gammapy__gammapy-5674

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  "stdout": "31:def squash_fluxpoints(flux_point, axis):\n869:            fp_new = squash_fluxpoints(fp, axis_new)\n",
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  "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``",
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  "filepath": "gammapy/estimators/points/core.py",
  "content": "        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)",
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  "stdout": "__init__.py\ntest_core.py\ntest_energydependentmorphology.py\ntest_flux.py\ntest_metadata.py\ntest_parameter_estimator.py\ntest_profile.py\ntest_utils.py\n",
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  "stdout": "gammapy/estimators/tests/test_utils.py:10:from gammapy.estimators import ExcessMapEstimator, FluxPoints\ngammapy/estimators/tests/test_utils.py:46:    return FluxPoints.from_table(table=table, format=\"lightcurve\")\ngammapy/estimators/tests/test_utils.py:80:    return FluxPoints.from_table(table=table, format=\"lightcurve\")\ngammapy/estimators/tests/test_utils.py:276:    lc_1d = FluxPoints.read(\n",
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  "filepath": "gammapy/estimators/points/core.py",
  "content": "    * 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 = '$GAMMAPY_DATA/hawc_crab/HAWC19_flux_points.fits'\n    >>> flux_points = FluxPoints.read(filename)\n    >>> flux_points.plot() #doctest: +SKIP\n\n    An instance of `FluxPoints` can also be created by passing an instance of\n    `astropy.table.Table`, which contains the required columns, such as `'e_ref'`\n    and `'dnde'`. The corresponding `sed_type` has to be defined in the meta data\n    of the table::\n\n    >>> import numpy as np\n    >>> from astropy import units as u\n    >>> from astropy.table import Table\n    >>> from gammapy.estimators import FluxPoints\n    >>> from gammapy.modeling.models import PowerLawSpectralModel\n    >>> table = Table()\n    >>> pwl = PowerLawSpectralModel()\n    >>> e_ref = np.geomspace(1, 100, 7) * u.TeV\n    >>> table[\"e_ref\"] = e_ref\n    >>> table[\"dnde\"] = pwl(e_ref)\n    >>> table[\"dnde_err\"] = pwl.evaluate_error(e_ref)[0]\n    >>> table.meta[\"SED_TYPE\"] = \"dnde\"\n    >>> flux_points = FluxPoints.from_table(table)\n    >>> flux_points.plot(sed_type=\"flux\") #doctest: +SKIP\n\n    If you have flux points in a different data format, the format can be changed\n    by renaming the table columns and adding meta data::\n\n\n    >>> from astropy import units as u\n    >>> from astropy.table import Table\n    >>> from gammapy.estimators import FluxPoints\n    >>> from gammapy.utils.scripts import make_path\n\n    >>> filename = make_path('$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\n    Note: In order to reproduce the example you need the tests datasets folder.\n    You may download it with the command\n    ``gammapy download datasets --tests --out $GAMMAPY_DATA``\n    \"\"\"\n\n    @classmethod\n    def read(\n        cls,\n        filename,\n        sed_type=None,\n        format=None,\n        reference_model=None,\n        checksum=False,\n        table_format=\"ascii.ecsv\",\n        **kwargs,\n    ):\n        \"\"\"Read precomputed flux points.\n\n        Parameters\n        ----------\n        filename : str\n            Filename.\n        sed_type : {\"dnde\", \"flux\", \"eflux\", \"e2dnde\", \"likelihood\"}\n            SED type.\n        format : {\"gadf-sed\", \"lightcurve\", \"profile\"}, optional\n            Format string. If None, the format is extracted from the input.\n            Default is None.\n        reference_model : `SpectralModel`\n            Reference spectral model.\n        checksum : bool\n            If True checks both DATASUM and CHECKSUM cards in the file headers. Default is False.\n        table_format :  str\n            Format string for the ~astropy.Table object. Default is \"ascii.ecsv\"\n        **kwargs : dict, optional\n            Keyword arguments passed to `astropy.table.Table.read`.\n\n        Returns\n        -------\n        flux_points : `FluxPoints`\n            Flux points.\n        \"\"\"\n        filename = make_path(filename)\n        gti = None\n        try:\n            table = Table.read(filename, format=table_format, **kwargs)\n        except (IORegistryError, UnicodeDecodeError):\n            with fits.open(filename, checksum=checksum) as hdulist:\n                if \"FLUXPOINTS\" in hdulist:\n                    fp = hdulist[\"FLUXPOINTS\"]\n                else:\n                    fp = hdulist[\"\"]  # to handle older files\n                table = Table.read(fp)\n                if \"GTI\" in hdulist:\n                    gti = GTI.from_table_hdu(hdulist[\"GTI\"])\n\n        return cls.from_table(\n            table=table,\n            sed_type=sed_type,\n            reference_model=reference_model,\n            format=format,\n            gti=gti,\n        )\n\n    def write(\n        self, filename, sed_type=None, format=None, overwrite=False, checksum=False\n    ):\n        \"\"\"Write flux points.\n\n        Parameters\n        ----------\n        filename : str\n            Filename.\n        sed_type : {\"dnde\", \"flux\", \"eflux\", \"e2dnde\", \"likelihood\"}, optional\n            SED type. Default is None.\n        format : {\"gadf-sed\", \"lightcurve\", \"binned-time-series\", \"profile\"}, optional\n            Format specification. The following formats are supported:\n\n                * \"gadf-sed\": format for SED flux points see :ref:`gadf:flux-points`\n                  for details\n                * \"lightcurve\": Gammapy internal format to store energy dependent",
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  "stdout": "82:    combined_fp = FluxPoints.from_maps(\n373:        return cls.from_maps(\n",
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  "filepath": "gammapy/estimators/points/core.py",
  "content": "            sed_type = cls._guess_sed_type(table.colnames)\n\n        if sed_type is None:\n            raise ValueError(\"Specifying the SED type is required\")\n\n        if sed_type == \"likelihood\":\n            table = cls._convert_loglike_columns(table)\n            if reference_model is None:\n                reference_model = TemplateSpectralModel(\n                    energy=flat_if_equal(table[\"e_ref\"].quantity),\n                    values=flat_if_equal(table[\"ref_dnde\"].quantity),\n                )\n\n        maps = Maps()\n        table.meta.setdefault(\"SED_TYPE\", sed_type)\n\n        for name in cls.all_quantities(sed_type=sed_type):\n            if name in table.colnames:\n                maps[name] = RegionNDMap.from_table(\n                    table=table, colname=name, format=format\n                )\n\n        meta = cls._get_meta_gadf(table)\n        return cls.from_maps(\n            maps=maps,\n            reference_model=reference_model,\n            meta=meta,\n            sed_type=sed_type,\n            gti=gti,\n        )\n\n    @staticmethod\n    def _get_meta_gadf(table):\n        meta = {}\n        meta.update(table.meta)\n        conf_ul = table.meta.get(\"UL_CONF\")\n        if conf_ul:\n            n_sigma_ul = np.round(stats.norm.isf(0.5 * (1 - conf_ul)), 1)\n            meta[\"n_sigma_ul\"] = n_sigma_ul\n        meta[\"sed_type_init\"] = table.meta.get(\"SED_TYPE\")\n        return meta\n\n    @staticmethod\n    def _format_table(table):\n        \"\"\"Format table.\"\"\"\n        for column in table.colnames:\n            if column.startswith((\"dnde\", \"eflux\", \"flux\", \"e2dnde\", \"ref\")):\n                table[column].format = \".3e\"\n            elif column.startswith(\n                (\"e_min\", \"e_max\", \"e_ref\", \"sqrt_ts\", \"norm\", \"ts\", \"stat\")\n            ):\n                table[column].format = \".3f\"\n\n        return table\n\n    def _guess_format(self):\n        \"\"\"Format of the FluxPoints object.\"\"\"\n        names = self.geom.axes.names\n        if \"time\" in names:\n            return \"lightcurve\"\n        elif \"projected-distance\" in names:\n            return \"profile\"\n        else:\n            return \"gadf-sed\"\n\n    def to_table(self, sed_type=None, format=None, formatted=False):\n        \"\"\"Create table for a given SED type.\n\n        Parameters\n        ----------\n        sed_type : {\"likelihood\", \"dnde\", \"e2dnde\", \"flux\", \"eflux\"}",
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  "stdout": "919:    def from_maps(cls, maps, sed_type=None, reference_model=None, gti=None, meta=None):\n",
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{
  "status": "error",
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  "error_message": "Traceback (most recent call last):\n  File \"<stdin>\", line 3, in <module>\n  File \"/workspace/gammapy/estimators/__init__.py\", line 4, in <module>\n    from .core import Estimator\n  File \"/workspace/gammapy/estimators/core.py\", line 9, in <module>\n    from gammapy.modeling.models import ModelBase\n  File \"/workspace/gammapy/modeling/__init__.py\", line 8, in <module>\n    from .selection import select_nested_models\n  File \"/workspace/gammapy/modeling/selection.py\", line 4, in <module>\n    from gammapy.stats.utils import sigma_to_ts\n  File \"/workspace/gammapy/stats/__init__.py\", line 4, in <module>\n    from .counts_statistic import CashCountsStatistic, WStatCountsStatistic\n  File \"/workspace/gammapy/stats/counts_statistic.py\", line 8, in <module>\n    from .fit_statistics import cash, wstat\n  File \"/workspace/gammapy/stats/fit_statistics.py\", line 8, in <module>\n    from gammapy.stats.fit_statistics_cython import TRUNCATION_VALUE\nModuleNotFoundError: No module named 'gammapy.stats.fit_statistics_cython'\n",
  "details": {
    "stdout": "",
    "stderr": "Traceback (most recent call last):\n  File \"<stdin>\", line 3, in <module>\n  File \"/workspace/gammapy/estimators/__init__.py\", line 4, in <module>\n    from .core import Estimator\n  File \"/workspace/gammapy/estimators/core.py\", line 9, in <module>\n    from gammapy.modeling.models import ModelBase\n  File \"/workspace/gammapy/modeling/__init__.py\", line 8, in <module>\n    from .selection import select_nested_models\n  File \"/workspace/gammapy/modeling/selection.py\", line 4, in <module>\n    from gammapy.stats.utils import sigma_to_ts\n  File \"/workspace/gammapy/stats/__init__.py\", line 4, in <module>\n    from .counts_statistic import CashCountsStatistic, WStatCountsStatistic\n  File \"/workspace/gammapy/stats/counts_statistic.py\", line 8, in <module>\n    from .fit_statistics import cash, wstat\n  File \"/workspace/gammapy/stats/fit_statistics.py\", line 8, in <module>\n    from gammapy.stats.fit_statistics_cython import TRUNCATION_VALUE\nModuleNotFoundError: No module named 'gammapy.stats.fit_statistics_cython'\n",
    "exit_code": 1
  }
}

{
  "status": "error",
  "error_type": "CommandError",
  "error_message": "Traceback (most recent call last):\n  File \"<stdin>\", line 2, in <module>\n  File \"/workspace/gammapy/estimators/__init__.py\", line 4, in <module>\n    from .core import Estimator\n  File \"/workspace/gammapy/estimators/core.py\", line 9, in <module>\n    from gammapy.modeling.models import ModelBase\n  File \"/workspace/gammapy/modeling/__init__.py\", line 8, in <module>\n    from .selection import select_nested_models\n  File \"/workspace/gammapy/modeling/selection.py\", line 4, in <module>\n    from gammapy.stats.utils import sigma_to_ts\n  File \"/workspace/gammapy/stats/__init__.py\", line 4, in <module>\n    from .counts_statistic import CashCountsStatistic, WStatCountsStatistic\n  File \"/workspace/gammapy/stats/counts_statistic.py\", line 8, in <module>\n    from .fit_statistics import cash, wstat\n  File \"/workspace/gammapy/stats/fit_statistics.py\", line 8, in <module>\n    from gammapy.stats.fit_statistics_cython import TRUNCATION_VALUE\nModuleNotFoundError: No module named 'gammapy.stats.fit_statistics_cython'\n",
  "details": {
    "stdout": "",
    "stderr": "Traceback (most recent call last):\n  File \"<stdin>\", line 2, in <module>\n  File \"/workspace/gammapy/estimators/__init__.py\", line 4, in <module>\n    from .core import Estimator\n  File \"/workspace/gammapy/estimators/core.py\", line 9, in <module>\n    from gammapy.modeling.models import ModelBase\n  File \"/workspace/gammapy/modeling/__init__.py\", line 8, in <module>\n    from .selection import select_nested_models\n  File \"/workspace/gammapy/modeling/selection.py\", line 4, in <module>\n    from gammapy.stats.utils import sigma_to_ts\n  File \"/workspace/gammapy/stats/__init__.py\", line 4, in <module>\n    from .counts_statistic import CashCountsStatistic, WStatCountsStatistic\n  File \"/workspace/gammapy/stats/counts_statistic.py\", line 8, in <module>\n    from .fit_statistics import cash, wstat\n  File \"/workspace/gammapy/stats/fit_statistics.py\", line 8, in <module>\n    from gammapy.stats.fit_statistics_cython import TRUNCATION_VALUE\nModuleNotFoundError: No module named 'gammapy.stats.fit_statistics_cython'\n",
    "exit_code": 1
  },
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  "stdout": "/workspace/gammapy/stats/fit_statistics_cython.pyx\n",
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  "filepath": "gammapy/estimators/points/core.py",
  "content": "    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 = '$GAMMAPY_DATA/hawc_crab/HAWC19_flux_points.fits'\n    >>> flux_points = FluxPoints.read(filename)\n    >>> flux_points.plot() #doctest: +SKIP\n\n    An instance of `FluxPoints` can also be created by passing an instance of\n    `astropy.table.Table`, which contains the required columns, such as `'e_ref'`\n    and `'dnde'`. The corresponding `sed_type` has to be defined in the meta data\n    of the table::\n\n    >>> import numpy as np\n    >>> from astropy import units as u\n    >>> from astropy.table import Table\n    >>> from gammapy.estimators import FluxPoints\n    >>> from gammapy.modeling.models import PowerLawSpectralModel\n    >>> table = Table()\n    >>> pwl = PowerLawSpectralModel()\n    >>> e_ref = np.geomspace(1, 100, 7) * u.TeV\n    >>> table[\"e_ref\"] = e_ref\n    >>> table[\"dnde\"] = pwl(e_ref)\n    >>> table[\"dnde_err\"] = pwl.evaluate_error(e_ref)[0]\n    >>> table.meta[\"SED_TYPE\"] = \"dnde\"\n    >>> flux_points = FluxPoints.from_table(table)\n    >>> flux_points.plot(sed_type=\"flux\") #doctest: +SKIP\n\n    If you have flux points in a different data format, the format can be changed\n    by renaming the table columns and adding meta data::\n\n\n    >>> from astropy import units as u\n    >>> from astropy.table import Table\n    >>> from gammapy.estimators import FluxPoints\n    >>> from gammapy.utils.scripts import make_path\n\n    >>> filename = make_path('$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\n    Note: In order to reproduce the example you need the tests datasets folder.\n    You may download it with the command\n    ``gammapy download datasets --tests --out $GAMMAPY_DATA``\n    \"\"\"\n\n    @classmethod\n    def read(\n        cls,\n        filename,\n        sed_type=None,",
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  "stdout": "840:    def resample_axis(self, axis_new):\n",
  "stderr": "",
  "exit_code": 0,
  "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",
  "content": "def 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``",
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  "budget_warning": "Only 5 tool call(s) remaining (25/30 used). Finalize your edits and call submit_patch soon."
}

{
  "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:",
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  "is_truncated": false,
  "budget_warning": "Only 4 tool call(s) remaining (26/30 used). Finalize your edits and call submit_patch soon."
}

{
  "status": "ok",
  "filepath": "gammapy/estimators/points/core.py",
  "content": "        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)",
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  "budget_warning": "Only 3 tool call(s) remaining (27/30 used). Finalize your edits and call submit_patch soon."
}

{
  "status": "error",
  "error_type": "CommandError",
  "error_message": "",
  "details": {
    "stdout": "",
    "stderr": "",
    "exit_code": 1
  },
  "budget_warning": "Only 2 tool call(s) remaining (28/30 used). Finalize your edits and call submit_patch soon."
}

{
  "status": "ok",
  "stdout": "20:from gammapy.utils.interpolation import interpolate_profile\n",
  "stderr": "",
  "exit_code": 0,
  "budget_warning": "Only 1 tool call(s) remaining (29/30 used). Finalize your edits and call submit_patch soon."
}

{
  "status": "ok",
  "filepath": "gammapy/utils/interpolation.py",
  "content": "# Licensed under a 3-clause BSD style license - see LICENSE.rst\n\"\"\"Interpolation utilities.\"\"\"\n\nimport html\nfrom itertools import compress\nimport numpy as np\nimport scipy.interpolate\nfrom astropy import units as u\nfrom .compat import COPY_IF_NEEDED\n\n__all__ = [\n    \"interpolate_profile\",\n    \"interpolation_scale\",\n    \"ScaledRegularGridInterpolator\",\n]\n\nINTERPOLATION_ORDER = {None: 0, \"nearest\": 0, \"linear\": 1, \"quadratic\": 2, \"cubic\": 3}\n\n\nclass ScaledRegularGridInterpolator:\n    \"\"\"Thin wrapper around `scipy.interpolate.RegularGridInterpolator`.\n\n    The values are scaled before the interpolation and back-scaled after the\n    interpolation.\n\n    Dimensions of length 1 are ignored in the interpolation of the data.\n\n    Parameters\n    ----------\n    points : tuple of `~numpy.ndarray` or `~astropy.units.Quantity`\n        Tuple of points passed to `RegularGridInterpolator`.\n    values : `~numpy.ndarray`\n        Values passed to `RegularGridInterpolator`.\n    points_scale : tuple of str\n        Interpolation scale used for the points.\n    values_scale : {'lin', 'log', 'sqrt'}\n        Interpolation scaling applied to values. If the values vary over many magnitudes\n        a 'log' scaling is recommended.\n    axis : int or None\n        Axis along which to interpolate.\n    method : {\"linear\", \"nearest\"}\n        Default interpolation method. Can be overwritten when calling the\n        `ScaledRegularGridInterpolator`.\n    **kwargs : dict\n        Keyword arguments passed to `RegularGridInterpolator`.\n    \"\"\"\n\n    def __init__(\n        self,\n        points,\n        values,\n        points_scale=None,\n        values_scale=\"lin\",\n        extrapolate=True,\n        axis=None,\n        **kwargs,\n    ):\n        if points_scale is None:\n            points_scale = [\"lin\"] * len(points)\n\n        self.scale_points = [interpolation_scale(scale) for scale in points_scale]\n        self.scale = interpolation_scale(values_scale)\n        self.axis = axis\n\n        self._include_dimensions = [len(p) > 1 for p in points]\n\n        values_scaled = self.scale(values)\n        points_scaled = self._scale_points(points=points)\n\n        kwargs.setdefault(\"bounds_error\", False)\n\n        if extrapolate:\n            kwargs.setdefault(\"fill_value\", None)\n\n        method = kwargs.get(\"method\", None)\n\n        if not np.any(self._include_dimensions):\n            if method != \"nearest\":\n                raise ValueError(\n                    \"Interpolating scalar values requires using \"\n                    \"method='nearest' explicitly.\"\n                )\n\n        if np.any(self._include_dimensions):\n            values_scaled = np.squeeze(values_scaled)\n\n        if axis is None:\n            self._interpolate = scipy.interpolate.RegularGridInterpolator(\n                points=points_scaled, values=values_scaled, **kwargs\n            )\n        else:\n            self._interpolate = scipy.interpolate.interp1d(\n                points_scaled[0], values_scaled, axis=axis\n            )\n\n    def _repr_html_(self):\n        try:\n            return self.to_html()\n        except AttributeError:\n            return f\"<pre>{html.escape(str(self))}</pre>\"\n\n    def _scale_points(self, points):\n        points_scaled = [scale(p) for p, scale in zip(points, self.scale_points)]\n\n        if np.any(self._include_dimensions):\n            points_scaled = compress(points_scaled, self._include_dimensions)\n\n        return tuple(points_scaled)\n\n    def __call__(self, points, method=None, clip=True, **kwargs):\n        \"\"\"Interpolate data points.\n\n        Parameters\n        ----------\n        points : tuple of `~numpy.ndarray` or `~astropy.units.Quantity`\n            Tuple of coordinate arrays of the form (x_1, x_2, x_3, ...). Arrays are\n            broadcast internally.\n        method : {None, \"linear\", \"nearest\"}\n            Linear or nearest neighbour interpolation.\n            Default is None, which is `method` defined on init.\n        clip : bool\n            Clip values at zero after interpolation.\n        \"\"\"\n        points = self._scale_points(points=points)\n\n        if self.axis is None:\n            points = np.broadcast_arrays(*points)\n            points_interp = np.stack([_.flat for _ in points]).T\n            values = self._interpolate(points_interp, method, **kwargs)\n            values = self.scale.inverse(values.reshape(points[0].shape))\n        else:\n            values = self._interpolate(points[0])\n            values = self.scale.inverse(values)\n\n        if clip:\n            values = np.clip(values, 0, np.inf)\n\n        return values\n\n\ndef interpolation_scale(scale=\"lin\"):\n    \"\"\"Interpolation scaling.\n\n    Parameters\n    ----------\n    scale : {\"lin\", \"log\", \"sqrt\"}\n        Choose interpolation scaling.\n    \"\"\"\n    if scale in [\"lin\", \"linear\"]:\n        return LinearScale()",
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  "is_truncated": true,
  "budget_warning": "Only 0 tool call(s) remaining (30/30 used). Finalize your edits and call submit_patch soon."
}