{
    "file_diffs": [
        {
            "old_file_content": "import numpy as np\n\nfrom AnyQt.QtCore import Qt\n\nfrom Orange.data import Table, Domain, ContinuousVariable\nfrom Orange.data.util import get_unique_names\nfrom Orange.preprocess import RemoveNaNColumns, Impute\nfrom Orange import distance\nfrom Orange.widgets import gui\nfrom Orange.widgets.settings import Setting\nfrom Orange.widgets.utils.signals import Input, Output\nfrom Orange.widgets.widget import OWWidget, Msg\nfrom Orange.widgets.utils.widgetpreview import WidgetPreview\n\nMETRICS = [\n    (\"Euclidean\", distance.Euclidean),\n    (\"Manhattan\", distance.Manhattan),\n    (\"Mahalanobis\", distance.Mahalanobis),\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]\n\n\nclass OWNeighbors(OWWidget):\n    name = \"Neighbors\"\n    description = \"Compute nearest neighbors in data according to reference.\"\n    icon = \"icons/Neighbors.svg\"\n    category = \"Unsupervised\"\n    replaces = [\"orangecontrib.prototypes.widgets.owneighbours.OWNeighbours\"]\n\n    class Inputs:\n        data = Input(\"Data\", Table)\n        reference = Input(\"Reference\", Table)\n\n    class Outputs:\n        data = Output(\"Neighbors\", Table)\n\n    class Info(OWWidget.Warning):\n        removed_references = \\\n            Msg(\"Input data includes reference instance(s).\\n\"\n                \"Reference instances are excluded from the output.\")\n\n    class Warning(OWWidget.Warning):\n        all_data_as_reference = \\\n            Msg(\"Every data instance is same as some reference\")\n\n    class Error(OWWidget.Error):\n        diff_domains = Msg(\"Data and reference have different features\")\n\n    n_neighbors: int\n    distance_index: int\n\n    n_neighbors = Setting(10)\n    limit_neighbors = Setting(True)\n    distance_index = Setting(0)\n    auto_apply = Setting(True)\n\n    want_main_area = False\n    resizing_enabled = False\n\n    def __init__(self):\n        super().__init__()\n\n        self.data = None\n        self.reference = None\n        self.distances = None\n\n        box = gui.vBox(self.controlArea, box=True)\n        gui.comboBox(\n            box, self, \"distance_index\", orientation=Qt.Horizontal,\n            label=\"Distance metric: \", items=[d[0] for d in METRICS],\n            callback=self.recompute)\n        gui.spin(\n            box, self, \"n_neighbors\", label=\"Limit number of neighbors to:\",\n            step=1, spinType=int, minv=0, maxv=100, checked='limit_neighbors',\n            # call apply by gui.auto_commit, pylint: disable=unnecessary-lambda\n            checkCallback=self.commit.deferred,\n            callback=self.commit.deferred)\n\n        self.apply_button = gui.auto_apply(self.buttonsArea, self)\n\n    @Inputs.data\n    def set_data(self, data):\n        self.controls.n_neighbors.setMaximum(len(data) if data else 100)\n        self.data = data\n\n    @Inputs.reference\n    def set_ref(self, refs):\n        self.reference = refs\n\n    def handleNewSignals(self):\n        self.compute_distances()\n        self.commit.now()\n\n    def recompute(self):\n        self.compute_distances()\n        self.commit.deferred()\n\n    def compute_distances(self):\n        self.Error.diff_domains.clear()\n        if not self.data or not self.reference:\n            self.distances = None\n            return\n        if set(self.reference.domain.attributes) != \\\n                set(self.data.domain.attributes):\n            self.Error.diff_domains()\n            self.distances = None\n            return\n\n        metric = METRICS[self.distance_index][1]\n        n_ref = len(self.reference)\n\n        # comparing only attributes, no metas and class-vars\n        new_domain = Domain(self.data.domain.attributes)\n        reference = self.reference.transform(new_domain)\n        data = self.data.transform(new_domain)\n\n        all_data = Table.concatenate([reference, data], 0)\n        pp_all_data = Impute()(RemoveNaNColumns()(all_data))\n        pp_reference, pp_data = pp_all_data[:n_ref], pp_all_data[n_ref:]\n        self.distances = metric(pp_data, pp_reference).min(axis=1)\n\n    @gui.deferred\n    def commit(self):\n        indices = self._compute_indices()\n\n        if indices is None:\n            neighbors = None\n        else:\n            neighbors = self._data_with_similarity(indices)\n        self.Outputs.data.send(neighbors)\n\n    def _compute_indices(self):\n        self.Warning.all_data_as_reference.clear()\n        self.Info.removed_references.clear()\n\n        if self.distances is None:\n            return None\n\n        inrefs = np.isin(self.data.ids, self.reference.ids)\n        if np.all(inrefs):\n            self.Warning.all_data_as_reference()\n            return None\n        if np.any(inrefs):\n            self.Info.removed_references()\n\n        dist = np.copy(self.distances)\n        dist[inrefs] = np.max(dist) + 1\n        up_to = len(dist) - np.sum(inrefs)\n        if self.limit_neighbors and self.n_neighbors < up_to:\n            up_to = self.n_neighbors\n        return np.argpartition(dist, up_to - 1)[:up_to]\n\n    def _data_with_similarity(self, indices):\n        domain = self.data.domain\n        dist_var = ContinuousVariable(get_unique_names(domain, \"distance\"))\n        metas = domain.metas + (dist_var, )\n        domain = Domain(domain.attributes, domain.class_vars, metas)\n        neighbours = self.data.from_table(domain, self.data, row_indices=indices)\n        distances = self.distances[indices]\n        with neighbours.unlocked(neighbours.metas):\n            if distances.size > 0:\n                neighbours.set_column(dist_var, distances)\n        return neighbours\n\n\nif __name__ == \"__main__\":  # pragma: no cover\n    iris = Table(\"iris.tab\")\n    WidgetPreview(OWNeighbors).run(\n        set_data=iris,\n        set_ref=iris[:1])\n",
            "new_file_content": "import numpy as np\n\nfrom AnyQt.QtCore import Qt\n\nfrom Orange.data import Table, Domain, ContinuousVariable\nfrom Orange.data.util import get_unique_names\nfrom Orange.preprocess import RemoveNaNColumns, Impute\nfrom Orange import distance\nfrom Orange.widgets import gui\nfrom Orange.widgets.settings import Setting\nfrom Orange.widgets.utils.signals import Input, Output\nfrom Orange.widgets.widget import OWWidget, Msg\nfrom Orange.widgets.utils.widgetpreview import WidgetPreview\n\nMETRICS = [\n    (\"Euclidean\", distance.Euclidean),\n    (\"Manhattan\", distance.Manhattan),\n    (\"Mahalanobis\", distance.Mahalanobis),\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]\n\n\nclass OWNeighbors(OWWidget):\n    name = \"Neighbors\"\n    description = \"Compute nearest neighbors in data according to reference.\"\n    icon = \"icons/Neighbors.svg\"\n    category = \"Unsupervised\"\n    replaces = [\"orangecontrib.prototypes.widgets.owneighbours.OWNeighbours\"]\n\n    class Inputs:\n        data = Input(\"Data\", Table)\n        reference = Input(\"Reference\", Table)\n\n    class Outputs:\n        data = Output(\"Neighbors\", Table)\n\n    class Info(OWWidget.Warning):\n        removed_references = \\\n            Msg(\"Input data includes reference instance(s).\\n\"\n                \"Reference instances are excluded from the output.\")\n\n    class Warning(OWWidget.Warning):\n        all_data_as_reference = \\\n            Msg(\"Every data instance is same as some reference\")\n\n    class Error(OWWidget.Error):\n        diff_domains = Msg(\"Data and reference have different features\")\n\n    n_neighbors: int\n    distance_index: int\n\n    n_neighbors = Setting(10)\n    limit_neighbors = Setting(True)\n    distance_index = Setting(0)\n    auto_apply = Setting(True)\n\n    want_main_area = False\n    resizing_enabled = False\n\n    def __init__(self):\n        super().__init__()\n\n        self.data = None\n        self.reference = None\n        self.distances = None\n\n        box = gui.vBox(self.controlArea, box=True)\n        gui.comboBox(\n            box, self, \"distance_index\", orientation=Qt.Horizontal,\n            label=\"Distance metric: \", items=[d[0] for d in METRICS],\n            callback=self.recompute)\n        gui.spin(\n            box, self, \"n_neighbors\", label=\"Limit number of neighbors to:\",\n            step=1, spinType=int, minv=0, maxv=100, checked='limit_neighbors',\n            # call apply by gui.auto_commit, pylint: disable=unnecessary-lambda\n            checkCallback=self.commit.deferred,\n            callback=self.commit.deferred)\n\n        self.apply_button = gui.auto_apply(self.buttonsArea, self)\n\n    @Inputs.data\n    def set_data(self, data):\n        self.controls.n_neighbors.setMaximum(len(data) if data else 100)\n        self.data = data\n\n    @Inputs.reference\n    def set_ref(self, refs):\n        self.reference = refs\n\n    def handleNewSignals(self):\n        self.compute_distances()\n        self.commit.now()\n\n    def recompute(self):\n        self.compute_distances()\n        self.commit.deferred()\n\n    def compute_distances(self):\n        self.Error.diff_domains.clear()\n        if not self.data or not self.reference:\n            self.distances = None\n            return\n        if set(self.reference.domain.attributes) != \\\n                set(self.data.domain.attributes):\n            self.Error.diff_domains()\n            self.distances = None\n            return\n\n        metric = METRICS[self.distance_index][1]\n        n_ref = len(self.reference)\n\n        # comparing only attributes, no metas and class-vars\n        new_domain = Domain(self.data.domain.attributes)\n        reference = self.reference.transform(new_domain)\n        data = self.data.transform(new_domain)\n\n        all_data = Table.concatenate([reference, data], 0)\n        pp_all_data = Impute()(RemoveNaNColumns()(all_data))\n        pp_reference, pp_data = pp_all_data[:n_ref], pp_all_data[n_ref:]\n        self.distances = metric(pp_data, pp_reference).min(axis=1)\n\n    @gui.deferred\n    def commit(self):\n        indices = self._compute_indices()\n\n        if indices is None:\n            neighbors = None\n        else:\n            neighbors = self._data_with_similarity(indices)\n        self.Outputs.data.send(neighbors)\n\n    def _compute_indices(self):\n        self.Warning.all_data_as_reference.clear()\n        self.Info.removed_references.clear()\n\n        if self.distances is None:\n            return None\n\n        inrefs = np.isin(self.data.ids, self.reference.ids)\n        if np.all(inrefs):\n            self.Warning.all_data_as_reference()\n            return None\n        if np.any(inrefs):\n            self.Info.removed_references()\n\n        dist = np.copy(self.distances)\n        dist[inrefs] = np.max(dist) + 1\n        up_to = len(dist) - np.sum(inrefs)\n        if self.limit_neighbors and self.n_neighbors < up_to:\n            up_to = self.n_neighbors\n        # get indexes of N neighbours in unsorted order - faster than argsort\n        idx = np.argpartition(dist, up_to - 1)[:up_to]\n        # sort selected N neighbours according to distances\n        sorted_subset_idx = np.argsort(dist[idx])\n        # map sorted indexes back to original index space\n        return idx[sorted_subset_idx]\n\n    def _data_with_similarity(self, indices):\n        domain = self.data.domain\n        dist_var = ContinuousVariable(get_unique_names(domain, \"distance\"))\n        metas = domain.metas + (dist_var, )\n        domain = Domain(domain.attributes, domain.class_vars, metas)\n        neighbours = self.data.from_table(domain, self.data, row_indices=indices)\n        distances = self.distances[indices]\n        with neighbours.unlocked(neighbours.metas):\n            if distances.size > 0:\n                neighbours.set_column(dist_var, distances)\n        return neighbours\n\n\nif __name__ == \"__main__\":  # pragma: no cover\n    iris = Table(\"iris.tab\")\n    WidgetPreview(OWNeighbors).run(\n        set_data=iris,\n        set_ref=iris[:1])\n",
            "header": {
                "file": {
                    "path": "Orange/widgets/data/owneighbors.py"
                },
                "misc_line": null
            },
            "index_line": {
                "old_commit_hash": "62d98a95c",
                "new_commit_hash": "ecf99fc5b",
                "mode": "100644"
            },
            "is_binary_file": false,
            "binary_line": null,
            "minus_file": {
                "path": "a/Orange/widgets/data/owneighbors.py"
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                "path": "b/Orange/widgets/data/owneighbors.py"
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                        "old_range": {
                            "start": 153,
                            "length": 7
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                            "start": 153,
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                        "section": "class OWNeighbors(OWWidget):"
                    },
                    "line_group": {
                        "all_lines": [
                            {
                                "content": "        up_to = len(dist) - np.sum(inrefs)",
                                "type": "context"
                            },
                            {
                                "content": "        if self.limit_neighbors and self.n_neighbors < up_to:",
                                "type": "context"
                            },
                            {
                                "content": "            up_to = self.n_neighbors",
                                "type": "context"
                            },
                            {
                                "content": "        return np.argpartition(dist, up_to - 1)[:up_to]",
                                "type": "deleted"
                            },
                            {
                                "content": "        # get indexes of N neighbours in unsorted order - faster than argsort",
                                "type": "added"
                            },
                            {
                                "content": "        idx = np.argpartition(dist, up_to - 1)[:up_to]",
                                "type": "added"
                            },
                            {
                                "content": "        # sort selected N neighbours according to distances",
                                "type": "added"
                            },
                            {
                                "content": "        sorted_subset_idx = np.argsort(dist[idx])",
                                "type": "added"
                            },
                            {
                                "content": "        # map sorted indexes back to original index space",
                                "type": "added"
                            },
                            {
                                "content": "        return idx[sorted_subset_idx]",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "    def _data_with_similarity(self, indices):",
                                "type": "context"
                            },
                            {
                                "content": "        domain = self.data.domain",
                                "type": "context"
                            }
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                        {
                            "file_name": "Orange/widgets/data/owneighbors.py",
                            "type": "class",
                            "name": "OWNeighbors",
                            "content": "class OWNeighbors(OWWidget):\n    name = \"Neighbors\"\n    description = \"Compute nearest neighbors in data according to reference.\"\n    icon = \"icons/Neighbors.svg\"\n    category = \"Unsupervised\"\n    replaces = [\"orangecontrib.prototypes.widgets.owneighbours.OWNeighbours\"]\n\n    class Inputs:\n        data = Input(\"Data\", Table)\n        reference = Input(\"Reference\", Table)\n\n    class Outputs:\n        data = Output(\"Neighbors\", Table)\n\n    class Info(OWWidget.Warning):\n        removed_references = \\\n            Msg(\"Input data includes reference instance(s).\\n\"\n                \"Reference instances are excluded from the output.\")\n\n    class Warning(OWWidget.Warning):\n        all_data_as_reference = \\\n            Msg(\"Every data instance is same as some reference\")\n\n    class Error(OWWidget.Error):\n        diff_domains = Msg(\"Data and reference have different features\")\n\n    n_neighbors: int\n    distance_index: int\n\n    n_neighbors = Setting(10)\n    limit_neighbors = Setting(True)\n    distance_index = Setting(0)\n    auto_apply = Setting(True)\n\n    want_main_area = False\n    resizing_enabled = False\n\n    def __init__(self):\n        super().__init__()\n\n        self.data = None\n        self.reference = None\n        self.distances = None\n\n        box = gui.vBox(self.controlArea, box=True)\n        gui.comboBox(\n            box, self, \"distance_index\", orientation=Qt.Horizontal,\n            label=\"Distance metric: \", items=[d[0] for d in METRICS],\n            callback=self.recompute)\n        gui.spin(\n            box, self, \"n_neighbors\", label=\"Limit number of neighbors to:\",\n            step=1, spinType=int, minv=0, maxv=100, checked='limit_neighbors',\n            # call apply by gui.auto_commit, pylint: disable=unnecessary-lambda\n            checkCallback=self.commit.deferred,\n            callback=self.commit.deferred)\n\n        self.apply_button = gui.auto_apply(self.buttonsArea, self)\n\n    @Inputs.data\n    def set_data(self, data):\n        self.controls.n_neighbors.setMaximum(len(data) if data else 100)\n        self.data = data\n\n    @Inputs.reference\n    def set_ref(self, refs):\n        self.reference = refs\n\n    def handleNewSignals(self):\n        self.compute_distances()\n        self.commit.now()\n\n    def recompute(self):\n        self.compute_distances()\n        self.commit.deferred()\n\n    def compute_distances(self):\n        self.Error.diff_domains.clear()\n        if not self.data or not self.reference:\n            self.distances = None\n            return\n        if set(self.reference.domain.attributes) != \\\n                set(self.data.domain.attributes):\n            self.Error.diff_domains()\n            self.distances = None\n            return\n\n        metric = METRICS[self.distance_index][1]\n        n_ref = len(self.reference)\n\n        # comparing only attributes, no metas and class-vars\n        new_domain = Domain(self.data.domain.attributes)\n        reference = self.reference.transform(new_domain)\n        data = self.data.transform(new_domain)\n\n        all_data = Table.concatenate([reference, data], 0)\n        pp_all_data = Impute()(RemoveNaNColumns()(all_data))\n        pp_reference, pp_data = pp_all_data[:n_ref], pp_all_data[n_ref:]\n        self.distances = metric(pp_data, pp_reference).min(axis=1)\n\n    @gui.deferred\n    def commit(self):\n        indices = self._compute_indices()\n\n        if indices is None:\n            neighbors = None\n        else:\n            neighbors = self._data_with_similarity(indices)\n        self.Outputs.data.send(neighbors)\n\n    def _compute_indices(self):\n        self.Warning.all_data_as_reference.clear()\n        self.Info.removed_references.clear()\n\n        if self.distances is None:\n            return None\n\n        inrefs = np.isin(self.data.ids, self.reference.ids)\n        if np.all(inrefs):\n            self.Warning.all_data_as_reference()\n            return None\n        if np.any(inrefs):\n            self.Info.removed_references()\n\n        dist = np.copy(self.distances)\n        dist[inrefs] = np.max(dist) + 1\n        up_to = len(dist) - np.sum(inrefs)\n        if self.limit_neighbors and self.n_neighbors < up_to:\n            up_to = self.n_neighbors\n        return np.argpartition(dist, up_to - 1)[:up_to]\n\n    def _data_with_similarity(self, indices):\n        domain = self.data.domain\n        dist_var = ContinuousVariable(get_unique_names(domain, \"distance\"))\n        metas = domain.metas + (dist_var, )\n        domain = Domain(domain.attributes, domain.class_vars, metas)\n        neighbours = self.data.from_table(domain, self.data, row_indices=indices)\n        distances = self.distances[indices]\n        with neighbours.unlocked(neighbours.metas):\n            if distances.size > 0:\n                neighbours.set_column(dist_var, distances)\n        return neighbours",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 28,
                            "end_lineno": 168,
                            "parent": null
                        },
                        {
                            "file_name": "Orange/widgets/data/owneighbors.py",
                            "type": "method",
                            "name": "OWNeighbors._compute_indices",
                            "content": "class OWNeighbors(OWWidget):\n    name = \"Neighbors\"\n    description = \"Compute nearest neighbors in data according to reference.\"\n    icon = \"icons/Neighbors.svg\"\n    category = \"Unsupervised\"\n    replaces = [\"orangecontrib.prototypes.widgets.owneighbours.OWNeighbours\"]\n\n    class Inputs:\n        data = Input(\"Data\", Table)\n        reference = Input(\"Reference\", Table)\n\n    class Outputs:\n        data = Output(\"Neighbors\", Table)\n\n    class Info(OWWidget.Warning):\n        removed_references = \\\n            Msg(\"Input data includes reference instance(s).\\n\"\n                \"Reference instances are excluded from the output.\")\n\n    class Warning(OWWidget.Warning):\n        all_data_as_reference = \\\n            Msg(\"Every data instance is same as some reference\")\n\n    class Error(OWWidget.Error):\n        diff_domains = Msg(\"Data and reference have different features\")\n\n    n_neighbors: int\n    distance_index: int\n\n    n_neighbors = Setting(10)\n    limit_neighbors = Setting(True)\n    distance_index = Setting(0)\n    auto_apply = Setting(True)\n\n    want_main_area = False\n    resizing_enabled = False\n\n    def __init__(self):\n        super().__init__()\n\n        self.data = None\n        self.reference = None\n        self.distances = None\n\n        box = gui.vBox(self.controlArea, box=True)\n        gui.comboBox(\n            box, self, \"distance_index\", orientation=Qt.Horizontal,\n            label=\"Distance metric: \", items=[d[0] for d in METRICS],\n            callback=self.recompute)\n        gui.spin(\n            box, self, \"n_neighbors\", label=\"Limit number of neighbors to:\",\n            step=1, spinType=int, minv=0, maxv=100, checked='limit_neighbors',\n            # call apply by gui.auto_commit, pylint: disable=unnecessary-lambda\n            checkCallback=self.commit.deferred,\n            callback=self.commit.deferred)\n\n        self.apply_button = gui.auto_apply(self.buttonsArea, self)\n\n    @Inputs.data\n    def set_data(self, data):\n        self.controls.n_neighbors.setMaximum(len(data) if data else 100)\n        self.data = data\n\n    @Inputs.reference\n    def set_ref(self, refs):\n        self.reference = refs\n\n    def handleNewSignals(self):\n        self.compute_distances()\n        self.commit.now()\n\n    def recompute(self):\n        self.compute_distances()\n        self.commit.deferred()\n\n    def compute_distances(self):\n        self.Error.diff_domains.clear()\n        if not self.data or not self.reference:\n            self.distances = None\n            return\n        if set(self.reference.domain.attributes) != \\\n                set(self.data.domain.attributes):\n            self.Error.diff_domains()\n            self.distances = None\n            return\n\n        metric = METRICS[self.distance_index][1]\n        n_ref = len(self.reference)\n\n        # comparing only attributes, no metas and class-vars\n        new_domain = Domain(self.data.domain.attributes)\n        reference = self.reference.transform(new_domain)\n        data = self.data.transform(new_domain)\n\n        all_data = Table.concatenate([reference, data], 0)\n        pp_all_data = Impute()(RemoveNaNColumns()(all_data))\n        pp_reference, pp_data = pp_all_data[:n_ref], pp_all_data[n_ref:]\n        self.distances = metric(pp_data, pp_reference).min(axis=1)\n\n    @gui.deferred\n    def commit(self):\n        indices = self._compute_indices()\n\n        if indices is None:\n            neighbors = None\n        else:\n            neighbors = self._data_with_similarity(indices)\n        self.Outputs.data.send(neighbors)\n\n    def _compute_indices(self):\n        self.Warning.all_data_as_reference.clear()\n        self.Info.removed_references.clear()\n\n        if self.distances is None:\n            return None\n\n        inrefs = np.isin(self.data.ids, self.reference.ids)\n        if np.all(inrefs):\n            self.Warning.all_data_as_reference()\n            return None\n        if np.any(inrefs):\n            self.Info.removed_references()\n\n        dist = np.copy(self.distances)\n        dist[inrefs] = np.max(dist) + 1\n        up_to = len(dist) - np.sum(inrefs)\n        if self.limit_neighbors and self.n_neighbors < up_to:\n            up_to = self.n_neighbors\n        return np.argpartition(dist, up_to - 1)[:up_to]\n\n    def _data_with_similarity(self, indices):\n        domain = self.data.domain\n        dist_var = ContinuousVariable(get_unique_names(domain, \"distance\"))\n        metas = domain.metas + (dist_var, )\n        domain = Domain(domain.attributes, domain.class_vars, metas)\n        neighbours = self.data.from_table(domain, self.data, row_indices=indices)\n        distances = self.distances[indices]\n        with neighbours.unlocked(neighbours.metas):\n            if distances.size > 0:\n                neighbours.set_column(dist_var, distances)\n        return neighbours",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 137,
                            "end_lineno": 156,
                            "parent": {
                                "file_name": "Orange/widgets/data/owneighbors.py",
                                "type": "class",
                                "name": "OWNeighbors",
                                "content": "class OWNeighbors(OWWidget):\n    name = \"Neighbors\"\n    description = \"Compute nearest neighbors in data according to reference.\"\n    icon = \"icons/Neighbors.svg\"\n    category = \"Unsupervised\"\n    replaces = [\"orangecontrib.prototypes.widgets.owneighbours.OWNeighbours\"]\n\n    class Inputs:\n        data = Input(\"Data\", Table)\n        reference = Input(\"Reference\", Table)\n\n    class Outputs:\n        data = Output(\"Neighbors\", Table)\n\n    class Info(OWWidget.Warning):\n        removed_references = \\\n            Msg(\"Input data includes reference instance(s).\\n\"\n                \"Reference instances are excluded from the output.\")\n\n    class Warning(OWWidget.Warning):\n        all_data_as_reference = \\\n            Msg(\"Every data instance is same as some reference\")\n\n    class Error(OWWidget.Error):\n        diff_domains = Msg(\"Data and reference have different features\")\n\n    n_neighbors: int\n    distance_index: int\n\n    n_neighbors = Setting(10)\n    limit_neighbors = Setting(True)\n    distance_index = Setting(0)\n    auto_apply = Setting(True)\n\n    want_main_area = False\n    resizing_enabled = False\n\n    def __init__(self):\n        super().__init__()\n\n        self.data = None\n        self.reference = None\n        self.distances = None\n\n        box = gui.vBox(self.controlArea, box=True)\n        gui.comboBox(\n            box, self, \"distance_index\", orientation=Qt.Horizontal,\n            label=\"Distance metric: \", items=[d[0] for d in METRICS],\n            callback=self.recompute)\n        gui.spin(\n            box, self, \"n_neighbors\", label=\"Limit number of neighbors to:\",\n            step=1, spinType=int, minv=0, maxv=100, checked='limit_neighbors',\n            # call apply by gui.auto_commit, pylint: disable=unnecessary-lambda\n            checkCallback=self.commit.deferred,\n            callback=self.commit.deferred)\n\n        self.apply_button = gui.auto_apply(self.buttonsArea, self)\n\n    @Inputs.data\n    def set_data(self, data):\n        self.controls.n_neighbors.setMaximum(len(data) if data else 100)\n        self.data = data\n\n    @Inputs.reference\n    def set_ref(self, refs):\n        self.reference = refs\n\n    def handleNewSignals(self):\n        self.compute_distances()\n        self.commit.now()\n\n    def recompute(self):\n        self.compute_distances()\n        self.commit.deferred()\n\n    def compute_distances(self):\n        self.Error.diff_domains.clear()\n        if not self.data or not self.reference:\n            self.distances = None\n            return\n        if set(self.reference.domain.attributes) != \\\n                set(self.data.domain.attributes):\n            self.Error.diff_domains()\n            self.distances = None\n            return\n\n        metric = METRICS[self.distance_index][1]\n        n_ref = len(self.reference)\n\n        # comparing only attributes, no metas and class-vars\n        new_domain = Domain(self.data.domain.attributes)\n        reference = self.reference.transform(new_domain)\n        data = self.data.transform(new_domain)\n\n        all_data = Table.concatenate([reference, data], 0)\n        pp_all_data = Impute()(RemoveNaNColumns()(all_data))\n        pp_reference, pp_data = pp_all_data[:n_ref], pp_all_data[n_ref:]\n        self.distances = metric(pp_data, pp_reference).min(axis=1)\n\n    @gui.deferred\n    def commit(self):\n        indices = self._compute_indices()\n\n        if indices is None:\n            neighbors = None\n        else:\n            neighbors = self._data_with_similarity(indices)\n        self.Outputs.data.send(neighbors)\n\n    def _compute_indices(self):\n        self.Warning.all_data_as_reference.clear()\n        self.Info.removed_references.clear()\n\n        if self.distances is None:\n            return None\n\n        inrefs = np.isin(self.data.ids, self.reference.ids)\n        if np.all(inrefs):\n            self.Warning.all_data_as_reference()\n            return None\n        if np.any(inrefs):\n            self.Info.removed_references()\n\n        dist = np.copy(self.distances)\n        dist[inrefs] = np.max(dist) + 1\n        up_to = len(dist) - np.sum(inrefs)\n        if self.limit_neighbors and self.n_neighbors < up_to:\n            up_to = self.n_neighbors\n        return np.argpartition(dist, up_to - 1)[:up_to]\n\n    def _data_with_similarity(self, indices):\n        domain = self.data.domain\n        dist_var = ContinuousVariable(get_unique_names(domain, \"distance\"))\n        metas = domain.metas + (dist_var, )\n        domain = Domain(domain.attributes, domain.class_vars, metas)\n        neighbours = self.data.from_table(domain, self.data, row_indices=indices)\n        distances = self.distances[indices]\n        with neighbours.unlocked(neighbours.metas):\n            if distances.size > 0:\n                neighbours.set_column(dist_var, distances)\n        return neighbours",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 28,
                                "end_lineno": 168,
                                "parent": null
                            }
                        }
                    ],
                    "added_entities": [],
                    "deleted_entities": []
                }
            ]
        },
        {
            "old_file_content": "#pylint: disable=missing-docstring,protected-access,unsubscriptable-object\nimport unittest\nfrom unittest.mock import Mock\n\nimport numpy as np\n\nfrom Orange.data import Table, Domain, ContinuousVariable\nfrom Orange.widgets.data.owneighbors import OWNeighbors, METRICS\nfrom Orange.widgets.tests.base import WidgetTest, ParameterMapping\n\n\nclass TestOWNeighbors(WidgetTest):\n    def setUp(self):\n        self.widget = self.create_widget(OWNeighbors,\n                                         stored_settings={\"auto_apply\": False})\n        self.iris = Table(\"iris\")\n\n    def test_input_data(self):\n        \"\"\"Check widget's data with data on the input\"\"\"\n        widget = self.widget\n        self.assertEqual(widget.data, None)\n        self.send_signal(widget.Inputs.data, self.iris)\n        self.assertEqual(widget.data, self.iris)\n\n    def test_input_data_disconnect(self):\n        \"\"\"Check widget's data after disconnecting data on the input\"\"\"\n        widget = self.widget\n        self.send_signal(widget.Inputs.data, self.iris)\n        self.assertEqual(widget.data, self.iris)\n        self.send_signal(widget.Inputs.data, None)\n        self.assertEqual(widget.data, None)\n\n    def test_input_reference(self):\n        \"\"\"Check widget's reference with reference on the input\"\"\"\n        widget = self.widget\n        self.assertEqual(widget.reference, None)\n        self.send_signal(widget.Inputs.reference, self.iris)\n        self.assertEqual(widget.reference, self.iris)\n\n    def test_input_reference_disconnect(self):\n        \"\"\"Check reference after disconnecting reference on the input\"\"\"\n        widget = self.widget\n        self.send_signal(widget.Inputs.data, self.iris)\n        self.send_signal(widget.Inputs.reference, self.iris)\n        self.assertEqual(widget.reference, self.iris)\n        self.send_signal(widget.Inputs.reference, None)\n        self.assertEqual(widget.reference, None)\n        widget.apply_button.button.click()\n        self.assertIsNone(self.get_output())\n\n    def test_output_neighbors(self):\n        \"\"\"Check if neighbors are on the output after apply\"\"\"\n        widget = self.widget\n        self.assertIsNone(self.get_output())\n        self.send_signals(((widget.Inputs.data, self.iris),\n                           (widget.Inputs.reference, self.iris[:10])))\n        widget.apply_button.button.click()\n        self.assertIsNotNone(self.get_output())\n        self.assertIsInstance(self.get_output(), Table)\n        self.assertTrue(all([i in self.iris.ids for i in\n                             self.get_output(widget.Outputs.data).ids])\n                        )\n\n    def test_settings(self):\n        \"\"\"Check neighbors for various distance metrics\"\"\"\n        widget = self.widget\n        settings = [\n            ParameterMapping(\"\", widget.controls.distance_index, METRICS),\n            ParameterMapping(\"\", widget.controls.n_neighbors)]\n        for setting in settings:\n            for val in setting.values:\n                self.send_signal(widget.Inputs.data, self.iris)\n                self.send_signal(widget.Inputs.reference, self.iris[:10])\n                setting.set_value(val)\n                widget.apply_button.button.click()\n                if METRICS[widget.distance_index][0] != \"Jaccard\" \\\n                        and widget.n_neighbors != 0:\n                    self.assertIsNotNone(self.get_output())\n\n    def test_exclude_reference(self):\n        \"\"\"Check neighbors when reference is excluded\"\"\"\n        widget = self.widget\n        reference = self.iris[:5]\n        self.send_signal(widget.Inputs.data, self.iris)\n        self.send_signal(widget.Inputs.reference, reference)\n        self.widget.exclude_reference = True\n        widget.apply_button.button.click()\n        neighbors = self.get_output(widget.Outputs.data)\n        for inst in reference:\n            self.assertNotIn(inst, neighbors)\n\n    def test_similarity(self):\n        widget = self.widget\n        reference = self.iris[:10]\n        self.send_signal(widget.Inputs.data, self.iris)\n        self.send_signal(widget.Inputs.reference, reference)\n        widget.apply_button.button.click()\n        neighbors = self.get_output()\n        self.assertEqual(self.iris.domain.attributes,\n                         neighbors.domain.attributes)\n        self.assertEqual(self.iris.domain.class_vars,\n                         neighbors.domain.class_vars)\n        self.assertIn(\"distance\", neighbors.domain)\n        self.assertTrue(all(100 >= ins[\"distance\"] >= 0 for ins in neighbors))\n\n    def test_missing_values(self):\n        widget = self.widget\n        data = Table(\"iris\")\n        reference = data[:3]\n        with data.unlocked():\n            data.X[0:10, 0] = np.nan\n        self.send_signal(widget.Inputs.data, self.iris)\n        self.send_signal(widget.Inputs.reference, reference)\n        widget.apply_button.button.click()\n        self.assertIsNotNone(self.get_output())\n\n    def test_compute_distances_apply_called(self):\n        \"\"\"Check compute distances and apply are called when receiving signal\"\"\"\n        widget = self.widget\n        cdist = widget.compute_distances = Mock()\n        def_commit = widget.commit.now = Mock()\n        self.widget.auto_apply = False\n        data = Table(\"iris\")\n        self.send_signal(widget.Inputs.data, data)\n        cdist.assert_called()\n        def_commit.assert_called()\n        cdist.reset_mock()\n        def_commit.reset_mock()\n\n        self.send_signal(widget.Inputs.reference, data[:10])\n        cdist.assert_called()\n        def_commit.assert_called()\n        cdist.reset_mock()\n        def_commit.reset_mock()\n\n        self.send_signals([(widget.Inputs.data, data),\n                           (widget.Inputs.reference, data[:10])])\n        self.assertEqual(cdist.call_count, 1)\n        self.assertEqual(def_commit.call_count, 1)\n\n    def test_compute_distances_calls_distance(self):\n        widget = self.widget\n        widget.distance_index = 2\n        dists = np.random.random((10, 5))\n        distance = Mock(return_value=dists)\n        try:\n            orig_metrics = METRICS[widget.distance_index]\n            METRICS[widget.distance_index] = (\"foo\", distance)\n            data = Table(\"iris\")\n            data, refs = data[:10], data[-5:]\n            self.send_signals([(widget.Inputs.data, data),\n                               (widget.Inputs.reference, refs)])\n            # false positive, pylint: disable=unsubscriptable-object\n            d1, d2 = distance.call_args[0]\n            np.testing.assert_almost_equal(d1.X, data.X)\n            np.testing.assert_almost_equal(d2.X, refs.X)\n            # false positive, pylint: disable=no-member\n            np.testing.assert_almost_equal(widget.distances, dists.min(axis=1))\n        finally:\n            METRICS[widget.distance_index] = orig_metrics\n\n    def test_compute_distances_distance_no_data(self):\n        widget = self.widget\n        distance = Mock()\n        try:\n            orig_metrics = METRICS[widget.distance_index]\n            METRICS[widget.distance_index] = (\"foo\", distance)\n            data = Table(\"iris\")\n            data, refs = data[:10], data[-5:]\n            self.send_signals([(widget.Inputs.data, data),\n                               (widget.Inputs.reference, None)])\n            distance.assert_not_called()\n            self.assertIsNone(widget.distances)\n            self.send_signal(widget.Inputs.data, data)\n            distance.assert_not_called()\n            self.assertIsNone(widget.distances)\n            self.send_signals([(widget.Inputs.data, None),\n                               (widget.Inputs.reference, refs)])\n            distance.assert_not_called()\n            self.assertIsNone(widget.distances)\n            self.send_signals([(widget.Inputs.data, data),\n                               (widget.Inputs.reference, None)])\n            distance.assert_not_called()\n            self.assertIsNone(widget.distances)\n\n            data, refs = data[:0], data[:0]\n            self.send_signals([(widget.Inputs.data, data),\n                               (widget.Inputs.reference, None)])\n            distance.assert_not_called()\n            self.assertIsNone(widget.distances)\n        finally:\n            METRICS[widget.distance_index] = orig_metrics\n\n    def test_compute_indices_without_reference(self):\n        widget = self.widget\n        widget.limit_neighbours = True\n\n        # Indices for easier reading: 0 1  2  3  4  5  6  7  8  9 10 11 12\n        widget.distances = np.array([4, 1, 7, 0, 5, 2, 4, 0, 2, 2, 2, 9, 8])\n\n        widget.data = Mock()\n        widget.data.ids = np.arange(13)\n        widget.reference = Mock()\n        widget.reference.ids = np.array([1, 3])\n\n        widget.n_neighbors = 5\n        self.assertEqual(sorted(widget._compute_indices()), [5, 7, 8, 9, 10])\n\n        widget.n_neighbors = 1\n        self.assertEqual(list(widget._compute_indices()), [7])\n\n        widget.n_neighbors = 3\n        ind = set(widget._compute_indices())\n        self.assertEqual(len(ind), 3)\n        self.assertIn(7, ind)\n        self.assertTrue(len({5, 8, 9, 10} & ind) == 2)\n\n        widget.n_neighbors = 100\n        self.assertEqual(\n            sorted(widget._compute_indices()),\n            [0, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12])\n\n        widget.n_neighbors = 10\n        self.assertEqual(\n            sorted(widget._compute_indices()),\n            [0, 2, 4, 5, 6, 7, 8, 9, 10, 12])\n\n        widget.limit_neighbours = False\n        self.assertEqual(\n            sorted(widget._compute_indices()),\n            [0, 2, 4, 5, 6, 7, 8, 9, 10, 12])\n\n    def test_data_with_similarity(self):\n        widget = self.widget\n        indices = np.array([5, 10, 15, 100])\n\n        data = Table(\"iris\")\n        widget.data = data\n\n        widget.distances = np.arange(1000, 1150).astype(float)\n        neighbours = widget._data_with_similarity(indices)\n        self.assertEqual(neighbours.metas.shape, (4, 1))\n        np.testing.assert_almost_equal(\n            neighbours.metas.flatten(), indices + 1000)\n        np.testing.assert_almost_equal(neighbours.X, data.X[indices])\n\n        domain = data.domain\n        domain2 = Domain([domain[2]], domain.class_var, metas=domain[:2])\n        data2 = data.transform(domain2)\n        widget.data = data2\n\n        widget.distances = np.arange(1000, 1150).astype(float)\n        neighbours = widget._data_with_similarity(indices)\n        self.assertEqual(len(neighbours.domain.metas), 3)\n        self.assertEqual(neighbours.metas.shape, (4, 3))\n        np.testing.assert_almost_equal(\n            neighbours.get_column(\"distance\"), indices + 1000)\n        np.testing.assert_almost_equal(neighbours.X, data2.X[indices])\n\n    def test_apply(self):\n        widget = self.widget\n        widget.auto_apply = True\n        data = Table(\"iris\")\n        indices = np.array([5, 10, 15, 100])\n\n        widget._compute_indices = lambda: \\\n            indices if widget.distances is not None else None\n        self.send_signal(widget.Inputs.data, data)\n        self.send_signal(widget.Inputs.reference, data[42:43])\n        neigh = self.get_output(widget.Outputs.data)\n        np.testing.assert_almost_equal(neigh.X, data.X[indices])\n        np.testing.assert_almost_equal(\n            neigh.metas.flatten(), widget.distances[indices])\n\n    def test_all_equal_ref(self):\n        widget = self.widget\n        widget.auto_apply = True\n\n        data = Table(\"iris\")\n        self.send_signal(widget.Inputs.data, data[:10])\n        self.send_signal(widget.Inputs.reference, data[:10])\n        self.assertTrue(widget.Warning.all_data_as_reference.is_shown())\n        self.assertFalse(widget.Info.removed_references.is_shown())\n        self.assertIsNone(self.get_output(widget.Outputs.data))\n\n        self.send_signal(widget.Inputs.data, data[:15])\n        self.assertFalse(widget.Warning.all_data_as_reference.is_shown())\n        self.assertTrue(widget.Info.removed_references.is_shown())\n        self.assertIsNotNone(self.get_output(widget.Outputs.data))\n\n    def test_different_domains(self):\n        \"\"\"\n        Test weather widget show error when data and a reference have different\n        domains.\n        \"\"\"\n        w = self.widget\n\n        domain = Domain([ContinuousVariable(\"a\")])\n        domain_ref = Domain([ContinuousVariable(\"b\")])\n        data = Table(domain, np.random.rand(2, len(domain.variables)))\n        reference = Table(\n            domain_ref, np.random.rand(1, len(domain_ref.variables)))\n\n        # no error if one or both of the signals is missing\n        self.send_signal(w.Inputs.data, data)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n\n        self.send_signal(w.Inputs.data, None)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n\n        self.send_signal(w.Inputs.reference, data[:1])\n        self.assertFalse(w.Error.diff_domains.is_shown())\n\n        # same domain - no error\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, data[:1])\n        self.assertFalse(w.Error.diff_domains.is_shown())\n\n        # one attribute different attribute name\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, reference)\n        self.assertTrue(w.Error.diff_domains.is_shown())\n\n        # different number of attributes\n        domain_ref = Domain([ContinuousVariable(\"a\"), ContinuousVariable(\"b\")])\n        reference = Table(domain_ref, np.random.rand(1, len(domain_ref.variables)))\n\n        # error disappears when data is set to None\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, reference)\n        self.assertTrue(w.Error.diff_domains.is_shown())\n\n        self.send_signal(w.Inputs.data, None)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n\n        # error disappears when reference is set to None\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, reference)\n        self.assertTrue(w.Error.diff_domains.is_shown())\n\n        self.send_signal(w.Inputs.reference, None)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n\n    def test_different_metas(self):\n        \"\"\"\n        Test weather widget do not show error when data and a reference have\n        domain that differ only in metas\n        \"\"\"\n        w = self.widget\n\n        domain = Domain([ContinuousVariable(\"a\"), ContinuousVariable(\"b\")],\n                        metas=[ContinuousVariable(\"c\")])\n        data = Table(\n            domain, np.random.rand(15, len(domain.attributes)),\n            metas=np.random.rand(15, len(domain.metas)))\n\n        # same domain with same metas no error\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, data[:1])\n        self.assertFalse(w.Error.diff_domains.is_shown())\n        output = self.get_output(w.Outputs.data)\n        self.assertEqual(10, len(output))\n\n        # same domain with different metas no error\n        domain_ref = Domain(domain.attributes,\n                            metas=[ContinuousVariable(\"d\")])\n        reference = Table(\n            domain_ref, np.random.rand(1, len(domain_ref.attributes)),\n            metas=np.random.rand(1, len(domain.metas)))\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, reference)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n        output = self.get_output(w.Outputs.data)\n        self.assertEqual(10, len(output))\n\n        # same domain with different order - no error\n        domain_ref = Domain(domain.attributes[::-1])\n        reference = Table(\n            domain_ref, np.random.rand(1, len(domain_ref.attributes)))\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, reference)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n        output = self.get_output(w.Outputs.data)\n        self.assertEqual(10, len(output))\n\n        # same domain with different number of metas no error\n        domain_ref = Domain(\n            domain.attributes,\n            metas=[ContinuousVariable(\"d\"), ContinuousVariable(\"e\")])\n        reference = Table(\n            domain_ref, np.random.rand(1, len(domain_ref.attributes)),\n            metas=np.random.rand(1, len(domain_ref.metas)))\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, reference)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n        output = self.get_output(w.Outputs.data)\n        self.assertEqual(10, len(output))\n\n        # different domain with same metas - error shown\n        domain_ref = Domain(domain.attributes + (ContinuousVariable(\"e\"),),\n                            metas=[ContinuousVariable(\"c\")])\n        reference = Table(\n            domain_ref, np.random.rand(1, len(domain_ref.attributes)),\n            metas=np.random.rand(1, len(domain_ref.metas)))\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, reference)\n        self.assertTrue(w.Error.diff_domains.is_shown())\n        output = self.get_output(w.Outputs.data)\n        self.assertIsNone(output)\n\n    def test_different_domains_same_names(self):\n        \"\"\"\n        Here we create two domains with same names of attributes - the case\n        with image-analytics (two embeddings). Widget must not show the\n        error in this case.\n        \"\"\"\n        w = self.widget\n        domain1 = Domain([ContinuousVariable(\"a\"), ContinuousVariable(\"b\")],\n                         metas=[ContinuousVariable(\"c\")])\n        domain2 = Domain([ContinuousVariable(\"a\"), ContinuousVariable(\"b\")],\n                         metas=[ContinuousVariable(\"d\")])\n        data = Table(\n            domain1, np.random.rand(15, len(domain1.attributes)),\n            metas=np.random.rand(15, len(domain1.metas)))\n        ref = Table(\n            domain2, np.random.rand(2, len(domain2.attributes)),\n            metas=np.random.rand(2, len(domain2.metas)))\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, ref)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n        output = self.get_output(w.Outputs.data)\n        self.assertEqual(10, len(output))\n\n    def test_n_neighbours_spin_max(self):\n        w = self.widget\n        sb = w.controls.n_neighbors\n        default = sb.maximum()\n        self.send_signal(w.Inputs.data, self.iris)\n        self.assertEqual(sb.maximum(), len(self.iris))\n        self.send_signal(w.Inputs.data, self.iris[:20])\n        self.assertEqual(sb.maximum(), 20)\n        self.send_signal(w.Inputs.data, None)\n        self.assertEqual(sb.maximum(), default)\n\n    def test_inherited_table(self):\n        # pylint: disable=abstract-method\n        class Table2(Table):\n            pass\n\n        data = Table2(self.iris)\n        self.send_signal(self.widget.Inputs.data, data)\n        self.send_signal(self.widget.Inputs.reference, data[0:1])\n        self.assertIsInstance(self.get_output(self.widget.Outputs.data), Table2)\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n",
            "new_file_content": "#pylint: disable=missing-docstring,protected-access,unsubscriptable-object\nimport unittest\nfrom unittest.mock import Mock\n\nimport numpy as np\n\nfrom Orange.data import Table, Domain, ContinuousVariable\nfrom Orange.widgets.data.owneighbors import OWNeighbors, METRICS\nfrom Orange.widgets.tests.base import WidgetTest, ParameterMapping\n\n\nclass TestOWNeighbors(WidgetTest):\n    def setUp(self):\n        self.widget = self.create_widget(OWNeighbors,\n                                         stored_settings={\"auto_apply\": False})\n        self.iris = Table(\"iris\")\n\n    def test_input_data(self):\n        \"\"\"Check widget's data with data on the input\"\"\"\n        widget = self.widget\n        self.assertEqual(widget.data, None)\n        self.send_signal(widget.Inputs.data, self.iris)\n        self.assertEqual(widget.data, self.iris)\n\n    def test_input_data_disconnect(self):\n        \"\"\"Check widget's data after disconnecting data on the input\"\"\"\n        widget = self.widget\n        self.send_signal(widget.Inputs.data, self.iris)\n        self.assertEqual(widget.data, self.iris)\n        self.send_signal(widget.Inputs.data, None)\n        self.assertEqual(widget.data, None)\n\n    def test_input_reference(self):\n        \"\"\"Check widget's reference with reference on the input\"\"\"\n        widget = self.widget\n        self.assertEqual(widget.reference, None)\n        self.send_signal(widget.Inputs.reference, self.iris)\n        self.assertEqual(widget.reference, self.iris)\n\n    def test_input_reference_disconnect(self):\n        \"\"\"Check reference after disconnecting reference on the input\"\"\"\n        widget = self.widget\n        self.send_signal(widget.Inputs.data, self.iris)\n        self.send_signal(widget.Inputs.reference, self.iris)\n        self.assertEqual(widget.reference, self.iris)\n        self.send_signal(widget.Inputs.reference, None)\n        self.assertEqual(widget.reference, None)\n        widget.apply_button.button.click()\n        self.assertIsNone(self.get_output())\n\n    def test_output_neighbors(self):\n        \"\"\"Check if neighbors are on the output after apply\"\"\"\n        widget = self.widget\n        self.assertIsNone(self.get_output())\n        self.send_signals(((widget.Inputs.data, self.iris),\n                           (widget.Inputs.reference, self.iris[:10])))\n        widget.apply_button.button.click()\n        self.assertIsNotNone(self.get_output())\n        self.assertIsInstance(self.get_output(), Table)\n        self.assertTrue(all([i in self.iris.ids for i in\n                             self.get_output(widget.Outputs.data).ids])\n                        )\n\n    def test_settings(self):\n        \"\"\"Check neighbors for various distance metrics\"\"\"\n        widget = self.widget\n        settings = [\n            ParameterMapping(\"\", widget.controls.distance_index, METRICS),\n            ParameterMapping(\"\", widget.controls.n_neighbors)]\n        for setting in settings:\n            for val in setting.values:\n                self.send_signal(widget.Inputs.data, self.iris)\n                self.send_signal(widget.Inputs.reference, self.iris[:10])\n                setting.set_value(val)\n                widget.apply_button.button.click()\n                if METRICS[widget.distance_index][0] != \"Jaccard\" \\\n                        and widget.n_neighbors != 0:\n                    self.assertIsNotNone(self.get_output())\n\n    def test_exclude_reference(self):\n        \"\"\"Check neighbors when reference is excluded\"\"\"\n        widget = self.widget\n        reference = self.iris[:5]\n        self.send_signal(widget.Inputs.data, self.iris)\n        self.send_signal(widget.Inputs.reference, reference)\n        self.widget.exclude_reference = True\n        widget.apply_button.button.click()\n        neighbors = self.get_output(widget.Outputs.data)\n        for inst in reference:\n            self.assertNotIn(inst, neighbors)\n\n    def test_similarity(self):\n        widget = self.widget\n        reference = self.iris[:10]\n        self.send_signal(widget.Inputs.data, self.iris)\n        self.send_signal(widget.Inputs.reference, reference)\n        widget.apply_button.button.click()\n        neighbors = self.get_output()\n        self.assertEqual(self.iris.domain.attributes,\n                         neighbors.domain.attributes)\n        self.assertEqual(self.iris.domain.class_vars,\n                         neighbors.domain.class_vars)\n        self.assertIn(\"distance\", neighbors.domain)\n        self.assertTrue(all(100 >= ins[\"distance\"] >= 0 for ins in neighbors))\n\n    def test_missing_values(self):\n        widget = self.widget\n        data = Table(\"iris\")\n        reference = data[:3]\n        with data.unlocked():\n            data.X[0:10, 0] = np.nan\n        self.send_signal(widget.Inputs.data, self.iris)\n        self.send_signal(widget.Inputs.reference, reference)\n        widget.apply_button.button.click()\n        self.assertIsNotNone(self.get_output())\n\n    def test_compute_distances_apply_called(self):\n        \"\"\"Check compute distances and apply are called when receiving signal\"\"\"\n        widget = self.widget\n        cdist = widget.compute_distances = Mock()\n        def_commit = widget.commit.now = Mock()\n        self.widget.auto_apply = False\n        data = Table(\"iris\")\n        self.send_signal(widget.Inputs.data, data)\n        cdist.assert_called()\n        def_commit.assert_called()\n        cdist.reset_mock()\n        def_commit.reset_mock()\n\n        self.send_signal(widget.Inputs.reference, data[:10])\n        cdist.assert_called()\n        def_commit.assert_called()\n        cdist.reset_mock()\n        def_commit.reset_mock()\n\n        self.send_signals([(widget.Inputs.data, data),\n                           (widget.Inputs.reference, data[:10])])\n        self.assertEqual(cdist.call_count, 1)\n        self.assertEqual(def_commit.call_count, 1)\n\n    def test_compute_distances_calls_distance(self):\n        widget = self.widget\n        widget.distance_index = 2\n        dists = np.random.random((10, 5))\n        distance = Mock(return_value=dists)\n        try:\n            orig_metrics = METRICS[widget.distance_index]\n            METRICS[widget.distance_index] = (\"foo\", distance)\n            data = Table(\"iris\")\n            data, refs = data[:10], data[-5:]\n            self.send_signals([(widget.Inputs.data, data),\n                               (widget.Inputs.reference, refs)])\n            # false positive, pylint: disable=unsubscriptable-object\n            d1, d2 = distance.call_args[0]\n            np.testing.assert_almost_equal(d1.X, data.X)\n            np.testing.assert_almost_equal(d2.X, refs.X)\n            # false positive, pylint: disable=no-member\n            np.testing.assert_almost_equal(widget.distances, dists.min(axis=1))\n        finally:\n            METRICS[widget.distance_index] = orig_metrics\n\n    def test_compute_distances_distance_no_data(self):\n        widget = self.widget\n        distance = Mock()\n        try:\n            orig_metrics = METRICS[widget.distance_index]\n            METRICS[widget.distance_index] = (\"foo\", distance)\n            data = Table(\"iris\")\n            data, refs = data[:10], data[-5:]\n            self.send_signals([(widget.Inputs.data, data),\n                               (widget.Inputs.reference, None)])\n            distance.assert_not_called()\n            self.assertIsNone(widget.distances)\n            self.send_signal(widget.Inputs.data, data)\n            distance.assert_not_called()\n            self.assertIsNone(widget.distances)\n            self.send_signals([(widget.Inputs.data, None),\n                               (widget.Inputs.reference, refs)])\n            distance.assert_not_called()\n            self.assertIsNone(widget.distances)\n            self.send_signals([(widget.Inputs.data, data),\n                               (widget.Inputs.reference, None)])\n            distance.assert_not_called()\n            self.assertIsNone(widget.distances)\n\n            data, refs = data[:0], data[:0]\n            self.send_signals([(widget.Inputs.data, data),\n                               (widget.Inputs.reference, None)])\n            distance.assert_not_called()\n            self.assertIsNone(widget.distances)\n        finally:\n            METRICS[widget.distance_index] = orig_metrics\n\n    def test_compute_indices_without_reference(self):\n        widget = self.widget\n        widget.limit_neighbours = True\n\n        # Indices for easier reading: 0 1  2  3  4  5  6  7  8  9 10 11 12\n        widget.distances = np.array([4, 1, 7, 0, 5, 2, 4, 0, 2, 2, 2, 9, 8])\n\n        widget.data = Mock()\n        widget.data.ids = np.arange(13)\n        widget.reference = Mock()\n        widget.reference.ids = np.array([1, 3])\n\n        widget.n_neighbors = 5\n        self.assertEqual(sorted(widget._compute_indices()), [5, 7, 8, 9, 10])\n\n        widget.n_neighbors = 1\n        self.assertEqual(list(widget._compute_indices()), [7])\n\n        widget.n_neighbors = 3\n        ind = set(widget._compute_indices())\n        self.assertEqual(len(ind), 3)\n        self.assertIn(7, ind)\n        self.assertTrue(len({5, 8, 9, 10} & ind) == 2)\n\n        widget.n_neighbors = 100\n        self.assertEqual(\n            sorted(widget._compute_indices()),\n            [0, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12])\n\n        widget.n_neighbors = 10\n        self.assertEqual(\n            sorted(widget._compute_indices()),\n            [0, 2, 4, 5, 6, 7, 8, 9, 10, 12])\n\n        widget.limit_neighbours = False\n        self.assertEqual(\n            sorted(widget._compute_indices()),\n            [0, 2, 4, 5, 6, 7, 8, 9, 10, 12])\n\n    def test_data_with_similarity(self):\n        widget = self.widget\n        indices = np.array([5, 10, 15, 100])\n\n        data = Table(\"iris\")\n        widget.data = data\n\n        widget.distances = np.arange(1000, 1150).astype(float)\n        neighbours = widget._data_with_similarity(indices)\n        self.assertEqual(neighbours.metas.shape, (4, 1))\n        np.testing.assert_almost_equal(\n            neighbours.metas.flatten(), indices + 1000)\n        np.testing.assert_almost_equal(neighbours.X, data.X[indices])\n\n        domain = data.domain\n        domain2 = Domain([domain[2]], domain.class_var, metas=domain[:2])\n        data2 = data.transform(domain2)\n        widget.data = data2\n\n        widget.distances = np.arange(1000, 1150).astype(float)\n        neighbours = widget._data_with_similarity(indices)\n        self.assertEqual(len(neighbours.domain.metas), 3)\n        self.assertEqual(neighbours.metas.shape, (4, 3))\n        np.testing.assert_almost_equal(\n            neighbours.get_column(\"distance\"), indices + 1000)\n        np.testing.assert_almost_equal(neighbours.X, data2.X[indices])\n\n    def test_apply(self):\n        widget = self.widget\n        widget.auto_apply = True\n        data = Table(\"iris\")\n        indices = np.array([5, 10, 15, 100])\n\n        widget._compute_indices = lambda: \\\n            indices if widget.distances is not None else None\n        self.send_signal(widget.Inputs.data, data)\n        self.send_signal(widget.Inputs.reference, data[42:43])\n        neigh = self.get_output(widget.Outputs.data)\n        np.testing.assert_almost_equal(neigh.X, data.X[indices])\n        np.testing.assert_almost_equal(\n            neigh.metas.flatten(), widget.distances[indices])\n\n    def test_all_equal_ref(self):\n        widget = self.widget\n        widget.auto_apply = True\n\n        data = Table(\"iris\")\n        self.send_signal(widget.Inputs.data, data[:10])\n        self.send_signal(widget.Inputs.reference, data[:10])\n        self.assertTrue(widget.Warning.all_data_as_reference.is_shown())\n        self.assertFalse(widget.Info.removed_references.is_shown())\n        self.assertIsNone(self.get_output(widget.Outputs.data))\n\n        self.send_signal(widget.Inputs.data, data[:15])\n        self.assertFalse(widget.Warning.all_data_as_reference.is_shown())\n        self.assertTrue(widget.Info.removed_references.is_shown())\n        self.assertIsNotNone(self.get_output(widget.Outputs.data))\n\n    def test_different_domains(self):\n        \"\"\"\n        Test weather widget show error when data and a reference have different\n        domains.\n        \"\"\"\n        w = self.widget\n\n        domain = Domain([ContinuousVariable(\"a\")])\n        domain_ref = Domain([ContinuousVariable(\"b\")])\n        data = Table(domain, np.random.rand(2, len(domain.variables)))\n        reference = Table(\n            domain_ref, np.random.rand(1, len(domain_ref.variables)))\n\n        # no error if one or both of the signals is missing\n        self.send_signal(w.Inputs.data, data)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n\n        self.send_signal(w.Inputs.data, None)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n\n        self.send_signal(w.Inputs.reference, data[:1])\n        self.assertFalse(w.Error.diff_domains.is_shown())\n\n        # same domain - no error\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, data[:1])\n        self.assertFalse(w.Error.diff_domains.is_shown())\n\n        # one attribute different attribute name\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, reference)\n        self.assertTrue(w.Error.diff_domains.is_shown())\n\n        # different number of attributes\n        domain_ref = Domain([ContinuousVariable(\"a\"), ContinuousVariable(\"b\")])\n        reference = Table(domain_ref, np.random.rand(1, len(domain_ref.variables)))\n\n        # error disappears when data is set to None\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, reference)\n        self.assertTrue(w.Error.diff_domains.is_shown())\n\n        self.send_signal(w.Inputs.data, None)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n\n        # error disappears when reference is set to None\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, reference)\n        self.assertTrue(w.Error.diff_domains.is_shown())\n\n        self.send_signal(w.Inputs.reference, None)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n\n    def test_different_metas(self):\n        \"\"\"\n        Test weather widget do not show error when data and a reference have\n        domain that differ only in metas\n        \"\"\"\n        w = self.widget\n\n        domain = Domain([ContinuousVariable(\"a\"), ContinuousVariable(\"b\")],\n                        metas=[ContinuousVariable(\"c\")])\n        data = Table(\n            domain, np.random.rand(15, len(domain.attributes)),\n            metas=np.random.rand(15, len(domain.metas)))\n\n        # same domain with same metas no error\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, data[:1])\n        self.assertFalse(w.Error.diff_domains.is_shown())\n        output = self.get_output(w.Outputs.data)\n        self.assertEqual(10, len(output))\n\n        # same domain with different metas no error\n        domain_ref = Domain(domain.attributes,\n                            metas=[ContinuousVariable(\"d\")])\n        reference = Table(\n            domain_ref, np.random.rand(1, len(domain_ref.attributes)),\n            metas=np.random.rand(1, len(domain.metas)))\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, reference)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n        output = self.get_output(w.Outputs.data)\n        self.assertEqual(10, len(output))\n\n        # same domain with different order - no error\n        domain_ref = Domain(domain.attributes[::-1])\n        reference = Table(\n            domain_ref, np.random.rand(1, len(domain_ref.attributes)))\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, reference)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n        output = self.get_output(w.Outputs.data)\n        self.assertEqual(10, len(output))\n\n        # same domain with different number of metas no error\n        domain_ref = Domain(\n            domain.attributes,\n            metas=[ContinuousVariable(\"d\"), ContinuousVariable(\"e\")])\n        reference = Table(\n            domain_ref, np.random.rand(1, len(domain_ref.attributes)),\n            metas=np.random.rand(1, len(domain_ref.metas)))\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, reference)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n        output = self.get_output(w.Outputs.data)\n        self.assertEqual(10, len(output))\n\n        # different domain with same metas - error shown\n        domain_ref = Domain(domain.attributes + (ContinuousVariable(\"e\"),),\n                            metas=[ContinuousVariable(\"c\")])\n        reference = Table(\n            domain_ref, np.random.rand(1, len(domain_ref.attributes)),\n            metas=np.random.rand(1, len(domain_ref.metas)))\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, reference)\n        self.assertTrue(w.Error.diff_domains.is_shown())\n        output = self.get_output(w.Outputs.data)\n        self.assertIsNone(output)\n\n    def test_different_domains_same_names(self):\n        \"\"\"\n        Here we create two domains with same names of attributes - the case\n        with image-analytics (two embeddings). Widget must not show the\n        error in this case.\n        \"\"\"\n        w = self.widget\n        domain1 = Domain([ContinuousVariable(\"a\"), ContinuousVariable(\"b\")],\n                         metas=[ContinuousVariable(\"c\")])\n        domain2 = Domain([ContinuousVariable(\"a\"), ContinuousVariable(\"b\")],\n                         metas=[ContinuousVariable(\"d\")])\n        data = Table(\n            domain1, np.random.rand(15, len(domain1.attributes)),\n            metas=np.random.rand(15, len(domain1.metas)))\n        ref = Table(\n            domain2, np.random.rand(2, len(domain2.attributes)),\n            metas=np.random.rand(2, len(domain2.metas)))\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, ref)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n        output = self.get_output(w.Outputs.data)\n        self.assertEqual(10, len(output))\n\n    def test_n_neighbours_spin_max(self):\n        w = self.widget\n        sb = w.controls.n_neighbors\n        default = sb.maximum()\n        self.send_signal(w.Inputs.data, self.iris)\n        self.assertEqual(sb.maximum(), len(self.iris))\n        self.send_signal(w.Inputs.data, self.iris[:20])\n        self.assertEqual(sb.maximum(), 20)\n        self.send_signal(w.Inputs.data, None)\n        self.assertEqual(sb.maximum(), default)\n\n    def test_inherited_table(self):\n        # pylint: disable=abstract-method\n        class Table2(Table):\n            pass\n\n        data = Table2(self.iris)\n        self.send_signal(self.widget.Inputs.data, data)\n        self.send_signal(self.widget.Inputs.reference, data[0:1])\n        self.assertIsInstance(self.get_output(self.widget.Outputs.data), Table2)\n\n    def test_order_by_distance(self):\n        domain = Domain([ContinuousVariable(x) for x in \"ab\"])\n        reference = Table.from_numpy(domain, [[1, 0]])\n        data = Table.from_numpy(domain, [[1, 0.1], [2, 0], [1, 0], [0, 0.1], [0.1, 0]])\n        self.send_signal(self.widget.Inputs.data, data)\n        self.send_signal(self.widget.Inputs.reference, reference)\n\n        output = self.get_output(self.widget.Outputs.data)\n        expected = [[1, 0], [1, 0.1], [0.1, 0], [2, 0], [0, 0.1]]\n        np.testing.assert_array_equal(output.X, expected)\n        dst = output.get_column(\"distance\").tolist()\n        self.assertTrue(dst == sorted(dst))  # check distance in ascending order\n\n        # test on bigger set\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.send_signal(self.widget.Inputs.reference, self.iris[:1])\n        dst = self.get_output(self.widget.Outputs.data).get_column(\"distance\").tolist()\n        self.assertTrue(dst == sorted(dst))  # check distance in ascending order\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n",
            "header": {
                "file": {
                    "path": "Orange/widgets/data/tests/test_owneighbors.py"
                },
                "misc_line": null
            },
            "index_line": {
                "old_commit_hash": "ce6442852",
                "new_commit_hash": "e66f5337b",
                "mode": "100644"
            },
            "is_binary_file": false,
            "binary_line": null,
            "minus_file": {
                "path": "a/Orange/widgets/data/tests/test_owneighbors.py"
            },
            "plus_file": {
                "path": "b/Orange/widgets/data/tests/test_owneighbors.py"
            },
            "hunks": [
                {
                    "descriptor": {
                        "old_range": {
                            "start": 452,
                            "length": 6
                        },
                        "new_range": {
                            "start": 452,
                            "length": 25
                        },
                        "section": "class TestOWNeighbors(WidgetTest):"
                    },
                    "line_group": {
                        "all_lines": [
                            {
                                "content": "        self.send_signal(self.widget.Inputs.reference, data[0:1])",
                                "type": "context"
                            },
                            {
                                "content": "        self.assertIsInstance(self.get_output(self.widget.Outputs.data), Table2)",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "    def test_order_by_distance(self):",
                                "type": "added"
                            },
                            {
                                "content": "        domain = Domain([ContinuousVariable(x) for x in \"ab\"])",
                                "type": "added"
                            },
                            {
                                "content": "        reference = Table.from_numpy(domain, [[1, 0]])",
                                "type": "added"
                            },
                            {
                                "content": "        data = Table.from_numpy(domain, [[1, 0.1], [2, 0], [1, 0], [0, 0.1], [0.1, 0]])",
                                "type": "added"
                            },
                            {
                                "content": "        self.send_signal(self.widget.Inputs.data, data)",
                                "type": "added"
                            },
                            {
                                "content": "        self.send_signal(self.widget.Inputs.reference, reference)",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "        output = self.get_output(self.widget.Outputs.data)",
                                "type": "added"
                            },
                            {
                                "content": "        expected = [[1, 0], [1, 0.1], [0.1, 0], [2, 0], [0, 0.1]]",
                                "type": "added"
                            },
                            {
                                "content": "        np.testing.assert_array_equal(output.X, expected)",
                                "type": "added"
                            },
                            {
                                "content": "        dst = output.get_column(\"distance\").tolist()",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertTrue(dst == sorted(dst))  # check distance in ascending order",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "        # test on bigger set",
                                "type": "added"
                            },
                            {
                                "content": "        self.send_signal(self.widget.Inputs.data, self.iris)",
                                "type": "added"
                            },
                            {
                                "content": "        self.send_signal(self.widget.Inputs.reference, self.iris[:1])",
                                "type": "added"
                            },
                            {
                                "content": "        dst = self.get_output(self.widget.Outputs.data).get_column(\"distance\").tolist()",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertTrue(dst == sorted(dst))  # check distance in ascending order",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "if __name__ == \"__main__\":",
                                "type": "context"
                            },
                            {
                                "content": "    unittest.main()",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            }
                        ]
                    },
                    "modified_entities": [
                        {
                            "file_name": "Orange/widgets/data/tests/test_owneighbors.py",
                            "type": "class",
                            "name": "TestOWNeighbors",
                            "content": "class TestOWNeighbors(WidgetTest):\n    def setUp(self):\n        self.widget = self.create_widget(OWNeighbors,\n                                         stored_settings={\"auto_apply\": False})\n        self.iris = Table(\"iris\")\n\n    def test_input_data(self):\n        \"\"\"Check widget's data with data on the input\"\"\"\n        widget = self.widget\n        self.assertEqual(widget.data, None)\n        self.send_signal(widget.Inputs.data, self.iris)\n        self.assertEqual(widget.data, self.iris)\n\n    def test_input_data_disconnect(self):\n        \"\"\"Check widget's data after disconnecting data on the input\"\"\"\n        widget = self.widget\n        self.send_signal(widget.Inputs.data, self.iris)\n        self.assertEqual(widget.data, self.iris)\n        self.send_signal(widget.Inputs.data, None)\n        self.assertEqual(widget.data, None)\n\n    def test_input_reference(self):\n        \"\"\"Check widget's reference with reference on the input\"\"\"\n        widget = self.widget\n        self.assertEqual(widget.reference, None)\n        self.send_signal(widget.Inputs.reference, self.iris)\n        self.assertEqual(widget.reference, self.iris)\n\n    def test_input_reference_disconnect(self):\n        \"\"\"Check reference after disconnecting reference on the input\"\"\"\n        widget = self.widget\n        self.send_signal(widget.Inputs.data, self.iris)\n        self.send_signal(widget.Inputs.reference, self.iris)\n        self.assertEqual(widget.reference, self.iris)\n        self.send_signal(widget.Inputs.reference, None)\n        self.assertEqual(widget.reference, None)\n        widget.apply_button.button.click()\n        self.assertIsNone(self.get_output())\n\n    def test_output_neighbors(self):\n        \"\"\"Check if neighbors are on the output after apply\"\"\"\n        widget = self.widget\n        self.assertIsNone(self.get_output())\n        self.send_signals(((widget.Inputs.data, self.iris),\n                           (widget.Inputs.reference, self.iris[:10])))\n        widget.apply_button.button.click()\n        self.assertIsNotNone(self.get_output())\n        self.assertIsInstance(self.get_output(), Table)\n        self.assertTrue(all([i in self.iris.ids for i in\n                             self.get_output(widget.Outputs.data).ids])\n                        )\n\n    def test_settings(self):\n        \"\"\"Check neighbors for various distance metrics\"\"\"\n        widget = self.widget\n        settings = [\n            ParameterMapping(\"\", widget.controls.distance_index, METRICS),\n            ParameterMapping(\"\", widget.controls.n_neighbors)]\n        for setting in settings:\n            for val in setting.values:\n                self.send_signal(widget.Inputs.data, self.iris)\n                self.send_signal(widget.Inputs.reference, self.iris[:10])\n                setting.set_value(val)\n                widget.apply_button.button.click()\n                if METRICS[widget.distance_index][0] != \"Jaccard\" \\\n                        and widget.n_neighbors != 0:\n                    self.assertIsNotNone(self.get_output())\n\n    def test_exclude_reference(self):\n        \"\"\"Check neighbors when reference is excluded\"\"\"\n        widget = self.widget\n        reference = self.iris[:5]\n        self.send_signal(widget.Inputs.data, self.iris)\n        self.send_signal(widget.Inputs.reference, reference)\n        self.widget.exclude_reference = True\n        widget.apply_button.button.click()\n        neighbors = self.get_output(widget.Outputs.data)\n        for inst in reference:\n            self.assertNotIn(inst, neighbors)\n\n    def test_similarity(self):\n        widget = self.widget\n        reference = self.iris[:10]\n        self.send_signal(widget.Inputs.data, self.iris)\n        self.send_signal(widget.Inputs.reference, reference)\n        widget.apply_button.button.click()\n        neighbors = self.get_output()\n        self.assertEqual(self.iris.domain.attributes,\n                         neighbors.domain.attributes)\n        self.assertEqual(self.iris.domain.class_vars,\n                         neighbors.domain.class_vars)\n        self.assertIn(\"distance\", neighbors.domain)\n        self.assertTrue(all(100 >= ins[\"distance\"] >= 0 for ins in neighbors))\n\n    def test_missing_values(self):\n        widget = self.widget\n        data = Table(\"iris\")\n        reference = data[:3]\n        with data.unlocked():\n            data.X[0:10, 0] = np.nan\n        self.send_signal(widget.Inputs.data, self.iris)\n        self.send_signal(widget.Inputs.reference, reference)\n        widget.apply_button.button.click()\n        self.assertIsNotNone(self.get_output())\n\n    def test_compute_distances_apply_called(self):\n        \"\"\"Check compute distances and apply are called when receiving signal\"\"\"\n        widget = self.widget\n        cdist = widget.compute_distances = Mock()\n        def_commit = widget.commit.now = Mock()\n        self.widget.auto_apply = False\n        data = Table(\"iris\")\n        self.send_signal(widget.Inputs.data, data)\n        cdist.assert_called()\n        def_commit.assert_called()\n        cdist.reset_mock()\n        def_commit.reset_mock()\n\n        self.send_signal(widget.Inputs.reference, data[:10])\n        cdist.assert_called()\n        def_commit.assert_called()\n        cdist.reset_mock()\n        def_commit.reset_mock()\n\n        self.send_signals([(widget.Inputs.data, data),\n                           (widget.Inputs.reference, data[:10])])\n        self.assertEqual(cdist.call_count, 1)\n        self.assertEqual(def_commit.call_count, 1)\n\n    def test_compute_distances_calls_distance(self):\n        widget = self.widget\n        widget.distance_index = 2\n        dists = np.random.random((10, 5))\n        distance = Mock(return_value=dists)\n        try:\n            orig_metrics = METRICS[widget.distance_index]\n            METRICS[widget.distance_index] = (\"foo\", distance)\n            data = Table(\"iris\")\n            data, refs = data[:10], data[-5:]\n            self.send_signals([(widget.Inputs.data, data),\n                               (widget.Inputs.reference, refs)])\n            # false positive, pylint: disable=unsubscriptable-object\n            d1, d2 = distance.call_args[0]\n            np.testing.assert_almost_equal(d1.X, data.X)\n            np.testing.assert_almost_equal(d2.X, refs.X)\n            # false positive, pylint: disable=no-member\n            np.testing.assert_almost_equal(widget.distances, dists.min(axis=1))\n        finally:\n            METRICS[widget.distance_index] = orig_metrics\n\n    def test_compute_distances_distance_no_data(self):\n        widget = self.widget\n        distance = Mock()\n        try:\n            orig_metrics = METRICS[widget.distance_index]\n            METRICS[widget.distance_index] = (\"foo\", distance)\n            data = Table(\"iris\")\n            data, refs = data[:10], data[-5:]\n            self.send_signals([(widget.Inputs.data, data),\n                               (widget.Inputs.reference, None)])\n            distance.assert_not_called()\n            self.assertIsNone(widget.distances)\n            self.send_signal(widget.Inputs.data, data)\n            distance.assert_not_called()\n            self.assertIsNone(widget.distances)\n            self.send_signals([(widget.Inputs.data, None),\n                               (widget.Inputs.reference, refs)])\n            distance.assert_not_called()\n            self.assertIsNone(widget.distances)\n            self.send_signals([(widget.Inputs.data, data),\n                               (widget.Inputs.reference, None)])\n            distance.assert_not_called()\n            self.assertIsNone(widget.distances)\n\n            data, refs = data[:0], data[:0]\n            self.send_signals([(widget.Inputs.data, data),\n                               (widget.Inputs.reference, None)])\n            distance.assert_not_called()\n            self.assertIsNone(widget.distances)\n        finally:\n            METRICS[widget.distance_index] = orig_metrics\n\n    def test_compute_indices_without_reference(self):\n        widget = self.widget\n        widget.limit_neighbours = True\n\n        # Indices for easier reading: 0 1  2  3  4  5  6  7  8  9 10 11 12\n        widget.distances = np.array([4, 1, 7, 0, 5, 2, 4, 0, 2, 2, 2, 9, 8])\n\n        widget.data = Mock()\n        widget.data.ids = np.arange(13)\n        widget.reference = Mock()\n        widget.reference.ids = np.array([1, 3])\n\n        widget.n_neighbors = 5\n        self.assertEqual(sorted(widget._compute_indices()), [5, 7, 8, 9, 10])\n\n        widget.n_neighbors = 1\n        self.assertEqual(list(widget._compute_indices()), [7])\n\n        widget.n_neighbors = 3\n        ind = set(widget._compute_indices())\n        self.assertEqual(len(ind), 3)\n        self.assertIn(7, ind)\n        self.assertTrue(len({5, 8, 9, 10} & ind) == 2)\n\n        widget.n_neighbors = 100\n        self.assertEqual(\n            sorted(widget._compute_indices()),\n            [0, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12])\n\n        widget.n_neighbors = 10\n        self.assertEqual(\n            sorted(widget._compute_indices()),\n            [0, 2, 4, 5, 6, 7, 8, 9, 10, 12])\n\n        widget.limit_neighbours = False\n        self.assertEqual(\n            sorted(widget._compute_indices()),\n            [0, 2, 4, 5, 6, 7, 8, 9, 10, 12])\n\n    def test_data_with_similarity(self):\n        widget = self.widget\n        indices = np.array([5, 10, 15, 100])\n\n        data = Table(\"iris\")\n        widget.data = data\n\n        widget.distances = np.arange(1000, 1150).astype(float)\n        neighbours = widget._data_with_similarity(indices)\n        self.assertEqual(neighbours.metas.shape, (4, 1))\n        np.testing.assert_almost_equal(\n            neighbours.metas.flatten(), indices + 1000)\n        np.testing.assert_almost_equal(neighbours.X, data.X[indices])\n\n        domain = data.domain\n        domain2 = Domain([domain[2]], domain.class_var, metas=domain[:2])\n        data2 = data.transform(domain2)\n        widget.data = data2\n\n        widget.distances = np.arange(1000, 1150).astype(float)\n        neighbours = widget._data_with_similarity(indices)\n        self.assertEqual(len(neighbours.domain.metas), 3)\n        self.assertEqual(neighbours.metas.shape, (4, 3))\n        np.testing.assert_almost_equal(\n            neighbours.get_column(\"distance\"), indices + 1000)\n        np.testing.assert_almost_equal(neighbours.X, data2.X[indices])\n\n    def test_apply(self):\n        widget = self.widget\n        widget.auto_apply = True\n        data = Table(\"iris\")\n        indices = np.array([5, 10, 15, 100])\n\n        widget._compute_indices = lambda: \\\n            indices if widget.distances is not None else None\n        self.send_signal(widget.Inputs.data, data)\n        self.send_signal(widget.Inputs.reference, data[42:43])\n        neigh = self.get_output(widget.Outputs.data)\n        np.testing.assert_almost_equal(neigh.X, data.X[indices])\n        np.testing.assert_almost_equal(\n            neigh.metas.flatten(), widget.distances[indices])\n\n    def test_all_equal_ref(self):\n        widget = self.widget\n        widget.auto_apply = True\n\n        data = Table(\"iris\")\n        self.send_signal(widget.Inputs.data, data[:10])\n        self.send_signal(widget.Inputs.reference, data[:10])\n        self.assertTrue(widget.Warning.all_data_as_reference.is_shown())\n        self.assertFalse(widget.Info.removed_references.is_shown())\n        self.assertIsNone(self.get_output(widget.Outputs.data))\n\n        self.send_signal(widget.Inputs.data, data[:15])\n        self.assertFalse(widget.Warning.all_data_as_reference.is_shown())\n        self.assertTrue(widget.Info.removed_references.is_shown())\n        self.assertIsNotNone(self.get_output(widget.Outputs.data))\n\n    def test_different_domains(self):\n        \"\"\"\n        Test weather widget show error when data and a reference have different\n        domains.\n        \"\"\"\n        w = self.widget\n\n        domain = Domain([ContinuousVariable(\"a\")])\n        domain_ref = Domain([ContinuousVariable(\"b\")])\n        data = Table(domain, np.random.rand(2, len(domain.variables)))\n        reference = Table(\n            domain_ref, np.random.rand(1, len(domain_ref.variables)))\n\n        # no error if one or both of the signals is missing\n        self.send_signal(w.Inputs.data, data)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n\n        self.send_signal(w.Inputs.data, None)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n\n        self.send_signal(w.Inputs.reference, data[:1])\n        self.assertFalse(w.Error.diff_domains.is_shown())\n\n        # same domain - no error\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, data[:1])\n        self.assertFalse(w.Error.diff_domains.is_shown())\n\n        # one attribute different attribute name\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, reference)\n        self.assertTrue(w.Error.diff_domains.is_shown())\n\n        # different number of attributes\n        domain_ref = Domain([ContinuousVariable(\"a\"), ContinuousVariable(\"b\")])\n        reference = Table(domain_ref, np.random.rand(1, len(domain_ref.variables)))\n\n        # error disappears when data is set to None\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, reference)\n        self.assertTrue(w.Error.diff_domains.is_shown())\n\n        self.send_signal(w.Inputs.data, None)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n\n        # error disappears when reference is set to None\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, reference)\n        self.assertTrue(w.Error.diff_domains.is_shown())\n\n        self.send_signal(w.Inputs.reference, None)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n\n    def test_different_metas(self):\n        \"\"\"\n        Test weather widget do not show error when data and a reference have\n        domain that differ only in metas\n        \"\"\"\n        w = self.widget\n\n        domain = Domain([ContinuousVariable(\"a\"), ContinuousVariable(\"b\")],\n                        metas=[ContinuousVariable(\"c\")])\n        data = Table(\n            domain, np.random.rand(15, len(domain.attributes)),\n            metas=np.random.rand(15, len(domain.metas)))\n\n        # same domain with same metas no error\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, data[:1])\n        self.assertFalse(w.Error.diff_domains.is_shown())\n        output = self.get_output(w.Outputs.data)\n        self.assertEqual(10, len(output))\n\n        # same domain with different metas no error\n        domain_ref = Domain(domain.attributes,\n                            metas=[ContinuousVariable(\"d\")])\n        reference = Table(\n            domain_ref, np.random.rand(1, len(domain_ref.attributes)),\n            metas=np.random.rand(1, len(domain.metas)))\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, reference)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n        output = self.get_output(w.Outputs.data)\n        self.assertEqual(10, len(output))\n\n        # same domain with different order - no error\n        domain_ref = Domain(domain.attributes[::-1])\n        reference = Table(\n            domain_ref, np.random.rand(1, len(domain_ref.attributes)))\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, reference)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n        output = self.get_output(w.Outputs.data)\n        self.assertEqual(10, len(output))\n\n        # same domain with different number of metas no error\n        domain_ref = Domain(\n            domain.attributes,\n            metas=[ContinuousVariable(\"d\"), ContinuousVariable(\"e\")])\n        reference = Table(\n            domain_ref, np.random.rand(1, len(domain_ref.attributes)),\n            metas=np.random.rand(1, len(domain_ref.metas)))\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, reference)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n        output = self.get_output(w.Outputs.data)\n        self.assertEqual(10, len(output))\n\n        # different domain with same metas - error shown\n        domain_ref = Domain(domain.attributes + (ContinuousVariable(\"e\"),),\n                            metas=[ContinuousVariable(\"c\")])\n        reference = Table(\n            domain_ref, np.random.rand(1, len(domain_ref.attributes)),\n            metas=np.random.rand(1, len(domain_ref.metas)))\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, reference)\n        self.assertTrue(w.Error.diff_domains.is_shown())\n        output = self.get_output(w.Outputs.data)\n        self.assertIsNone(output)\n\n    def test_different_domains_same_names(self):\n        \"\"\"\n        Here we create two domains with same names of attributes - the case\n        with image-analytics (two embeddings). Widget must not show the\n        error in this case.\n        \"\"\"\n        w = self.widget\n        domain1 = Domain([ContinuousVariable(\"a\"), ContinuousVariable(\"b\")],\n                         metas=[ContinuousVariable(\"c\")])\n        domain2 = Domain([ContinuousVariable(\"a\"), ContinuousVariable(\"b\")],\n                         metas=[ContinuousVariable(\"d\")])\n        data = Table(\n            domain1, np.random.rand(15, len(domain1.attributes)),\n            metas=np.random.rand(15, len(domain1.metas)))\n        ref = Table(\n            domain2, np.random.rand(2, len(domain2.attributes)),\n            metas=np.random.rand(2, len(domain2.metas)))\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, ref)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n        output = self.get_output(w.Outputs.data)\n        self.assertEqual(10, len(output))\n\n    def test_n_neighbours_spin_max(self):\n        w = self.widget\n        sb = w.controls.n_neighbors\n        default = sb.maximum()\n        self.send_signal(w.Inputs.data, self.iris)\n        self.assertEqual(sb.maximum(), len(self.iris))\n        self.send_signal(w.Inputs.data, self.iris[:20])\n        self.assertEqual(sb.maximum(), 20)\n        self.send_signal(w.Inputs.data, None)\n        self.assertEqual(sb.maximum(), default)\n\n    def test_inherited_table(self):\n        # pylint: disable=abstract-method\n        class Table2(Table):\n            pass\n\n        data = Table2(self.iris)\n        self.send_signal(self.widget.Inputs.data, data)\n        self.send_signal(self.widget.Inputs.reference, data[0:1])\n        self.assertIsInstance(self.get_output(self.widget.Outputs.data), Table2)\n\n    def test_order_by_distance(self):\n        domain = Domain([ContinuousVariable(x) for x in \"ab\"])\n        reference = Table.from_numpy(domain, [[1, 0]])\n        data = Table.from_numpy(domain, [[1, 0.1], [2, 0], [1, 0], [0, 0.1], [0.1, 0]])\n        self.send_signal(self.widget.Inputs.data, data)\n        self.send_signal(self.widget.Inputs.reference, reference)\n\n        output = self.get_output(self.widget.Outputs.data)\n        expected = [[1, 0], [1, 0.1], [0.1, 0], [2, 0], [0, 0.1]]\n        np.testing.assert_array_equal(output.X, expected)\n        dst = output.get_column(\"distance\").tolist()\n        self.assertTrue(dst == sorted(dst))  # check distance in ascending order\n\n        # test on bigger set\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.send_signal(self.widget.Inputs.reference, self.iris[:1])\n        dst = self.get_output(self.widget.Outputs.data).get_column(\"distance\").tolist()\n        self.assertTrue(dst == sorted(dst))",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 12,
                            "end_lineno": 472,
                            "parent": null
                        }
                    ],
                    "added_entities": [
                        {
                            "file_name": "Orange/widgets/data/tests/test_owneighbors.py",
                            "type": "method",
                            "name": "TestOWNeighbors.test_order_by_distance",
                            "content": "class TestOWNeighbors(WidgetTest):\n    def setUp(self):\n        self.widget = self.create_widget(OWNeighbors,\n                                         stored_settings={\"auto_apply\": False})\n        self.iris = Table(\"iris\")\n\n    def test_input_data(self):\n        \"\"\"Check widget's data with data on the input\"\"\"\n        widget = self.widget\n        self.assertEqual(widget.data, None)\n        self.send_signal(widget.Inputs.data, self.iris)\n        self.assertEqual(widget.data, self.iris)\n\n    def test_input_data_disconnect(self):\n        \"\"\"Check widget's data after disconnecting data on the input\"\"\"\n        widget = self.widget\n        self.send_signal(widget.Inputs.data, self.iris)\n        self.assertEqual(widget.data, self.iris)\n        self.send_signal(widget.Inputs.data, None)\n        self.assertEqual(widget.data, None)\n\n    def test_input_reference(self):\n        \"\"\"Check widget's reference with reference on the input\"\"\"\n        widget = self.widget\n        self.assertEqual(widget.reference, None)\n        self.send_signal(widget.Inputs.reference, self.iris)\n        self.assertEqual(widget.reference, self.iris)\n\n    def test_input_reference_disconnect(self):\n        \"\"\"Check reference after disconnecting reference on the input\"\"\"\n        widget = self.widget\n        self.send_signal(widget.Inputs.data, self.iris)\n        self.send_signal(widget.Inputs.reference, self.iris)\n        self.assertEqual(widget.reference, self.iris)\n        self.send_signal(widget.Inputs.reference, None)\n        self.assertEqual(widget.reference, None)\n        widget.apply_button.button.click()\n        self.assertIsNone(self.get_output())\n\n    def test_output_neighbors(self):\n        \"\"\"Check if neighbors are on the output after apply\"\"\"\n        widget = self.widget\n        self.assertIsNone(self.get_output())\n        self.send_signals(((widget.Inputs.data, self.iris),\n                           (widget.Inputs.reference, self.iris[:10])))\n        widget.apply_button.button.click()\n        self.assertIsNotNone(self.get_output())\n        self.assertIsInstance(self.get_output(), Table)\n        self.assertTrue(all([i in self.iris.ids for i in\n                             self.get_output(widget.Outputs.data).ids])\n                        )\n\n    def test_settings(self):\n        \"\"\"Check neighbors for various distance metrics\"\"\"\n        widget = self.widget\n        settings = [\n            ParameterMapping(\"\", widget.controls.distance_index, METRICS),\n            ParameterMapping(\"\", widget.controls.n_neighbors)]\n        for setting in settings:\n            for val in setting.values:\n                self.send_signal(widget.Inputs.data, self.iris)\n                self.send_signal(widget.Inputs.reference, self.iris[:10])\n                setting.set_value(val)\n                widget.apply_button.button.click()\n                if METRICS[widget.distance_index][0] != \"Jaccard\" \\\n                        and widget.n_neighbors != 0:\n                    self.assertIsNotNone(self.get_output())\n\n    def test_exclude_reference(self):\n        \"\"\"Check neighbors when reference is excluded\"\"\"\n        widget = self.widget\n        reference = self.iris[:5]\n        self.send_signal(widget.Inputs.data, self.iris)\n        self.send_signal(widget.Inputs.reference, reference)\n        self.widget.exclude_reference = True\n        widget.apply_button.button.click()\n        neighbors = self.get_output(widget.Outputs.data)\n        for inst in reference:\n            self.assertNotIn(inst, neighbors)\n\n    def test_similarity(self):\n        widget = self.widget\n        reference = self.iris[:10]\n        self.send_signal(widget.Inputs.data, self.iris)\n        self.send_signal(widget.Inputs.reference, reference)\n        widget.apply_button.button.click()\n        neighbors = self.get_output()\n        self.assertEqual(self.iris.domain.attributes,\n                         neighbors.domain.attributes)\n        self.assertEqual(self.iris.domain.class_vars,\n                         neighbors.domain.class_vars)\n        self.assertIn(\"distance\", neighbors.domain)\n        self.assertTrue(all(100 >= ins[\"distance\"] >= 0 for ins in neighbors))\n\n    def test_missing_values(self):\n        widget = self.widget\n        data = Table(\"iris\")\n        reference = data[:3]\n        with data.unlocked():\n            data.X[0:10, 0] = np.nan\n        self.send_signal(widget.Inputs.data, self.iris)\n        self.send_signal(widget.Inputs.reference, reference)\n        widget.apply_button.button.click()\n        self.assertIsNotNone(self.get_output())\n\n    def test_compute_distances_apply_called(self):\n        \"\"\"Check compute distances and apply are called when receiving signal\"\"\"\n        widget = self.widget\n        cdist = widget.compute_distances = Mock()\n        def_commit = widget.commit.now = Mock()\n        self.widget.auto_apply = False\n        data = Table(\"iris\")\n        self.send_signal(widget.Inputs.data, data)\n        cdist.assert_called()\n        def_commit.assert_called()\n        cdist.reset_mock()\n        def_commit.reset_mock()\n\n        self.send_signal(widget.Inputs.reference, data[:10])\n        cdist.assert_called()\n        def_commit.assert_called()\n        cdist.reset_mock()\n        def_commit.reset_mock()\n\n        self.send_signals([(widget.Inputs.data, data),\n                           (widget.Inputs.reference, data[:10])])\n        self.assertEqual(cdist.call_count, 1)\n        self.assertEqual(def_commit.call_count, 1)\n\n    def test_compute_distances_calls_distance(self):\n        widget = self.widget\n        widget.distance_index = 2\n        dists = np.random.random((10, 5))\n        distance = Mock(return_value=dists)\n        try:\n            orig_metrics = METRICS[widget.distance_index]\n            METRICS[widget.distance_index] = (\"foo\", distance)\n            data = Table(\"iris\")\n            data, refs = data[:10], data[-5:]\n            self.send_signals([(widget.Inputs.data, data),\n                               (widget.Inputs.reference, refs)])\n            # false positive, pylint: disable=unsubscriptable-object\n            d1, d2 = distance.call_args[0]\n            np.testing.assert_almost_equal(d1.X, data.X)\n            np.testing.assert_almost_equal(d2.X, refs.X)\n            # false positive, pylint: disable=no-member\n            np.testing.assert_almost_equal(widget.distances, dists.min(axis=1))\n        finally:\n            METRICS[widget.distance_index] = orig_metrics\n\n    def test_compute_distances_distance_no_data(self):\n        widget = self.widget\n        distance = Mock()\n        try:\n            orig_metrics = METRICS[widget.distance_index]\n            METRICS[widget.distance_index] = (\"foo\", distance)\n            data = Table(\"iris\")\n            data, refs = data[:10], data[-5:]\n            self.send_signals([(widget.Inputs.data, data),\n                               (widget.Inputs.reference, None)])\n            distance.assert_not_called()\n            self.assertIsNone(widget.distances)\n            self.send_signal(widget.Inputs.data, data)\n            distance.assert_not_called()\n            self.assertIsNone(widget.distances)\n            self.send_signals([(widget.Inputs.data, None),\n                               (widget.Inputs.reference, refs)])\n            distance.assert_not_called()\n            self.assertIsNone(widget.distances)\n            self.send_signals([(widget.Inputs.data, data),\n                               (widget.Inputs.reference, None)])\n            distance.assert_not_called()\n            self.assertIsNone(widget.distances)\n\n            data, refs = data[:0], data[:0]\n            self.send_signals([(widget.Inputs.data, data),\n                               (widget.Inputs.reference, None)])\n            distance.assert_not_called()\n            self.assertIsNone(widget.distances)\n        finally:\n            METRICS[widget.distance_index] = orig_metrics\n\n    def test_compute_indices_without_reference(self):\n        widget = self.widget\n        widget.limit_neighbours = True\n\n        # Indices for easier reading: 0 1  2  3  4  5  6  7  8  9 10 11 12\n        widget.distances = np.array([4, 1, 7, 0, 5, 2, 4, 0, 2, 2, 2, 9, 8])\n\n        widget.data = Mock()\n        widget.data.ids = np.arange(13)\n        widget.reference = Mock()\n        widget.reference.ids = np.array([1, 3])\n\n        widget.n_neighbors = 5\n        self.assertEqual(sorted(widget._compute_indices()), [5, 7, 8, 9, 10])\n\n        widget.n_neighbors = 1\n        self.assertEqual(list(widget._compute_indices()), [7])\n\n        widget.n_neighbors = 3\n        ind = set(widget._compute_indices())\n        self.assertEqual(len(ind), 3)\n        self.assertIn(7, ind)\n        self.assertTrue(len({5, 8, 9, 10} & ind) == 2)\n\n        widget.n_neighbors = 100\n        self.assertEqual(\n            sorted(widget._compute_indices()),\n            [0, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12])\n\n        widget.n_neighbors = 10\n        self.assertEqual(\n            sorted(widget._compute_indices()),\n            [0, 2, 4, 5, 6, 7, 8, 9, 10, 12])\n\n        widget.limit_neighbours = False\n        self.assertEqual(\n            sorted(widget._compute_indices()),\n            [0, 2, 4, 5, 6, 7, 8, 9, 10, 12])\n\n    def test_data_with_similarity(self):\n        widget = self.widget\n        indices = np.array([5, 10, 15, 100])\n\n        data = Table(\"iris\")\n        widget.data = data\n\n        widget.distances = np.arange(1000, 1150).astype(float)\n        neighbours = widget._data_with_similarity(indices)\n        self.assertEqual(neighbours.metas.shape, (4, 1))\n        np.testing.assert_almost_equal(\n            neighbours.metas.flatten(), indices + 1000)\n        np.testing.assert_almost_equal(neighbours.X, data.X[indices])\n\n        domain = data.domain\n        domain2 = Domain([domain[2]], domain.class_var, metas=domain[:2])\n        data2 = data.transform(domain2)\n        widget.data = data2\n\n        widget.distances = np.arange(1000, 1150).astype(float)\n        neighbours = widget._data_with_similarity(indices)\n        self.assertEqual(len(neighbours.domain.metas), 3)\n        self.assertEqual(neighbours.metas.shape, (4, 3))\n        np.testing.assert_almost_equal(\n            neighbours.get_column(\"distance\"), indices + 1000)\n        np.testing.assert_almost_equal(neighbours.X, data2.X[indices])\n\n    def test_apply(self):\n        widget = self.widget\n        widget.auto_apply = True\n        data = Table(\"iris\")\n        indices = np.array([5, 10, 15, 100])\n\n        widget._compute_indices = lambda: \\\n            indices if widget.distances is not None else None\n        self.send_signal(widget.Inputs.data, data)\n        self.send_signal(widget.Inputs.reference, data[42:43])\n        neigh = self.get_output(widget.Outputs.data)\n        np.testing.assert_almost_equal(neigh.X, data.X[indices])\n        np.testing.assert_almost_equal(\n            neigh.metas.flatten(), widget.distances[indices])\n\n    def test_all_equal_ref(self):\n        widget = self.widget\n        widget.auto_apply = True\n\n        data = Table(\"iris\")\n        self.send_signal(widget.Inputs.data, data[:10])\n        self.send_signal(widget.Inputs.reference, data[:10])\n        self.assertTrue(widget.Warning.all_data_as_reference.is_shown())\n        self.assertFalse(widget.Info.removed_references.is_shown())\n        self.assertIsNone(self.get_output(widget.Outputs.data))\n\n        self.send_signal(widget.Inputs.data, data[:15])\n        self.assertFalse(widget.Warning.all_data_as_reference.is_shown())\n        self.assertTrue(widget.Info.removed_references.is_shown())\n        self.assertIsNotNone(self.get_output(widget.Outputs.data))\n\n    def test_different_domains(self):\n        \"\"\"\n        Test weather widget show error when data and a reference have different\n        domains.\n        \"\"\"\n        w = self.widget\n\n        domain = Domain([ContinuousVariable(\"a\")])\n        domain_ref = Domain([ContinuousVariable(\"b\")])\n        data = Table(domain, np.random.rand(2, len(domain.variables)))\n        reference = Table(\n            domain_ref, np.random.rand(1, len(domain_ref.variables)))\n\n        # no error if one or both of the signals is missing\n        self.send_signal(w.Inputs.data, data)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n\n        self.send_signal(w.Inputs.data, None)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n\n        self.send_signal(w.Inputs.reference, data[:1])\n        self.assertFalse(w.Error.diff_domains.is_shown())\n\n        # same domain - no error\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, data[:1])\n        self.assertFalse(w.Error.diff_domains.is_shown())\n\n        # one attribute different attribute name\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, reference)\n        self.assertTrue(w.Error.diff_domains.is_shown())\n\n        # different number of attributes\n        domain_ref = Domain([ContinuousVariable(\"a\"), ContinuousVariable(\"b\")])\n        reference = Table(domain_ref, np.random.rand(1, len(domain_ref.variables)))\n\n        # error disappears when data is set to None\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, reference)\n        self.assertTrue(w.Error.diff_domains.is_shown())\n\n        self.send_signal(w.Inputs.data, None)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n\n        # error disappears when reference is set to None\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, reference)\n        self.assertTrue(w.Error.diff_domains.is_shown())\n\n        self.send_signal(w.Inputs.reference, None)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n\n    def test_different_metas(self):\n        \"\"\"\n        Test weather widget do not show error when data and a reference have\n        domain that differ only in metas\n        \"\"\"\n        w = self.widget\n\n        domain = Domain([ContinuousVariable(\"a\"), ContinuousVariable(\"b\")],\n                        metas=[ContinuousVariable(\"c\")])\n        data = Table(\n            domain, np.random.rand(15, len(domain.attributes)),\n            metas=np.random.rand(15, len(domain.metas)))\n\n        # same domain with same metas no error\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, data[:1])\n        self.assertFalse(w.Error.diff_domains.is_shown())\n        output = self.get_output(w.Outputs.data)\n        self.assertEqual(10, len(output))\n\n        # same domain with different metas no error\n        domain_ref = Domain(domain.attributes,\n                            metas=[ContinuousVariable(\"d\")])\n        reference = Table(\n            domain_ref, np.random.rand(1, len(domain_ref.attributes)),\n            metas=np.random.rand(1, len(domain.metas)))\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, reference)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n        output = self.get_output(w.Outputs.data)\n        self.assertEqual(10, len(output))\n\n        # same domain with different order - no error\n        domain_ref = Domain(domain.attributes[::-1])\n        reference = Table(\n            domain_ref, np.random.rand(1, len(domain_ref.attributes)))\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, reference)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n        output = self.get_output(w.Outputs.data)\n        self.assertEqual(10, len(output))\n\n        # same domain with different number of metas no error\n        domain_ref = Domain(\n            domain.attributes,\n            metas=[ContinuousVariable(\"d\"), ContinuousVariable(\"e\")])\n        reference = Table(\n            domain_ref, np.random.rand(1, len(domain_ref.attributes)),\n            metas=np.random.rand(1, len(domain_ref.metas)))\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, reference)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n        output = self.get_output(w.Outputs.data)\n        self.assertEqual(10, len(output))\n\n        # different domain with same metas - error shown\n        domain_ref = Domain(domain.attributes + (ContinuousVariable(\"e\"),),\n                            metas=[ContinuousVariable(\"c\")])\n        reference = Table(\n            domain_ref, np.random.rand(1, len(domain_ref.attributes)),\n            metas=np.random.rand(1, len(domain_ref.metas)))\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, reference)\n        self.assertTrue(w.Error.diff_domains.is_shown())\n        output = self.get_output(w.Outputs.data)\n        self.assertIsNone(output)\n\n    def test_different_domains_same_names(self):\n        \"\"\"\n        Here we create two domains with same names of attributes - the case\n        with image-analytics (two embeddings). Widget must not show the\n        error in this case.\n        \"\"\"\n        w = self.widget\n        domain1 = Domain([ContinuousVariable(\"a\"), ContinuousVariable(\"b\")],\n                         metas=[ContinuousVariable(\"c\")])\n        domain2 = Domain([ContinuousVariable(\"a\"), ContinuousVariable(\"b\")],\n                         metas=[ContinuousVariable(\"d\")])\n        data = Table(\n            domain1, np.random.rand(15, len(domain1.attributes)),\n            metas=np.random.rand(15, len(domain1.metas)))\n        ref = Table(\n            domain2, np.random.rand(2, len(domain2.attributes)),\n            metas=np.random.rand(2, len(domain2.metas)))\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, ref)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n        output = self.get_output(w.Outputs.data)\n        self.assertEqual(10, len(output))\n\n    def test_n_neighbours_spin_max(self):\n        w = self.widget\n        sb = w.controls.n_neighbors\n        default = sb.maximum()\n        self.send_signal(w.Inputs.data, self.iris)\n        self.assertEqual(sb.maximum(), len(self.iris))\n        self.send_signal(w.Inputs.data, self.iris[:20])\n        self.assertEqual(sb.maximum(), 20)\n        self.send_signal(w.Inputs.data, None)\n        self.assertEqual(sb.maximum(), default)\n\n    def test_inherited_table(self):\n        # pylint: disable=abstract-method\n        class Table2(Table):\n            pass\n\n        data = Table2(self.iris)\n        self.send_signal(self.widget.Inputs.data, data)\n        self.send_signal(self.widget.Inputs.reference, data[0:1])\n        self.assertIsInstance(self.get_output(self.widget.Outputs.data), Table2)\n\n    def test_order_by_distance(self):\n        domain = Domain([ContinuousVariable(x) for x in \"ab\"])\n        reference = Table.from_numpy(domain, [[1, 0]])\n        data = Table.from_numpy(domain, [[1, 0.1], [2, 0], [1, 0], [0, 0.1], [0.1, 0]])\n        self.send_signal(self.widget.Inputs.data, data)\n        self.send_signal(self.widget.Inputs.reference, reference)\n\n        output = self.get_output(self.widget.Outputs.data)\n        expected = [[1, 0], [1, 0.1], [0.1, 0], [2, 0], [0, 0.1]]\n        np.testing.assert_array_equal(output.X, expected)\n        dst = output.get_column(\"distance\").tolist()\n        self.assertTrue(dst == sorted(dst))  # check distance in ascending order\n\n        # test on bigger set\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.send_signal(self.widget.Inputs.reference, self.iris[:1])\n        dst = self.get_output(self.widget.Outputs.data).get_column(\"distance\").tolist()\n        self.assertTrue(dst == sorted(dst))",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 455,
                            "end_lineno": 472,
                            "parent": {
                                "file_name": "Orange/widgets/data/tests/test_owneighbors.py",
                                "type": "class",
                                "name": "TestOWNeighbors",
                                "content": "class TestOWNeighbors(WidgetTest):\n    def setUp(self):\n        self.widget = self.create_widget(OWNeighbors,\n                                         stored_settings={\"auto_apply\": False})\n        self.iris = Table(\"iris\")\n\n    def test_input_data(self):\n        \"\"\"Check widget's data with data on the input\"\"\"\n        widget = self.widget\n        self.assertEqual(widget.data, None)\n        self.send_signal(widget.Inputs.data, self.iris)\n        self.assertEqual(widget.data, self.iris)\n\n    def test_input_data_disconnect(self):\n        \"\"\"Check widget's data after disconnecting data on the input\"\"\"\n        widget = self.widget\n        self.send_signal(widget.Inputs.data, self.iris)\n        self.assertEqual(widget.data, self.iris)\n        self.send_signal(widget.Inputs.data, None)\n        self.assertEqual(widget.data, None)\n\n    def test_input_reference(self):\n        \"\"\"Check widget's reference with reference on the input\"\"\"\n        widget = self.widget\n        self.assertEqual(widget.reference, None)\n        self.send_signal(widget.Inputs.reference, self.iris)\n        self.assertEqual(widget.reference, self.iris)\n\n    def test_input_reference_disconnect(self):\n        \"\"\"Check reference after disconnecting reference on the input\"\"\"\n        widget = self.widget\n        self.send_signal(widget.Inputs.data, self.iris)\n        self.send_signal(widget.Inputs.reference, self.iris)\n        self.assertEqual(widget.reference, self.iris)\n        self.send_signal(widget.Inputs.reference, None)\n        self.assertEqual(widget.reference, None)\n        widget.apply_button.button.click()\n        self.assertIsNone(self.get_output())\n\n    def test_output_neighbors(self):\n        \"\"\"Check if neighbors are on the output after apply\"\"\"\n        widget = self.widget\n        self.assertIsNone(self.get_output())\n        self.send_signals(((widget.Inputs.data, self.iris),\n                           (widget.Inputs.reference, self.iris[:10])))\n        widget.apply_button.button.click()\n        self.assertIsNotNone(self.get_output())\n        self.assertIsInstance(self.get_output(), Table)\n        self.assertTrue(all([i in self.iris.ids for i in\n                             self.get_output(widget.Outputs.data).ids])\n                        )\n\n    def test_settings(self):\n        \"\"\"Check neighbors for various distance metrics\"\"\"\n        widget = self.widget\n        settings = [\n            ParameterMapping(\"\", widget.controls.distance_index, METRICS),\n            ParameterMapping(\"\", widget.controls.n_neighbors)]\n        for setting in settings:\n            for val in setting.values:\n                self.send_signal(widget.Inputs.data, self.iris)\n                self.send_signal(widget.Inputs.reference, self.iris[:10])\n                setting.set_value(val)\n                widget.apply_button.button.click()\n                if METRICS[widget.distance_index][0] != \"Jaccard\" \\\n                        and widget.n_neighbors != 0:\n                    self.assertIsNotNone(self.get_output())\n\n    def test_exclude_reference(self):\n        \"\"\"Check neighbors when reference is excluded\"\"\"\n        widget = self.widget\n        reference = self.iris[:5]\n        self.send_signal(widget.Inputs.data, self.iris)\n        self.send_signal(widget.Inputs.reference, reference)\n        self.widget.exclude_reference = True\n        widget.apply_button.button.click()\n        neighbors = self.get_output(widget.Outputs.data)\n        for inst in reference:\n            self.assertNotIn(inst, neighbors)\n\n    def test_similarity(self):\n        widget = self.widget\n        reference = self.iris[:10]\n        self.send_signal(widget.Inputs.data, self.iris)\n        self.send_signal(widget.Inputs.reference, reference)\n        widget.apply_button.button.click()\n        neighbors = self.get_output()\n        self.assertEqual(self.iris.domain.attributes,\n                         neighbors.domain.attributes)\n        self.assertEqual(self.iris.domain.class_vars,\n                         neighbors.domain.class_vars)\n        self.assertIn(\"distance\", neighbors.domain)\n        self.assertTrue(all(100 >= ins[\"distance\"] >= 0 for ins in neighbors))\n\n    def test_missing_values(self):\n        widget = self.widget\n        data = Table(\"iris\")\n        reference = data[:3]\n        with data.unlocked():\n            data.X[0:10, 0] = np.nan\n        self.send_signal(widget.Inputs.data, self.iris)\n        self.send_signal(widget.Inputs.reference, reference)\n        widget.apply_button.button.click()\n        self.assertIsNotNone(self.get_output())\n\n    def test_compute_distances_apply_called(self):\n        \"\"\"Check compute distances and apply are called when receiving signal\"\"\"\n        widget = self.widget\n        cdist = widget.compute_distances = Mock()\n        def_commit = widget.commit.now = Mock()\n        self.widget.auto_apply = False\n        data = Table(\"iris\")\n        self.send_signal(widget.Inputs.data, data)\n        cdist.assert_called()\n        def_commit.assert_called()\n        cdist.reset_mock()\n        def_commit.reset_mock()\n\n        self.send_signal(widget.Inputs.reference, data[:10])\n        cdist.assert_called()\n        def_commit.assert_called()\n        cdist.reset_mock()\n        def_commit.reset_mock()\n\n        self.send_signals([(widget.Inputs.data, data),\n                           (widget.Inputs.reference, data[:10])])\n        self.assertEqual(cdist.call_count, 1)\n        self.assertEqual(def_commit.call_count, 1)\n\n    def test_compute_distances_calls_distance(self):\n        widget = self.widget\n        widget.distance_index = 2\n        dists = np.random.random((10, 5))\n        distance = Mock(return_value=dists)\n        try:\n            orig_metrics = METRICS[widget.distance_index]\n            METRICS[widget.distance_index] = (\"foo\", distance)\n            data = Table(\"iris\")\n            data, refs = data[:10], data[-5:]\n            self.send_signals([(widget.Inputs.data, data),\n                               (widget.Inputs.reference, refs)])\n            # false positive, pylint: disable=unsubscriptable-object\n            d1, d2 = distance.call_args[0]\n            np.testing.assert_almost_equal(d1.X, data.X)\n            np.testing.assert_almost_equal(d2.X, refs.X)\n            # false positive, pylint: disable=no-member\n            np.testing.assert_almost_equal(widget.distances, dists.min(axis=1))\n        finally:\n            METRICS[widget.distance_index] = orig_metrics\n\n    def test_compute_distances_distance_no_data(self):\n        widget = self.widget\n        distance = Mock()\n        try:\n            orig_metrics = METRICS[widget.distance_index]\n            METRICS[widget.distance_index] = (\"foo\", distance)\n            data = Table(\"iris\")\n            data, refs = data[:10], data[-5:]\n            self.send_signals([(widget.Inputs.data, data),\n                               (widget.Inputs.reference, None)])\n            distance.assert_not_called()\n            self.assertIsNone(widget.distances)\n            self.send_signal(widget.Inputs.data, data)\n            distance.assert_not_called()\n            self.assertIsNone(widget.distances)\n            self.send_signals([(widget.Inputs.data, None),\n                               (widget.Inputs.reference, refs)])\n            distance.assert_not_called()\n            self.assertIsNone(widget.distances)\n            self.send_signals([(widget.Inputs.data, data),\n                               (widget.Inputs.reference, None)])\n            distance.assert_not_called()\n            self.assertIsNone(widget.distances)\n\n            data, refs = data[:0], data[:0]\n            self.send_signals([(widget.Inputs.data, data),\n                               (widget.Inputs.reference, None)])\n            distance.assert_not_called()\n            self.assertIsNone(widget.distances)\n        finally:\n            METRICS[widget.distance_index] = orig_metrics\n\n    def test_compute_indices_without_reference(self):\n        widget = self.widget\n        widget.limit_neighbours = True\n\n        # Indices for easier reading: 0 1  2  3  4  5  6  7  8  9 10 11 12\n        widget.distances = np.array([4, 1, 7, 0, 5, 2, 4, 0, 2, 2, 2, 9, 8])\n\n        widget.data = Mock()\n        widget.data.ids = np.arange(13)\n        widget.reference = Mock()\n        widget.reference.ids = np.array([1, 3])\n\n        widget.n_neighbors = 5\n        self.assertEqual(sorted(widget._compute_indices()), [5, 7, 8, 9, 10])\n\n        widget.n_neighbors = 1\n        self.assertEqual(list(widget._compute_indices()), [7])\n\n        widget.n_neighbors = 3\n        ind = set(widget._compute_indices())\n        self.assertEqual(len(ind), 3)\n        self.assertIn(7, ind)\n        self.assertTrue(len({5, 8, 9, 10} & ind) == 2)\n\n        widget.n_neighbors = 100\n        self.assertEqual(\n            sorted(widget._compute_indices()),\n            [0, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12])\n\n        widget.n_neighbors = 10\n        self.assertEqual(\n            sorted(widget._compute_indices()),\n            [0, 2, 4, 5, 6, 7, 8, 9, 10, 12])\n\n        widget.limit_neighbours = False\n        self.assertEqual(\n            sorted(widget._compute_indices()),\n            [0, 2, 4, 5, 6, 7, 8, 9, 10, 12])\n\n    def test_data_with_similarity(self):\n        widget = self.widget\n        indices = np.array([5, 10, 15, 100])\n\n        data = Table(\"iris\")\n        widget.data = data\n\n        widget.distances = np.arange(1000, 1150).astype(float)\n        neighbours = widget._data_with_similarity(indices)\n        self.assertEqual(neighbours.metas.shape, (4, 1))\n        np.testing.assert_almost_equal(\n            neighbours.metas.flatten(), indices + 1000)\n        np.testing.assert_almost_equal(neighbours.X, data.X[indices])\n\n        domain = data.domain\n        domain2 = Domain([domain[2]], domain.class_var, metas=domain[:2])\n        data2 = data.transform(domain2)\n        widget.data = data2\n\n        widget.distances = np.arange(1000, 1150).astype(float)\n        neighbours = widget._data_with_similarity(indices)\n        self.assertEqual(len(neighbours.domain.metas), 3)\n        self.assertEqual(neighbours.metas.shape, (4, 3))\n        np.testing.assert_almost_equal(\n            neighbours.get_column(\"distance\"), indices + 1000)\n        np.testing.assert_almost_equal(neighbours.X, data2.X[indices])\n\n    def test_apply(self):\n        widget = self.widget\n        widget.auto_apply = True\n        data = Table(\"iris\")\n        indices = np.array([5, 10, 15, 100])\n\n        widget._compute_indices = lambda: \\\n            indices if widget.distances is not None else None\n        self.send_signal(widget.Inputs.data, data)\n        self.send_signal(widget.Inputs.reference, data[42:43])\n        neigh = self.get_output(widget.Outputs.data)\n        np.testing.assert_almost_equal(neigh.X, data.X[indices])\n        np.testing.assert_almost_equal(\n            neigh.metas.flatten(), widget.distances[indices])\n\n    def test_all_equal_ref(self):\n        widget = self.widget\n        widget.auto_apply = True\n\n        data = Table(\"iris\")\n        self.send_signal(widget.Inputs.data, data[:10])\n        self.send_signal(widget.Inputs.reference, data[:10])\n        self.assertTrue(widget.Warning.all_data_as_reference.is_shown())\n        self.assertFalse(widget.Info.removed_references.is_shown())\n        self.assertIsNone(self.get_output(widget.Outputs.data))\n\n        self.send_signal(widget.Inputs.data, data[:15])\n        self.assertFalse(widget.Warning.all_data_as_reference.is_shown())\n        self.assertTrue(widget.Info.removed_references.is_shown())\n        self.assertIsNotNone(self.get_output(widget.Outputs.data))\n\n    def test_different_domains(self):\n        \"\"\"\n        Test weather widget show error when data and a reference have different\n        domains.\n        \"\"\"\n        w = self.widget\n\n        domain = Domain([ContinuousVariable(\"a\")])\n        domain_ref = Domain([ContinuousVariable(\"b\")])\n        data = Table(domain, np.random.rand(2, len(domain.variables)))\n        reference = Table(\n            domain_ref, np.random.rand(1, len(domain_ref.variables)))\n\n        # no error if one or both of the signals is missing\n        self.send_signal(w.Inputs.data, data)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n\n        self.send_signal(w.Inputs.data, None)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n\n        self.send_signal(w.Inputs.reference, data[:1])\n        self.assertFalse(w.Error.diff_domains.is_shown())\n\n        # same domain - no error\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, data[:1])\n        self.assertFalse(w.Error.diff_domains.is_shown())\n\n        # one attribute different attribute name\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, reference)\n        self.assertTrue(w.Error.diff_domains.is_shown())\n\n        # different number of attributes\n        domain_ref = Domain([ContinuousVariable(\"a\"), ContinuousVariable(\"b\")])\n        reference = Table(domain_ref, np.random.rand(1, len(domain_ref.variables)))\n\n        # error disappears when data is set to None\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, reference)\n        self.assertTrue(w.Error.diff_domains.is_shown())\n\n        self.send_signal(w.Inputs.data, None)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n\n        # error disappears when reference is set to None\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, reference)\n        self.assertTrue(w.Error.diff_domains.is_shown())\n\n        self.send_signal(w.Inputs.reference, None)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n\n    def test_different_metas(self):\n        \"\"\"\n        Test weather widget do not show error when data and a reference have\n        domain that differ only in metas\n        \"\"\"\n        w = self.widget\n\n        domain = Domain([ContinuousVariable(\"a\"), ContinuousVariable(\"b\")],\n                        metas=[ContinuousVariable(\"c\")])\n        data = Table(\n            domain, np.random.rand(15, len(domain.attributes)),\n            metas=np.random.rand(15, len(domain.metas)))\n\n        # same domain with same metas no error\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, data[:1])\n        self.assertFalse(w.Error.diff_domains.is_shown())\n        output = self.get_output(w.Outputs.data)\n        self.assertEqual(10, len(output))\n\n        # same domain with different metas no error\n        domain_ref = Domain(domain.attributes,\n                            metas=[ContinuousVariable(\"d\")])\n        reference = Table(\n            domain_ref, np.random.rand(1, len(domain_ref.attributes)),\n            metas=np.random.rand(1, len(domain.metas)))\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, reference)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n        output = self.get_output(w.Outputs.data)\n        self.assertEqual(10, len(output))\n\n        # same domain with different order - no error\n        domain_ref = Domain(domain.attributes[::-1])\n        reference = Table(\n            domain_ref, np.random.rand(1, len(domain_ref.attributes)))\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, reference)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n        output = self.get_output(w.Outputs.data)\n        self.assertEqual(10, len(output))\n\n        # same domain with different number of metas no error\n        domain_ref = Domain(\n            domain.attributes,\n            metas=[ContinuousVariable(\"d\"), ContinuousVariable(\"e\")])\n        reference = Table(\n            domain_ref, np.random.rand(1, len(domain_ref.attributes)),\n            metas=np.random.rand(1, len(domain_ref.metas)))\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, reference)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n        output = self.get_output(w.Outputs.data)\n        self.assertEqual(10, len(output))\n\n        # different domain with same metas - error shown\n        domain_ref = Domain(domain.attributes + (ContinuousVariable(\"e\"),),\n                            metas=[ContinuousVariable(\"c\")])\n        reference = Table(\n            domain_ref, np.random.rand(1, len(domain_ref.attributes)),\n            metas=np.random.rand(1, len(domain_ref.metas)))\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, reference)\n        self.assertTrue(w.Error.diff_domains.is_shown())\n        output = self.get_output(w.Outputs.data)\n        self.assertIsNone(output)\n\n    def test_different_domains_same_names(self):\n        \"\"\"\n        Here we create two domains with same names of attributes - the case\n        with image-analytics (two embeddings). Widget must not show the\n        error in this case.\n        \"\"\"\n        w = self.widget\n        domain1 = Domain([ContinuousVariable(\"a\"), ContinuousVariable(\"b\")],\n                         metas=[ContinuousVariable(\"c\")])\n        domain2 = Domain([ContinuousVariable(\"a\"), ContinuousVariable(\"b\")],\n                         metas=[ContinuousVariable(\"d\")])\n        data = Table(\n            domain1, np.random.rand(15, len(domain1.attributes)),\n            metas=np.random.rand(15, len(domain1.metas)))\n        ref = Table(\n            domain2, np.random.rand(2, len(domain2.attributes)),\n            metas=np.random.rand(2, len(domain2.metas)))\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, ref)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n        output = self.get_output(w.Outputs.data)\n        self.assertEqual(10, len(output))\n\n    def test_n_neighbours_spin_max(self):\n        w = self.widget\n        sb = w.controls.n_neighbors\n        default = sb.maximum()\n        self.send_signal(w.Inputs.data, self.iris)\n        self.assertEqual(sb.maximum(), len(self.iris))\n        self.send_signal(w.Inputs.data, self.iris[:20])\n        self.assertEqual(sb.maximum(), 20)\n        self.send_signal(w.Inputs.data, None)\n        self.assertEqual(sb.maximum(), default)\n\n    def test_inherited_table(self):\n        # pylint: disable=abstract-method\n        class Table2(Table):\n            pass\n\n        data = Table2(self.iris)\n        self.send_signal(self.widget.Inputs.data, data)\n        self.send_signal(self.widget.Inputs.reference, data[0:1])\n        self.assertIsInstance(self.get_output(self.widget.Outputs.data), Table2)\n\n    def test_order_by_distance(self):\n        domain = Domain([ContinuousVariable(x) for x in \"ab\"])\n        reference = Table.from_numpy(domain, [[1, 0]])\n        data = Table.from_numpy(domain, [[1, 0.1], [2, 0], [1, 0], [0, 0.1], [0.1, 0]])\n        self.send_signal(self.widget.Inputs.data, data)\n        self.send_signal(self.widget.Inputs.reference, reference)\n\n        output = self.get_output(self.widget.Outputs.data)\n        expected = [[1, 0], [1, 0.1], [0.1, 0], [2, 0], [0, 0.1]]\n        np.testing.assert_array_equal(output.X, expected)\n        dst = output.get_column(\"distance\").tolist()\n        self.assertTrue(dst == sorted(dst))  # check distance in ascending order\n\n        # test on bigger set\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.send_signal(self.widget.Inputs.reference, self.iris[:1])\n        dst = self.get_output(self.widget.Outputs.data).get_column(\"distance\").tolist()\n        self.assertTrue(dst == sorted(dst))",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 12,
                                "end_lineno": 472,
                                "parent": null
                            }
                        }
                    ],
                    "deleted_entities": []
                }
            ]
        }
    ],
    "old_commit_hash": "9009f53bcd87ba30da69c9e73294c33e0c0aa035^",
    "new_commit_hash": "9009f53bcd87ba30da69c9e73294c33e0c0aa035",
    "commit_message": "Merge pull request #6425 from PrimozGodec/neighbours-order\n\n[ENH] Neighbours - Sort resulting instances in order according to distance",
    "commit_date": "2023-04-28T19:25:43+02:00",
    "metadata": {}
}
{
    "repo_name": "orange3",
    "new_commit_hash": "9009f53bcd87ba30da69c9e73294c33e0c0aa035",
    "test_file_codes": [
        "#pylint: disable=missing-docstring,protected-access,unsubscriptable-object\nimport unittest\nfrom unittest.mock import Mock\n\nimport numpy as np\n\nfrom Orange.data import Table, Domain, ContinuousVariable\nfrom Orange.widgets.data.owneighbors import OWNeighbors, METRICS\nfrom Orange.widgets.tests.base import WidgetTest, ParameterMapping\n\n\nclass TestOWNeighbors(WidgetTest):\n    def setUp(self):\n        self.widget = self.create_widget(OWNeighbors,\n                                         stored_settings={\"auto_apply\": False})\n        self.iris = Table(\"iris\")\n\n    def test_input_data(self):\n        \"\"\"Check widget's data with data on the input\"\"\"\n        widget = self.widget\n        self.assertEqual(widget.data, None)\n        self.send_signal(widget.Inputs.data, self.iris)\n        self.assertEqual(widget.data, self.iris)\n\n    def test_input_data_disconnect(self):\n        \"\"\"Check widget's data after disconnecting data on the input\"\"\"\n        widget = self.widget\n        self.send_signal(widget.Inputs.data, self.iris)\n        self.assertEqual(widget.data, self.iris)\n        self.send_signal(widget.Inputs.data, None)\n        self.assertEqual(widget.data, None)\n\n    def test_input_reference(self):\n        \"\"\"Check widget's reference with reference on the input\"\"\"\n        widget = self.widget\n        self.assertEqual(widget.reference, None)\n        self.send_signal(widget.Inputs.reference, self.iris)\n        self.assertEqual(widget.reference, self.iris)\n\n    def test_input_reference_disconnect(self):\n        \"\"\"Check reference after disconnecting reference on the input\"\"\"\n        widget = self.widget\n        self.send_signal(widget.Inputs.data, self.iris)\n        self.send_signal(widget.Inputs.reference, self.iris)\n        self.assertEqual(widget.reference, self.iris)\n        self.send_signal(widget.Inputs.reference, None)\n        self.assertEqual(widget.reference, None)\n        widget.apply_button.button.click()\n        self.assertIsNone(self.get_output())\n\n    def test_output_neighbors(self):\n        \"\"\"Check if neighbors are on the output after apply\"\"\"\n        widget = self.widget\n        self.assertIsNone(self.get_output())\n        self.send_signals(((widget.Inputs.data, self.iris),\n                           (widget.Inputs.reference, self.iris[:10])))\n        widget.apply_button.button.click()\n        self.assertIsNotNone(self.get_output())\n        self.assertIsInstance(self.get_output(), Table)\n        self.assertTrue(all([i in self.iris.ids for i in\n                             self.get_output(widget.Outputs.data).ids])\n                        )\n\n    def test_settings(self):\n        \"\"\"Check neighbors for various distance metrics\"\"\"\n        widget = self.widget\n        settings = [\n            ParameterMapping(\"\", widget.controls.distance_index, METRICS),\n            ParameterMapping(\"\", widget.controls.n_neighbors)]\n        for setting in settings:\n            for val in setting.values:\n                self.send_signal(widget.Inputs.data, self.iris)\n                self.send_signal(widget.Inputs.reference, self.iris[:10])\n                setting.set_value(val)\n                widget.apply_button.button.click()\n                if METRICS[widget.distance_index][0] != \"Jaccard\" \\\n                        and widget.n_neighbors != 0:\n                    self.assertIsNotNone(self.get_output())\n\n    def test_exclude_reference(self):\n        \"\"\"Check neighbors when reference is excluded\"\"\"\n        widget = self.widget\n        reference = self.iris[:5]\n        self.send_signal(widget.Inputs.data, self.iris)\n        self.send_signal(widget.Inputs.reference, reference)\n        self.widget.exclude_reference = True\n        widget.apply_button.button.click()\n        neighbors = self.get_output(widget.Outputs.data)\n        for inst in reference:\n            self.assertNotIn(inst, neighbors)\n\n    def test_similarity(self):\n        widget = self.widget\n        reference = self.iris[:10]\n        self.send_signal(widget.Inputs.data, self.iris)\n        self.send_signal(widget.Inputs.reference, reference)\n        widget.apply_button.button.click()\n        neighbors = self.get_output()\n        self.assertEqual(self.iris.domain.attributes,\n                         neighbors.domain.attributes)\n        self.assertEqual(self.iris.domain.class_vars,\n                         neighbors.domain.class_vars)\n        self.assertIn(\"distance\", neighbors.domain)\n        self.assertTrue(all(100 >= ins[\"distance\"] >= 0 for ins in neighbors))\n\n    def test_missing_values(self):\n        widget = self.widget\n        data = Table(\"iris\")\n        reference = data[:3]\n        with data.unlocked():\n            data.X[0:10, 0] = np.nan\n        self.send_signal(widget.Inputs.data, self.iris)\n        self.send_signal(widget.Inputs.reference, reference)\n        widget.apply_button.button.click()\n        self.assertIsNotNone(self.get_output())\n\n    def test_compute_distances_apply_called(self):\n        \"\"\"Check compute distances and apply are called when receiving signal\"\"\"\n        widget = self.widget\n        cdist = widget.compute_distances = Mock()\n        def_commit = widget.commit.now = Mock()\n        self.widget.auto_apply = False\n        data = Table(\"iris\")\n        self.send_signal(widget.Inputs.data, data)\n        cdist.assert_called()\n        def_commit.assert_called()\n        cdist.reset_mock()\n        def_commit.reset_mock()\n\n        self.send_signal(widget.Inputs.reference, data[:10])\n        cdist.assert_called()\n        def_commit.assert_called()\n        cdist.reset_mock()\n        def_commit.reset_mock()\n\n        self.send_signals([(widget.Inputs.data, data),\n                           (widget.Inputs.reference, data[:10])])\n        self.assertEqual(cdist.call_count, 1)\n        self.assertEqual(def_commit.call_count, 1)\n\n    def test_compute_distances_calls_distance(self):\n        widget = self.widget\n        widget.distance_index = 2\n        dists = np.random.random((10, 5))\n        distance = Mock(return_value=dists)\n        try:\n            orig_metrics = METRICS[widget.distance_index]\n            METRICS[widget.distance_index] = (\"foo\", distance)\n            data = Table(\"iris\")\n            data, refs = data[:10], data[-5:]\n            self.send_signals([(widget.Inputs.data, data),\n                               (widget.Inputs.reference, refs)])\n            # false positive, pylint: disable=unsubscriptable-object\n            d1, d2 = distance.call_args[0]\n            np.testing.assert_almost_equal(d1.X, data.X)\n            np.testing.assert_almost_equal(d2.X, refs.X)\n            # false positive, pylint: disable=no-member\n            np.testing.assert_almost_equal(widget.distances, dists.min(axis=1))\n        finally:\n            METRICS[widget.distance_index] = orig_metrics\n\n    def test_compute_distances_distance_no_data(self):\n        widget = self.widget\n        distance = Mock()\n        try:\n            orig_metrics = METRICS[widget.distance_index]\n            METRICS[widget.distance_index] = (\"foo\", distance)\n            data = Table(\"iris\")\n            data, refs = data[:10], data[-5:]\n            self.send_signals([(widget.Inputs.data, data),\n                               (widget.Inputs.reference, None)])\n            distance.assert_not_called()\n            self.assertIsNone(widget.distances)\n            self.send_signal(widget.Inputs.data, data)\n            distance.assert_not_called()\n            self.assertIsNone(widget.distances)\n            self.send_signals([(widget.Inputs.data, None),\n                               (widget.Inputs.reference, refs)])\n            distance.assert_not_called()\n            self.assertIsNone(widget.distances)\n            self.send_signals([(widget.Inputs.data, data),\n                               (widget.Inputs.reference, None)])\n            distance.assert_not_called()\n            self.assertIsNone(widget.distances)\n\n            data, refs = data[:0], data[:0]\n            self.send_signals([(widget.Inputs.data, data),\n                               (widget.Inputs.reference, None)])\n            distance.assert_not_called()\n            self.assertIsNone(widget.distances)\n        finally:\n            METRICS[widget.distance_index] = orig_metrics\n\n    def test_compute_indices_without_reference(self):\n        widget = self.widget\n        widget.limit_neighbours = True\n\n        # Indices for easier reading: 0 1  2  3  4  5  6  7  8  9 10 11 12\n        widget.distances = np.array([4, 1, 7, 0, 5, 2, 4, 0, 2, 2, 2, 9, 8])\n\n        widget.data = Mock()\n        widget.data.ids = np.arange(13)\n        widget.reference = Mock()\n        widget.reference.ids = np.array([1, 3])\n\n        widget.n_neighbors = 5\n        self.assertEqual(sorted(widget._compute_indices()), [5, 7, 8, 9, 10])\n\n        widget.n_neighbors = 1\n        self.assertEqual(list(widget._compute_indices()), [7])\n\n        widget.n_neighbors = 3\n        ind = set(widget._compute_indices())\n        self.assertEqual(len(ind), 3)\n        self.assertIn(7, ind)\n        self.assertTrue(len({5, 8, 9, 10} & ind) == 2)\n\n        widget.n_neighbors = 100\n        self.assertEqual(\n            sorted(widget._compute_indices()),\n            [0, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12])\n\n        widget.n_neighbors = 10\n        self.assertEqual(\n            sorted(widget._compute_indices()),\n            [0, 2, 4, 5, 6, 7, 8, 9, 10, 12])\n\n        widget.limit_neighbours = False\n        self.assertEqual(\n            sorted(widget._compute_indices()),\n            [0, 2, 4, 5, 6, 7, 8, 9, 10, 12])\n\n    def test_data_with_similarity(self):\n        widget = self.widget\n        indices = np.array([5, 10, 15, 100])\n\n        data = Table(\"iris\")\n        widget.data = data\n\n        widget.distances = np.arange(1000, 1150).astype(float)\n        neighbours = widget._data_with_similarity(indices)\n        self.assertEqual(neighbours.metas.shape, (4, 1))\n        np.testing.assert_almost_equal(\n            neighbours.metas.flatten(), indices + 1000)\n        np.testing.assert_almost_equal(neighbours.X, data.X[indices])\n\n        domain = data.domain\n        domain2 = Domain([domain[2]], domain.class_var, metas=domain[:2])\n        data2 = data.transform(domain2)\n        widget.data = data2\n\n        widget.distances = np.arange(1000, 1150).astype(float)\n        neighbours = widget._data_with_similarity(indices)\n        self.assertEqual(len(neighbours.domain.metas), 3)\n        self.assertEqual(neighbours.metas.shape, (4, 3))\n        np.testing.assert_almost_equal(\n            neighbours.get_column(\"distance\"), indices + 1000)\n        np.testing.assert_almost_equal(neighbours.X, data2.X[indices])\n\n    def test_apply(self):\n        widget = self.widget\n        widget.auto_apply = True\n        data = Table(\"iris\")\n        indices = np.array([5, 10, 15, 100])\n\n        widget._compute_indices = lambda: \\\n            indices if widget.distances is not None else None\n        self.send_signal(widget.Inputs.data, data)\n        self.send_signal(widget.Inputs.reference, data[42:43])\n        neigh = self.get_output(widget.Outputs.data)\n        np.testing.assert_almost_equal(neigh.X, data.X[indices])\n        np.testing.assert_almost_equal(\n            neigh.metas.flatten(), widget.distances[indices])\n\n    def test_all_equal_ref(self):\n        widget = self.widget\n        widget.auto_apply = True\n\n        data = Table(\"iris\")\n        self.send_signal(widget.Inputs.data, data[:10])\n        self.send_signal(widget.Inputs.reference, data[:10])\n        self.assertTrue(widget.Warning.all_data_as_reference.is_shown())\n        self.assertFalse(widget.Info.removed_references.is_shown())\n        self.assertIsNone(self.get_output(widget.Outputs.data))\n\n        self.send_signal(widget.Inputs.data, data[:15])\n        self.assertFalse(widget.Warning.all_data_as_reference.is_shown())\n        self.assertTrue(widget.Info.removed_references.is_shown())\n        self.assertIsNotNone(self.get_output(widget.Outputs.data))\n\n    def test_different_domains(self):\n        \"\"\"\n        Test weather widget show error when data and a reference have different\n        domains.\n        \"\"\"\n        w = self.widget\n\n        domain = Domain([ContinuousVariable(\"a\")])\n        domain_ref = Domain([ContinuousVariable(\"b\")])\n        data = Table(domain, np.random.rand(2, len(domain.variables)))\n        reference = Table(\n            domain_ref, np.random.rand(1, len(domain_ref.variables)))\n\n        # no error if one or both of the signals is missing\n        self.send_signal(w.Inputs.data, data)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n\n        self.send_signal(w.Inputs.data, None)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n\n        self.send_signal(w.Inputs.reference, data[:1])\n        self.assertFalse(w.Error.diff_domains.is_shown())\n\n        # same domain - no error\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, data[:1])\n        self.assertFalse(w.Error.diff_domains.is_shown())\n\n        # one attribute different attribute name\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, reference)\n        self.assertTrue(w.Error.diff_domains.is_shown())\n\n        # different number of attributes\n        domain_ref = Domain([ContinuousVariable(\"a\"), ContinuousVariable(\"b\")])\n        reference = Table(domain_ref, np.random.rand(1, len(domain_ref.variables)))\n\n        # error disappears when data is set to None\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, reference)\n        self.assertTrue(w.Error.diff_domains.is_shown())\n\n        self.send_signal(w.Inputs.data, None)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n\n        # error disappears when reference is set to None\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, reference)\n        self.assertTrue(w.Error.diff_domains.is_shown())\n\n        self.send_signal(w.Inputs.reference, None)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n\n    def test_different_metas(self):\n        \"\"\"\n        Test weather widget do not show error when data and a reference have\n        domain that differ only in metas\n        \"\"\"\n        w = self.widget\n\n        domain = Domain([ContinuousVariable(\"a\"), ContinuousVariable(\"b\")],\n                        metas=[ContinuousVariable(\"c\")])\n        data = Table(\n            domain, np.random.rand(15, len(domain.attributes)),\n            metas=np.random.rand(15, len(domain.metas)))\n\n        # same domain with same metas no error\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, data[:1])\n        self.assertFalse(w.Error.diff_domains.is_shown())\n        output = self.get_output(w.Outputs.data)\n        self.assertEqual(10, len(output))\n\n        # same domain with different metas no error\n        domain_ref = Domain(domain.attributes,\n                            metas=[ContinuousVariable(\"d\")])\n        reference = Table(\n            domain_ref, np.random.rand(1, len(domain_ref.attributes)),\n            metas=np.random.rand(1, len(domain.metas)))\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, reference)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n        output = self.get_output(w.Outputs.data)\n        self.assertEqual(10, len(output))\n\n        # same domain with different order - no error\n        domain_ref = Domain(domain.attributes[::-1])\n        reference = Table(\n            domain_ref, np.random.rand(1, len(domain_ref.attributes)))\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, reference)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n        output = self.get_output(w.Outputs.data)\n        self.assertEqual(10, len(output))\n\n        # same domain with different number of metas no error\n        domain_ref = Domain(\n            domain.attributes,\n            metas=[ContinuousVariable(\"d\"), ContinuousVariable(\"e\")])\n        reference = Table(\n            domain_ref, np.random.rand(1, len(domain_ref.attributes)),\n            metas=np.random.rand(1, len(domain_ref.metas)))\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, reference)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n        output = self.get_output(w.Outputs.data)\n        self.assertEqual(10, len(output))\n\n        # different domain with same metas - error shown\n        domain_ref = Domain(domain.attributes + (ContinuousVariable(\"e\"),),\n                            metas=[ContinuousVariable(\"c\")])\n        reference = Table(\n            domain_ref, np.random.rand(1, len(domain_ref.attributes)),\n            metas=np.random.rand(1, len(domain_ref.metas)))\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, reference)\n        self.assertTrue(w.Error.diff_domains.is_shown())\n        output = self.get_output(w.Outputs.data)\n        self.assertIsNone(output)\n\n    def test_different_domains_same_names(self):\n        \"\"\"\n        Here we create two domains with same names of attributes - the case\n        with image-analytics (two embeddings). Widget must not show the\n        error in this case.\n        \"\"\"\n        w = self.widget\n        domain1 = Domain([ContinuousVariable(\"a\"), ContinuousVariable(\"b\")],\n                         metas=[ContinuousVariable(\"c\")])\n        domain2 = Domain([ContinuousVariable(\"a\"), ContinuousVariable(\"b\")],\n                         metas=[ContinuousVariable(\"d\")])\n        data = Table(\n            domain1, np.random.rand(15, len(domain1.attributes)),\n            metas=np.random.rand(15, len(domain1.metas)))\n        ref = Table(\n            domain2, np.random.rand(2, len(domain2.attributes)),\n            metas=np.random.rand(2, len(domain2.metas)))\n        self.send_signal(w.Inputs.data, data)\n        self.send_signal(w.Inputs.reference, ref)\n        self.assertFalse(w.Error.diff_domains.is_shown())\n        output = self.get_output(w.Outputs.data)\n        self.assertEqual(10, len(output))\n\n    def test_n_neighbours_spin_max(self):\n        w = self.widget\n        sb = w.controls.n_neighbors\n        default = sb.maximum()\n        self.send_signal(w.Inputs.data, self.iris)\n        self.assertEqual(sb.maximum(), len(self.iris))\n        self.send_signal(w.Inputs.data, self.iris[:20])\n        self.assertEqual(sb.maximum(), 20)\n        self.send_signal(w.Inputs.data, None)\n        self.assertEqual(sb.maximum(), default)\n\n    def test_inherited_table(self):\n        # pylint: disable=abstract-method\n        class Table2(Table):\n            pass\n\n        data = Table2(self.iris)\n        self.send_signal(self.widget.Inputs.data, data)\n        self.send_signal(self.widget.Inputs.reference, data[0:1])\n        self.assertIsInstance(self.get_output(self.widget.Outputs.data), Table2)\n\n    def test_order_by_distance(self):\n        domain = Domain([ContinuousVariable(x) for x in \"ab\"])\n        reference = Table.from_numpy(domain, [[1, 0]])\n        data = Table.from_numpy(domain, [[1, 0.1], [2, 0], [1, 0], [0, 0.1], [0.1, 0]])\n        self.send_signal(self.widget.Inputs.data, data)\n        self.send_signal(self.widget.Inputs.reference, reference)\n\n        output = self.get_output(self.widget.Outputs.data)\n        expected = [[1, 0], [1, 0.1], [0.1, 0], [2, 0], [0, 0.1]]\n        np.testing.assert_array_equal(output.X, expected)\n        dst = output.get_column(\"distance\").tolist()\n        self.assertTrue(dst == sorted(dst))  # check distance in ascending order\n\n        # test on bigger set\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.send_signal(self.widget.Inputs.reference, self.iris[:1])\n        dst = self.get_output(self.widget.Outputs.data).get_column(\"distance\").tolist()\n        self.assertTrue(dst == sorted(dst))  # check distance in ascending order\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_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.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_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.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_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.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_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.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_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.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_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.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_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.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_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.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_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.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_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.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_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.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_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.7/site-packages/Orange3.egg-link (link to .)\nAdding Orange3 3.35.0.dev0+9009f53 to easy-install.pth file\nInstalling orange-canvas script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035\nProcessing dependencies for Orange3==3.35.0.dev0+9009f53\nSearching for xlsxwriter\nReading https://pypi.org/simple/xlsxwriter/\nDownloading https://files.pythonhosted.org/packages/a7/ea/53d1fe468e63e092cf16e2c18d16f50c29851242f9dd12d6a66e0d7f0d02/XlsxWriter-3.2.0-py3-none-any.whl#sha256=ecfd5405b3e0e228219bcaf24c2ca0915e012ca9464a14048021d21a995d490e\nBest match: XlsxWriter 3.2.0\nProcessing XlsxWriter-3.2.0-py3-none-any.whl\nInstalling XlsxWriter-3.2.0-py3-none-any.whl to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.7/site-packages\nAdding XlsxWriter 3.2.0 to easy-install.pth file\nInstalling vba_extract.py script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.7/site-packages/XlsxWriter-3.2.0-py3.7.egg\nSearching for xlrd>=1.2.0\nReading https://pypi.org/simple/xlrd/\nDownloading https://files.pythonhosted.org/packages/a6/0c/c2a72d51fe56e08a08acc85d13013558a2d793028ae7385448a6ccdfae64/xlrd-2.0.1-py2.py3-none-any.whl#sha256=6a33ee89877bd9abc1158129f6e94be74e2679636b8a205b43b85206c3f0bbdd\nBest match: xlrd 2.0.1\nProcessing xlrd-2.0.1-py2.py3-none-any.whl\nInstalling xlrd-2.0.1-py2.py3-none-any.whl to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.7/site-packages\nAdding xlrd 2.0.1 to easy-install.pth file\nInstalling runxlrd.py script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.7/site-packages/xlrd-2.0.1-py3.7.egg\nSearching for serverfiles\nReading https://pypi.org/simple/serverfiles/\nDownloading https://files.pythonhosted.org/packages/33/36/794fe1c4ed29b0f56a26d72e956baf0285d8fe9e29faf36797a28a8ad45b/serverfiles-0.3.1.tar.gz#sha256=5e10fc32e758791e3735bc08bce2ed4702a83b1e45ab96c5d59cc8aeecfbebe1\nBest match: serverfiles 0.3.1\nProcessing serverfiles-0.3.1.tar.gz\nWriting /tmp/easy_install-14ybp7bv/serverfiles-0.3.1/setup.cfg\nRunning serverfiles-0.3.1/setup.py -q bdist_egg --dist-dir /tmp/easy_install-14ybp7bv/serverfiles-0.3.1/egg-dist-tmp-89igovbl\ncreating /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.7/site-packages/serverfiles-0.3.1-py3.7.egg\nExtracting serverfiles-0.3.1-py3.7.egg to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.7/site-packages\nAdding serverfiles 0.3.1 to easy-install.pth file\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.7/site-packages/serverfiles-0.3.1-py3.7.egg\nSearching for scipy>=1.9\nReading https://pypi.org/simple/scipy/\nDownloading https://files.pythonhosted.org/packages/d9/7b/2b8ac283cf32465ed08bc20a83d559fe7b174a484781702ba8accea001d6/scipy-1.15.0.tar.gz#sha256=300742e2cc94e36a2880ebe464a1c8b4352a7b0f3e36ec3d2ac006cdbe0219ac\nBest match: scipy 1.15.0\nProcessing scipy-1.15.0.tar.gz\nVerifying Orange installation...\n❌ Orange verification failed\nPython 3.7 installation failed, trying Python 3.8...\nAttempting installation with Python 3.8...\nrunning build_ext\nbuilding 'Orange.classification._tree_scorers' extension\ncreating build/temp.linux-x86_64-cpython-38/Orange/classification\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/classification/_tree_scorers.c -o build/temp.linux-x86_64-cpython-38/Orange/classification/_tree_scorers.o\ncreating build/lib.linux-x86_64-cpython-38/Orange/classification\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/classification/_tree_scorers.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/classification/_tree_scorers.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.data._contingency' extension\ncreating build/temp.linux-x86_64-cpython-38/Orange/data\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/data/_contingency.c -o build/temp.linux-x86_64-cpython-38/Orange/data/_contingency.o\ncreating build/lib.linux-x86_64-cpython-38/Orange/data\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/data/_contingency.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/data/_contingency.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.data._io' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/data/_io.c -o build/temp.linux-x86_64-cpython-38/Orange/data/_io.o\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/data/_io.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/data/_io.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.data._valuecount' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/data/_valuecount.c -o build/temp.linux-x86_64-cpython-38/Orange/data/_valuecount.o\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/data/_valuecount.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/data/_valuecount.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.data._variable' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/data/_variable.c -o build/temp.linux-x86_64-cpython-38/Orange/data/_variable.o\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/data/_variable.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/data/_variable.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.distance._distance' extension\ncreating build/temp.linux-x86_64-cpython-38/Orange/distance\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/distance/_distance.c -o build/temp.linux-x86_64-cpython-38/Orange/distance/_distance.o\ncreating build/lib.linux-x86_64-cpython-38/Orange/distance\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/distance/_distance.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/distance/_distance.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.preprocess._discretize' extension\ncreating build/temp.linux-x86_64-cpython-38/Orange/preprocess\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/preprocess/_discretize.c -o build/temp.linux-x86_64-cpython-38/Orange/preprocess/_discretize.o\ncreating build/lib.linux-x86_64-cpython-38/Orange/preprocess\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/preprocess/_discretize.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/preprocess/_discretize.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.preprocess._relieff' extension\nc++ -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/preprocess/_relieff.cpp -o build/temp.linux-x86_64-cpython-38/Orange/preprocess/_relieff.o\nc++ -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -pthread -shared build/temp.linux-x86_64-cpython-38/Orange/preprocess/_relieff.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/preprocess/_relieff.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.projection._som' extension\ncreating build/temp.linux-x86_64-cpython-38/Orange/projection\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/projection/_som.c -o build/temp.linux-x86_64-cpython-38/Orange/projection/_som.o\ncreating build/lib.linux-x86_64-cpython-38/Orange/projection\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/projection/_som.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/projection/_som.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.classification._simple_tree' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/classification/_simple_tree.c -o build/temp.linux-x86_64-cpython-38/Orange/classification/_simple_tree.o\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/classification/_simple_tree.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/classification/_simple_tree.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.widgets.utils._grid_density' extension\ncreating build/temp.linux-x86_64-cpython-38/Orange/widgets/utils\nc++ -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/widgets/utils/_grid_density.cpp -o build/temp.linux-x86_64-cpython-38/Orange/widgets/utils/_grid_density.o\ncreating build/lib.linux-x86_64-cpython-38/Orange/widgets/utils\nc++ -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -pthread -shared build/temp.linux-x86_64-cpython-38/Orange/widgets/utils/_grid_density.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/widgets/utils/_grid_density.cpython-38-x86_64-linux-gnu.so\ncopying build/lib.linux-x86_64-cpython-38/Orange/classification/_tree_scorers.cpython-38-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_contingency.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_io.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_valuecount.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_variable.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/distance/_distance.cpython-38-x86_64-linux-gnu.so -> Orange/distance\ncopying build/lib.linux-x86_64-cpython-38/Orange/preprocess/_discretize.cpython-38-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-cpython-38/Orange/preprocess/_relieff.cpython-38-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-cpython-38/Orange/projection/_som.cpython-38-x86_64-linux-gnu.so -> Orange/projection\ncopying build/lib.linux-x86_64-cpython-38/Orange/classification/_simple_tree.cpython-38-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-cpython-38/Orange/widgets/utils/_grid_density.cpython-38-x86_64-linux-gnu.so -> Orange/widgets/utils\nrunning develop\nrunning egg_info\nwriting Orange3.egg-info/PKG-INFO\nwriting dependency_links to Orange3.egg-info/dependency_links.txt\nwriting entry points to Orange3.egg-info/entry_points.txt\nwriting requirements to Orange3.egg-info/requires.txt\nwriting top-level names to Orange3.egg-info/top_level.txt\ndependency 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path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_math.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_math.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative\nreading manifest file 'Orange3.egg-info/SOURCES.txt'\nreading manifest template 'MANIFEST.in'\nadding license file 'LICENSE'\nwriting manifest file 'Orange3.egg-info/SOURCES.txt'\nrunning build_ext\ncopying build/lib.linux-x86_64-cpython-38/Orange/classification/_tree_scorers.cpython-38-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_contingency.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_io.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_valuecount.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_variable.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/distance/_distance.cpython-38-x86_64-linux-gnu.so -> Orange/distance\ncopying build/lib.linux-x86_64-cpython-38/Orange/preprocess/_discretize.cpython-38-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-cpython-38/Orange/preprocess/_relieff.cpython-38-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-cpython-38/Orange/projection/_som.cpython-38-x86_64-linux-gnu.so -> Orange/projection\ncopying build/lib.linux-x86_64-cpython-38/Orange/classification/_simple_tree.cpython-38-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-cpython-38/Orange/widgets/utils/_grid_density.cpython-38-x86_64-linux-gnu.so -> Orange/widgets/utils\nCreating /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/Orange3.egg-link (link to .)\nAdding Orange3 3.35.0.dev0+9009f53 to easy-install.pth file\nInstalling orange-canvas script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035\nProcessing dependencies for Orange3==3.35.0.dev0+9009f53\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_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for xlrd==2.0.1\nBest match: xlrd 2.0.1\nAdding xlrd 2.0.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for setuptools==75.3.0\nBest match: setuptools 75.3.0\nAdding setuptools 75.3.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for serverfiles==0.3.1\nBest match: serverfiles 0.3.1\nAdding serverfiles 0.3.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for scipy==1.10.1\nBest match: scipy 1.10.1\nAdding scipy 1.10.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for scikit-learn==1.1.3\nBest match: scikit-learn 1.1.3\nAdding scikit-learn 1.1.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for requests==2.32.3\nBest match: requests 2.32.3\nAdding requests 2.32.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for qtconsole==5.6.1\nBest match: qtconsole 5.6.1\nAdding qtconsole 5.6.1 to easy-install.pth file\nInstalling jupyter-qtconsole script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for PyYAML==6.0.2\nBest match: PyYAML 6.0.2\nAdding PyYAML 6.0.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for python-louvain==0.16\nBest match: python-louvain 0.16\nAdding python-louvain 0.16 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for pyqtgraph==0.13.3\nBest match: pyqtgraph 0.13.3\nAdding pyqtgraph 0.13.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for pygments==2.19.1\nBest match: pygments 2.19.1\nAdding pygments 2.19.1 to easy-install.pth file\nInstalling pygmentize script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for pip==24.3.1\nBest match: pip 24.3.1\nAdding pip 24.3.1 to easy-install.pth file\nInstalling pip script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/bin\nInstalling pip3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for pandas==2.0.3\nBest match: pandas 2.0.3\nAdding pandas 2.0.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for orange-widget-base==4.22.0\nBest match: orange-widget-base 4.22.0\nAdding orange-widget-base 4.22.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for orange-canvas-core==0.1.35\nBest match: orange-canvas-core 0.1.35\nAdding orange-canvas-core 0.1.35 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for openpyxl==3.1.5\nBest match: openpyxl 3.1.5\nAdding openpyxl 3.1.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for openTSNE==1.0.1\nBest match: openTSNE 1.0.1\nAdding openTSNE 1.0.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for numpy==1.24.4\nBest match: numpy 1.24.4\nAdding numpy 1.24.4 to easy-install.pth file\nInstalling f2py script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/bin\nInstalling f2py3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/bin\nInstalling f2py3.8 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for networkx==3.1\nBest match: networkx 3.1\nAdding networkx 3.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for matplotlib==3.7.5\nBest match: matplotlib 3.7.5\nAdding matplotlib 3.7.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for keyrings.alt==5.0.2\nBest match: keyrings.alt 5.0.2\nAdding keyrings.alt 5.0.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for keyring==25.5.0\nBest match: keyring 25.5.0\nAdding keyring 25.5.0 to easy-install.pth file\nInstalling keyring script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for joblib==1.4.2\nBest match: joblib 1.4.2\nAdding joblib 1.4.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for httpx==0.28.1\nBest match: httpx 0.28.1\nAdding httpx 0.28.1 to easy-install.pth file\nInstalling httpx script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for chardet==5.2.0\nBest match: chardet 5.2.0\nAdding chardet 5.2.0 to easy-install.pth file\nInstalling chardetect script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for Bottleneck==1.4.0\nBest match: Bottleneck 1.4.0\nAdding Bottleneck 1.4.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for baycomp==1.0.3\nBest match: baycomp 1.0.3\nAdding baycomp 1.0.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for AnyQt==0.2.0\nBest match: AnyQt 0.2.0\nAdding AnyQt 0.2.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for threadpoolctl==3.5.0\nBest match: threadpoolctl 3.5.0\nAdding threadpoolctl 3.5.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for certifi==2024.12.14\nBest match: certifi 2024.12.14\nAdding certifi 2024.12.14 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for urllib3==2.2.3\nBest match: urllib3 2.2.3\nAdding urllib3 2.2.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for idna==3.10\nBest match: idna 3.10\nAdding idna 3.10 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for charset-normalizer==3.4.1\nBest match: charset-normalizer 3.4.1\nAdding charset-normalizer 3.4.1 to easy-install.pth file\nInstalling normalizer script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for packaging==24.2\nBest match: packaging 24.2\nAdding packaging 24.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for QtPy==2.4.2\nBest match: QtPy 2.4.2\nAdding QtPy 2.4.2 to easy-install.pth file\nInstalling qtpy script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for ipykernel==6.29.5\nBest match: ipykernel 6.29.5\nAdding ipykernel 6.29.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for jupyter-client==8.6.3\nBest match: jupyter-client 8.6.3\nAdding jupyter-client 8.6.3 to easy-install.pth file\nInstalling jupyter-kernel script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/bin\nInstalling jupyter-kernelspec script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/bin\nInstalling jupyter-run script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for jupyter-core==5.7.2\nBest match: jupyter-core 5.7.2\nAdding jupyter-core 5.7.2 to easy-install.pth file\nInstalling jupyter script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/bin\nInstalling jupyter-migrate script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/bin\nInstalling jupyter-troubleshoot script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for traitlets==5.14.3\nBest match: traitlets 5.14.3\nAdding traitlets 5.14.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for tzdata==2024.2\nBest match: tzdata 2024.2\nAdding tzdata 2024.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for pytz==2024.2\nBest match: pytz 2024.2\nAdding pytz 2024.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for python-dateutil==2.9.0.post0\nBest match: python-dateutil 2.9.0.post0\nAdding python-dateutil 2.9.0.post0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for typing-extensions==4.12.2\nBest match: typing-extensions 4.12.2\nAdding typing-extensions 4.12.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/setuptools/_vendor\nSearching for qasync==0.27.1\nBest match: qasync 0.27.1\nAdding qasync 0.27.1 to easy-install.pth file\ndetected new path './setuptools/_vendor'\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for dictdiffer==0.9.0\nBest match: dictdiffer 0.9.0\nAdding dictdiffer 0.9.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for requests-cache==1.2.1\nBest match: requests-cache 1.2.1\nAdding requests-cache 1.2.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for commonmark==0.9.1\nBest match: commonmark 0.9.1\nAdding commonmark 0.9.1 to easy-install.pth file\nInstalling cmark script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for docutils==0.20.1\nBest match: docutils 0.20.1\nAdding docutils 0.20.1 to easy-install.pth file\nInstalling docutils script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for et-xmlfile==2.0.0\nBest match: et-xmlfile 2.0.0\nAdding et-xmlfile 2.0.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for importlib-resources==6.4.5\nBest match: importlib-resources 6.4.5\nAdding importlib-resources 6.4.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for pyparsing==3.1.4\nBest match: pyparsing 3.1.4\nAdding pyparsing 3.1.4 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for pillow==10.4.0\nBest match: pillow 10.4.0\nAdding pillow 10.4.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for kiwisolver==1.4.7\nBest match: kiwisolver 1.4.7\nAdding kiwisolver 1.4.7 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for fonttools==4.55.3\nBest match: fonttools 4.55.3\nAdding fonttools 4.55.3 to easy-install.pth file\nInstalling fonttools script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/bin\nInstalling pyftmerge script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/bin\nInstalling pyftsubset script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/bin\nInstalling ttx script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for cycler==0.12.1\nBest match: cycler 0.12.1\nAdding cycler 0.12.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for contourpy==1.1.1\nBest match: contourpy 1.1.1\nAdding contourpy 1.1.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for jaraco.context==6.0.1\nBest match: jaraco.context 6.0.1\nAdding jaraco.context 6.0.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for jaraco.classes==3.4.0\nBest match: jaraco.classes 3.4.0\nAdding jaraco.classes 3.4.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for jeepney==0.8.0\nBest match: jeepney 0.8.0\nAdding jeepney 0.8.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for SecretStorage==3.3.3\nBest match: SecretStorage 3.3.3\nAdding SecretStorage 3.3.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for importlib-metadata==8.5.0\nBest match: importlib-metadata 8.5.0\nAdding importlib-metadata 8.5.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for jaraco.functools==4.1.0\nBest match: jaraco.functools 4.1.0\nAdding jaraco.functools 4.1.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for httpcore==1.0.7\nBest match: httpcore 1.0.7\nAdding httpcore 1.0.7 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for anyio==4.5.2\nBest match: anyio 4.5.2\nAdding anyio 4.5.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for tornado==6.4.2\nBest match: tornado 6.4.2\nAdding tornado 6.4.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for pyzmq==26.2.0\nBest match: pyzmq 26.2.0\nAdding pyzmq 26.2.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for psutil==6.1.1\nBest match: psutil 6.1.1\nAdding psutil 6.1.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for nest-asyncio==1.6.0\nBest match: nest-asyncio 1.6.0\nAdding nest-asyncio 1.6.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for matplotlib-inline==0.1.7\nBest match: matplotlib-inline 0.1.7\nAdding matplotlib-inline 0.1.7 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for ipython==8.12.3\nBest match: ipython 8.12.3\nAdding ipython 8.12.3 to easy-install.pth file\nInstalling ipython script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/bin\nInstalling ipython3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for debugpy==1.8.11\nBest match: debugpy 1.8.11\nAdding debugpy 1.8.11 to easy-install.pth file\nInstalling debugpy script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for comm==0.2.2\nBest match: comm 0.2.2\nAdding comm 0.2.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for platformdirs==4.3.6\nBest match: platformdirs 4.3.6\nAdding platformdirs 4.3.6 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for six==1.17.0\nBest match: six 1.17.0\nAdding six 1.17.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for url-normalize==1.4.3\nBest match: url-normalize 1.4.3\nAdding url-normalize 1.4.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for cattrs==24.1.2\nBest match: cattrs 24.1.2\nAdding cattrs 24.1.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for attrs==24.3.0\nBest match: attrs 24.3.0\nAdding attrs 24.3.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for zipp==3.20.2\nBest match: zipp 3.20.2\nAdding zipp 3.20.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for backports.tarfile==1.2.0\nBest match: backports.tarfile 1.2.0\nbackports.tarfile 1.2.0 is already the active version in easy-install.pth\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/setuptools/_vendor\nSearching for more-itertools==10.5.0\nBest match: more-itertools 10.5.0\nAdding more-itertools 10.5.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for cryptography==44.0.0\nBest match: cryptography 44.0.0\nAdding cryptography 44.0.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for h11==0.14.0\nBest match: h11 0.14.0\nAdding h11 0.14.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for exceptiongroup==1.2.2\nBest match: exceptiongroup 1.2.2\nAdding exceptiongroup 1.2.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for sniffio==1.3.1\nBest match: sniffio 1.3.1\nAdding sniffio 1.3.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for pexpect==4.9.0\nBest match: pexpect 4.9.0\nAdding pexpect 4.9.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for stack-data==0.6.3\nBest match: stack-data 0.6.3\nAdding stack-data 0.6.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for prompt-toolkit==3.0.48\nBest match: prompt-toolkit 3.0.48\nAdding prompt-toolkit 3.0.48 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for pickleshare==0.7.5\nBest match: pickleshare 0.7.5\nAdding pickleshare 0.7.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for jedi==0.19.2\nBest match: jedi 0.19.2\nAdding jedi 0.19.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for decorator==5.1.1\nBest match: decorator 5.1.1\nAdding decorator 5.1.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for backcall==0.2.0\nBest match: backcall 0.2.0\nAdding backcall 0.2.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for cffi==1.17.1\nBest match: cffi 1.17.1\nAdding cffi 1.17.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for ptyprocess==0.7.0\nBest match: ptyprocess 0.7.0\nAdding ptyprocess 0.7.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for pure-eval==0.2.3\nBest match: pure-eval 0.2.3\nAdding pure-eval 0.2.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for asttokens==3.0.0\nBest match: asttokens 3.0.0\nAdding asttokens 3.0.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for executing==2.1.0\nBest match: executing 2.1.0\nAdding executing 2.1.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for wcwidth==0.2.13\nBest match: wcwidth 0.2.13\nAdding wcwidth 0.2.13 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for parso==0.8.4\nBest match: parso 0.8.4\nAdding parso 0.8.4 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nSearching for pycparser==2.22\nBest match: pycparser 2.22\nAdding pycparser 2.22 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages\nFinished processing dependencies for Orange3==3.35.0.dev0+9009f53\nVerifying Orange installation...\n✅ Orange installation successful!\nSuccessfully installed orange using Python 3.8\n",
    "setup_res_stderr": "Using CPython 3.7.9\nCreating virtual environment at: .venv\nActivate with: source .venv/bin/activate\nResolved 18 packages in 174ms\nPrepared 18 packages in 2ms\nInstalled 18 packages in 172ms\n + cython==0.29.37\n + exceptiongroup==1.2.2\n + importlib-metadata==6.7.0\n + iniconfig==2.0.0\n + numpy==1.17.5\n + packaging==24.0\n + pluggy==1.2.0\n + pyqt5==5.15.10\n + pyqt5-qt5==5.15.16\n + pyqt5-sip==12.13.0\n + pyqtwebengine==5.15.6\n + pyqtwebengine-qt5==5.15.16\n + pytest==7.4.4\n + setuptools==59.8.0\n + tomli==2.0.1\n + typing-extensions==4.7.1\n + wheel==0.42.0\n + zipp==3.15.0\n  × No solution found when resolving dependencies:\n  ╰─▶ Because the current Python version (3.7.9) does not satisfy Python>=3.8 and all of:\n          scipy>=1.9.0,<=1.9.1\n          scipy>=1.10.0,<=1.10.1\n      depend on Python>=3.8,<3.12, we can conclude that all of:\n          scipy>=1.9.0,<=1.9.1\n          scipy>=1.10.0,<=1.10.1\n       cannot be used.\n      And because only the following versions of scipy are available:\n          scipy<=1.9.0\n          scipy==1.9.1\n          scipy==1.9.2\n          scipy==1.9.3\n          scipy==1.10.0\n          scipy==1.10.1\n          scipy==1.11.0\n          scipy==1.11.1\n          scipy==1.11.2\n          scipy==1.11.3\n          scipy==1.11.4\n          scipy==1.12.0\n          scipy==1.13.0\n          scipy==1.13.1\n          scipy==1.14.0\n          scipy==1.14.1\n          scipy==1.15.0\n      we can conclude that all of:\n          scipy>=1.9.0,<1.9.2\n          scipy>1.9.3,<1.11.0\n       cannot be used. (1)\n\n      Because the current Python version (3.7.9) does not satisfy Python>=3.8 and scipy>=1.9.2,<=1.9.3 depends on Python>=3.8,\n      we can conclude that scipy>=1.9.2,<=1.9.3 cannot be used.\n      And because we know from (1) that all of:\n          scipy>=1.9.0,<1.9.2\n          scipy>1.9.3,<1.11.0\n       cannot be used, we can conclude that scipy>=1.9.0,<1.11.0 cannot be used.\n      And because scipy==1.11.0 was yanked (reason: License Violation), we can conclude that scipy>=1.9.0,<1.11.1 cannot be\n      used. (2)\n\n      Because the current Python version (3.7.9) does not satisfy Python>=3.8 and scipy>=1.11.1,<=1.11.3 depends on\n      Python>=3.9,<3.13, we can conclude that scipy>=1.11.1,<=1.11.3 cannot be used.\n      And because we know from (2) that scipy>=1.9.0,<1.11.1 cannot be used, we can conclude that scipy>=1.9.0,<1.11.4 cannot\n      be used. (3)\n\n      Because the current Python version (3.7.9) does not satisfy Python>=3.8 and scipy>=1.11.4,<=1.13.1 depends on\n      Python>=3.9, we can conclude that scipy>=1.11.4,<=1.13.1 cannot be used.\n      And because we know from (3) that scipy>=1.9.0,<1.11.4 cannot be used, we can conclude that scipy>=1.9.0,<1.14.0 cannot\n      be used. (4)\n\n      Because the current Python version (3.7.9) does not satisfy Python>=3.8 and scipy>=1.14.0 depends on Python>=3.10, we can\n      conclude that scipy>=1.14.0 cannot be used.\n      And because we know from (4) that scipy>=1.9.0,<1.14.0 cannot be used, we can conclude that scipy>=1.9.0 cannot be used.\n      And because you require scipy>=1.9, we can conclude that your requirements are unsatisfiable.\n\n      hint: Pre-releases are available for `scipy` in the requested range (e.g., 1.15.0rc2), but pre-releases weren't enabled\n      (try: `--prerelease=allow`)\n  × No solution found when resolving dependencies:\n  ╰─▶ Because the current Python version (3.7.9) does not satisfy Python>=3.8 and pyqtgraph>=0.13.1,<=0.13.3 depends on\n      Python>=3.8, we can conclude that pyqtgraph>=0.13.1,<=0.13.3 cannot be used.\n      And because only the following versions of pyqtgraph are available:\n          pyqtgraph<=0.13.1\n          pyqtgraph==0.13.2\n          pyqtgraph==0.13.3\n          pyqtgraph==0.13.4\n          pyqtgraph==0.13.5\n          pyqtgraph==0.13.6\n          pyqtgraph==0.13.7\n      we can conclude that pyqtgraph>=0.13.1,<0.13.4 cannot be used. (1)\n\n      Because the current Python version (3.7.9) does not satisfy Python>=3.9 and pyqtgraph>=0.13.4 depends on Python>=3.9, we\n      can conclude that pyqtgraph>=0.13.4 cannot be used.\n      And because we know from (1) that pyqtgraph>=0.13.1,<0.13.4 cannot be used, we can conclude that pyqtgraph>=0.13.1 cannot\n      be used.\n      And because you require pyqtgraph>=0.13.1, we can conclude that your requirements are unsatisfiable.\nResolved 2 packages in 45ms\nInstalled 2 packages in 11ms\n + psycopg2==2.9.9\n + pymssql==2.3.2\nResolved 19 packages in 205ms\nInstalled 16 packages in 2.20s\n + catboost==1.2.7\n + cycler==0.11.0\n + fonttools==4.38.0\n + graphviz==0.20.1\n + kiwisolver==1.4.5\n + matplotlib==3.5.3\n + pandas==1.3.5\n + pillow==9.5.0\n + plotly==5.18.0\n + pyparsing==3.1.4\n + python-dateutil==2.9.0.post0\n + pytz==2024.2\n + scipy==1.7.3\n + six==1.17.0\n + tenacity==8.2.3\n + xgboost==1.6.2\nResolved 5 packages in 44ms\nInstalled 3 packages in 126ms\n + joblib==1.3.2\n + scikit-learn==1.0.2\n + threadpoolctl==3.1.0\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.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_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.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_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.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_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.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_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.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_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.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_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.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_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.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_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.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_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.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_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.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_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.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_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.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_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.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_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.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_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.7/site-packages/setuptools/command/install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.\n  setuptools.SetuptoolsDeprecationWarning,\nwarning: no files found matching 'README*' under directory 'Orange'\nwarning: no files found matching 'LICENSE' under directory 'Orange'\nwarning: no files found matching '*.basket' under directory 'Orange/datasets'\nwarning: no files found matching '*.svg' under directory 'Orange/canvas'\nwarning: no files found matching '*.js' under directory 'Orange/widgets'\nwarning: no files found matching '*.css' under directory 'Orange/widgets'\nwarning: no files found matching '*.html' under directory 'Orange/widgets'\nwarning: no files found matching '*.svg' under directory 'distribute'\nwarning: no files found matching 'COPYING'\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.7/site-packages/pkg_resources/__init__.py:119: PkgResourcesDeprecationWarning:  is an invalid version and will not be supported in a future release\n  PkgResourcesDeprecationWarning,\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.7/site-packages/setuptools/command/install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.\n  setuptools.SetuptoolsDeprecationWarning,\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.7/site-packages/pkg_resources/__init__.py:119: PkgResourcesDeprecationWarning:  is an invalid version and will not be supported in a future release\n  PkgResourcesDeprecationWarning,\nerror: Couldn't find a setup script in /tmp/easy_install-mez7tuwe/scipy-1.15.0.tar.gz\nUsing CPython 3.8.20\nCreating virtual environment at: .venv\nActivate with: source .venv/bin/activate\nResolved 15 packages in 80ms\nPrepared 15 packages in 0.99ms\nInstalled 15 packages in 25ms\n + cython==0.29.37\n + exceptiongroup==1.2.2\n + iniconfig==2.0.0\n + numpy==1.24.4\n + packaging==24.2\n + pluggy==1.5.0\n + pyqt5==5.15.11\n + pyqt5-qt5==5.15.16\n + pyqt5-sip==12.15.0\n + pyqtwebengine==5.15.7\n + pyqtwebengine-qt5==5.15.16\n + pytest==8.3.4\n + setuptools==75.3.0\n + tomli==2.2.1\n + wheel==0.45.1\nResolved 59 packages in 104ms\nInstalled 55 packages in 107ms\n + anyio==4.5.2\n + backports-tarfile==1.2.0\n + baycomp==1.0.3\n + bottleneck==1.4.0\n + certifi==2024.12.14\n + cffi==1.17.1\n + chardet==5.2.0\n + charset-normalizer==3.4.1\n + contourpy==1.1.1\n + cryptography==44.0.0\n + cycler==0.12.1\n + et-xmlfile==2.0.0\n + fonttools==4.55.3\n + h11==0.14.0\n + httpcore==1.0.7\n + httpx==0.28.1\n + idna==3.10\n + importlib-metadata==8.5.0\n + importlib-resources==6.4.5\n + jaraco-classes==3.4.0\n + jaraco-context==6.0.1\n + jaraco-functools==4.1.0\n + jeepney==0.8.0\n + joblib==1.4.2\n + keyring==25.5.0\n + keyrings-alt==5.0.2\n + kiwisolver==1.4.7\n + matplotlib==3.7.5\n + more-itertools==10.5.0\n + networkx==3.1\n + openpyxl==3.1.5\n + opentsne==1.0.1\n + pandas==2.0.3\n + pillow==10.4.0\n + pip==24.3.1\n + pycparser==2.22\n + pyparsing==3.1.4\n + python-dateutil==2.9.0.post0\n + python-louvain==0.16\n + pytz==2024.2\n + pyyaml==6.0.2\n + requests==2.32.3\n + scikit-learn==1.1.3\n + scipy==1.10.1\n + secretstorage==3.3.3\n + serverfiles==0.3.1\n + six==1.17.0\n + sniffio==1.3.1\n + threadpoolctl==3.5.0\n + typing-extensions==4.12.2\n + tzdata==2024.2\n + urllib3==2.2.3\n + xlrd==2.0.1\n + xlsxwriter==3.2.0\n + zipp==3.20.2\nResolved 64 packages in 178ms\nInstalled 41 packages in 37ms\n + anyqt==0.2.0\n + asttokens==3.0.0\n + attrs==24.3.0\n + backcall==0.2.0\n + cattrs==24.1.2\n + comm==0.2.2\n + commonmark==0.9.1\n + debugpy==1.8.11\n + decorator==5.1.1\n + dictdiffer==0.9.0\n + docutils==0.20.1\n + executing==2.1.0\n + ipykernel==6.29.5\n + ipython==8.12.3\n + jedi==0.19.2\n + jupyter-client==8.6.3\n + jupyter-core==5.7.2\n + matplotlib-inline==0.1.7\n + nest-asyncio==1.6.0\n + orange-canvas-core==0.1.35\n + orange-widget-base==4.22.0\n + parso==0.8.4\n + pexpect==4.9.0\n + pickleshare==0.7.5\n + platformdirs==4.3.6\n + prompt-toolkit==3.0.48\n + psutil==6.1.1\n + ptyprocess==0.7.0\n + pure-eval==0.2.3\n + pygments==2.19.1\n + pyqtgraph==0.13.3\n + pyzmq==26.2.0\n + qasync==0.27.1\n + qtconsole==5.6.1\n + qtpy==2.4.2\n + requests-cache==1.2.1\n + stack-data==0.6.3\n + tornado==6.4.2\n + traitlets==5.14.3\n + url-normalize==1.4.3\n + wcwidth==0.2.13\nResolved 2 packages in 2ms\nInstalled 2 packages in 3ms\n + psycopg2==2.9.10\n + pymssql==2.3.2\nResolved 23 packages in 15ms\nInstalled 6 packages in 391ms\n + catboost==1.2.7\n + graphviz==0.20.3\n + nvidia-nccl-cu12==2.24.3\n + plotly==5.24.1\n + tenacity==9.0.0\n + xgboost==2.1.3\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/classification/_tree_scorers.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_2find_threshold_entropy’:\nOrange/classification/_tree_scorers.c:3427:27: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 3427 |   __pyx_t_7 = ((__pyx_v_N <= __pyx_v_min_leaf) != 0);\n      |                           ^~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_6find_threshold_MSE’:\nOrange/classification/_tree_scorers.c:4883:27: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 4883 |   __pyx_t_1 = ((__pyx_v_N <= __pyx_v_min_leaf) != 0);\n      |                           ^~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_8find_binarization_MSE’:\nOrange/classification/_tree_scorers.c:5629:43: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5629 |           for (__pyx_t_19 = 0; __pyx_t_19 < __pyx_t_18; __pyx_t_19+=1) {\n      |                                           ^\nOrange/classification/_tree_scorers.c:5750:39: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5750 |           __pyx_t_20 = ((__pyx_v_left >= __pyx_v_min_leaf) != 0);\n      |                                       ^~\nOrange/classification/_tree_scorers.c:5756:53: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5756 |           __pyx_t_20 = (((__pyx_v_N - __pyx_v_left) >= __pyx_v_min_leaf) != 0);\n      |                                                     ^~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_14compute_predictions_csr’:\nOrange/classification/_tree_scorers.c:7217:41: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 7217 |         for (__pyx_t_12 = 0; __pyx_t_12 < __pyx_t_11; __pyx_t_12+=1) {\n      |                                         ^\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_16compute_predictions_csc’:\nOrange/classification/_tree_scorers.c:7874:41: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 7874 |         for (__pyx_t_12 = 0; __pyx_t_12 < __pyx_t_11; __pyx_t_12+=1) {\n      |                                         ^\nOrange/classification/_tree_scorers.c:7934:150: warning: comparison of integer expressions of different signedness: ‘__pyx_t_5numpy_int32_t’ {aka ‘int’} and ‘unsigned int’ [-Wsign-compare]\n 7934 |               __pyx_t_16 = (((*((__pyx_t_5numpy_int32_t const  *) ( /* dim=0 */ (__pyx_v_indices.data + __pyx_t_15 * __pyx_v_indices.strides[0]) ))) != __pyx_v_i) != 0);\n      |                                                                                                                                                      ^~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/data/_contingency.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nOrange/data/_io.c: In function ‘__pyx_pf_6Orange_4data_3_io_sparse_read_float.constprop’:\nOrange/data/_io.c:4120:43: warning: ‘__pyx_v_decs’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4120 |           __pyx_v_value = ((__pyx_v_value * __pyx_v_decs) + (__pyx_v_c & 0xf));\n      |                            ~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/data/_io.c:4318:24: warning: ‘__pyx_v_value’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4318 |           __pyx_t_10 = PyFloat_FromDouble(__pyx_v_value); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 223, __pyx_L5_error)\n      |                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOrange/data/_io.c:683:40: warning: ‘__pyx_v_attr_index’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n  683 |   #define PyInt_FromLong               PyLong_FromLong\n      |                                        ^~~~~~~~~~~~~~~\nOrange/data/_io.c:1590:7: note: ‘__pyx_v_attr_index’ was declared here\n 1590 |   int __pyx_v_attr_index;\n      |       ^~~~~~~~~~~~~~~~~~\nOrange/data/_io.c:3183:25: warning: ‘__pyx_v_atomp’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3183 |           __pyx_v_atomp = (__pyx_v_atomp + 1);\n      |           ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/data/_valuecount.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/distance/_distance.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/preprocess/_discretize.c:774:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/preprocess/_relieff.cpp:790:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/projection/_som.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_5update_hex’:\nOrange/projection/_som.c:3863:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3863 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:3862:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3862 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pf_6Orange_10projection_4_som_8update_sparse.constprop’:\nOrange/projection/_som.c:4934:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4934 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:4933:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4933 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pf_6Orange_10projection_4_som_10update_sparse_hex.constprop’:\nOrange/projection/_som.c:5682:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5682 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:5681:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5681 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_1get_winners’:\nOrange/projection/_som.c:3184:223: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3184 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_22, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_23, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_y;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:2887:7: note: ‘__pyx_v_win_y’ was declared here\n 2887 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:3173:223: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3173 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_23, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_22, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_x;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:2886:7: note: ‘__pyx_v_win_x’ was declared here\n 2886 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_3update’:\nOrange/projection/_som.c:3357:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3357 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_7get_winners_sparse’:\nOrange/projection/_som.c:4732:223: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4732 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_26, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_27, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_y;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:4307:7: note: ‘__pyx_v_win_y’ was declared here\n 4307 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:4721:223: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4721 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_27, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_26, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_x;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:4306:7: note: ‘__pyx_v_win_x’ was declared here\n 4306 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/classification/_simple_tree.c: In function ‘gain_ratio_c’:\nOrange/classification/_simple_tree.c:63:9: warning: implicit declaration of function ‘qsort_r’; did you mean ‘qsort’? [-Wimplicit-function-declaration]\n   63 |         qsort_r(base, nel, size, compar, thunk)\n      |         ^~~~~~~\nOrange/classification/_simple_tree.c:147:9: note: in expansion of macro ‘QSORT_R_FUNC’\n  147 |         QSORT_R_FUNC(examples, size, sizeof(struct Example), (void*) &compar_attr, compar_examples);\n      |         ^~~~~~~~~~~~\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/setuptools/_distutils/dist.py:261: UserWarning: Unknown distribution option: 'test_suite'\n  warnings.warn(msg)\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/setuptools/_distutils/dist.py:261: UserWarning: Unknown distribution option: 'test_suite'\n  warnings.warn(msg)\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/setuptools/command/develop.py:41: EasyInstallDeprecationWarning: easy_install command is deprecated.\n!!\n\n        ********************************************************************************\n        Please avoid running ``setup.py`` and ``easy_install``.\n        Instead, use pypa/build, pypa/installer or other\n        standards-based tools.\n\n        See https://github.com/pypa/setuptools/issues/917 for details.\n        ********************************************************************************\n\n!!\n  easy_install.initialize_options(self)\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035/.venv/lib/python3.8/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated.\n!!\n\n        ********************************************************************************\n        Please avoid running ``setup.py`` directly.\n        Instead, use pypa/build, pypa/installer or other\n        standards-based tools.\n\n        See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.\n        ********************************************************************************\n\n!!\n  self.initialize_options()\nwarning: no files found matching 'README*' under directory 'Orange'\nwarning: no files found matching 'LICENSE' under directory 'Orange'\nwarning: no files found matching '*.basket' under directory 'Orange/datasets'\nwarning: no files found matching '*.svg' under directory 'Orange/canvas'\nwarning: no files found matching '*.js' under directory 'Orange/widgets'\nwarning: no files found matching '*.css' under directory 'Orange/widgets'\nwarning: no files found matching '*.html' under directory 'Orange/widgets'\nwarning: no files found matching '*.svg' under directory 'distribute'\nwarning: no files found matching 'COPYING'\n",
    "new_commit_res_code": 0,
    "new_commit_res_stdout": "============================= test session starts ==============================\nplatform linux -- Python 3.8.20, pytest-8.3.4, pluggy-1.5.0\nrootdir: /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035\nconfigfile: pyproject.toml\nplugins: anyio-4.5.2, typeguard-4.3.0\ncollected 28 items\n\nr2e_tests/test_1.py sss.........................                         [100%]\n\n==================================== PASSES ====================================\n=========================== short test summary info ============================\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_all_equal_ref\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_apply\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_compute_distances_apply_called\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_compute_distances_calls_distance\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_compute_distances_distance_no_data\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_compute_indices_without_reference\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_data_with_similarity\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_different_domains\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_different_domains_same_names\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_different_metas\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_exclude_reference\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_image_export\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_inherited_table\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_input_data\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_input_data_disconnect\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_input_reference\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_input_reference_disconnect\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_minimum_size\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_missing_values\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_msg_base_class\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_n_neighbours_spin_max\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_order_by_distance\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_output_neighbors\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_settings\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_similarity\nSKIPPED [1] .venv/lib/python3.8/site-packages/orangewidget/tests/base.py:244: image exporting not tested as .widget was not set\nSKIPPED [1] .venv/lib/python3.8/site-packages/orangewidget/tests/base.py:238: minimum size not tested as .widget was not set\nSKIPPED [1] .venv/lib/python3.8/site-packages/orangewidget/tests/base.py:250: msg base class not tested as .widget was not set\n======================== 25 passed, 3 skipped in 4.79s =========================\nWidgets left: 0    Max widgets: 1350 \n\n",
    "new_commit_res_stderr": "",
    "old_commit_res_code": 1,
    "old_commit_res_stdout": "============================= test session starts ==============================\nplatform linux -- Python 3.8.20, pytest-8.3.4, pluggy-1.5.0\nrootdir: /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9009f53bcd87ba30da69c9e73294c33e0c0aa035\nconfigfile: pyproject.toml\nplugins: anyio-4.5.2, typeguard-4.3.0\ncollected 28 items\n\nr2e_tests/test_1.py sss.....................F...                         [100%]\n\n=================================== FAILURES ===================================\n____________________ TestOWNeighbors.test_order_by_distance ____________________\n\nself = <r2e_tests.test_1.TestOWNeighbors testMethod=test_order_by_distance>\n\n    def test_order_by_distance(self):\n        domain = Domain([ContinuousVariable(x) for x in \"ab\"])\n        reference = Table.from_numpy(domain, [[1, 0]])\n        data = Table.from_numpy(domain, [[1, 0.1], [2, 0], [1, 0], [0, 0.1], [0.1, 0]])\n        self.send_signal(self.widget.Inputs.data, data)\n        self.send_signal(self.widget.Inputs.reference, reference)\n    \n        output = self.get_output(self.widget.Outputs.data)\n        expected = [[1, 0], [1, 0.1], [0.1, 0], [2, 0], [0, 0.1]]\n>       np.testing.assert_array_equal(output.X, expected)\n\nr2e_tests/test_1.py:464: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\nargs = (<built-in function eq>, array([[1. , 0.1],\n       [2. , 0. ],\n       [1. , 0. ],\n       [0.1, 0. ],\n       [0. , 0.1]]), [[1, 0], [1, 0.1], [0.1, 0], [2, 0], [0, 0.1]])\nkwds = {'err_msg': '', 'header': 'Arrays are not equal', 'strict': False, 'verbose': True}\n\n    @wraps(func)\n    def inner(*args, **kwds):\n        with self._recreate_cm():\n>           return func(*args, **kwds)\nE           AssertionError: \nE           Arrays are not equal\nE           \nE           Mismatched elements: 5 / 10 (50%)\nE           Max absolute difference: 1.9\nE           Max relative difference: 9.\nE            x: array([[1. , 0.1],\nE                  [2. , 0. ],\nE                  [1. , 0. ],...\nE            y: array([[1. , 0. ],\nE                  [1. , 0.1],\nE                  [0.1, 0. ],...\n\n../../../../.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib/python3.8/contextlib.py:75: AssertionError\n==================================== PASSES ====================================\n=========================== short test summary info ============================\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_all_equal_ref\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_apply\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_compute_distances_apply_called\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_compute_distances_calls_distance\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_compute_distances_distance_no_data\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_compute_indices_without_reference\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_data_with_similarity\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_different_domains\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_different_domains_same_names\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_different_metas\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_exclude_reference\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_image_export\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_inherited_table\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_input_data\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_input_data_disconnect\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_input_reference\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_input_reference_disconnect\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_minimum_size\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_missing_values\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_msg_base_class\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_n_neighbours_spin_max\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_output_neighbors\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_settings\nPASSED r2e_tests/test_1.py::TestOWNeighbors::test_similarity\nSKIPPED [1] .venv/lib/python3.8/site-packages/orangewidget/tests/base.py:244: image exporting not tested as .widget was not set\nSKIPPED [1] .venv/lib/python3.8/site-packages/orangewidget/tests/base.py:238: minimum size not tested as .widget was not set\nSKIPPED [1] .venv/lib/python3.8/site-packages/orangewidget/tests/base.py:250: msg base class not tested as .widget was not set\nFAILED r2e_tests/test_1.py::TestOWNeighbors::test_order_by_distance - Asserti...\n=================== 1 failed, 24 passed, 3 skipped in 3.59s ====================\nWidgets left: 0    Max widgets: 1350 \n\n",
    "old_commit_res_stderr": ""
}
Orange/widgets/data/owneighbors.py Orange/widgets/data/tests/test_owneighbors.py
We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
[ISSUE]
**Title:** Neighbors Not Sorted by Distance, Causing Incorrect Output Order

**Description:**
When using the Neighbors widget, the resulting instances are not sorted in ascending order based on their distance from the reference instance. This leads to unexpected ordering of neighbors, which can affect downstream analysis and results.

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

# Define domain and reference
domain = Domain([ContinuousVariable(x) for x in "ab"])
reference = Table.from_numpy(domain, [[1, 0]])

# Define data with unsorted neighbors
data = Table.from_numpy(domain, [
    [1, 0.1],
    [2, 0],
    [1, 0],
    [0.1, 0],
    [0, 0.1]
])

# Send signals to widget
widget.send_signal(widget.Inputs.data, data)
widget.send_signal(widget.Inputs.reference, reference)

# Get and check output
output = widget.get_output(widget.Outputs.data)
expected_order = [
    [1, 0],
    [1, 0.1],
    [0.1, 0],
    [2, 0],
    [0, 0.1]
]
# The output does not match the expected sorted order
```

**Expected Behavior:**
The neighbors should be sorted in ascending order of their distance from the reference instance. For the given data and reference, the output should have the instances ordered as `[[1, 0], [1, 0.1], [0.1, 0], [2, 0], [0, 0.1]]`.

**Actual Behavior:**
The output instances are not sorted by distance, resulting in an order like `[[1, 0.1], [2, 0], [1, 0], [0.1, 0], [0, 0.1]]`, which fails assertions checking for the correct order based on distance.

[/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/data/owneighbors.py
import numpy as np

from AnyQt.QtCore import Qt

from Orange.data import Table, Domain, ContinuousVariable
from Orange.data.util import get_unique_names
from Orange.preprocess import RemoveNaNColumns, Impute
from Orange import distance
from Orange.widgets import gui
from Orange.widgets.settings import Setting
from Orange.widgets.utils.signals import Input, Output
from Orange.widgets.widget import OWWidget, Msg
from Orange.widgets.utils.widgetpreview import WidgetPreview

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


class OWNeighbors(OWWidget):
    name = "Neighbors"
    description = "Compute nearest neighbors in data according to reference."
    icon = "icons/Neighbors.svg"
    category = "Unsupervised"
    replaces = ["orangecontrib.prototypes.widgets.owneighbours.OWNeighbours"]

    class Inputs:
        data = Input("Data", Table)
        reference = Input("Reference", Table)

    class Outputs:
        data = Output("Neighbors", Table)

    class Info(OWWidget.Warning):
        removed_references = \
            Msg("Input data includes reference instance(s).\n"
                "Reference instances are excluded from the output.")

    class Warning(OWWidget.Warning):
        all_data_as_reference = \
            Msg("Every data instance is same as some reference")

    class Error(OWWidget.Error):
        diff_domains = Msg("Data and reference have different features")

    n_neighbors: int
    distance_index: int

    n_neighbors = Setting(10)
    limit_neighbors = Setting(True)
    distance_index = Setting(0)
    auto_apply = Setting(True)

    want_main_area = False
    resizing_enabled = False

    def __init__(self):
        super().__init__()

        self.data = None
        self.reference = None
        self.distances = None

        box = gui.vBox(self.controlArea, box=True)
        gui.comboBox(
            box, self, "distance_index", orientation=Qt.Horizontal,
            label="Distance metric: ", items=[d[0] for d in METRICS],
            callback=self.recompute)
        gui.spin(
            box, self, "n_neighbors", label="Limit number of neighbors to:",
            step=1, spinType=int, minv=0, maxv=100, checked='limit_neighbors',
            # call apply by gui.auto_commit, pylint: disable=unnecessary-lambda
            checkCallback=self.commit.deferred,
            callback=self.commit.deferred)

        self.apply_button = gui.auto_apply(self.buttonsArea, self)

    @Inputs.data
    def set_data(self, data):
        self.controls.n_neighbors.setMaximum(len(data) if data else 100)
        self.data = data

    @Inputs.reference
    def set_ref(self, refs):
        self.reference = refs

    def handleNewSignals(self):
        self.compute_distances()
        self.commit.now()

    def recompute(self):
        self.compute_distances()
        self.commit.deferred()

    def compute_distances(self):
        self.Error.diff_domains.clear()
        if not self.data or not self.reference:
            self.distances = None
            return
        if set(self.reference.domain.attributes) != \
                set(self.data.domain.attributes):
            self.Error.diff_domains()
            self.distances = None
            return

        metric = METRICS[self.distance_index][1]
        n_ref = len(self.reference)

        # comparing only attributes, no metas and class-vars
        new_domain = Domain(self.data.domain.attributes)
        reference = self.reference.transform(new_domain)
        data = self.data.transform(new_domain)

        all_data = Table.concatenate([reference, data], 0)
        pp_all_data = Impute()(RemoveNaNColumns()(all_data))
        pp_reference, pp_data = pp_all_data[:n_ref], pp_all_data[n_ref:]
        self.distances = metric(pp_data, pp_reference).min(axis=1)

    @gui.deferred
    def commit(self):
        indices = self._compute_indices()

        if indices is None:
            neighbors = None
        else:
            neighbors = self._data_with_similarity(indices)
        self.Outputs.data.send(neighbors)

    def _compute_indices(self):
        self.Warning.all_data_as_reference.clear()
        self.Info.removed_references.clear()

        if self.distances is None:
            return None

        inrefs = np.isin(self.data.ids, self.reference.ids)
        if np.all(inrefs):
            self.Warning.all_data_as_reference()
            return None
        if np.any(inrefs):
            self.Info.removed_references()

        dist = np.copy(self.distances)
        dist[inrefs] = np.max(dist) + 1
        up_to = len(dist) - np.sum(inrefs)
        if self.limit_neighbors and self.n_neighbors < up_to:
            up_to = self.n_neighbors
        return np.argpartition(dist, up_to - 1)[:up_to]

    def _data_with_similarity(self, indices):
        domain = self.data.domain
        dist_var = ContinuousVariable(get_unique_names(domain, "distance"))
        metas = domain.metas + (dist_var, )
        domain = Domain(domain.attributes, domain.class_vars, metas)
        neighbours = self.data.from_table(domain, self.data, row_indices=indices)
        distances = self.distances[indices]
        with neighbours.unlocked(neighbours.metas):
            if distances.size > 0:
                neighbours.set_column(dist_var, distances)
        return neighbours


if __name__ == "__main__":  # pragma: no cover
    iris = Table("iris.tab")
    WidgetPreview(OWNeighbors).run(
        set_data=iris,
        set_ref=iris[:1])

```
--- 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:** Neighbors Not Sorted by Distance, Causing Incorrect Output Order

**Description:**
When using the Neighbors widget, the resulting instances are not sorted in ascending order based on their distance from the reference instance. This leads to unexpected ordering of neighbors, which can affect downstream analysis and results.

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

# Define domain and reference
domain = Domain([ContinuousVariable(x) for x in "ab"])
reference = Table.from_numpy(domain, [[1, 0]])

# Define data with unsorted neighbors
data = Table.from_numpy(domain, [
    [1, 0.1],
    [2, 0],
    [1, 0],
    [0.1, 0],
    [0, 0.1]
])

# Send signals to widget
widget.send_signal(widget.Inputs.data, data)
widget.send_signal(widget.Inputs.reference, reference)

# Get and check output
output = widget.get_output(widget.Outputs.data)
expected_order = [
    [1, 0],
    [1, 0.1],
    [0.1, 0],
    [2, 0],
    [0, 0.1]
]
# The output does not match the expected sorted order
```

**Expected Behavior:**
The neighbors should be sorted in ascending order of their distance from the reference instance. For the given data and reference, the output should have the instances ordered as `[[1, 0], [1, 0.1], [0.1, 0], [2, 0], [0, 0.1]]`.

**Actual Behavior:**
The output instances are not sorted by distance, resulting in an order like `[[1, 0.1], [2, 0], [1, 0], [0.1, 0], [0, 0.1]]`, which fails assertions checking for the correct order based on distance.

[/ISSUE]
{
    "TestOWNeighbors.test_all_equal_ref": "PASSED",
    "TestOWNeighbors.test_apply": "PASSED",
    "TestOWNeighbors.test_compute_distances_apply_called": "PASSED",
    "TestOWNeighbors.test_compute_distances_calls_distance": "PASSED",
    "TestOWNeighbors.test_compute_distances_distance_no_data": "PASSED",
    "TestOWNeighbors.test_compute_indices_without_reference": "PASSED",
    "TestOWNeighbors.test_data_with_similarity": "PASSED",
    "TestOWNeighbors.test_different_domains": "PASSED",
    "TestOWNeighbors.test_different_domains_same_names": "PASSED",
    "TestOWNeighbors.test_different_metas": "PASSED",
    "TestOWNeighbors.test_exclude_reference": "PASSED",
    "TestOWNeighbors.test_image_export": "PASSED",
    "TestOWNeighbors.test_inherited_table": "PASSED",
    "TestOWNeighbors.test_input_data": "PASSED",
    "TestOWNeighbors.test_input_data_disconnect": "PASSED",
    "TestOWNeighbors.test_input_reference": "PASSED",
    "TestOWNeighbors.test_input_reference_disconnect": "PASSED",
    "TestOWNeighbors.test_minimum_size": "PASSED",
    "TestOWNeighbors.test_missing_values": "PASSED",
    "TestOWNeighbors.test_msg_base_class": "PASSED",
    "TestOWNeighbors.test_n_neighbours_spin_max": "PASSED",
    "TestOWNeighbors.test_order_by_distance": "PASSED",
    "TestOWNeighbors.test_output_neighbors": "PASSED",
    "TestOWNeighbors.test_settings": "PASSED",
    "TestOWNeighbors.test_similarity": "PASSED"
}
diff --git a/Orange/widgets/data/owneighbors.py b/Orange/widgets/data/owneighbors.py
index 62d98a95c..ecf99fc5b 100644
--- a/Orange/widgets/data/owneighbors.py
+++ b/Orange/widgets/data/owneighbors.py
@@ -153,7 +153,12 @@ class OWNeighbors(OWWidget):
         up_to = len(dist) - np.sum(inrefs)
         if self.limit_neighbors and self.n_neighbors < up_to:
             up_to = self.n_neighbors
-        return np.argpartition(dist, up_to - 1)[:up_to]
+        # get indexes of N neighbours in unsorted order - faster than argsort
+        idx = np.argpartition(dist, up_to - 1)[:up_to]
+        # sort selected N neighbours according to distances
+        sorted_subset_idx = np.argsort(dist[idx])
+        # map sorted indexes back to original index space
+        return idx[sorted_subset_idx]
 
     def _data_with_similarity(self, indices):
         domain = self.data.domain

{"Orange/widgets/data/owneighbors.py": "import numpy as np\n\nfrom AnyQt.QtCore import Qt\n\nfrom Orange.data import Table, Domain, ContinuousVariable\nfrom Orange.data.util import get_unique_names\nfrom Orange.preprocess import RemoveNaNColumns, Impute\nfrom Orange import distance\nfrom Orange.widgets import gui\nfrom Orange.widgets.settings import Setting\nfrom Orange.widgets.utils.signals import Input, Output\nfrom Orange.widgets.widget import OWWidget, Msg\nfrom Orange.widgets.utils.widgetpreview import WidgetPreview\n\nMETRICS = [\n    (\"Euclidean\", distance.Euclidean),\n    (\"Manhattan\", distance.Manhattan),\n    (\"Mahalanobis\", distance.Mahalanobis),\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]\n\n\nclass OWNeighbors(OWWidget):\n    name = \"Neighbors\"\n    description = \"Compute nearest neighbors in data according to reference.\"\n    icon = \"icons/Neighbors.svg\"\n    category = \"Unsupervised\"\n    replaces = [\"orangecontrib.prototypes.widgets.owneighbours.OWNeighbours\"]\n\n    class Inputs:\n        data = Input(\"Data\", Table)\n        reference = Input(\"Reference\", Table)\n\n    class Outputs:\n        data = Output(\"Neighbors\", Table)\n\n    class Info(OWWidget.Warning):\n        removed_references = \\\n            Msg(\"Input data includes reference instance(s).\\n\"\n                \"Reference instances are excluded from the output.\")\n\n    class Warning(OWWidget.Warning):\n        all_data_as_reference = \\\n            Msg(\"Every data instance is same as some reference\")\n\n    class Error(OWWidget.Error):\n        diff_domains = Msg(\"Data and reference have different features\")\n\n    n_neighbors: int\n    distance_index: int\n\n    n_neighbors = Setting(10)\n    limit_neighbors = Setting(True)\n    distance_index = Setting(0)\n    auto_apply = Setting(True)\n\n    want_main_area = False\n    resizing_enabled = False\n\n    def __init__(self):\n        super().__init__()\n\n        self.data = None\n        self.reference = None\n        self.distances = None\n\n        box = gui.vBox(self.controlArea, box=True)\n        gui.comboBox(\n            box, self, \"distance_index\", orientation=Qt.Horizontal,\n            label=\"Distance metric: \", items=[d[0] for d in METRICS],\n            callback=self.recompute)\n        gui.spin(\n            box, self, \"n_neighbors\", label=\"Limit number of neighbors to:\",\n            step=1, spinType=int, minv=0, maxv=100, checked='limit_neighbors',\n            # call apply by gui.auto_commit, pylint: disable=unnecessary-lambda\n            checkCallback=self.commit.deferred,\n            callback=self.commit.deferred)\n\n        self.apply_button = gui.auto_apply(self.buttonsArea, self)\n\n    @Inputs.data\n    def set_data(self, data):\n        self.controls.n_neighbors.setMaximum(len(data) if data else 100)\n        self.data = data\n\n    @Inputs.reference\n    def set_ref(self, refs):\n        self.reference = refs\n\n    def handleNewSignals(self):\n        self.compute_distances()\n        self.commit.now()\n\n    def recompute(self):\n        self.compute_distances()\n        self.commit.deferred()\n\n    def compute_distances(self):\n        self.Error.diff_domains.clear()\n        if not self.data or not self.reference:\n            self.distances = None\n            return\n        if set(self.reference.domain.attributes) != \\\n                set(self.data.domain.attributes):\n            self.Error.diff_domains()\n            self.distances = None\n            return\n\n        metric = METRICS[self.distance_index][1]\n        n_ref = len(self.reference)\n\n        # comparing only attributes, no metas and class-vars\n        new_domain = Domain(self.data.domain.attributes)\n        reference = self.reference.transform(new_domain)\n        data = self.data.transform(new_domain)\n\n        all_data = Table.concatenate([reference, data], 0)\n        pp_all_data = Impute()(RemoveNaNColumns()(all_data))\n        pp_reference, pp_data = pp_all_data[:n_ref], pp_all_data[n_ref:]\n        self.distances = metric(pp_data, pp_reference).min(axis=1)\n\n    @gui.deferred\n    def commit(self):\n        indices = self._compute_indices()\n\n        if indices is None:\n            neighbors = None\n        else:\n            neighbors = self._data_with_similarity(indices)\n        self.Outputs.data.send(neighbors)\n\n    def _compute_indices(self):\n        self.Warning.all_data_as_reference.clear()\n        self.Info.removed_references.clear()\n\n        if self.distances is None:\n            return None\n\n        inrefs = np.isin(self.data.ids, self.reference.ids)\n        if np.all(inrefs):\n            self.Warning.all_data_as_reference()\n            return None\n        if np.any(inrefs):\n            self.Info.removed_references()\n\n        dist = np.copy(self.distances)\n        dist[inrefs] = np.max(dist) + 1\n        up_to = len(dist) - np.sum(inrefs)\n        if self.limit_neighbors and self.n_neighbors < up_to:\n            up_to = self.n_neighbors\n        return np.argpartition(dist, up_to - 1)[:up_to]\n\n    def _data_with_similarity(self, indices):\n        domain = self.data.domain\n        dist_var = ContinuousVariable(get_unique_names(domain, \"distance\"))\n        metas = domain.metas + (dist_var, )\n        domain = Domain(domain.attributes, domain.class_vars, metas)\n        neighbours = self.data.from_table(domain, self.data, row_indices=indices)\n        distances = self.distances[indices]\n        with neighbours.unlocked(neighbours.metas):\n            if distances.size > 0:\n                neighbours.set_column(dist_var, distances)\n        return neighbours\n\n\nif __name__ == \"__main__\":  # pragma: no cover\n    iris = Table(\"iris.tab\")\n    WidgetPreview(OWNeighbors).run(\n        set_data=iris,\n        set_ref=iris[:1])\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:** Neighbors Not Sorted by Distance, Causing Incorrect Output Order

**Description:**
When using the Neighbors widget, the resulting instances are not sorted in ascending order based on their distance from the reference instance. This leads to unexpected ordering of neighbors, which can affect downstream analysis and results.

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

# Define domain and reference
domain = Domain([ContinuousVariable(x) for x in "ab"])
reference = Table.from_numpy(domain, [[1, 0]])

# Define data with unsorted neighbors
data = Table.from_numpy(domain, [
    [1, 0.1],
    [2, 0],
    [1, 0],
    [0.1, 0],
    [0, 0.1]
])

# Send signals to widget
widget.send_signal(widget.Inputs.data, data)
widget.send_signal(widget.Inputs.reference, reference)

# Get and check output
output = widget.get_output(widget.Outputs.data)
expected_order = [
    [1, 0],
    [1, 0.1],
    [0.1, 0],
    [2, 0],
    [0, 0.1]
]
# The output does not match the expected sorted order
```

**Expected Behavior:**
The neighbors should be sorted in ascending order of their distance from the reference instance. For the given data and reference, the output should have the instances ordered as `[[1, 0], [1, 0.1], [0.1, 0], [2, 0], [0, 0.1]]`.

**Actual Behavior:**
The output instances are not sorted by distance, resulting in an order like `[[1, 0.1], [2, 0], [1, 0], [0.1, 0], [0, 0.1]]`, which fails assertions checking for the correct order based on distance.

[/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/data/owneighbors.py
import numpy as np

from AnyQt.QtCore import Qt

from Orange.data import Table, Domain, ContinuousVariable
from Orange.data.util import get_unique_names
from Orange.preprocess import RemoveNaNColumns, Impute
from Orange import distance
from Orange.widgets import gui
from Orange.widgets.settings import Setting
from Orange.widgets.utils.signals import Input, Output
from Orange.widgets.widget import OWWidget, Msg
from Orange.widgets.utils.widgetpreview import WidgetPreview

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


class OWNeighbors(OWWidget):
    name = "Neighbors"
    description = "Compute nearest neighbors in data according to reference."
    icon = "icons/Neighbors.svg"
    category = "Unsupervised"
    replaces = ["orangecontrib.prototypes.widgets.owneighbours.OWNeighbours"]

    class Inputs:
        data = Input("Data", Table)
        reference = Input("Reference", Table)

    class Outputs:
        data = Output("Neighbors", Table)

    class Info(OWWidget.Warning):
        removed_references = \
            Msg("Input data includes reference instance(s).\n"
                "Reference instances are excluded from the output.")

    class Warning(OWWidget.Warning):
        all_data_as_reference = \
            Msg("Every data instance is same as some reference")

    class Error(OWWidget.Error):
        diff_domains = Msg("Data and reference have different features")

    n_neighbors: int
    distance_index: int

    n_neighbors = Setting(10)
    limit_neighbors = Setting(True)
    distance_index = Setting(0)
    auto_apply = Setting(True)

    want_main_area = False
    resizing_enabled = False

    def __init__(self):
        super().__init__()

        self.data = None
        self.reference = None
        self.distances = None

        box = gui.vBox(self.controlArea, box=True)
        gui.comboBox(
            box, self, "distance_index", orientation=Qt.Horizontal,
            label="Distance metric: ", items=[d[0] for d in METRICS],
            callback=self.recompute)
        gui.spin(
            box, self, "n_neighbors", label="Limit number of neighbors to:",
            step=1, spinType=int, minv=0, maxv=100, checked='limit_neighbors',
            # call apply by gui.auto_commit, pylint: disable=unnecessary-lambda
            checkCallback=self.commit.deferred,
            callback=self.commit.deferred)

        self.apply_button = gui.auto_apply(self.buttonsArea, self)

    @Inputs.data
    def set_data(self, data):
        self.controls.n_neighbors.setMaximum(len(data) if data else 100)
        self.data = data

    @Inputs.reference
    def set_ref(self, refs):
        self.reference = refs

    def handleNewSignals(self):
        self.compute_distances()
        self.commit.now()

    def recompute(self):
        self.compute_distances()
        self.commit.deferred()

    def compute_distances(self):
        self.Error.diff_domains.clear()
        if not self.data or not self.reference:
            self.distances = None
            return
        if set(self.reference.domain.attributes) != \
                set(self.data.domain.attributes):
            self.Error.diff_domains()
            self.distances = None
            return

        metric = METRICS[self.distance_index][1]
        n_ref = len(self.reference)

        # comparing only attributes, no metas and class-vars
        new_domain = Domain(self.data.domain.attributes)
        reference = self.reference.transform(new_domain)
        data = self.data.transform(new_domain)

        all_data = Table.concatenate([reference, data], 0)
        pp_all_data = Impute()(RemoveNaNColumns()(all_data))
        pp_reference, pp_data = pp_all_data[:n_ref], pp_all_data[n_ref:]
        self.distances = metric(pp_data, pp_reference).min(axis=1)

    @gui.deferred
    def commit(self):
        indices = self._compute_indices()

        if indices is None:
            neighbors = None
        else:
            neighbors = self._data_with_similarity(indices)
        self.Outputs.data.send(neighbors)

    def _compute_indices(self):
        self.Warning.all_data_as_reference.clear()
        self.Info.removed_references.clear()

        if self.distances is None:
            return None

        inrefs = np.isin(self.data.ids, self.reference.ids)
        if np.all(inrefs):
            self.Warning.all_data_as_reference()
            return None
        if np.any(inrefs):
            self.Info.removed_references()

        dist = np.copy(self.distances)
        dist[inrefs] = np.max(dist) + 1
        up_to = len(dist) - np.sum(inrefs)
        if self.limit_neighbors and self.n_neighbors < up_to:
            up_to = self.n_neighbors
        return np.argpartition(dist, up_to - 1)[:up_to]

    def _data_with_similarity(self, indices):
        domain = self.data.domain
        dist_var = ContinuousVariable(get_unique_names(domain, "distance"))
        metas = domain.metas + (dist_var, )
        domain = Domain(domain.attributes, domain.class_vars, metas)
        neighbours = self.data.from_table(domain, self.data, row_indices=indices)
        distances = self.distances[indices]
        with neighbours.unlocked(neighbours.metas):
            if distances.size > 0:
                neighbours.set_column(dist_var, distances)
        return neighbours


if __name__ == "__main__":  # pragma: no cover
    iris = Table("iris.tab")
    WidgetPreview(OWNeighbors).run(
        set_data=iris,
        set_ref=iris[:1])

```
--- 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