{
    "file_diffs": [
        {
            "old_file_content": "from scipy.sparse import issparse\nimport bottleneck as bn\n\nfrom AnyQt.QtCore import Qt\n\nimport Orange.data\nimport Orange.misc\nfrom Orange import distance\nfrom Orange.widgets import gui\nfrom Orange.widgets.settings import Setting\nfrom Orange.widgets.utils.concurrent import TaskState, ConcurrentWidgetMixin\nfrom Orange.widgets.utils.sql import check_sql_input\nfrom Orange.widgets.utils.widgetpreview import WidgetPreview\nfrom Orange.widgets.widget import OWWidget, Msg, Input, Output\n\n\nMETRICS = [\n    (\"Euclidean\", distance.Euclidean),\n    (\"Manhattan\", distance.Manhattan),\n    (\"Cosine\", distance.Cosine),\n    (\"Jaccard\", distance.Jaccard),\n    (\"Spearman\", distance.SpearmanR),\n    (\"Absolute Spearman\", distance.SpearmanRAbsolute),\n    (\"Pearson\", distance.PearsonR),\n    (\"Absolute Pearson\", distance.PearsonRAbsolute),\n    (\"Hamming\", distance.Hamming),\n    (\"Mahalanobis\", distance.Mahalanobis),\n    ('Bhattacharyya', distance.Bhattacharyya)\n]\n\n\nclass InterruptException(Exception):\n    pass\n\n\nclass DistanceRunner:\n    @staticmethod\n    def run(data: Orange.data.Table, metric: distance, normalized_dist: bool,\n            axis: int, state: TaskState) -> Orange.misc.DistMatrix:\n        if data is None:\n            return None\n\n        def callback(i: float) -> bool:\n            state.set_progress_value(i)\n            if state.is_interruption_requested():\n                raise InterruptException\n\n        state.set_status(\"Calculating...\")\n        kwargs = {\"axis\": 1 - axis, \"impute\": True, \"callback\": callback}\n        if metric.supports_normalization and normalized_dist:\n            kwargs[\"normalize\"] = True\n        return metric(data, **kwargs)\n\n\nclass OWDistances(OWWidget, ConcurrentWidgetMixin):\n    name = \"Distances\"\n    description = \"Compute a matrix of pairwise distances.\"\n    icon = \"icons/Distance.svg\"\n    keywords = []\n\n    class Inputs:\n        data = Input(\"Data\", Orange.data.Table)\n\n    class Outputs:\n        distances = Output(\"Distances\", Orange.misc.DistMatrix, dynamic=False)\n\n    settings_version = 3\n\n    axis = Setting(0)        # type: int\n    metric_idx = Setting(0)  # type: int\n\n    #: Use normalized distances if the metric supports it.\n    #: The default is `True`, expect when restoring from old pre v2 settings\n    #: (see `migrate_settings`).\n    normalized_dist = Setting(True)  # type: bool\n    autocommit = Setting(True)       # type: bool\n\n    want_main_area = False\n    resizing_enabled = False\n\n    class Error(OWWidget.Error):\n        no_continuous_features = Msg(\"No numeric features\")\n        no_binary_features = Msg(\"No binary features\")\n        dense_metric_sparse_data = Msg(\"{} requires dense data.\")\n        distances_memory_error = Msg(\"Not enough memory\")\n        distances_value_error = Msg(\"Problem in calculation:\\n{}\")\n        data_too_large_for_mahalanobis = Msg(\n            \"Mahalanobis handles up to 1000 {}.\")\n\n    class Warning(OWWidget.Warning):\n        ignoring_discrete = Msg(\"Ignoring categorical features\")\n        ignoring_nonbinary = Msg(\"Ignoring non-binary features\")\n        imputing_data = Msg(\"Missing values were imputed\")\n\n    def __init__(self):\n        OWWidget.__init__(self)\n        ConcurrentWidgetMixin.__init__(self)\n\n        self.data = None\n\n        gui.radioButtons(\n            self.controlArea, self, \"axis\", [\"Rows\", \"Columns\"],\n            box=\"Distances between\", callback=self._invalidate\n        )\n        box = gui.widgetBox(self.controlArea, \"Distance Metric\")\n        self.metrics_combo = gui.comboBox(\n            box, self, \"metric_idx\",\n            items=[m[0] for m in METRICS],\n            callback=self._metric_changed\n        )\n        self.normalization_check = gui.checkBox(\n            box, self, \"normalized_dist\", \"Normalized\",\n            callback=self._invalidate,\n            tooltip=(\"All dimensions are (implicitly) scaled to a common\"\n                     \"scale to normalize the influence across the domain.\"),\n            stateWhenDisabled=False, attribute=Qt.WA_LayoutUsesWidgetRect\n        )\n        _, metric = METRICS[self.metric_idx]\n        self.normalization_check.setEnabled(metric.supports_normalization)\n\n        gui.auto_apply(self.buttonsArea, self, \"autocommit\")\n\n    @Inputs.data\n    @check_sql_input\n    def set_data(self, data):\n        self.cancel()\n        self.data = data\n        self.refresh_metrics()\n        self.commit.now()\n\n    def refresh_metrics(self):\n        sparse = self.data is not None and issparse(self.data.X)\n        for i, metric in enumerate(METRICS):\n            item = self.metrics_combo.model().item(i)\n            item.setEnabled(not sparse or metric[1].supports_sparse)\n\n    @gui.deferred\n    def commit(self):\n        # pylint: disable=invalid-sequence-index\n        metric = METRICS[self.metric_idx][1]\n        self.compute_distances(metric, self.data)\n\n    def compute_distances(self, metric, data):\n        def _check_sparse():\n            # pylint: disable=invalid-sequence-index\n            if issparse(data.X) and not metric.supports_sparse:\n                self.Error.dense_metric_sparse_data(METRICS[self.metric_idx][0])\n                return False\n            return True\n\n        def _fix_discrete():\n            nonlocal data\n            if data.domain.has_discrete_attributes() \\\n                    and metric is not distance.Jaccard \\\n                    and (issparse(data.X) and getattr(metric, \"fallback\", None)\n                         or not metric.supports_discrete\n                         or self.axis == 1):\n                if not data.domain.has_continuous_attributes():\n                    self.Error.no_continuous_features()\n                    return False\n                self.Warning.ignoring_discrete()\n                data = distance.remove_discrete_features(data, to_metas=True)\n            return True\n\n        def _fix_nonbinary():\n            nonlocal data\n            if metric is distance.Jaccard and not issparse(data.X):\n                nbinary = sum(a.is_discrete and len(a.values) == 2\n                              for a in data.domain.attributes)\n                if not nbinary:\n                    self.Error.no_binary_features()\n                    return False\n                elif nbinary < len(data.domain.attributes):\n                    self.Warning.ignoring_nonbinary()\n                    data = distance.remove_nonbinary_features(data,\n                                                              to_metas=True)\n            return True\n\n        def _fix_missing():\n            nonlocal data\n            if not metric.supports_missing and bn.anynan(data.X):\n                self.Warning.imputing_data()\n                data = distance.impute(data)\n            return True\n\n        def _check_tractability():\n            if metric is distance.Mahalanobis:\n                if self.axis == 1:\n                    # when computing distances by columns, we want < 100 rows\n                    if len(data) > 1000:\n                        self.Error.data_too_large_for_mahalanobis(\"rows\")\n                        return False\n                else:\n                    if len(data.domain.attributes) > 1000:\n                        self.Error.data_too_large_for_mahalanobis(\"columns\")\n                        return False\n            return True\n\n        self.clear_messages()\n        if data is not None:\n            for check in (_check_sparse, _check_tractability,\n                          _fix_discrete, _fix_missing, _fix_nonbinary):\n                if not check():\n                    data = None\n                    break\n\n        self.start(DistanceRunner.run, data, metric,\n                   self.normalized_dist, self.axis)\n\n    def on_partial_result(self, _):\n        pass\n\n    def on_done(self, result: Orange.misc.DistMatrix):\n        assert isinstance(result, Orange.misc.DistMatrix) or result is None\n        self.Outputs.distances.send(result)\n\n    def on_exception(self, ex):\n        if isinstance(ex, ValueError):\n            self.Error.distances_value_error(ex)\n        elif isinstance(ex, MemoryError):\n            self.Error.distances_memory_error()\n        elif isinstance(ex, InterruptException):\n            pass\n        else:\n            raise ex\n\n    def onDeleteWidget(self):\n        self.shutdown()\n        super().onDeleteWidget()\n\n    def _invalidate(self):\n        self.commit.deferred()\n\n    def _metric_changed(self):\n        metric = METRICS[self.metric_idx][1]\n        self.normalization_check.setEnabled(metric.supports_normalization)\n        self._invalidate()\n\n    def send_report(self):\n        # pylint: disable=invalid-sequence-index\n        self.report_items((\n            (\"Distances Between\", [\"Rows\", \"Columns\"][self.axis]),\n            (\"Metric\", METRICS[self.metric_idx][0])\n        ))\n\n    @classmethod\n    def migrate_settings(cls, settings, version):\n        if version is None or version < 2 and \"normalized_dist\" not in settings:\n            # normalize_dist is set to False when restoring settings from\n            # an older version to preserve old semantics.\n            settings[\"normalized_dist\"] = False\n        if version is None or version < 3:\n            # Mahalanobis was moved from idx = 2 to idx = 9\n            metric_idx = settings[\"metric_idx\"]\n            if metric_idx == 2:\n                settings[\"metric_idx\"] = 9\n            elif 2 < metric_idx <= 9:\n                settings[\"metric_idx\"] -= 1\n\n\nif __name__ == \"__main__\":  # pragma: no cover\n    WidgetPreview(OWDistances).run(Orange.data.Table(\"iris\"))\n",
            "new_file_content": "from scipy.sparse import issparse\nimport bottleneck as bn\n\nfrom AnyQt.QtCore import Qt\n\nimport Orange.data\nimport Orange.misc\nfrom Orange import distance\nfrom Orange.widgets import gui\nfrom Orange.widgets.settings import Setting\nfrom Orange.widgets.utils.concurrent import TaskState, ConcurrentWidgetMixin\nfrom Orange.widgets.utils.sql import check_sql_input\nfrom Orange.widgets.utils.widgetpreview import WidgetPreview\nfrom Orange.widgets.widget import OWWidget, Msg, Input, Output\n\n\nMETRICS = [\n    (\"Euclidean\", distance.Euclidean),\n    (\"Manhattan\", distance.Manhattan),\n    (\"Cosine\", distance.Cosine),\n    (\"Jaccard\", distance.Jaccard),\n    (\"Spearman\", distance.SpearmanR),\n    (\"Absolute Spearman\", distance.SpearmanRAbsolute),\n    (\"Pearson\", distance.PearsonR),\n    (\"Absolute Pearson\", distance.PearsonRAbsolute),\n    (\"Hamming\", distance.Hamming),\n    (\"Mahalanobis\", distance.Mahalanobis),\n    ('Bhattacharyya', distance.Bhattacharyya)\n]\n\n\nclass InterruptException(Exception):\n    pass\n\n\nclass DistanceRunner:\n    @staticmethod\n    def run(data: Orange.data.Table, metric: distance, normalized_dist: bool,\n            axis: int, state: TaskState) -> Orange.misc.DistMatrix:\n        if data is None:\n            return None\n\n        def callback(i: float) -> bool:\n            state.set_progress_value(i)\n            if state.is_interruption_requested():\n                raise InterruptException\n\n        state.set_status(\"Calculating...\")\n        kwargs = {\"axis\": 1 - axis, \"impute\": True, \"callback\": callback}\n        if metric.supports_normalization and normalized_dist:\n            kwargs[\"normalize\"] = True\n        return metric(data, **kwargs)\n\n\nclass OWDistances(OWWidget, ConcurrentWidgetMixin):\n    name = \"Distances\"\n    description = \"Compute a matrix of pairwise distances.\"\n    icon = \"icons/Distance.svg\"\n    keywords = []\n\n    class Inputs:\n        data = Input(\"Data\", Orange.data.Table)\n\n    class Outputs:\n        distances = Output(\"Distances\", Orange.misc.DistMatrix, dynamic=False)\n\n    settings_version = 3\n\n    axis = Setting(0)        # type: int\n    metric_idx = Setting(0)  # type: int\n\n    #: Use normalized distances if the metric supports it.\n    #: The default is `True`, expect when restoring from old pre v2 settings\n    #: (see `migrate_settings`).\n    normalized_dist = Setting(True)  # type: bool\n    autocommit = Setting(True)       # type: bool\n\n    want_main_area = False\n    resizing_enabled = False\n\n    class Error(OWWidget.Error):\n        no_continuous_features = Msg(\"No numeric features\")\n        no_binary_features = Msg(\"No binary features\")\n        dense_metric_sparse_data = Msg(\"{} requires dense data.\")\n        distances_memory_error = Msg(\"Not enough memory\")\n        distances_value_error = Msg(\"Problem in calculation:\\n{}\")\n        data_too_large_for_mahalanobis = Msg(\n            \"Mahalanobis handles up to 1000 {}.\")\n\n    class Warning(OWWidget.Warning):\n        ignoring_discrete = Msg(\"Ignoring categorical features\")\n        ignoring_nonbinary = Msg(\"Ignoring non-binary features\")\n        imputing_data = Msg(\"Missing values were imputed\")\n\n    def __init__(self):\n        OWWidget.__init__(self)\n        ConcurrentWidgetMixin.__init__(self)\n\n        self.data = None\n\n        gui.radioButtons(\n            self.controlArea, self, \"axis\", [\"Rows\", \"Columns\"],\n            box=\"Distances between\", callback=self._invalidate\n        )\n        box = gui.widgetBox(self.controlArea, \"Distance Metric\")\n        self.metrics_combo = gui.comboBox(\n            box, self, \"metric_idx\",\n            items=[m[0] for m in METRICS],\n            callback=self._metric_changed\n        )\n        self.normalization_check = gui.checkBox(\n            box, self, \"normalized_dist\", \"Normalized\",\n            callback=self._invalidate,\n            tooltip=(\"All dimensions are (implicitly) scaled to a common\"\n                     \"scale to normalize the influence across the domain.\"),\n            stateWhenDisabled=False, attribute=Qt.WA_LayoutUsesWidgetRect\n        )\n        _, metric = METRICS[self.metric_idx]\n        self.normalization_check.setEnabled(metric.supports_normalization)\n\n        gui.auto_apply(self.buttonsArea, self, \"autocommit\")\n\n    @Inputs.data\n    @check_sql_input\n    def set_data(self, data):\n        self.cancel()\n        self.data = data\n        self.refresh_metrics()\n        self.commit.now()\n\n    def refresh_metrics(self):\n        sparse = self.data is not None and issparse(self.data.X)\n        for i, metric in enumerate(METRICS):\n            item = self.metrics_combo.model().item(i)\n            item.setEnabled(not sparse or metric[1].supports_sparse)\n\n    @gui.deferred\n    def commit(self):\n        # pylint: disable=invalid-sequence-index\n        metric = METRICS[self.metric_idx][1]\n        self.compute_distances(metric, self.data)\n\n    def compute_distances(self, metric, data):\n        def _check_sparse():\n            # pylint: disable=invalid-sequence-index\n            if issparse(data.X) and not metric.supports_sparse:\n                self.Error.dense_metric_sparse_data(METRICS[self.metric_idx][0])\n                return False\n            return True\n\n        def _fix_discrete():\n            nonlocal data\n            if data.domain.has_discrete_attributes() \\\n                    and metric is not distance.Jaccard \\\n                    and (issparse(data.X) and getattr(metric, \"fallback\", None)\n                         or not metric.supports_discrete\n                         or self.axis == 1):\n                if not data.domain.has_continuous_attributes():\n                    self.Error.no_continuous_features()\n                    return False\n                self.Warning.ignoring_discrete()\n                data = distance.remove_discrete_features(data, to_metas=True)\n            return True\n\n        def _fix_nonbinary():\n            nonlocal data\n            if metric is distance.Jaccard and not issparse(data.X):\n                nbinary = sum(a.is_discrete and len(a.values) == 2\n                              for a in data.domain.attributes)\n                if not nbinary:\n                    self.Error.no_binary_features()\n                    return False\n                elif nbinary < len(data.domain.attributes):\n                    self.Warning.ignoring_nonbinary()\n                    data = distance.remove_nonbinary_features(data,\n                                                              to_metas=True)\n            return True\n\n        def _fix_missing():\n            nonlocal data\n            if not metric.supports_missing and bn.anynan(data.X):\n                self.Warning.imputing_data()\n                data = distance.impute(data)\n            return True\n\n        def _check_tractability():\n            if metric is distance.Mahalanobis:\n                if self.axis == 0:\n                    # when computing distances by columns, we want < 1000 rows\n                    if len(data) > 1000:\n                        self.Error.data_too_large_for_mahalanobis(\"rows\")\n                        return False\n                else:\n                    if len(data.domain.attributes) > 1000:\n                        self.Error.data_too_large_for_mahalanobis(\"columns\")\n                        return False\n            return True\n\n        self.clear_messages()\n        if data is not None:\n            for check in (_check_sparse, _check_tractability,\n                          _fix_discrete, _fix_missing, _fix_nonbinary):\n                if not check():\n                    data = None\n                    break\n\n        self.start(DistanceRunner.run, data, metric,\n                   self.normalized_dist, self.axis)\n\n    def on_partial_result(self, _):\n        pass\n\n    def on_done(self, result: Orange.misc.DistMatrix):\n        assert isinstance(result, Orange.misc.DistMatrix) or result is None\n        self.Outputs.distances.send(result)\n\n    def on_exception(self, ex):\n        if isinstance(ex, ValueError):\n            self.Error.distances_value_error(ex)\n        elif isinstance(ex, MemoryError):\n            self.Error.distances_memory_error()\n        elif isinstance(ex, InterruptException):\n            pass\n        else:\n            raise ex\n\n    def onDeleteWidget(self):\n        self.shutdown()\n        super().onDeleteWidget()\n\n    def _invalidate(self):\n        self.commit.deferred()\n\n    def _metric_changed(self):\n        metric = METRICS[self.metric_idx][1]\n        self.normalization_check.setEnabled(metric.supports_normalization)\n        self._invalidate()\n\n    def send_report(self):\n        # pylint: disable=invalid-sequence-index\n        self.report_items((\n            (\"Distances Between\", [\"Rows\", \"Columns\"][self.axis]),\n            (\"Metric\", METRICS[self.metric_idx][0])\n        ))\n\n    @classmethod\n    def migrate_settings(cls, settings, version):\n        if version is None or version < 2 and \"normalized_dist\" not in settings:\n            # normalize_dist is set to False when restoring settings from\n            # an older version to preserve old semantics.\n            settings[\"normalized_dist\"] = False\n        if version is None or version < 3:\n            # Mahalanobis was moved from idx = 2 to idx = 9\n            metric_idx = settings[\"metric_idx\"]\n            if metric_idx == 2:\n                settings[\"metric_idx\"] = 9\n            elif 2 < metric_idx <= 9:\n                settings[\"metric_idx\"] -= 1\n\n\nif __name__ == \"__main__\":  # pragma: no cover\n    WidgetPreview(OWDistances).run(Orange.data.Table(\"iris\"))\n",
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                            "start": 185,
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                        "section": "class OWDistances(OWWidget, ConcurrentWidgetMixin):"
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                                "content": "",
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                                "content": "        def _check_tractability():",
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                                "content": "            if metric is distance.Mahalanobis:",
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                            },
                            {
                                "content": "                if self.axis == 1:",
                                "type": "deleted"
                            },
                            {
                                "content": "                    # when computing distances by columns, we want < 100 rows",
                                "type": "deleted"
                            },
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                            },
                            {
                                "content": "                    # when computing distances by columns, we want < 1000 rows",
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                                "content": "                        self.Error.data_too_large_for_mahalanobis(\"rows\")",
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                        {
                            "file_name": "Orange/widgets/unsupervised/owdistances.py",
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                            "name": "OWDistances.compute_distances",
                            "content": "class OWDistances(OWWidget, ConcurrentWidgetMixin):\n    name = \"Distances\"\n    description = \"Compute a matrix of pairwise distances.\"\n    icon = \"icons/Distance.svg\"\n    keywords = []\n\n    class Inputs:\n        data = Input(\"Data\", Orange.data.Table)\n\n    class Outputs:\n        distances = Output(\"Distances\", Orange.misc.DistMatrix, dynamic=False)\n\n    settings_version = 3\n\n    axis = Setting(0)        # type: int\n    metric_idx = Setting(0)  # type: int\n\n    #: Use normalized distances if the metric supports it.\n    #: The default is `True`, expect when restoring from old pre v2 settings\n    #: (see `migrate_settings`).\n    normalized_dist = Setting(True)  # type: bool\n    autocommit = Setting(True)       # type: bool\n\n    want_main_area = False\n    resizing_enabled = False\n\n    class Error(OWWidget.Error):\n        no_continuous_features = Msg(\"No numeric features\")\n        no_binary_features = Msg(\"No binary features\")\n        dense_metric_sparse_data = Msg(\"{} requires dense data.\")\n        distances_memory_error = Msg(\"Not enough memory\")\n        distances_value_error = Msg(\"Problem in calculation:\\n{}\")\n        data_too_large_for_mahalanobis = Msg(\n            \"Mahalanobis handles up to 1000 {}.\")\n\n    class Warning(OWWidget.Warning):\n        ignoring_discrete = Msg(\"Ignoring categorical features\")\n        ignoring_nonbinary = Msg(\"Ignoring non-binary features\")\n        imputing_data = Msg(\"Missing values were imputed\")\n\n    def __init__(self):\n        OWWidget.__init__(self)\n        ConcurrentWidgetMixin.__init__(self)\n\n        self.data = None\n\n        gui.radioButtons(\n            self.controlArea, self, \"axis\", [\"Rows\", \"Columns\"],\n            box=\"Distances between\", callback=self._invalidate\n        )\n        box = gui.widgetBox(self.controlArea, \"Distance Metric\")\n        self.metrics_combo = gui.comboBox(\n            box, self, \"metric_idx\",\n            items=[m[0] for m in METRICS],\n            callback=self._metric_changed\n        )\n        self.normalization_check = gui.checkBox(\n            box, self, \"normalized_dist\", \"Normalized\",\n            callback=self._invalidate,\n            tooltip=(\"All dimensions are (implicitly) scaled to a common\"\n                     \"scale to normalize the influence across the domain.\"),\n            stateWhenDisabled=False, attribute=Qt.WA_LayoutUsesWidgetRect\n        )\n        _, metric = METRICS[self.metric_idx]\n        self.normalization_check.setEnabled(metric.supports_normalization)\n\n        gui.auto_apply(self.buttonsArea, self, \"autocommit\")\n\n    @Inputs.data\n    @check_sql_input\n    def set_data(self, data):\n        self.cancel()\n        self.data = data\n        self.refresh_metrics()\n        self.commit.now()\n\n    def refresh_metrics(self):\n        sparse = self.data is not None and issparse(self.data.X)\n        for i, metric in enumerate(METRICS):\n            item = self.metrics_combo.model().item(i)\n            item.setEnabled(not sparse or metric[1].supports_sparse)\n\n    @gui.deferred\n    def commit(self):\n        # pylint: disable=invalid-sequence-index\n        metric = METRICS[self.metric_idx][1]\n        self.compute_distances(metric, self.data)\n\n    def compute_distances(self, metric, data):\n        def _check_sparse():\n            # pylint: disable=invalid-sequence-index\n            if issparse(data.X) and not metric.supports_sparse:\n                self.Error.dense_metric_sparse_data(METRICS[self.metric_idx][0])\n                return False\n            return True\n\n        def _fix_discrete():\n            nonlocal data\n            if data.domain.has_discrete_attributes() \\\n                    and metric is not distance.Jaccard \\\n                    and (issparse(data.X) and getattr(metric, \"fallback\", None)\n                         or not metric.supports_discrete\n                         or self.axis == 1):\n                if not data.domain.has_continuous_attributes():\n                    self.Error.no_continuous_features()\n                    return False\n                self.Warning.ignoring_discrete()\n                data = distance.remove_discrete_features(data, to_metas=True)\n            return True\n\n        def _fix_nonbinary():\n            nonlocal data\n            if metric is distance.Jaccard and not issparse(data.X):\n                nbinary = sum(a.is_discrete and len(a.values) == 2\n                              for a in data.domain.attributes)\n                if not nbinary:\n                    self.Error.no_binary_features()\n                    return False\n                elif nbinary < len(data.domain.attributes):\n                    self.Warning.ignoring_nonbinary()\n                    data = distance.remove_nonbinary_features(data,\n                                                              to_metas=True)\n            return True\n\n        def _fix_missing():\n            nonlocal data\n            if not metric.supports_missing and bn.anynan(data.X):\n                self.Warning.imputing_data()\n                data = distance.impute(data)\n            return True\n\n        def _check_tractability():\n            if metric is distance.Mahalanobis:\n                if self.axis == 1:\n                    # when computing distances by columns, we want < 100 rows\n                    if len(data) > 1000:\n                        self.Error.data_too_large_for_mahalanobis(\"rows\")\n                        return False\n                else:\n                    if len(data.domain.attributes) > 1000:\n                        self.Error.data_too_large_for_mahalanobis(\"columns\")\n                        return False\n            return True\n\n        self.clear_messages()\n        if data is not None:\n            for check in (_check_sparse, _check_tractability,\n                          _fix_discrete, _fix_missing, _fix_nonbinary):\n                if not check():\n                    data = None\n                    break\n\n        self.start(DistanceRunner.run, data, metric,\n                   self.normalized_dist, self.axis)\n\n    def on_partial_result(self, _):\n        pass\n\n    def on_done(self, result: Orange.misc.DistMatrix):\n        assert isinstance(result, Orange.misc.DistMatrix) or result is None\n        self.Outputs.distances.send(result)\n\n    def on_exception(self, ex):\n        if isinstance(ex, ValueError):\n            self.Error.distances_value_error(ex)\n        elif isinstance(ex, MemoryError):\n            self.Error.distances_memory_error()\n        elif isinstance(ex, InterruptException):\n            pass\n        else:\n            raise ex\n\n    def onDeleteWidget(self):\n        self.shutdown()\n        super().onDeleteWidget()\n\n    def _invalidate(self):\n        self.commit.deferred()\n\n    def _metric_changed(self):\n        metric = METRICS[self.metric_idx][1]\n        self.normalization_check.setEnabled(metric.supports_normalization)\n        self._invalidate()\n\n    def send_report(self):\n        # pylint: disable=invalid-sequence-index\n        self.report_items((\n            (\"Distances Between\", [\"Rows\", \"Columns\"][self.axis]),\n            (\"Metric\", METRICS[self.metric_idx][0])\n        ))\n\n    @classmethod\n    def migrate_settings(cls, settings, version):\n        if version is None or version < 2 and \"normalized_dist\" not in settings:\n            # normalize_dist is set to False when restoring settings from\n            # an older version to preserve old semantics.\n            settings[\"normalized_dist\"] = False\n        if version is None or version < 3:\n            # Mahalanobis was moved from idx = 2 to idx = 9\n            metric_idx = settings[\"metric_idx\"]\n            if metric_idx == 2:\n                settings[\"metric_idx\"] = 9\n            elif 2 < metric_idx <= 9:\n                settings[\"metric_idx\"] -= 1",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 143,
                            "end_lineno": 208,
                            "parent": {
                                "file_name": "Orange/widgets/unsupervised/owdistances.py",
                                "type": "class",
                                "name": "OWDistances",
                                "content": "class OWDistances(OWWidget, ConcurrentWidgetMixin):\n    name = \"Distances\"\n    description = \"Compute a matrix of pairwise distances.\"\n    icon = \"icons/Distance.svg\"\n    keywords = []\n\n    class Inputs:\n        data = Input(\"Data\", Orange.data.Table)\n\n    class Outputs:\n        distances = Output(\"Distances\", Orange.misc.DistMatrix, dynamic=False)\n\n    settings_version = 3\n\n    axis = Setting(0)        # type: int\n    metric_idx = Setting(0)  # type: int\n\n    #: Use normalized distances if the metric supports it.\n    #: The default is `True`, expect when restoring from old pre v2 settings\n    #: (see `migrate_settings`).\n    normalized_dist = Setting(True)  # type: bool\n    autocommit = Setting(True)       # type: bool\n\n    want_main_area = False\n    resizing_enabled = False\n\n    class Error(OWWidget.Error):\n        no_continuous_features = Msg(\"No numeric features\")\n        no_binary_features = Msg(\"No binary features\")\n        dense_metric_sparse_data = Msg(\"{} requires dense data.\")\n        distances_memory_error = Msg(\"Not enough memory\")\n        distances_value_error = Msg(\"Problem in calculation:\\n{}\")\n        data_too_large_for_mahalanobis = Msg(\n            \"Mahalanobis handles up to 1000 {}.\")\n\n    class Warning(OWWidget.Warning):\n        ignoring_discrete = Msg(\"Ignoring categorical features\")\n        ignoring_nonbinary = Msg(\"Ignoring non-binary features\")\n        imputing_data = Msg(\"Missing values were imputed\")\n\n    def __init__(self):\n        OWWidget.__init__(self)\n        ConcurrentWidgetMixin.__init__(self)\n\n        self.data = None\n\n        gui.radioButtons(\n            self.controlArea, self, \"axis\", [\"Rows\", \"Columns\"],\n            box=\"Distances between\", callback=self._invalidate\n        )\n        box = gui.widgetBox(self.controlArea, \"Distance Metric\")\n        self.metrics_combo = gui.comboBox(\n            box, self, \"metric_idx\",\n            items=[m[0] for m in METRICS],\n            callback=self._metric_changed\n        )\n        self.normalization_check = gui.checkBox(\n            box, self, \"normalized_dist\", \"Normalized\",\n            callback=self._invalidate,\n            tooltip=(\"All dimensions are (implicitly) scaled to a common\"\n                     \"scale to normalize the influence across the domain.\"),\n            stateWhenDisabled=False, attribute=Qt.WA_LayoutUsesWidgetRect\n        )\n        _, metric = METRICS[self.metric_idx]\n        self.normalization_check.setEnabled(metric.supports_normalization)\n\n        gui.auto_apply(self.buttonsArea, self, \"autocommit\")\n\n    @Inputs.data\n    @check_sql_input\n    def set_data(self, data):\n        self.cancel()\n        self.data = data\n        self.refresh_metrics()\n        self.commit.now()\n\n    def refresh_metrics(self):\n        sparse = self.data is not None and issparse(self.data.X)\n        for i, metric in enumerate(METRICS):\n            item = self.metrics_combo.model().item(i)\n            item.setEnabled(not sparse or metric[1].supports_sparse)\n\n    @gui.deferred\n    def commit(self):\n        # pylint: disable=invalid-sequence-index\n        metric = METRICS[self.metric_idx][1]\n        self.compute_distances(metric, self.data)\n\n    def compute_distances(self, metric, data):\n        def _check_sparse():\n            # pylint: disable=invalid-sequence-index\n            if issparse(data.X) and not metric.supports_sparse:\n                self.Error.dense_metric_sparse_data(METRICS[self.metric_idx][0])\n                return False\n            return True\n\n        def _fix_discrete():\n            nonlocal data\n            if data.domain.has_discrete_attributes() \\\n                    and metric is not distance.Jaccard \\\n                    and (issparse(data.X) and getattr(metric, \"fallback\", None)\n                         or not metric.supports_discrete\n                         or self.axis == 1):\n                if not data.domain.has_continuous_attributes():\n                    self.Error.no_continuous_features()\n                    return False\n                self.Warning.ignoring_discrete()\n                data = distance.remove_discrete_features(data, to_metas=True)\n            return True\n\n        def _fix_nonbinary():\n            nonlocal data\n            if metric is distance.Jaccard and not issparse(data.X):\n                nbinary = sum(a.is_discrete and len(a.values) == 2\n                              for a in data.domain.attributes)\n                if not nbinary:\n                    self.Error.no_binary_features()\n                    return False\n                elif nbinary < len(data.domain.attributes):\n                    self.Warning.ignoring_nonbinary()\n                    data = distance.remove_nonbinary_features(data,\n                                                              to_metas=True)\n            return True\n\n        def _fix_missing():\n            nonlocal data\n            if not metric.supports_missing and bn.anynan(data.X):\n                self.Warning.imputing_data()\n                data = distance.impute(data)\n            return True\n\n        def _check_tractability():\n            if metric is distance.Mahalanobis:\n                if self.axis == 1:\n                    # when computing distances by columns, we want < 100 rows\n                    if len(data) > 1000:\n                        self.Error.data_too_large_for_mahalanobis(\"rows\")\n                        return False\n                else:\n                    if len(data.domain.attributes) > 1000:\n                        self.Error.data_too_large_for_mahalanobis(\"columns\")\n                        return False\n            return True\n\n        self.clear_messages()\n        if data is not None:\n            for check in (_check_sparse, _check_tractability,\n                          _fix_discrete, _fix_missing, _fix_nonbinary):\n                if not check():\n                    data = None\n                    break\n\n        self.start(DistanceRunner.run, data, metric,\n                   self.normalized_dist, self.axis)\n\n    def on_partial_result(self, _):\n        pass\n\n    def on_done(self, result: Orange.misc.DistMatrix):\n        assert isinstance(result, Orange.misc.DistMatrix) or result is None\n        self.Outputs.distances.send(result)\n\n    def on_exception(self, ex):\n        if isinstance(ex, ValueError):\n            self.Error.distances_value_error(ex)\n        elif isinstance(ex, MemoryError):\n            self.Error.distances_memory_error()\n        elif isinstance(ex, InterruptException):\n            pass\n        else:\n            raise ex\n\n    def onDeleteWidget(self):\n        self.shutdown()\n        super().onDeleteWidget()\n\n    def _invalidate(self):\n        self.commit.deferred()\n\n    def _metric_changed(self):\n        metric = METRICS[self.metric_idx][1]\n        self.normalization_check.setEnabled(metric.supports_normalization)\n        self._invalidate()\n\n    def send_report(self):\n        # pylint: disable=invalid-sequence-index\n        self.report_items((\n            (\"Distances Between\", [\"Rows\", \"Columns\"][self.axis]),\n            (\"Metric\", METRICS[self.metric_idx][0])\n        ))\n\n    @classmethod\n    def migrate_settings(cls, settings, version):\n        if version is None or version < 2 and \"normalized_dist\" not in settings:\n            # normalize_dist is set to False when restoring settings from\n            # an older version to preserve old semantics.\n            settings[\"normalized_dist\"] = False\n        if version is None or version < 3:\n            # Mahalanobis was moved from idx = 2 to idx = 9\n            metric_idx = settings[\"metric_idx\"]\n            if metric_idx == 2:\n                settings[\"metric_idx\"] = 9\n            elif 2 < metric_idx <= 9:\n                settings[\"metric_idx\"] -= 1",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 55,
                                "end_lineno": 258,
                                "parent": null
                            }
                        },
                        {
                            "file_name": "Orange/widgets/unsupervised/owdistances.py",
                            "type": "class",
                            "name": "OWDistances",
                            "content": "class OWDistances(OWWidget, ConcurrentWidgetMixin):\n    name = \"Distances\"\n    description = \"Compute a matrix of pairwise distances.\"\n    icon = \"icons/Distance.svg\"\n    keywords = []\n\n    class Inputs:\n        data = Input(\"Data\", Orange.data.Table)\n\n    class Outputs:\n        distances = Output(\"Distances\", Orange.misc.DistMatrix, dynamic=False)\n\n    settings_version = 3\n\n    axis = Setting(0)        # type: int\n    metric_idx = Setting(0)  # type: int\n\n    #: Use normalized distances if the metric supports it.\n    #: The default is `True`, expect when restoring from old pre v2 settings\n    #: (see `migrate_settings`).\n    normalized_dist = Setting(True)  # type: bool\n    autocommit = Setting(True)       # type: bool\n\n    want_main_area = False\n    resizing_enabled = False\n\n    class Error(OWWidget.Error):\n        no_continuous_features = Msg(\"No numeric features\")\n        no_binary_features = Msg(\"No binary features\")\n        dense_metric_sparse_data = Msg(\"{} requires dense data.\")\n        distances_memory_error = Msg(\"Not enough memory\")\n        distances_value_error = Msg(\"Problem in calculation:\\n{}\")\n        data_too_large_for_mahalanobis = Msg(\n            \"Mahalanobis handles up to 1000 {}.\")\n\n    class Warning(OWWidget.Warning):\n        ignoring_discrete = Msg(\"Ignoring categorical features\")\n        ignoring_nonbinary = Msg(\"Ignoring non-binary features\")\n        imputing_data = Msg(\"Missing values were imputed\")\n\n    def __init__(self):\n        OWWidget.__init__(self)\n        ConcurrentWidgetMixin.__init__(self)\n\n        self.data = None\n\n        gui.radioButtons(\n            self.controlArea, self, \"axis\", [\"Rows\", \"Columns\"],\n            box=\"Distances between\", callback=self._invalidate\n        )\n        box = gui.widgetBox(self.controlArea, \"Distance Metric\")\n        self.metrics_combo = gui.comboBox(\n            box, self, \"metric_idx\",\n            items=[m[0] for m in METRICS],\n            callback=self._metric_changed\n        )\n        self.normalization_check = gui.checkBox(\n            box, self, \"normalized_dist\", \"Normalized\",\n            callback=self._invalidate,\n            tooltip=(\"All dimensions are (implicitly) scaled to a common\"\n                     \"scale to normalize the influence across the domain.\"),\n            stateWhenDisabled=False, attribute=Qt.WA_LayoutUsesWidgetRect\n        )\n        _, metric = METRICS[self.metric_idx]\n        self.normalization_check.setEnabled(metric.supports_normalization)\n\n        gui.auto_apply(self.buttonsArea, self, \"autocommit\")\n\n    @Inputs.data\n    @check_sql_input\n    def set_data(self, data):\n        self.cancel()\n        self.data = data\n        self.refresh_metrics()\n        self.commit.now()\n\n    def refresh_metrics(self):\n        sparse = self.data is not None and issparse(self.data.X)\n        for i, metric in enumerate(METRICS):\n            item = self.metrics_combo.model().item(i)\n            item.setEnabled(not sparse or metric[1].supports_sparse)\n\n    @gui.deferred\n    def commit(self):\n        # pylint: disable=invalid-sequence-index\n        metric = METRICS[self.metric_idx][1]\n        self.compute_distances(metric, self.data)\n\n    def compute_distances(self, metric, data):\n        def _check_sparse():\n            # pylint: disable=invalid-sequence-index\n            if issparse(data.X) and not metric.supports_sparse:\n                self.Error.dense_metric_sparse_data(METRICS[self.metric_idx][0])\n                return False\n            return True\n\n        def _fix_discrete():\n            nonlocal data\n            if data.domain.has_discrete_attributes() \\\n                    and metric is not distance.Jaccard \\\n                    and (issparse(data.X) and getattr(metric, \"fallback\", None)\n                         or not metric.supports_discrete\n                         or self.axis == 1):\n                if not data.domain.has_continuous_attributes():\n                    self.Error.no_continuous_features()\n                    return False\n                self.Warning.ignoring_discrete()\n                data = distance.remove_discrete_features(data, to_metas=True)\n            return True\n\n        def _fix_nonbinary():\n            nonlocal data\n            if metric is distance.Jaccard and not issparse(data.X):\n                nbinary = sum(a.is_discrete and len(a.values) == 2\n                              for a in data.domain.attributes)\n                if not nbinary:\n                    self.Error.no_binary_features()\n                    return False\n                elif nbinary < len(data.domain.attributes):\n                    self.Warning.ignoring_nonbinary()\n                    data = distance.remove_nonbinary_features(data,\n                                                              to_metas=True)\n            return True\n\n        def _fix_missing():\n            nonlocal data\n            if not metric.supports_missing and bn.anynan(data.X):\n                self.Warning.imputing_data()\n                data = distance.impute(data)\n            return True\n\n        def _check_tractability():\n            if metric is distance.Mahalanobis:\n                if self.axis == 1:\n                    # when computing distances by columns, we want < 100 rows\n                    if len(data) > 1000:\n                        self.Error.data_too_large_for_mahalanobis(\"rows\")\n                        return False\n                else:\n                    if len(data.domain.attributes) > 1000:\n                        self.Error.data_too_large_for_mahalanobis(\"columns\")\n                        return False\n            return True\n\n        self.clear_messages()\n        if data is not None:\n            for check in (_check_sparse, _check_tractability,\n                          _fix_discrete, _fix_missing, _fix_nonbinary):\n                if not check():\n                    data = None\n                    break\n\n        self.start(DistanceRunner.run, data, metric,\n                   self.normalized_dist, self.axis)\n\n    def on_partial_result(self, _):\n        pass\n\n    def on_done(self, result: Orange.misc.DistMatrix):\n        assert isinstance(result, Orange.misc.DistMatrix) or result is None\n        self.Outputs.distances.send(result)\n\n    def on_exception(self, ex):\n        if isinstance(ex, ValueError):\n            self.Error.distances_value_error(ex)\n        elif isinstance(ex, MemoryError):\n            self.Error.distances_memory_error()\n        elif isinstance(ex, InterruptException):\n            pass\n        else:\n            raise ex\n\n    def onDeleteWidget(self):\n        self.shutdown()\n        super().onDeleteWidget()\n\n    def _invalidate(self):\n        self.commit.deferred()\n\n    def _metric_changed(self):\n        metric = METRICS[self.metric_idx][1]\n        self.normalization_check.setEnabled(metric.supports_normalization)\n        self._invalidate()\n\n    def send_report(self):\n        # pylint: disable=invalid-sequence-index\n        self.report_items((\n            (\"Distances Between\", [\"Rows\", \"Columns\"][self.axis]),\n            (\"Metric\", METRICS[self.metric_idx][0])\n        ))\n\n    @classmethod\n    def migrate_settings(cls, settings, version):\n        if version is None or version < 2 and \"normalized_dist\" not in settings:\n            # normalize_dist is set to False when restoring settings from\n            # an older version to preserve old semantics.\n            settings[\"normalized_dist\"] = False\n        if version is None or version < 3:\n            # Mahalanobis was moved from idx = 2 to idx = 9\n            metric_idx = settings[\"metric_idx\"]\n            if metric_idx == 2:\n                settings[\"metric_idx\"] = 9\n            elif 2 < metric_idx <= 9:\n                settings[\"metric_idx\"] -= 1",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 55,
                            "end_lineno": 258,
                            "parent": null
                        }
                    ],
                    "added_entities": [],
                    "deleted_entities": []
                }
            ]
        },
        {
            "old_file_content": "# Test methods with long descriptive names can omit docstrings\n# pylint: disable=missing-docstring, protected-access\nimport unittest\nfrom unittest.mock import Mock\n\nimport numpy as np\n\nfrom Orange import distance\nfrom Orange.data import Table, Domain, ContinuousVariable\nfrom Orange.misc import DistMatrix\nfrom Orange.widgets.unsupervised.owdistances import OWDistances, METRICS, \\\n    DistanceRunner\nfrom Orange.widgets.tests.base import WidgetTest\n\n\nclass TestDistanceRunner(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        super().setUpClass()\n        cls.iris = Table(\"iris\")[::5].copy()\n        with cls.iris.unlocked():\n            cls.iris.X[0, 2] = np.nan\n            cls.iris.X[1, 3] = np.nan\n            cls.iris.X[2, 1] = np.nan\n\n        cls.zoo = Table(\"zoo\")[::5].copy()\n        with cls.zoo.unlocked():\n            cls.zoo.X[0, 2] = np.nan\n            cls.zoo.X[1, 3] = np.nan\n            cls.zoo.X[2, 1] = np.nan\n\n    def test_run(self):\n        state = Mock()\n        state.is_interruption_requested = Mock(return_value=False)\n        for name, metric in METRICS:\n            data = self.iris\n            if not metric.supports_missing or name == \"Bhattacharyya\":\n                data = distance.impute(data)\n            elif name == \"Jaccard\":\n                data = self.zoo\n\n            # between rows, normalized\n            dist1 = DistanceRunner.run(data, metric, True, 0, state)\n            dist2 = metric(data, axis=1, impute=True, normalize=True)\n            self.assertDistMatrixEqual(dist1, dist2)\n\n            # between rows, not normalized\n            dist1 = DistanceRunner.run(data, metric, False, 0, state)\n            dist2 = metric(data, axis=1, impute=True, normalize=False)\n            self.assertDistMatrixEqual(dist1, dist2)\n\n            # between columns, normalized\n            dist1 = DistanceRunner.run(data, metric, True, 1, state)\n            dist2 = metric(data, axis=0, impute=True, normalize=True)\n            self.assertDistMatrixEqual(dist1, dist2)\n\n            # between columns, not normalized\n            dist1 = DistanceRunner.run(data, metric, False, 1, state)\n            dist2 = metric(data, axis=0, impute=True, normalize=False)\n            self.assertDistMatrixEqual(dist1, dist2)\n\n    def assertDistMatrixEqual(self, dist1, dist2):\n        self.assertIsInstance(dist1, DistMatrix)\n        self.assertIsInstance(dist2, DistMatrix)\n        self.assertEqual(dist1.axis, dist2.axis)\n        self.assertEqual(dist1.row_items, dist2.row_items)\n        self.assertEqual(dist1.col_items, dist2.col_items)\n        np.testing.assert_array_almost_equal(dist1, dist2)\n\n\nclass TestOWDistances(WidgetTest):\n    def setUp(self):\n        super().setUp()\n        self.iris = Table(\"iris\")[::5].copy()\n        self.titanic = Table(\"titanic\")[::10].copy()\n        self.widget = self.create_widget(OWDistances)\n\n    def test_distance_combo(self):\n        \"\"\"Check distances when the metric changes\"\"\"\n        self.assertEqual(self.widget.metrics_combo.count(), len(METRICS))\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        for i, (_, metric) in enumerate(METRICS):\n            self.widget.metrics_combo.activated.emit(i)\n            self.widget.metrics_combo.setCurrentIndex(i)\n            self.wait_until_stop_blocking()\n            if metric.supports_normalization:\n                expected = metric(self.iris, normalize=self.widget.normalized_dist)\n            else:\n                expected = metric(self.iris)\n\n            if metric is not distance.Jaccard:\n                np.testing.assert_array_almost_equal(\n                    expected, self.get_output(self.widget.Outputs.distances))\n\n    def test_error_message(self):\n        \"\"\"Check if error message appears and then disappears when\n        data is removed from input\"\"\"\n        self.widget.metric_idx = 2\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_continuous_features.is_shown())\n        self.send_signal(self.widget.Inputs.data, self.titanic)\n        self.wait_until_stop_blocking()\n        self.assertTrue(self.widget.Error.no_continuous_features.is_shown())\n        self.send_signal(self.widget.Inputs.data, None)\n        self.assertFalse(self.widget.Error.no_continuous_features.is_shown())\n\n    def test_jaccard_messages(self):\n        for self.widget.metric_idx, (name, _) in enumerate(METRICS):\n            if name == \"Jaccard\":\n                break\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.wait_until_stop_blocking()\n        self.assertTrue(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, self.titanic)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertTrue(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, self.titanic)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertTrue(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        dom = self.titanic.domain\n        dom = Domain(dom.attributes[1:], dom.class_var)\n        self.send_signal(self.widget.Inputs.data, self.titanic.transform(dom))\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, Table(\"heart_disease\"))\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_discrete.is_shown())\n\n    def test_too_big_array(self):\n        \"\"\"\n        Users sees an error message when calculating too large arrays and Orange\n        does not crash.\n        GH-2315\n        \"\"\"\n        self.assertEqual(len(self.widget.Error.active), 0)\n        self.send_signal(self.widget.Inputs.data, self.iris)\n\n        mock = Mock(side_effect=ValueError)\n        self.widget.compute_distances(mock, self.iris)\n        self.wait_until_finished()\n        self.assertTrue(self.widget.Error.distances_value_error.is_shown())\n\n        mock = Mock(side_effect=MemoryError)\n        self.widget.compute_distances(mock, self.iris)\n        self.wait_until_finished()\n        self.assertEqual(len(self.widget.Error.active), 1)\n        self.assertTrue(self.widget.Error.distances_memory_error.is_shown())\n\n    def test_migrates_normalized_dist(self):\n        w = self.create_widget(OWDistances, stored_settings={\"metric_idx\": 0})\n        self.assertFalse(w.normalized_dist)\n\n    def test_negative_values_bhattacharyya(self):\n        with self.iris.unlocked():\n            self.iris.X[0, 0] *= -1\n        for self.widget.metric_idx, (_, metric) in enumerate(METRICS):\n            if metric == distance.Bhattacharyya:\n                break\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.wait_until_finished()\n        self.assertTrue(self.widget.Error.distances_value_error.is_shown())\n\n    def test_limit_mahalanobis(self):\n        def assert_error_shown():\n            self.assertTrue(\n                self.widget.Error.data_too_large_for_mahalanobis.is_shown())\n\n        def assert_no_error():\n            self.assertFalse(\n                self.widget.Error.data_too_large_for_mahalanobis.is_shown())\n\n        widget = self.widget\n        axis_buttons = widget.controls.axis.buttons\n\n        self.assertEqual(widget.metrics_combo.count(), len(METRICS))\n        for i, (_, metric) in enumerate(METRICS):\n            if metric == distance.Mahalanobis:\n                widget.metrics_combo.setCurrentIndex(i)\n                widget.metrics_combo.activated.emit(i)\n                break\n\n        X = np.random.random((1010, 4))\n        bigrows = Table.from_numpy(Domain(self.iris.domain.attributes), X)\n        bigcols = Table.from_numpy(\n            Domain([ContinuousVariable(f\"{i}\") for i in range(1010)]), X.T)\n\n        self.send_signal(widget.Inputs.data, self.iris)\n        assert_no_error()\n\n        axis_buttons[0].click()\n        assert_no_error()\n        axis_buttons[1].click()\n        assert_no_error()\n\n        # by columns -- cannot handle too many rows\n        self.send_signal(self.widget.Inputs.data, bigrows)\n        assert_error_shown()\n        axis_buttons[0].click()\n        assert_no_error()\n        axis_buttons[1].click()\n        assert_error_shown()\n\n        self.send_signal(self.widget.Inputs.data, bigcols)\n        assert_no_error()\n        axis_buttons[0].click()\n        assert_error_shown()\n\n        self.send_signal(widget.Inputs.data, self.iris)\n        assert_no_error()\n\n    def test_discrete_in_metas(self):\n        domain = self.iris.domain\n        data = self.iris.transform(\n            Domain(domain.attributes[:-1] + (domain.class_var, ),\n                   [],\n                   domain.attributes[-1:])\n        )\n        for self.widget.metric_idx, (_, metric) in enumerate(METRICS):\n            if metric == distance.Cosine:\n                break\n        self.send_signal(self.widget.Inputs.data, data)\n        self.wait_until_finished()\n        out = self.get_output(self.widget.Outputs.distances)\n        out_domain = out.row_items.domain\n        self.assertEqual(out_domain.attributes, domain.attributes[:-1])\n        self.assertEqual(out_domain.metas,\n                         (domain.attributes[-1], domain.class_var))\n\n    def test_non_binary_in_metas(self):\n        for self.widget.metric_idx, (_, metric) in enumerate(METRICS):\n            if metric == distance.Jaccard:\n                break\n        zoo = Table(\"zoo\")[:20]\n        self.send_signal(self.widget.Inputs.data, zoo)\n        self.wait_until_finished()\n        out = self.get_output(self.widget.Outputs.distances)\n        domain = zoo.domain\n        out_domain = out.row_items.domain\n        self.assertEqual(out_domain.metas, (domain[\"name\"], domain[\"legs\"]))\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n",
            "new_file_content": "# Test methods with long descriptive names can omit docstrings\n# pylint: disable=missing-docstring, protected-access\nimport unittest\nfrom unittest.mock import Mock\n\nimport numpy as np\n\nfrom Orange import distance\nfrom Orange.data import Table, Domain, ContinuousVariable\nfrom Orange.misc import DistMatrix\nfrom Orange.widgets.unsupervised.owdistances import OWDistances, METRICS, \\\n    DistanceRunner\nfrom Orange.widgets.tests.base import WidgetTest\n\n\nclass TestDistanceRunner(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        super().setUpClass()\n        cls.iris = Table(\"iris\")[::5].copy()\n        with cls.iris.unlocked():\n            cls.iris.X[0, 2] = np.nan\n            cls.iris.X[1, 3] = np.nan\n            cls.iris.X[2, 1] = np.nan\n\n        cls.zoo = Table(\"zoo\")[::5].copy()\n        with cls.zoo.unlocked():\n            cls.zoo.X[0, 2] = np.nan\n            cls.zoo.X[1, 3] = np.nan\n            cls.zoo.X[2, 1] = np.nan\n\n    def test_run(self):\n        state = Mock()\n        state.is_interruption_requested = Mock(return_value=False)\n        for name, metric in METRICS:\n            data = self.iris\n            if not metric.supports_missing or name == \"Bhattacharyya\":\n                data = distance.impute(data)\n            elif name == \"Jaccard\":\n                data = self.zoo\n\n            # between rows, normalized\n            dist1 = DistanceRunner.run(data, metric, True, 0, state)\n            dist2 = metric(data, axis=1, impute=True, normalize=True)\n            self.assertDistMatrixEqual(dist1, dist2)\n\n            # between rows, not normalized\n            dist1 = DistanceRunner.run(data, metric, False, 0, state)\n            dist2 = metric(data, axis=1, impute=True, normalize=False)\n            self.assertDistMatrixEqual(dist1, dist2)\n\n            # between columns, normalized\n            dist1 = DistanceRunner.run(data, metric, True, 1, state)\n            dist2 = metric(data, axis=0, impute=True, normalize=True)\n            self.assertDistMatrixEqual(dist1, dist2)\n\n            # between columns, not normalized\n            dist1 = DistanceRunner.run(data, metric, False, 1, state)\n            dist2 = metric(data, axis=0, impute=True, normalize=False)\n            self.assertDistMatrixEqual(dist1, dist2)\n\n    def assertDistMatrixEqual(self, dist1, dist2):\n        self.assertIsInstance(dist1, DistMatrix)\n        self.assertIsInstance(dist2, DistMatrix)\n        self.assertEqual(dist1.axis, dist2.axis)\n        self.assertEqual(dist1.row_items, dist2.row_items)\n        self.assertEqual(dist1.col_items, dist2.col_items)\n        np.testing.assert_array_almost_equal(dist1, dist2)\n\n\nclass TestOWDistances(WidgetTest):\n    def setUp(self):\n        super().setUp()\n        self.iris = Table(\"iris\")[::5].copy()\n        self.titanic = Table(\"titanic\")[::10].copy()\n        self.widget = self.create_widget(OWDistances)\n\n    def test_distance_combo(self):\n        \"\"\"Check distances when the metric changes\"\"\"\n        self.assertEqual(self.widget.metrics_combo.count(), len(METRICS))\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        for i, (_, metric) in enumerate(METRICS):\n            self.widget.metrics_combo.activated.emit(i)\n            self.widget.metrics_combo.setCurrentIndex(i)\n            self.wait_until_stop_blocking()\n            if metric.supports_normalization:\n                expected = metric(self.iris, normalize=self.widget.normalized_dist)\n            else:\n                expected = metric(self.iris)\n\n            if metric is not distance.Jaccard:\n                np.testing.assert_array_almost_equal(\n                    expected, self.get_output(self.widget.Outputs.distances))\n\n    def test_error_message(self):\n        \"\"\"Check if error message appears and then disappears when\n        data is removed from input\"\"\"\n        self.widget.metric_idx = 2\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_continuous_features.is_shown())\n        self.send_signal(self.widget.Inputs.data, self.titanic)\n        self.wait_until_stop_blocking()\n        self.assertTrue(self.widget.Error.no_continuous_features.is_shown())\n        self.send_signal(self.widget.Inputs.data, None)\n        self.assertFalse(self.widget.Error.no_continuous_features.is_shown())\n\n    def test_jaccard_messages(self):\n        for self.widget.metric_idx, (name, _) in enumerate(METRICS):\n            if name == \"Jaccard\":\n                break\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.wait_until_stop_blocking()\n        self.assertTrue(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, self.titanic)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertTrue(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, self.titanic)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertTrue(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        dom = self.titanic.domain\n        dom = Domain(dom.attributes[1:], dom.class_var)\n        self.send_signal(self.widget.Inputs.data, self.titanic.transform(dom))\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, Table(\"heart_disease\"))\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_discrete.is_shown())\n\n    def test_too_big_array(self):\n        \"\"\"\n        Users sees an error message when calculating too large arrays and Orange\n        does not crash.\n        GH-2315\n        \"\"\"\n        self.assertEqual(len(self.widget.Error.active), 0)\n        self.send_signal(self.widget.Inputs.data, self.iris)\n\n        mock = Mock(side_effect=ValueError)\n        self.widget.compute_distances(mock, self.iris)\n        self.wait_until_finished()\n        self.assertTrue(self.widget.Error.distances_value_error.is_shown())\n\n        mock = Mock(side_effect=MemoryError)\n        self.widget.compute_distances(mock, self.iris)\n        self.wait_until_finished()\n        self.assertEqual(len(self.widget.Error.active), 1)\n        self.assertTrue(self.widget.Error.distances_memory_error.is_shown())\n\n    def test_migrates_normalized_dist(self):\n        w = self.create_widget(OWDistances, stored_settings={\"metric_idx\": 0})\n        self.assertFalse(w.normalized_dist)\n\n    def test_negative_values_bhattacharyya(self):\n        with self.iris.unlocked():\n            self.iris.X[0, 0] *= -1\n        for self.widget.metric_idx, (_, metric) in enumerate(METRICS):\n            if metric == distance.Bhattacharyya:\n                break\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.wait_until_finished()\n        self.assertTrue(self.widget.Error.distances_value_error.is_shown())\n\n    def test_limit_mahalanobis(self):\n        def assert_error_shown():\n            self.assertTrue(\n                self.widget.Error.data_too_large_for_mahalanobis.is_shown())\n\n        def assert_no_error():\n            self.assertFalse(\n                self.widget.Error.data_too_large_for_mahalanobis.is_shown())\n\n        widget = self.widget\n        axis_buttons = widget.controls.axis.buttons\n\n        self.assertEqual(widget.metrics_combo.count(), len(METRICS))\n        for i, (_, metric) in enumerate(METRICS):\n            if metric == distance.Mahalanobis:\n                widget.metrics_combo.setCurrentIndex(i)\n                widget.metrics_combo.activated.emit(i)\n                break\n\n        X = np.random.random((1010, 4))\n        bigrows = Table.from_numpy(Domain(self.iris.domain.attributes), X)\n        bigcols = Table.from_numpy(\n            Domain([ContinuousVariable(f\"{i}\") for i in range(1010)]), X.T)\n\n        self.send_signal(widget.Inputs.data, self.iris)\n        assert_no_error()\n\n        axis_buttons[0].click()\n        assert_no_error()\n        axis_buttons[1].click()\n        assert_no_error()\n\n        # by columns -- cannot handle too many rows\n        self.send_signal(self.widget.Inputs.data, bigrows)\n        assert_no_error()\n        axis_buttons[0].click()\n        assert_error_shown()\n        axis_buttons[1].click()\n        assert_no_error()\n\n        self.send_signal(self.widget.Inputs.data, bigcols)\n        assert_error_shown()\n        axis_buttons[0].click()\n        assert_no_error()\n        axis_buttons[1].click()\n        assert_error_shown()\n\n        self.send_signal(widget.Inputs.data, self.iris)\n        assert_no_error()\n\n    def test_discrete_in_metas(self):\n        domain = self.iris.domain\n        data = self.iris.transform(\n            Domain(domain.attributes[:-1] + (domain.class_var, ),\n                   [],\n                   domain.attributes[-1:])\n        )\n        for self.widget.metric_idx, (_, metric) in enumerate(METRICS):\n            if metric == distance.Cosine:\n                break\n        self.send_signal(self.widget.Inputs.data, data)\n        self.wait_until_finished()\n        out = self.get_output(self.widget.Outputs.distances)\n        out_domain = out.row_items.domain\n        self.assertEqual(out_domain.attributes, domain.attributes[:-1])\n        self.assertEqual(out_domain.metas,\n                         (domain.attributes[-1], domain.class_var))\n\n    def test_non_binary_in_metas(self):\n        for self.widget.metric_idx, (_, metric) in enumerate(METRICS):\n            if metric == distance.Jaccard:\n                break\n        zoo = Table(\"zoo\")[:20]\n        self.send_signal(self.widget.Inputs.data, zoo)\n        self.wait_until_finished()\n        out = self.get_output(self.widget.Outputs.distances)\n        domain = zoo.domain\n        out_domain = out.row_items.domain\n        self.assertEqual(out_domain.metas, (domain[\"name\"], domain[\"legs\"]))\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n",
            "header": {
                "file": {
                    "path": "Orange/widgets/unsupervised/tests/test_owdistances.py"
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                "new_commit_hash": "6fd5b0541",
                "mode": "100644"
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            "binary_line": null,
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                "path": "a/Orange/widgets/unsupervised/tests/test_owdistances.py"
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                "path": "b/Orange/widgets/unsupervised/tests/test_owdistances.py"
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                                "content": "",
                                "type": "context"
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                            {
                                "content": "        # by columns -- cannot handle too many rows",
                                "type": "context"
                            },
                            {
                                "content": "        self.send_signal(self.widget.Inputs.data, bigrows)",
                                "type": "context"
                            },
                            {
                                "content": "        assert_error_shown()",
                                "type": "deleted"
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                                "content": "        axis_buttons[0].click()",
                                "type": "deleted"
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                                "content": "        self.send_signal(self.widget.Inputs.data, bigcols)",
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                            "type": "method",
                            "name": "TestOWDistances.test_limit_mahalanobis",
                            "content": "class TestOWDistances(WidgetTest):\n    def setUp(self):\n        super().setUp()\n        self.iris = Table(\"iris\")[::5].copy()\n        self.titanic = Table(\"titanic\")[::10].copy()\n        self.widget = self.create_widget(OWDistances)\n\n    def test_distance_combo(self):\n        \"\"\"Check distances when the metric changes\"\"\"\n        self.assertEqual(self.widget.metrics_combo.count(), len(METRICS))\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        for i, (_, metric) in enumerate(METRICS):\n            self.widget.metrics_combo.activated.emit(i)\n            self.widget.metrics_combo.setCurrentIndex(i)\n            self.wait_until_stop_blocking()\n            if metric.supports_normalization:\n                expected = metric(self.iris, normalize=self.widget.normalized_dist)\n            else:\n                expected = metric(self.iris)\n\n            if metric is not distance.Jaccard:\n                np.testing.assert_array_almost_equal(\n                    expected, self.get_output(self.widget.Outputs.distances))\n\n    def test_error_message(self):\n        \"\"\"Check if error message appears and then disappears when\n        data is removed from input\"\"\"\n        self.widget.metric_idx = 2\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_continuous_features.is_shown())\n        self.send_signal(self.widget.Inputs.data, self.titanic)\n        self.wait_until_stop_blocking()\n        self.assertTrue(self.widget.Error.no_continuous_features.is_shown())\n        self.send_signal(self.widget.Inputs.data, None)\n        self.assertFalse(self.widget.Error.no_continuous_features.is_shown())\n\n    def test_jaccard_messages(self):\n        for self.widget.metric_idx, (name, _) in enumerate(METRICS):\n            if name == \"Jaccard\":\n                break\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.wait_until_stop_blocking()\n        self.assertTrue(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, self.titanic)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertTrue(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, self.titanic)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertTrue(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        dom = self.titanic.domain\n        dom = Domain(dom.attributes[1:], dom.class_var)\n        self.send_signal(self.widget.Inputs.data, self.titanic.transform(dom))\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, Table(\"heart_disease\"))\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_discrete.is_shown())\n\n    def test_too_big_array(self):\n        \"\"\"\n        Users sees an error message when calculating too large arrays and Orange\n        does not crash.\n        GH-2315\n        \"\"\"\n        self.assertEqual(len(self.widget.Error.active), 0)\n        self.send_signal(self.widget.Inputs.data, self.iris)\n\n        mock = Mock(side_effect=ValueError)\n        self.widget.compute_distances(mock, self.iris)\n        self.wait_until_finished()\n        self.assertTrue(self.widget.Error.distances_value_error.is_shown())\n\n        mock = Mock(side_effect=MemoryError)\n        self.widget.compute_distances(mock, self.iris)\n        self.wait_until_finished()\n        self.assertEqual(len(self.widget.Error.active), 1)\n        self.assertTrue(self.widget.Error.distances_memory_error.is_shown())\n\n    def test_migrates_normalized_dist(self):\n        w = self.create_widget(OWDistances, stored_settings={\"metric_idx\": 0})\n        self.assertFalse(w.normalized_dist)\n\n    def test_negative_values_bhattacharyya(self):\n        with self.iris.unlocked():\n            self.iris.X[0, 0] *= -1\n        for self.widget.metric_idx, (_, metric) in enumerate(METRICS):\n            if metric == distance.Bhattacharyya:\n                break\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.wait_until_finished()\n        self.assertTrue(self.widget.Error.distances_value_error.is_shown())\n\n    def test_limit_mahalanobis(self):\n        def assert_error_shown():\n            self.assertTrue(\n                self.widget.Error.data_too_large_for_mahalanobis.is_shown())\n\n        def assert_no_error():\n            self.assertFalse(\n                self.widget.Error.data_too_large_for_mahalanobis.is_shown())\n\n        widget = self.widget\n        axis_buttons = widget.controls.axis.buttons\n\n        self.assertEqual(widget.metrics_combo.count(), len(METRICS))\n        for i, (_, metric) in enumerate(METRICS):\n            if metric == distance.Mahalanobis:\n                widget.metrics_combo.setCurrentIndex(i)\n                widget.metrics_combo.activated.emit(i)\n                break\n\n        X = np.random.random((1010, 4))\n        bigrows = Table.from_numpy(Domain(self.iris.domain.attributes), X)\n        bigcols = Table.from_numpy(\n            Domain([ContinuousVariable(f\"{i}\") for i in range(1010)]), X.T)\n\n        self.send_signal(widget.Inputs.data, self.iris)\n        assert_no_error()\n\n        axis_buttons[0].click()\n        assert_no_error()\n        axis_buttons[1].click()\n        assert_no_error()\n\n        # by columns -- cannot handle too many rows\n        self.send_signal(self.widget.Inputs.data, bigrows)\n        assert_error_shown()\n        axis_buttons[0].click()\n        assert_no_error()\n        axis_buttons[1].click()\n        assert_error_shown()\n\n        self.send_signal(self.widget.Inputs.data, bigcols)\n        assert_no_error()\n        axis_buttons[0].click()\n        assert_error_shown()\n\n        self.send_signal(widget.Inputs.data, self.iris)\n        assert_no_error()\n\n    def test_discrete_in_metas(self):\n        domain = self.iris.domain\n        data = self.iris.transform(\n            Domain(domain.attributes[:-1] + (domain.class_var, ),\n                   [],\n                   domain.attributes[-1:])\n        )\n        for self.widget.metric_idx, (_, metric) in enumerate(METRICS):\n            if metric == distance.Cosine:\n                break\n        self.send_signal(self.widget.Inputs.data, data)\n        self.wait_until_finished()\n        out = self.get_output(self.widget.Outputs.distances)\n        out_domain = out.row_items.domain\n        self.assertEqual(out_domain.attributes, domain.attributes[:-1])\n        self.assertEqual(out_domain.metas,\n                         (domain.attributes[-1], domain.class_var))\n\n    def test_non_binary_in_metas(self):\n        for self.widget.metric_idx, (_, metric) in enumerate(METRICS):\n            if metric == distance.Jaccard:\n                break\n        zoo = Table(\"zoo\")[:20]\n        self.send_signal(self.widget.Inputs.data, zoo)\n        self.wait_until_finished()\n        out = self.get_output(self.widget.Outputs.distances)\n        domain = zoo.domain\n        out_domain = out.row_items.domain\n        self.assertEqual(out_domain.metas, (domain[\"name\"], domain[\"legs\"]))",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 183,
                            "end_lineno": 229,
                            "parent": {
                                "file_name": "Orange/widgets/unsupervised/tests/test_owdistances.py",
                                "type": "class",
                                "name": "TestOWDistances",
                                "content": "class TestOWDistances(WidgetTest):\n    def setUp(self):\n        super().setUp()\n        self.iris = Table(\"iris\")[::5].copy()\n        self.titanic = Table(\"titanic\")[::10].copy()\n        self.widget = self.create_widget(OWDistances)\n\n    def test_distance_combo(self):\n        \"\"\"Check distances when the metric changes\"\"\"\n        self.assertEqual(self.widget.metrics_combo.count(), len(METRICS))\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        for i, (_, metric) in enumerate(METRICS):\n            self.widget.metrics_combo.activated.emit(i)\n            self.widget.metrics_combo.setCurrentIndex(i)\n            self.wait_until_stop_blocking()\n            if metric.supports_normalization:\n                expected = metric(self.iris, normalize=self.widget.normalized_dist)\n            else:\n                expected = metric(self.iris)\n\n            if metric is not distance.Jaccard:\n                np.testing.assert_array_almost_equal(\n                    expected, self.get_output(self.widget.Outputs.distances))\n\n    def test_error_message(self):\n        \"\"\"Check if error message appears and then disappears when\n        data is removed from input\"\"\"\n        self.widget.metric_idx = 2\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_continuous_features.is_shown())\n        self.send_signal(self.widget.Inputs.data, self.titanic)\n        self.wait_until_stop_blocking()\n        self.assertTrue(self.widget.Error.no_continuous_features.is_shown())\n        self.send_signal(self.widget.Inputs.data, None)\n        self.assertFalse(self.widget.Error.no_continuous_features.is_shown())\n\n    def test_jaccard_messages(self):\n        for self.widget.metric_idx, (name, _) in enumerate(METRICS):\n            if name == \"Jaccard\":\n                break\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.wait_until_stop_blocking()\n        self.assertTrue(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, self.titanic)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertTrue(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, self.titanic)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertTrue(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        dom = self.titanic.domain\n        dom = Domain(dom.attributes[1:], dom.class_var)\n        self.send_signal(self.widget.Inputs.data, self.titanic.transform(dom))\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, Table(\"heart_disease\"))\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_discrete.is_shown())\n\n    def test_too_big_array(self):\n        \"\"\"\n        Users sees an error message when calculating too large arrays and Orange\n        does not crash.\n        GH-2315\n        \"\"\"\n        self.assertEqual(len(self.widget.Error.active), 0)\n        self.send_signal(self.widget.Inputs.data, self.iris)\n\n        mock = Mock(side_effect=ValueError)\n        self.widget.compute_distances(mock, self.iris)\n        self.wait_until_finished()\n        self.assertTrue(self.widget.Error.distances_value_error.is_shown())\n\n        mock = Mock(side_effect=MemoryError)\n        self.widget.compute_distances(mock, self.iris)\n        self.wait_until_finished()\n        self.assertEqual(len(self.widget.Error.active), 1)\n        self.assertTrue(self.widget.Error.distances_memory_error.is_shown())\n\n    def test_migrates_normalized_dist(self):\n        w = self.create_widget(OWDistances, stored_settings={\"metric_idx\": 0})\n        self.assertFalse(w.normalized_dist)\n\n    def test_negative_values_bhattacharyya(self):\n        with self.iris.unlocked():\n            self.iris.X[0, 0] *= -1\n        for self.widget.metric_idx, (_, metric) in enumerate(METRICS):\n            if metric == distance.Bhattacharyya:\n                break\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.wait_until_finished()\n        self.assertTrue(self.widget.Error.distances_value_error.is_shown())\n\n    def test_limit_mahalanobis(self):\n        def assert_error_shown():\n            self.assertTrue(\n                self.widget.Error.data_too_large_for_mahalanobis.is_shown())\n\n        def assert_no_error():\n            self.assertFalse(\n                self.widget.Error.data_too_large_for_mahalanobis.is_shown())\n\n        widget = self.widget\n        axis_buttons = widget.controls.axis.buttons\n\n        self.assertEqual(widget.metrics_combo.count(), len(METRICS))\n        for i, (_, metric) in enumerate(METRICS):\n            if metric == distance.Mahalanobis:\n                widget.metrics_combo.setCurrentIndex(i)\n                widget.metrics_combo.activated.emit(i)\n                break\n\n        X = np.random.random((1010, 4))\n        bigrows = Table.from_numpy(Domain(self.iris.domain.attributes), X)\n        bigcols = Table.from_numpy(\n            Domain([ContinuousVariable(f\"{i}\") for i in range(1010)]), X.T)\n\n        self.send_signal(widget.Inputs.data, self.iris)\n        assert_no_error()\n\n        axis_buttons[0].click()\n        assert_no_error()\n        axis_buttons[1].click()\n        assert_no_error()\n\n        # by columns -- cannot handle too many rows\n        self.send_signal(self.widget.Inputs.data, bigrows)\n        assert_error_shown()\n        axis_buttons[0].click()\n        assert_no_error()\n        axis_buttons[1].click()\n        assert_error_shown()\n\n        self.send_signal(self.widget.Inputs.data, bigcols)\n        assert_no_error()\n        axis_buttons[0].click()\n        assert_error_shown()\n\n        self.send_signal(widget.Inputs.data, self.iris)\n        assert_no_error()\n\n    def test_discrete_in_metas(self):\n        domain = self.iris.domain\n        data = self.iris.transform(\n            Domain(domain.attributes[:-1] + (domain.class_var, ),\n                   [],\n                   domain.attributes[-1:])\n        )\n        for self.widget.metric_idx, (_, metric) in enumerate(METRICS):\n            if metric == distance.Cosine:\n                break\n        self.send_signal(self.widget.Inputs.data, data)\n        self.wait_until_finished()\n        out = self.get_output(self.widget.Outputs.distances)\n        out_domain = out.row_items.domain\n        self.assertEqual(out_domain.attributes, domain.attributes[:-1])\n        self.assertEqual(out_domain.metas,\n                         (domain.attributes[-1], domain.class_var))\n\n    def test_non_binary_in_metas(self):\n        for self.widget.metric_idx, (_, metric) in enumerate(METRICS):\n            if metric == distance.Jaccard:\n                break\n        zoo = Table(\"zoo\")[:20]\n        self.send_signal(self.widget.Inputs.data, zoo)\n        self.wait_until_finished()\n        out = self.get_output(self.widget.Outputs.distances)\n        domain = zoo.domain\n        out_domain = out.row_items.domain\n        self.assertEqual(out_domain.metas, (domain[\"name\"], domain[\"legs\"]))",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 71,
                                "end_lineno": 259,
                                "parent": null
                            }
                        },
                        {
                            "file_name": "Orange/widgets/unsupervised/tests/test_owdistances.py",
                            "type": "class",
                            "name": "TestOWDistances",
                            "content": "class TestOWDistances(WidgetTest):\n    def setUp(self):\n        super().setUp()\n        self.iris = Table(\"iris\")[::5].copy()\n        self.titanic = Table(\"titanic\")[::10].copy()\n        self.widget = self.create_widget(OWDistances)\n\n    def test_distance_combo(self):\n        \"\"\"Check distances when the metric changes\"\"\"\n        self.assertEqual(self.widget.metrics_combo.count(), len(METRICS))\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        for i, (_, metric) in enumerate(METRICS):\n            self.widget.metrics_combo.activated.emit(i)\n            self.widget.metrics_combo.setCurrentIndex(i)\n            self.wait_until_stop_blocking()\n            if metric.supports_normalization:\n                expected = metric(self.iris, normalize=self.widget.normalized_dist)\n            else:\n                expected = metric(self.iris)\n\n            if metric is not distance.Jaccard:\n                np.testing.assert_array_almost_equal(\n                    expected, self.get_output(self.widget.Outputs.distances))\n\n    def test_error_message(self):\n        \"\"\"Check if error message appears and then disappears when\n        data is removed from input\"\"\"\n        self.widget.metric_idx = 2\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_continuous_features.is_shown())\n        self.send_signal(self.widget.Inputs.data, self.titanic)\n        self.wait_until_stop_blocking()\n        self.assertTrue(self.widget.Error.no_continuous_features.is_shown())\n        self.send_signal(self.widget.Inputs.data, None)\n        self.assertFalse(self.widget.Error.no_continuous_features.is_shown())\n\n    def test_jaccard_messages(self):\n        for self.widget.metric_idx, (name, _) in enumerate(METRICS):\n            if name == \"Jaccard\":\n                break\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.wait_until_stop_blocking()\n        self.assertTrue(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, self.titanic)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertTrue(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, self.titanic)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertTrue(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        dom = self.titanic.domain\n        dom = Domain(dom.attributes[1:], dom.class_var)\n        self.send_signal(self.widget.Inputs.data, self.titanic.transform(dom))\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, Table(\"heart_disease\"))\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_discrete.is_shown())\n\n    def test_too_big_array(self):\n        \"\"\"\n        Users sees an error message when calculating too large arrays and Orange\n        does not crash.\n        GH-2315\n        \"\"\"\n        self.assertEqual(len(self.widget.Error.active), 0)\n        self.send_signal(self.widget.Inputs.data, self.iris)\n\n        mock = Mock(side_effect=ValueError)\n        self.widget.compute_distances(mock, self.iris)\n        self.wait_until_finished()\n        self.assertTrue(self.widget.Error.distances_value_error.is_shown())\n\n        mock = Mock(side_effect=MemoryError)\n        self.widget.compute_distances(mock, self.iris)\n        self.wait_until_finished()\n        self.assertEqual(len(self.widget.Error.active), 1)\n        self.assertTrue(self.widget.Error.distances_memory_error.is_shown())\n\n    def test_migrates_normalized_dist(self):\n        w = self.create_widget(OWDistances, stored_settings={\"metric_idx\": 0})\n        self.assertFalse(w.normalized_dist)\n\n    def test_negative_values_bhattacharyya(self):\n        with self.iris.unlocked():\n            self.iris.X[0, 0] *= -1\n        for self.widget.metric_idx, (_, metric) in enumerate(METRICS):\n            if metric == distance.Bhattacharyya:\n                break\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.wait_until_finished()\n        self.assertTrue(self.widget.Error.distances_value_error.is_shown())\n\n    def test_limit_mahalanobis(self):\n        def assert_error_shown():\n            self.assertTrue(\n                self.widget.Error.data_too_large_for_mahalanobis.is_shown())\n\n        def assert_no_error():\n            self.assertFalse(\n                self.widget.Error.data_too_large_for_mahalanobis.is_shown())\n\n        widget = self.widget\n        axis_buttons = widget.controls.axis.buttons\n\n        self.assertEqual(widget.metrics_combo.count(), len(METRICS))\n        for i, (_, metric) in enumerate(METRICS):\n            if metric == distance.Mahalanobis:\n                widget.metrics_combo.setCurrentIndex(i)\n                widget.metrics_combo.activated.emit(i)\n                break\n\n        X = np.random.random((1010, 4))\n        bigrows = Table.from_numpy(Domain(self.iris.domain.attributes), X)\n        bigcols = Table.from_numpy(\n            Domain([ContinuousVariable(f\"{i}\") for i in range(1010)]), X.T)\n\n        self.send_signal(widget.Inputs.data, self.iris)\n        assert_no_error()\n\n        axis_buttons[0].click()\n        assert_no_error()\n        axis_buttons[1].click()\n        assert_no_error()\n\n        # by columns -- cannot handle too many rows\n        self.send_signal(self.widget.Inputs.data, bigrows)\n        assert_error_shown()\n        axis_buttons[0].click()\n        assert_no_error()\n        axis_buttons[1].click()\n        assert_error_shown()\n\n        self.send_signal(self.widget.Inputs.data, bigcols)\n        assert_no_error()\n        axis_buttons[0].click()\n        assert_error_shown()\n\n        self.send_signal(widget.Inputs.data, self.iris)\n        assert_no_error()\n\n    def test_discrete_in_metas(self):\n        domain = self.iris.domain\n        data = self.iris.transform(\n            Domain(domain.attributes[:-1] + (domain.class_var, ),\n                   [],\n                   domain.attributes[-1:])\n        )\n        for self.widget.metric_idx, (_, metric) in enumerate(METRICS):\n            if metric == distance.Cosine:\n                break\n        self.send_signal(self.widget.Inputs.data, data)\n        self.wait_until_finished()\n        out = self.get_output(self.widget.Outputs.distances)\n        out_domain = out.row_items.domain\n        self.assertEqual(out_domain.attributes, domain.attributes[:-1])\n        self.assertEqual(out_domain.metas,\n                         (domain.attributes[-1], domain.class_var))\n\n    def test_non_binary_in_metas(self):\n        for self.widget.metric_idx, (_, metric) in enumerate(METRICS):\n            if metric == distance.Jaccard:\n                break\n        zoo = Table(\"zoo\")[:20]\n        self.send_signal(self.widget.Inputs.data, zoo)\n        self.wait_until_finished()\n        out = self.get_output(self.widget.Outputs.distances)\n        domain = zoo.domain\n        out_domain = out.row_items.domain\n        self.assertEqual(out_domain.metas, (domain[\"name\"], domain[\"legs\"]))",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 71,
                            "end_lineno": 259,
                            "parent": null
                        }
                    ],
                    "added_entities": [],
                    "deleted_entities": []
                }
            ]
        }
    ],
    "old_commit_hash": "dd84366eca09cf0348884fb9354f12658f4be676^",
    "new_commit_hash": "dd84366eca09cf0348884fb9354f12658f4be676",
    "commit_message": "Distance: Fix check and error message for Mahalanobis distance",
    "commit_date": "2022-10-21T12:07:22+02:00",
    "metadata": {}
}
{
    "repo_name": "orange3",
    "new_commit_hash": "dd84366eca09cf0348884fb9354f12658f4be676",
    "test_file_codes": [
        "# Test methods with long descriptive names can omit docstrings\n# pylint: disable=missing-docstring, protected-access\nimport unittest\nfrom unittest.mock import Mock\n\nimport numpy as np\n\nfrom Orange import distance\nfrom Orange.data import Table, Domain, ContinuousVariable\nfrom Orange.misc import DistMatrix\nfrom Orange.widgets.unsupervised.owdistances import OWDistances, METRICS, \\\n    DistanceRunner\nfrom Orange.widgets.tests.base import WidgetTest\n\n\nclass TestDistanceRunner(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        super().setUpClass()\n        cls.iris = Table(\"iris\")[::5].copy()\n        with cls.iris.unlocked():\n            cls.iris.X[0, 2] = np.nan\n            cls.iris.X[1, 3] = np.nan\n            cls.iris.X[2, 1] = np.nan\n\n        cls.zoo = Table(\"zoo\")[::5].copy()\n        with cls.zoo.unlocked():\n            cls.zoo.X[0, 2] = np.nan\n            cls.zoo.X[1, 3] = np.nan\n            cls.zoo.X[2, 1] = np.nan\n\n    def test_run(self):\n        state = Mock()\n        state.is_interruption_requested = Mock(return_value=False)\n        for name, metric in METRICS:\n            data = self.iris\n            if not metric.supports_missing or name == \"Bhattacharyya\":\n                data = distance.impute(data)\n            elif name == \"Jaccard\":\n                data = self.zoo\n\n            # between rows, normalized\n            dist1 = DistanceRunner.run(data, metric, True, 0, state)\n            dist2 = metric(data, axis=1, impute=True, normalize=True)\n            self.assertDistMatrixEqual(dist1, dist2)\n\n            # between rows, not normalized\n            dist1 = DistanceRunner.run(data, metric, False, 0, state)\n            dist2 = metric(data, axis=1, impute=True, normalize=False)\n            self.assertDistMatrixEqual(dist1, dist2)\n\n            # between columns, normalized\n            dist1 = DistanceRunner.run(data, metric, True, 1, state)\n            dist2 = metric(data, axis=0, impute=True, normalize=True)\n            self.assertDistMatrixEqual(dist1, dist2)\n\n            # between columns, not normalized\n            dist1 = DistanceRunner.run(data, metric, False, 1, state)\n            dist2 = metric(data, axis=0, impute=True, normalize=False)\n            self.assertDistMatrixEqual(dist1, dist2)\n\n    def assertDistMatrixEqual(self, dist1, dist2):\n        self.assertIsInstance(dist1, DistMatrix)\n        self.assertIsInstance(dist2, DistMatrix)\n        self.assertEqual(dist1.axis, dist2.axis)\n        self.assertEqual(dist1.row_items, dist2.row_items)\n        self.assertEqual(dist1.col_items, dist2.col_items)\n        np.testing.assert_array_almost_equal(dist1, dist2)\n\n\nclass TestOWDistances(WidgetTest):\n    def setUp(self):\n        super().setUp()\n        self.iris = Table(\"iris\")[::5].copy()\n        self.titanic = Table(\"titanic\")[::10].copy()\n        self.widget = self.create_widget(OWDistances)\n\n    def test_distance_combo(self):\n        \"\"\"Check distances when the metric changes\"\"\"\n        self.assertEqual(self.widget.metrics_combo.count(), len(METRICS))\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        for i, (_, metric) in enumerate(METRICS):\n            self.widget.metrics_combo.activated.emit(i)\n            self.widget.metrics_combo.setCurrentIndex(i)\n            self.wait_until_stop_blocking()\n            if metric.supports_normalization:\n                expected = metric(self.iris, normalize=self.widget.normalized_dist)\n            else:\n                expected = metric(self.iris)\n\n            if metric is not distance.Jaccard:\n                np.testing.assert_array_almost_equal(\n                    expected, self.get_output(self.widget.Outputs.distances))\n\n    def test_error_message(self):\n        \"\"\"Check if error message appears and then disappears when\n        data is removed from input\"\"\"\n        self.widget.metric_idx = 2\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_continuous_features.is_shown())\n        self.send_signal(self.widget.Inputs.data, self.titanic)\n        self.wait_until_stop_blocking()\n        self.assertTrue(self.widget.Error.no_continuous_features.is_shown())\n        self.send_signal(self.widget.Inputs.data, None)\n        self.assertFalse(self.widget.Error.no_continuous_features.is_shown())\n\n    def test_jaccard_messages(self):\n        for self.widget.metric_idx, (name, _) in enumerate(METRICS):\n            if name == \"Jaccard\":\n                break\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.wait_until_stop_blocking()\n        self.assertTrue(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, self.titanic)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertTrue(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, self.titanic)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertTrue(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        dom = self.titanic.domain\n        dom = Domain(dom.attributes[1:], dom.class_var)\n        self.send_signal(self.widget.Inputs.data, self.titanic.transform(dom))\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, Table(\"heart_disease\"))\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_discrete.is_shown())\n\n    def test_too_big_array(self):\n        \"\"\"\n        Users sees an error message when calculating too large arrays and Orange\n        does not crash.\n        GH-2315\n        \"\"\"\n        self.assertEqual(len(self.widget.Error.active), 0)\n        self.send_signal(self.widget.Inputs.data, self.iris)\n\n        mock = Mock(side_effect=ValueError)\n        self.widget.compute_distances(mock, self.iris)\n        self.wait_until_finished()\n        self.assertTrue(self.widget.Error.distances_value_error.is_shown())\n\n        mock = Mock(side_effect=MemoryError)\n        self.widget.compute_distances(mock, self.iris)\n        self.wait_until_finished()\n        self.assertEqual(len(self.widget.Error.active), 1)\n        self.assertTrue(self.widget.Error.distances_memory_error.is_shown())\n\n    def test_migrates_normalized_dist(self):\n        w = self.create_widget(OWDistances, stored_settings={\"metric_idx\": 0})\n        self.assertFalse(w.normalized_dist)\n\n    def test_negative_values_bhattacharyya(self):\n        with self.iris.unlocked():\n            self.iris.X[0, 0] *= -1\n        for self.widget.metric_idx, (_, metric) in enumerate(METRICS):\n            if metric == distance.Bhattacharyya:\n                break\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.wait_until_finished()\n        self.assertTrue(self.widget.Error.distances_value_error.is_shown())\n\n    def test_limit_mahalanobis(self):\n        def assert_error_shown():\n            self.assertTrue(\n                self.widget.Error.data_too_large_for_mahalanobis.is_shown())\n\n        def assert_no_error():\n            self.assertFalse(\n                self.widget.Error.data_too_large_for_mahalanobis.is_shown())\n\n        widget = self.widget\n        axis_buttons = widget.controls.axis.buttons\n\n        self.assertEqual(widget.metrics_combo.count(), len(METRICS))\n        for i, (_, metric) in enumerate(METRICS):\n            if metric == distance.Mahalanobis:\n                widget.metrics_combo.setCurrentIndex(i)\n                widget.metrics_combo.activated.emit(i)\n                break\n\n        X = np.random.random((1010, 4))\n        bigrows = Table.from_numpy(Domain(self.iris.domain.attributes), X)\n        bigcols = Table.from_numpy(\n            Domain([ContinuousVariable(f\"{i}\") for i in range(1010)]), X.T)\n\n        self.send_signal(widget.Inputs.data, self.iris)\n        assert_no_error()\n\n        axis_buttons[0].click()\n        assert_no_error()\n        axis_buttons[1].click()\n        assert_no_error()\n\n        # by columns -- cannot handle too many rows\n        self.send_signal(self.widget.Inputs.data, bigrows)\n        assert_no_error()\n        axis_buttons[0].click()\n        assert_error_shown()\n        axis_buttons[1].click()\n        assert_no_error()\n\n        self.send_signal(self.widget.Inputs.data, bigcols)\n        assert_error_shown()\n        axis_buttons[0].click()\n        assert_no_error()\n        axis_buttons[1].click()\n        assert_error_shown()\n\n        self.send_signal(widget.Inputs.data, self.iris)\n        assert_no_error()\n\n    def test_discrete_in_metas(self):\n        domain = self.iris.domain\n        data = self.iris.transform(\n            Domain(domain.attributes[:-1] + (domain.class_var, ),\n                   [],\n                   domain.attributes[-1:])\n        )\n        for self.widget.metric_idx, (_, metric) in enumerate(METRICS):\n            if metric == distance.Cosine:\n                break\n        self.send_signal(self.widget.Inputs.data, data)\n        self.wait_until_finished()\n        out = self.get_output(self.widget.Outputs.distances)\n        out_domain = out.row_items.domain\n        self.assertEqual(out_domain.attributes, domain.attributes[:-1])\n        self.assertEqual(out_domain.metas,\n                         (domain.attributes[-1], domain.class_var))\n\n    def test_non_binary_in_metas(self):\n        for self.widget.metric_idx, (_, metric) in enumerate(METRICS):\n            if metric == distance.Jaccard:\n                break\n        zoo = Table(\"zoo\")[:20]\n        self.send_signal(self.widget.Inputs.data, zoo)\n        self.wait_until_finished()\n        out = self.get_output(self.widget.Outputs.distances)\n        domain = zoo.domain\n        out_domain = out.row_items.domain\n        self.assertEqual(out_domain.metas, (domain[\"name\"], domain[\"legs\"]))\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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages/Orange3.egg-link (link to .)\nAdding Orange3 3.34.0.dev0+dd84366 to easy-install.pth file\nInstalling orange-canvas script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676\nProcessing dependencies for Orange3==3.34.0.dev0+dd84366\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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for setuptools==59.8.0\nBest match: setuptools 59.8.0\nAdding setuptools 59.8.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for scipy==1.7.3\nBest match: scipy 1.7.3\nAdding scipy 1.7.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for scikit-learn==1.0.2\nBest match: scikit-learn 1.0.2\nAdding scikit-learn 1.0.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for requests==2.31.0\nBest match: requests 2.31.0\nAdding requests 2.31.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for qtconsole==5.4.4\nBest match: qtconsole 5.4.4\nAdding qtconsole 5.4.4 to easy-install.pth file\nInstalling jupyter-qtconsole script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for PyYAML==6.0.1\nBest match: PyYAML 6.0.1\nAdding PyYAML 6.0.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for pyqtgraph==0.12.3\nBest match: pyqtgraph 0.12.3\nAdding pyqtgraph 0.12.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for pygments==2.17.2\nBest match: pygments 2.17.2\nAdding pygments 2.17.2 to easy-install.pth file\nInstalling pygmentize script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for pip==24.0\nBest match: pip 24.0\nAdding pip 24.0 to easy-install.pth file\nInstalling pip script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/bin\nInstalling pip3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/bin\nInstalling pip3.10 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for pandas==1.3.5\nBest match: pandas 1.3.5\nAdding pandas 1.3.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for openpyxl==3.1.3\nBest match: openpyxl 3.1.3\nAdding openpyxl 3.1.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for numpy==1.17.5\nBest match: numpy 1.17.5\nAdding numpy 1.17.5 to easy-install.pth file\nInstalling f2py script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/bin\nInstalling f2py3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/bin\nInstalling f2py3.7 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for networkx==2.6.3\nBest match: networkx 2.6.3\nAdding networkx 2.6.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for matplotlib==3.5.3\nBest match: matplotlib 3.5.3\nAdding matplotlib 3.5.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for keyrings.alt==4.2.0\nBest match: keyrings.alt 4.2.0\nAdding keyrings.alt 4.2.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for keyring==24.1.1\nBest match: keyring 24.1.1\nAdding keyring 24.1.1 to easy-install.pth file\nInstalling keyring script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for joblib==1.3.2\nBest match: joblib 1.3.2\nAdding joblib 1.3.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for httpx==0.24.1\nBest match: httpx 0.24.1\nAdding httpx 0.24.1 to easy-install.pth file\nInstalling httpx script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for PyQtWebEngine==5.15.6\nBest match: PyQtWebEngine 5.15.6\nAdding PyQtWebEngine 5.15.6 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for PyQt5==5.15.10\nBest match: PyQt5 5.15.10\nAdding PyQt5 5.15.10 to easy-install.pth file\nInstalling pylupdate5 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/bin\nInstalling pyrcc5 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/bin\nInstalling pyuic5 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for threadpoolctl==3.1.0\nBest match: threadpoolctl 3.1.0\nAdding threadpoolctl 3.1.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for urllib3==2.0.7\nBest match: urllib3 2.0.7\nAdding urllib3 2.0.7 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for ipykernel==6.16.2\nBest match: ipykernel 6.16.2\nAdding ipykernel 6.16.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for jupyter-core==4.12.0\nBest match: jupyter-core 4.12.0\nAdding jupyter-core 4.12.0 to easy-install.pth file\nInstalling jupyter script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/bin\nInstalling jupyter-migrate script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/bin\nInstalling jupyter-troubleshoot script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for packaging==24.0\nBest match: packaging 24.0\nAdding packaging 24.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for jupyter-client==7.4.9\nBest match: jupyter-client 7.4.9\nAdding jupyter-client 7.4.9 to easy-install.pth file\nInstalling jupyter-kernel script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/bin\nInstalling jupyter-kernelspec script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/bin\nInstalling jupyter-run script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for traitlets==5.9.0\nBest match: traitlets 5.9.0\nAdding traitlets 5.9.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for ipython-genutils==0.2.0\nBest match: ipython-genutils 0.2.0\nAdding ipython-genutils 0.2.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for typing-extensions==4.7.1\nBest match: typing-extensions 4.7.1\nAdding typing-extensions 4.7.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for requests-cache==1.1.1\nBest match: requests-cache 1.1.1\nAdding requests-cache 1.1.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for importlib-metadata==6.7.0\nBest match: importlib-metadata 6.7.0\nAdding importlib-metadata 6.7.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for qasync==0.25.0\nBest match: qasync 0.25.0\nAdding qasync 0.25.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for et-xmlfile==1.1.0\nBest match: et-xmlfile 1.1.0\nAdding et-xmlfile 1.1.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for cycler==0.11.0\nBest match: cycler 0.11.0\nAdding cycler 0.11.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for Pillow==9.5.0\nBest match: Pillow 9.5.0\nAdding Pillow 9.5.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for kiwisolver==1.4.5\nBest match: kiwisolver 1.4.5\nAdding kiwisolver 1.4.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for fonttools==4.38.0\nBest match: fonttools 4.38.0\nAdding fonttools 4.38.0 to easy-install.pth file\nInstalling fonttools script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/bin\nInstalling pyftmerge script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/bin\nInstalling pyftsubset script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/bin\nInstalling ttx script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for jaraco.classes==3.2.3\nBest match: jaraco.classes 3.2.3\nAdding jaraco.classes 3.2.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for importlib-resources==5.12.0\nBest match: importlib-resources 5.12.0\nAdding importlib-resources 5.12.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for httpcore==0.17.3\nBest match: httpcore 0.17.3\nAdding httpcore 0.17.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for PyQtWebEngine-Qt5==5.15.16\nBest match: PyQtWebEngine-Qt5 5.15.16\nAdding PyQtWebEngine-Qt5 5.15.16 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for PyQt5-sip==12.13.0\nBest match: PyQt5-sip 12.13.0\nAdding PyQt5-sip 12.13.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for PyQt5-Qt5==5.15.16\nBest match: PyQt5-Qt5 5.15.16\nAdding PyQt5-Qt5 5.15.16 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for ipython==7.34.0\nBest match: ipython 7.34.0\nAdding ipython 7.34.0 to easy-install.pth file\nInstalling iptest script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/bin\nInstalling iptest3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/bin\nInstalling ipython script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/bin\nInstalling ipython3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for matplotlib-inline==0.1.6\nBest match: matplotlib-inline 0.1.6\nAdding matplotlib-inline 0.1.6 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for debugpy==1.7.0\nBest match: debugpy 1.7.0\nAdding debugpy 1.7.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for tornado==6.2\nBest match: tornado 6.2\nAdding tornado 6.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for entrypoints==0.4\nBest match: entrypoints 0.4\nAdding entrypoints 0.4 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for cattrs==23.1.2\nBest match: cattrs 23.1.2\nAdding cattrs 23.1.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for platformdirs==4.0.0\nBest match: platformdirs 4.0.0\nAdding platformdirs 4.0.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for attrs==24.2.0\nBest match: attrs 24.2.0\nAdding attrs 24.2.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for zipp==3.15.0\nBest match: zipp 3.15.0\nAdding zipp 3.15.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for more-itertools==9.1.0\nBest match: more-itertools 9.1.0\nAdding more-itertools 9.1.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for anyio==3.7.1\nBest match: anyio 3.7.1\nAdding anyio 3.7.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for cffi==1.15.1\nBest match: cffi 1.15.1\nAdding cffi 1.15.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nSearching for pycparser==2.21\nBest match: pycparser 2.21\nAdding pycparser 2.21 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages\nFinished processing dependencies for Orange3==3.34.0.dev0+dd84366\nVerifying Orange installation...\n✅ Orange installation successful!\nSuccessfully installed orange using Python 3.7\n",
    "setup_res_stderr": "Using CPython 3.7.9\nCreating virtual environment at: .venv\nActivate with: source .venv/bin/activate\nResolved 18 packages in 93ms\nPrepared 18 packages in 32ms\nInstalled 18 packages in 153ms\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\nResolved 54 packages in 541ms\nInstalled 47 packages in 733ms\n + anyio==3.7.1\n + baycomp==1.0.3\n + bottleneck==1.4.0\n + certifi==2024.12.14\n + cffi==1.15.1\n + chardet==5.2.0\n + charset-normalizer==3.4.1\n + cryptography==44.0.0\n + cycler==0.11.0\n + et-xmlfile==1.1.0\n + fonttools==4.38.0\n + h11==0.14.0\n + httpcore==0.17.3\n + httpx==0.24.1\n + idna==3.10\n + importlib-resources==5.12.0\n + jaraco-classes==3.2.3\n + jeepney==0.8.0\n + joblib==1.3.2\n + keyring==24.1.1\n + keyrings-alt==4.2.0\n + kiwisolver==1.4.5\n + matplotlib==3.5.3\n + more-itertools==9.1.0\n + networkx==2.6.3\n + openpyxl==3.1.3\n + opentsne==1.0.1\n + pandas==1.3.5\n + pillow==9.5.0\n + pip==24.0\n + pycparser==2.21\n + pyparsing==3.1.4\n + python-dateutil==2.9.0.post0\n + python-louvain==0.16\n + pytz==2024.2\n + pyyaml==6.0.1\n + requests==2.31.0\n + scikit-learn==1.0.2\n + scipy==1.7.3\n + secretstorage==3.3.3\n + serverfiles==0.3.1\n + six==1.17.0\n + sniffio==1.3.1\n + threadpoolctl==3.1.0\n + urllib3==2.0.7\n + xlrd==2.0.1\n + xlsxwriter==3.2.0\nResolved 59 packages in 478ms\nInstalled 38 packages in 488ms\n + anyqt==0.2.0\n + attrs==24.2.0\n + backcall==0.2.0\n + cattrs==23.1.2\n + commonmark==0.9.1\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 + 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 + 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 + 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 + pyqtgraph==0.12.3\n + pyzmq==26.2.0\n + qasync==0.25.0\n + qtconsole==5.4.4\n + qtpy==2.4.2\n + requests-cache==1.1.1\n + tornado==6.2\n + traitlets==5.9.0\n + url-normalize==1.4.3\n + wcwidth==0.2.13\nResolved 2 packages in 3ms\nInstalled 2 packages in 7ms\n + psycopg2==2.9.9\n + pymssql==2.3.2\nResolved 19 packages in 52ms\nInstalled 5 packages in 778ms\n + catboost==1.2.7\n + graphviz==0.20.1\n + plotly==5.18.0\n + tenacity==8.2.3\n + xgboost==1.6.2\nAudited 2 packages in 1ms\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.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_dd84366eca09cf0348884fb9354f12658f4be676/.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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.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_dd84366eca09cf0348884fb9354f12658f4be676/.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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.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_dd84366eca09cf0348884fb9354f12658f4be676/.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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.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_dd84366eca09cf0348884fb9354f12658f4be676/.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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.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_dd84366eca09cf0348884fb9354f12658f4be676/.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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.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_dd84366eca09cf0348884fb9354f12658f4be676/.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_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676/.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_dd84366eca09cf0348884fb9354f12658f4be676/.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_dd84366eca09cf0348884fb9354f12658f4be676/.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_dd84366eca09cf0348884fb9354f12658f4be676/.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",
    "new_commit_res_code": 0,
    "new_commit_res_stdout": "============================= test session starts ==============================\nplatform linux -- Python 3.7.9, pytest-7.4.4, pluggy-1.2.0\nrootdir: /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676\nplugins: anyio-3.7.1\ncollected 16 items\n\nr2e_tests/test_1.py sss.............                                     [100%]\n\n==================================== PASSES ====================================\n=========================== short test summary info ============================\nPASSED r2e_tests/test_1.py::TestDistanceRunner::test_run\nPASSED r2e_tests/test_1.py::TestOWDistances::test_discrete_in_metas\nPASSED r2e_tests/test_1.py::TestOWDistances::test_distance_combo\nPASSED r2e_tests/test_1.py::TestOWDistances::test_error_message\nPASSED r2e_tests/test_1.py::TestOWDistances::test_image_export\nPASSED r2e_tests/test_1.py::TestOWDistances::test_jaccard_messages\nPASSED r2e_tests/test_1.py::TestOWDistances::test_limit_mahalanobis\nPASSED r2e_tests/test_1.py::TestOWDistances::test_migrates_normalized_dist\nPASSED r2e_tests/test_1.py::TestOWDistances::test_minimum_size\nPASSED r2e_tests/test_1.py::TestOWDistances::test_msg_base_class\nPASSED r2e_tests/test_1.py::TestOWDistances::test_negative_values_bhattacharyya\nPASSED r2e_tests/test_1.py::TestOWDistances::test_non_binary_in_metas\nPASSED r2e_tests/test_1.py::TestOWDistances::test_too_big_array\nSKIPPED [1] .venv/lib/python3.7/site-packages/orangewidget/tests/base.py:244: image exporting not tested as .widget was not set\nSKIPPED [1] .venv/lib/python3.7/site-packages/orangewidget/tests/base.py:238: minimum size not tested as .widget was not set\nSKIPPED [1] .venv/lib/python3.7/site-packages/orangewidget/tests/base.py:250: msg base class not tested as .widget was not set\n======================== 13 passed, 3 skipped in 6.76s =========================\nWidgets left: 0    Max widgets: 689 \n\n",
    "new_commit_res_stderr": "",
    "old_commit_res_code": 1,
    "old_commit_res_stdout": "============================= test session starts ==============================\nplatform linux -- Python 3.7.9, pytest-7.4.4, pluggy-1.2.0\nrootdir: /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_dd84366eca09cf0348884fb9354f12658f4be676\nplugins: anyio-3.7.1\ncollected 16 items\n\nr2e_tests/test_1.py sss......F......                                     [100%]\n\n=================================== FAILURES ===================================\n____________________ TestOWDistances.test_limit_mahalanobis ____________________\n\nself = <r2e_tests.test_1.TestOWDistances testMethod=test_limit_mahalanobis>\n\n    def test_limit_mahalanobis(self):\n        def assert_error_shown():\n            self.assertTrue(\n                self.widget.Error.data_too_large_for_mahalanobis.is_shown())\n    \n        def assert_no_error():\n            self.assertFalse(\n                self.widget.Error.data_too_large_for_mahalanobis.is_shown())\n    \n        widget = self.widget\n        axis_buttons = widget.controls.axis.buttons\n    \n        self.assertEqual(widget.metrics_combo.count(), len(METRICS))\n        for i, (_, metric) in enumerate(METRICS):\n            if metric == distance.Mahalanobis:\n                widget.metrics_combo.setCurrentIndex(i)\n                widget.metrics_combo.activated.emit(i)\n                break\n    \n        X = np.random.random((1010, 4))\n        bigrows = Table.from_numpy(Domain(self.iris.domain.attributes), X)\n        bigcols = Table.from_numpy(\n            Domain([ContinuousVariable(f\"{i}\") for i in range(1010)]), X.T)\n    \n        self.send_signal(widget.Inputs.data, self.iris)\n        assert_no_error()\n    \n        axis_buttons[0].click()\n        assert_no_error()\n        axis_buttons[1].click()\n        assert_no_error()\n    \n        # by columns -- cannot handle too many rows\n        self.send_signal(self.widget.Inputs.data, bigrows)\n>       assert_no_error()\n\nr2e_tests/test_1.py:217: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \nr2e_tests/test_1.py:190: in assert_no_error\n    self.widget.Error.data_too_large_for_mahalanobis.is_shown())\nE   AssertionError: True is not false\n==================================== PASSES ====================================\n=========================== short test summary info ============================\nPASSED r2e_tests/test_1.py::TestDistanceRunner::test_run\nPASSED r2e_tests/test_1.py::TestOWDistances::test_discrete_in_metas\nPASSED r2e_tests/test_1.py::TestOWDistances::test_distance_combo\nPASSED r2e_tests/test_1.py::TestOWDistances::test_error_message\nPASSED r2e_tests/test_1.py::TestOWDistances::test_image_export\nPASSED r2e_tests/test_1.py::TestOWDistances::test_jaccard_messages\nPASSED r2e_tests/test_1.py::TestOWDistances::test_migrates_normalized_dist\nPASSED r2e_tests/test_1.py::TestOWDistances::test_minimum_size\nPASSED r2e_tests/test_1.py::TestOWDistances::test_msg_base_class\nPASSED r2e_tests/test_1.py::TestOWDistances::test_negative_values_bhattacharyya\nPASSED r2e_tests/test_1.py::TestOWDistances::test_non_binary_in_metas\nPASSED r2e_tests/test_1.py::TestOWDistances::test_too_big_array\nSKIPPED [1] .venv/lib/python3.7/site-packages/orangewidget/tests/base.py:244: image exporting not tested as .widget was not set\nSKIPPED [1] .venv/lib/python3.7/site-packages/orangewidget/tests/base.py:238: minimum size not tested as .widget was not set\nSKIPPED [1] .venv/lib/python3.7/site-packages/orangewidget/tests/base.py:250: msg base class not tested as .widget was not set\nFAILED r2e_tests/test_1.py::TestOWDistances::test_limit_mahalanobis - Asserti...\n=================== 1 failed, 12 passed, 3 skipped in 3.47s ====================\nWidgets left: 0    Max widgets: 689 \n\n",
    "old_commit_res_stderr": ""
}
Orange/widgets/unsupervised/owdistances.py Orange/widgets/unsupervised/tests/test_owdistances.py
We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
[ISSUE]

**Title:** Incorrect Row Limit and Axis Check for Mahalanobis Distance Calculations

**Description:**
When computing Mahalanobis distances by columns, the widget incorrectly restricts the number of rows to less than 100 and triggers an error for larger datasets. This limitation prevents processing datasets with up to 1000 rows as intended.

**Example Code:**
```python
widget = OWDistances()
dataset = Table.from_numpy(Domain([...]), np.random.random((1010, 4)))
widget.axis = 1  # Intended to compute distances by columns
widget.compute_distances(metric=distance.Mahalanobis, data=dataset)
# Unexpectedly shows error: "Data too large for Mahalanobis distance"
```

**Expected Behavior:**
When computing distances by columns (axis=0) using Mahalanobis distance, the widget should allow datasets with up to 1000 rows without displaying an error. Only datasets exceeding 1000 rows should trigger the "Data too large for Mahalanobis distance" error.

**Actual Behavior:**
The widget incorrectly checks for axis=1 when computing by columns and restricts the number of rows to less than 100. As a result, even datasets with up to 1000 rows erroneously display the error message, preventing proper distance calculations.

[/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/widgets/unsupervised/owdistances.py
from scipy.sparse import issparse
import bottleneck as bn

from AnyQt.QtCore import Qt

import Orange.data
import Orange.misc
from Orange import distance
from Orange.widgets import gui
from Orange.widgets.settings import Setting
from Orange.widgets.utils.concurrent import TaskState, ConcurrentWidgetMixin
from Orange.widgets.utils.sql import check_sql_input
from Orange.widgets.utils.widgetpreview import WidgetPreview
from Orange.widgets.widget import OWWidget, Msg, Input, Output


METRICS = [
    ("Euclidean", distance.Euclidean),
    ("Manhattan", distance.Manhattan),
    ("Cosine", distance.Cosine),
    ("Jaccard", distance.Jaccard),
    ("Spearman", distance.SpearmanR),
    ("Absolute Spearman", distance.SpearmanRAbsolute),
    ("Pearson", distance.PearsonR),
    ("Absolute Pearson", distance.PearsonRAbsolute),
    ("Hamming", distance.Hamming),
    ("Mahalanobis", distance.Mahalanobis),
    ('Bhattacharyya', distance.Bhattacharyya)
]


class InterruptException(Exception):
    pass


class DistanceRunner:
    @staticmethod
    def run(data: Orange.data.Table, metric: distance, normalized_dist: bool,
            axis: int, state: TaskState) -> Orange.misc.DistMatrix:
        if data is None:
            return None

        def callback(i: float) -> bool:
            state.set_progress_value(i)
            if state.is_interruption_requested():
                raise InterruptException

        state.set_status("Calculating...")
        kwargs = {"axis": 1 - axis, "impute": True, "callback": callback}
        if metric.supports_normalization and normalized_dist:
            kwargs["normalize"] = True
        return metric(data, **kwargs)


class OWDistances(OWWidget, ConcurrentWidgetMixin):
    name = "Distances"
    description = "Compute a matrix of pairwise distances."
    icon = "icons/Distance.svg"
    keywords = []

    class Inputs:
        data = Input("Data", Orange.data.Table)

    class Outputs:
        distances = Output("Distances", Orange.misc.DistMatrix, dynamic=False)

    settings_version = 3

    axis = Setting(0)        # type: int
    metric_idx = Setting(0)  # type: int

    #: Use normalized distances if the metric supports it.
    #: The default is `True`, expect when restoring from old pre v2 settings
    #: (see `migrate_settings`).
    normalized_dist = Setting(True)  # type: bool
    autocommit = Setting(True)       # type: bool

    want_main_area = False
    resizing_enabled = False

    class Error(OWWidget.Error):
        no_continuous_features = Msg("No numeric features")
        no_binary_features = Msg("No binary features")
        dense_metric_sparse_data = Msg("{} requires dense data.")
        distances_memory_error = Msg("Not enough memory")
        distances_value_error = Msg("Problem in calculation:\n{}")
        data_too_large_for_mahalanobis = Msg(
            "Mahalanobis handles up to 1000 {}.")

    class Warning(OWWidget.Warning):
        ignoring_discrete = Msg("Ignoring categorical features")
        ignoring_nonbinary = Msg("Ignoring non-binary features")
        imputing_data = Msg("Missing values were imputed")

    def __init__(self):
        OWWidget.__init__(self)
        ConcurrentWidgetMixin.__init__(self)

        self.data = None

        gui.radioButtons(
            self.controlArea, self, "axis", ["Rows", "Columns"],
            box="Distances between", callback=self._invalidate
        )
        box = gui.widgetBox(self.controlArea, "Distance Metric")
        self.metrics_combo = gui.comboBox(
            box, self, "metric_idx",
            items=[m[0] for m in METRICS],
            callback=self._metric_changed
        )
        self.normalization_check = gui.checkBox(
            box, self, "normalized_dist", "Normalized",
            callback=self._invalidate,
            tooltip=("All dimensions are (implicitly) scaled to a common"
                     "scale to normalize the influence across the domain."),
            stateWhenDisabled=False, attribute=Qt.WA_LayoutUsesWidgetRect
        )
        _, metric = METRICS[self.metric_idx]
        self.normalization_check.setEnabled(metric.supports_normalization)

        gui.auto_apply(self.buttonsArea, self, "autocommit")

    @Inputs.data
    @check_sql_input
    def set_data(self, data):
        self.cancel()
        self.data = data
        self.refresh_metrics()
        self.commit.now()

    def refresh_metrics(self):
        sparse = self.data is not None and issparse(self.data.X)
        for i, metric in enumerate(METRICS):
            item = self.metrics_combo.model().item(i)
            item.setEnabled(not sparse or metric[1].supports_sparse)

    @gui.deferred
    def commit(self):
        # pylint: disable=invalid-sequence-index
        metric = METRICS[self.metric_idx][1]
        self.compute_distances(metric, self.data)

    def compute_distances(self, metric, data):
        def _check_sparse():
            # pylint: disable=invalid-sequence-index
            if issparse(data.X) and not metric.supports_sparse:
                self.Error.dense_metric_sparse_data(METRICS[self.metric_idx][0])
                return False
            return True

        def _fix_discrete():
            nonlocal data
            if data.domain.has_discrete_attributes() \
                    and metric is not distance.Jaccard \
                    and (issparse(data.X) and getattr(metric, "fallback", None)
                         or not metric.supports_discrete
                         or self.axis == 1):
                if not data.domain.has_continuous_attributes():
                    self.Error.no_continuous_features()
                    return False
                self.Warning.ignoring_discrete()
                data = distance.remove_discrete_features(data, to_metas=True)
            return True

        def _fix_nonbinary():
            nonlocal data
            if metric is distance.Jaccard and not issparse(data.X):
                nbinary = sum(a.is_discrete and len(a.values) == 2
                              for a in data.domain.attributes)
                if not nbinary:
                    self.Error.no_binary_features()
                    return False
                elif nbinary < len(data.domain.attributes):
                    self.Warning.ignoring_nonbinary()
                    data = distance.remove_nonbinary_features(data,
                                                              to_metas=True)
            return True

        def _fix_missing():
            nonlocal data
            if not metric.supports_missing and bn.anynan(data.X):
                self.Warning.imputing_data()
                data = distance.impute(data)
            return True

        def _check_tractability():
            if metric is distance.Mahalanobis:
                if self.axis == 1:
                    # when computing distances by columns, we want < 100 rows
                    if len(data) > 1000:
                        self.Error.data_too_large_for_mahalanobis("rows")
                        return False
                else:
                    if len(data.domain.attributes) > 1000:
                        self.Error.data_too_large_for_mahalanobis("columns")
                        return False
            return True

        self.clear_messages()
        if data is not None:
            for check in (_check_sparse, _check_tractability,
                          _fix_discrete, _fix_missing, _fix_nonbinary):
                if not check():
                    data = None
                    break

        self.start(DistanceRunner.run, data, metric,
                   self.normalized_dist, self.axis)

    def on_partial_result(self, _):
        pass

    def on_done(self, result: Orange.misc.DistMatrix):
        assert isinstance(result, Orange.misc.DistMatrix) or result is None
        self.Outputs.distances.send(result)

    def on_exception(self, ex):
        if isinstance(ex, ValueError):
            self.Error.distances_value_error(ex)
        elif isinstance(ex, MemoryError):
            self.Error.distances_memory_error()
        elif isinstance(ex, InterruptException):
            pass
        else:
            raise ex

    def onDeleteWidget(self):
        self.shutdown()
        super().onDeleteWidget()

    def _invalidate(self):
        self.commit.deferred()

    def _metric_changed(self):
        metric = METRICS[self.metric_idx][1]
        self.normalization_check.setEnabled(metric.supports_normalization)
        self._invalidate()

    def send_report(self):
        # pylint: disable=invalid-sequence-index
        self.report_items((
            ("Distances Between", ["Rows", "Columns"][self.axis]),
            ("Metric", METRICS[self.metric_idx][0])
        ))

    @classmethod
    def migrate_settings(cls, settings, version):
        if version is None or version < 2 and "normalized_dist" not in settings:
            # normalize_dist is set to False when restoring settings from
            # an older version to preserve old semantics.
            settings["normalized_dist"] = False
        if version is None or version < 3:
            # Mahalanobis was moved from idx = 2 to idx = 9
            metric_idx = settings["metric_idx"]
            if metric_idx == 2:
                settings["metric_idx"] = 9
            elif 2 < metric_idx <= 9:
                settings["metric_idx"] -= 1


if __name__ == "__main__":  # pragma: no cover
    WidgetPreview(OWDistances).run(Orange.data.Table("iris"))

```
--- END FILE ---

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

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

Here is an example:

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

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

**Title:** Incorrect Row Limit and Axis Check for Mahalanobis Distance Calculations

**Description:**
When computing Mahalanobis distances by columns, the widget incorrectly restricts the number of rows to less than 100 and triggers an error for larger datasets. This limitation prevents processing datasets with up to 1000 rows as intended.

**Example Code:**
```python
widget = OWDistances()
dataset = Table.from_numpy(Domain([...]), np.random.random((1010, 4)))
widget.axis = 1  # Intended to compute distances by columns
widget.compute_distances(metric=distance.Mahalanobis, data=dataset)
# Unexpectedly shows error: "Data too large for Mahalanobis distance"
```

**Expected Behavior:**
When computing distances by columns (axis=0) using Mahalanobis distance, the widget should allow datasets with up to 1000 rows without displaying an error. Only datasets exceeding 1000 rows should trigger the "Data too large for Mahalanobis distance" error.

**Actual Behavior:**
The widget incorrectly checks for axis=1 when computing by columns and restricts the number of rows to less than 100. As a result, even datasets with up to 1000 rows erroneously display the error message, preventing proper distance calculations.

[/ISSUE]
{
    "TestDistanceRunner.test_run": "PASSED",
    "TestOWDistances.test_discrete_in_metas": "PASSED",
    "TestOWDistances.test_distance_combo": "PASSED",
    "TestOWDistances.test_error_message": "PASSED",
    "TestOWDistances.test_image_export": "PASSED",
    "TestOWDistances.test_jaccard_messages": "PASSED",
    "TestOWDistances.test_limit_mahalanobis": "PASSED",
    "TestOWDistances.test_migrates_normalized_dist": "PASSED",
    "TestOWDistances.test_minimum_size": "PASSED",
    "TestOWDistances.test_msg_base_class": "PASSED",
    "TestOWDistances.test_negative_values_bhattacharyya": "PASSED",
    "TestOWDistances.test_non_binary_in_metas": "PASSED",
    "TestOWDistances.test_too_big_array": "PASSED"
}
diff --git a/Orange/widgets/unsupervised/owdistances.py b/Orange/widgets/unsupervised/owdistances.py
index f75fb2f93..a7882a057 100644
--- a/Orange/widgets/unsupervised/owdistances.py
+++ b/Orange/widgets/unsupervised/owdistances.py
@@ -185,8 +185,8 @@ class OWDistances(OWWidget, ConcurrentWidgetMixin):
 
         def _check_tractability():
             if metric is distance.Mahalanobis:
-                if self.axis == 1:
-                    # when computing distances by columns, we want < 100 rows
+                if self.axis == 0:
+                    # when computing distances by columns, we want < 1000 rows
                     if len(data) > 1000:
                         self.Error.data_too_large_for_mahalanobis("rows")
                         return False

{"Orange/widgets/unsupervised/owdistances.py": "from scipy.sparse import issparse\nimport bottleneck as bn\n\nfrom AnyQt.QtCore import Qt\n\nimport Orange.data\nimport Orange.misc\nfrom Orange import distance\nfrom Orange.widgets import gui\nfrom Orange.widgets.settings import Setting\nfrom Orange.widgets.utils.concurrent import TaskState, ConcurrentWidgetMixin\nfrom Orange.widgets.utils.sql import check_sql_input\nfrom Orange.widgets.utils.widgetpreview import WidgetPreview\nfrom Orange.widgets.widget import OWWidget, Msg, Input, Output\n\n\nMETRICS = [\n    (\"Euclidean\", distance.Euclidean),\n    (\"Manhattan\", distance.Manhattan),\n    (\"Cosine\", distance.Cosine),\n    (\"Jaccard\", distance.Jaccard),\n    (\"Spearman\", distance.SpearmanR),\n    (\"Absolute Spearman\", distance.SpearmanRAbsolute),\n    (\"Pearson\", distance.PearsonR),\n    (\"Absolute Pearson\", distance.PearsonRAbsolute),\n    (\"Hamming\", distance.Hamming),\n    (\"Mahalanobis\", distance.Mahalanobis),\n    ('Bhattacharyya', distance.Bhattacharyya)\n]\n\n\nclass InterruptException(Exception):\n    pass\n\n\nclass DistanceRunner:\n    @staticmethod\n    def run(data: Orange.data.Table, metric: distance, normalized_dist: bool,\n            axis: int, state: TaskState) -> Orange.misc.DistMatrix:\n        if data is None:\n            return None\n\n        def callback(i: float) -> bool:\n            state.set_progress_value(i)\n            if state.is_interruption_requested():\n                raise InterruptException\n\n        state.set_status(\"Calculating...\")\n        kwargs = {\"axis\": 1 - axis, \"impute\": True, \"callback\": callback}\n        if metric.supports_normalization and normalized_dist:\n            kwargs[\"normalize\"] = True\n        return metric(data, **kwargs)\n\n\nclass OWDistances(OWWidget, ConcurrentWidgetMixin):\n    name = \"Distances\"\n    description = \"Compute a matrix of pairwise distances.\"\n    icon = \"icons/Distance.svg\"\n    keywords = []\n\n    class Inputs:\n        data = Input(\"Data\", Orange.data.Table)\n\n    class Outputs:\n        distances = Output(\"Distances\", Orange.misc.DistMatrix, dynamic=False)\n\n    settings_version = 3\n\n    axis = Setting(0)        # type: int\n    metric_idx = Setting(0)  # type: int\n\n    #: Use normalized distances if the metric supports it.\n    #: The default is `True`, expect when restoring from old pre v2 settings\n    #: (see `migrate_settings`).\n    normalized_dist = Setting(True)  # type: bool\n    autocommit = Setting(True)       # type: bool\n\n    want_main_area = False\n    resizing_enabled = False\n\n    class Error(OWWidget.Error):\n        no_continuous_features = Msg(\"No numeric features\")\n        no_binary_features = Msg(\"No binary features\")\n        dense_metric_sparse_data = Msg(\"{} requires dense data.\")\n        distances_memory_error = Msg(\"Not enough memory\")\n        distances_value_error = Msg(\"Problem in calculation:\\n{}\")\n        data_too_large_for_mahalanobis = Msg(\n            \"Mahalanobis handles up to 1000 {}.\")\n\n    class Warning(OWWidget.Warning):\n        ignoring_discrete = Msg(\"Ignoring categorical features\")\n        ignoring_nonbinary = Msg(\"Ignoring non-binary features\")\n        imputing_data = Msg(\"Missing values were imputed\")\n\n    def __init__(self):\n        OWWidget.__init__(self)\n        ConcurrentWidgetMixin.__init__(self)\n\n        self.data = None\n\n        gui.radioButtons(\n            self.controlArea, self, \"axis\", [\"Rows\", \"Columns\"],\n            box=\"Distances between\", callback=self._invalidate\n        )\n        box = gui.widgetBox(self.controlArea, \"Distance Metric\")\n        self.metrics_combo = gui.comboBox(\n            box, self, \"metric_idx\",\n            items=[m[0] for m in METRICS],\n            callback=self._metric_changed\n        )\n        self.normalization_check = gui.checkBox(\n            box, self, \"normalized_dist\", \"Normalized\",\n            callback=self._invalidate,\n            tooltip=(\"All dimensions are (implicitly) scaled to a common\"\n                     \"scale to normalize the influence across the domain.\"),\n            stateWhenDisabled=False, attribute=Qt.WA_LayoutUsesWidgetRect\n        )\n        _, metric = METRICS[self.metric_idx]\n        self.normalization_check.setEnabled(metric.supports_normalization)\n\n        gui.auto_apply(self.buttonsArea, self, \"autocommit\")\n\n    @Inputs.data\n    @check_sql_input\n    def set_data(self, data):\n        self.cancel()\n        self.data = data\n        self.refresh_metrics()\n        self.commit.now()\n\n    def refresh_metrics(self):\n        sparse = self.data is not None and issparse(self.data.X)\n        for i, metric in enumerate(METRICS):\n            item = self.metrics_combo.model().item(i)\n            item.setEnabled(not sparse or metric[1].supports_sparse)\n\n    @gui.deferred\n    def commit(self):\n        # pylint: disable=invalid-sequence-index\n        metric = METRICS[self.metric_idx][1]\n        self.compute_distances(metric, self.data)\n\n    def compute_distances(self, metric, data):\n        def _check_sparse():\n            # pylint: disable=invalid-sequence-index\n            if issparse(data.X) and not metric.supports_sparse:\n                self.Error.dense_metric_sparse_data(METRICS[self.metric_idx][0])\n                return False\n            return True\n\n        def _fix_discrete():\n            nonlocal data\n            if data.domain.has_discrete_attributes() \\\n                    and metric is not distance.Jaccard \\\n                    and (issparse(data.X) and getattr(metric, \"fallback\", None)\n                         or not metric.supports_discrete\n                         or self.axis == 1):\n                if not data.domain.has_continuous_attributes():\n                    self.Error.no_continuous_features()\n                    return False\n                self.Warning.ignoring_discrete()\n                data = distance.remove_discrete_features(data, to_metas=True)\n            return True\n\n        def _fix_nonbinary():\n            nonlocal data\n            if metric is distance.Jaccard and not issparse(data.X):\n                nbinary = sum(a.is_discrete and len(a.values) == 2\n                              for a in data.domain.attributes)\n                if not nbinary:\n                    self.Error.no_binary_features()\n                    return False\n                elif nbinary < len(data.domain.attributes):\n                    self.Warning.ignoring_nonbinary()\n                    data = distance.remove_nonbinary_features(data,\n                                                              to_metas=True)\n            return True\n\n        def _fix_missing():\n            nonlocal data\n            if not metric.supports_missing and bn.anynan(data.X):\n                self.Warning.imputing_data()\n                data = distance.impute(data)\n            return True\n\n        def _check_tractability():\n            if metric is distance.Mahalanobis:\n                if self.axis == 1:\n                    # when computing distances by columns, we want < 100 rows\n                    if len(data) > 1000:\n                        self.Error.data_too_large_for_mahalanobis(\"rows\")\n                        return False\n                else:\n                    if len(data.domain.attributes) > 1000:\n                        self.Error.data_too_large_for_mahalanobis(\"columns\")\n                        return False\n            return True\n\n        self.clear_messages()\n        if data is not None:\n            for check in (_check_sparse, _check_tractability,\n                          _fix_discrete, _fix_missing, _fix_nonbinary):\n                if not check():\n                    data = None\n                    break\n\n        self.start(DistanceRunner.run, data, metric,\n                   self.normalized_dist, self.axis)\n\n    def on_partial_result(self, _):\n        pass\n\n    def on_done(self, result: Orange.misc.DistMatrix):\n        assert isinstance(result, Orange.misc.DistMatrix) or result is None\n        self.Outputs.distances.send(result)\n\n    def on_exception(self, ex):\n        if isinstance(ex, ValueError):\n            self.Error.distances_value_error(ex)\n        elif isinstance(ex, MemoryError):\n            self.Error.distances_memory_error()\n        elif isinstance(ex, InterruptException):\n            pass\n        else:\n            raise ex\n\n    def onDeleteWidget(self):\n        self.shutdown()\n        super().onDeleteWidget()\n\n    def _invalidate(self):\n        self.commit.deferred()\n\n    def _metric_changed(self):\n        metric = METRICS[self.metric_idx][1]\n        self.normalization_check.setEnabled(metric.supports_normalization)\n        self._invalidate()\n\n    def send_report(self):\n        # pylint: disable=invalid-sequence-index\n        self.report_items((\n            (\"Distances Between\", [\"Rows\", \"Columns\"][self.axis]),\n            (\"Metric\", METRICS[self.metric_idx][0])\n        ))\n\n    @classmethod\n    def migrate_settings(cls, settings, version):\n        if version is None or version < 2 and \"normalized_dist\" not in settings:\n            # normalize_dist is set to False when restoring settings from\n            # an older version to preserve old semantics.\n            settings[\"normalized_dist\"] = False\n        if version is None or version < 3:\n            # Mahalanobis was moved from idx = 2 to idx = 9\n            metric_idx = settings[\"metric_idx\"]\n            if metric_idx == 2:\n                settings[\"metric_idx\"] = 9\n            elif 2 < metric_idx <= 9:\n                settings[\"metric_idx\"] -= 1\n\n\nif __name__ == \"__main__\":  # pragma: no cover\n    WidgetPreview(OWDistances).run(Orange.data.Table(\"iris\"))\n"}
A user will ask you to solve a task. You should first draft your thinking process (inner monologue). Then, generate the solution.

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

**Title:** Incorrect Row Limit and Axis Check for Mahalanobis Distance Calculations

**Description:**
When computing Mahalanobis distances by columns, the widget incorrectly restricts the number of rows to less than 100 and triggers an error for larger datasets. This limitation prevents processing datasets with up to 1000 rows as intended.

**Example Code:**
```python
widget = OWDistances()
dataset = Table.from_numpy(Domain([...]), np.random.random((1010, 4)))
widget.axis = 1  # Intended to compute distances by columns
widget.compute_distances(metric=distance.Mahalanobis, data=dataset)
# Unexpectedly shows error: "Data too large for Mahalanobis distance"
```

**Expected Behavior:**
When computing distances by columns (axis=0) using Mahalanobis distance, the widget should allow datasets with up to 1000 rows without displaying an error. Only datasets exceeding 1000 rows should trigger the "Data too large for Mahalanobis distance" error.

**Actual Behavior:**
The widget incorrectly checks for axis=1 when computing by columns and restricts the number of rows to less than 100. As a result, even datasets with up to 1000 rows erroneously display the error message, preventing proper distance calculations.

[/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/widgets/unsupervised/owdistances.py
from scipy.sparse import issparse
import bottleneck as bn

from AnyQt.QtCore import Qt

import Orange.data
import Orange.misc
from Orange import distance
from Orange.widgets import gui
from Orange.widgets.settings import Setting
from Orange.widgets.utils.concurrent import TaskState, ConcurrentWidgetMixin
from Orange.widgets.utils.sql import check_sql_input
from Orange.widgets.utils.widgetpreview import WidgetPreview
from Orange.widgets.widget import OWWidget, Msg, Input, Output


METRICS = [
    ("Euclidean", distance.Euclidean),
    ("Manhattan", distance.Manhattan),
    ("Cosine", distance.Cosine),
    ("Jaccard", distance.Jaccard),
    ("Spearman", distance.SpearmanR),
    ("Absolute Spearman", distance.SpearmanRAbsolute),
    ("Pearson", distance.PearsonR),
    ("Absolute Pearson", distance.PearsonRAbsolute),
    ("Hamming", distance.Hamming),
    ("Mahalanobis", distance.Mahalanobis),
    ('Bhattacharyya', distance.Bhattacharyya)
]


class InterruptException(Exception):
    pass


class DistanceRunner:
    @staticmethod
    def run(data: Orange.data.Table, metric: distance, normalized_dist: bool,
            axis: int, state: TaskState) -> Orange.misc.DistMatrix:
        if data is None:
            return None

        def callback(i: float) -> bool:
            state.set_progress_value(i)
            if state.is_interruption_requested():
                raise InterruptException

        state.set_status("Calculating...")
        kwargs = {"axis": 1 - axis, "impute": True, "callback": callback}
        if metric.supports_normalization and normalized_dist:
            kwargs["normalize"] = True
        return metric(data, **kwargs)


class OWDistances(OWWidget, ConcurrentWidgetMixin):
    name = "Distances"
    description = "Compute a matrix of pairwise distances."
    icon = "icons/Distance.svg"
    keywords = []

    class Inputs:
        data = Input("Data", Orange.data.Table)

    class Outputs:
        distances = Output("Distances", Orange.misc.DistMatrix, dynamic=False)

    settings_version = 3

    axis = Setting(0)        # type: int
    metric_idx = Setting(0)  # type: int

    #: Use normalized distances if the metric supports it.
    #: The default is `True`, expect when restoring from old pre v2 settings
    #: (see `migrate_settings`).
    normalized_dist = Setting(True)  # type: bool
    autocommit = Setting(True)       # type: bool

    want_main_area = False
    resizing_enabled = False

    class Error(OWWidget.Error):
        no_continuous_features = Msg("No numeric features")
        no_binary_features = Msg("No binary features")
        dense_metric_sparse_data = Msg("{} requires dense data.")
        distances_memory_error = Msg("Not enough memory")
        distances_value_error = Msg("Problem in calculation:\n{}")
        data_too_large_for_mahalanobis = Msg(
            "Mahalanobis handles up to 1000 {}.")

    class Warning(OWWidget.Warning):
        ignoring_discrete = Msg("Ignoring categorical features")
        ignoring_nonbinary = Msg("Ignoring non-binary features")
        imputing_data = Msg("Missing values were imputed")

    def __init__(self):
        OWWidget.__init__(self)
        ConcurrentWidgetMixin.__init__(self)

        self.data = None

        gui.radioButtons(
            self.controlArea, self, "axis", ["Rows", "Columns"],
            box="Distances between", callback=self._invalidate
        )
        box = gui.widgetBox(self.controlArea, "Distance Metric")
        self.metrics_combo = gui.comboBox(
            box, self, "metric_idx",
            items=[m[0] for m in METRICS],
            callback=self._metric_changed
        )
        self.normalization_check = gui.checkBox(
            box, self, "normalized_dist", "Normalized",
            callback=self._invalidate,
            tooltip=("All dimensions are (implicitly) scaled to a common"
                     "scale to normalize the influence across the domain."),
            stateWhenDisabled=False, attribute=Qt.WA_LayoutUsesWidgetRect
        )
        _, metric = METRICS[self.metric_idx]
        self.normalization_check.setEnabled(metric.supports_normalization)

        gui.auto_apply(self.buttonsArea, self, "autocommit")

    @Inputs.data
    @check_sql_input
    def set_data(self, data):
        self.cancel()
        self.data = data
        self.refresh_metrics()
        self.commit.now()

    def refresh_metrics(self):
        sparse = self.data is not None and issparse(self.data.X)
        for i, metric in enumerate(METRICS):
            item = self.metrics_combo.model().item(i)
            item.setEnabled(not sparse or metric[1].supports_sparse)

    @gui.deferred
    def commit(self):
        # pylint: disable=invalid-sequence-index
        metric = METRICS[self.metric_idx][1]
        self.compute_distances(metric, self.data)

    def compute_distances(self, metric, data):
        def _check_sparse():
            # pylint: disable=invalid-sequence-index
            if issparse(data.X) and not metric.supports_sparse:
                self.Error.dense_metric_sparse_data(METRICS[self.metric_idx][0])
                return False
            return True

        def _fix_discrete():
            nonlocal data
            if data.domain.has_discrete_attributes() \
                    and metric is not distance.Jaccard \
                    and (issparse(data.X) and getattr(metric, "fallback", None)
                         or not metric.supports_discrete
                         or self.axis == 1):
                if not data.domain.has_continuous_attributes():
                    self.Error.no_continuous_features()
                    return False
                self.Warning.ignoring_discrete()
                data = distance.remove_discrete_features(data, to_metas=True)
            return True

        def _fix_nonbinary():
            nonlocal data
            if metric is distance.Jaccard and not issparse(data.X):
                nbinary = sum(a.is_discrete and len(a.values) == 2
                              for a in data.domain.attributes)
                if not nbinary:
                    self.Error.no_binary_features()
                    return False
                elif nbinary < len(data.domain.attributes):
                    self.Warning.ignoring_nonbinary()
                    data = distance.remove_nonbinary_features(data,
                                                              to_metas=True)
            return True

        def _fix_missing():
            nonlocal data
            if not metric.supports_missing and bn.anynan(data.X):
                self.Warning.imputing_data()
                data = distance.impute(data)
            return True

        def _check_tractability():
            if metric is distance.Mahalanobis:
                if self.axis == 1:
                    # when computing distances by columns, we want < 100 rows
                    if len(data) > 1000:
                        self.Error.data_too_large_for_mahalanobis("rows")
                        return False
                else:
                    if len(data.domain.attributes) > 1000:
                        self.Error.data_too_large_for_mahalanobis("columns")
                        return False
            return True

        self.clear_messages()
        if data is not None:
            for check in (_check_sparse, _check_tractability,
                          _fix_discrete, _fix_missing, _fix_nonbinary):
                if not check():
                    data = None
                    break

        self.start(DistanceRunner.run, data, metric,
                   self.normalized_dist, self.axis)

    def on_partial_result(self, _):
        pass

    def on_done(self, result: Orange.misc.DistMatrix):
        assert isinstance(result, Orange.misc.DistMatrix) or result is None
        self.Outputs.distances.send(result)

    def on_exception(self, ex):
        if isinstance(ex, ValueError):
            self.Error.distances_value_error(ex)
        elif isinstance(ex, MemoryError):
            self.Error.distances_memory_error()
        elif isinstance(ex, InterruptException):
            pass
        else:
            raise ex

    def onDeleteWidget(self):
        self.shutdown()
        super().onDeleteWidget()

    def _invalidate(self):
        self.commit.deferred()

    def _metric_changed(self):
        metric = METRICS[self.metric_idx][1]
        self.normalization_check.setEnabled(metric.supports_normalization)
        self._invalidate()

    def send_report(self):
        # pylint: disable=invalid-sequence-index
        self.report_items((
            ("Distances Between", ["Rows", "Columns"][self.axis]),
            ("Metric", METRICS[self.metric_idx][0])
        ))

    @classmethod
    def migrate_settings(cls, settings, version):
        if version is None or version < 2 and "normalized_dist" not in settings:
            # normalize_dist is set to False when restoring settings from
            # an older version to preserve old semantics.
            settings["normalized_dist"] = False
        if version is None or version < 3:
            # Mahalanobis was moved from idx = 2 to idx = 9
            metric_idx = settings["metric_idx"]
            if metric_idx == 2:
                settings["metric_idx"] = 9
            elif 2 < metric_idx <= 9:
                settings["metric_idx"] -= 1


if __name__ == "__main__":  # pragma: no cover
    WidgetPreview(OWDistances).run(Orange.data.Table("iris"))

```
--- 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