                "type": "context"
                            },
                            {
                                "content": "                          self.col_items or self.row_items)",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "    @staticmethod",
                                "type": "deleted"
                            },
                            {
                                "content": "    def _trivial_labels(items):",
                                "type": "deleted"
                            },
                            {
                                "content": "    def _trivial_labels(self, items):",
                                "type": "added"
                            },
                            {
                                "content": "        # prevent circular imports, pylint: disable=import-outside-toplevel",
                                "type": "context"
                            },
                            {
                                "content": "        from Orange.data import Table, StringVariable",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "        return items and \\",
                                "type": "deleted"
                            },
                            {
                                "content": "               isinstance(items, Table) and \\",
                                "type": "deleted"
                            },
                            {
                                "content": "               len(items.domain.metas) == 1 and \\",
                                "type": "deleted"
                            },
                            {
                                "content": "               isinstance(items.domain.metas[0], StringVariable)",
                                "type": "deleted"
                            },
                            {
                                "content": "        return (isinstance(items, (list, tuple))",
                                "type": "added"
                            },
                            {
                                "content": "                and all(isinstance(item, str) for item in items)",
                                "type": "added"
                            },
                            {
                                "content": "                or",
                                "type": "added"
                            },
                            {
                                "content": "                isinstance(items, Table)",
                                "type": "added"
                            },
                            {
                                "content": "                and (self.axis == 0 or",
                                "type": "added"
                            },
                            {
                                "content": "                     sum(isinstance(meta, StringVariable)",
                                "type": "added"
                            },
                            {
                                "content": "                         for meta in items.domain.metas) == 1",
                                "type": "added"
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                            {
                                "content": "                     )",
                                "type": "added"
                            },
                            {
                                "content": "                )",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "    def is_symmetric(self):",
                                "type": "context"
                            },
                            {
                                "content": "        # prevent circular imports, pylint: disable=import-outside-toplevel",
                                "type": "context"
                            }
                        ]
                    },
                    "modified_entities": [
                        {
                            "file_name": "Orange/misc/distmatrix.py",
                            "type": "method",
                            "name": "DistMatrix._trivial_labels",
                            "content": "class DistMatrix(np.ndarray):\n    \"\"\"\n    Distance matrix. Extends ``numpy.ndarray``.\n\n    .. attribute:: row_items\n\n        Items corresponding to matrix rows.\n\n    .. attribute:: col_items\n\n        Items corresponding to matrix columns.\n\n    .. attribute:: axis\n\n        If axis=1 we calculate distances between rows,\n        if axis=0 we calculate distances between columns.\n    \"\"\"\n    def __new__(cls, data, row_items=None, col_items=None, axis=1):\n        \"\"\"Construct a new distance matrix containing the given data.\n\n        :param data: Distance matrix\n        :type data: numpy array\n        :param row_items: Items in matrix rows\n        :type row_items: `Orange.data.Table` or `Orange.data.Instance`\n        :param col_items: Items in matrix columns\n        :type col_items: `Orange.data.Table` or `Orange.data.Instance`\n        :param axis: The axis along which the distances are calculated\n        :type axis: int\n\n        \"\"\"\n        obj = np.asarray(data).view(cls)\n        obj.row_items = row_items\n        obj.col_items = col_items\n        obj.axis = axis\n        return obj\n\n    def __array_finalize__(self, obj):\n        \"\"\"See http://docs.scipy.org/doc/numpy/user/basics.subclassing.html\"\"\"\n        if obj is None:\n            return\n        self.row_items = getattr(obj, 'row_items', None)\n        self.col_items = getattr(obj, 'col_items', None)\n        self.axis = getattr(obj, 'axis', 1)\n\n    def __array_wrap__(self, out_arr, context=None):\n        if out_arr.ndim == 0:  # a single scalar\n            return out_arr[()]\n        return np.ndarray.__array_wrap__(self, out_arr, context)\n\n    # __reduce__() and __setstate__() ensure DistMatrix is picklable.\n    def __reduce__(self):\n        state = super().__reduce__()\n        newstate = state[2] + (self.row_items, self.col_items, self.axis)\n        return state[0], state[1], newstate\n\n    # noinspection PyMethodOverriding,PyArgumentList\n    def __setstate__(self, state):\n        self.row_items = state[-3]\n        self.col_items = state[-2]\n        self.axis = state[-1]\n        super().__setstate__(state[0:-3])\n\n    @property\n    @deprecated\n    def dim(self):\n        \"\"\"Returns the single dimension of the symmetric square matrix.\"\"\"\n        return self.shape[0]\n\n    # noinspection PyPep8Naming\n    @property\n    @deprecated\n    def X(self):\n        return self\n\n    @property\n    def flat(self):\n        return self[np.triu_indices(self.shape[0], 1)]\n\n    def submatrix(self, row_items, col_items=None):\n        \"\"\"\n        Return a submatrix\n\n        Args:\n            row_items: indices of rows\n            col_items: incides of columns\n        \"\"\"\n        if not col_items:\n            col_items = row_items\n        obj = self[np.ix_(row_items, col_items)]\n        if self.row_items is not None:\n            obj.row_items = self.row_items[row_items]\n        if self.col_items is not None:\n            if self.col_items is self.row_items and row_items is col_items:\n                obj.col_items = obj.row_items\n            else:\n                obj.col_items = self.col_items[col_items]\n        return obj\n\n    @classmethod\n    def from_file(cls, filename, sheet=None):\n        \"\"\"\n        Load distance matrix from a file\n\n        The file should be preferrably encoded in ascii/utf-8. White space at\n        the beginning and end of lines is ignored.\n\n        The first line of the file starts with the matrix dimension. It\n        can be followed by a list flags\n\n        - *axis=<number>*: the axis number\n        - *symmetric*: the matrix is symmetric; when reading the element (i, j)\n          it's value is also assigned to (j, i)\n        - *asymmetric*: the matrix is asymmetric\n        - *row_labels*: the file contains row labels\n        - *col_labels*: the file contains column labels\n\n        By default, matrices are symmetric, have axis 1 and no labels are given.\n        Flags *labeled* and *labelled* are obsolete aliases for *row_labels*.\n\n        If the file has column labels, they follow in the second line.\n        Row labels appear at the beginning of each row.\n        Labels are arbitrary strings that cannot contain newlines and\n        tabulators. Labels are stored as instances of `Table` with a single\n        meta attribute named \"label\".\n\n        The remaining lines contain tab-separated numbers, preceded with labels,\n        if present. Lines are padded with zeros if necessary. If the matrix is\n        symmetric, the file contains the lower triangle; any data above the\n        diagonal is ignored.\n\n        Args:\n            filename: file name\n        \"\"\"\n        _, ext = os.path.splitext(filename)\n        if ext == \".xlsx\":\n            matrix, row_labels, col_labels, axis \\\n                = _distmatrix_xlsx.read_matrix(filename, sheet)\n        else:\n            assert sheet is None\n            matrix, row_labels, col_labels, axis = cls._from_dst(filename)\n        return cls(matrix,\n                   cls._labels_to_tables(row_labels),\n                   cls._labels_to_tables(col_labels),\n                   axis)\n\n    @staticmethod\n    def _labels_to_tables(labels):\n        # prevent circular imports, pylint: disable=import-outside-toplevel\n        from Orange.data import Table, StringVariable, Domain\n\n        if labels is None or isinstance(labels, Table):\n            return labels\n        return Table.from_numpy(\n            Domain([], metas=[StringVariable(\"label\")]),\n            np.empty((len(labels), 0)), None, np.array(labels)[:, None])\n\n    @classmethod\n    def _from_dst(cls, filename):\n        # prevent circular imports, pylint: disable=import-outside-toplevel\n        from Orange.data.io import detect_encoding\n\n        with open(filename, encoding=detect_encoding(filename)) as fle:\n            line = fle.readline()\n            if not line:\n                raise ValueError(\"empty file\")\n            data = line.strip().split()\n            if not data[0].strip().isdigit():\n                raise ValueError(\"distance file must begin with dimension\")\n            n = int(data.pop(0))\n            symmetric = True\n            axis = 1\n            col_labels = row_labels = None\n            for flag in data:\n                if flag in (\"labelled\", \"labeled\", \"row_labels\"):\n                    row_labels = []\n                elif flag == \"col_labels\":\n                    col_labels = []\n                elif flag == \"symmetric\":\n                    symmetric = True\n                elif flag == \"asymmetric\":\n                    symmetric = False\n                else:\n                    flag_data = flag.split(\"=\")\n                    if len(flag_data) == 2:\n                        name, value = map(str.strip, flag_data)\n                    else:\n                        name, value = \"\", None\n                    if name == \"axis\" and value.isdigit():\n                        axis = int(value)\n                    else:\n                        raise ValueError(f\"invalid flag '{flag}'\")\n            if col_labels is not None:\n                col_labels = [x.strip()\n                              for x in fle.readline().strip().split(\"\\t\")]\n                if len(col_labels) != n:\n                    raise ValueError(\"mismatching number of column labels, \"\n                                     f\"{len(col_labels)} != {n}\")\n\n            def num_or_lab(n, labels):\n                return f\"'{labels[n]}'\" if labels else str(n + 1)\n\n            matrix = np.zeros((n, n))\n            for i, line in enumerate(fle):\n                if i >= n:\n                    raise ValueError(\"too many rows\")\n                line = line.strip().split(\"\\t\")\n                if row_labels is not None:\n                    row_labels.append(line.pop(0).strip())\n                if len(line) > n:\n                    raise ValueError(\n                        f\"too many columns in matrix row \"\n                        f\"{num_or_lab(i, row_labels)}\")\n                for j, e in enumerate(line[:i + 1 if symmetric else n]):\n                    try:\n                        matrix[i, j] = float(e)\n                    except ValueError as exc:\n                        raise ValueError(\n                            \"invalid element at \"\n                            f\"row {num_or_lab(i, row_labels)}, \"\n                            f\"column {num_or_lab(j, col_labels)}\") from exc\n                    if symmetric:\n                        matrix[j, i] = matrix[i, j]\n            return matrix, row_labels, col_labels, axis\n\n    def auto_symmetricized(self, copy=False):\n        def self_or_copy():\n            return self.copy() if copy else self\n\n        def get_labels(labels):\n            return np.array(labels) if isinstance(labels, list) \\\n                else labels.metas[:, 0] if self._trivial_labels(labels) \\\n                else object()\n\n        h, w = self.shape\n        m = max(w, h)\n        if (abs(h - w) > 1\n                or self.row_items and self.col_items\n                   and np.any(get_labels(self.row_items)\n                              != get_labels(self.col_items))\n                or self.row_items and len(self.row_items) != m\n                or self.col_items and len(self.col_items) != m):\n            return self_or_copy()\n\n        nans = np.isnan(self)\n        low_indices = np.tril_indices(h, -1)\n        low_empty = np.all(nans[low_indices])\n        high_indices = np.triu_indices(w, 1)\n        high_empty = np.all(nans[high_indices])\n        if low_empty is high_empty:  # both non-empty, or both empty (only diagonal)\n            return self_or_copy()\n\n        indices = low_indices if low_empty else high_indices\n        if w == h:\n            matrix = np.array(self)\n        else:\n            if low_empty:\n                row = np.vstack((self[:, -1, None], [[0]])).T\n                matrix = np.vstack((self, row))\n            else:\n                col = np.hstack((self[-1, None], [[0]])).T\n                matrix = np.hstack((self, col))\n            diag_indices = np.diag_indices(len(matrix))\n            matrix[diag_indices] = np.nan_to_num(matrix[diag_indices])\n        matrix[indices] = self.T[indices]\n        return type(self)(matrix,\n                          self.row_items or self.col_items,\n                          self.col_items or self.row_items)\n\n    @staticmethod\n    def _trivial_labels(items):\n        # prevent circular imports, pylint: disable=import-outside-toplevel\n        from Orange.data import Table, StringVariable\n\n        return items and \\\n               isinstance(items, Table) and \\\n               len(items.domain.metas) == 1 and \\\n               isinstance(items.domain.metas[0], StringVariable)\n\n    def is_symmetric(self):\n        # prevent circular imports, pylint: disable=import-outside-toplevel\n        from Orange.data import Table\n\n        if self.shape[0] != self.shape[1] or not np.allclose(self, self.T):\n            return False\n        if self.row_items is None or self.col_items is None:\n            return True\n        if isinstance(self.row_items, Table):\n            return (isinstance(self.col_items, Table)\n                and self.col_items.domain == self.row_items.domain\n                and np.array_equal(self.col_items.X, self.row_items.X)\n                and np.array_equal(self.col_items.Y, self.row_items.Y)\n                and np.array_equal(self.col_items.metas, self.row_items.metas))\n        else:\n            return (not isinstance(self.col_items, Table)\n                and np.array_equal(self.row_items, self.col_items))\n\n    def has_row_labels(self):\n        \"\"\"\n        Returns `True` if row labels can be automatically determined from data\n\n        For this, the `row_items` must be an instance of `Orange.data.Table`\n        whose domain contains a single meta attribute, which has to be a string.\n        The domain may contain other variables, but not meta attributes.\n        \"\"\"\n        return self._trivial_labels(self.row_items)\n\n    def has_col_labels(self):\n        \"\"\"\n        Returns `True` if column labels can be automatically determined from\n        data\n\n        For this, the `col_items` must be an instance of `Orange.data.Table`\n        whose domain contains a single meta attribute, which has to be a string.\n        The domain may contain other variables, but not meta attributes.\n        \"\"\"\n        return self._trivial_labels(self.col_items)\n\n    def get_labels(self, items):\n        if self._trivial_labels(items):\n            return items.get_column(items.domain.metas[0])\n        elif isinstance(items, (list, tuple)) \\\n                and all(isinstance(x, str) for x in items):\n            return items\n        else:\n            return None\n\n    def save(self, filename):\n        if os.path.splitext(filename)[1] == \".xlsx\":\n            _distmatrix_xlsx.write_matrix(self, filename)\n        else:\n            self._save_dst(filename)\n\n    def _save_dst(self, filename):\n        \"\"\"\n        Save the distance matrix to a file in the file format described at\n        :obj:`~Orange.misc.distmatrix.DistMatrix.from_file`.\n\n        Args:\n            filename: file name\n        \"\"\"\n        n = len(self)\n        data = \"{}\\taxis={}\".format(n, self.axis)\n        row_labels = col_labels = None\n        if self.has_col_labels():\n            data += \"\\tcol_labels\"\n            col_labels = self.col_items\n        if self.has_row_labels():\n            data += \"\\trow_labels\"\n            row_labels = self.row_items\n        symmetric = self.is_symmetric()\n        if not symmetric:\n            data += \"\\tasymmetric\"\n        with open(filename, \"wt\") as fle:\n            fle.write(data + \"\\n\")\n            if col_labels is not None:\n                fle.write(\"\\t\".join(str(e.metas[0]) for e in col_labels) + \"\\n\")\n            for i, row in enumerate(self):\n                if row_labels is not None:\n                    fle.write(str(row_labels[i].metas[0]) + \"\\t\")\n                if symmetric:\n                    fle.write(\"\\t\".join(map(str, row[:i + 1])) + \"\\n\")\n                else:\n                    fle.write(\"\\t\".join(map(str, row)) + \"\\n\")",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 278,
                            "end_lineno": 285,
                            "parent": {
                                "file_name": "Orange/misc/distmatrix.py",
                                "type": "class",
                                "name": "DistMatrix",
                                "content": "class DistMatrix(np.ndarray):\n    \"\"\"\n    Distance matrix. Extends ``numpy.ndarray``.\n\n    .. attribute:: row_items\n\n        Items corresponding to matrix rows.\n\n    .. attribute:: col_items\n\n        Items corresponding to matrix columns.\n\n    .. attribute:: axis\n\n        If axis=1 we calculate distances between rows,\n        if axis=0 we calculate distances between columns.\n    \"\"\"\n    def __new__(cls, data, row_items=None, col_items=None, axis=1):\n        \"\"\"Construct a new distance matrix containing the given data.\n\n        :param data: Distance matrix\n        :type data: numpy array\n        :param row_items: Items in matrix rows\n        :type row_items: `Orange.data.Table` or `Orange.data.Instance`\n        :param col_items: Items in matrix columns\n        :type col_items: `Orange.data.Table` or `Orange.data.Instance`\n        :param axis: The axis along which the distances are calculated\n        :type axis: int\n\n        \"\"\"\n        obj = np.asarray(data).view(cls)\n        obj.row_items = row_items\n        obj.col_items = col_items\n        obj.axis = axis\n        return obj\n\n    def __array_finalize__(self, obj):\n        \"\"\"See http://docs.scipy.org/doc/numpy/user/basics.subclassing.html\"\"\"\n        if obj is None:\n            return\n        self.row_items = getattr(obj, 'row_items', None)\n        self.col_items = getattr(obj, 'col_items', None)\n        self.axis = getattr(obj, 'axis', 1)\n\n    def __array_wrap__(self, out_arr, context=None):\n        if out_arr.ndim == 0:  # a single scalar\n            return out_arr[()]\n        return np.ndarray.__array_wrap__(self, out_arr, context)\n\n    # __reduce__() and __setstate__() ensure DistMatrix is picklable.\n    def __reduce__(self):\n        state = super().__reduce__()\n        newstate = state[2] + (self.row_items, self.col_items, self.axis)\n        return state[0], state[1], newstate\n\n    # noinspection PyMethodOverriding,PyArgumentList\n    def __setstate__(self, state):\n        self.row_items = state[-3]\n        self.col_items = state[-2]\n        self.axis = state[-1]\n        super().__setstate__(state[0:-3])\n\n    @property\n    @deprecated\n    def dim(self):\n        \"\"\"Returns the single dimension of the symmetric square matrix.\"\"\"\n        return self.shape[0]\n\n    # noinspection PyPep8Naming\n    @property\n    @deprecated\n    def X(self):\n        return self\n\n    @property\n    def flat(self):\n        return self[np.triu_indices(self.shape[0], 1)]\n\n    def submatrix(self, row_items, col_items=None):\n        \"\"\"\n        Return a submatrix\n\n        Args:\n            row_items: indices of rows\n            col_items: incides of columns\n        \"\"\"\n        if not col_items:\n            col_items = row_items\n        obj = self[np.ix_(row_items, col_items)]\n        if self.row_items is not None:\n            obj.row_items = self.row_items[row_items]\n        if self.col_items is not None:\n            if self.col_items is self.row_items and row_items is col_items:\n                obj.col_items = obj.row_items\n            else:\n                obj.col_items = self.col_items[col_items]\n        return obj\n\n    @classmethod\n    def from_file(cls, filename, sheet=None):\n        \"\"\"\n        Load distance matrix from a file\n\n        The file should be preferrably encoded in ascii/utf-8. White space at\n        the beginning and end of lines is ignored.\n\n        The first line of the file starts with the matrix dimension. It\n        can be followed by a list flags\n\n        - *axis=<number>*: the axis number\n        - *symmetric*: the matrix is symmetric; when reading the element (i, j)\n          it's value is also assigned to (j, i)\n        - *asymmetric*: the matrix is asymmetric\n        - *row_labels*: the file contains row labels\n        - *col_labels*: the file contains column labels\n\n        By default, matrices are symmetric, have axis 1 and no labels are given.\n        Flags *labeled* and *labelled* are obsolete aliases for *row_labels*.\n\n        If the file has column labels, they follow in the second line.\n        Row labels appear at the beginning of each row.\n        Labels are arbitrary strings that cannot contain newlines and\n        tabulators. Labels are stored as instances of `Table` with a single\n        meta attribute named \"label\".\n\n        The remaining lines contain tab-separated numbers, preceded with labels,\n        if present. Lines are padded with zeros if necessary. If the matrix is\n        symmetric, the file contains the lower triangle; any data above the\n        diagonal is ignored.\n\n        Args:\n            filename: file name\n        \"\"\"\n        _, ext = os.path.splitext(filename)\n        if ext == \".xlsx\":\n            matrix, row_labels, col_labels, axis \\\n                = _distmatrix_xlsx.read_matrix(filename, sheet)\n        else:\n            assert sheet is None\n            matrix, row_labels, col_labels, axis = cls._from_dst(filename)\n        return cls(matrix,\n                   cls._labels_to_tables(row_labels),\n                   cls._labels_to_tables(col_labels),\n                   axis)\n\n    @staticmethod\n    def _labels_to_tables(labels):\n        # prevent circular imports, pylint: disable=import-outside-toplevel\n        from Orange.data import Table, StringVariable, Domain\n\n        if labels is None or isinstance(labels, Table):\n            return labels\n        return Table.from_numpy(\n            Domain([], metas=[StringVariable(\"label\")]),\n            np.empty((len(labels), 0)), None, np.array(labels)[:, None])\n\n    @classmethod\n    def _from_dst(cls, filename):\n        # prevent circular imports, pylint: disable=import-outside-toplevel\n        from Orange.data.io import detect_encoding\n\n        with open(filename, encoding=detect_encoding(filename)) as fle:\n            line = fle.readline()\n            if not line:\n                raise ValueError(\"empty file\")\n            data = line.strip().split()\n            if not data[0].strip().isdigit():\n                raise ValueError(\"distance file must begin with dimension\")\n            n = int(data.pop(0))\n            symmetric = True\n            axis = 1\n            col_labels = row_labels = None\n            for flag in data:\n                if flag in (\"labelled\", \"labeled\", \"row_labels\"):\n                    row_labels = []\n                elif flag == \"col_labels\":\n                    col_labels = []\n                elif flag == \"symmetric\":\n                    symmetric = True\n                elif flag == \"asymmetric\":\n                    symmetric = False\n                else:\n                    flag_data = flag.split(\"=\")\n                    if len(flag_data) == 2:\n                        name, value = map(str.strip, flag_data)\n                    else:\n                        name, value = \"\", None\n                    if name == \"axis\" and value.isdigit():\n                        axis = int(value)\n                    else:\n                        raise ValueError(f\"invalid flag '{flag}'\")\n            if col_labels is not None:\n                col_labels = [x.strip()\n                              for x in fle.readline().strip().split(\"\\t\")]\n                if len(col_labels) != n:\n                    raise ValueError(\"mismatching number of column labels, \"\n                                     f\"{len(col_labels)} != {n}\")\n\n            def num_or_lab(n, labels):\n                return f\"'{labels[n]}'\" if labels else str(n + 1)\n\n            matrix = np.zeros((n, n))\n            for i, line in enumerate(fle):\n                if i >= n:\n                    raise ValueError(\"too many rows\")\n                line = line.strip().split(\"\\t\")\n                if row_labels is not None:\n                    row_labels.append(line.pop(0).strip())\n                if len(line) > n:\n                    raise ValueError(\n                        f\"too many columns in matrix row \"\n                        f\"{num_or_lab(i, row_labels)}\")\n                for j, e in enumerate(line[:i + 1 if symmetric else n]):\n                    try:\n                        matrix[i, j] = float(e)\n                    except ValueError as exc:\n                        raise ValueError(\n                            \"invalid element at \"\n                            f\"row {num_or_lab(i, row_labels)}, \"\n                            f\"column {num_or_lab(j, col_labels)}\") from exc\n                    if symmetric:\n                        matrix[j, i] = matrix[i, j]\n            return matrix, row_labels, col_labels, axis\n\n    def auto_symmetricized(self, copy=False):\n        def self_or_copy():\n            return self.copy() if copy else self\n\n        def get_labels(labels):\n            return np.array(labels) if isinstance(labels, list) \\\n                else labels.metas[:, 0] if self._trivial_labels(labels) \\\n                else object()\n\n        h, w = self.shape\n        m = max(w, h)\n        if (abs(h - w) > 1\n                or self.row_items and self.col_items\n                   and np.any(get_labels(self.row_items)\n                              != get_labels(self.col_items))\n                or self.row_items and len(self.row_items) != m\n                or self.col_items and len(self.col_items) != m):\n            return self_or_copy()\n\n        nans = np.isnan(self)\n        low_indices = np.tril_indices(h, -1)\n        low_empty = np.all(nans[low_indices])\n        high_indices = np.triu_indices(w, 1)\n        high_empty = np.all(nans[high_indices])\n        if low_empty is high_empty:  # both non-empty, or both empty (only diagonal)\n            return self_or_copy()\n\n        indices = low_indices if low_empty else high_indices\n        if w == h:\n            matrix = np.array(self)\n        else:\n            if low_empty:\n                row = np.vstack((self[:, -1, None], [[0]])).T\n                matrix = np.vstack((self, row))\n            else:\n                col = np.hstack((self[-1, None], [[0]])).T\n                matrix = np.hstack((self, col))\n            diag_indices = np.diag_indices(len(matrix))\n            matrix[diag_indices] = np.nan_to_num(matrix[diag_indices])\n        matrix[indices] = self.T[indices]\n        return type(self)(matrix,\n                          self.row_items or self.col_items,\n                          self.col_items or self.row_items)\n\n    @staticmethod\n    def _trivial_labels(items):\n        # prevent circular imports, pylint: disable=import-outside-toplevel\n        from Orange.data import Table, StringVariable\n\n        return items and \\\n               isinstance(items, Table) and \\\n               len(items.domain.metas) == 1 and \\\n               isinstance(items.domain.metas[0], StringVariable)\n\n    def is_symmetric(self):\n        # prevent circular imports, pylint: disable=import-outside-toplevel\n        from Orange.data import Table\n\n        if self.shape[0] != self.shape[1] or not np.allclose(self, self.T):\n            return False\n        if self.row_items is None or self.col_items is None:\n            return True\n        if isinstance(self.row_items, Table):\n            return (isinstance(self.col_items, Table)\n                and self.col_items.domain == self.row_items.domain\n                and np.array_equal(self.col_items.X, self.row_items.X)\n                and np.array_equal(self.col_items.Y, self.row_items.Y)\n                and np.array_equal(self.col_items.metas, self.row_items.metas))\n        else:\n            return (not isinstance(self.col_items, Table)\n                and np.array_equal(self.row_items, self.col_items))\n\n    def has_row_labels(self):\n        \"\"\"\n        Returns `True` if row labels can be automatically determined from data\n\n        For this, the `row_items` must be an instance of `Orange.data.Table`\n        whose domain contains a single meta attribute, which has to be a string.\n        The domain may contain other variables, but not meta attributes.\n        \"\"\"\n        return self._trivial_labels(self.row_items)\n\n    def has_col_labels(self):\n        \"\"\"\n        Returns `True` if column labels can be automatically determined from\n        data\n\n        For this, the `col_items` must be an instance of `Orange.data.Table`\n        whose domain contains a single meta attribute, which has to be a string.\n        The domain may contain other variables, but not meta attributes.\n        \"\"\"\n        return self._trivial_labels(self.col_items)\n\n    def get_labels(self, items):\n        if self._trivial_labels(items):\n            return items.get_column(items.domain.metas[0])\n        elif isinstance(items, (list, tuple)) \\\n                and all(isinstance(x, str) for x in items):\n            return items\n        else:\n            return None\n\n    def save(self, filename):\n        if os.path.splitext(filename)[1] == \".xlsx\":\n            _distmatrix_xlsx.write_matrix(self, filename)\n        else:\n            self._save_dst(filename)\n\n    def _save_dst(self, filename):\n        \"\"\"\n        Save the distance matrix to a file in the file format described at\n        :obj:`~Orange.misc.distmatrix.DistMatrix.from_file`.\n\n        Args:\n            filename: file name\n        \"\"\"\n        n = len(self)\n        data = \"{}\\taxis={}\".format(n, self.axis)\n        row_labels = col_labels = None\n        if self.has_col_labels():\n            data += \"\\tcol_labels\"\n            col_labels = self.col_items\n        if self.has_row_labels():\n            data += \"\\trow_labels\"\n            row_labels = self.row_items\n        symmetric = self.is_symmetric()\n        if not symmetric:\n            data += \"\\tasymmetric\"\n        with open(filename, \"wt\") as fle:\n            fle.write(data + \"\\n\")\n            if col_labels is not None:\n                fle.write(\"\\t\".join(str(e.metas[0]) for e in col_labels) + \"\\n\")\n            for i, row in enumerate(self):\n                if row_labels is not None:\n                    fle.write(str(row_labels[i].metas[0]) + \"\\t\")\n                if symmetric:\n                    fle.write(\"\\t\".join(map(str, row[:i + 1])) + \"\\n\")\n                else:\n                    fle.write(\"\\t\".join(map(str, row)) + \"\\n\")",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 9,
                                "end_lineno": 371,
                                "parent": null
                            }
                        },
                        {
                            "file_name": "Orange/misc/distmatrix.py",
                            "type": "class",
                            "name": "DistMatrix",
                            "content": "class DistMatrix(np.ndarray):\n    \"\"\"\n    Distance matrix. Extends ``numpy.ndarray``.\n\n    .. attribute:: row_items\n\n        Items corresponding to matrix rows.\n\n    .. attribute:: col_items\n\n        Items corresponding to matrix columns.\n\n    .. attribute:: axis\n\n        If axis=1 we calculate distances between rows,\n        if axis=0 we calculate distances between columns.\n    \"\"\"\n    def __new__(cls, data, row_items=None, col_items=None, axis=1):\n        \"\"\"Construct a new distance matrix containing the given data.\n\n        :param data: Distance matrix\n        :type data: numpy array\n        :param row_items: Items in matrix rows\n        :type row_items: `Orange.data.Table` or `Orange.data.Instance`\n        :param col_items: Items in matrix columns\n        :type col_items: `Orange.data.Table` or `Orange.data.Instance`\n        :param axis: The axis along which the distances are calculated\n        :type axis: int\n\n        \"\"\"\n        obj = np.asarray(data).view(cls)\n        obj.row_items = row_items\n        obj.col_items = col_items\n        obj.axis = axis\n        return obj\n\n    def __array_finalize__(self, obj):\n        \"\"\"See http://docs.scipy.org/doc/numpy/user/basics.subclassing.html\"\"\"\n        if obj is None:\n            return\n        self.row_items = getattr(obj, 'row_items', None)\n        self.col_items = getattr(obj, 'col_items', None)\n        self.axis = getattr(obj, 'axis', 1)\n\n    def __array_wrap__(self, out_arr, context=None):\n        if out_arr.ndim == 0:  # a single scalar\n            return out_arr[()]\n        return np.ndarray.__array_wrap__(self, out_arr, context)\n\n    # __reduce__() and __setstate__() ensure DistMatrix is picklable.\n    def __reduce__(self):\n        state = super().__reduce__()\n        newstate = state[2] + (self.row_items, self.col_items, self.axis)\n        return state[0], state[1], newstate\n\n    # noinspection PyMethodOverriding,PyArgumentList\n    def __setstate__(self, state):\n        self.row_items = state[-3]\n        self.col_items = state[-2]\n        self.axis = state[-1]\n        super().__setstate__(state[0:-3])\n\n    @property\n    @deprecated\n    def dim(self):\n        \"\"\"Returns the single dimension of the symmetric square matrix.\"\"\"\n        return self.shape[0]\n\n    # noinspection PyPep8Naming\n    @property\n    @deprecated\n    def X(self):\n        return self\n\n    @property\n    def flat(self):\n        return self[np.triu_indices(self.shape[0], 1)]\n\n    def submatrix(self, row_items, col_items=None):\n        \"\"\"\n        Return a submatrix\n\n        Args:\n            row_items: indices of rows\n            col_items: incides of columns\n        \"\"\"\n        if not col_items:\n            col_items = row_items\n        obj = self[np.ix_(row_items, col_items)]\n        if self.row_items is not None:\n            obj.row_items = self.row_items[row_items]\n        if self.col_items is not None:\n            if self.col_items is self.row_items and row_items is col_items:\n                obj.col_items = obj.row_items\n            else:\n                obj.col_items = self.col_items[col_items]\n        return obj\n\n    @classmethod\n    def from_file(cls, filename, sheet=None):\n        \"\"\"\n        Load distance matrix from a file\n\n        The file should be preferrably encoded in ascii/utf-8. White space at\n        the beginning and end of lines is ignored.\n\n        The first line of the file starts with the matrix dimension. It\n        can be followed by a list flags\n\n        - *axis=<number>*: the axis number\n        - *symmetric*: the matrix is symmetric; when reading the element (i, j)\n          it's value is also assigned to (j, i)\n        - *asymmetric*: the matrix is asymmetric\n        - *row_labels*: the file contains row labels\n        - *col_labels*: the file contains column labels\n\n        By default, matrices are symmetric, have axis 1 and no labels are given.\n        Flags *labeled* and *labelled* are obsolete aliases for *row_labels*.\n\n        If the file has column labels, they follow in the second line.\n        Row labels appear at the beginning of each row.\n        Labels are arbitrary strings that cannot contain newlines and\n        tabulators. Labels are stored as instances of `Table` with a single\n        meta attribute named \"label\".\n\n        The remaining lines contain tab-separated numbers, preceded with labels,\n        if present. Lines are padded with zeros if necessary. If the matrix is\n        symmetric, the file contains the lower triangle; any data above the\n        diagonal is ignored.\n\n        Args:\n            filename: file name\n        \"\"\"\n        _, ext = os.path.splitext(filename)\n        if ext == \".xlsx\":\n            matrix, row_labels, col_labels, axis \\\n                = _distmatrix_xlsx.read_matrix(filename, sheet)\n        else:\n            assert sheet is None\n            matrix, row_labels, col_labels, axis = cls._from_dst(filename)\n        return cls(matrix,\n                   cls._labels_to_tables(row_labels),\n                   cls._labels_to_tables(col_labels),\n                   axis)\n\n    @staticmethod\n    def _labels_to_tables(labels):\n        # prevent circular imports, pylint: disable=import-outside-toplevel\n        from Orange.data import Table, StringVariable, Domain\n\n        if labels is None or isinstance(labels, Table):\n            return labels\n        return Table.from_numpy(\n            Domain([], metas=[StringVariable(\"label\")]),\n            np.empty((len(labels), 0)), None, np.array(labels)[:, None])\n\n    @classmethod\n    def _from_dst(cls, filename):\n        # prevent circular imports, pylint: disable=import-outside-toplevel\n        from Orange.data.io import detect_encoding\n\n        with open(filename, encoding=detect_encoding(filename)) as fle:\n            line = fle.readline()\n            if not line:\n                raise ValueError(\"empty file\")\n            data = line.strip().split()\n            if not data[0].strip().isdigit():\n                raise ValueError(\"distance file must begin with dimension\")\n            n = int(data.pop(0))\n            symmetric = True\n            axis = 1\n            col_labels = row_labels = None\n            for flag in data:\n                if flag in (\"labelled\", \"labeled\", \"row_labels\"):\n                    row_labels = []\n                elif flag == \"col_labels\":\n                    col_labels = []\n                elif flag == \"symmetric\":\n                    symmetric = True\n                elif flag == \"asymmetric\":\n                    symmetric = False\n                else:\n                    flag_data = flag.split(\"=\")\n                    if len(flag_data) == 2:\n                        name, value = map(str.strip, flag_data)\n                    else:\n                        name, value = \"\", None\n                    if name == \"axis\" and value.isdigit():\n                        axis = int(value)\n                    else:\n                        raise ValueError(f\"invalid flag '{flag}'\")\n            if col_labels is not None:\n                col_labels = [x.strip()\n                              for x in fle.readline().strip().split(\"\\t\")]\n                if len(col_labels) != n:\n                    raise ValueError(\"mismatching number of column labels, \"\n                                     f\"{len(col_labels)} != {n}\")\n\n            def num_or_lab(n, labels):\n                return f\"'{labels[n]}'\" if labels else str(n + 1)\n\n            matrix = np.zeros((n, n))\n            for i, line in enumerate(fle):\n                if i >= n:\n                    raise ValueError(\"too many rows\")\n                line = line.strip().split(\"\\t\")\n                if row_labels is not None:\n                    row_labels.append(line.pop(0).strip())\n                if len(line) > n:\n                    raise ValueError(\n                        f\"too many columns in matrix row \"\n                        f\"{num_or_lab(i, row_labels)}\")\n                for j, e in enumerate(line[:i + 1 if symmetric else n]):\n                    try:\n                        matrix[i, j] = float(e)\n                    except ValueError as exc:\n                        raise ValueError(\n                            \"invalid element at \"\n                            f\"row {num_or_lab(i, row_labels)}, \"\n                            f\"column {num_or_lab(j, col_labels)}\") from exc\n                    if symmetric:\n                        matrix[j, i] = matrix[i, j]\n            return matrix, row_labels, col_labels, axis\n\n    def auto_symmetricized(self, copy=False):\n        def self_or_copy():\n            return self.copy() if copy else self\n\n        def get_labels(labels):\n            return np.array(labels) if isinstance(labels, list) \\\n                else labels.metas[:, 0] if self._trivial_labels(labels) \\\n                else object()\n\n        h, w = self.shape\n        m = max(w, h)\n        if (abs(h - w) > 1\n                or self.row_items and self.col_items\n                   and np.any(get_labels(self.row_items)\n                              != get_labels(self.col_items))\n                or self.row_items and len(self.row_items) != m\n                or self.col_items and len(self.col_items) != m):\n            return self_or_copy()\n\n        nans = np.isnan(self)\n        low_indices = np.tril_indices(h, -1)\n        low_empty = np.all(nans[low_indices])\n        high_indices = np.triu_indices(w, 1)\n        high_empty = np.all(nans[high_indices])\n        if low_empty is high_empty:  # both non-empty, or both empty (only diagonal)\n            return self_or_copy()\n\n        indices = low_indices if low_empty else high_indices\n        if w == h:\n            matrix = np.array(self)\n        else:\n            if low_empty:\n                row = np.vstack((self[:, -1, None], [[0]])).T\n                matrix = np.vstack((self, row))\n            else:\n                col = np.hstack((self[-1, None], [[0]])).T\n                matrix = np.hstack((self, col))\n            diag_indices = np.diag_indices(len(matrix))\n            matrix[diag_indices] = np.nan_to_num(matrix[diag_indices])\n        matrix[indices] = self.T[indices]\n        return type(self)(matrix,\n                          self.row_items or self.col_items,\n                          self.col_items or self.row_items)\n\n    @staticmethod\n    def _trivial_labels(items):\n        # prevent circular imports, pylint: disable=import-outside-toplevel\n        from Orange.data import Table, StringVariable\n\n        return items and \\\n               isinstance(items, Table) and \\\n               len(items.domain.metas) == 1 and \\\n               isinstance(items.domain.metas[0], StringVariable)\n\n    def is_symmetric(self):\n        # prevent circular imports, pylint: disable=import-outside-toplevel\n        from Orange.data import Table\n\n        if self.shape[0] != self.shape[1] or not np.allclose(self, self.T):\n            return False\n        if self.row_items is None or self.col_items is None:\n            return True\n        if isinstance(self.row_items, Table):\n            return (isinstance(self.col_items, Table)\n                and self.col_items.domain == self.row_items.domain\n                and np.array_equal(self.col_items.X, self.row_items.X)\n                and np.array_equal(self.col_items.Y, self.row_items.Y)\n                and np.array_equal(self.col_items.metas, self.row_items.metas))\n        else:\n            return (not isinstance(self.col_items, Table)\n                and np.array_equal(self.row_items, self.col_items))\n\n    def has_row_labels(self):\n        \"\"\"\n        Returns `True` if row labels can be automatically determined from data\n\n        For this, the `row_items` must be an instance of `Orange.data.Table`\n        whose domain contains a single meta attribute, which has to be a string.\n        The domain may contain other variables, but not meta attributes.\n        \"\"\"\n        return self._trivial_labels(self.row_items)\n\n    def has_col_labels(self):\n        \"\"\"\n        Returns `True` if column labels can be automatically determined from\n        data\n\n        For this, the `col_items` must be an instance of `Orange.data.Table`\n        whose domain contains a single meta attribute, which has to be a string.\n        The domain may contain other variables, but not meta attributes.\n        \"\"\"\n        return self._trivial_labels(self.col_items)\n\n    def get_labels(self, items):\n        if self._trivial_labels(items):\n            return items.get_column(items.domain.metas[0])\n        elif isinstance(items, (list, tuple)) \\\n                and all(isinstance(x, str) for x in items):\n            return items\n        else:\n            return None\n\n    def save(self, filename):\n        if os.path.splitext(filename)[1] == \".xlsx\":\n            _distmatrix_xlsx.write_matrix(self, filename)\n        else:\n            self._save_dst(filename)\n\n    def _save_dst(self, filename):\n        \"\"\"\n        Save the distance matrix to a file in the file format described at\n        :obj:`~Orange.misc.distmatrix.DistMatrix.from_file`.\n\n        Args:\n            filename: file name\n        \"\"\"\n        n = len(self)\n        data = \"{}\\taxis={}\".format(n, self.axis)\n        row_labels = col_labels = None\n        if self.has_col_labels():\n            data += \"\\tcol_labels\"\n            col_labels = self.col_items\n        if self.has_row_labels():\n            data += \"\\trow_labels\"\n            row_labels = self.row_items\n        symmetric = self.is_symmetric()\n        if not symmetric:\n            data += \"\\tasymmetric\"\n        with open(filename, \"wt\") as fle:\n            fle.write(data + \"\\n\")\n            if col_labels is not None:\n                fle.write(\"\\t\".join(str(e.metas[0]) for e in col_labels) + \"\\n\")\n            for i, row in enumerate(self):\n                if row_labels is not None:\n                    fle.write(str(row_labels[i].metas[0]) + \"\\t\")\n                if symmetric:\n                    fle.write(\"\\t\".join(map(str, row[:i + 1])) + \"\\n\")\n                else:\n                    fle.write(\"\\t\".join(map(str, row)) + \"\\n\")",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 9,
                            "end_lineno": 371,
                            "parent": null
                        }
                    ],
                    "added_entities": [],
                    "deleted_entities": []
                },
                {
                    "descriptor": {
                        "old_range": {
                            "start": 324,
                            "length": 13
                        },
                        "new_range": {
                            "start": 328,
                            "length": 20
                        },
                        "section": "class DistMatrix(np.ndarray):"
                    },
                    "line_group": {
                        "all_lines": [
                            {
                                "content": "        return self._trivial_labels(self.col_items)",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "    def get_labels(self, items):",
                                "type": "context"
                            },
                            {
                                "content": "        if self._trivial_labels(items):",
                                "type": "deleted"
                            },
                            {
                                "content": "            return items.get_column(items.domain.metas[0])",
                                "type": "deleted"
                            },
                            {
                                "content": "        elif isinstance(items, (list, tuple)) \\",
                                "type": "deleted"
                            },
                            {
                                "content": "        # prevent circular imports, pylint: disable=import-outside-toplevel",
                                "type": "added"
                            },
                            {
                                "content": "        from Orange.data import StringVariable",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "        if not self._trivial_labels(items):",
                                "type": "added"
                            },
                            {
                                "content": "            return None",
                                "type": "added"
                            },
                            {
                                "content": "        if isinstance(items, (list, tuple)) \\",
                                "type": "added"
                            },
                            {
                                "content": "                and all(isinstance(x, str) for x in items):",
                                "type": "context"
                            },
                            {
                                "content": "            return items",
                                "type": "context"
                            },
                            {
                                "content": "        if self.axis == 0:",
                                "type": "added"
                            },
                            {
                                "content": "            return [attr.name for attr in items.domain.attributes]",
                                "type": "added"
                            },
                            {
                                "content": "        else:",
                                "type": "context"
                            },
                            {
                                "content": "            return None",
                                "type": "deleted"
                            },
                            {
                                "content": "            string_var = next(var for var in items.domain.metas",
                                "type": "added"
                            },
                            {
                                "content": "                              if isinstance(var, StringVariable))",
                                "type": "added"
                            },
                            {
                                "content": "            return items.get_column(string_var)",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "    def save(self, filename):",
                                "type": "context"
                            },
                            {
                                "content": "        if os.path.splitext(filename)[1] == \".xlsx\":",
                                "type": "context"
                            }
                        ]
                    },
                    "modified_entities": [
                        {
                            "file_name": "Orange/misc/distmatrix.py",
                            "type": "method",
                            "name": "DistMatrix.get_labels",
                            "content": "class DistMatrix(np.ndarray):\n    \"\"\"\n    Distance matrix. Extends ``numpy.ndarray``.\n\n    .. attribute:: row_items\n\n        Items corresponding to matrix rows.\n\n    .. attribute:: col_items\n\n        Items corresponding to matrix columns.\n\n    .. attribute:: axis\n\n        If axis=1 we calculate distances between rows,\n        if axis=0 we calculate distances between columns.\n    \"\"\"\n    def __new__(cls, data, row_items=None, col_items=None, axis=1):\n        \"\"\"Construct a new distance matrix containing the given data.\n\n        :param data: Distance matrix\n        :type data: numpy array\n        :param row_items: Items in matrix rows\n        :type row_items: `Orange.data.Table` or `Orange.data.Instance`\n        :param col_items: Items in matrix columns\n        :type col_items: `Orange.data.Table` or `Orange.data.Instance`\n        :param axis: The axis along which the distances are calculated\n        :type axis: int\n\n        \"\"\"\n        obj = np.asarray(data).view(cls)\n        obj.row_items = row_items\n        obj.col_items = col_items\n        obj.axis = axis\n        return obj\n\n    def __array_finalize__(self, obj):\n        \"\"\"See http://docs.scipy.org/doc/numpy/user/basics.subclassing.html\"\"\"\n        if obj is None:\n            return\n        self.row_items = getattr(obj, 'row_items', None)\n        self.col_items = getattr(obj, 'col_items', None)\n        self.axis = getattr(obj, 'axis', 1)\n\n    def __array_wrap__(self, out_arr, context=None):\n        if out_arr.ndim == 0:  # a single scalar\n            return out_arr[()]\n        return np.ndarray.__array_wrap__(self, out_arr, context)\n\n    # __reduce__() and __setstate__() ensure DistMatrix is picklable.\n    def __reduce__(self):\n        state = super().__reduce__()\n        newstate = state[2] + (self.row_items, self.col_items, self.axis)\n        return state[0], state[1], newstate\n\n    # noinspection PyMethodOverriding,PyArgumentList\n    def __setstate__(self, state):\n        self.row_items = state[-3]\n        self.col_items = state[-2]\n        self.axis = state[-1]\n        super().__setstate__(state[0:-3])\n\n    @property\n    @deprecated\n    def dim(self):\n        \"\"\"Returns the single dimension of the symmetric square matrix.\"\"\"\n        return self.shape[0]\n\n    # noinspection PyPep8Naming\n    @property\n    @deprecated\n    def X(self):\n        return self\n\n    @property\n    def flat(self):\n        return self[np.triu_indices(self.shape[0], 1)]\n\n    def submatrix(self, row_items, col_items=None):\n        \"\"\"\n        Return a submatrix\n\n        Args:\n            row_items: indices of rows\n            col_items: incides of columns\n        \"\"\"\n        if not col_items:\n            col_items = row_items\n        obj = self[np.ix_(row_items, col_items)]\n        if self.row_items is not None:\n            obj.row_items = self.row_items[row_items]\n        if self.col_items is not None:\n            if self.col_items is self.row_items and row_items is col_items:\n                obj.col_items = obj.row_items\n            else:\n                obj.col_items = self.col_items[col_items]\n        return obj\n\n    @classmethod\n    def from_file(cls, filename, sheet=None):\n        \"\"\"\n        Load distance matrix from a file\n\n        The file should be preferrably encoded in ascii/utf-8. White space at\n        the beginning and end of lines is ignored.\n\n        The first line of the file starts with the matrix dimension. It\n        can be followed by a list flags\n\n        - *axis=<number>*: the axis number\n        - *symmetric*: the matrix is symmetric; when reading the element (i, j)\n          it's value is also assigned to (j, i)\n        - *asymmetric*: the matrix is asymmetric\n        - *row_labels*: the file contains row labels\n        - *col_labels*: the file contains column labels\n\n        By default, matrices are symmetric, have axis 1 and no labels are given.\n        Flags *labeled* and *labelled* are obsolete aliases for *row_labels*.\n\n        If the file has column labels, they follow in the second line.\n        Row labels appear at the beginning of each row.\n        Labels are arbitrary strings that cannot contain newlines and\n        tabulators. Labels are stored as instances of `Table` with a single\n        meta attribute named \"label\".\n\n        The remaining lines contain tab-separated numbers, preceded with labels,\n        if present. Lines are padded with zeros if necessary. If the matrix is\n        symmetric, the file contains the lower triangle; any data above the\n        diagonal is ignored.\n\n        Args:\n            filename: file name\n        \"\"\"\n        _, ext = os.path.splitext(filename)\n        if ext == \".xlsx\":\n            matrix, row_labels, col_labels, axis \\\n                = _distmatrix_xlsx.read_matrix(filename, sheet)\n        else:\n            assert sheet is None\n            matrix, row_labels, col_labels, axis = cls._from_dst(filename)\n        return cls(matrix,\n                   cls._labels_to_tables(row_labels),\n                   cls._labels_to_tables(col_labels),\n                   axis)\n\n    @staticmethod\n    def _labels_to_tables(labels):\n        # prevent circular imports, pylint: disable=import-outside-toplevel\n        from Orange.data import Table, StringVariable, Domain\n\n        if labels is None or isinstance(labels, Table):\n            return labels\n        return Table.from_numpy(\n            Domain([], metas=[StringVariable(\"label\")]),\n            np.empty((len(labels), 0)), None, np.array(labels)[:, None])\n\n    @classmethod\n    def _from_dst(cls, filename):\n        # prevent circular imports, pylint: disable=import-outside-toplevel\n        from Orange.data.io import detect_encoding\n\n        with open(filename, encoding=detect_encoding(filename)) as fle:\n            line = fle.readline()\n            if not line:\n                raise ValueError(\"empty file\")\n            data = line.strip().split()\n            if not data[0].strip().isdigit():\n                raise ValueError(\"distance file must begin with dimension\")\n            n = int(data.pop(0))\n            symmetric = True\n            axis = 1\n            col_labels = row_labels = None\n            for flag in data:\n                if flag in (\"labelled\", \"labeled\", \"row_labels\"):\n                    row_labels = []\n                elif flag == \"col_labels\":\n                    col_labels = []\n                elif flag == \"symmetric\":\n                    symmetric = True\n                elif flag == \"asymmetric\":\n                    symmetric = False\n                else:\n                    flag_data = flag.split(\"=\")\n                    if len(flag_data) == 2:\n                        name, value = map(str.strip, flag_data)\n                    else:\n                        name, value = \"\", None\n                    if name == \"axis\" and value.isdigit():\n                        axis = int(value)\n                    else:\n                        raise ValueError(f\"invalid flag '{flag}'\")\n            if col_labels is not None:\n                col_labels = [x.strip()\n                              for x in fle.readline().strip().split(\"\\t\")]\n                if len(col_labels) != n:\n                    raise ValueError(\"mismatching number of column labels, \"\n                                     f\"{len(col_labels)} != {n}\")\n\n            def num_or_lab(n, labels):\n                return f\"'{labels[n]}'\" if labels else str(n + 1)\n\n            matrix = np.zeros((n, n))\n            for i, line in enumerate(fle):\n                if i >= n:\n                    raise ValueError(\"too many rows\")\n                line = line.strip().split(\"\\t\")\n                if row_labels is not None:\n                    row_labels.append(line.pop(0).strip())\n                if len(line) > n:\n                    raise ValueError(\n                        f\"too many columns in matrix row \"\n                        f\"{num_or_lab(i, row_labels)}\")\n                for j, e in enumerate(line[:i + 1 if symmetric else n]):\n                    try:\n                        matrix[i, j] = float(e)\n                    except ValueError as exc:\n                        raise ValueError(\n                            \"invalid element at \"\n                            f\"row {num_or_lab(i, row_labels)}, \"\n                            f\"column {num_or_lab(j, col_labels)}\") from exc\n                    if symmetric:\n                        matrix[j, i] = matrix[i, j]\n            return matrix, row_labels, col_labels, axis\n\n    def auto_symmetricized(self, copy=False):\n        def self_or_copy():\n            return self.copy() if copy else self\n\n        def get_labels(labels):\n            return np.array(labels) if isinstance(labels, list) \\\n                else labels.metas[:, 0] if self._trivial_labels(labels) \\\n                else object()\n\n        h, w = self.shape\n        m = max(w, h)\n        if (abs(h - w) > 1\n                or self.row_items and self.col_items\n                   and np.any(get_labels(self.row_items)\n                              != get_labels(self.col_items))\n                or self.row_items and len(self.row_items) != m\n                or self.col_items and len(self.col_items) != m):\n            return self_or_copy()\n\n        nans = np.isnan(self)\n        low_indices = np.tril_indices(h, -1)\n        low_empty = np.all(nans[low_indices])\n        high_indices = np.triu_indices(w, 1)\n        high_empty = np.all(nans[high_indices])\n        if low_empty is high_empty:  # both non-empty, or both empty (only diagonal)\n            return self_or_copy()\n\n        indices = low_indices if low_empty else high_indices\n        if w == h:\n            matrix = np.array(self)\n        else:\n            if low_empty:\n                row = np.vstack((self[:, -1, None], [[0]])).T\n                matrix = np.vstack((self, row))\n            else:\n                col = np.hstack((self[-1, None], [[0]])).T\n                matrix = np.hstack((self, col))\n            diag_indices = np.diag_indices(len(matrix))\n            matrix[diag_indices] = np.nan_to_num(matrix[diag_indices])\n        matrix[indices] = self.T[indices]\n        return type(self)(matrix,\n                          self.row_items or self.col_items,\n                          self.col_items or self.row_items)\n\n    @staticmethod\n    def _trivial_labels(items):\n        # prevent circular imports, pylint: disable=import-outside-toplevel\n        from Orange.data import Table, StringVariable\n\n        return items and \\\n               isinstance(items, Table) and \\\n               len(items.domain.metas) == 1 and \\\n               isinstance(items.domain.metas[0], StringVariable)\n\n    def is_symmetric(self):\n        # prevent circular imports, pylint: disable=import-outside-toplevel\n        from Orange.data import Table\n\n        if self.shape[0] != self.shape[1] or not np.allclose(self, self.T):\n            return False\n        if self.row_items is None or self.col_items is None:\n            return True\n        if isinstance(self.row_items, Table):\n            return (isinstance(self.col_items, Table)\n                and self.col_items.domain == self.row_items.domain\n                and np.array_equal(self.col_items.X, self.row_items.X)\n                and np.array_equal(self.col_items.Y, self.row_items.Y)\n                and np.array_equal(self.col_items.metas, self.row_items.metas))\n        else:\n            return (not isinstance(self.col_items, Table)\n                and np.array_equal(self.row_items, self.col_items))\n\n    def has_row_labels(self):\n        \"\"\"\n        Returns `True` if row labels can be automatically determined from data\n\n        For this, the `row_items` must be an instance of `Orange.data.Table`\n        whose domain contains a single meta attribute, which has to be a string.\n        The domain may contain other variables, but not meta attributes.\n        \"\"\"\n        return self._trivial_labels(self.row_items)\n\n    def has_col_labels(self):\n        \"\"\"\n        Returns `True` if column labels can be automatically determined from\n        data\n\n        For this, the `col_items` must be an instance of `Orange.data.Table`\n        whose domain contains a single meta attribute, which has to be a string.\n        The domain may contain other variables, but not meta attributes.\n        \"\"\"\n        return self._trivial_labels(self.col_items)\n\n    def get_labels(self, items):\n        if self._trivial_labels(items):\n            return items.get_column(items.domain.metas[0])\n        elif isinstance(items, (list, tuple)) \\\n                and all(isinstance(x, str) for x in items):\n            return items\n        else:\n            return None\n\n    def save(self, filename):\n        if os.path.splitext(filename)[1] == \".xlsx\":\n            _distmatrix_xlsx.write_matrix(self, filename)\n        else:\n            self._save_dst(filename)\n\n    def _save_dst(self, filename):\n        \"\"\"\n        Save the distance matrix to a file in the file format described at\n        :obj:`~Orange.misc.distmatrix.DistMatrix.from_file`.\n\n        Args:\n            filename: file name\n        \"\"\"\n        n = len(self)\n        data = \"{}\\taxis={}\".format(n, self.axis)\n        row_labels = col_labels = None\n        if self.has_col_labels():\n            data += \"\\tcol_labels\"\n            col_labels = self.col_items\n        if self.has_row_labels():\n            data += \"\\trow_labels\"\n            row_labels = self.row_items\n        symmetric = self.is_symmetric()\n        if not symmetric:\n            data += \"\\tasymmetric\"\n        with open(filename, \"wt\") as fle:\n            fle.write(data + \"\\n\")\n            if col_labels is not None:\n                fle.write(\"\\t\".join(str(e.metas[0]) for e in col_labels) + \"\\n\")\n            for i, row in enumerate(self):\n                if row_labels is not None:\n                    fle.write(str(row_labels[i].metas[0]) + \"\\t\")\n                if symmetric:\n                    fle.write(\"\\t\".join(map(str, row[:i + 1])) + \"\\n\")\n                else:\n                    fle.write(\"\\t\".join(map(str, row)) + \"\\n\")",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 326,
                            "end_lineno": 333,
                            "parent": {
                                "file_name": "Orange/misc/distmatrix.py",
                                "type": "class",
                                "name": "DistMatrix",
                                "content": "class DistMatrix(np.ndarray):\n    \"\"\"\n    Distance matrix. Extends ``numpy.ndarray``.\n\n    .. attribute:: row_items\n\n        Items corresponding to matrix rows.\n\n    .. attribute:: col_items\n\n        Items corresponding to matrix columns.\n\n    .. attribute:: axis\n\n        If axis=1 we calculate distances between rows,\n        if axis=0 we calculate distances between columns.\n    \"\"\"\n    def __new__(cls, data, row_items=None, col_items=None, axis=1):\n        \"\"\"Construct a new distance matrix containing the given data.\n\n        :param data: Distance matrix\n        :type data: numpy array\n        :param row_items: Items in matrix rows\n        :type row_items: `Orange.data.Table` or `Orange.data.Instance`\n        :param col_items: Items in matrix columns\n        :type col_items: `Orange.data.Table` or `Orange.data.Instance`\n        :param axis: The axis along which the distances are calculated\n        :type axis: int\n\n        \"\"\"\n        obj = np.asarray(data).view(cls)\n        obj.row_items = row_items\n        obj.col_items = col_items\n        obj.axis = axis\n        return obj\n\n    def __array_finalize__(self, obj):\n        \"\"\"See http://docs.scipy.org/doc/numpy/user/basics.subclassing.html\"\"\"\n        if obj is None:\n            return\n        self.row_items = getattr(obj, 'row_items', None)\n        self.col_items = getattr(obj, 'col_items', None)\n        self.axis = getattr(obj, 'axis', 1)\n\n    def __array_wrap__(self, out_arr, context=None):\n        if out_arr.ndim == 0:  # a single scalar\n            return out_arr[()]\n        return np.ndarray.__array_wrap__(self, out_arr, context)\n\n    # __reduce__() and __setstate__() ensure DistMatrix is picklable.\n    def __reduce__(self):\n        state = super().__reduce__()\n        newstate = state[2] + (self.row_items, self.col_items, self.axis)\n        return state[0], state[1], newstate\n\n    # noinspection PyMethodOverriding,PyArgumentList\n    def __setstate__(self, state):\n        self.row_items = state[-3]\n        self.col_items = state[-2]\n        self.axis = state[-1]\n        super().__setstate__(state[0:-3])\n\n    @property\n    @deprecated\n    def dim(self):\n        \"\"\"Returns the single dimension of the symmetric square matrix.\"\"\"\n        return self.shape[0]\n\n    # noinspection PyPep8Naming\n    @property\n    @deprecated\n    def X(self):\n        return self\n\n    @property\n    def flat(self):\n        return self[np.triu_indices(self.shape[0], 1)]\n\n    def submatrix(self, row_items, col_items=None):\n        \"\"\"\n        Return a submatrix\n\n        Args:\n            row_items: indices of rows\n            col_items: incides of columns\n        \"\"\"\n        if not col_items:\n            col_items = row_items\n        obj = self[np.ix_(row_items, col_items)]\n        if self.row_items is not None:\n            obj.row_items = self.row_items[row_items]\n        if self.col_items is not None:\n            if self.col_items is self.row_items and row_items is col_items:\n                obj.col_items = obj.row_items\n            else:\n                obj.col_items = self.col_items[col_items]\n        return obj\n\n    @classmethod\n    def from_file(cls, filename, sheet=None):\n        \"\"\"\n        Load distance matrix from a file\n\n        The file should be preferrably encoded in ascii/utf-8. White space at\n        the beginning and end of lines is ignored.\n\n        The first line of the file starts with the matrix dimension. It\n        can be followed by a list flags\n\n        - *axis=<number>*: the axis number\n        - *symmetric*: the matrix is symmetric; when reading the element (i, j)\n          it's value is also assigned to (j, i)\n        - *asymmetric*: the matrix is asymmetric\n        - *row_labels*: the file contains row labels\n        - *col_labels*: the file contains column labels\n\n        By default, matrices are symmetric, have axis 1 and no labels are given.\n        Flags *labeled* and *labelled* are obsolete aliases for *row_labels*.\n\n        If the file has column labels, they follow in the second line.\n        Row labels appear at the beginning of each row.\n        Labels are arbitrary strings that cannot contain newlines and\n        tabulators. Labels are stored as instances of `Table` with a single\n        meta attribute named \"label\".\n\n        The remaining lines contain tab-separated numbers, preceded with labels,\n        if present. Lines are padded with zeros if necessary. If the matrix is\n        symmetric, the file contains the lower triangle; any data above the\n        diagonal is ignored.\n\n        Args:\n            filename: file name\n        \"\"\"\n        _, ext = os.path.splitext(filename)\n        if ext == \".xlsx\":\n            matrix, row_labels, col_labels, axis \\\n                = _distmatrix_xlsx.read_matrix(filename, sheet)\n        else:\n            assert sheet is None\n            matrix, row_labels, col_labels, axis = cls._from_dst(filename)\n        return cls(matrix,\n                   cls._labels_to_tables(row_labels),\n                   cls._labels_to_tables(col_labels),\n                   axis)\n\n    @staticmethod\n    def _labels_to_tables(labels):\n        # prevent circular imports, pylint: disable=import-outside-toplevel\n        from Orange.data import Table, StringVariable, Domain\n\n        if labels is None or isinstance(labels, Table):\n            return labels\n        return Table.from_numpy(\n            Domain([], metas=[StringVariable(\"label\")]),\n            np.empty((len(labels), 0)), None, np.array(labels)[:, None])\n\n    @classmethod\n    def _from_dst(cls, filename):\n        # prevent circular imports, pylint: disable=import-outside-toplevel\n        from Orange.data.io import detect_encoding\n\n        with open(filename, encoding=detect_encoding(filename)) as fle:\n            line = fle.readline()\n            if not line:\n                raise ValueError(\"empty file\")\n            data = line.strip().split()\n            if not data[0].strip().isdigit():\n                raise ValueError(\"distance file must begin with dimension\")\n            n = int(data.pop(0))\n            symmetric = True\n            axis = 1\n            col_labels = row_labels = None\n            for flag in data:\n                if flag in (\"labelled\", \"labeled\", \"row_labels\"):\n                    row_labels = []\n                elif flag == \"col_labels\":\n                    col_labels = []\n                elif flag == \"symmetric\":\n                    symmetric = True\n                elif flag == \"asymmetric\":\n                    symmetric = False\n                else:\n                    flag_data = flag.split(\"=\")\n                    if len(flag_data) == 2:\n                        name, value = map(str.strip, flag_data)\n                    else:\n                        name, value = \"\", None\n                    if name == \"axis\" and value.isdigit():\n                        axis = int(value)\n                    else:\n                        raise ValueError(f\"invalid flag '{flag}'\")\n            if col_labels is not None:\n                col_labels = [x.strip()\n                              for x in fle.readline().strip().split(\"\\t\")]\n                if len(col_labels) != n:\n                    raise ValueError(\"mismatching number of column labels, \"\n                                     f\"{len(col_labels)} != {n}\")\n\n            def num_or_lab(n, labels):\n                return f\"'{labels[n]}'\" if labels else str(n + 1)\n\n            matrix = np.zeros((n, n))\n            for i, line in enumerate(fle):\n                if i >= n:\n                    raise ValueError(\"too many rows\")\n                line = line.strip().split(\"\\t\")\n                if row_labels is not None:\n                    row_labels.append(line.pop(0).strip())\n                if len(line) > n:\n                    raise ValueError(\n                        f\"too many columns in matrix row \"\n                        f\"{num_or_lab(i, row_labels)}\")\n                for j, e in enumerate(line[:i + 1 if symmetric else n]):\n                    try:\n                        matrix[i, j] = float(e)\n                    except ValueError as exc:\n                        raise ValueError(\n                            \"invalid element at \"\n                            f\"row {num_or_lab(i, row_labels)}, \"\n                            f\"column {num_or_lab(j, col_labels)}\") from exc\n                    if symmetric:\n                        matrix[j, i] = matrix[i, j]\n            return matrix, row_labels, col_labels, axis\n\n    def auto_symmetricized(self, copy=False):\n        def self_or_copy():\n            return self.copy() if copy else self\n\n        def get_labels(labels):\n            return np.array(labels) if isinstance(labels, list) \\\n                else labels.metas[:, 0] if self._trivial_labels(labels) \\\n                else object()\n\n        h, w = self.shape\n        m = max(w, h)\n        if (abs(h - w) > 1\n                or self.row_items and self.col_items\n                   and np.any(get_labels(self.row_items)\n                              != get_labels(self.col_items))\n                or self.row_items and len(self.row_items) != m\n                or self.col_items and len(self.col_items) != m):\n            return self_or_copy()\n\n        nans = np.isnan(self)\n        low_indices = np.tril_indices(h, -1)\n        low_empty = np.all(nans[low_indices])\n        high_indices = np.triu_indices(w, 1)\n        high_empty = np.all(nans[high_indices])\n        if low_empty is high_empty:  # both non-empty, or both empty (only diagonal)\n            return self_or_copy()\n\n        indices = low_indices if low_empty else high_indices\n        if w == h:\n            matrix = np.array(self)\n        else:\n            if low_empty:\n                row = np.vstack((self[:, -1, None], [[0]])).T\n                matrix = np.vstack((self, row))\n            else:\n                col = np.hstack((self[-1, None], [[0]])).T\n                matrix = np.hstack((self, col))\n            diag_indices = np.diag_indices(len(matrix))\n            matrix[diag_indices] = np.nan_to_num(matrix[diag_indices])\n        matrix[indices] = self.T[indices]\n        return type(self)(matrix,\n                          self.row_items or self.col_items,\n                          self.col_items or self.row_items)\n\n    @staticmethod\n    def _trivial_labels(items):\n        # prevent circular imports, pylint: disable=import-outside-toplevel\n        from Orange.data import Table, StringVariable\n\n        return items and \\\n               isinstance(items, Table) and \\\n               len(items.domain.metas) == 1 and \\\n               isinstance(items.domain.metas[0], StringVariable)\n\n    def is_symmetric(self):\n        # prevent circular imports, pylint: disable=import-outside-toplevel\n        from Orange.data import Table\n\n        if self.shape[0] != self.shape[1] or not np.allclose(self, self.T):\n            return False\n        if self.row_items is None or self.col_items is None:\n            return True\n        if isinstance(self.row_items, Table):\n            return (isinstance(self.col_items, Table)\n                and self.col_items.domain == self.row_items.domain\n                and np.array_equal(self.col_items.X, self.row_items.X)\n                and np.array_equal(self.col_items.Y, self.row_items.Y)\n                and np.array_equal(self.col_items.metas, self.row_items.metas))\n        else:\n            return (not isinstance(self.col_items, Table)\n                and np.array_equal(self.row_items, self.col_items))\n\n    def has_row_labels(self):\n        \"\"\"\n        Returns `True` if row labels can be automatically determined from data\n\n        For this, the `row_items` must be an instance of `Orange.data.Table`\n        whose domain contains a single meta attribute, which has to be a string.\n        The domain may contain other variables, but not meta attributes.\n        \"\"\"\n        return self._trivial_labels(self.row_items)\n\n    def has_col_labels(self):\n        \"\"\"\n        Returns `True` if column labels can be automatically determined from\n        data\n\n        For this, the `col_items` must be an instance of `Orange.data.Table`\n        whose domain contains a single meta attribute, which has to be a string.\n        The domain may contain other variables, but not meta attributes.\n        \"\"\"\n        return self._trivial_labels(self.col_items)\n\n    def get_labels(self, items):\n        if self._trivial_labels(items):\n            return items.get_column(items.domain.metas[0])\n        elif isinstance(items, (list, tuple)) \\\n                and all(isinstance(x, str) for x in items):\n            return items\n        else:\n            return None\n\n    def save(self, filename):\n        if os.path.splitext(filename)[1] == \".xlsx\":\n            _distmatrix_xlsx.write_matrix(self, filename)\n        else:\n            self._save_dst(filename)\n\n    def _save_dst(self, filename):\n        \"\"\"\n        Save the distance matrix to a file in the file format described at\n        :obj:`~Orange.misc.distmatrix.DistMatrix.from_file`.\n\n        Args:\n            filename: file name\n        \"\"\"\n        n = len(self)\n        data = \"{}\\taxis={}\".format(n, self.axis)\n        row_labels = col_labels = None\n        if self.has_col_labels():\n            data += \"\\tcol_labels\"\n            col_labels = self.col_items\n        if self.has_row_labels():\n            data += \"\\trow_labels\"\n            row_labels = self.row_items\n        symmetric = self.is_symmetric()\n        if not symmetric:\n            data += \"\\tasymmetric\"\n        with open(filename, \"wt\") as fle:\n            fle.write(data + \"\\n\")\n            if col_labels is not None:\n                fle.write(\"\\t\".join(str(e.metas[0]) for e in col_labels) + \"\\n\")\n            for i, row in enumerate(self):\n                if row_labels is not None:\n                    fle.write(str(row_labels[i].metas[0]) + \"\\t\")\n                if symmetric:\n                    fle.write(\"\\t\".join(map(str, row[:i + 1])) + \"\\n\")\n                else:\n                    fle.write(\"\\t\".join(map(str, row)) + \"\\n\")",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 9,
                                "end_lineno": 371,
                                "parent": null
                            }
                        },
                        {
                            "file_name": "Orange/misc/distmatrix.py",
                            "type": "class",
                            "name": "DistMatrix",
                            "content": "class DistMatrix(np.ndarray):\n    \"\"\"\n    Distance matrix. Extends ``numpy.ndarray``.\n\n    .. attribute:: row_items\n\n        Items corresponding to matrix rows.\n\n    .. attribute:: col_items\n\n        Items corresponding to matrix columns.\n\n    .. attribute:: axis\n\n        If axis=1 we calculate distances between rows,\n        if axis=0 we calculate distances between columns.\n    \"\"\"\n    def __new__(cls, data, row_items=None, col_items=None, axis=1):\n        \"\"\"Construct a new distance matrix containing the given data.\n\n        :param data: Distance matrix\n        :type data: numpy array\n        :param row_items: Items in matrix rows\n        :type row_items: `Orange.data.Table` or `Orange.data.Instance`\n        :param col_items: Items in matrix columns\n        :type col_items: `Orange.data.Table` or `Orange.data.Instance`\n        :param axis: The axis along which the distances are calculated\n        :type axis: int\n\n        \"\"\"\n        obj = np.asarray(data).view(cls)\n        obj.row_items = row_items\n        obj.col_items = col_items\n        obj.axis = axis\n        return obj\n\n    def __array_finalize__(self, obj):\n        \"\"\"See http://docs.scipy.org/doc/numpy/user/basics.subclassing.html\"\"\"\n        if obj is None:\n            return\n        self.row_items = getattr(obj, 'row_items', None)\n        self.col_items = getattr(obj, 'col_items', None)\n        self.axis = getattr(obj, 'axis', 1)\n\n    def __array_wrap__(self, out_arr, context=None):\n        if out_arr.ndim == 0:  # a single scalar\n            return out_arr[()]\n        return np.ndarray.__array_wrap__(self, out_arr, context)\n\n    # __reduce__() and __setstate__() ensure DistMatrix is picklable.\n    def __reduce__(self):\n        state = super().__reduce__()\n        newstate = state[2] + (self.row_items, self.col_items, self.axis)\n        return state[0], state[1], newstate\n\n    # noinspection PyMethodOverriding,PyArgumentList\n    def __setstate__(self, state):\n        self.row_items = state[-3]\n        self.col_items = state[-2]\n        self.axis = state[-1]\n        super().__setstate__(state[0:-3])\n\n    @property\n    @deprecated\n    def dim(self):\n        \"\"\"Returns the single dimension of the symmetric square matrix.\"\"\"\n        return self.shape[0]\n\n    # noinspection PyPep8Naming\n    @property\n    @deprecated\n    def X(self):\n        return self\n\n    @property\n    def flat(self):\n        return self[np.triu_indices(self.shape[0], 1)]\n\n    def submatrix(self, row_items, col_items=None):\n        \"\"\"\n        Return a submatrix\n\n        Args:\n            row_items: indices of rows\n            col_items: incides of columns\n        \"\"\"\n        if not col_items:\n            col_items = row_items\n        obj = self[np.ix_(row_items, col_items)]\n        if self.row_items is not None:\n            obj.row_items = self.row_items[row_items]\n        if self.col_items is not None:\n            if self.col_items is self.row_items and row_items is col_items:\n                obj.col_items = obj.row_items\n            else:\n                obj.col_items = self.col_items[col_items]\n        return obj\n\n    @classmethod\n    def from_file(cls, filename, sheet=None):\n        \"\"\"\n        Load distance matrix from a file\n\n        The file should be preferrably encoded in ascii/utf-8. White space at\n        the beginning and end of lines is ignored.\n\n        The first line of the file starts with the matrix dimension. It\n        can be followed by a list flags\n\n        - *axis=<number>*: the axis number\n        - *symmetric*: the matrix is symmetric; when reading the element (i, j)\n          it's value is also assigned to (j, i)\n        - *asymmetric*: the matrix is asymmetric\n        - *row_labels*: the file contains row labels\n        - *col_labels*: the file contains column labels\n\n        By default, matrices are symmetric, have axis 1 and no labels are given.\n        Flags *labeled* and *labelled* are obsolete aliases for *row_labels*.\n\n        If the file has column labels, they follow in the second line.\n        Row labels appear at the beginning of each row.\n        Labels are arbitrary strings that cannot contain newlines and\n        tabulators. Labels are stored as instances of `Table` with a single\n        meta attribute named \"label\".\n\n        The remaining lines contain tab-separated numbers, preceded with labels,\n        if present. Lines are padded with zeros if necessary. If the matrix is\n        symmetric, the file contains the lower triangle; any data above the\n        diagonal is ignored.\n\n        Args:\n            filename: file name\n        \"\"\"\n        _, ext = os.path.splitext(filename)\n        if ext == \".xlsx\":\n            matrix, row_labels, col_labels, axis \\\n                = _distmatrix_xlsx.read_matrix(filename, sheet)\n        else:\n            assert sheet is None\n            matrix, row_labels, col_labels, axis = cls._from_dst(filename)\n        return cls(matrix,\n                   cls._labels_to_tables(row_labels),\n                   cls._labels_to_tables(col_labels),\n                   axis)\n\n    @staticmethod\n    def _labels_to_tables(labels):\n        # prevent circular imports, pylint: disable=import-outside-toplevel\n        from Orange.data import Table, StringVariable, Domain\n\n        if labels is None or isinstance(labels, Table):\n            return labels\n        return Table.from_numpy(\n            Domain([], metas=[StringVariable(\"label\")]),\n            np.empty((len(labels), 0)), None, np.array(labels)[:, None])\n\n    @classmethod\n    def _from_dst(cls, filename):\n        # prevent circular imports, pylint: disable=import-outside-toplevel\n        from Orange.data.io import detect_encoding\n\n        with open(filename, encoding=detect_encoding(filename)) as fle:\n            line = fle.readline()\n            if not line:\n                raise ValueError(\"empty file\")\n            data = line.strip().split()\n            if not data[0].strip().isdigit():\n                raise ValueError(\"distance file must begin with dimension\")\n            n = int(data.pop(0))\n            symmetric = True\n            axis = 1\n            col_labels = row_labels = None\n            for flag in data:\n                if flag in (\"labelled\", \"labeled\", \"row_labels\"):\n                    row_labels = []\n                elif flag == \"col_labels\":\n                    col_labels = []\n                elif flag == \"symmetric\":\n                    symmetric = True\n                elif flag == \"asymmetric\":\n                    symmetric = False\n                else:\n                    flag_data = flag.split(\"=\")\n                    if len(flag_data) == 2:\n                        name, value = map(str.strip, flag_data)\n                    else:\n                        name, value = \"\", None\n                    if name == \"axis\" and value.isdigit():\n                        axis = int(value)\n                    else:\n                        raise ValueError(f\"invalid flag '{flag}'\")\n            if col_labels is not None:\n                col_labels = [x.strip()\n                              for x in fle.readline().strip().split(\"\\t\")]\n                if len(col_labels) != n:\n                    raise ValueError(\"mismatching number of column labels, \"\n                                     f\"{len(col_labels)} != {n}\")\n\n            def num_or_lab(n, labels):\n                return f\"'{labels[n]}'\" if labels else str(n + 1)\n\n            matrix = np.zeros((n, n))\n            for i, line in enumerate(fle):\n                if i >= n:\n                    raise ValueError(\"too many rows\")\n                line = line.strip().split(\"\\t\")\n                if row_labels is not None:\n                    row_labels.append(line.pop(0).strip())\n                if len(line) > n:\n                    raise ValueError(\n                        f\"too many columns in matrix row \"\n                        f\"{num_or_lab(i, row_labels)}\")\n                for j, e in enumerate(line[:i + 1 if symmetric else n]):\n                    try:\n                        matrix[i, j] = float(e)\n                    except ValueError as exc:\n                        raise ValueError(\n                            \"invalid element at \"\n                            f\"row {num_or_lab(i, row_labels)}, \"\n                            f\"column {num_or_lab(j, col_labels)}\") from exc\n                    if symmetric:\n                        matrix[j, i] = matrix[i, j]\n            return matrix, row_labels, col_labels, axis\n\n    def auto_symmetricized(self, copy=False):\n        def self_or_copy():\n            return self.copy() if copy else self\n\n        def get_labels(labels):\n            return np.array(labels) if isinstance(labels, list) \\\n                else labels.metas[:, 0] if self._trivial_labels(labels) \\\n                else object()\n\n        h, w = self.shape\n        m = max(w, h)\n        if (abs(h - w) > 1\n                or self.row_items and self.col_items\n                   and np.any(get_labels(self.row_items)\n                              != get_labels(self.col_items))\n                or self.row_items and len(self.row_items) != m\n                or self.col_items and len(self.col_items) != m):\n            return self_or_copy()\n\n        nans = np.isnan(self)\n        low_indices = np.tril_indices(h, -1)\n        low_empty = np.all(nans[low_indices])\n        high_indices = np.triu_indices(w, 1)\n        high_empty = np.all(nans[high_indices])\n        if low_empty is high_empty:  # both non-empty, or both empty (only diagonal)\n            return self_or_copy()\n\n        indices = low_indices if low_empty else high_indices\n        if w == h:\n            matrix = np.array(self)\n        else:\n            if low_empty:\n                row = np.vstack((self[:, -1, None], [[0]])).T\n                matrix = np.vstack((self, row))\n            else:\n                col = np.hstack((self[-1, None], [[0]])).T\n                matrix = np.hstack((self, col))\n            diag_indices = np.diag_indices(len(matrix))\n            matrix[diag_indices] = np.nan_to_num(matrix[diag_indices])\n        matrix[indices] = self.T[indices]\n        return type(self)(matrix,\n                          self.row_items or self.col_items,\n                          self.col_items or self.row_items)\n\n    @staticmethod\n    def _trivial_labels(items):\n        # prevent circular imports, pylint: disable=import-outside-toplevel\n        from Orange.data import Table, StringVariable\n\n        return items and \\\n               isinstance(items, Table) and \\\n               len(items.domain.metas) == 1 and \\\n               isinstance(items.domain.metas[0], StringVariable)\n\n    def is_symmetric(self):\n        # prevent circular imports, pylint: disable=import-outside-toplevel\n        from Orange.data import Table\n\n        if self.shape[0] != self.shape[1] or not np.allclose(self, self.T):\n            return False\n        if self.row_items is None or self.col_items is None:\n            return True\n        if isinstance(self.row_items, Table):\n            return (isinstance(self.col_items, Table)\n                and self.col_items.domain == self.row_items.domain\n                and np.array_equal(self.col_items.X, self.row_items.X)\n                and np.array_equal(self.col_items.Y, self.row_items.Y)\n                and np.array_equal(self.col_items.metas, self.row_items.metas))\n        else:\n            return (not isinstance(self.col_items, Table)\n                and np.array_equal(self.row_items, self.col_items))\n\n    def has_row_labels(self):\n        \"\"\"\n        Returns `True` if row labels can be automatically determined from data\n\n        For this, the `row_items` must be an instance of `Orange.data.Table`\n        whose domain contains a single meta attribute, which has to be a string.\n        The domain may contain other variables, but not meta attributes.\n        \"\"\"\n        return self._trivial_labels(self.row_items)\n\n    def has_col_labels(self):\n        \"\"\"\n        Returns `True` if column labels can be automatically determined from\n        data\n\n        For this, the `col_items` must be an instance of `Orange.data.Table`\n        whose domain contains a single meta attribute, which has to be a string.\n        The domain may contain other variables, but not meta attributes.\n        \"\"\"\n        return self._trivial_labels(self.col_items)\n\n    def get_labels(self, items):\n        if self._trivial_labels(items):\n            return items.get_column(items.domain.metas[0])\n        elif isinstance(items, (list, tuple)) \\\n                and all(isinstance(x, str) for x in items):\n            return items\n        else:\n            return None\n\n    def save(self, filename):\n        if os.path.splitext(filename)[1] == \".xlsx\":\n            _distmatrix_xlsx.write_matrix(self, filename)\n        else:\n            self._save_dst(filename)\n\n    def _save_dst(self, filename):\n        \"\"\"\n        Save the distance matrix to a file in the file format described at\n        :obj:`~Orange.misc.distmatrix.DistMatrix.from_file`.\n\n        Args:\n            filename: file name\n        \"\"\"\n        n = len(self)\n        data = \"{}\\taxis={}\".format(n, self.axis)\n        row_labels = col_labels = None\n        if self.has_col_labels():\n            data += \"\\tcol_labels\"\n            col_labels = self.col_items\n        if self.has_row_labels():\n            data += \"\\trow_labels\"\n            row_labels = self.row_items\n        symmetric = self.is_symmetric()\n        if not symmetric:\n            data += \"\\tasymmetric\"\n        with open(filename, \"wt\") as fle:\n            fle.write(data + \"\\n\")\n            if col_labels is not None:\n                fle.write(\"\\t\".join(str(e.metas[0]) for e in col_labels) + \"\\n\")\n            for i, row in enumerate(self):\n                if row_labels is not None:\n                    fle.write(str(row_labels[i].metas[0]) + \"\\t\")\n                if symmetric:\n                    fle.write(\"\\t\".join(map(str, row[:i + 1])) + \"\\n\")\n                else:\n                    fle.write(\"\\t\".join(map(str, row)) + \"\\n\")",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 9,
                            "end_lineno": 371,
                            "parent": null
                        }
                    ],
                    "added_entities": [],
                    "deleted_entities": []
                }
            ]
        },
        {
            "old_file_content": "import unittest\nfrom unittest.mock import patch\n\nimport numpy as np\nfrom Orange.misc import DistMatrix\n\n\nclass DistMatrixTest(unittest.TestCase):\n    def test_reader_selection(self):\n        with patch(\"Orange.misc._distmatrix_xlsx.read_matrix\") as read_matrix, \\\n                patch.object(DistMatrix, \"_from_dst\") as _from_dst:\n            read_matrix.return_value = (np.zeros((3, 4)), None, None, 1)\n            _from_dst.return_value = (np.zeros((2, 2)), None, None, 1)\n\n            matrix = DistMatrix.from_file(\"test.dst\")\n            self.assertEqual(matrix.shape, (2, 2))\n\n            matrix = DistMatrix.from_file(\"test.xlsx\")\n            self.assertEqual(matrix.shape, (3, 4))\n\n    def test_auto_symmetricized_result(self):\n        data = np.array([[np.nan, np.nan, np.nan],\n                         [1528.13, np.nan, np.nan],\n                         [1497.61, 999.25, np.nan],\n                         [1062.89, 1372.59, 651.62]])\n\n        exp_sym = np.array([[1528.13, 1497.61, 1062.89],\n                            [1497.61, 999.25, 1372.59],\n                            [1062.89, 1372.59, 651.62]])\n\n        labels = list(\"ABC\")\n        for ri, li in ((labels, labels),\n                       (labels, None),\n                       (None, labels),\n                       (None, None)):\n            matrix = DistMatrix(data[1:], ri, li)\n            sym = matrix.auto_symmetricized()\n            np.testing.assert_almost_equal(sym, exp_sym)\n            self.assertEqual(sym.row_items, sym.col_items)\n            self.assertIs((ri or li), sym.row_items)\n\n        matrix = DistMatrix(data[1:].T)\n        sym = matrix.auto_symmetricized()\n        np.testing.assert_almost_equal(sym, exp_sym)\n\n        labels = list(\"ABCD\")\n        data[1, 1] =  2\n        exp_sym = np.array(\n            [[   0.  , 1528.13, 1497.61, 1062.89],\n             [1528.13,    2.  ,  999.25, 1372.59],\n             [1497.61,  999.25,    0.  ,  651.62],\n             [1062.89, 1372.59,  651.62,    0.  ]])\n        matrix = DistMatrix(data, labels)\n        sym = matrix.auto_symmetricized()\n        np.testing.assert_almost_equal(sym, exp_sym)\n\n        matrix = DistMatrix(data.T, None, labels)\n        sym = matrix.auto_symmetricized()\n        np.testing.assert_almost_equal(sym, exp_sym)\n\n    def test_auto_symmetricized_dont_apply(self):\n        data = DistMatrix(np.array([[np.nan, np.nan]] * 3 + [[1, np.nan]]))\n        self.assertIs(data.auto_symmetricized(), data)\n\n        data = np.array([[np.nan, np.nan, 1],\n                         [1528.13, np.nan, np.nan],\n                         [1497.61, 999.25, np.nan],\n                         [1062.89, 1372.59, 651.62]])\n        matrix = DistMatrix(data)\n        self.assertIs(matrix.auto_symmetricized(), matrix)\n\n        data = np.array([[np.nan, np.nan, 1],\n                         [1528.13, np.nan, np.nan],\n                         [1497.61, 999.25, np.nan],\n                         [1062.89, 1372.59, 651.62]])\n        matrix = DistMatrix(data)\n        sym = matrix.auto_symmetricized(copy=True)\n        np.testing.assert_equal(matrix, sym)\n        self.assertIsNot(sym, matrix)\n\n        matrix = DistMatrix(data.T)\n        self.assertIs(matrix.auto_symmetricized(), matrix)\n\n        data = np.array([[np.nan, np.nan, np.nan],\n                         [1528.13, np.nan, np.nan],\n                         [1497.61, 999.25, np.nan],\n                         [1062.89, 1372.59, 651.62]])\n        matrix = DistMatrix(data, None, list(\"abc\"))\n        self.assertIs(matrix.auto_symmetricized(), matrix)\n\n        matrix = DistMatrix(data.T, list(\"abc\"))\n        self.assertIs(matrix.auto_symmetricized(), matrix)\n\n        data = np.array([[1528.13, np.nan, np.nan],\n                         [1497.61, 999.25, np.nan],\n                         [1062.89, 1372.59, 651.62]])\n        matrix = DistMatrix(data, list(\"def\"), list(\"abc\"))\n        self.assertIs(matrix.auto_symmetricized(), matrix)\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n",
            "new_file_content": "import unittest\nfrom unittest.mock import patch\n\nimport numpy as np\nfrom Orange.data import ContinuousVariable, StringVariable, Table, Domain\nfrom Orange.misc import DistMatrix\n\n\nclass DistMatrixTest(unittest.TestCase):\n    def test_reader_selection(self):\n        with patch(\"Orange.misc._distmatrix_xlsx.read_matrix\") as read_matrix, \\\n                patch.object(DistMatrix, \"_from_dst\") as _from_dst:\n            read_matrix.return_value = (np.zeros((3, 4)), None, None, 1)\n            _from_dst.return_value = (np.zeros((2, 2)), None, None, 1)\n\n            matrix = DistMatrix.from_file(\"test.dst\")\n            self.assertEqual(matrix.shape, (2, 2))\n\n            matrix = DistMatrix.from_file(\"test.xlsx\")\n            self.assertEqual(matrix.shape, (3, 4))\n\n    def test_auto_symmetricized_result(self):\n        data = np.array([[np.nan, np.nan, np.nan],\n                         [1528.13, np.nan, np.nan],\n                         [1497.61, 999.25, np.nan],\n                         [1062.89, 1372.59, 651.62]])\n\n        exp_sym = np.array([[1528.13, 1497.61, 1062.89],\n                            [1497.61, 999.25, 1372.59],\n                            [1062.89, 1372.59, 651.62]])\n\n        labels = list(\"ABC\")\n        for ri, li in ((labels, labels),\n                       (labels, None),\n                       (None, labels),\n                       (None, None)):\n            matrix = DistMatrix(data[1:], ri, li)\n            sym = matrix.auto_symmetricized()\n            np.testing.assert_almost_equal(sym, exp_sym)\n            self.assertEqual(sym.row_items, sym.col_items)\n            self.assertIs((ri or li), sym.row_items)\n\n        matrix = DistMatrix(data[1:].T)\n        sym = matrix.auto_symmetricized()\n        np.testing.assert_almost_equal(sym, exp_sym)\n\n        labels = list(\"ABCD\")\n        data[1, 1] =  2\n        exp_sym = np.array(\n            [[   0.  , 1528.13, 1497.61, 1062.89],\n             [1528.13,    2.  ,  999.25, 1372.59],\n             [1497.61,  999.25,    0.  ,  651.62],\n             [1062.89, 1372.59,  651.62,    0.  ]])\n        matrix = DistMatrix(data, labels)\n        sym = matrix.auto_symmetricized()\n        np.testing.assert_almost_equal(sym, exp_sym)\n\n        matrix = DistMatrix(data.T, None, labels)\n        sym = matrix.auto_symmetricized()\n        np.testing.assert_almost_equal(sym, exp_sym)\n\n    def test_auto_symmetricized_dont_apply(self):\n        data = DistMatrix(np.array([[np.nan, np.nan]] * 3 + [[1, np.nan]]))\n        self.assertIs(data.auto_symmetricized(), data)\n\n        data = np.array([[np.nan, np.nan, 1],\n                         [1528.13, np.nan, np.nan],\n                         [1497.61, 999.25, np.nan],\n                         [1062.89, 1372.59, 651.62]])\n        matrix = DistMatrix(data)\n        self.assertIs(matrix.auto_symmetricized(), matrix)\n\n        data = np.array([[np.nan, np.nan, 1],\n                         [1528.13, np.nan, np.nan],\n                         [1497.61, 999.25, np.nan],\n                         [1062.89, 1372.59, 651.62]])\n        matrix = DistMatrix(data)\n        sym = matrix.auto_symmetricized(copy=True)\n        np.testing.assert_equal(matrix, sym)\n        self.assertIsNot(sym, matrix)\n\n        matrix = DistMatrix(data.T)\n        self.assertIs(matrix.auto_symmetricized(), matrix)\n\n        data = np.array([[np.nan, np.nan, np.nan],\n                         [1528.13, np.nan, np.nan],\n                         [1497.61, 999.25, np.nan],\n                         [1062.89, 1372.59, 651.62]])\n        matrix = DistMatrix(data, None, list(\"abc\"))\n        self.assertIs(matrix.auto_symmetricized(), matrix)\n\n        matrix = DistMatrix(data.T, list(\"abc\"))\n        self.assertIs(matrix.auto_symmetricized(), matrix)\n\n        data = np.array([[1528.13, np.nan, np.nan],\n                         [1497.61, 999.25, np.nan],\n                         [1062.89, 1372.59, 651.62]])\n        matrix = DistMatrix(data, list(\"def\"), list(\"abc\"))\n        self.assertIs(matrix.auto_symmetricized(), matrix)\n\n    def test_trivial_labels(self):\n        matrix = DistMatrix(np.array([[1, 2, 3], [4, 5, 6]]))\n\n        self.assertFalse(matrix._trivial_labels(matrix.row_items))\n        self.assertIsNone(matrix.get_labels(matrix.row_items))\n\n        matrix.row_items = list(\"abc\")\n        self.assertTrue(matrix._trivial_labels(matrix.row_items))\n        self.assertEqual(matrix.get_labels(matrix.row_items), list(\"abc\"))\n\n        matrix.row_items = [\"a\", 1, \"c\"]\n        self.assertFalse(matrix._trivial_labels(matrix.row_items))\n        self.assertIsNone(matrix.get_labels(matrix.row_items))\n\n        c1, c2 = (ContinuousVariable(c) for c in \"xy\")\n        s1, s2 = (StringVariable(c) for c in \"st\")\n        data = Table.from_list(Domain([c1], None, [c2, s1]),\n                               [[1, 0, \"a\"], [2, 2, \"b\"], [3, 1, \"c\"]])\n        matrix.row_items = data\n\n        matrix.axis = 1\n        self.assertTrue(matrix._trivial_labels(matrix.row_items))\n        self.assertEqual(list(matrix.get_labels(matrix.row_items)), list(\"abc\"))\n\n        matrix.axis = 0\n        self.assertTrue(matrix._trivial_labels(matrix.row_items))\n        self.assertEqual(list(matrix.get_labels(matrix.row_items)), list(\"x\"))\n\n\n        data = Table.from_list(Domain([c1], None, [c2, s1, s2]),\n                               [[1, 2, \"a\", \"2\"],\n                                [2, 4, \"b\", \"5\"],\n                                [3, 0, \"c\", \"g\"]])\n        matrix.row_items = data\n\n        matrix.axis = 1\n        self.assertFalse(matrix._trivial_labels(matrix.row_items))\n        self.assertIsNone(matrix.get_labels(matrix.row_items))\n\n        matrix.axis = 0\n        self.assertTrue(matrix._trivial_labels(matrix.row_items))\n        self.assertEqual(list(matrix.get_labels(matrix.row_items)), list(\"x\"))\n\n        data = Table.from_list(Domain([c1], None, [c2]),\n                               [[1, 2],\n                                [2, 4],\n                                [3, 0]])\n        matrix.row_items = data\n        matrix.axis = 1\n        self.assertFalse(matrix._trivial_labels(matrix.row_items))\n        self.assertIsNone(matrix.get_labels(matrix.row_items))\n\n        matrix.axis = 0\n        self.assertTrue(matrix._trivial_labels(matrix.row_items))\n        self.assertEqual(matrix.get_labels(matrix.row_items), list(\"x\"))\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n",
            "header": {
                "file": {
                    "path": "Orange/misc/tests/test_distmatrix.py"
                },
                "misc_line": null
            },
            "index_line": {
                "old_commit_hash": "f0e8ba0b0",
                "new_commit_hash": "3da2fdbe7",
                "mode": "100644"
            },
            "is_binary_file": false,
            "binary_line": null,
            "minus_file": {
                "path": "a/Orange/misc/tests/test_distmatrix.py"
            },
            "plus_file": {
                "path": "b/Orange/misc/tests/test_distmatrix.py"
            },
            "hunks": [
                {
                    "descriptor": {
                        "old_range": {
                            "start": 2,
                            "length": 6
                        },
                        "new_range": {
                            "start": 2,
                            "length": 7
                        },
                        "section": "import unittest"
                    },
                    "line_group": {
                        "all_lines": [
                            {
                                "content": "from unittest.mock import patch",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "import numpy as np",
                                "type": "context"
                            },
                            {
                                "content": "from Orange.data import ContinuousVariable, StringVariable, Table, Domain",
                                "type": "added"
                            },
                            {
                                "content": "from Orange.misc import DistMatrix",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            }
                        ]
                    },
                    "modified_entities": [],
                    "added_entities": [
                        {
                            "file_name": "Orange/misc/tests/test_distmatrix.py",
                            "type": "statement",
                            "name": "",
                            "content": "from Orange.data import ContinuousVariable, StringVariable, Table, Domain",
                            "ast_type_str": "ImportFrom",
                            "start_lineno": 5,
                            "end_lineno": 5,
                            "parent": null
                        }
                    ],
                    "deleted_entities": []
                },
                {
                    "descriptor": {
                        "old_range": {
                            "start": 97,
                            "length": 6
                        },
                        "new_range": {
                            "start": 98,
                            "length": 62
                        },
                        "section": "class DistMatrixTest(unittest.TestCase):"
                    },
                    "line_group": {
                        "all_lines": [
                            {
                                "content": "        matrix = DistMatrix(data, list(\"def\"), list(\"abc\"))",
                                "type": "context"
                            },
                            {
                                "content": "        self.assertIs(matrix.auto_symmetricized(), matrix)",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "    def test_trivial_labels(self):",
                                "type": "added"
                            },
                            {
                                "content": "        matrix = DistMatrix(np.array([[1, 2, 3], [4, 5, 6]]))",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertFalse(matrix._trivial_labels(matrix.row_items))",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertIsNone(matrix.get_labels(matrix.row_items))",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "        matrix.row_items = list(\"abc\")",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertTrue(matrix._trivial_labels(matrix.row_items))",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertEqual(matrix.get_labels(matrix.row_items), list(\"abc\"))",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "        matrix.row_items = [\"a\", 1, \"c\"]",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertFalse(matrix._trivial_labels(matrix.row_items))",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertIsNone(matrix.get_labels(matrix.row_items))",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "        c1, c2 = (ContinuousVariable(c) for c in \"xy\")",
                                "type": "added"
                            },
                            {
                                "content": "        s1, s2 = (StringVariable(c) for c in \"st\")",
                                "type": "added"
                            },
                            {
                                "content": "        data = Table.from_list(Domain([c1], None, [c2, s1]),",
                                "type": "added"
                            },
                            {
                                "content": "                               [[1, 0, \"a\"], [2, 2, \"b\"], [3, 1, \"c\"]])",
                                "type": "added"
                            },
                            {
                                "content": "        matrix.row_items = data",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "        matrix.axis = 1",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertTrue(matrix._trivial_labels(matrix.row_items))",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertEqual(list(matrix.get_labels(matrix.row_items)), list(\"abc\"))",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "        matrix.axis = 0",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertTrue(matrix._trivial_labels(matrix.row_items))",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertEqual(list(matrix.get_labels(matrix.row_items)), list(\"x\"))",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "        data = Table.from_list(Domain([c1], None, [c2, s1, s2]),",
                                "type": "added"
                            },
                            {
                                "content": "                               [[1, 2, \"a\", \"2\"],",
                                "type": "added"
                            },
                            {
                                "content": "                                [2, 4, \"b\", \"5\"],",
                                "type": "added"
                            },
                            {
                                "content": "                                [3, 0, \"c\", \"g\"]])",
                                "type": "added"
                            },
                            {
                                "content": "        matrix.row_items = data",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "        matrix.axis = 1",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertFalse(matrix._trivial_labels(matrix.row_items))",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertIsNone(matrix.get_labels(matrix.row_items))",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "        matrix.axis = 0",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertTrue(matrix._trivial_labels(matrix.row_items))",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertEqual(list(matrix.get_labels(matrix.row_items)), list(\"x\"))",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "        data = Table.from_list(Domain([c1], None, [c2]),",
                                "type": "added"
                            },
                            {
                                "content": "                               [[1, 2],",
                                "type": "added"
                            },
                            {
                                "content": "                                [2, 4],",
                                "type": "added"
                            },
                            {
                                "content": "                                [3, 0]])",
                                "type": "added"
                            },
                            {
                                "content": "        matrix.row_items = data",
                                "type": "added"
                            },
                            {
                                "content": "        matrix.axis = 1",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertFalse(matrix._trivial_labels(matrix.row_items))",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertIsNone(matrix.get_labels(matrix.row_items))",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "        matrix.axis = 0",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertTrue(matrix._trivial_labels(matrix.row_items))",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertEqual(matrix.get_labels(matrix.row_items), list(\"x\"))",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "if __name__ == \"__main__\":",
                                "type": "context"
                            },
                            {
                                "content": "    unittest.main()",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            }
                        ]
                    },
                    "modified_entities": [
                        {
                            "file_name": "Orange/misc/tests/test_distmatrix.py",
                            "type": "class",
                            "name": "DistMatrixTest",
                            "content": "class DistMatrixTest(unittest.TestCase):\n    def test_reader_selection(self):\n        with patch(\"Orange.misc._distmatrix_xlsx.read_matrix\") as read_matrix, \\\n                patch.object(DistMatrix, \"_from_dst\") as _from_dst:\n            read_matrix.return_value = (np.zeros((3, 4)), None, None, 1)\n            _from_dst.return_value = (np.zeros((2, 2)), None, None, 1)\n\n            matrix = DistMatrix.from_file(\"test.dst\")\n            self.assertEqual(matrix.shape, (2, 2))\n\n            matrix = DistMatrix.from_file(\"test.xlsx\")\n            self.assertEqual(matrix.shape, (3, 4))\n\n    def test_auto_symmetricized_result(self):\n        data = np.array([[np.nan, np.nan, np.nan],\n                         [1528.13, np.nan, np.nan],\n                         [1497.61, 999.25, np.nan],\n                         [1062.89, 1372.59, 651.62]])\n\n        exp_sym = np.array([[1528.13, 1497.61, 1062.89],\n                            [1497.61, 999.25, 1372.59],\n                            [1062.89, 1372.59, 651.62]])\n\n        labels = list(\"ABC\")\n        for ri, li in ((labels, labels),\n                       (labels, None),\n                       (None, labels),\n                       (None, None)):\n            matrix = DistMatrix(data[1:], ri, li)\n            sym = matrix.auto_symmetricized()\n            np.testing.assert_almost_equal(sym, exp_sym)\n            self.assertEqual(sym.row_items, sym.col_items)\n            self.assertIs((ri or li), sym.row_items)\n\n        matrix = DistMatrix(data[1:].T)\n        sym = matrix.auto_symmetricized()\n        np.testing.assert_almost_equal(sym, exp_sym)\n\n        labels = list(\"ABCD\")\n        data[1, 1] =  2\n        exp_sym = np.array(\n            [[   0.  , 1528.13, 1497.61, 1062.89],\n             [1528.13,    2.  ,  999.25, 1372.59],\n             [1497.61,  999.25,    0.  ,  651.62],\n             [1062.89, 1372.59,  651.62,    0.  ]])\n        matrix = DistMatrix(data, labels)\n        sym = matrix.auto_symmetricized()\n        np.testing.assert_almost_equal(sym, exp_sym)\n\n        matrix = DistMatrix(data.T, None, labels)\n        sym = matrix.auto_symmetricized()\n        np.testing.assert_almost_equal(sym, exp_sym)\n\n    def test_auto_symmetricized_dont_apply(self):\n        data = DistMatrix(np.array([[np.nan, np.nan]] * 3 + [[1, np.nan]]))\n        self.assertIs(data.auto_symmetricized(), data)\n\n        data = np.array([[np.nan, np.nan, 1],\n                         [1528.13, np.nan, np.nan],\n                         [1497.61, 999.25, np.nan],\n                         [1062.89, 1372.59, 651.62]])\n        matrix = DistMatrix(data)\n        self.assertIs(matrix.auto_symmetricized(), matrix)\n\n        data = np.array([[np.nan, np.nan, 1],\n                         [1528.13, np.nan, np.nan],\n                         [1497.61, 999.25, np.nan],\n                         [1062.89, 1372.59, 651.62]])\n        matrix = DistMatrix(data)\n        sym = matrix.auto_symmetricized(copy=True)\n        np.testing.assert_equal(matrix, sym)\n        self.assertIsNot(sym, matrix)\n\n        matrix = DistMatrix(data.T)\n        self.assertIs(matrix.auto_symmetricized(), matrix)\n\n        data = np.array([[np.nan, np.nan, np.nan],\n                         [1528.13, np.nan, np.nan],\n                         [1497.61, 999.25, np.nan],\n                         [1062.89, 1372.59, 651.62]])\n        matrix = DistMatrix(data, None, list(\"abc\"))\n        self.assertIs(matrix.auto_symmetricized(), matrix)\n\n        matrix = DistMatrix(data.T, list(\"abc\"))\n        self.assertIs(matrix.auto_symmetricized(), matrix)\n\n        data = np.array([[1528.13, np.nan, np.nan],\n                         [1497.61, 999.25, np.nan],\n                         [1062.89, 1372.59, 651.62]])\n        matrix = DistMatrix(data, list(\"def\"), list(\"abc\"))\n        self.assertIs(matrix.auto_symmetricized(), matrix)\n\n    def test_trivial_labels(self):\n        matrix = DistMatrix(np.array([[1, 2, 3], [4, 5, 6]]))\n\n        self.assertFalse(matrix._trivial_labels(matrix.row_items))\n        self.assertIsNone(matrix.get_labels(matrix.row_items))\n\n        matrix.row_items = list(\"abc\")\n        self.assertTrue(matrix._trivial_labels(matrix.row_items))\n        self.assertEqual(matrix.get_labels(matrix.row_items), list(\"abc\"))\n\n        matrix.row_items = [\"a\", 1, \"c\"]\n        self.assertFalse(matrix._trivial_labels(matrix.row_items))\n        self.assertIsNone(matrix.get_labels(matrix.row_items))\n\n        c1, c2 = (ContinuousVariable(c) for c in \"xy\")\n        s1, s2 = (StringVariable(c) for c in \"st\")\n        data = Table.from_list(Domain([c1], None, [c2, s1]),\n                               [[1, 0, \"a\"], [2, 2, \"b\"], [3, 1, \"c\"]])\n        matrix.row_items = data\n\n        matrix.axis = 1\n        self.assertTrue(matrix._trivial_labels(matrix.row_items))\n        self.assertEqual(list(matrix.get_labels(matrix.row_items)), list(\"abc\"))\n\n        matrix.axis = 0\n        self.assertTrue(matrix._trivial_labels(matrix.row_items))\n        self.assertEqual(list(matrix.get_labels(matrix.row_items)), list(\"x\"))\n\n\n        data = Table.from_list(Domain([c1], None, [c2, s1, s2]),\n                               [[1, 2, \"a\", \"2\"],\n                                [2, 4, \"b\", \"5\"],\n                                [3, 0, \"c\", \"g\"]])\n        matrix.row_items = data\n\n        matrix.axis = 1\n        self.assertFalse(matrix._trivial_labels(matrix.row_items))\n        self.assertIsNone(matrix.get_labels(matrix.row_items))\n\n        matrix.axis = 0\n        self.assertTrue(matrix._trivial_labels(matrix.row_items))\n        self.assertEqual(list(matrix.get_labels(matrix.row_items)), list(\"x\"))\n\n        data = Table.from_list(Domain([c1], None, [c2]),\n                               [[1, 2],\n                                [2, 4],\n                                [3, 0]])\n        matrix.row_items = data\n        matrix.axis = 1\n        self.assertFalse(matrix._trivial_labels(matrix.row_items))\n        self.assertIsNone(matrix.get_labels(matrix.row_items))\n\n        matrix.axis = 0\n        self.assertTrue(matrix._trivial_labels(matrix.row_items))\n        self.assertEqual(matrix.get_labels(matrix.row_items), list(\"x\"))",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 9,
                            "end_lineno": 155,
                            "parent": null
                        }
                    ],
                    "added_entities": [
                        {
                            "file_name": "Orange/misc/tests/test_distmatrix.py",
                            "type": "method",
                            "name": "DistMatrixTest.test_trivial_labels",
                            "content": "class DistMatrixTest(unittest.TestCase):\n    def test_reader_selection(self):\n        with patch(\"Orange.misc._distmatrix_xlsx.read_matrix\") as read_matrix, \\\n                patch.object(DistMatrix, \"_from_dst\") as _from_dst:\n            read_matrix.return_value = (np.zeros((3, 4)), None, None, 1)\n            _from_dst.return_value = (np.zeros((2, 2)), None, None, 1)\n\n            matrix = DistMatrix.from_file(\"test.dst\")\n            self.assertEqual(matrix.shape, (2, 2))\n\n            matrix = DistMatrix.from_file(\"test.xlsx\")\n            self.assertEqual(matrix.shape, (3, 4))\n\n    def test_auto_symmetricized_result(self):\n        data = np.array([[np.nan, np.nan, np.nan],\n                         [1528.13, np.nan, np.nan],\n                         [1497.61, 999.25, np.nan],\n                         [1062.89, 1372.59, 651.62]])\n\n        exp_sym = np.array([[1528.13, 1497.61, 1062.89],\n                            [1497.61, 999.25, 1372.59],\n                            [1062.89, 1372.59, 651.62]])\n\n        labels = list(\"ABC\")\n        for ri, li in ((labels, labels),\n                       (labels, None),\n                       (None, labels),\n                       (None, None)):\n            matrix = DistMatrix(data[1:], ri, li)\n            sym = matrix.auto_symmetricized()\n            np.testing.assert_almost_equal(sym, exp_sym)\n            self.assertEqual(sym.row_items, sym.col_items)\n            self.assertIs((ri or li), sym.row_items)\n\n        matrix = DistMatrix(data[1:].T)\n        sym = matrix.auto_symmetricized()\n        np.testing.assert_almost_equal(sym, exp_sym)\n\n        labels = list(\"ABCD\")\n        data[1, 1] =  2\n        exp_sym = np.array(\n            [[   0.  , 1528.13, 1497.61, 1062.89],\n             [1528.13,    2.  ,  999.25, 1372.59],\n             [1497.61,  999.25,    0.  ,  651.62],\n             [1062.89, 1372.59,  651.62,    0.  ]])\n        matrix = DistMatrix(data, labels)\n        sym = matrix.auto_symmetricized()\n        np.testing.assert_almost_equal(sym, exp_sym)\n\n        matrix = DistMatrix(data.T, None, labels)\n        sym = matrix.auto_symmetricized()\n        np.testing.assert_almost_equal(sym, exp_sym)\n\n    def test_auto_symmetricized_dont_apply(self):\n        data = DistMatrix(np.array([[np.nan, np.nan]] * 3 + [[1, np.nan]]))\n        self.assertIs(data.auto_symmetricized(), data)\n\n        data = np.array([[np.nan, np.nan, 1],\n                         [1528.13, np.nan, np.nan],\n                         [1497.61, 999.25, np.nan],\n                         [1062.89, 1372.59, 651.62]])\n        matrix = DistMatrix(data)\n        self.assertIs(matrix.auto_symmetricized(), matrix)\n\n        data = np.array([[np.nan, np.nan, 1],\n                         [1528.13, np.nan, np.nan],\n                         [1497.61, 999.25, np.nan],\n                         [1062.89, 1372.59, 651.62]])\n        matrix = DistMatrix(data)\n        sym = matrix.auto_symmetricized(copy=True)\n        np.testing.assert_equal(matrix, sym)\n        self.assertIsNot(sym, matrix)\n\n        matrix = DistMatrix(data.T)\n        self.assertIs(matrix.auto_symmetricized(), matrix)\n\n        data = np.array([[np.nan, np.nan, np.nan],\n                         [1528.13, np.nan, np.nan],\n                         [1497.61, 999.25, np.nan],\n                         [1062.89, 1372.59, 651.62]])\n        matrix = DistMatrix(data, None, list(\"abc\"))\n        self.assertIs(matrix.auto_symmetricized(), matrix)\n\n        matrix = DistMatrix(data.T, list(\"abc\"))\n        self.assertIs(matrix.auto_symmetricized(), matrix)\n\n        data = np.array([[1528.13, np.nan, np.nan],\n                         [1497.61, 999.25, np.nan],\n                         [1062.89, 1372.59, 651.62]])\n        matrix = DistMatrix(data, list(\"def\"), list(\"abc\"))\n        self.assertIs(matrix.auto_symmetricized(), matrix)\n\n    def test_trivial_labels(self):\n        matrix = DistMatrix(np.array([[1, 2, 3], [4, 5, 6]]))\n\n        self.assertFalse(matrix._trivial_labels(matrix.row_items))\n        self.assertIsNone(matrix.get_labels(matrix.row_items))\n\n        matrix.row_items = list(\"abc\")\n        self.assertTrue(matrix._trivial_labels(matrix.row_items))\n        self.assertEqual(matrix.get_labels(matrix.row_items), list(\"abc\"))\n\n        matrix.row_items = [\"a\", 1, \"c\"]\n        self.assertFalse(matrix._trivial_labels(matrix.row_items))\n        self.assertIsNone(matrix.get_labels(matrix.row_items))\n\n        c1, c2 = (ContinuousVariable(c) for c in \"xy\")\n        s1, s2 = (StringVariable(c) for c in \"st\")\n        data = Table.from_list(Domain([c1], None, [c2, s1]),\n                               [[1, 0, \"a\"], [2, 2, \"b\"], [3, 1, \"c\"]])\n        matrix.row_items = data\n\n        matrix.axis = 1\n        self.assertTrue(matrix._trivial_labels(matrix.row_items))\n        self.assertEqual(list(matrix.get_labels(matrix.row_items)), list(\"abc\"))\n\n        matrix.axis = 0\n        self.assertTrue(matrix._trivial_labels(matrix.row_items))\n        self.assertEqual(list(matrix.get_labels(matrix.row_items)), list(\"x\"))\n\n\n        data = Table.from_list(Domain([c1], None, [c2, s1, s2]),\n                               [[1, 2, \"a\", \"2\"],\n                                [2, 4, \"b\", \"5\"],\n                                [3, 0, \"c\", \"g\"]])\n        matrix.row_items = data\n\n        matrix.axis = 1\n        self.assertFalse(matrix._trivial_labels(matrix.row_items))\n        self.assertIsNone(matrix.get_labels(matrix.row_items))\n\n        matrix.axis = 0\n        self.assertTrue(matrix._trivial_labels(matrix.row_items))\n        self.assertEqual(list(matrix.get_labels(matrix.row_items)), list(\"x\"))\n\n        data = Table.from_list(Domain([c1], None, [c2]),\n                               [[1, 2],\n                                [2, 4],\n                                [3, 0]])\n        matrix.row_items = data\n        matrix.axis = 1\n        self.assertFalse(matrix._trivial_labels(matrix.row_items))\n        self.assertIsNone(matrix.get_labels(matrix.row_items))\n\n        matrix.axis = 0\n        self.assertTrue(matrix._trivial_labels(matrix.row_items))\n        self.assertEqual(matrix.get_labels(matrix.row_items), list(\"x\"))",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 101,
                            "end_lineno": 155,
                            "parent": {
                                "file_name": "Orange/misc/tests/test_distmatrix.py",
                                "type": "class",
                                "name": "DistMatrixTest",
                                "content": "class DistMatrixTest(unittest.TestCase):\n    def test_reader_selection(self):\n        with patch(\"Orange.misc._distmatrix_xlsx.read_matrix\") as read_matrix, \\\n                patch.object(DistMatrix, \"_from_dst\") as _from_dst:\n            read_matrix.return_value = (np.zeros((3, 4)), None, None, 1)\n            _from_dst.return_value = (np.zeros((2, 2)), None, None, 1)\n\n            matrix = DistMatrix.from_file(\"test.dst\")\n            self.assertEqual(matrix.shape, (2, 2))\n\n            matrix = DistMatrix.from_file(\"test.xlsx\")\n            self.assertEqual(matrix.shape, (3, 4))\n\n    def test_auto_symmetricized_result(self):\n        data = np.array([[np.nan, np.nan, np.nan],\n                         [1528.13, np.nan, np.nan],\n                         [1497.61, 999.25, np.nan],\n                         [1062.89, 1372.59, 651.62]])\n\n        exp_sym = np.array([[1528.13, 1497.61, 1062.89],\n                            [1497.61, 999.25, 1372.59],\n                            [1062.89, 1372.59, 651.62]])\n\n        labels = list(\"ABC\")\n        for ri, li in ((labels, labels),\n                       (labels, None),\n                       (None, labels),\n                       (None, None)):\n            matrix = DistMatrix(data[1:], ri, li)\n            sym = matrix.auto_symmetricized()\n            np.testing.assert_almost_equal(sym, exp_sym)\n            self.assertEqual(sym.row_items, sym.col_items)\n            self.assertIs((ri or li), sym.row_items)\n\n        matrix = DistMatrix(data[1:].T)\n        sym = matrix.auto_symmetricized()\n        np.testing.assert_almost_equal(sym, exp_sym)\n\n        labels = list(\"ABCD\")\n        data[1, 1] =  2\n        exp_sym = np.array(\n            [[   0.  , 1528.13, 1497.61, 1062.89],\n             [1528.13,    2.  ,  999.25, 1372.59],\n             [1497.61,  999.25,    0.  ,  651.62],\n             [1062.89, 1372.59,  651.62,    0.  ]])\n        matrix = DistMatrix(data, labels)\n        sym = matrix.auto_symmetricized()\n        np.testing.assert_almost_equal(sym, exp_sym)\n\n        matrix = DistMatrix(data.T, None, labels)\n        sym = matrix.auto_symmetricized()\n        np.testing.assert_almost_equal(sym, exp_sym)\n\n    def test_auto_symmetricized_dont_apply(self):\n        data = DistMatrix(np.array([[np.nan, np.nan]] * 3 + [[1, np.nan]]))\n        self.assertIs(data.auto_symmetricized(), data)\n\n        data = np.array([[np.nan, np.nan, 1],\n                         [1528.13, np.nan, np.nan],\n                         [1497.61, 999.25, np.nan],\n                         [1062.89, 1372.59, 651.62]])\n        matrix = DistMatrix(data)\n        self.assertIs(matrix.auto_symmetricized(), matrix)\n\n        data = np.array([[np.nan, np.nan, 1],\n                         [1528.13, np.nan, np.nan],\n                         [1497.61, 999.25, np.nan],\n                         [1062.89, 1372.59, 651.62]])\n        matrix = DistMatrix(data)\n        sym = matrix.auto_symmetricized(copy=True)\n        np.testing.assert_equal(matrix, sym)\n        self.assertIsNot(sym, matrix)\n\n        matrix = DistMatrix(data.T)\n        self.assertIs(matrix.auto_symmetricized(), matrix)\n\n        data = np.array([[np.nan, np.nan, np.nan],\n                         [1528.13, np.nan, np.nan],\n                         [1497.61, 999.25, np.nan],\n                         [1062.89, 1372.59, 651.62]])\n        matrix = DistMatrix(data, None, list(\"abc\"))\n        self.assertIs(matrix.auto_symmetricized(), matrix)\n\n        matrix = DistMatrix(data.T, list(\"abc\"))\n        self.assertIs(matrix.auto_symmetricized(), matrix)\n\n        data = np.array([[1528.13, np.nan, np.nan],\n                         [1497.61, 999.25, np.nan],\n                         [1062.89, 1372.59, 651.62]])\n        matrix = DistMatrix(data, list(\"def\"), list(\"abc\"))\n        self.assertIs(matrix.auto_symmetricized(), matrix)\n\n    def test_trivial_labels(self):\n        matrix = DistMatrix(np.array([[1, 2, 3], [4, 5, 6]]))\n\n        self.assertFalse(matrix._trivial_labels(matrix.row_items))\n        self.assertIsNone(matrix.get_labels(matrix.row_items))\n\n        matrix.row_items = list(\"abc\")\n        self.assertTrue(matrix._trivial_labels(matrix.row_items))\n        self.assertEqual(matrix.get_labels(matrix.row_items), list(\"abc\"))\n\n        matrix.row_items = [\"a\", 1, \"c\"]\n        self.assertFalse(matrix._trivial_labels(matrix.row_items))\n        self.assertIsNone(matrix.get_labels(matrix.row_items))\n\n        c1, c2 = (ContinuousVariable(c) for c in \"xy\")\n        s1, s2 = (StringVariable(c) for c in \"st\")\n        data = Table.from_list(Domain([c1], None, [c2, s1]),\n                               [[1, 0, \"a\"], [2, 2, \"b\"], [3, 1, \"c\"]])\n        matrix.row_items = data\n\n        matrix.axis = 1\n        self.assertTrue(matrix._trivial_labels(matrix.row_items))\n        self.assertEqual(list(matrix.get_labels(matrix.row_items)), list(\"abc\"))\n\n        matrix.axis = 0\n        self.assertTrue(matrix._trivial_labels(matrix.row_items))\n        self.assertEqual(list(matrix.get_labels(matrix.row_items)), list(\"x\"))\n\n\n        data = Table.from_list(Domain([c1], None, [c2, s1, s2]),\n                               [[1, 2, \"a\", \"2\"],\n                                [2, 4, \"b\", \"5\"],\n                                [3, 0, \"c\", \"g\"]])\n        matrix.row_items = data\n\n        matrix.axis = 1\n        self.assertFalse(matrix._trivial_labels(matrix.row_items))\n        self.assertIsNone(matrix.get_labels(matrix.row_items))\n\n        matrix.axis = 0\n        self.assertTrue(matrix._trivial_labels(matrix.row_items))\n        self.assertEqual(list(matrix.get_labels(matrix.row_items)), list(\"x\"))\n\n        data = Table.from_list(Domain([c1], None, [c2]),\n                               [[1, 2],\n                                [2, 4],\n                                [3, 0]])\n        matrix.row_items = data\n        matrix.axis = 1\n        self.assertFalse(matrix._trivial_labels(matrix.row_items))\n        self.assertIsNone(matrix.get_labels(matrix.row_items))\n\n        matrix.axis = 0\n        self.assertTrue(matrix._trivial_labels(matrix.row_items))\n        self.assertEqual(matrix.get_labels(matrix.row_items), list(\"x\"))",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 9,
                                "end_lineno": 155,
                                "parent": null
                            }
                        }
                    ],
                    "deleted_entities": []
                }
            ]
        }
    ],
    "old_commit_hash": "6d1df8d358406c30c66d192599092605d51da431^",
    "new_commit_hash": "6d1df8d358406c30c66d192599092605d51da431",
    "commit_message": "DistMatrix: improve deduction of labels",
    "commit_date": "2023-01-10T21:12:18+01:00",
    "metadata": {}
}
{
    "repo_name": "orange3",
    "new_commit_hash": "6d1df8d358406c30c66d192599092605d51da431",
    "test_file_codes": [
        "import unittest\nfrom unittest.mock import patch\n\nimport numpy as np\nfrom Orange.data import ContinuousVariable, StringVariable, Table, Domain\nfrom Orange.misc import DistMatrix\n\n\nclass DistMatrixTest(unittest.TestCase):\n    def test_reader_selection(self):\n        with patch(\"Orange.misc._distmatrix_xlsx.read_matrix\") as read_matrix, \\\n                patch.object(DistMatrix, \"_from_dst\") as _from_dst:\n            read_matrix.return_value = (np.zeros((3, 4)), None, None, 1)\n            _from_dst.return_value = (np.zeros((2, 2)), None, None, 1)\n\n            matrix = DistMatrix.from_file(\"test.dst\")\n            self.assertEqual(matrix.shape, (2, 2))\n\n            matrix = DistMatrix.from_file(\"test.xlsx\")\n            self.assertEqual(matrix.shape, (3, 4))\n\n    def test_auto_symmetricized_result(self):\n        data = np.array([[np.nan, np.nan, np.nan],\n                         [1528.13, np.nan, np.nan],\n                         [1497.61, 999.25, np.nan],\n                         [1062.89, 1372.59, 651.62]])\n\n        exp_sym = np.array([[1528.13, 1497.61, 1062.89],\n                            [1497.61, 999.25, 1372.59],\n                            [1062.89, 1372.59, 651.62]])\n\n        labels = list(\"ABC\")\n        for ri, li in ((labels, labels),\n                       (labels, None),\n                       (None, labels),\n                       (None, None)):\n            matrix = DistMatrix(data[1:], ri, li)\n            sym = matrix.auto_symmetricized()\n            np.testing.assert_almost_equal(sym, exp_sym)\n            self.assertEqual(sym.row_items, sym.col_items)\n            self.assertIs((ri or li), sym.row_items)\n\n        matrix = DistMatrix(data[1:].T)\n        sym = matrix.auto_symmetricized()\n        np.testing.assert_almost_equal(sym, exp_sym)\n\n        labels = list(\"ABCD\")\n        data[1, 1] =  2\n        exp_sym = np.array(\n            [[   0.  , 1528.13, 1497.61, 1062.89],\n             [1528.13,    2.  ,  999.25, 1372.59],\n             [1497.61,  999.25,    0.  ,  651.62],\n             [1062.89, 1372.59,  651.62,    0.  ]])\n        matrix = DistMatrix(data, labels)\n        sym = matrix.auto_symmetricized()\n        np.testing.assert_almost_equal(sym, exp_sym)\n\n        matrix = DistMatrix(data.T, None, labels)\n        sym = matrix.auto_symmetricized()\n        np.testing.assert_almost_equal(sym, exp_sym)\n\n    def test_auto_symmetricized_dont_apply(self):\n        data = DistMatrix(np.array([[np.nan, np.nan]] * 3 + [[1, np.nan]]))\n        self.assertIs(data.auto_symmetricized(), data)\n\n        data = np.array([[np.nan, np.nan, 1],\n                         [1528.13, np.nan, np.nan],\n                         [1497.61, 999.25, np.nan],\n                         [1062.89, 1372.59, 651.62]])\n        matrix = DistMatrix(data)\n        self.assertIs(matrix.auto_symmetricized(), matrix)\n\n        data = np.array([[np.nan, np.nan, 1],\n                         [1528.13, np.nan, np.nan],\n                         [1497.61, 999.25, np.nan],\n                         [1062.89, 1372.59, 651.62]])\n        matrix = DistMatrix(data)\n        sym = matrix.auto_symmetricized(copy=True)\n        np.testing.assert_equal(matrix, sym)\n        self.assertIsNot(sym, matrix)\n\n        matrix = DistMatrix(data.T)\n        self.assertIs(matrix.auto_symmetricized(), matrix)\n\n        data = np.array([[np.nan, np.nan, np.nan],\n                         [1528.13, np.nan, np.nan],\n                         [1497.61, 999.25, np.nan],\n                         [1062.89, 1372.59, 651.62]])\n        matrix = DistMatrix(data, None, list(\"abc\"))\n        self.assertIs(matrix.auto_symmetricized(), matrix)\n\n        matrix = DistMatrix(data.T, list(\"abc\"))\n        self.assertIs(matrix.auto_symmetricized(), matrix)\n\n        data = np.array([[1528.13, np.nan, np.nan],\n                         [1497.61, 999.25, np.nan],\n                         [1062.89, 1372.59, 651.62]])\n        matrix = DistMatrix(data, list(\"def\"), list(\"abc\"))\n        self.assertIs(matrix.auto_symmetricized(), matrix)\n\n    def test_trivial_labels(self):\n        matrix = DistMatrix(np.array([[1, 2, 3], [4, 5, 6]]))\n\n        self.assertFalse(matrix._trivial_labels(matrix.row_items))\n        self.assertIsNone(matrix.get_labels(matrix.row_items))\n\n        matrix.row_items = list(\"abc\")\n        self.assertTrue(matrix._trivial_labels(matrix.row_items))\n        self.assertEqual(matrix.get_labels(matrix.row_items), list(\"abc\"))\n\n        matrix.row_items = [\"a\", 1, \"c\"]\n        self.assertFalse(matrix._trivial_labels(matrix.row_items))\n        self.assertIsNone(matrix.get_labels(matrix.row_items))\n\n        c1, c2 = (ContinuousVariable(c) for c in \"xy\")\n        s1, s2 = (StringVariable(c) for c in \"st\")\n        data = Table.from_list(Domain([c1], None, [c2, s1]),\n                               [[1, 0, \"a\"], [2, 2, \"b\"], [3, 1, \"c\"]])\n        matrix.row_items = data\n\n        matrix.axis = 1\n        self.assertTrue(matrix._trivial_labels(matrix.row_items))\n        self.assertEqual(list(matrix.get_labels(matrix.row_items)), list(\"abc\"))\n\n        matrix.axis = 0\n        self.assertTrue(matrix._trivial_labels(matrix.row_items))\n        self.assertEqual(list(matrix.get_labels(matrix.row_items)), list(\"x\"))\n\n\n        data = Table.from_list(Domain([c1], None, [c2, s1, s2]),\n                               [[1, 2, \"a\", \"2\"],\n                                [2, 4, \"b\", \"5\"],\n                                [3, 0, \"c\", \"g\"]])\n        matrix.row_items = data\n\n        matrix.axis = 1\n        self.assertFalse(matrix._trivial_labels(matrix.row_items))\n        self.assertIsNone(matrix.get_labels(matrix.row_items))\n\n        matrix.axis = 0\n        self.assertTrue(matrix._trivial_labels(matrix.row_items))\n        self.assertEqual(list(matrix.get_labels(matrix.row_items)), list(\"x\"))\n\n        data = Table.from_list(Domain([c1], None, [c2]),\n                               [[1, 2],\n                                [2, 4],\n                                [3, 0]])\n        matrix.row_items = data\n        matrix.axis = 1\n        self.assertFalse(matrix._trivial_labels(matrix.row_items))\n        self.assertIsNone(matrix.get_labels(matrix.row_items))\n\n        matrix.axis = 0\n        self.assertTrue(matrix._trivial_labels(matrix.row_items))\n        self.assertEqual(matrix.get_labels(matrix.row_items), list(\"x\"))\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_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.7/site-packages/Orange3.egg-link (link to .)\nAdding Orange3 3.35.0.dev0+6d1df8d to easy-install.pth file\nInstalling orange-canvas script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431\nProcessing dependencies for Orange3==3.35.0.dev0+6d1df8d\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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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-f7utyq0_/serverfiles-0.3.1/setup.cfg\nRunning serverfiles-0.3.1/setup.py -q bdist_egg --dist-dir /tmp/easy_install-f7utyq0_/serverfiles-0.3.1/egg-dist-tmp-elygm7zr\ncreating /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.8/site-packages/Orange3.egg-link (link to .)\nAdding Orange3 3.35.0.dev0+6d1df8d to easy-install.pth file\nInstalling orange-canvas script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431\nProcessing dependencies for Orange3==3.35.0.dev0+6d1df8d\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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/bin\nInstalling pip3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/bin\nInstalling f2py3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.venv/bin\nInstalling f2py3.8 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/bin\nInstalling jupyter-kernelspec script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.venv/bin\nInstalling jupyter-run script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/bin\nInstalling jupyter-migrate script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.venv/bin\nInstalling jupyter-troubleshoot script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/bin\nInstalling pyftmerge script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.venv/bin\nInstalling pyftsubset script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.venv/bin\nInstalling ttx script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/bin\nInstalling ipython3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.8/site-packages\nFinished processing dependencies for Orange3==3.35.0.dev0+6d1df8d\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 80ms\nPrepared 18 packages in 23ms\nInstalled 18 packages in 211ms\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`)\nResolved 59 packages in 647ms\nInstalled 52 packages in 480ms\n + anyqt==0.2.0\n + attrs==24.2.0\n + backcall==0.2.0\n + cattrs==23.1.2\n + certifi==2024.12.14\n + charset-normalizer==3.4.1\n + commonmark==0.9.1\n + cycler==0.11.0\n + debugpy==1.7.0\n + decorator==5.1.1\n + dictdiffer==0.9.0\n + docutils==0.20.1\n + entrypoints==0.4\n + fonttools==4.38.0\n + idna==3.10\n + ipykernel==6.16.2\n + ipython==7.34.0\n + ipython-genutils==0.2.0\n + jedi==0.19.2\n + jupyter-client==7.4.9\n + jupyter-core==4.12.0\n + kiwisolver==1.4.5\n + matplotlib==3.5.3\n + matplotlib-inline==0.1.6\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 + pillow==9.5.0\n + pip==24.0\n + platformdirs==4.0.0\n + prompt-toolkit==3.0.48\n + psutil==6.1.1\n + ptyprocess==0.7.0\n + pygments==2.17.2\n + pyparsing==3.1.4\n + pyqtgraph==0.12.3\n + python-dateutil==2.9.0.post0\n + pyzmq==26.2.0\n + qasync==0.25.0\n + qtconsole==5.4.4\n + qtpy==2.4.2\n + requests==2.31.0\n + requests-cache==1.1.1\n + six==1.17.0\n + tornado==6.2\n + traitlets==5.9.0\n + url-normalize==1.4.3\n + urllib3==2.0.7\n + wcwidth==0.2.13\nResolved 2 packages in 7ms\nInstalled 2 packages in 39ms\n + psycopg2==2.9.9\n + pymssql==2.3.2\nResolved 19 packages in 163ms\nInstalled 8 packages in 1.50s\n + catboost==1.2.7\n + graphviz==0.20.1\n + pandas==1.3.5\n + plotly==5.18.0\n + pytz==2024.2\n + scipy==1.7.3\n + tenacity==8.2.3\n + xgboost==1.6.2\nResolved 5 packages in 46ms\nInstalled 3 packages in 92ms\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_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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-wb8ivh6s/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 128ms\nPrepared 15 packages in 1ms\nInstalled 15 packages in 105ms\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 94ms\nInstalled 55 packages in 92ms\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 87ms\nInstalled 41 packages in 97ms\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 11ms\nInstalled 2 packages in 14ms\n + psycopg2==2.9.10\n + pymssql==2.3.2\nResolved 23 packages in 68ms\nInstalled 6 packages in 608ms\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_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431/.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_6d1df8d358406c30c66d192599092605d51da431\nconfigfile: pyproject.toml\nplugins: anyio-4.5.2, typeguard-4.3.0\ncollected 4 items\n\nr2e_tests/test_1.py ....                                                 [100%]\n\n==================================== PASSES ====================================\n=========================== short test summary info ============================\nPASSED r2e_tests/test_1.py::DistMatrixTest::test_auto_symmetricized_dont_apply\nPASSED r2e_tests/test_1.py::DistMatrixTest::test_auto_symmetricized_result\nPASSED r2e_tests/test_1.py::DistMatrixTest::test_reader_selection\nPASSED r2e_tests/test_1.py::DistMatrixTest::test_trivial_labels\n============================== 4 passed in 1.59s ===============================\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_6d1df8d358406c30c66d192599092605d51da431\nconfigfile: pyproject.toml\nplugins: anyio-4.5.2, typeguard-4.3.0\ncollected 4 items\n\nr2e_tests/test_1.py ...F                                                 [100%]\n\n=================================== FAILURES ===================================\n______________________ DistMatrixTest.test_trivial_labels ______________________\n\nself = <r2e_tests.test_1.DistMatrixTest testMethod=test_trivial_labels>\n\n    def test_trivial_labels(self):\n        matrix = DistMatrix(np.array([[1, 2, 3], [4, 5, 6]]))\n    \n        self.assertFalse(matrix._trivial_labels(matrix.row_items))\n        self.assertIsNone(matrix.get_labels(matrix.row_items))\n    \n        matrix.row_items = list(\"abc\")\n>       self.assertTrue(matrix._trivial_labels(matrix.row_items))\nE       AssertionError: False is not true\n\nr2e_tests/test_1.py:108: AssertionError\n==================================== PASSES ====================================\n=========================== short test summary info ============================\nPASSED r2e_tests/test_1.py::DistMatrixTest::test_auto_symmetricized_dont_apply\nPASSED r2e_tests/test_1.py::DistMatrixTest::test_auto_symmetricized_result\nPASSED r2e_tests/test_1.py::DistMatrixTest::test_reader_selection\nFAILED r2e_tests/test_1.py::DistMatrixTest::test_trivial_labels - AssertionEr...\n========================= 1 failed, 3 passed in 1.33s ==========================\n",
    "old_commit_res_stderr": ""
}
Orange/misc/distmatrix.py Orange/misc/tests/test_distmatrix.py
We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
**Analysis:**

- **Key Parts:**
  - **Commit Message:** "DistMatrix: improve deduction of labels."
  - **Test Result:** The `test_trivial_labels` test failed in the old commit, asserting that `_trivial_labels` should return `True` when `row_items` is a list of strings (`list("abc")`), but it returned `False` instead.
  
- **Main Problem:**
  - The `_trivial_labels` method fails to correctly identify when `row_items` is a list of strings, resulting in `get_labels` returning `None` instead of the expected label list.

- **Expected Behavior:**
  - When `row_items` is a list of strings, `_trivial_labels` should return `True`.
  - Consequently, `get_labels` should return the list of labels provided.

- **Actual Behavior:**
  - `_trivial_labels` returns `False` for a list of strings, causing `get_labels` to return `None`.

- **Issue Description:**
  - The bug occurs when `row_items` is provided as a list of strings. The `_trivial_labels` method incorrectly identifies the labels as non-trivial, preventing the retrieval of labels through `get_labels`.

---

[ISSUE]

**Title:** DistMatrix fails to deduce labels correctly for list of strings

**Description:**

When initializing a `DistMatrix` with `row_items` as a list of strings, the matrix fails to recognize the labels as trivial. This causes the `get_labels` method to return `None` instead of the expected list of labels.

**Example Code:**

```python
import numpy as np
from Orange.misc import DistMatrix

# Initialize DistMatrix with row_items as a list of strings
matrix = DistMatrix(np.array([[1, 2, 3], [4, 5, 6]]))
matrix.row_items = list("abc")

# Attempt to retrieve labels
labels = matrix.get_labels(matrix.row_items)
print(labels)  # Expected: ['a', 'b', 'c']
```

**Expected Behavior:**
- The `get_labels` method should return the list `['a', 'b', 'c']` when `row_items` is a list of strings.

**Actual Behavior:**
- The `get_labels` method returns `None`, indicating that it failed to recognize the labels as trivial.

[/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/misc/distmatrix.py
import os.path

import numpy as np

from Orange.misc import _distmatrix_xlsx
from Orange.util import deprecated


class DistMatrix(np.ndarray):
    """
    Distance matrix. Extends ``numpy.ndarray``.

    .. attribute:: row_items

        Items corresponding to matrix rows.

    .. attribute:: col_items

        Items corresponding to matrix columns.

    .. attribute:: axis

        If axis=1 we calculate distances between rows,
        if axis=0 we calculate distances between columns.
    """
    def __new__(cls, data, row_items=None, col_items=None, axis=1):
        """Construct a new distance matrix containing the given data.

        :param data: Distance matrix
        :type data: numpy array
        :param row_items: Items in matrix rows
        :type row_items: `Orange.data.Table` or `Orange.data.Instance`
        :param col_items: Items in matrix columns
        :type col_items: `Orange.data.Table` or `Orange.data.Instance`
        :param axis: The axis along which the distances are calculated
        :type axis: int

        """
        obj = np.asarray(data).view(cls)
        obj.row_items = row_items
        obj.col_items = col_items
        obj.axis = axis
        return obj

    def __array_finalize__(self, obj):
        """See http://docs.scipy.org/doc/numpy/user/basics.subclassing.html"""
        if obj is None:
            return
        self.row_items = getattr(obj, 'row_items', None)
        self.col_items = getattr(obj, 'col_items', None)
        self.axis = getattr(obj, 'axis', 1)

    def __array_wrap__(self, out_arr, context=None):
        if out_arr.ndim == 0:  # a single scalar
            return out_arr[()]
        return np.ndarray.__array_wrap__(self, out_arr, context)

    # __reduce__() and __setstate__() ensure DistMatrix is picklable.
    def __reduce__(self):
        state = super().__reduce__()
        newstate = state[2] + (self.row_items, self.col_items, self.axis)
        return state[0], state[1], newstate

    # noinspection PyMethodOverriding,PyArgumentList
    def __setstate__(self, state):
        self.row_items = state[-3]
        self.col_items = state[-2]
        self.axis = state[-1]
        super().__setstate__(state[0:-3])

    @property
    @deprecated
    def dim(self):
        """Returns the single dimension of the symmetric square matrix."""
        return self.shape[0]

    # noinspection PyPep8Naming
    @property
    @deprecated
    def X(self):
        return self

    @property
    def flat(self):
        return self[np.triu_indices(self.shape[0], 1)]

    def submatrix(self, row_items, col_items=None):
        """
        Return a submatrix

        Args:
            row_items: indices of rows
            col_items: incides of columns
        """
        if not col_items:
            col_items = row_items
        obj = self[np.ix_(row_items, col_items)]
        if self.row_items is not None:
            obj.row_items = self.row_items[row_items]
        if self.col_items is not None:
            if self.col_items is self.row_items and row_items is col_items:
                obj.col_items = obj.row_items
            else:
                obj.col_items = self.col_items[col_items]
        return obj

    @classmethod
    def from_file(cls, filename, sheet=None):
        """
        Load distance matrix from a file

        The file should be preferrably encoded in ascii/utf-8. White space at
        the beginning and end of lines is ignored.

        The first line of the file starts with the matrix dimension. It
        can be followed by a list flags

        - *axis=<number>*: the axis number
        - *symmetric*: the matrix is symmetric; when reading the element (i, j)
          it's value is also assigned to (j, i)
        - *asymmetric*: the matrix is asymmetric
        - *row_labels*: the file contains row labels
        - *col_labels*: the file contains column labels

        By default, matrices are symmetric, have axis 1 and no labels are given.
        Flags *labeled* and *labelled* are obsolete aliases for *row_labels*.

        If the file has column labels, they follow in the second line.
        Row labels appear at the beginning of each row.
        Labels are arbitrary strings that cannot contain newlines and
        tabulators. Labels are stored as instances of `Table` with a single
        meta attribute named "label".

        The remaining lines contain tab-separated numbers, preceded with labels,
        if present. Lines are padded with zeros if necessary. If the matrix is
        symmetric, the file contains the lower triangle; any data above the
        diagonal is ignored.

        Args:
            filename: file name
        """
        _, ext = os.path.splitext(filename)
        if ext == ".xlsx":
            matrix, row_labels, col_labels, axis \
                = _distmatrix_xlsx.read_matrix(filename, sheet)
        else:
            assert sheet is None
            matrix, row_labels, col_labels, axis = cls._from_dst(filename)
        return cls(matrix,
                   cls._labels_to_tables(row_labels),
                   cls._labels_to_tables(col_labels),
                   axis)

    @staticmethod
    def _labels_to_tables(labels):
        # prevent circular imports, pylint: disable=import-outside-toplevel
        from Orange.data import Table, StringVariable, Domain

        if labels is None or isinstance(labels, Table):
            return labels
        return Table.from_numpy(
            Domain([], metas=[StringVariable("label")]),
            np.empty((len(labels), 0)), None, np.array(labels)[:, None])

    @classmethod
    def _from_dst(cls, filename):
        # prevent circular imports, pylint: disable=import-outside-toplevel
        from Orange.data.io import detect_encoding

        with open(filename, encoding=detect_encoding(filename)) as fle:
            line = fle.readline()
            if not line:
                raise ValueError("empty file")
            data = line.strip().split()
            if not data[0].strip().isdigit():
                raise ValueError("distance file must begin with dimension")
            n = int(data.pop(0))
            symmetric = True
            axis = 1
            col_labels = row_labels = None
            for flag in data:
                if flag in ("labelled", "labeled", "row_labels"):
                    row_labels = []
                elif flag == "col_labels":
                    col_labels = []
                elif flag == "symmetric":
                    symmetric = True
                elif flag == "asymmetric":
                    symmetric = False
                else:
                    flag_data = flag.split("=")
                    if len(flag_data) == 2:
                        name, value = map(str.strip, flag_data)
                    else:
                        name, value = "", None
                    if name == "axis" and value.isdigit():
                        axis = int(value)
                    else:
                        raise ValueError(f"invalid flag '{flag}'")
            if col_labels is not None:
                col_labels = [x.strip()
                              for x in fle.readline().strip().split("\t")]
                if len(col_labels) != n:
                    raise ValueError("mismatching number of column labels, "
                                     f"{len(col_labels)} != {n}")

            def num_or_lab(n, labels):
                return f"'{labels[n]}'" if labels else str(n + 1)

            matrix = np.zeros((n, n))
            for i, line in enumerate(fle):
                if i >= n:
                    raise ValueError("too many rows")
                line = line.strip().split("\t")
                if row_labels is not None:
                    row_labels.append(line.pop(0).strip())
                if len(line) > n:
                    raise ValueError(
                        f"too many columns in matrix row "
                        f"{num_or_lab(i, row_labels)}")
                for j, e in enumerate(line[:i + 1 if symmetric else n]):
                    try:
                        matrix[i, j] = float(e)
                    except ValueError as exc:
                        raise ValueError(
                            "invalid element at "
                            f"row {num_or_lab(i, row_labels)}, "
                            f"column {num_or_lab(j, col_labels)}") from exc
                    if symmetric:
                        matrix[j, i] = matrix[i, j]
            return matrix, row_labels, col_labels, axis

    def auto_symmetricized(self, copy=False):
        def self_or_copy():
            return self.copy() if copy else self

        def get_labels(labels):
            return np.array(labels) if isinstance(labels, list) \
                else labels.metas[:, 0] if self._trivial_labels(labels) \
                else object()

        h, w = self.shape
        m = max(w, h)
        if (abs(h - w) > 1
                or self.row_items and self.col_items
                   and np.any(get_labels(self.row_items)
                              != get_labels(self.col_items))
                or self.row_items and len(self.row_items) != m
                or self.col_items and len(self.col_items) != m):
            return self_or_copy()

        nans = np.isnan(self)
        low_indices = np.tril_indices(h, -1)
        low_empty = np.all(nans[low_indices])
        high_indices = np.triu_indices(w, 1)
        high_empty = np.all(nans[high_indices])
        if low_empty is high_empty:  # both non-empty, or both empty (only diagonal)
            return self_or_copy()

        indices = low_indices if low_empty else high_indices
        if w == h:
            matrix = np.array(self)
        else:
            if low_empty:
                row = np.vstack((self[:, -1, None], [[0]])).T
                matrix = np.vstack((self, row))
            else:
                col = np.hstack((self[-1, None], [[0]])).T
                matrix = np.hstack((self, col))
            diag_indices = np.diag_indices(len(matrix))
            matrix[diag_indices] = np.nan_to_num(matrix[diag_indices])
        matrix[indices] = self.T[indices]
        return type(self)(matrix,
                          self.row_items or self.col_items,
                          self.col_items or self.row_items)

    @staticmethod
    def _trivial_labels(items):
        # prevent circular imports, pylint: disable=import-outside-toplevel
        from Orange.data import Table, StringVariable

        return items and \
               isinstance(items, Table) and \
               len(items.domain.metas) == 1 and \
               isinstance(items.domain.metas[0], StringVariable)

    def is_symmetric(self):
        # prevent circular imports, pylint: disable=import-outside-toplevel
        from Orange.data import Table

        if self.shape[0] != self.shape[1] or not np.allclose(self, self.T):
            return False
        if self.row_items is None or self.col_items is None:
            return True
        if isinstance(self.row_items, Table):
            return (isinstance(self.col_items, Table)
                and self.col_items.domain == self.row_items.domain
                and np.array_equal(self.col_items.X, self.row_items.X)
                and np.array_equal(self.col_items.Y, self.row_items.Y)
                and np.array_equal(self.col_items.metas, self.row_items.metas))
        else:
            return (not isinstance(self.col_items, Table)
                and np.array_equal(self.row_items, self.col_items))

    def has_row_labels(self):
        """
        Returns `True` if row labels can be automatically determined from data

        For this, the `row_items` must be an instance of `Orange.data.Table`
        whose domain contains a single meta attribute, which has to be a string.
        The domain may contain other variables, but not meta attributes.
        """
        return self._trivial_labels(self.row_items)

    def has_col_labels(self):
        """
        Returns `True` if column labels can be automatically determined from
        data

        For this, the `col_items` must be an instance of `Orange.data.Table`
        whose domain contains a single meta attribute, which has to be a string.
        The domain may contain other variables, but not meta attributes.
        """
        return self._trivial_labels(self.col_items)

    def get_labels(self, items):
        if self._trivial_labels(items):
            return items.get_column(items.domain.metas[0])
        elif isinstance(items, (list, tuple)) \
                and all(isinstance(x, str) for x in items):
            return items
        else:
            return None

    def save(self, filename):
        if os.path.splitext(filename)[1] == ".xlsx":
            _distmatrix_xlsx.write_matrix(self, filename)
        else:
            self._save_dst(filename)

    def _save_dst(self, filename):
        """
        Save the distance matrix to a file in the file format described at
        :obj:`~Orange.misc.distmatrix.DistMatrix.from_file`.

        Args:
            filename: file name
        """
        n = len(self)
        data = "{}\taxis={}".format(n, self.axis)
        row_labels = col_labels = None
        if self.has_col_labels():
            data += "\tcol_labels"
            col_labels = self.col_items
        if self.has_row_labels():
            data += "\trow_labels"
            row_labels = self.row_items
        symmetric = self.is_symmetric()
        if not symmetric:
            data += "\tasymmetric"
        with open(filename, "wt") as fle:
            fle.write(data + "\n")
            if col_labels is not None:
                fle.write("\t".join(str(e.metas[0]) for e in col_labels) + "\n")
            for i, row in enumerate(self):
                if row_labels is not None:
                    fle.write(str(row_labels[i].metas[0]) + "\t")
                if symmetric:
                    fle.write("\t".join(map(str, row[:i + 1])) + "\n")
                else:
                    fle.write("\t".join(map(str, row)) + "\n")

```
--- 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.
**Analysis:**

- **Key Parts:**
  - **Commit Message:** "DistMatrix: improve deduction of labels."
  - **Test Result:** The `test_trivial_labels` test failed in the old commit, asserting that `_trivial_labels` should return `True` when `row_items` is a list of strings (`list("abc")`), but it returned `False` instead.
  
- **Main Problem:**
  - The `_trivial_labels` method fails to correctly identify when `row_items` is a list of strings, resulting in `get_labels` returning `None` instead of the expected label list.

- **Expected Behavior:**
  - When `row_items` is a list of strings, `_trivial_labels` should return `True`.
  - Consequently, `get_labels` should return the list of labels provided.

- **Actual Behavior:**
  - `_trivial_labels` returns `False` for a list of strings, causing `get_labels` to return `None`.

- **Issue Description:**
  - The bug occurs when `row_items` is provided as a list of strings. The `_trivial_labels` method incorrectly identifies the labels as non-trivial, preventing the retrieval of labels through `get_labels`.

---

[ISSUE]

**Title:** DistMatrix fails to deduce labels correctly for list of strings

**Description:**

When initializing a `DistMatrix` with `row_items` as a list of strings, the matrix fails to recognize the labels as trivial. This causes the `get_labels` method to return `None` instead of the expected list of labels.

**Example Code:**

```python
import numpy as np
from Orange.misc import DistMatrix

# Initialize DistMatrix with row_items as a list of strings
matrix = DistMatrix(np.array([[1, 2, 3], [4, 5, 6]]))
matrix.row_items = list("abc")

# Attempt to retrieve labels
labels = matrix.get_labels(matrix.row_items)
print(labels)  # Expected: ['a', 'b', 'c']
```

**Expected Behavior:**
- The `get_labels` method should return the list `['a', 'b', 'c']` when `row_items` is a list of strings.

**Actual Behavior:**
- The `get_labels` method returns `None`, indicating that it failed to recognize the labels as trivial.

[/ISSUE]
{
    "DistMatrixTest.test_auto_symmetricized_dont_apply": "PASSED",
    "DistMatrixTest.test_auto_symmetricized_result": "PASSED",
    "DistMatrixTest.test_reader_selection": "PASSED",
    "DistMatrixTest.test_trivial_labels": "PASSED"
}
diff --git a/Orange/misc/distmatrix.py b/Orange/misc/distmatrix.py
index 68827da7c..c56fa169c 100644
--- a/Orange/misc/distmatrix.py
+++ b/Orange/misc/distmatrix.py
@@ -274,15 +274,19 @@ class DistMatrix(np.ndarray):
                           self.row_items or self.col_items,
                           self.col_items or self.row_items)
 
-    @staticmethod
-    def _trivial_labels(items):
+    def _trivial_labels(self, items):
         # prevent circular imports, pylint: disable=import-outside-toplevel
         from Orange.data import Table, StringVariable
 
-        return items and \
-               isinstance(items, Table) and \
-               len(items.domain.metas) == 1 and \
-               isinstance(items.domain.metas[0], StringVariable)
+        return (isinstance(items, (list, tuple))
+                and all(isinstance(item, str) for item in items)
+                or
+                isinstance(items, Table)
+                and (self.axis == 0 or
+                     sum(isinstance(meta, StringVariable)
+                         for meta in items.domain.metas) == 1
+                     )
+                )
 
     def is_symmetric(self):
         # prevent circular imports, pylint: disable=import-outside-toplevel
@@ -324,13 +328,20 @@ class DistMatrix(np.ndarray):
         return self._trivial_labels(self.col_items)
 
     def get_labels(self, items):
-        if self._trivial_labels(items):
-            return items.get_column(items.domain.metas[0])
-        elif isinstance(items, (list, tuple)) \
+        # prevent circular imports, pylint: disable=import-outside-toplevel
+        from Orange.data import StringVariable
+
+        if not self._trivial_labels(items):
+            return None
+        if isinstance(items, (list, tuple)) \
                 and all(isinstance(x, str) for x in items):
             return items
+        if self.axis == 0:
+            return [attr.name for attr in items.domain.attributes]
         else:
-            return None
+            string_var = next(var for var in items.domain.metas
+                              if isinstance(var, StringVariable))
+            return items.get_column(string_var)
 
     def save(self, filename):
         if os.path.splitext(filename)[1] == ".xlsx":

{"Orange/misc/distmatrix.py": "import os.path\n\nimport numpy as np\n\nfrom Orange.misc import _distmatrix_xlsx\nfrom Orange.util import deprecated\n\n\nclass DistMatrix(np.ndarray):\n    \"\"\"\n    Distance matrix. Extends ``numpy.ndarray``.\n\n    .. attribute:: row_items\n\n        Items corresponding to matrix rows.\n\n    .. attribute:: col_items\n\n        Items corresponding to matrix columns.\n\n    .. attribute:: axis\n\n        If axis=1 we calculate distances between rows,\n        if axis=0 we calculate distances between columns.\n    \"\"\"\n    def __new__(cls, data, row_items=None, col_items=None, axis=1):\n        \"\"\"Construct a new distance matrix containing the given data.\n\n        :param data: Distance matrix\n        :type data: numpy array\n        :param row_items: Items in matrix rows\n        :type row_items: `Orange.data.Table` or `Orange.data.Instance`\n        :param col_items: Items in matrix columns\n        :type col_items: `Orange.data.Table` or `Orange.data.Instance`\n        :param axis: The axis along which the distances are calculated\n        :type axis: int\n\n        \"\"\"\n        obj = np.asarray(data).view(cls)\n        obj.row_items = row_items\n        obj.col_items = col_items\n        obj.axis = axis\n        return obj\n\n    def __array_finalize__(self, obj):\n        \"\"\"See http://docs.scipy.org/doc/numpy/user/basics.subclassing.html\"\"\"\n        if obj is None:\n            return\n        self.row_items = getattr(obj, 'row_items', None)\n        self.col_items = getattr(obj, 'col_items', None)\n        self.axis = getattr(obj, 'axis', 1)\n\n    def __array_wrap__(self, out_arr, context=None):\n        if out_arr.ndim == 0:  # a single scalar\n            return out_arr[()]\n        return np.ndarray.__array_wrap__(self, out_arr, context)\n\n    # __reduce__() and __setstate__() ensure DistMatrix is picklable.\n    def __reduce__(self):\n        state = super().__reduce__()\n        newstate = state[2] + (self.row_items, self.col_items, self.axis)\n        return state[0], state[1], newstate\n\n    # noinspection PyMethodOverriding,PyArgumentList\n    def __setstate__(self, state):\n        self.row_items = state[-3]\n        self.col_items = state[-2]\n        self.axis = state[-1]\n        super().__setstate__(state[0:-3])\n\n    @property\n    @deprecated\n    def dim(self):\n        \"\"\"Returns the single dimension of the symmetric square matrix.\"\"\"\n        return self.shape[0]\n\n    # noinspection PyPep8Naming\n    @property\n    @deprecated\n    def X(self):\n        return self\n\n    @property\n    def flat(self):\n        return self[np.triu_indices(self.shape[0], 1)]\n\n    def submatrix(self, row_items, col_items=None):\n        \"\"\"\n        Return a submatrix\n\n        Args:\n            row_items: indices of rows\n            col_items: incides of columns\n        \"\"\"\n        if not col_items:\n            col_items = row_items\n        obj = self[np.ix_(row_items, col_items)]\n        if self.row_items is not None:\n            obj.row_items = self.row_items[row_items]\n        if self.col_items is not None:\n            if self.col_items is self.row_items and row_items is col_items:\n                obj.col_items = obj.row_items\n            else:\n                obj.col_items = self.col_items[col_items]\n        return obj\n\n    @classmethod\n    def from_file(cls, filename, sheet=None):\n        \"\"\"\n        Load distance matrix from a file\n\n        The file should be preferrably encoded in ascii/utf-8. White space at\n        the beginning and end of lines is ignored.\n\n        The first line of the file starts with the matrix dimension. It\n        can be followed by a list flags\n\n        - *axis=<number>*: the axis number\n        - *symmetric*: the matrix is symmetric; when reading the element (i, j)\n          it's value is also assigned to (j, i)\n        - *asymmetric*: the matrix is asymmetric\n        - *row_labels*: the file contains row labels\n        - *col_labels*: the file contains column labels\n\n        By default, matrices are symmetric, have axis 1 and no labels are given.\n        Flags *labeled* and *labelled* are obsolete aliases for *row_labels*.\n\n        If the file has column labels, they follow in the second line.\n        Row labels appear at the beginning of each row.\n        Labels are arbitrary strings that cannot contain newlines and\n        tabulators. Labels are stored as instances of `Table` with a single\n        meta attribute named \"label\".\n\n        The remaining lines contain tab-separated numbers, preceded with labels,\n        if present. Lines are padded with zeros if necessary. If the matrix is\n        symmetric, the file contains the lower triangle; any data above the\n        diagonal is ignored.\n\n        Args:\n            filename: file name\n        \"\"\"\n        _, ext = os.path.splitext(filename)\n        if ext == \".xlsx\":\n            matrix, row_labels, col_labels, axis \\\n                = _distmatrix_xlsx.read_matrix(filename, sheet)\n        else:\n            assert sheet is None\n            matrix, row_labels, col_labels, axis = cls._from_dst(filename)\n        return cls(matrix,\n                   cls._labels_to_tables(row_labels),\n                   cls._labels_to_tables(col_labels),\n                   axis)\n\n    @staticmethod\n    def _labels_to_tables(labels):\n        # prevent circular imports, pylint: disable=import-outside-toplevel\n        from Orange.data import Table, StringVariable, Domain\n\n        if labels is None or isinstance(labels, Table):\n            return labels\n        return Table.from_numpy(\n            Domain([], metas=[StringVariable(\"label\")]),\n            np.empty((len(labels), 0)), None, np.array(labels)[:, None])\n\n    @classmethod\n    def _from_dst(cls, filename):\n        # prevent circular imports, pylint: disable=import-outside-toplevel\n        from Orange.data.io import detect_encoding\n\n        with open(filename, encoding=detect_encoding(filename)) as fle:\n            line = fle.readline()\n            if not line:\n                raise ValueError(\"empty file\")\n            data = line.strip().split()\n            if not data[0].strip().isdigit():\n                raise ValueError(\"distance file must begin with dimension\")\n            n = int(data.pop(0))\n            symmetric = True\n            axis = 1\n            col_labels = row_labels = None\n            for flag in data:\n                if flag in (\"labelled\", \"labeled\", \"row_labels\"):\n                    row_labels = []\n                elif flag == \"col_labels\":\n                    col_labels = []\n                elif flag == \"symmetric\":\n                    symmetric = True\n                elif flag == \"asymmetric\":\n                    symmetric = False\n                else:\n                    flag_data = flag.split(\"=\")\n                    if len(flag_data) == 2:\n                        name, value = map(str.strip, flag_data)\n                    else:\n                        name, value = \"\", None\n                    if name == \"axis\" and value.isdigit():\n                        axis = int(value)\n                    else:\n                        raise ValueError(f\"invalid flag '{flag}'\")\n            if col_labels is not None:\n                col_labels = [x.strip()\n                              for x in fle.readline().strip().split(\"\\t\")]\n                if len(col_labels) != n:\n                    raise ValueError(\"mismatching number of column labels, \"\n                                     f\"{len(col_labels)} != {n}\")\n\n            def num_or_lab(n, labels):\n                return f\"'{labels[n]}'\" if labels else str(n + 1)\n\n            matrix = np.zeros((n, n))\n            for i, line in enumerate(fle):\n                if i >= n:\n                    raise ValueError(\"too many rows\")\n                line = line.strip().split(\"\\t\")\n                if row_labels is not None:\n                    row_labels.append(line.pop(0).strip())\n                if len(line) > n:\n                    raise ValueError(\n                        f\"too many columns in matrix row \"\n                        f\"{num_or_lab(i, row_labels)}\")\n                for j, e in enumerate(line[:i + 1 if symmetric else n]):\n                    try:\n                        matrix[i, j] = float(e)\n                    except ValueError as exc:\n                        raise ValueError(\n                            \"invalid element at \"\n                            f\"row {num_or_lab(i, row_labels)}, \"\n                            f\"column {num_or_lab(j, col_labels)}\") from exc\n                    if symmetric:\n                        matrix[j, i] = matrix[i, j]\n            return matrix, row_labels, col_labels, axis\n\n    def auto_symmetricized(self, copy=False):\n        def self_or_copy():\n            return self.copy() if copy else self\n\n        def get_labels(labels):\n            return np.array(labels) if isinstance(labels, list) \\\n                else labels.metas[:, 0] if self._trivial_labels(labels) \\\n                else object()\n\n        h, w = self.shape\n        m = max(w, h)\n        if (abs(h - w) > 1\n                or self.row_items and self.col_items\n                   and np.any(get_labels(self.row_items)\n                              != get_labels(self.col_items))\n                or self.row_items and len(self.row_items) != m\n                or self.col_items and len(self.col_items) != m):\n            return self_or_copy()\n\n        nans = np.isnan(self)\n        low_indices = np.tril_indices(h, -1)\n        low_empty = np.all(nans[low_indices])\n        high_indices = np.triu_indices(w, 1)\n        high_empty = np.all(nans[high_indices])\n        if low_empty is high_empty:  # both non-empty, or both empty (only diagonal)\n            return self_or_copy()\n\n        indices = low_indices if low_empty else high_indices\n        if w == h:\n            matrix = np.array(self)\n        else:\n            if low_empty:\n                row = np.vstack((self[:, -1, None], [[0]])).T\n                matrix = np.vstack((self, row))\n            else:\n                col = np.hstack((self[-1, None], [[0]])).T\n                matrix = np.hstack((self, col))\n            diag_indices = np.diag_indices(len(matrix))\n            matrix[diag_indices] = np.nan_to_num(matrix[diag_indices])\n        matrix[indices] = self.T[indices]\n        return type(self)(matrix,\n                          self.row_items or self.col_items,\n                          self.col_items or self.row_items)\n\n    @staticmethod\n    def _trivial_labels(items):\n        # prevent circular imports, pylint: disable=import-outside-toplevel\n        from Orange.data import Table, StringVariable\n\n        return items and \\\n               isinstance(items, Table) and \\\n               len(items.domain.metas) == 1 and \\\n               isinstance(items.domain.metas[0], StringVariable)\n\n    def is_symmetric(self):\n        # prevent circular imports, pylint: disable=import-outside-toplevel\n        from Orange.data import Table\n\n        if self.shape[0] != self.shape[1] or not np.allclose(self, self.T):\n            return False\n        if self.row_items is None or self.col_items is None:\n            return True\n        if isinstance(self.row_items, Table):\n            return (isinstance(self.col_items, Table)\n                and self.col_items.domain == self.row_items.domain\n                and np.array_equal(self.col_items.X, self.row_items.X)\n                and np.array_equal(self.col_items.Y, self.row_items.Y)\n                and np.array_equal(self.col_items.metas, self.row_items.metas))\n        else:\n            return (not isinstance(self.col_items, Table)\n                and np.array_equal(self.row_items, self.col_items))\n\n    def has_row_labels(self):\n        \"\"\"\n        Returns `True` if row labels can be automatically determined from data\n\n        For this, the `row_items` must be an instance of `Orange.data.Table`\n        whose domain contains a single meta attribute, which has to be a string.\n        The domain may contain other variables, but not meta attributes.\n        \"\"\"\n        return self._trivial_labels(self.row_items)\n\n    def has_col_labels(self):\n        \"\"\"\n        Returns `True` if column labels can be automatically determined from\n        data\n\n        For this, the `col_items` must be an instance of `Orange.data.Table`\n        whose domain contains a single meta attribute, which has to be a string.\n        The domain may contain other variables, but not meta attributes.\n        \"\"\"\n        return self._trivial_labels(self.col_items)\n\n    def get_labels(self, items):\n        if self._trivial_labels(items):\n            return items.get_column(items.domain.metas[0])\n        elif isinstance(items, (list, tuple)) \\\n                and all(isinstance(x, str) for x in items):\n            return items\n        else:\n            return None\n\n    def save(self, filename):\n        if os.path.splitext(filename)[1] == \".xlsx\":\n            _distmatrix_xlsx.write_matrix(self, filename)\n        else:\n            self._save_dst(filename)\n\n    def _save_dst(self, filename):\n        \"\"\"\n        Save the distance matrix to a file in the file format described at\n        :obj:`~Orange.misc.distmatrix.DistMatrix.from_file`.\n\n        Args:\n            filename: file name\n        \"\"\"\n        n = len(self)\n        data = \"{}\\taxis={}\".format(n, self.axis)\n        row_labels = col_labels = None\n        if self.has_col_labels():\n            data += \"\\tcol_labels\"\n            col_labels = self.col_items\n        if self.has_row_labels():\n            data += \"\\trow_labels\"\n            row_labels = self.row_items\n        symmetric = self.is_symmetric()\n        if not symmetric:\n            data += \"\\tasymmetric\"\n        with open(filename, \"wt\") as fle:\n            fle.write(data + \"\\n\")\n            if col_labels is not None:\n                fle.write(\"\\t\".join(str(e.metas[0]) for e in col_labels) + \"\\n\")\n            for i, row in enumerate(self):\n                if row_labels is not None:\n                    fle.write(str(row_labels[i].metas[0]) + \"\\t\")\n                if symmetric:\n                    fle.write(\"\\t\".join(map(str, row[:i + 1])) + \"\\n\")\n                else:\n                    fle.write(\"\\t\".join(map(str, row)) + \"\\n\")\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 ---
**Analysis:**

- **Key Parts:**
  - **Commit Message:** "DistMatrix: improve deduction of labels."
  - **Test Result:** The `test_trivial_labels` test failed in the old commit, asserting that `_trivial_labels` should return `True` when `row_items` is a list of strings (`list("abc")`), but it returned `False` instead.
  
- **Main Problem:**
  - The `_trivial_labels` method fails to correctly identify when `row_items` is a list of strings, resulting in `get_labels` returning `None` instead of the expected label list.

- **Expected Behavior:**
  - When `row_items` is a list of strings, `_trivial_labels` should return `True`.
  - Consequently, `get_labels` should return the list of labels provided.

- **Actual Behavior:**
  - `_trivial_labels` returns `False` for a list of strings, causing `get_labels` to return `None`.

- **Issue Description:**
  - The bug occurs when `row_items` is provided as a list of strings. The `_trivial_labels` method incorrectly identifies the labels as non-trivial, preventing the retrieval of labels through `get_labels`.

---

[ISSUE]

**Title:** DistMatrix fails to deduce labels correctly for list of strings

**Description:**

When initializing a `DistMatrix` with `row_items` as a list of strings, the matrix fails to recognize the labels as trivial. This causes the `get_labels` method to return `None` instead of the expected list of labels.

**Example Code:**

```python
import numpy as np
from Orange.misc import DistMatrix

# Initialize DistMatrix with row_items as a list of strings
matrix = DistMatrix(np.array([[1, 2, 3], [4, 5, 6]]))
matrix.row_items = list("abc")

# Attempt to retrieve labels
labels = matrix.get_labels(matrix.row_items)
print(labels)  # Expected: ['a', 'b', 'c']
```

**Expected Behavior:**
- The `get_labels` method should return the list `['a', 'b', 'c']` when `row_items` is a list of strings.

**Actual Behavior:**
- The `get_labels` method returns `None`, indicating that it failed to recognize the labels as trivial.

[/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/misc/distmatrix.py
import os.path

import numpy as np

from Orange.misc import _distmatrix_xlsx
from Orange.util import deprecated


class DistMatrix(np.ndarray):
    """
    Distance matrix. Extends ``numpy.ndarray``.

    .. attribute:: row_items

        Items corresponding to matrix rows.

    .. attribute:: col_items

        Items corresponding to matrix columns.

    .. attribute:: axis

        If axis=1 we calculate distances between rows,
        if axis=0 we calculate distances between columns.
    """
    def __new__(cls, data, row_items=None, col_items=None, axis=1):
        """Construct a new distance matrix containing the given data.

        :param data: Distance matrix
        :type data: numpy array
        :param row_items: Items in matrix rows
        :type row_items: `Orange.data.Table` or `Orange.data.Instance`
        :param col_items: Items in matrix columns
        :type col_items: `Orange.data.Table` or `Orange.data.Instance`
        :param axis: The axis along which the distances are calculated
        :type axis: int

        """
        obj = np.asarray(data).view(cls)
        obj.row_items = row_items
        obj.col_items = col_items
        obj.axis = axis
        return obj

    def __array_finalize__(self, obj):
        """See http://docs.scipy.org/doc/numpy/user/basics.subclassing.html"""
        if obj is None:
            return
        self.row_items = getattr(obj, 'row_items', None)
        self.col_items = getattr(obj, 'col_items', None)
        self.axis = getattr(obj, 'axis', 1)

    def __array_wrap__(self, out_arr, context=None):
        if out_arr.ndim == 0:  # a single scalar
            return out_arr[()]
        return np.ndarray.__array_wrap__(self, out_arr, context)

    # __reduce__() and __setstate__() ensure DistMatrix is picklable.
    def __reduce__(self):
        state = super().__reduce__()
        newstate = state[2] + (self.row_items, self.col_items, self.axis)
        return state[0], state[1], newstate

    # noinspection PyMethodOverriding,PyArgumentList
    def __setstate__(self, state):
        self.row_items = state[-3]
        self.col_items = state[-2]
        self.axis = state[-1]
        super().__setstate__(state[0:-3])

    @property
    @deprecated
    def dim(self):
        """Returns the single dimension of the symmetric square matrix."""
        return self.shape[0]

    # noinspection PyPep8Naming
    @property
    @deprecated
    def X(self):
        return self

    @property
    def flat(self):
        return self[np.triu_indices(self.shape[0], 1)]

    def submatrix(self, row_items, col_items=None):
        """
        Return a submatrix

        Args:
            row_items: indices of rows
            col_items: incides of columns
        """
        if not col_items:
            col_items = row_items
        obj = self[np.ix_(row_items, col_items)]
        if self.row_items is not None:
            obj.row_items = self.row_items[row_items]
        if self.col_items is not None:
            if self.col_items is self.row_items and row_items is col_items:
                obj.col_items = obj.row_items
            else:
                obj.col_items = self.col_items[col_items]
        return obj

    @classmethod
    def from_file(cls, filename, sheet=None):
        """
        Load distance matrix from a file

        The file should be preferrably encoded in ascii/utf-8. White space at
        the beginning and end of lines is ignored.

        The first line of the file starts with the matrix dimension. It
        can be followed by a list flags

        - *axis=<number>*: the axis number
        - *symmetric*: the matrix is symmetric; when reading the element (i, j)
          it's value is also assigned to (j, i)
        - *asymmetric*: the matrix is asymmetric
        - *row_labels*: the file contains row labels
        - *col_labels*: the file contains column labels

        By default, matrices are symmetric, have axis 1 and no labels are given.
        Flags *labeled* and *labelled* are obsolete aliases for *row_labels*.

        If the file has column labels, they follow in the second line.
        Row labels appear at the beginning of each row.
        Labels are arbitrary strings that cannot contain newlines and
        tabulators. Labels are stored as instances of `Table` with a single
        meta attribute named "label".

        The remaining lines contain tab-separated numbers, preceded with labels,
        if present. Lines are padded with zeros if necessary. If the matrix is
        symmetric, the file contains the lower triangle; any data above the
        diagonal is ignored.

        Args:
            filename: file name
        """
        _, ext = os.path.splitext(filename)
        if ext == ".xlsx":
            matrix, row_labels, col_labels, axis \
                = _distmatrix_xlsx.read_matrix(filename, sheet)
        else:
            assert sheet is None
            matrix, row_labels, col_labels, axis = cls._from_dst(filename)
        return cls(matrix,
                   cls._labels_to_tables(row_labels),
                   cls._labels_to_tables(col_labels),
                   axis)

    @staticmethod
    def _labels_to_tables(labels):
        # prevent circular imports, pylint: disable=import-outside-toplevel
        from Orange.data import Table, StringVariable, Domain

        if labels is None or isinstance(labels, Table):
            return labels
        return Table.from_numpy(
            Domain([], metas=[StringVariable("label")]),
            np.empty((len(labels), 0)), None, np.array(labels)[:, None])

    @classmethod
    def _from_dst(cls, filename):
        # prevent circular imports, pylint: disable=import-outside-toplevel
        from Orange.data.io import detect_encoding

        with open(filename, encoding=detect_encoding(filename)) as fle:
            line = fle.readline()
            if not line:
                raise ValueError("empty file")
            data = line.strip().split()
            if not data[0].strip().isdigit():
                raise ValueError("distance file must begin with dimension")
            n = int(data.pop(0))
            symmetric = True
            axis = 1
            col_labels = row_labels = None
            for flag in data:
                if flag in ("labelled", "labeled", "row_labels"):
                    row_labels = []
                elif flag == "col_labels":
                    col_labels = []
                elif flag == "symmetric":
                    symmetric = True
                elif flag == "asymmetric":
                    symmetric = False
                else:
                    flag_data = flag.split("=")
                    if len(flag_data) == 2:
                        name, value = map(str.strip, flag_data)
                    else:
                        name, value = "", None
                    if name == "axis" and value.isdigit():
                        axis = int(value)
                    else:
                        raise ValueError(f"invalid flag '{flag}'")
            if col_labels is not None:
                col_labels = [x.strip()
                              for x in fle.readline().strip().split("\t")]
                if len(col_labels) != n:
                    raise ValueError("mismatching number of column labels, "
                                     f"{len(col_labels)} != {n}")

            def num_or_lab(n, labels):
                return f"'{labels[n]}'" if labels else str(n + 1)

            matrix = np.zeros((n, n))
            for i, line in enumerate(fle):
                if i >= n:
                    raise ValueError("too many rows")
                line = line.strip().split("\t")
                if row_labels is not None:
                    row_labels.append(line.pop(0).strip())
                if len(line) > n:
                    raise ValueError(
                        f"too many columns in matrix row "
                        f"{num_or_lab(i, row_labels)}")
                for j, e in enumerate(line[:i + 1 if symmetric else n]):
                    try:
                        matrix[i, j] = float(e)
                    except ValueError as exc:
                        raise ValueError(
                            "invalid element at "
                            f"row {num_or_lab(i, row_labels)}, "
                            f"column {num_or_lab(j, col_labels)}") from exc
                    if symmetric:
                        matrix[j, i] = matrix[i, j]
            return matrix, row_labels, col_labels, axis

    def auto_symmetricized(self, copy=False):
        def self_or_copy():
            return self.copy() if copy else self

        def get_labels(labels):
            return np.array(labels) if isinstance(labels, list) \
                else labels.metas[:, 0] if self._trivial_labels(labels) \
                else object()

        h, w = self.shape
        m = max(w, h)
        if (abs(h - w) > 1
                or self.row_items and self.col_items
                   and np.any(get_labels(self.row_items)
                              != get_labels(self.col_items))
                or self.row_items and len(self.row_items) != m
                or self.col_items and len(self.col_items) != m):
            return self_or_copy()

        nans = np.isnan(self)
        low_indices = np.tril_indices(h, -1)
        low_empty = np.all(nans[low_indices])
        high_indices = np.triu_indices(w, 1)
        high_empty = np.all(nans[high_indices])
        if low_empty is high_empty:  # both non-empty, or both empty (only diagonal)
            return self_or_copy()

        indices = low_indices if low_empty else high_indices
        if w == h:
            matrix = np.array(self)
        else:
            if low_empty:
                row = np.vstack((self[:, -1, None], [[0]])).T
                matrix = np.vstack((self, row))
            else:
                col = np.hstack((self[-1, None], [[0]])).T
                matrix = np.hstack((self, col))
            diag_indices = np.diag_indices(len(matrix))
            matrix[diag_indices] = np.nan_to_num(matrix[diag_indices])
        matrix[indices] = self.T[indices]
        return type(self)(matrix,
                          self.row_items or self.col_items,
                          self.col_items or self.row_items)

    @staticmethod
    def _trivial_labels(items):
        # prevent circular imports, pylint: disable=import-outside-toplevel
        from Orange.data import Table, StringVariable

        return items and \
               isinstance(items, Table) and \
               len(items.domain.metas) == 1 and \
               isinstance(items.domain.metas[0], StringVariable)

    def is_symmetric(self):
        # prevent circular imports, pylint: disable=import-outside-toplevel
        from Orange.data import Table

        if self.shape[0] != self.shape[1] or not np.allclose(self, self.T):
            return False
        if self.row_items is None or self.col_items is None:
            return True
        if isinstance(self.row_items, Table):
            return (isinstance(self.col_items, Table)
                and self.col_items.domain == self.row_items.domain
                and np.array_equal(self.col_items.X, self.row_items.X)
                and np.array_equal(self.col_items.Y, self.row_items.Y)
                and np.array_equal(self.col_items.metas, self.row_items.metas))
        else:
            return (not isinstance(self.col_items, Table)
                and np.array_equal(self.row_items, self.col_items))

    def has_row_labels(self):
        """
        Returns `True` if row labels can be automatically determined from data

        For this, the `row_items` must be an instance of `Orange.data.Table`
        whose domain contains a single meta attribute, which has to be a string.
        The domain may contain other variables, but not meta attributes.
        """
        return self._trivial_labels(self.row_items)

    def has_col_labels(self):
        """
        Returns `True` if column labels can be automatically determined from
        data

        For this, the `col_items` must be an instance of `Orange.data.Table`
        whose domain contains a single meta attribute, which has to be a string.
        The domain may contain other variables, but not meta attributes.
        """
        return self._trivial_labels(self.col_items)

    def get_labels(self, items):
        if self._trivial_labels(items):
            return items.get_column(items.domain.metas[0])
        elif isinstance(items, (list, tuple)) \
                and all(isinstance(x, str) for x in items):
            return items
        else:
            return None

    def save(self, filename):
        if os.path.splitext(filename)[1] == ".xlsx":
            _distmatrix_xlsx.write_matrix(self, filename)
        else:
            self._save_dst(filename)

    def _save_dst(self, filename):
        """
        Save the distance matrix to a file in the file format described at
        :obj:`~Orange.misc.distmatrix.DistMatrix.from_file`.

        Args:
            filename: file name
        """
        n = len(self)
        data = "{}\taxis={}".format(n, self.axis)
        row_labels = col_labels = None
        if self.has_col_labels():
            data += "\tcol_labels"
            col_labels = self.col_items
        if self.has_row_labels():
            data += "\trow_labels"
            row_labels = self.row_items
        symmetric = self.is_symmetric()
        if not symmetric:
            data += "\tasymmetric"
        with open(filename, "wt") as fle:
            fle.write(data + "\n")
            if col_labels is not None:
                fle.write("\t".join(str(e.metas[0]) for e in col_labels) + "\n")
            for i, row in enumerate(self):
                if row_labels is not None:
                    fle.write(str(row_labels[i].metas[0]) + "\t")
                if symmetric:
                    fle.write("\t".join(map(str, row[:i + 1])) + "\n")
                else:
                    fle.write("\t".join(map(str, row)) + "\n")

```
--- 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