gammapy__gammapy-5674

{
  "status": "ok",
  "stdout": "./gammapy/estimators/points/core.py:31:def squash_fluxpoints(flux_point, axis):\n./gammapy/estimators/points/core.py:869:            fp_new = squash_fluxpoints(fp, axis_new)\n",
  "stderr": "",
  "exit_code": 0
}

{
  "status": "ok",
  "filepath": "gammapy/estimators/points/core.py",
  "content": "# Licensed under a 3-clause BSD style license - see LICENSE.rst\nimport logging\nfrom copy import deepcopy\nimport numpy as np\nfrom scipy import stats\nfrom scipy.interpolate import interp1d\nfrom scipy.optimize import minimize\nfrom astropy.io import fits\nfrom astropy.io.registry import IORegistryError\nfrom astropy.table import Table, vstack\nfrom astropy.time import Time\nfrom astropy.visualization import quantity_support\nimport matplotlib.pyplot as plt\nfrom gammapy.data import GTI\nfrom gammapy.maps import Map, MapAxis, Maps, RegionNDMap, TimeMapAxis\nfrom gammapy.maps.axes import UNIT_STRING_FORMAT, flat_if_equal\nfrom gammapy.modeling.models import TemplateSpectralModel\nfrom gammapy.modeling.models.spectral import scale_plot_flux\nfrom gammapy.modeling.scipy import stat_profile_ul_scipy\nfrom gammapy.utils.interpolation import interpolate_profile\nfrom gammapy.utils.scripts import make_path\nfrom gammapy.utils.table import table_standardise_units_copy\nfrom gammapy.utils.time import time_ref_to_dict\nfrom ..map.core import DEFAULT_UNIT, FluxMaps\n\n__all__ = [\"FluxPoints\"]\n\nlog = logging.getLogger(__name__)\n\n\ndef squash_fluxpoints(flux_point, axis):\n    \"\"\"Squash a `~FluxPoints` object into one point.\n\n    The log-likelihoods profiles in each bin are summed\n    to compute the resultant quantities. Stat profiles\n    must be present on the `~FluxPoints` object for\n    this method to work.\n    \"\"\"\n    value_scan = flux_point.stat_scan.geom.axes[\"norm\"].center\n    stat_scan = np.sum(flux_point.stat_scan.data, axis=0).ravel()\n    f = interp1d(value_scan, stat_scan, kind=\"quadratic\", bounds_error=False)\n    f = interpolate_profile(value_scan, stat_scan)\n    minimizer = minimize(\n        f,\n        x0=value_scan[int(len(value_scan) / 2)],\n        bounds=[(value_scan[0], value_scan[-1])],\n        method=\"L-BFGS-B\",\n    )\n\n    maps = Maps()\n    geom = flux_point.geom.to_image()\n    if axis.name != \"energy\":\n        geom = geom.to_cube([flux_point.geom.axes[\"energy\"]])\n\n    maps[\"norm\"] = Map.from_geom(geom, data=minimizer.x)\n    maps[\"norm_err\"] = Map.from_geom(\n        geom, data=np.sqrt(minimizer.hess_inv.todense()).reshape(geom.data_shape)\n    )\n    maps[\"n_dof\"] = Map.from_geom(geom, data=flux_point.geom.axes[axis.name].nbin)\n\n    if \"norm_ul\" in flux_point.available_quantities:\n        delta_ts = flux_point.meta.get(\"n_sigma_ul\", 2) ** 2\n        ul = stat_profile_ul_scipy(value_scan, stat_scan, delta_ts=delta_ts)\n        maps[\"norm_ul\"] = Map.from_geom(geom, data=ul.value)\n\n    maps[\"stat\"] = Map.from_geom(geom, data=f(minimizer.x))\n\n    geom_scan = geom.to_cube([MapAxis.from_nodes(value_scan, name=\"norm\")])\n    maps[\"stat_scan\"] = Map.from_geom(\n        geom=geom_scan, data=stat_scan.reshape(geom_scan.data_shape)\n    )\n    try:\n        maps[\"stat_null\"] = Map.from_geom(geom, data=np.sum(flux_point.stat_null.data))\n        maps[\"ts\"] = maps[\"stat_null\"] - maps[\"stat\"]\n    except AttributeError:\n        log.info(\n            \"Stat null info not present on original FluxPoints object. TS not computed\"\n        )\n\n    maps[\"success\"] = Map.from_geom(geom=geom, data=minimizer.success, dtype=bool)\n\n    combined_fp = FluxPoints.from_maps(\n        maps=maps,\n        sed_type=flux_point.sed_type_init,\n        reference_model=flux_point.reference_model,\n        gti=flux_point.gti,\n        meta=flux_point.meta,\n    )\n    return combined_fp\n\n\nclass FluxPoints(FluxMaps):\n    \"\"\"Flux points container.\n\n    The supported formats are described here: :ref:`gadf:flux-points`.\n\n    In summary, the following formats and minimum required columns are:\n\n    * Format ``dnde``: columns ``e_ref`` and ``dnde``\n    * Format ``e2dnde``: columns ``e_ref``, ``e2dnde``",
  "start_line": 1,
  "end_line": 100,
  "total_lines": 872,
  "is_truncated": false
}

{
  "status": "ok",
  "filepath": "gammapy/estimators/points/core.py",
  "content": "    def resample_axis(self, axis_new):\n        \"\"\"Rebin the flux point object along the new axis.\n\n        The log-likelihoods profiles in each bin are summed\n        to compute the resultant quantities.\n        Stat profiles must be present on the `~gammapy.estimators.FluxPoints` object for\n        this method to work.\n\n        For now, works only for flat `~gammapy.estimators.FluxPoints`.\n\n        Parameters\n        ----------\n        axis_new : `MapAxis` or `TimeMapAxis`\n            The new axis to resample along\n\n        Returns\n        -------\n        flux_points : `~gammapy.estimators.FluxPoints`\n            A new FluxPoints object with modified axis.\n        \"\"\"\n        if not self.has_stat_profiles:\n            raise ValueError(\"Stat profiles not present, rebinning is not possible\")\n\n        fluxpoints = []\n        for edge_min, edge_max in zip(axis_new.edges_min, axis_new.edges_max):\n            if isinstance(axis_new, TimeMapAxis):\n                edge_min = edge_min + axis_new.reference_time\n                edge_max = edge_max + axis_new.reference_time\n            fp = self.slice_by_coord({axis_new.name: slice(edge_min, edge_max)})\n            fp_new = squash_fluxpoints(fp, axis_new)\n            fluxpoints.append(fp_new)\n\n        return self.__class__.from_stack(fluxpoints, axis=axis_new)",
  "start_line": 840,
  "end_line": 872,
  "total_lines": 872,
  "is_truncated": false
}

{
  "status": "ok",
  "filepath": "gammapy/maps/region/ndmap.py",
  "content": "            data = np.zeros(geom.data_shape, dtype=dtype)\n\n        if meta is None:\n            meta = {}\n\n        self._geom = geom\n        self.data = data\n        self.meta = meta\n        self._unit = u.Unit(unit)\n\n    @property\n    def data(self):\n        return self._data\n\n    @data.setter\n    def data(self, value):\n        \"\"\"Set data.\n        Parameters\n        ----------\n        value : array-like\n            Data array.\n        \"\"\"\n        if np.isscalar(value):\n            value = value * np.ones(self.geom.data_shape, dtype=type(value))\n\n        if isinstance(value, u.Quantity):\n            raise TypeError(\"Map data must be a Numpy array. Set unit separately\")\n\n        input_shape = value.shape\n        if len(self.geom.data_shape) == 2 + len(value.shape):\n            if self.geom.data_shape[: len(value.shape)] == value.shape:\n                value = np.expand_dims(value, (-2, -1))\n\n        if self.geom.data_shape != value.shape:\n            raise ValueError(\n                f\"Input shape {input_shape} is not compatible with shape from geometry {self.geom.data_shape}\"\n            )\n\n        self._data = value\n\n    def plot(self, ax=None, axis_name=None, **kwargs):",
  "start_line": 50,
  "end_line": 90,
  "total_lines": 837,
  "is_truncated": false
}

{
  "status": "ok",
  "filepath": "gammapy/estimators/points/core.py",
  "content": "    >>> from gammapy.estimators import FluxPoints\n    >>> from gammapy.utils.scripts import make_path\n\n    >>> filename = make_path('$GAMMAPY_DATA/tests/spectrum/flux_points/flux_points_ctb_37b.txt')\n    >>> table = Table.read(filename ,format='ascii.csv', delimiter=' ', comment='#')\n    >>> table.rename_column('Differential_Flux', 'dnde')\n    >>> table['dnde'].unit = 'cm-2 s-1 TeV-1'\n\n    >>> table.rename_column('lower_error', 'dnde_errn')\n    >>> table['dnde_errn'].unit = 'cm-2 s-1 TeV-1'\n\n    >>> table.rename_column('upper_error', 'dnde_errp')\n    >>> table['dnde_errp'].unit = 'cm-2 s-1 TeV-1'\n\n    >>> table.rename_column('E', 'e_ref')\n    >>> table['e_ref'].unit = 'TeV'\n\n    >>> flux_points = FluxPoints.from_table(table, sed_type=\"dnde\")\n    >>> flux_points.plot(sed_type=\"e2dnde\") #doctest: +SKIP\n\n",
  "start_line": 150,
  "end_line": 170,
  "total_lines": 872,
  "is_truncated": false
}

{
  "status": "error",
  "error_type": "CommandError",
  "error_message": "",
  "details": {
    "stdout": "",
    "stderr": "",
    "exit_code": 1
  }
}

{
  "status": "error",
  "error_type": "CommandError",
  "error_message": "",
  "details": {
    "stdout": "",
    "stderr": "",
    "exit_code": 1
  }
}

{
  "status": "ok",
  "stdout": "498:            if self.has_stat_profiles:\n819:        if not self.has_stat_profiles:\n860:        if not self.has_stat_profiles:\n",
  "stderr": "",
  "exit_code": 0
}

{
  "status": "ok",
  "filepath": "gammapy/estimators/points/core.py",
  "content": "            if not self.is_convertible_to_flux_sed_type:\n                table.remove_columns([\"e_min\", \"e_max\"])\n\n            if self.n_sigma_ul:\n                table.meta[\"UL_CONF\"] = np.round(\n                    1 - 2 * stats.norm.sf(self.n_sigma_ul), 7\n                )\n\n            if sed_type == \"likelihood\":\n                table[\"ref_dnde\"] = self.dnde_ref[idx]\n                table[\"ref_flux\"] = self.flux_ref[idx]\n                table[\"ref_eflux\"] = self.eflux_ref[idx]\n\n            for quantity in self.all_quantities(sed_type=sed_type):\n                data = getattr(self, quantity, None)\n                if data:\n                    table[quantity] = data.quantity[idx]\n\n            if self.has_stat_profiles:\n                norm_axis = self.stat_scan.geom.axes[\"norm\"]\n                table[\"norm_scan\"] = norm_axis.center.reshape((1, -1))\n                table[\"stat\"] = self.stat.data[idx]\n                table[\"stat_scan\"] = self.stat_scan.data[idx]\n\n            table[\"is_ul\"] = self.is_ul.data[idx]\n            if not self.has_ul:\n                table.remove_columns(\"is_ul\")\n\n        elif format == \"lightcurve\":\n            time_axis = self.geom.axes[\"time\"]\n",
  "start_line": 480,
  "end_line": 510,
  "total_lines": 872,
  "is_truncated": false
}

{
  "status": "ok",
  "filepath": "gammapy/estimators/points/core.py",
  "content": "            for quantity in self.all_quantities(sed_type=sed_type):\n                data = getattr(self, quantity, None)\n                if data:\n                    table[quantity] = data.quantity.squeeze()\n        elif format == \"profile\":\n            x_axis = self.geom.axes[\"projected-distance\"]\n\n            tables = []\n            for idx, (x_min, x_max) in enumerate(x_axis.iter_by_edges):\n                table_flat = Table()\n                table_flat[\"x_min\"] = [x_min]\n                table_flat[\"x_max\"] = [x_max]\n                table_flat[\"x_ref\"] = [(x_max + x_min) / 2]\n\n                fp = self.slice_by_idx(slices={\"projected-distance\": idx})\n                table = fp.to_table(sed_type=sed_type, format=\"gadf-sed\")\n\n                for column in table.columns:\n                    table_flat[column] = table[column][np.newaxis]\n\n                tables.append(table_flat)\n\n            table = vstack(tables)\n\n        else:\n            raise ValueError(f\"Not a supported format {format}\")\n\n        if formatted:\n            table = self._format_table(table=table)\n\n        return table\n\n    @staticmethod\n    def _energy_ref_lafferty(model, energy_min, energy_max):\n        \"\"\"Helper for `to_sed_type`.\n\n        Compute energy_ref that the value at energy_ref corresponds\n        to the mean value between energy_min and energy_max.\n        \"\"\"\n        flux = model.integral(energy_min, energy_max)\n        dnde_mean = flux / (energy_max - energy_min)\n        return model.inverse(dnde_mean)\n\n    def _plot_get_flux_err(self, sed_type=None):\n        \"\"\"Compute flux error for given SED type.\"\"\"\n        y_errn, y_errp = None, None\n\n        if \"norm_err\" in self.available_quantities:\n            # symmetric error\n            y_errn = getattr(self, sed_type + \"_err\")\n            y_errp = y_errn.copy()",
  "start_line": 550,
  "end_line": 600,
  "total_lines": 872,
  "is_truncated": false
}

{
  "status": "ok",
  "stdout": "498:            if self.has_stat_profiles:\n819:        if not self.has_stat_profiles:\n860:        if not self.has_stat_profiles:\n",
  "stderr": "",
  "exit_code": 0
}

{
  "status": "ok",
  "stdout": "__init__.py\nasmooth.py\ncore.py\nexcess.py\ntests\nts.py\n",
  "stderr": "",
  "exit_code": 0
}

{
  "status": "ok",
  "filepath": "gammapy/estimators/map/core.py",
  "content": "# Licensed under a 3-clause BSD style license - see LICENSE.rst\nimport logging\nfrom copy import deepcopy\nimport numpy as np\nfrom astropy import units as u\nfrom astropy.io import fits\nfrom astropy.utils import classproperty\nfrom gammapy.data import GTI\nfrom gammapy.maps import Map, Maps, TimeMapAxis\nfrom gammapy.modeling.models import (\n    Models,\n    PowerLawSpectralModel,\n    SkyModel,\n    SpectralModel,\n)\nfrom gammapy.utils.scripts import make_path\n\n__all__ = [\"FluxMaps\"]\n\nlog = logging.getLogger(__name__)\n\n\nDEFAULT_UNIT = {\n    \"dnde\": u.Unit(\"cm-2 s-1 TeV-1\"),\n    \"e2dnde\": u.Unit(\"erg cm-2 s-1\"),\n    \"flux\": u.Unit(\"cm-2 s-1\"),\n    \"eflux\": u.Unit(\"erg cm-2 s-1\"),\n    \"norm\": u.Unit(\"\"),\n}\n\nREQUIRED_MAPS = {\n    \"dnde\": [\"dnde\"],\n    \"e2dnde\": [\"e2dnde\"],\n    \"flux\": [\"flux\"],\n    \"eflux\": [\"eflux\"],\n    \"likelihood\": [\"norm\"],\n}\n\nREQUIRED_COLUMNS = {\n    \"dnde\": [\"e_ref\", \"dnde\"],\n    \"e2dnde\": [\"e_ref\", \"e2dnde\"],\n    \"flux\": [\"e_min\", \"e_max\", \"flux\"],\n    \"eflux\": [\"e_min\", \"e_max\", \"eflux\"],\n    # TODO: extend required columns\n    \"likelihood\": [\n        \"e_min\",\n        \"e_max\",\n        \"e_ref\",\n        \"ref_dnde\",\n        \"ref_flux\",\n        \"ref_eflux\",\n        \"norm\",\n    ],\n}\n\n\nREQUIRED_QUANTITIES_SCAN = [\"stat_scan\", \"stat\"]\n\nOPTIONAL_QUANTITIES = {\n    \"dnde\": [\"dnde_err\", \"dnde_errp\", \"dnde_errn\", \"dnde_ul\"],\n    \"e2dnde\": [\"e2dnde_err\", \"e2dnde_errp\", \"e2dnde_errn\", \"e2dnde_ul\"],\n    \"flux\": [\"flux_err\", \"flux_errp\", \"flux_errn\", \"flux_ul\", \"flux_sensitivity\"],\n    \"eflux\": [\"eflux_err\", \"eflux_errp\", \"eflux_errn\", \"eflux_ul\"],\n    \"likelihood\": [\"norm_err\", \"norm_errn\", \"norm_errp\", \"norm_ul\"],\n}\n\nVALID_QUANTITIES = [\n    \"norm\",\n    \"norm_err\",\n    \"norm_errn\",\n    \"norm_errp\",\n    \"norm_ul\",\n    \"norm_sensitivity\",\n    \"ts\",\n    \"sqrt_ts\",\n    \"npred\",\n    \"npred_excess\",\n    \"stat\",\n    \"stat_null\",\n    \"stat_scan\",\n    \"dnde_scan_values\",\n    \"niter\",\n    \"is_ul\",\n    \"counts\",\n    \"success\",\n    \"n_dof\",\n    \"alpha\",\n    \"acceptance_on\",\n    \"acceptance_off\",\n]\n\n\nOPTIONAL_QUANTITIES_COMMON = [\n    \"ts\",\n    \"sqrt_ts\",\n    \"npred\",\n    \"npred_excess\",\n    \"stat\",\n    \"stat_null\",\n    \"stat_scan\",\n    \"dnde_scan_values\",\n    \"niter\",\n    \"is_ul\",\n    \"counts\",\n    \"success\",\n    \"n_dof\",\n]\n\n\nclass FluxMaps:\n    \"\"\"A flux map / points container.\n\n    It contains a set of `~gammapy.maps.Map` objects that store the estimated\n    flux as a function of energy as well as associated quantities (typically\n    errors, upper limits, delta TS and possibly raw quantities such counts,\n    excesses etc). It also contains a reference model to convert the flux\n    values in different formats. Usually, this should be the model used to\n    produce the flux map.\n\n    The associated map geometry can use a `RegionGeom` to store the equivalent\n    of flux points, or a `WcsGeom`/`HpxGeom` to store an energy dependent flux map.\n\n    The container relies internally on the 'Likelihood' SED type defined in\n    :ref:`gadf:flux-points` and offers convenience properties to convert to\n    other flux formats, namely: ``dnde``, ``flux``, ``eflux`` or ``e2dnde``.\n    The conversion is done according to the reference model spectral shape.\n\n    Parameters\n    ----------\n    data : dict of `~gammapy.maps.Map`\n        The maps dictionary. Expected entries are the following:\n\n        * norm : the norm factor.\n        * norm_err : optional, the error on the norm factor.\n        * norm_errn : optional, the negative error on the norm factor.\n        * norm_errp : optional, the positive error on the norm factor.\n        * norm_ul : optional, the upper limit on the norm factor.\n        * norm_scan : optional, the norm values of the test statistic scan.\n        * stat_scan : optional, the test statistic scan values.\n        * ts : optional, the delta test statistic associated with the flux value.\n        * sqrt_ts : optional, the square root of the test statistic, when relevant.\n        * success : optional, a boolean tagging the validity of the estimation.\n        * n_dof : optional, the number of degrees of freedom used in TS computation\n        * alpha : optional, normalisation factor to accounts for differences between the test region and the background\n        * acceptance_off : optional, acceptance from the off region\n        * acceptance_on : optional, acceptance from the on region\n\n    reference_model : `~gammapy.modeling.models.SkyModel`, optional\n        The reference model to use for conversions. If None, a model consisting\n        of a point source with a power law spectrum of index 2 is assumed.",
  "start_line": 1,
  "end_line": 150,
  "total_lines": 1298,
  "is_truncated": true
}

{
  "status": "ok",
  "stdout": "271:    def has_stat_profiles(self):\n",
  "stderr": "",
  "exit_code": 0
}

{
  "status": "ok",
  "filepath": "gammapy/estimators/map/core.py",
  "content": "    @property\n    def has_any_ts(self):\n        \"\"\"Whether the flux estimate has either sqrt(TS) or test statistic defined.\"\"\"\n        return any(_ in self._data for _ in [\"ts\", \"sqrt_ts\"])\n\n    @property\n    def has_stat_profiles(self):\n        \"\"\"Whether the flux estimate has test statistic profiles.\"\"\"\n        return \"stat_scan\" in self._data\n\n    @property",
  "start_line": 265,
  "end_line": 275,
  "total_lines": 1298,
  "is_truncated": false
}

{
  "status": "ok",
  "stdout": "gammapy/estimators/map/excess.py:290:            reco_exposure = reco_exposure.resample_axis(\ngammapy/estimators/map/excess.py:299:            resampled_dataset.background = dataset.npred().resample_axis(\ngammapy/estimators/points/core.py:840:    def resample_axis(self, axis_new):\ngammapy/estimators/points/tests/test_core.py:419:def test_resample_axis():\ngammapy/estimators/points/tests/test_core.py:427:    l1 = lc_1d.resample_axis(axis_new=axis_new)\ngammapy/estimators/points/tests/test_core.py:436:    l1 = lc_1d.resample_axis(axis_new=axis_new)\ngammapy/estimators/points/tests/test_core.py:447:        fp.resample_axis(axis_new=MapAxis.from_nodes([0, 1, 2]))\n",
  "stderr": "",
  "exit_code": 0
}

{
  "status": "ok",
  "filepath": "gammapy/estimators/points/core.py",
  "content": "        for idx in np.ndindex(shape_axes):\n            stat_scan = np.abs(\n                self.stat_scan.data[idx].squeeze() - self.stat.data[idx].squeeze()\n            )\n            flux_points.norm_ul.data[idx] = stat_profile_ul_scipy(\n                value_scan, stat_scan, delta_ts=delta_ts, **kwargs\n            )\n        flux_points.meta[\"n_sigma_ul\"] = n_sigma_ul\n        return flux_points\n\n    def resample_axis(self, axis_new):\n        \"\"\"Rebin the flux point object along the new axis.\n\n        The log-likelihoods profiles in each bin are summed\n        to compute the resultant quantities.\n        Stat profiles must be present on the `~gammapy.estimators.FluxPoints` object for\n        this method to work.\n\n        For now, works only for flat `~gammapy.estimators.FluxPoints`.\n\n        Parameters\n        ----------\n        axis_new : `MapAxis` or `TimeMapAxis`\n            The new axis to resample along\n\n        Returns\n        -------\n        flux_points : `~gammapy.estimators.FluxPoints`\n            A new FluxPoints object with modified axis.\n        \"\"\"\n        if not self.has_stat_profiles:\n            raise ValueError(\"Stat profiles not present, rebinning is not possible\")\n\n        fluxpoints = []\n        for edge_min, edge_max in zip(axis_new.edges_min, axis_new.edges_max):\n            if isinstance(axis_new, TimeMapAxis):\n                edge_min = edge_min + axis_new.reference_time\n                edge_max = edge_max + axis_new.reference_time\n            fp = self.slice_by_coord({axis_new.name: slice(edge_min, edge_max)})\n            fp_new = squash_fluxpoints(fp, axis_new)\n            fluxpoints.append(fp_new)\n\n        return self.__class__.from_stack(fluxpoints, axis=axis_new)",
  "start_line": 830,
  "end_line": 872,
  "total_lines": 872,
  "is_truncated": false
}

{
  "status": "ok",
  "stdout": "31:def squash_fluxpoints(flux_point, axis):\n",
  "stderr": "",
  "exit_code": 0
}

{
  "status": "ok",
  "filepath": "gammapy/estimators/points/core.py",
  "content": "\ndef squash_fluxpoints(flux_point, axis):\n    \"\"\"Squash a `~FluxPoints` object into one point.\n\n    The log-likelihoods profiles in each bin are summed\n    to compute the resultant quantities. Stat profiles\n    must be present on the `~FluxPoints` object for\n    this method to work.\n    \"\"\"\n    value_scan = flux_point.stat_scan.geom.axes[\"norm\"].center\n    stat_scan = np.sum(flux_point.stat_scan.data, axis=0).ravel()\n    f = interp1d(value_scan, stat_scan, kind=\"quadratic\", bounds_error=False)\n    f = interpolate_profile(value_scan, stat_scan)\n    minimizer = minimize(\n        f,\n        x0=value_scan[int(len(value_scan) / 2)],\n        bounds=[(value_scan[0], value_scan[-1])],\n        method=\"L-BFGS-B\",\n    )\n\n    maps = Maps()\n    geom = flux_point.geom.to_image()\n    if axis.name != \"energy\":\n        geom = geom.to_cube([flux_point.geom.axes[\"energy\"]])\n\n    maps[\"norm\"] = Map.from_geom(geom, data=minimizer.x)\n    maps[\"norm_err\"] = Map.from_geom(\n        geom, data=np.sqrt(minimizer.hess_inv.todense()).reshape(geom.data_shape)\n    )\n    maps[\"n_dof\"] = Map.from_geom(geom, data=flux_point.geom.axes[axis.name].nbin)\n\n    if \"norm_ul\" in flux_point.available_quantities:\n        delta_ts = flux_point.meta.get(\"n_sigma_ul\", 2) ** 2\n        ul = stat_profile_ul_scipy(value_scan, stat_scan, delta_ts=delta_ts)\n        maps[\"norm_ul\"] = Map.from_geom(geom, data=ul.value)\n\n    maps[\"stat\"] = Map.from_geom(geom, data=f(minimizer.x))\n\n    geom_scan = geom.to_cube([MapAxis.from_nodes(value_scan, name=\"norm\")])\n    maps[\"stat_scan\"] = Map.from_geom(\n        geom=geom_scan, data=stat_scan.reshape(geom_scan.data_shape)\n    )\n    try:\n        maps[\"stat_null\"] = Map.from_geom(geom, data=np.sum(flux_point.stat_null.data))\n        maps[\"ts\"] = maps[\"stat_null\"] - maps[\"stat\"]\n    except AttributeError:\n        log.info(\n            \"Stat null info not present on original FluxPoints object. TS not computed\"\n        )\n\n    maps[\"success\"] = Map.from_geom(geom=geom, data=minimizer.success, dtype=bool)\n\n    combined_fp = FluxPoints.from_maps(\n        maps=maps,\n        sed_type=flux_point.sed_type_init,\n        reference_model=flux_point.reference_model,\n        gti=flux_point.gti,\n        meta=flux_point.meta,\n    )\n    return combined_fp\n\n\nclass FluxPoints(FluxMaps):\n    \"\"\"Flux points container.\n\n    The supported formats are described here: :ref:`gadf:flux-points`.\n\n    In summary, the following formats and minimum required columns are:\n\n    * Format ``dnde``: columns ``e_ref`` and ``dnde``\n    * Format ``e2dnde``: columns ``e_ref``, ``e2dnde``\n    * Format ``flux``: columns ``e_min``, ``e_max``, ``flux``\n    * Format ``eflux``: columns ``e_min``, ``e_max``, ``eflux``\n\n    Parameters\n    ----------\n    table : `~astropy.table.Table`\n        Table with flux point data.\n\n    Attributes\n    ----------\n    table : `~astropy.table.Table`\n        Table with flux point data.\n\n    Examples\n    --------\n    The `FluxPoints` object is most easily created by reading a file with\n    flux points given in one of the formats documented above::\n\n    >>> from gammapy.estimators import FluxPoints\n    >>> filename = '$GAMMAPY_DATA/hawc_crab/HAWC19_flux_points.fits'",
  "start_line": 30,
  "end_line": 120,
  "total_lines": 872,
  "is_truncated": false
}

{
  "status": "ok",
  "filepath": "gammapy/estimators/points/tests/test_core.py",
  "content": "        model, table[\"e_min\"], table[\"e_max\"]\n    )\n    fp = FluxPoints.from_table(table, reference_model=model)\n\n    assert fp.available_quantities == [\"norm\", \"n_dof\"]\n    assert_allclose(fp.n_dof.data.ravel(), table[\"n_dof\"])\n\n\n@requires_data()\ndef test_resample_axis():\n    lc_1d = FluxPoints.read(\n        \"$GAMMAPY_DATA/estimators/pks2155_hess_lc/pks2155_hess_lc.fits\",\n        format=\"lightcurve\",\n    )\n    axis_new = get_rebinned_axis(\n        lc_1d, method=\"fixed-bins\", group_size=5, axis_name=\"time\"\n    )\n    l1 = lc_1d.resample_axis(axis_new=axis_new)\n    assert_allclose(l1.norm.data.ravel()[0:2], [1.56321943, 2.12845751], rtol=1e-3)\n    assert_allclose(l1.norm_err.data.ravel()[0:2], [0.03904136, 0.03977413], rtol=1e-3)\n    assert_allclose(l1.n_dof.data.ravel()[0], 5.0)\n    assert l1.success.data.ravel()[0]\n\n    axis_new = get_rebinned_axis(\n        lc_1d, method=\"min-ts\", ts_threshold=300, axis_name=\"time\"\n    )\n    l1 = lc_1d.resample_axis(axis_new=axis_new)\n    assert_allclose(l1.norm_err.data.ravel()[0:2], [0.072236, 0.092942], rtol=1e-3)\n    assert_allclose(l1.ts.data.ravel()[0:2], [312.742222, 454.99609], rtol=1e-3)\n    assert_allclose(l1.stat_null.data.ravel()[0:2], [319.8675, 462.329], rtol=1e-3)\n    assert l1.success.data.ravel()[0]\n    assert_allclose(l1.n_dof.data[0][0][0][0], 2)\n\n    path = make_path(\"$GAMMAPY_DATA/tests/spectrum/flux_points/flux_points.fits\")\n    table = Table.read(path)\n    fp = FluxPoints.from_table(table)\n    with pytest.raises(ValueError):\n        fp.resample_axis(axis_new=MapAxis.from_nodes([0, 1, 2]))",
  "start_line": 410,
  "end_line": 447,
  "total_lines": 447,
  "is_truncated": false,
  "budget_warning": "Only 10 tool call(s) remaining (20/30 used). Finalize your edits and call submit_patch soon."
}

{
  "status": "error",
  "error_type": "CommandError",
  "error_message": "Traceback (most recent call last):\n  File \"<stdin>\", line 2, in <module>\nModuleNotFoundError: No module named 'gammapy.geometry'\n",
  "details": {
    "stdout": "",
    "stderr": "Traceback (most recent call last):\n  File \"<stdin>\", line 2, in <module>\nModuleNotFoundError: No module named 'gammapy.geometry'\n",
    "exit_code": 1
  },
  "budget_warning": "Only 9 tool call(s) remaining (21/30 used). Finalize your edits and call submit_patch soon."
}

{
  "status": "ok",
  "filepath": "gammapy/estimators/points/core.py",
  "content": "    stat_scan = np.sum(flux_point.stat_scan.data, axis=0).ravel()\n    f = interp1d(value_scan, stat_scan, kind=\"quadratic\", bounds_error=False)\n    f = interpolate_profile(value_scan, stat_scan)\n    minimizer = minimize(\n        f,\n        x0=value_scan[int(len(value_scan) / 2)],\n        bounds=[(value_scan[0], value_scan[-1])],\n        method=\"L-BFGS-B\",\n    )\n\n    maps = Maps()\n    geom = flux_point.geom.to_image()\n    if axis.name != \"energy\":\n        geom = geom.to_cube([flux_point.geom.axes[\"energy\"]])\n\n    maps[\"norm\"] = Map.from_geom(geom, data=minimizer.x)\n    maps[\"norm_err\"] = Map.from_geom(\n        geom, data=np.sqrt(minimizer.hess_inv.todense()).reshape(geom.data_shape)\n    )\n    maps[\"n_dof\"] = Map.from_geom(geom, data=flux_point.geom.axes[axis.name].nbin)\n",
  "start_line": 40,
  "end_line": 60,
  "total_lines": 872,
  "is_truncated": false,
  "budget_warning": "Only 8 tool call(s) remaining (22/30 used). Finalize your edits and call submit_patch soon."
}

{
  "status": "ok",
  "filepath": "gammapy/estimators/points/core.py",
  "content": "    maps[\"norm\"] = Map.from_geom(geom, data=minimizer.x)\n    maps[\"norm_err\"] = Map.from_geom(\n        geom, data=np.sqrt(minimizer.hess_inv.todense()).reshape(geom.data_shape)\n    )\n    maps[\"n_dof\"] = Map.from_geom(geom, data=flux_point.geom.axes[axis.name].nbin)\n",
  "start_line": 55,
  "end_line": 60,
  "total_lines": 872,
  "is_truncated": false,
  "budget_warning": "Only 7 tool call(s) remaining (23/30 used). Finalize your edits and call submit_patch soon."
}

{
  "status": "ok",
  "filepath": "gammapy/estimators/points/core.py",
  "content": "\ndef squash_fluxpoints(flux_point, axis):\n    \"\"\"Squash a `~FluxPoints` object into one point.\n\n    The log-likelihoods profiles in each bin are summed\n    to compute the resultant quantities. Stat profiles\n    must be present on the `~FluxPoints` object for\n    this method to work.\n    \"\"\"\n    value_scan = flux_point.stat_scan.geom.axes[\"norm\"].center\n    stat_scan = np.sum(flux_point.stat_scan.data, axis=0).ravel()\n    f = interp1d(value_scan, stat_scan, kind=\"quadratic\", bounds_error=False)\n    f = interpolate_profile(value_scan, stat_scan)\n    minimizer = minimize(\n        f,\n        x0=value_scan[int(len(value_scan) / 2)],\n        bounds=[(value_scan[0], value_scan[-1])],\n        method=\"L-BFGS-B\",\n    )\n\n    maps = Maps()\n    geom = flux_point.geom.to_image()\n    if axis.name != \"energy\":\n        geom = geom.to_cube([flux_point.geom.axes[\"energy\"]])\n\n    maps[\"norm\"] = Map.from_geom(geom, data=minimizer.x)\n    maps[\"norm_err\"] = Map.from_geom(\n        geom, data=np.sqrt(minimizer.hess_inv.todense()).reshape(geom.data_shape)\n    )\n    maps[\"n_dof\"] = Map.from_geom(geom, data=flux_point.geom.axes[axis.name].nbin)\n\n    if \"norm_ul\" in flux_point.available_quantities:\n        delta_ts = flux_point.meta.get(\"n_sigma_ul\", 2) ** 2\n        ul = stat_profile_ul_scipy(value_scan, stat_scan, delta_ts=delta_ts)\n        maps[\"norm_ul\"] = Map.from_geom(geom, data=ul.value)\n\n    maps[\"stat\"] = Map.from_geom(geom, data=f(minimizer.x))\n\n    geom_scan = geom.to_cube([MapAxis.from_nodes(value_scan, name=\"norm\")])\n    maps[\"stat_scan\"] = Map.from_geom(\n        geom=geom_scan, data=stat_scan.reshape(geom_scan.data_shape)\n    )\n    try:\n        maps[\"stat_null\"] = Map.from_geom(geom, data=np.sum(flux_point.stat_null.data))\n        maps[\"ts\"] = maps[\"stat_null\"] - maps[\"stat\"]\n    except AttributeError:",
  "start_line": 30,
  "end_line": 75,
  "total_lines": 872,
  "is_truncated": false,
  "budget_warning": "Only 6 tool call(s) remaining (24/30 used). Finalize your edits and call submit_patch soon."
}

{
  "status": "ok",
  "filepath": "gammapy/estimators/points/core.py",
  "occurrences": 1,
  "strategy": "unescaped",
  "diff": "--- a/gammapy/estimators/points/core.py\n+++ b/gammapy/estimators/points/core.py\n@@ -52,7 +52,7 @@\n     if axis.name != \"energy\":\n         geom = geom.to_cube([flux_point.geom.axes[\"energy\"]])\n \n-    maps[\"norm\"] = Map.from_geom(geom, data=minimizer.x)\n+    maps[\"norm\"] = Map.from_geom(geom, data=minimizer.x.item())\n     maps[\"norm_err\"] = Map.from_geom(\n         geom, data=np.sqrt(minimizer.hess_inv.todense()).reshape(geom.data_shape)\n     )\n@@ -63,7 +63,7 @@\n         ul = stat_profile_ul_scipy(value_scan, stat_scan, delta_ts=delta_ts)\n         maps[\"norm_ul\"] = Map.from_geom(geom, data=ul.value)\n \n-    maps[\"stat\"] = Map.from_geom(geom, data=f(minimizer.x))\n+    maps[\"stat\"] = Map.from_geom(geom, data=f(minimizer.x.item()))\n \n     geom_scan = geom.to_cube([MapAxis.from_nodes(value_scan, name=\"norm\")])\n     maps[\"stat_scan\"] = Map.from_geom(\n",
  "is_truncated": false,
  "budget_warning": "Only 5 tool call(s) remaining (25/30 used). Finalize your edits and call submit_patch soon."
}

{
  "status": "error",
  "error_type": "CommandError",
  "error_message": "In /usr/local/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/classic.mplstyle: 'parseString' deprecated - use 'parse_string'\nIn /usr/local/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/classic.mplstyle: 'resetCache' deprecated - use 'reset_cache'\nIn /usr/local/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/classic.mplstyle: 'parseString' deprecated - use 'parse_string'\nIn /usr/local/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/classic.mplstyle: 'resetCache' deprecated - use 'reset_cache'\nIn /usr/local/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/classic.mplstyle: 'parseString' deprecated - use 'parse_string'\nIn /usr/local/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/classic.mplstyle: 'resetCache' deprecated - use 'reset_cache'\nIn /usr/local/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/classic.mplstyle: 'parseString' deprecated - use 'parse_string'\nIn /usr/local/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/classic.mplstyle: 'resetCache' deprecated - use 'reset_cache'\nImportError while loading conftest '/workspace/gammapy/conftest.py'.\n/gammapy/gammapy/conftest.py:12: in <module>\n    from gammapy.data import GTI\n/gammapy/gammapy/data/__init__.py:4: in <module>\n    from .data_store import DataStore\n/gammapy/gammapy/data/data_store.py:17: in <module>\n    from .observations import Observation, ObservationChecker, Observations\n/gammapy/gammapy/data/observations.py:20: in <module>\n    from gammapy.utils.metadata import CreatorMetaData, TargetMetaData, TimeInfoMetaData\n/gammapy/gammapy/utils/metadata.py:16: in <module>\n    from gammapy.version import version\nE   ModuleNotFoundError: No module named 'gammapy.version'\n",
  "details": {
    "stdout": "",
    "stderr": "In /usr/local/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/classic.mplstyle: 'parseString' deprecated - use 'parse_string'\nIn /usr/local/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/classic.mplstyle: 'resetCache' deprecated - use 'reset_cache'\nIn /usr/local/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/classic.mplstyle: 'parseString' deprecated - use 'parse_string'\nIn /usr/local/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/classic.mplstyle: 'resetCache' deprecated - use 'reset_cache'\nIn /usr/local/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/classic.mplstyle: 'parseString' deprecated - use 'parse_string'\nIn /usr/local/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/classic.mplstyle: 'resetCache' deprecated - use 'reset_cache'\nIn /usr/local/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/classic.mplstyle: 'parseString' deprecated - use 'parse_string'\nIn /usr/local/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/classic.mplstyle: 'resetCache' deprecated - use 'reset_cache'\nImportError while loading conftest '/workspace/gammapy/conftest.py'.\n/gammapy/gammapy/conftest.py:12: in <module>\n    from gammapy.data import GTI\n/gammapy/gammapy/data/__init__.py:4: in <module>\n    from .data_store import DataStore\n/gammapy/gammapy/data/data_store.py:17: in <module>\n    from .observations import Observation, ObservationChecker, Observations\n/gammapy/gammapy/data/observations.py:20: in <module>\n    from gammapy.utils.metadata import CreatorMetaData, TargetMetaData, TimeInfoMetaData\n/gammapy/gammapy/utils/metadata.py:16: in <module>\n    from gammapy.version import version\nE   ModuleNotFoundError: No module named 'gammapy.version'\n",
    "exit_code": 4
  },
  "budget_warning": "Only 4 tool call(s) remaining (26/30 used). Finalize your edits and call submit_patch soon."
}

{
  "status": "error",
  "error_type": "CommandError",
  "error_message": "In /usr/local/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/classic.mplstyle: 'parseString' deprecated - use 'parse_string'\nIn /usr/local/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/classic.mplstyle: 'resetCache' deprecated - use 'reset_cache'\nIn /usr/local/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/classic.mplstyle: 'parseString' deprecated - use 'parse_string'\nIn /usr/local/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/classic.mplstyle: 'resetCache' deprecated - use 'reset_cache'\nIn /usr/local/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/classic.mplstyle: 'parseString' deprecated - use 'parse_string'\nIn /usr/local/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/classic.mplstyle: 'resetCache' deprecated - use 'reset_cache'\nIn /usr/local/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/classic.mplstyle: 'parseString' deprecated - use 'parse_string'\nIn /usr/local/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/classic.mplstyle: 'resetCache' deprecated - use 'reset_cache'\nImportError while loading conftest '/workspace/gammapy/conftest.py'.\ngammapy/conftest.py:12: in <module>\n    from gammapy.data import GTI\ngammapy/data/__init__.py:4: in <module>\n    from .data_store import DataStore\ngammapy/data/data_store.py:17: in <module>\n    from .observations import Observation, ObservationChecker, Observations\ngammapy/data/observations.py:20: in <module>\n    from gammapy.utils.metadata import CreatorMetaData, TargetMetaData, TimeInfoMetaData\ngammapy/utils/metadata.py:16: in <module>\n    from gammapy.version import version\nE   ModuleNotFoundError: No module named 'gammapy.version'\n",
  "details": {
    "stdout": "",
    "stderr": "In /usr/local/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/classic.mplstyle: 'parseString' deprecated - use 'parse_string'\nIn /usr/local/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/classic.mplstyle: 'resetCache' deprecated - use 'reset_cache'\nIn /usr/local/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/classic.mplstyle: 'parseString' deprecated - use 'parse_string'\nIn /usr/local/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/classic.mplstyle: 'resetCache' deprecated - use 'reset_cache'\nIn /usr/local/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/classic.mplstyle: 'parseString' deprecated - use 'parse_string'\nIn /usr/local/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/classic.mplstyle: 'resetCache' deprecated - use 'reset_cache'\nIn /usr/local/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/classic.mplstyle: 'parseString' deprecated - use 'parse_string'\nIn /usr/local/lib/python3.10/site-packages/matplotlib/mpl-data/stylelib/classic.mplstyle: 'resetCache' deprecated - use 'reset_cache'\nImportError while loading conftest '/workspace/gammapy/conftest.py'.\ngammapy/conftest.py:12: in <module>\n    from gammapy.data import GTI\ngammapy/data/__init__.py:4: in <module>\n    from .data_store import DataStore\ngammapy/data/data_store.py:17: in <module>\n    from .observations import Observation, ObservationChecker, Observations\ngammapy/data/observations.py:20: in <module>\n    from gammapy.utils.metadata import CreatorMetaData, TargetMetaData, TimeInfoMetaData\ngammapy/utils/metadata.py:16: in <module>\n    from gammapy.version import version\nE   ModuleNotFoundError: No module named 'gammapy.version'\n",
    "exit_code": 4
  },
  "budget_warning": "Only 3 tool call(s) remaining (27/30 used). Finalize your edits and call submit_patch soon."
}

{
  "status": "ok",
  "stdout": "",
  "stderr": "",
  "exit_code": 0,
  "budget_warning": "Only 2 tool call(s) remaining (28/30 used). Finalize your edits and call submit_patch soon."
}