diff --git a/doc/sphinx/concepts/dataset.ipynb b/doc/sphinx/concepts/dataset.ipynb index 7b316ce27e..22089b6e77 100644 --- a/doc/sphinx/concepts/dataset.ipynb +++ b/doc/sphinx/concepts/dataset.ipynb @@ -19,10 +19,10 @@ "collection of individual datasets, one for each partial IceCube detector\n", "configuration.\n", "\n", - "SkyLLh provides the :py:class:`skyllh.core.dataset.Dataset` class to create an\n", + "SkyLLH provides the :py:class:`skyllh.core.dataset.Dataset` class to create an\n", "individual dataset definition. Such a definition defines the experimental and\n", "monte-carlo data files and possibly additional information like data binning\n", - "definitions or auxilary data files.\n", + "definitions or auxiliary data files.\n", "\n", "Individual datasets can be combined into a dataset collection via the \n", ":py:class:`skyllh.core.dataset.DatasetCollection` class.\n", @@ -30,7 +30,7 @@ "A dataset collection is usually defined within one Python module providing the\n", "function ``create_dataset_collection``. For instance the 10-year public \n", "point-source data sample is defined in the \n", - ":py:mod:`skyllh.datasets.i3.PublicData_10y_ps` module, and the its dataset \n", + ":py:mod:`skyllh.datasets.i3.PublicData_10y_ps` module, and its dataset \n", "collection can be created via the \n", ":py:func:`~skyllh.datasets.i3.PublicData_10y_ps.create_dataset_collection`\n", "function. This function requires a configuration. If no data repository base \n", @@ -71,7 +71,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -128,9 +128,9 @@ " Dataset \"My Dataset\": v001patch00\n", " { livetime = 365.000 days }\n", " Experimental data:\n", - " [\u001b[92mFOUND\u001b[0m] /data/ana/analyses/my_dataset_v001_p00/exp.npy\n", + " [\u001b[91mNOT FOUND\u001b[0m] /data/ana/analyses/my_dataset_v001_p00/exp.npy\n", " MC data:\n", - " [\u001b[92mFOUND\u001b[0m] /data/ana/analyses/my_dataset_v001_p00/mc.npy\n", + " [\u001b[91mNOT FOUND\u001b[0m] /data/ana/analyses/my_dataset_v001_p00/mc.npy\n", " \n" ] } @@ -161,9 +161,9 @@ "Dataset \"My Dataset\": v001patch00\n", " { livetime = 365.000 days }\n", " Experimental data:\n", - " [\u001b[92mFOUND\u001b[0m] /data/ana/analyses/my_dataset_v001_p00/exp.npy\n", + " [\u001b[91mNOT FOUND\u001b[0m] /data/ana/analyses/my_dataset_v001_p00/exp.npy\n", " MC data:\n", - " [\u001b[92mFOUND\u001b[0m] /data/ana/analyses/my_dataset_v001_p00/mc.npy\n", + " [\u001b[91mNOT FOUND\u001b[0m] /data/ana/analyses/my_dataset_v001_p00/mc.npy\n", " \n" ] } @@ -211,12 +211,12 @@ "Dataset \"My Dataset\": v001patch00\n", " { livetime = 365.000 days }\n", " Experimental data:\n", - " [\u001b[92mFOUND\u001b[0m] /data/ana/analyses/my_dataset_v001_p00/exp.npy\n", + " [\u001b[91mNOT FOUND\u001b[0m] /data/ana/analyses/my_dataset_v001_p00/exp.npy\n", " MC data:\n", - " [\u001b[92mFOUND\u001b[0m] /data/ana/analyses/my_dataset_v001_p00/mc.npy\n", + " [\u001b[91mNOT FOUND\u001b[0m] /data/ana/analyses/my_dataset_v001_p00/mc.npy\n", " Auxiliary data:\n", " aux_file_key_1: \n", - " [\u001b[92mFOUND\u001b[0m] /data/ana/analyses/my_dataset_v001_p00/aux_data/aux_file1.dat\n" + " [\u001b[91mNOT FOUND\u001b[0m] /data/ana/analyses/my_dataset_v001_p00/aux_data/aux_file1.dat\n" ] } ], @@ -265,7 +265,11 @@ "at the origin, and a transfer function which will be used to perform the actual\n", "data transfer.\n", "\n", - "SkyLLH provides two dataset transfer methods, ``wget`` and ``rsync``. " + "SkyLLH provides three dataset transfer methods: ``rsync`` via the\n", + ":py:class:`~skyllh.core.dataset.RSYNCDatasetTransfer` class, ``wget`` via the\n", + ":py:class:`~skyllh.core.dataset.WGETDatasetTransfer` class, and Python's\n", + ":py:func:`urllib.request.urlretrieve` function via the\n", + ":py:class:`~skyllh.core.dataset.URLRetrieveDatasetTransfer` class." ] }, { @@ -276,7 +280,9 @@ "source": [ "from skyllh.core.dataset import (\n", " DatasetOrigin,\n", + " URLRetrieveDatasetTransfer,\n", " WGETDatasetTransfer,\n", + " post_transfer_unarchive,\n", ")" ] }, @@ -302,7 +308,7 @@ }, "source": [ "In the example above we specified that the dataset is available at the URL\n", - "``data.mydomain.com/downloads/data/my_dataset``, which can be transfered\n", + "``data.mydomain.com/downloads/data/my_dataset``, which can be transferred\n", "using ``wget`` via the https protocol.\n", "\n", "Hence, the experimental and monte-carlo files ``exp.npy`` and ``mc.npy`` \n", @@ -328,14 +334,32 @@ "webserver. In such cases the ``filename`` argument of the \n", ":py:class:`~skyllh.core.dataset.DatasetOrigin` class constructor can be used in\n", "combination with a post transfer function specified via the \n", - "``post_transfer_func`` argument of the constructor:" + "``post_transfer_func`` argument of the constructor. SkyLLH provides the\n", + ":py:func:`~skyllh.core.dataset.post_transfer_unarchive` function, which\n", + "extracts ``.zip``, ``.tar.gz``, ``.tgz``, ``.tar.bz2``, and ``.tar.xz`` archive\n", + "files:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DatasetOrigin {\n", + " base_path = \"/downloads/data\"\n", + " sub_path = \"\"\n", + " filename = my_dataset.zip\n", + " user@host:port = None@data.mydomain.com:None\n", + " password = not set\n", + " transfer class = WGETDatasetTransfer\n", + " }\n" + ] + } + ], "source": [ "origin = DatasetOrigin(\n", " host='data.mydomain.com',\n", @@ -343,8 +367,9 @@ " sub_path='',\n", " filename='my_dataset.zip',\n", " transfer_func=WGETDatasetTransfer(protocol='https').transfer,\n", - " post_transfer_func=WGETDatasetTransfer.post_transfer_unzip,\n", - ")" + " post_transfer_func=post_transfer_unarchive,\n", + ")\n", + "print(origin)" ] }, { @@ -354,8 +379,58 @@ }, "source": [ "The example above will transfer the single archive file \n", - "``https://data.mydomain.com/downloads/data/my_dataset.zip`` and unzip the file\n", - "on the local host." + "``https://data.mydomain.com/downloads/data/my_dataset.zip`` and extract the\n", + "archive on the local host. The archive file is removed after the extraction." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Origin as download URL" + ] + }, + { + "cell_type": "raw", + "metadata": { + "raw_mimetype": "text/restructuredtext" + }, + "source": [ + "If the dataset is provided through a complete download URL, e.g. an API\n", + "endpoint with query parameters, the URL can be specified via the ``url``\n", + "argument of the :py:class:`~skyllh.core.dataset.DatasetOrigin` class\n", + "constructor. The ``filename`` argument then specifies the local filename of the\n", + "downloaded file. This is how the IceCube public data samples are downloaded\n", + "from the Harvard Dataverse, e.g. in the\n", + ":py:mod:`skyllh.datasets.i3.PublicData_14y_ps_v2` module:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "origin = DatasetOrigin(\n", + " url='https://data.mydomain.com/api/access/dataset?id=my_dataset',\n", + " base_path='',\n", + " sub_path='my_dataset',\n", + " filename='my_dataset.zip',\n", + " transfer_func=URLRetrieveDatasetTransfer(protocol='https').transfer,\n", + " post_transfer_func=post_transfer_unarchive,\n", + ")\n", + "my_dataset.origin = origin" + ] + }, + { + "cell_type": "raw", + "metadata": { + "raw_mimetype": "text/restructuredtext" + }, + "source": [ + "The example above will download the archive file from the given URL, save it as\n", + "``my_dataset.zip`` into the ``my_dataset`` sub directory of the dataset's base\n", + "path on the local host, and extract it there." ] }, { diff --git a/doc/sphinx/tutorials/dataset_collections.ipynb b/doc/sphinx/tutorials/dataset_collections.ipynb index d3bd1c1460..1435cece04 100644 --- a/doc/sphinx/tutorials/dataset_collections.ipynb +++ b/doc/sphinx/tutorials/dataset_collections.ipynb @@ -35,7 +35,7 @@ "id": "3cd8d21f", "metadata": {}, "source": [ - "The available data samples are accessible through the `skyllh.data_samples` dictionary" + "The available data samples are accessible through the `skyllh.datasets.data_samples` dictionary" ] }, { @@ -47,12 +47,12 @@ { "data": { "text/plain": [ - "{'IceTracks-DR1': ,\n", - " 'IceTracks-DR2': ,\n", - " 'IceTracks-DR2-v1': ,\n", - " 'IceTracks-DR2-v2': ,\n", - " 'IceTracks-DR1_wMC': ,\n", - " 'TestData': }" + "['IceTracks-DR1',\n", + " 'IceTracks-DR2',\n", + " 'IceTracks-DR2-v1',\n", + " 'IceTracks-DR2-v2',\n", + " 'IceTracks-DR1_wMC',\n", + " 'TestData']" ] }, "execution_count": 2, @@ -63,7 +63,7 @@ "source": [ "from skyllh.datasets import data_samples\n", "\n", - "data_samples" + "list(data_samples.keys())" ] }, { diff --git a/doc/sphinx/tutorials/fitting_a_source.ipynb b/doc/sphinx/tutorials/fitting_a_source.ipynb index e5f197ff2d..07695b3f97 100644 --- a/doc/sphinx/tutorials/fitting_a_source.ipynb +++ b/doc/sphinx/tutorials/fitting_a_source.ipynb @@ -15,7 +15,7 @@ "source": [ "This tutorial shows how to fit a neutrino point-like steady source (time-integrated) using the IceCube public 14-year point-source data with SkyLLH.\n", "\n", - "We assume that you have installed the SkyLLH package in your environment by following the [Installation](../installation.rst) guide. If that's not the case you'll need to add the path to the SkyLLH folder to your `PYTHONPATH`." + "We assume that you have installed the SkyLLH package in your environment by following the [Installation](../installation.rst) guide." ] }, { @@ -226,7 +226,7 @@ }, "source": [ ".. note::\n", - " IceTracks-DR2 is a pretty heavy dataset (~2GB) and it might take a while to initialize the analysis when it has to be downloaded for the first time. Be patient, and the next time it will be loaded from `~/.cache/skyllh` :)" + " IceTracks-DR2 is a pretty heavy dataset (~2GB) and it might take a while to initialize the analysis when it has to be downloaded for the first time. Be patient, and the next time it will be loaded from ``~/.cache/skyllh`` :)" ] }, { @@ -234,7 +234,7 @@ "id": "e3aeb290", "metadata": {}, "source": [ - "To create the correct dataset object, we need to import the 14-yr dataset definition:" + "The public datasets are available through the top-level `skyllh` package:" ] }, { @@ -314,10 +314,10 @@ "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "2026-09-04 22:49:00,894 MainProcess skyllh.datasets.datasets INFO: Loaded 4 dataset(s) from sample \"IceTracks-DR2\": IC40, IC59, IC79, IC86_I-XI\n" + "2026-10-28 21:35:47,797 MainProcess skyllh.datasets.datasets INFO: Loaded 4 dataset(s) from sample \"IceTracks-DR2\": IC40, IC59, IC79, IC86_I-XI\n" ] } ], @@ -364,102 +364,102 @@ "Help on function create_analysis in module skyllh.analyses.i3.publicdata_ps.time_integrated_ps:\n", "\n", "create_analysis(\n", - " cfg,\n", - " datasets,\n", - " source,\n", - " refplflux_Phi0=1,\n", - " refplflux_E0=1000.0,\n", - " refplflux_gamma=2.0,\n", - " refplflux_Ec=inf,\n", - " energy_range=None,\n", - " ns_seed=10.0,\n", - " ns_min=0.0,\n", - " ns_max=1000.0,\n", - " gamma_seed=3.0,\n", - " gamma_min=1.0,\n", - " gamma_max=4.0,\n", - " kde_smoothing=False,\n", - " minimizer_impl='LBFGS',\n", - " minimizer_max_rep=100,\n", - " compress_data=False,\n", - " keep_data_fields=None,\n", - " evt_sel_delta_angle_deg=10,\n", - " construct_sig_generator=True,\n", - " tl=None,\n", - " ppbar=None,\n", - " logger_name=None\n", - ")\n", + " cfg: Config,\n", + " datasets: list[Dataset],\n", + " source: PointLikeSource,\n", + " refplflux_Phi0: float = 1,\n", + " refplflux_E0: float = 1000.0,\n", + " refplflux_gamma: float = 2.0,\n", + " refplflux_Ec: float = inf,\n", + " energy_range: tuple[float, float] | None = None,\n", + " ns_seed: float = 10.0,\n", + " ns_min: float = 0.0,\n", + " ns_max: float = 1000.0,\n", + " gamma_seed: float | None = 3.0,\n", + " gamma_min: float = 1.0,\n", + " gamma_max: float = 4.0,\n", + " kde_smoothing: bool = False,\n", + " minimizer_impl: str = 'LBFGS',\n", + " minimizer_max_rep: int = 100,\n", + " compress_data: bool = False,\n", + " keep_data_fields: list[str] | None = None,\n", + " evt_sel_delta_angle_deg: float = 10,\n", + " construct_sig_generator: bool = True,\n", + " tl: TimeLord | None = None,\n", + " ppbar: ProgressBar | None = None,\n", + " logger_name: str | None = None\n", + ") -> SingleSourceMultiDatasetLLHRatioAnalysis\n", " Creates the Analysis instance for this particular analysis.\n", "\n", " Parameters\n", " ----------\n", - " cfg : instance of Config\n", + " cfg\n", " The instance of Config holding the local configuration.\n", - " datasets : list of Dataset instances\n", + " datasets\n", " The list of Dataset instances, which should be used in the\n", " analysis.\n", - " source : PointLikeSource instance\n", + " source\n", " The PointLikeSource instance defining the point source position.\n", - " refplflux_Phi0 : float\n", + " refplflux_Phi0\n", " The flux normalization to use for the reference power law flux model.\n", - " refplflux_E0 : float\n", + " refplflux_E0\n", " The reference energy to use for the reference power law flux model.\n", - " refplflux_gamma : float\n", + " refplflux_gamma\n", " The spectral index to use for the reference power law flux model.\n", - " refplflux_Ec: float,\n", + " refplflux_Ec\n", " The cutoff energy for the cutoff power law flux model.\n", - " energy_range: 2-element tuple of float (low energy, high energy) | None\n", + " energy_range\n", " The energy range for signal generation. Both low and high energies are\n", " given in GeV. If set to ``None``, the entire energy range of the\n", " dataset is used.\n", - " ns_seed : float\n", + " ns_seed\n", " Value to seed the minimizer with for the ns fit.\n", - " ns_min : float\n", + " ns_min\n", " Lower bound for ns fit.\n", - " ns_max : float\n", + " ns_max\n", " Upper bound for ns fit.\n", - " gamma_seed : float | None\n", + " gamma_seed\n", " Value to seed the minimizer with for the gamma fit. If set to None,\n", " the refplflux_gamma value will be set as gamma_seed.\n", - " gamma_min : float\n", + " gamma_min\n", " Lower bound for gamma fit.\n", - " gamma_max : float\n", + " gamma_max\n", " Upper bound for gamma fit.\n", - " kde_smoothing : bool\n", + " kde_smoothing\n", " Deprecated: use of ``kde_smoothing=True`` is deprecated and will be\n", " removed in a future version.\n", " Apply a KDE-based smoothing to the data-driven background pdf.\n", - " Default: False.\n", - " minimizer_impl : str\n", + " Default\n", + " minimizer_impl\n", " Minimizer implementation to be used. Supported options are ``\"LBFGS\"``\n", " (L-BFG-S minimizer used from the :mod:`scipy.optimize` module),\n", " or ``\"minuit\"`` (Minuit minimizer used by the :mod:`iminuit` module).\n", " Default: \"LBFGS\".\n", - " minimizer_max_rep : int\n", + " minimizer_max_rep\n", " In case the minimization process did not converge at the first time\n", " this option specifies the maximum number of repetitions with\n", " different initials. Default is 100.\n", - " compress_data : bool\n", + " compress_data\n", " Flag if the data should get converted from float64 into float32.\n", - " keep_data_fields : list of str | None\n", + " keep_data_fields\n", " List of additional data field names that should get kept when loading\n", " the data.\n", - " evt_sel_delta_angle_deg : float\n", + " evt_sel_delta_angle_deg\n", " The delta angle in degrees for the event selection optimization methods.\n", - " construct_sig_generator : bool\n", + " construct_sig_generator\n", " Flag if the signal generator should be constructed (``True``) or not\n", " (``False``).\n", - " tl : TimeLord instance | None\n", + " tl\n", " The TimeLord instance to use to time the creation of the analysis.\n", - " ppbar : ProgressBar instance | None\n", + " ppbar\n", " The instance of ProgressBar for the optional parent progress bar.\n", - " logger_name : str | None\n", + " logger_name\n", " The name of the logger to be used. If set to ``None``, ``__name__`` will\n", " be used.\n", "\n", " Returns\n", " -------\n", - " ana : instance of SingleSourceMultiDatasetLLHRatioAnalysis\n", + " ana\n", " The Analysis instance for this analysis.\n", "\n" ] @@ -478,7 +478,7 @@ "source": [ "Defining the Source: Here we use the location of NGC 1068 as an example source.\n", "\n", - "Instantiating a `PointLikeSource` object required the source location (R.A. and Dec.) in radians. Optionally, the source can have a `name` and a `weight`. The latter is in principle useful when performing a stacking analysis which is not supported for public data yet." + "Instantiating a `PointLikeSource` object requires the source location (R.A. and Dec.) in radians. Optionally, the source can have a `name` and a `weight`. The latter is in principle useful when performing a stacking analysis which is not supported for public data yet." ] }, { @@ -494,7 +494,13 @@ "Help on class PointLikeSource in module skyllh.core.source_model:\n", "\n", "class PointLikeSource(SourceModel, IsPointlike)\n", - " | PointLikeSource(ra, dec, name=None, weight=None, **kwargs)\n", + " | PointLikeSource(\n", + " | ra: float,\n", + " | dec: float,\n", + " | name: str | None = None,\n", + " | weight: float | None = None,\n", + " | **kwargs\n", + " | )\n", " |\n", " | The PointLikeSource class is a source model for a point-like source\n", " | object in the sky at a given location (right-ascention and declination).\n", @@ -508,19 +514,26 @@ " |\n", " | Methods defined here:\n", " |\n", - " | __init__(self, ra, dec, name=None, weight=None, **kwargs)\n", + " | __init__(\n", + " | self,\n", + " | ra: float,\n", + " | dec: float,\n", + " | name: str | None = None,\n", + " | weight: float | None = None,\n", + " | **kwargs\n", + " | )\n", " | Creates a new PointLikeSource instance for defining a point-like\n", " | source.\n", " |\n", " | Parameters\n", " | ----------\n", - " | ra : float\n", + " | ra\n", " | The right-ascention coordinate of the source in radians.\n", - " | dec : float\n", + " | dec\n", " | The declination coordinate of the source in radians.\n", - " | name : str | None\n", + " | name\n", " | The name of the source.\n", - " | weight : float | None\n", + " | weight\n", " | The relative weight of the source w.r.t. other sources.\n", " | If set to None, unity will be used.\n", " |\n", @@ -592,7 +605,7 @@ "id": "f7dc082a", "metadata": {}, "source": [ - "If not otherwise specified through the optional arguments, the `time_integrated_ps.create_analysis` method source assumes a signal hypothesis such that the defined source emits a powerlaw flux with spectral index $\\gamma=2.0$:\n", + "If not otherwise specified through the optional arguments, the `time_integrated_ps.create_analysis` method assumes a signal hypothesis in which the defined source emits a powerlaw flux with spectral index $\\gamma=2.0$:\n", "\n", "$$\\phi(E_{\\nu}) = \\mathrm{refplflux\\_Phi0} \\cdot \\left(\\frac{E_{\\nu}}{\\mathrm{refplflux\\_E0}}\\right)^{-\\mathrm{refplflux\\_gamma}}$$" ] @@ -604,21 +617,21 @@ "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "2026-09-04 22:49:02,011 MainProcess skyllh.analyses.i3.publicdata_ps.time_integrated_ps INFO: SourceHypoGroupManager\n", + "2026-10-28 21:35:48,696 MainProcess skyllh.analyses.i3.publicdata_ps.time_integrated_ps INFO: SourceHypoGroupManager\n", " Source Hypothesis Groups:\n", " 0: SourceHypoGroup:\n", " sources (1):\n", - " 0: PointLikeSource: \"4591836800\": { ra=40.670 deg, dec=-0.010 deg }\n", + " 0: PointLikeSource: \"4659955424\": { ra=40.670 deg, dec=-0.010 deg }\n", " fluxmodel:\n", " 1.000e+00 * (E / (1000 GeV))^-2 * 1 (GeV cm^2 s)^-1\n", " detector signal yield builders (1):\n", " PDSingleParamFluxPointLikeSourceI3DetSigYieldBuilder\n", " signal generation method:\n", " NoneType\n", - "2026-09-04 22:49:02,013 MainProcess skyllh.analyses.i3.publicdata_ps.time_integrated_ps INFO: ParameterModelMapper: 2 global parameters, 2 models (1 source)\n", + "2026-10-28 21:35:48,697 MainProcess skyllh.analyses.i3.publicdata_ps.time_integrated_ps INFO: ParameterModelMapper: 2 global parameters, 2 models (1 source)\n", " Parameters: \n", " ns [floating (0 <= 10 <= 1000)]\n", " in models:\n", @@ -626,14 +639,20 @@ " \n", " gamma [floating (1 <= 3 <= 4)]\n", " in models:\n", - " - 4591836800: gamma\n", - " \n", - "100%|██████████| 33/33 [00:00<00:00, 502.58it/s]\n", - "100%|██████████| 33/33 [00:00<00:00, 645.65it/s]\n", - "100%|██████████| 33/33 [00:00<00:00, 704.21it/s]\n", - "100%|██████████| 33/33 [00:00<00:00, 378.95it/s]\n", - "100%|██████████| 4/4 [00:21<00:00, 5.26s/it]\n", - "100%|██████████| 136/136 [00:00<00:00, 873.32it/s]\n" + " - 4659955424: gamma\n", + " \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 33/33 [00:00<00:00, 480.94it/s]\n", + "100%|██████████| 33/33 [00:00<00:00, 487.45it/s]\n", + "100%|██████████| 33/33 [00:00<00:00, 370.73it/s]\n", + "100%|██████████| 33/33 [00:00<00:00, 480.19it/s]\n", + "100%|██████████| 4/4 [00:24<00:00, 6.12s/it]\n", + "100%|██████████| 136/136 [00:00<00:00, 836.92it/s]\n" ] } ], @@ -814,26 +833,26 @@ "text": [ "Help on method unblind in module skyllh.core.analysis:\n", "\n", - "unblind(minimizer_rss, tl=None) method of skyllh.core.analysis.SingleSourceMultiDatasetLLHRatioAnalysis instance\n", + "unblind(minimizer_rss: RandomStateService, tl: TimeLord | None = None) -> tuple[float, dict, dict] method of skyllh.core.analysis.SingleSourceMultiDatasetLLHRatioAnalysis instance\n", " Evaluates the unscrambled data, i.e. unblinds the data.\n", "\n", " Parameters\n", " ----------\n", - " minimizer_rss : instance of RandomStateService\n", + " minimizer_rss\n", " The instance of RandomStateService that should be used by the\n", " minimizer to generate new random initial fit parameter values.\n", - " tl : instance of TimeLord | None\n", + " tl\n", " The optional instance of TimeLord that should be used to time the\n", " maximization of the LLH ratio function.\n", "\n", " Returns\n", " -------\n", - " TS : float\n", + " TS\n", " The test-statistic value.\n", - " global_params_dict : dict\n", + " global_params_dict\n", " The dictionary holding the global parameter names and their\n", " best fit values. It includes fixed and floating parameters.\n", - " status : dict\n", + " status\n", " The status dictionary with information about the performed\n", " minimization process of the negative of the log-likelihood ratio\n", " function.\n", @@ -896,7 +915,7 @@ "id": "3e5d967c", "metadata": {}, "source": [ - "By default the analysis assuming a powerlaw signal spectrum is created with a flux normalization of 1 $\\text{GeV}^{-1}\\text{s}^{-1}\\text{cm}^{-2}\\text{sr}^{-1}$ at 1 TeV (see `refplflux_Phi0` and `refplflux_E0` arguments of the `create_analysis` method).\n", + "By default the analysis assuming a powerlaw signal spectrum is created with a flux normalization of 1 $\\text{GeV}^{-1}\\text{cm}^{-2}\\text{s}^{-1}$ at 1 TeV. Note that the differential flux of a point source has no solid-angle term, hence no $\\text{sr}^{-1}$ in its unit (see `refplflux_Phi0` and `refplflux_E0` arguments of the `create_analysis` method).\n", "\n", "The analysis instance offers the method `ana.calculate_fluxmodel_scaling_factor` that calculates the scaling factor that, for a given spectral model, converts a mean number of signal events of 1 into a flux normalization `refplflux_Phi0` at `refplflux_E0`." ] @@ -911,7 +930,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "1 event = 3.083e-17 (E/1000 GeV)^{-2.0} 1/(GeV s cm^2 sr)\n" + "1 event = 3.083e-17 (E/1000 GeV)^{-2.0} 1/(GeV s cm^2)\n" ] } ], @@ -920,7 +939,7 @@ "refplflux_gamma = 2.0\n", "\n", "scaling_factor = ana.calculate_fluxmodel_scaling_factor()\n", - "print(f'1 event = {scaling_factor:.3e} (E/1000 GeV)^{{-{refplflux_gamma:.1f}}} 1/(GeV s cm^2 sr)')" + "print(f'1 event = {scaling_factor:.3e} (E/1000 GeV)^{{-{refplflux_gamma:.1f}}} 1/(GeV s cm^2)')" ] }, { @@ -928,7 +947,7 @@ "id": "e5e7874f", "metadata": {}, "source": [ - "By default the conversion factor is returned for the spectral index with which the analysis\n", + "By default, the conversion factor is returned for the spectral index with which the analysis was created, i.e. `refplflux_gamma` (2.0 by default).\n", "\n", "The method optionally takes a `fitparam_values` argument where a specific spectral index can be specified. Note that this won't change the internal `refplflux_gamma` value with which the analysis was initialized." ] @@ -943,7 +962,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "best-fit flux = 2.845e-14 (E/1000 GeV)^{-3.2} 1/(GeV s cm^2 sr)\n" + "best-fit flux = 2.845e-14 (E/1000 GeV)^{-3.2} 1/(GeV s cm^2)\n" ] } ], @@ -952,7 +971,7 @@ "\n", "scaling_factor = ana.calculate_fluxmodel_scaling_factor(fitparam_values=[1, refplflux_gamma])\n", "print(\n", - " f'best-fit flux = {fitparam_values[\"ns\"] * scaling_factor:.3e} (E/1000 GeV)^{{-{refplflux_gamma:.1f}}} 1/(GeV s cm^2 sr)'\n", + " f'best-fit flux = {fitparam_values[\"ns\"] * scaling_factor:.3e} (E/1000 GeV)^{{-{refplflux_gamma:.1f}}} 1/(GeV s cm^2)'\n", ")" ] }, @@ -968,7 +987,7 @@ "\n", "respectively.\n", "\n", - "They also take a different spectral parameters as optional arguments" + "They also take different spectral parameter values via the optional `fitparam_values` argument." ] }, { @@ -981,13 +1000,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Flux normalization for 10 events = 3.083e-16 1/(GeV s cm^2 sr)\n" + "Flux normalization for 10 events = 3.083e-16 1/(GeV s cm^2)\n" ] } ], "source": [ "phi0 = ana.mu2flux(10)\n", - "print(f'Flux normalization for 10 events = {phi0:.3e} 1/(GeV s cm^2 sr)')" + "print(f'Flux normalization for 10 events = {phi0:.3e} 1/(GeV s cm^2)')" ] }, { @@ -1000,19 +1019,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "Mean number of events for a flux normalization at 1000 GeV of 5e-15 1/(GeV s cm^2 sr) = 162.18\n" + "Mean number of events for a flux normalization at 1000 GeV of 5e-15 1/(GeV s cm^2) = 162.18\n" ] } ], "source": [ "mean_ns = ana.flux2mu(5e-15)\n", - "print(f'Mean number of events for a flux normalization at 1000 GeV of 5e-15 1/(GeV s cm^2 sr) = {mean_ns:.2f}')" + "print(f'Mean number of events for a flux normalization at 1000 GeV of 5e-15 1/(GeV s cm^2) = {mean_ns:.2f}')" ] } ], "metadata": { "kernelspec": { - "display_name": "skyllh (3.14.5)", + "display_name": "skyllh (3.14.x)", "language": "python", "name": "python3" }, @@ -1026,7 +1045,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.14.5" + "version": "3.14.-1" } }, "nbformat": 4, diff --git a/doc/sphinx/tutorials/fixed_spectral_index.ipynb b/doc/sphinx/tutorials/fixed_spectral_index.ipynb index 24eeb0d928..f6b42717dc 100644 --- a/doc/sphinx/tutorials/fixed_spectral_index.ipynb +++ b/doc/sphinx/tutorials/fixed_spectral_index.ipynb @@ -132,9 +132,9 @@ "id": "bcf2e6df", "metadata": {}, "source": [ - "As a test, we can try to fit the location of NGC 1068 with the power-law spectral index in the likelihood fixed to 2.0, which is rather far from the best-fit value we obtain when the parameter is left floating (gamma = 3.2).\n", + "As a test, we can try to fit the location of NGC 1068 with the power-law spectral index in the likelihood fixed to 2.0, which is rather far from the best-fit value obtained when the spectral index is left floating (see the [Fitting a steady point-source with the public 14-year IceCube track data](fitting_a_source.ipynb) tutorial).\n", "\n", - "The resulting TS value is much lower when the fluxmodel does not match sources spectrum (free gamma index best-fit was: TS=29.154; ns=80.09; gamma=3.21), indicating reduced statistical power." + "The resulting TS value is much lower than the one of the fit with a floating spectral index, because the flux model does not match the spectrum of the source, indicating reduced statistical power." ] }, { diff --git a/doc/sphinx/tutorials/p_value_method_comparison.ipynb b/doc/sphinx/tutorials/p_value_method_comparison.ipynb index ba982786cd..15452c4a4e 100644 --- a/doc/sphinx/tutorials/p_value_method_comparison.ipynb +++ b/doc/sphinx/tutorials/p_value_method_comparison.ipynb @@ -30,12 +30,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 33/33 [00:00<00:00, 528.12it/s]\n", - "100%|██████████| 33/33 [00:00<00:00, 517.08it/s]\n", - "100%|██████████| 33/33 [00:00<00:00, 352.20it/s]\n", - "100%|██████████| 33/33 [00:00<00:00, 531.65it/s]\n", - "100%|██████████| 4/4 [00:20<00:00, 5.11s/it]\n", - "100%|██████████| 136/136 [00:00<00:00, 10463.83it/s]\n" + "100%|██████████| 33/33 [00:00<00:00, 513.21it/s]\n", + "100%|██████████| 33/33 [00:00<00:00, 521.56it/s]\n", + "100%|██████████| 33/33 [00:00<00:00, 531.66it/s]\n", + "100%|██████████| 33/33 [00:00<00:00, 514.15it/s]\n", + "100%|██████████| 4/4 [00:23<00:00, 5.81s/it]\n", + "100%|██████████| 136/136 [00:00<00:00, 9478.65it/s]\n" ] } ], @@ -52,7 +52,7 @@ "\n", "datasets = skyllh.create_datasets('IceTracks-DR2', cfg=cfg)\n", "\n", - "# Location of the blazatar TXS 0506+056\n", + "# Location of the blazar TXS 0506+056\n", "src_ra = 77.35 # degrees\n", "src_dec = 5.7 # degrees\n", "\n", @@ -100,7 +100,7 @@ "id": "03aab5c3", "metadata": {}, "source": [ - "## Calculating the significance (local p-value) by generating the test-statistic distribution of background-only pseudo_experiments (trials)" + "## Calculating the significance (local p-value) by generating the test-statistic distribution of background-only pseudo-experiments (trials)" ] }, { @@ -108,7 +108,7 @@ "id": "e7f3b101", "metadata": {}, "source": [ - "The local significance of the source, i.e. its p-value, can be calculated by generating the test-statistic distribution of background-only pseudo-experiment, i.e. for zero injected signal events. SkyLLH provides the helper function ``create_trial_data_file`` to generate these trials and store them in a numpy file for later usage." + "The local significance of the source, i.e. its p-value, can be calculated by generating the test-statistic distribution of background-only pseudo-experiments, i.e. for zero injected signal events. SkyLLH provides the helper function ``create_trial_data_file`` to generate these trials and store them in a numpy file for later usage." ] }, { @@ -134,34 +134,34 @@ "Help on function create_trial_data_file in module skyllh.core.utils.analysis:\n", "\n", "create_trial_data_file(\n", - " ana,\n", - " rss,\n", - " n_trials,\n", + " ana: Analysis,\n", + " rss: RandomStateService,\n", + " n_trials: int,\n", " mean_n_sig=0,\n", " mean_n_sig_null=0,\n", - " mean_n_bkg_list=None,\n", - " minimizer_rss=None,\n", - " bkg_kwargs=None,\n", - " sig_kwargs=None,\n", - " pathfilename=None,\n", - " ncpu=None,\n", - " ppbar=None,\n", - " tl=None\n", - ")\n", + " mean_n_bkg_list: list[float] | None = None,\n", + " minimizer_rss: RandomStateService | None = None,\n", + " bkg_kwargs: dict | None = None,\n", + " sig_kwargs: dict | None = None,\n", + " pathfilename: str | None = None,\n", + " ncpu: int | None = None,\n", + " ppbar: ProgressBar | None = None,\n", + " tl: TimeLord | None = None\n", + ") -> tuple[int, np.ndarray, np.ndarray, np.ndarray]\n", " Creates and fills a trial data file with `n_trials` generated trials for\n", " each mean number of injected signal events specified by `mean_n_sig` for a\n", " given analysis.\n", "\n", " Parameters\n", " ----------\n", - " ana : instance of Analysis\n", + " ana\n", " The Analysis instance to use for the trial generation.\n", - " rss : instance of RandomStateService\n", + " rss\n", " The RandomStateService instance to use for generating random\n", " numbers.\n", - " n_trials : int\n", + " n_trials\n", " The number of trials to perform for each hypothesis test.\n", - " mean_n_sig : ndarray of float | float | 2- or 3-element sequence of float\n", + " mean_n_sig\n", " The array of mean number of injected signal events (MNOISEs) for which\n", " to generate trials. If this argument is not a ndarray, an array of\n", " MNOISEs is generated based on this argument.\n", @@ -170,7 +170,7 @@ " MNOISEs with a step size of one.\n", " If a 3-element sequence of floats is given, it specifies the range plus\n", " the step size of the MNOISEs.\n", - " mean_n_sig_null : ndarray of float | float | 2- or 3-element sequence of float\n", + " mean_n_sig_null\n", " The array of the fixed mean number of signal events (FMNOSEs) for the\n", " null-hypothesis for which to generate trials. If this argument is not a\n", " ndarray, an array of FMNOSEs is generated based on this argument.\n", @@ -179,46 +179,46 @@ " FMNOSEs with a step size of one.\n", " If a 3-element sequence of floats is given, it specifies the range plus\n", " the step size of the FMNOSEs.\n", - " mean_n_bkg_list : list of float | None\n", + " mean_n_bkg_list\n", " The mean number of background events that should be generated for\n", " each dataset. This parameter is passed to the ``do_trials`` method of\n", " the ``Analysis`` class. If set to None (the default), the background\n", " generation method needs to obtain this number itself.\n", - " minimizer_rss : instance of RandomStateService | None\n", + " minimizer_rss\n", " The instance of RandomStateService to use for generating random\n", " numbers for the minimizer, e.g. new initial fit parameter values.\n", " If set to ``None``, a rss with the same seed as ``rss`` will be\n", " initialized.\n", - " bkg_kwargs : dict | None\n", + " bkg_kwargs\n", " Additional keyword arguments for the `generate_events` method of the\n", " background generation method class. An usual keyword argument is\n", " `poisson`.\n", - " sig_kwargs : dict | None\n", + " sig_kwargs\n", " Additional keyword arguments for the `generate_signal_events` method\n", " of the `SignalGenerator` class. An usual keyword argument is\n", " `poisson`.\n", - " pathfilename : string | None\n", + " pathfilename\n", " Trial data file path including the filename.\n", " If set to None generated trials won't be saved.\n", - " ncpu : int | None\n", + " ncpu\n", " The number of CPUs to use.\n", - " ppbar : instance of ProgressBar | None\n", + " ppbar\n", " The optional instance of the parent progress bar.\n", - " tl: instance of TimeLord | None\n", + " tl\n", " The instance of TimeLord that should be used to measure individual\n", " tasks.\n", "\n", " Returns\n", " -------\n", - " seed : int\n", + " seed\n", " The seed used to generate the trials.\n", - " mean_n_sig : 1d ndarray\n", + " mean_n_sig\n", " The array holding the mean number of signal events used to generate the\n", " trials.\n", - " mean_n_sig_null : 1d ndarray\n", + " mean_n_sig_null\n", " The array holding the fixed mean number of signal events for the\n", " null-hypothesis used to generate the trials.\n", - " trial_data : structured numpy ndarray\n", + " trial_data\n", " The generated trial data.\n", "\n" ] @@ -237,7 +237,10 @@ "source": [ "from skyllh.core.timing import TimeLord\n", "\n", - "tl = TimeLord()" + "tl = TimeLord()\n", + "\n", + "# The number of CPU cores to use for generating the trials.\n", + "ncpu = 8" ] }, { @@ -250,7 +253,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 1001/1001 [00:18<00:00, 54.47it/s]" + "100%|██████████| 1001/1001 [00:17<00:00, 56.05it/s]" ] }, { @@ -258,21 +261,21 @@ "output_type": "stream", "text": [ "TimeLord: Executed tasks:\n", - "[Generating background events for dataset \"IC40\". ] 6.6e-04 sec/iter (1000)\n", + "[Generating background events for dataset \"IC40\". ] 6.5e-04 sec/iter (1000)\n", "[Generating background events for dataset \"IC59\". ] 0.001 sec/iter (1000)\n", "[Generating background events for dataset \"IC79\". ] 0.001 sec/iter (1000)\n", "[Generating background events for dataset \"IC86_I-XI\".] 0.008 sec/iter (1000)\n", "[Generating pseudo data. ] 0.010 sec/iter (1000)\n", - "[Initializing trial. ] 0.019 sec/iter (1000)\n", - "[Get sig probability densities and grads. ] 9.5e-07 sec/iter (88192)\n", - "[Get bkg probability densities and grads. ] 6.9e-07 sec/iter (88192)\n", + "[Initializing trial. ] 0.018 sec/iter (1000)\n", + "[Get sig probability densities and grads. ] 1.1e-06 sec/iter (88192)\n", + "[Get bkg probability densities and grads. ] 7.5e-07 sec/iter (88192)\n", "[Calculate PDF ratios. ] 4.5e-05 sec/iter (88192)\n", "[Calc pdfratio value Ri ] 3.4e-04 sec/iter (44096)\n", "[Calc logLambda and grads ] 1.1e-04 sec/iter (44096)\n", "[Evaluate llh-ratio function. ] 0.002 sec/iter (11024)\n", "[Minimize -llhratio function. ] 0.018 sec/iter (1000)\n", "[Maximizing LLH ratio function. ] 0.018 sec/iter (1000)\n", - "[Calculating test statistic. ] 1.1e-05 sec/iter (1000)\n" + "[Calculating test statistic. ] 1.2e-05 sec/iter (1000)\n" ] }, { @@ -286,7 +289,7 @@ "source": [ "rss = RandomStateService(seed=1)\n", "(_, _, _, trials) = create_trial_data_file(\n", - " ana=ana, rss=rss, n_trials=1e3, mean_n_sig=0, pathfilename='txs_bkg_trials.npy', ncpu=8, tl=tl\n", + " ana=ana, rss=rss, n_trials=1000, mean_n_sig=0, pathfilename='txs_bkg_trials.npy', ncpu=ncpu, tl=tl\n", ")\n", "print(tl)" ] @@ -307,7 +310,7 @@ "outputs": [ { "data": { - "image/png": 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", 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eeOKJJjj503ChJ29vTUZw843WkOioJF3AN5r9aA2GXh8yZIi5rrVT2qSktS15eXkmlATXkNxzzz3mxK41K8E1JE6VP9KyqV9//TWgZic4mIXiDWbex7377rvmvdm1a5dpftJaOA0+xdFmLw0xWusUrrnvrbfeMu+5lst/VBbgNIIOEAE9UTnZfJRK9GTarVs3efPNN83QXz2Raf8OHQIcCw0rGpT+9a9/mWYZ7a+jzVd33XVXVGUI1rp1a/niiy8CtmlzSm5ubpHlWbdunSlDNPvRmpQtW7YE3EdrNbSmSy8aJoJrSCpVqmT6sIQaDu5k+SMpm/r6669NTZr3sdE0XQW/NxpydN+zZs2KKJRMmzZN9u7da0JuuNu1/5Y2jYW7D+AYj8sx6go2jvIoamSUjv758ssvfaNftm7d6unQoYPnD3/4g+8+uk1HSy1fvjyqfY8bN86TmZlphp57TZgwwYwSWrlyZVRlCDZ79mwzgue9994z13VotV5/6623fPe5//77PatXrza/676HDBniycjI8MyZMyeq/WjZ9PV//PHH5vr27ds9rVu39lxyySUxj7pyqvyRlq1Xr16e66+/3hMrLe8nn3xift+3b5/nlltu8dSqVStgBJWO7MrOzj7usRdccIGnc+fOIferI/rKlStnHuuW/49utS9FRl0RdBheDgv/sBYVRnSotw4T1kCiJy496ehJ0j+cqJ49e3r69esX8b51fpUqVap4xowZc9xj/vKXv3jOOeccz5EjR6IqQ7BnnnnGU7VqVXOpXLlywPwsaubMmZ4//vGPZvi6zufTrFkzsy3a/aj//Oc/Zq4fnUtG93XllVcGnOSjDTpOlr+4sv3++++eatWqeb766itPrHRem06dOpk5kDSoanhZsWJFwH28cyr5W7NmjQlnb7/9dsj91q5d2wQ8DUj+F52nx9b/j261L0WCTob+Iy62Z88eU+27e/fuqDrqRUKHBntH6jg9cgbxpR1ptcng1FNPPa6/SDrQETvaBKR9RoL7xnhpU4T2p9FOpqGG9mrziI74WbZsWUBH0XD71v9D2pdDt4fqs6HNWLof/1E7xZUhXLn1+fUx4UZAaVn0tooVK5ZoP9qBWPu6aP+a4kYbeTsjBw/Jjlf5iyqbjmpbsWKFvPHGG1JSWhbtZxPcodz791PfP//mOu/no0GDBiH75+hwee18HUz//uoIMhv/P7rV/jifAyM9f3PmBSyk/UX0UhQ9OfrPbxJMO6PqsObKlStHtG/9g6OXUPSEHarvSnFlCFfu4DlkQpXFif3oiTp4SHg4RQ2Nj0f5iyqbjt6KZB+RKGo/oUZyFffZi6QzM+Akgg6AsKINIUgNsc6BBNiImZEBAIC1CDoAAMBaBB0AAGAt1wYdnUE0Jyen2Mm64G4uH5QIpAT+H6IkXBt0+vbta6ZVX7x4cbKLghTkHersXScIQPLs37/f/Ix0QVHAH6OugBB0PpTMzEwzH4j+cQ23Xg+A+NbkaMjR+YJ0fqJI51oC/BF0gBB0IjwdoquTlG3YsIFjBCSRhpxI5zMCghF0gDB0ZtrGjRvTfAUkkdaoUpODkiDoAEXQJiumnAeA9EXHAwAAYC2CDgAAsBZBBwAAWIugAwAArEXQAQAA1iLoAAAAaxF0AACAtQg6AADAWgQdAABgLYIOAACwFkEHAABYi6ADAACsRdABAADWIugAAABrEXQAAIC1CDoAAMBaBB0AAGAtgg4AALAWQQcAAFiLoAMAAKxF0AEAANYi6AAAAGsRdAAAgLUIOgAAwFoEHQAAYC2CDgAAsJY1QefXX3+VwsLCZBcDAACkkLQPOitWrJDWrVtL8+bNpUaNGpKXl5fsIgEAgBSR9kFn2bJlMmbMGNm6davMmzdP7r//fvF4PMkuFgAASAFlJAXMnTtXfvzxR+nSpYvUqVPnuNv37t0rc+bMMT/btGkjjRs39t32t7/9zfd75cqVpWHDhpKRkZGwsgMAgNSV1BqdWbNmSbNmzWTAgAFy++23y6pVq467T35+vjRt2lSGDRsmM2bMkBYtWsjTTz993P1++eUXufnmm+W1115LUOkBAECqS2qNTvny5WX69OlSpUoVqV+/fsj73HrrrdKyZUsTirSmZtKkSXLDDTfI5ZdfLk2aNDH30YDUs2dPGTt2rOmrAwAAkPQanY4dO5oanXAKCgpkwYIF0qdPH19zVPfu3eXEE0+UqVOnmuuLFi2SSy+9VB5++GGpWLGirFmzRo4dOxZ2n4cOHZI9e/YEXAAAgJ1Soo9OOD/88IP5mZOT49tWqlQp05TlvU377qi+ffsGdFDWWqJQhg8fLoMHD45zyQEAQCpI6VFX3tqWatWqBWyvXr2677YHH3zQ1OL4X8KFHDVo0CDZvXu377Jp06Y4vwoAAJAsKV2jo01RSkdb+YcdDTk1a9aMuV+QXgAAgP1SukbHO4x83bp1AdvXr18vjRo1SlKpAABAukjpoKN9cXRk1cSJE33b5s+fb4KOjroCAABI2aarlStXyqeffiq7du0y19955x0zcWCrVq3MRY0ePVo6d+4shw8flgYNGpgh5D169DATBwIAAKRsjc6OHTtk+fLlpoamd+/esn//fnNdJ//z6tChgxlFVbduXROIRo0aJa+++mqJn1vXxNLRXLm5uSXeFwAASE0ZHpcvDKUdm7OysswIrKpVqzq67/2FRyTn4Q/M7/lDOklmuZTu+w0AQNqcAyM9f6d0Hx0AAICSIOgAAABrEXQAAIC1CDoAAMBaBB0AAGAt1wYdhpcDAGA/1wYdXe08Pz9fFi9enOyiAACAOHFt0AEAAPYj6AAAAGsRdAAAgLUIOgAAwFoEHQAAYC2CDgAAsJZrgw7z6AAAYD/XBh3m0QEAwH6uDToAAMB+BB0AAGAtgg4AALAWQQcAAFiLoAMAAKxF0AEAANZybdBhHh0AAOzn2qDDPDoAANjPtUEHAADYj6ADAACsRdABAADWIugAAABrEXQAAIC1CDoAAMBaBB0AAGAtgg4AALAWQQcAAFjLtUGHJSAAALCfa4MOS0AAAGA/1wYdAABgP4IOAACwFkEHAABYq0xJHuzxeGTfvn3Hba9cuXJJdmul/YVHA65XLFtaMjIyklYeAADcIKags2rVKundu7csXLhQDh06FDIAIVCroXMCr2dXl6m3tSHsAACQakHnlltuMbU2M2bMkOrVqztfKktorY0GmiUbdh53m247cPioZJYrUaUaAAAoQkxn2a+//lrWr18vNWrUiOXhrqFNU1pro4HGvwnLW7tDcxYAACkYdOrWrSuFhYXOl8bSsBOu1obmLAAAUnDUlU62179/f9m7d6/zJXJJc1Yo3uYsAACQxBqdJ554QrZs2SJTp06VWrVqHdehtqCgwKHiua85CwAAJDnoDBs2TGxY60ovR48eTanmLAAA4JyYzrY9evSQdKfNb3rZs2ePZGVlJbs4AAAgDpgZGQAAWCviGp1q1aqZn7t27fL9Ho7eBwAAIG2CzssvvxzydzgreG6dYCwdAQBAHIJO165dQ/4OZxU3+oqlIwAAiFzMQ390Pau5c+fKihUrzPWcnBxp3749azc5vFREMJaOAAAgzkFn3bp1ctVVV8m3334rderUMdt0Xp0WLVrItGnT5JRTTuE9KOHcOsGYawcAgASNutJFPWvXri0bN26UTZs2mYv+rpMH9urVK5Zdup53bp3wl9KuP0YAACSkRmfBggXy008/mTWvvPT3l156SRo1ahTLLgEAAFKjRqd+/fpy5MiR47brNr0NAAAgbYNO7969zezIq1at8m3T33Wb3gYAAJBWTVf16tULGHGlnY+bNm0qVatWNde9K5lr4NGVzQEAANIm6AwdOjS+JQEAAEhW0LFhIU8AAOAuLOoJAACsRdABAADWcm3QycvLM8tW5ObmJrsoAAAglYJOQUGBpLu+fftKfn6+LF68ONlFAQAAqTQzcoMGDeTYsWPOlwYJo1MCBK+tpYuL6lIUAAC4OujoQp5aq+M/tw7SK+R0HbtQlgatlq4rqOviooQdAICrm650QsB+/frJtm3bnC8R4k5rcoJDjlqyYWeRK6gDAOCKGp0RI0aYmZFnzpwpNWvWlHLlylnXh8ctljx0sfnZauicZBcFAIDUCDrDhg1zviRIisxypTnyAABrxRR0mCUZAABYP4/O1q1bZeHChc6VBgAAINlBZ+fOndK5c2c5+eST5dxzz/Vt79KliyxYsMDJ8gEAACQ26Nx3333m508//RSwXUdiPfroo7GXBgAAINl9dN5991356quvpH79+gHbdTmFzz//3KmyAQAAJL5GZ9euXZKVlWV+959cbs+ePVK6NKN4AABAGgcdrbmZPn16QNDR2XaHDx8e0GcH8bG/8KjsLzziu+ixT3VaRv8yp0u5AQAubLrSQHPJJZfI3Llzzclq0KBB8uGHH8qKFSvks88+c76UCBA8uV+qL93AkhMAgLSq0Wnbtq188cUX5gTWrFkzM0PyaaedJosWLZJWrVo5X0qYBTc10ISS6ks3sOQEACCtanRU8+bNZcKECc6WBmFpbY3W2vgHGm3CSrelG1hyAgCQFkFHa3O06Uqbq1ROTo60b98+ZZtPbKDHNrNczG9ZSmDJCQBAIsV01ly3bp1cddVV8u2330qdOnXMNl3ks0WLFjJt2jQ55ZRTnC4nkkDDbKgmMW1GI9ACAKwNOrfccovUrl1bZs+eLXXr1jXbNm/eLL169TKXjz76yOlyIkU6EKdD52cAAEoUdHSZB50V2RtylP7+0ksvSaNGjWLZJdKkA7F/5+d0b0YDANgvpjOVzoh85MiR47brtuDZkpEY2jE5Xk1L2oFY+9akY+dnAIC7xRR0evfuLT169JAXXnhBmjRpYratWrXKbNcLEs8/gDjdtKQhh9obAIBrgs7IkSNN5+OmTZtK1apVTX+OvXv3+gKP3u5VUFAgqSgvL89cjh5N3flnIp1bR5uS/NG0BABACYLOsGHDJN317dvXXHR9Lu+6Xek+tw5NSwAAOBB0tNkKqcGGuXUAAEipJSAAAADSAVUBLhiFlcqT/KVLOQEA6YmgY6l0WeE8XcoJAEhPNF1ZJF1WOE+XcgIAXFqjo0PLJ06cKAMGDDDXn332WXn88cflD3/4g0yaNEkaNGjgdDnhwArn3mai4OaiRK+HVZKV2Fl/CwAQ96Bz7733SteuXX3z5AwaNMgEHV0aQm976623Ytkt4jwKy+lZjUuyHlYso8VYfwsAkJCmqzlz5kiHDh3M7++//7506tRJ7rrrLhk1apTMmzcvll0iCc1Eul1vj+d6WE5K9PMBAFxao3Ps2DHZt2+fmWhPVyq/6KKLzPby5cun9UzDNgrVTBSPEU6JXg+L9bcAAHELOhdccIHcfPPN0q5dO5k1a5Y8+eSTZrs2XbVt2zaWXSLNJxVM9HpYrL8FAIhb05WuEVWtWjWZPXu2WdgzOzvbbJ8wYYI88sgjsewSAADAcTF9Ba9du7YZXRVsypQpTpQJAADAEcyjAwAArBVxjY42VUVq165dsZYHAAAg8UHn5Zdfdu5ZAQAAUinoeCcIBAAASBcs6omUw4rmAICkB51FixbJtGnTZOPGjXLkyJGA21gCAvFY0RwAgISMutKh5RdeeKEJOZMnT5bKlSvLihUrTPBxaqZduAsrmgMAUqZG57HHHjOrl19xxRUm6IwfP94suNi/f3/ZunWr86WE9UqyojkAAI4GndWrV5uFPFXZsmVl//79kpmZaVYxb9q0aSy7BBKyVAUAwF1iaro6dOiQVKxY0fxep04d02ylNPAcPnzY2RIibWmNzP7CI8d1LgYAIFFK/PX5qquukhtvvFG6detmFvj0rmQO0OwEAEjLoPP5558H9NcpX768zJ07V3Jzc2Xo0KFOlg9p2ql4yYadx92m2/V2AABSOuicd955vt8rVKggw4cPd7JMsKxTsZeGHEblAQASiZ6fCODtT1Ncv5qibo9Hp2L6+QAAErKopy7YWdwCnyzqaX+/mkT3v6G/DwAgYYt6ssCnO/vVhLtfvPreJPr5AAD2iWlRz3POOUfq1asXrzIhRfvVhLtfvPreJPr5AAD2iakjRYMGDeTYsWPOlwZJE2m/mkRP6sckggCAkojpjKWTBBYUFFCrg5SlS5LEUhMU6+MAABYFHV3Tql+/fvLiiy9KzZo1JZlWrlwpN998s/n9+uuvl9tuuy2p5UHyaVjpOnahLA3Rt0ebwsKFllgfBwCwLOiMGDFCtmzZIjNnzjRBp1y5cgG3a21PotStW1cef/xxmTp1qqxfvz5hz4vUpTUywWFFaadmvS1c01usjwMApK6Y/nIPGzbM0ULoN2ldP0sDU6lS4Zff0n5BwbdXrlzZTGC4ZMkS+eWXXxwtF9Lfkocujml4eqyPAwBYEHTWrFkTdqmHhx56KOL9bNu2TV555RV54YUXTG3Mp59+KhdccEHAfQoLC01T2WuvvWYWDdURX2PHjpXTTz89lqLDZTLLlU7o4wAAFqxeXlSNTjS1PWPGjJGdO3fKhAkTwt5n4MCBpolM19fSYHTKKadIp06dZN++fVGXGwAAuIujnQ6+//77qDonP/zww0X26dEwox2en3nmGTnjjDPMtueff15q1aolU6ZMkZ49ezpUcrhp+QoAgHtEFXRq1KgR8ndv/xld+kFHYzll+fLlcuDAAWnfvr1vW/Xq1eXMM8+UhQsXmqDz+++/yyWXXGI6Rx8+fFjmz58vkydPNp2UQ9G+QHrx2rNnj2PlRXLRnwYAUKKgM3r0aPPz2muv9f3uVbZsWdOsdNZZZ4lTtm7dan4G1xKddNJJvtsqVqxoRl35O/HEE8PuU1daHzx4sGNlROovXxFqxmcAgDtEFXSuueYaX23OxRf//6iURAiehVmve+c0KV26tBl1FalBgwbJvffeG1CjU79+fQdLi1RcvgIA4E4x9dHRkHPkyBEpUya+84rUrl3b/Pz1119NLY6XXs/NzY1pn+XLlzcX2INlIgAAjo668jZVeWm/mHho0aKFVKpUyQw799q+fbt888030rZt27g8JwAAcGnQ0f4tOsz74MGDAdvbtWsX05MfPXrU7MvbOVjnzNHrWlvk7X9z5513mjl79Hk3bNggvXr1kuzsbOnWrVtMzwnEg056ub/wSMBFtwEAkiuqtqdVq1aZCf68w8F1zhwd8RSriRMnmuCitDmpS5cuvkkHvRMPPvroo2Y25Ouuu0727t1ranI++ugjqVChQszPCziJNbIAwJKgM27cOPNTh3Lr8O3Vq1f7Rl9dffXVcv7555vanZYtW0a0vxtuuMFciqKdjbVGJ9xMzLHKy8szF61VAkqCNbIAwJKmq9dff13WrVsnderUMdfHjx9vQo/SuW60r07nzp0lHfTt21fy8/Nl8eLFyS4KLKJrZHnXyQIApGHQ0RmKvZPxvfrqq76FNLUvja4g7g0+gBvpGlmskwUAaRp0PvzwQzP78TvvvGOu6wzEDRs2NL/fddddJuj8/PPP8SkpAABAvIeXa58Z7+zHH3zwgRnurTIzM+W5556TU089NdpdAilP189iRBUApJ+YZ/w7++yzzU+d58Y79FwFDz0HbFxHS5eX0BmZmXkZACydMPDLL7/0/a7NVl7pMuxbR1zl5OTEPMMy3LOOVii6thZraAFA6nNkDYdnn31W0o2OutKLrnWVlZWV7OIgTdbR0iYsVkkHgPQR38WqgDTHOloA4NKmKwAAgFRH0AEAANYi6AAAAGvRRweuop2Jg0dWpfoQcV00NNQIL/+yR3IfAHAjgg5cJd3mwwm3Mrp/2VVx90nV1wcA8ebapivm0XGPdJ4PJ9zK6P5lj+Q+AOBWrq3RYR4d97BlPhxdFV0XDC2q7JHcBwDcxLVBB+5iw3w4/78yepkS3wcA3MS1TVcAAMB+BB0AAGAtgg4AALAWQQcAAFiLXotwPe8kgsGTCcbrcUUJnvjPiX1HO0lirJMPMmkhgFRE0IHrxToM2+nh20VNDpioSRIjmaDQyccBQLy5tumKCQPdrahJBHW73u7k4yJR1MR/0e471kkSY518kEkLAaQq19boMGGgu4WaRDCSJppYHxct78R/se7biUkSY518kEkLAaQS1wYdINZJBBMx+aATE/+VtJyxloFJCwGkEtc2XQEAAPsRdAAAgLUIOgAAwFoEHQAAYC2CDgAAsBajroA4inZW4kQJN+Oyk7M8A0AqIOgAcRTNrMSJ5PSszgCQqlzbdMXMyIiXWGclTma5nJ7lGQBShWtrdJgZGfHixKzEiSpXOKnSxAYAJeXaoAPEUyJmT7apXAAQL65tugIAAPYj6AAAAGsRdAAAgLUIOgAAwFoEHQAAYC2CDgAAsBZBBwAAWIugAwAArMXMYUCCpepCn9GWPZ3K7QSPx5PQWaVDPZ/bjjngBIIOkCILfaZb2VNlgdJE0NDRdexCWbphZ7H3deK4hHs+Nx1zwCmubbpiUU8kUqou9FmSsqd6uZ2krzOSkOPUcQn3fG465oBTXFujw6KeSKRUXegzlrKnS7njZclDF0tmueNXdo/XcdHnU24+5kBJuDboAImWzgtqpnPZnaYhJ5HHIlSoAhA51zZdAQAA+xF0AACAtQg6AADAWgQdAABgLYIOAACwFkEHAABYi6ADAACsRdABAADWIugAAABrEXQAAIC1CDoAAMBaLF4DpABdEDKdy11c+f1v19XQde0sL4/HE9GK3JE+Lvh+6SD4tZTk8+Dk8UzHYwkEI+gAKSBdV6aOtNz+92uVXd2shq4nUD25dh27UJZu2Fn8PiJ8nP/90kE0x8DJfUVyPNPtWAKhuLbpKi8vT3JyciQ3NzfZRYFL6bdlPZEE0216W7qVO7js4e63ZMNOX82B/oz0BB/p4/zvlw6Kei3RfhacPp7pdiyBUFxbo9O3b19z2bNnj2RlZSW7OHAh/Zas35bTrbkgXLmDyx58P22OKaoGaMlDF0tmueNP6pE+rrj7pYPgY1CSz0JJjqdK92MJiNuDDpAK9CSWWa6MteWO5vXpSTmWYxHr41KRk6+lJMcTsIlrm64AAID9CDoAAMBaBB0AAGAtgg4AALAWQQcAAFiLoAMAAKxF0AEAANYi6AAAAGsRdAAAgLUIOgAAwFoEHQAAYC2CDgAAsBZBBwAAWIugAwAArEXQAQAA1iLoAAAAaxF0AACAtQg6AADAWgQdAABgrTLJLgAA99lfeDTgZ7z2H07FsqUlIyNDksHj8ciBw/8tX7THIPj+8X4tkZQvljIEH4dI95Poxzkl1PMnugxuRdABkHCths5J6v5bZVeXqbe1SfgJRk92XcculKUbdjr22uL9WiJ5r6ItQ7jjUNx+Ev24RLzvyfosuolrm67y8vIkJydHcnNzk10UwBX0m6v+UQ9Ft+vt8dp/sCUbdob8dh1v+pzhQk5Rx6Co1xaP1xLNsYylDOGOQ3H7SfTjEvG+J+uz6CaurdHp27evuezZs0eysrKSXRzAevqNVb+5xqv6vqj9+zfDxLs2KVJLHrpYMsuVjugYhHpt8XwtkRxLp8qgx0FFu59EP87p9z2VPou2c23QAZB4egLNLFcmbffvJD3ZRVPWRL+2RD2ff9hL5ccl631Hybm26QoAANiPoAMAAKxF0AEAANYi6AAAAGsRdAAAgLUIOgAAwFoEHQAAYC2CDgAAsBZBBwAAWIugAwAArEXQAQAA1iLoAAAAaxF0AACAtQg6AADAWgQdAABgLYIOAACwFkEHAABYi6ADAACsRdABAADWIugAAABrEXQAAIC1CDoAAMBaBB0AAGAtgg4AALAWQQcAAFiLoAMAAKxF0AEAANYi6AAAAGsRdAAAgLWsCTq7d++WgwcPJrsYAAAghaR90Dl69Khcd911kp2dLTVr1pTnn38+2UUCAAApooykubffflvWrl0rW7duld9++01atmwp3bp1k9q1aye7aAAAIMlSokZn7ty5MnbsWNmyZUvI2/fu3SszZsyQ1157TVavXh1w24IFC6R79+5Svnx5qVOnjpx//vny1VdfJajkAAAglSU16MyaNUuaNWsmAwYMkNtvv11WrVp13H3y8/OladOmMmzYMBN2WrRoIU8//XRA35ysrCzf9WrVqsnOnTsT9hoAAEDqSmrTldbCTJ8+XapUqSL169cPeZ9bb73VNEdpKMrIyJBJkybJDTfcIJdffrk0adJETj75ZNm4caPv/hs2bJCrr746ga8CQKLsLzwa8NOJfYVTsWxp8zenpDwejxw47Fy5nTwuTh5Pt/B/P4v6vMT6vofafySfxeDHOfX5tUFSg07Hjh3Nz4KCgpC363Ztmnr33Xd9b5g2U919990ydepUefDBB+WKK64wl/bt28vmzZvl22+/Nc1X4Rw6dMhcvPbs2eP46wIQH62GzknYvlplV5ept7Up0clCTz5dxy6UpRt2puRxcfJ4ukFR76f/5yXW9z3c44r7LIZ6XP6QTpJZLu274drTRyecH374wfzMycnxbStVqpRpyvLe1qpVK9OsNWjQIBk/frzMnDlTKlasGHafw4cPN01d3ku4miQAqUG/meof+lB0u97uxL6CLdmwM+Q392jo48OdFKMpt5PHxcnj6Tbh3s/gz0us73u4xxX3WSyqXEjxUVfe2hbtd+OvevXqATUxf//7380lEhqI7r333oDnIOwAqUu/xeq32UiaC0qyLy9tZohHTceShy6WzHKlHWtWiPW4OHk83cz7fhb3eYn1fdfHqWg/i97nI7CmSdDx1szoqCv/sKPhROfMibVfkF4ApA89OThVDe/kvqKhJx+nnzfW15KsY2CTSN/PWN93bziKV7ncJKWbrho3bmx+rlu3LmD7+vXrpVGjRkkqFQAASBcpHXS0L46OrJo4caJv2/z5803Q0VFXAAAARUlq/dbKlSvl008/lV27dpnr77zzjvz444+mg7Fe1OjRo6Vz585y+PBhadCggZlYsEePHtKmTZtkFh0AAKSBpNbo7NixQ5YvX25qaHr37i379+8313/55RfffTp06CDLli2TunXrmkA0atQoefXVV0v83Hl5eWY0V25ubon3BQAAUlNSa3S0ViaSmhkNJEOGDHH0ufv27Wsu2rHZf2ZlAABgj5TuowMAAFASBB0AAGAtgg4AALAWQQcAAFiLoAMAAKzl2qDD8HIAAOzn2qCjQ8vz8/Nl8eLFyS4KAACIE9cGHQAAYD/XL3Hq8XjMgdCJA4F0sL/wiBw7tD/gc+t//QgrFzt6fPV4htrm1OPSWayvL9bPcKIfF8nrjefnJdbXlyqfs/1xLpf3GHnP4+FkeIq7h+UKCgqkfv36yS4GAACIwaZNm6RevXphb3d90Dl27Jhs2bJFqlSpIhkZGeJEwtTgpAe+atWqJd4fOOapiM85x9wt+Kyn7jHXepq9e/dKnTp1pFSp8D1xUqN+K4n04BSVBGOlbw5BJ7E45onHMeeYuwWf9dQ85pGsVUlnZAAAYC2CDgAAsBZBx2Hly5eXRx55xPxEYnDME49jzjF3Cz7r6X/MXd8ZGQAA2IsaHQAAYC2CDgAAsBZBBwAAWIug45Ddu3fLoEGD5MILL5QrrrhC3n77bad2jTC+/vprszjrRRddJNddd5289957HKsEGjFihJxzzjkyefJkjnucHTp0SJ5//nm57LLLpEuXLvLWW29xzOPss88+k7///e/mb/pVV10lr7zyihw9epTj7qBFixZJz549zd+RcAtsz58/X6655hq54IIL5I477pDNmzdH/TwEHQfoh79jx47yySefyD333CPt2rWTrl27yn/+8x8ndo8QNEj27t1bTj/9dHnggQekZcuWcvXVV8uzzz7L8UqAefPmyYsvvijfffed/PzzzxzzOIeciy++WF566SVz4r3zzjtl+vTpBPs4mjNnjgk4Ojuvjv7RgHnfffeZL7Nwhh7Xfv36SU5Ojgk8WlkQ7PPPP5c///nP0rBhQxk4cKCsXbtW2rZtG/K+RdK1rlAyU6ZM8ZQqVcpTUFDg29a/f39P/fr1PceOHePwxsHevXuP2zZo0CBzzBFf27dv92RnZ3u++OILT1ZWlmfkyJEc8jgaPny4Oc5bt24N2H7gwAGOe5z069fP06JFi4Bt999/v6dx48Ycc4fs2rXL/Pz55591vU3PRx99dNx9zj//fM/VV18d8JmvVq2a5/HHH4/quajRccDHH39sahTq1q3r2/aXv/zFrNOxatUqJ54CQSpXrhxyW2FhIccqznr06GEubdq04VgnwOuvv25qK0866aSA7RUqVOD4x0mrVq1kw4YNvmYS/buiTSutW7fmmDukuKUbDh48KAsWLJDLL7884DOvrSda4xYNgo4D9D+ELirmz3tdb0P87dy5U8aMGWP6RyF+tGlw+/bt8tBDD3GYE7To8I8//ijNmzeXf/7zn6Y5RfsrzJgxg+MfRzfeeKMMHjzYNI23aNHCfInVv+nafIjE0JCp3UJCnVujPa8SdBxw+PDh42ZwrFixou82xL8Pg37j1RXon3zySQ53HDt/Dx061PQ9K1PG9esBJ4T+/dCwoyddrVXQsHP22Webzvf0R4ufL7/8Uh5++GHp06ePPPPMMzJs2DD54IMPTL80JIb33Bnq3BrteZW/Vg444YQTZMeOHQHbfvvtN/PzxBNPdOIpUETI0RERmv7nzp3LivFxNGnSJPNTT7Jev//+uznhakf8d955h8+pw/SPfKVKlUxTyhNPPGG2aefMLVu2mFFYd999N8c8DjRQtm/fXh577DHfMdfAqcf71ltv9X2RRXzPqyrUuTXa8ypBxwHaP0eH2h45csT3TVd7kZctW9ZUfSI+9Buu1uSsXr3ahJzatWtzqONIR0jo8fbXoUMHufLKK01VP+LjrLPOOq76Xj/r2lyL+NCTa5MmTQK26XugX6z27dtH0EkA7ZOmTYbaN8q/S4KeW3Nzc6PaF01XDtBvuPrhHz16tLm+Z88e8y33r3/9q2lOgfO06lKH8Gtnbw05wScCOK9BgwZmvgv/S+nSpSU7O1v+9Kc/ccjjRKdRePfdd2XdunXmug6t1eZDHXKO+Dj//PNNP6iNGzf6OsZqs9Vpp50mNWrU4LAnyM033yyvvvqqGdjjnVbk22+/lZtuuimq/VCj4wCda+GNN94wb4pWJ2/bts1UNY8aNcqJ3SOEcePGyaxZs6RRo0am6crfp59+yjcuWONvf/ub/PDDD3LGGWfIqaeeajpiasjMy8tLdtGs9eijj5qQ07RpU2ncuLFpGq9Xr568+eabyS6aNT744AMzl463v43OD1W1alUzgaCGe/Xggw+aL7Nau6bn2YKCAnnuueeiHvHJ6uUO0tSvIyR02Jz+QUL8bN261fcNN5gOAS1VisrKRFiyZIk5AZx88skJeT4327Vrl6xfv97UXgYPNUd8aPOgBh6txdHjnpGRwaF2iI7eXLNmzXHb9e+JXvxpnzT9m69fbGNpJSHoAAAAa/G1FwAAWIugAwAArEXQAQAA1iLoAAAAaxF0AACAtQg6AADAWgQdAABgLWZGBpCWdMZUXVG9KJ06dZLq1aub33XCse+++84sznjmmWdKrVq1ElRSAMlE0AGQln766SeZOXNmwJTyNWvWNIvs+s+SrUFHV6N++umnzXVdDVwDj65npOsXZWZmJukVAEgEZkYGYIUWLVrIeeed51tc10sXfb3wwgtl/vz50rZtW7NNa3UmT54sl1xyia/GB4CdqNEBYLXvv/9eKlSoIOeee65vm66Fdu211ya1XAASg87IAKymzVWHDh2SPn36mNDj8XiSXSQACUTQAWB90Jk0aZLMmzdPmjdvLllZWXL55ZfLRx99lOyiAUgAgg4A63Xv3l3y8/OloKBAxo0bZ2p1OnbsKLNnz0520QDEGUEHgGvUrVtXrr76apk1a5bk5OTIxIkTk10kAHFG0AFgtS1btkhhYWHANh11pduqVauWtHIBSAxGXQGw2qJFi6R///5yxRVXSLNmzeTw4cMyZcoU2bZtm9xxxx3JLh6AOKNGB4AVdE6cs84667jtV155pXzxxReSnZ0tixcvNiOvNPSsWbPGNF8BsBsTBgIAAGtRowMAAKxF0AEAANYi6AAAAGsRdAAAgLUIOgAAwFoEHQAAYC2CDgAAsBZBBwAAWIugAwAArEXQAQAA1iLoAAAAaxF0AACA2Or/AL7Z5QPOSPXpAAAAAElFTkSuQmCC", "text/plain": [ "
" ] @@ -359,16 +362,16 @@ "Help on function extend_trial_data_file in module skyllh.core.utils.analysis:\n", "\n", "extend_trial_data_file(\n", - " ana,\n", - " rss,\n", - " n_trials,\n", - " trial_data,\n", + " ana: Analysis,\n", + " rss: RandomStateService,\n", + " n_trials: int,\n", + " trial_data: np.ndarray,\n", " mean_n_sig=0,\n", " mean_n_sig_null=0,\n", " mean_n_bkg_list=None,\n", - " bkg_kwargs=None,\n", - " sig_kwargs=None,\n", - " pathfilename=None,\n", + " bkg_kwargs: dict | None = None,\n", + " sig_kwargs: dict | None = None,\n", + " pathfilename: str | None = None,\n", " **kwargs\n", ")\n", " Appends to the trial data file `n_trials` generated trials for each\n", @@ -377,16 +380,16 @@ "\n", " Parameters\n", " ----------\n", - " ana : instance of Analysis\n", + " ana\n", " The Analysis instance to use for sensitivity estimation.\n", - " rss : instance of RandomStateService\n", + " rss\n", " The RandomStateService instance to use for generating random\n", " numbers.\n", - " n_trials : int\n", + " n_trials\n", " The number of trials the trial data file needs to be extended by.\n", - " trial_data : structured numpy ndarray\n", + " trial_data\n", " The structured numpy ndarray holding the trials.\n", - " mean_n_sig : ndarray of float | float | 2- or 3-element sequence of float\n", + " mean_n_sig\n", " The array of mean number of injected signal events (MNOISEs) for which\n", " to generate trials. If this argument is not a ndarray, an array of\n", " MNOISEs is generated based on this argument.\n", @@ -395,7 +398,7 @@ " MNOISEs with a step size of one.\n", " If a 3-element sequence of floats is given, it specifies the range plus\n", " the step size of the MNOISEs.\n", - " mean_n_sig_null : ndarray of float | float | 2- or 3-element sequence of float\n", + " mean_n_sig_null\n", " The array of the fixed mean number of signal events (FMNOSEs) for the\n", " null-hypothesis for which to generate trials. If this argument is not a\n", " ndarray, an array of FMNOSEs is generated based on this argument.\n", @@ -404,15 +407,15 @@ " FMNOSEs with a step size of one.\n", " If a 3-element sequence of floats is given, it specifies the range plus\n", " the step size of the FMNOSEs.\n", - " bkg_kwargs : dict | None\n", + " bkg_kwargs\n", " Additional keyword arguments for the `generate_events` method of the\n", " background generation method class. An usual keyword argument is\n", " `poisson`.\n", - " sig_kwargs : dict | None\n", + " sig_kwargs\n", " Additional keyword arguments for the `generate_signal_events` method\n", " of the `SignalGenerator` class. An usual keyword argument is\n", " `poisson`.\n", - " pathfilename : string | None\n", + " pathfilename\n", " Trial data file path including the filename.\n", "\n", " Additional keyword arguments\n", @@ -443,7 +446,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 4001/4001 [01:18<00:00, 51.16it/s]\n" + "100%|██████████| 4001/4001 [01:12<00:00, 55.26it/s]\n" ] } ], @@ -451,7 +454,7 @@ "tl = TimeLord()\n", "rss = RandomStateService(seed=2)\n", "trials = extend_trial_data_file(\n", - " ana=ana, rss=rss, n_trials=4e3, trial_data=trials, pathfilename='txs_bkg_trials.npy', ncpu=8, tl=tl\n", + " ana=ana, rss=rss, n_trials=4000, trial_data=trials, pathfilename='txs_bkg_trials.npy', ncpu=ncpu, tl=tl\n", ")" ] }, @@ -472,8 +475,8 @@ "[Generating background events for dataset \"IC86_I-XI\".] 0.008 sec/iter (4000)\n", "[Generating pseudo data. ] 0.009 sec/iter (4000)\n", "[Initializing trial. ] 0.016 sec/iter (4000)\n", - "[Get sig probability densities and grads. ] 9.5e-07 sec/iter (343864)\n", - "[Get bkg probability densities and grads. ] 6.9e-07 sec/iter (343864)\n", + "[Get sig probability densities and grads. ] 1.1e-06 sec/iter (343864)\n", + "[Get bkg probability densities and grads. ] 7.5e-07 sec/iter (343864)\n", "[Calculate PDF ratios. ] 4.4e-05 sec/iter (343864)\n", "[Calc pdfratio value Ri ] 3.1e-04 sec/iter (171932)\n", "[Calc logLambda and grads ] 1.1e-04 sec/iter (171932)\n", @@ -493,7 +496,7 @@ "id": "aaacf95d", "metadata": {}, "source": [ - "Among utility functions saved in the library ``skyllh.core.utils.analysis``, SkyLLH offers a function ``calculate_pval_from_trials`` that allows one to calculate the p-value emprically directly from the distribution of background trials. The function takes the created background-only pseudo-experiments and the observed TS value and it returns the corresponding p-value and the related statistical uncertainty. " + "Among utility functions saved in the library ``skyllh.core.utils.analysis``, SkyLLH offers a function ``calculate_pval_from_trials`` that allows one to calculate the p-value empirically directly from the distribution of background trials. The function takes the created background-only pseudo-experiments and the observed TS value and it returns the corresponding p-value and the related statistical uncertainty. " ] }, { @@ -518,7 +521,11 @@ "text": [ "Help on function calculate_pval_from_trials in module skyllh.core.utils.analysis:\n", "\n", - "calculate_pval_from_trials(ts_vals, ts_threshold, comp_operator='greater_equal')\n", + "calculate_pval_from_trials(\n", + " ts_vals: np.ndarray,\n", + " ts_threshold: float,\n", + " comp_operator: str = 'greater_equal'\n", + ") -> tuple[float, float]\n", " Calculates the percentage (p-value) of test-statistic trials that are\n", " above the given test-statistic critical value.\n", " In addition it calculates the standard deviation of the p-value assuming\n", @@ -526,11 +533,11 @@ "\n", " Parameters\n", " ----------\n", - " ts_vals : (n_trials,)-shaped 1D ndarray of float\n", + " ts_vals\n", " The ndarray holding the test-statistic values of the trials.\n", - " ts_threshold : float\n", + " ts_threshold\n", " The critical test-statistic value.\n", - " comp_operator: string, optional\n", + " comp_operator\n", " The comparison operator for p-value calculation. It can be set to one of\n", " the following options: 'greater' or 'greater_equal'.\n", "\n", @@ -590,7 +597,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -624,9 +631,9 @@ "id": "a4fe6a06", "metadata": {}, "source": [ - "It can happen (if your lucky or if you're estimating a $5\\sigma$ discovery potential, see the [Sensitivity study](sensitivity_study.ipynb) notebook) that the observed TS is high enough that computing background trials to calculate the corresponding p-value becomes extremely expensive.\n", + "It can happen (if you're lucky or if you're estimating a $5\\sigma$ discovery potential, see the [Sensitivity study](sensitivity_study.ipynb) notebook) that the observed TS is high enough that computing background trials to calculate the corresponding p-value becomes extremely expensive.\n", "\n", - "While, the emprical estimation remains the most robust way to compute a significance, a faster alternative is to fit the tail of the generated distribution of background-only pseudo-experiments with a truncated gamma function and use it to extrapolate p-values at higher TS values." + "While the empirical estimation remains the most robust way to compute a significance, a faster alternative is to fit the tail of the generated distribution of background-only pseudo-experiments with a truncated gamma function and use it to extrapolate p-values at higher TS values." ] }, { @@ -636,9 +643,19 @@ "source": [ "Among the analysis utility functions, SkyLLH offers the ``fit_truncated_gamma`` function, which fits the truncated gamma function to the TS distribution.\n", "\n", - "Additionally, the ``calculate_pval_from_gammafit_to_trials`` function provides a wrapper method that runs the fit and returns the p-value estimated from the fit.\n", - "\n", - "..note:: Please note that fitting the truncated gamma function requires the `iminuit` Python package." + "Additionally, the ``calculate_pval_from_gammafit_to_trials`` function provides a wrapper method that runs the fit and returns the p-value estimated from the fit." + ] + }, + { + "cell_type": "raw", + "id": "33ae416b", + "metadata": { + "raw_mimetype": "text/restructuredtext" + }, + "source": [ + ".. note::\n", + " Fitting the truncated gamma function requires the ``iminuit`` Python package, which can be installed via\n", + " ``pip install \"skyllh[extras]\"``." ] }, { @@ -673,7 +690,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -707,9 +724,6 @@ " ls='',\n", " color='C0',\n", ")\n", - "bins = bins[bins > eta]\n", - "bincenters = 0.5 * (bins[1:] + bins[:-1])\n", - "bw = bins[1:] - bins[:-1]\n", "ax.set_yscale('log')\n", "\n", "vals = np.linspace(eta, np.max(trials['ts']) + 2, 100)\n", @@ -720,7 +734,7 @@ "ax.axvline(ts, color='C1', linestyle='--', label='unblinded TS')\n", "\n", "ax.set_xlabel('Test statistic')\n", - "ax.set_ylabel('Counts')\n", + "ax.set_ylabel('Probability density')\n", "\n", "ax.legend()\n", "plt.show()" @@ -731,7 +745,7 @@ "id": "29dfadd1", "metadata": {}, "source": [ - "``calculate_critical_ts_from_gamma`` function calculates the TS you would need in your analysis to reach a given significance." + "``calculate_critical_ts_from_gamma`` function calculates the TS you would need in your analysis to reach a given significance. We use the same truncation value $\\eta$ as for the fit above." ] }, { @@ -744,8 +758,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "TS value for a 3 sigma detection: 12.48\n", - "TS value for a 5 sigma detection: 29.48\n" + "TS value for a 3 sigma detection: 12.10\n", + "TS value for a 5 sigma detection: 27.52\n" ] } ], @@ -753,13 +767,13 @@ "critical_ts = calculate_critical_ts_from_gamma(\n", " ts=trials['ts'],\n", " h0_ts_quantile=1.3e-3, # this is ~ 3 sigma\n", - " eta=1.0,\n", + " eta=eta,\n", ")\n", "print(f'TS value for a 3 sigma detection: {critical_ts:.2f}')\n", "critical_ts = calculate_critical_ts_from_gamma(\n", " ts=trials['ts'],\n", " h0_ts_quantile=2.9e-7, # this is ~ 5 sigma\n", - " eta=1.0,\n", + " eta=eta,\n", ")\n", "print(f'TS value for a 5 sigma detection: {critical_ts:.2f}')" ] @@ -769,7 +783,7 @@ "id": "9cc0c3ba", "metadata": {}, "source": [ - "And of course you can also calculate the p-value for the observed TS from the fitted function. This should result in a very similar result to the one estimated empirically from the trials." + "And of course you can also calculate the p-value for the observed TS from the fitted function. This should result in a p-value of the same order of magnitude as the one estimated empirically from the trials." ] }, { @@ -796,7 +810,7 @@ "id": "d906e843", "metadata": {}, "source": [ - "Practially, one might be interested in flip-flopping from one estimation method to the other depending on how high the TS value of interest is and on how many pseudo-experiments have been computed to populate the background TS distribution.\n", + "Practically, one might be interested in flip-flopping from one estimation method to the other depending on how high the TS value of interest is and on how many pseudo-experiments have been computed to populate the background TS distribution.\n", "\n", "This feature exists in the `calculate_pval_from_trials_mixed` method, which takes a `switch_at_ts` additional argument to set at what TS value one should switch from the empirical estimation to the evaluation of the truncated gamma fit." ] @@ -811,59 +825,27 @@ "name": "stdout", "output_type": "stream", "text": [ - "Help on function calculate_pval_from_trials_mixed in module skyllh.core.utils.analysis:\n", - "\n", - "calculate_pval_from_trials_mixed(\n", - " ts_vals,\n", - " ts_threshold,\n", - " switch_at_ts=3.0,\n", - " eta=None,\n", - " n_max=500000,\n", - " comp_operator='greater_equal'\n", - ")\n", - " Calculates the probability (p-value) of test-statistic exceeding\n", - " the given test-statistic threshold. This calculation relies on fitting\n", - " a gamma distribution to a list of ts values if ts_threshold is larger than\n", - " switch_at_ts. If ts_threshold is smaller then the pvalue will be taken\n", - " from the trials directly.\n", - "\n", - " Parameters\n", - " ----------\n", - " ts_vals : (n_trials,)-shaped 1D ndarray of float\n", - " The ndarray holding the test-statistic values of the trials.\n", - " ts_threshold : float\n", - " The critical test-statistic value.\n", - " switch_at_ts : float, optional\n", - " Test-statistic value below which p-value is computed from trials\n", - " directly. For thresholds greater than switch_at_ts the pvalue is\n", - " calculated using a gamma fit.\n", - " eta : float, optional\n", - " Test-statistic value at which the gamma function is truncated\n", - " from below. Default is None.\n", - " n_max : int, optional\n", - " The maximum number of trials that should be used during\n", - " fitting. Default = 500,000\n", - " comp_operator: string, optional\n", - " The comparison operator for p-value calculation. It can be set to one of\n", - " the following options: 'greater' or 'greater_equal'.\n", - "\n", - " Returns\n", - " -------\n", - " p, p_sigma: tuple(float, float)\n", - "\n" + "p_value calculated with the mixed method (switch_at_ts=2.0): 4.595e-03\n", + "p_value calculated with the mixed method (switch_at_ts=20.0): 2.400e-03\n" ] } ], "source": [ "from skyllh.core.utils.analysis import calculate_pval_from_trials_mixed\n", "\n", - "help(calculate_pval_from_trials_mixed)" + "# The observed TS is above switch_at_ts, hence the p-value is taken from the truncated gamma fit.\n", + "p_val_mixed, _ = calculate_pval_from_trials_mixed(trials['ts'], ts, switch_at_ts=eta, eta=eta)\n", + "print(f'p_value calculated with the mixed method (switch_at_ts={eta}): {p_val_mixed:.3e}')\n", + "\n", + "# The observed TS is below switch_at_ts, hence the p-value is calculated from the trials directly.\n", + "p_val_mixed, _ = calculate_pval_from_trials_mixed(trials['ts'], ts, switch_at_ts=20.0, eta=eta)\n", + "print(f'p_value calculated with the mixed method (switch_at_ts=20.0): {p_val_mixed:.3e}')" ] } ], "metadata": { "kernelspec": { - "display_name": "skyllh (3.14.5)", + "display_name": "skyllh (3.14.x)", "language": "python", "name": "python3" }, diff --git a/doc/sphinx/tutorials/sensitivity_study.ipynb b/doc/sphinx/tutorials/sensitivity_study.ipynb index 77ec5f50bd..e7671926f1 100644 --- a/doc/sphinx/tutorials/sensitivity_study.ipynb +++ b/doc/sphinx/tutorials/sensitivity_study.ipynb @@ -98,7 +98,7 @@ "\n", "The `estimate_sensitivity` function accepts pre-generated background trials via its `h0_trials`\n", "argument. If fewer trials are provided than what its internal precision criterion requires\n", - "(≈ 10 000 for the default 0.5 quantile and `eps_p=0.05`), it will automatically extend them —\n", + "(≈ 10 000 for the 0.5 quantile used here, independent of `eps_p`), it will automatically extend them —\n", "so the 500 trials generated here serve as a starting point." ] }, diff --git a/doc/sphinx/tutorials/setting_an_energy_range.ipynb b/doc/sphinx/tutorials/setting_an_energy_range.ipynb index 2a63dbe93e..1f125f5c41 100644 --- a/doc/sphinx/tutorials/setting_an_energy_range.ipynb +++ b/doc/sphinx/tutorials/setting_an_energy_range.ipynb @@ -57,7 +57,7 @@ "id": "8bc3d432", "metadata": {}, "source": [ - "The ``create_analysis`` instance can be created with the ``energy_range`` set to a specific energy range. This internally sets the energy range for the signal generator. Here in this example, the analysis object is created with the energy range $10^2\\, \\text{GeV}$ to $10^3\\, \\text{GeV}$. " + "The analysis instance can be created via ``create_analysis`` with the ``energy_range`` argument set to a specific energy range. This internally sets the energy range for the signal generator. Here in this example, the analysis object is created with the energy range $10^2\\, \\text{GeV}$ to $10^3\\, \\text{GeV}$. " ] }, { @@ -255,7 +255,7 @@ "id": "f98ec153", "metadata": {}, "source": [ - "Now we can look at the true neutrino energy of the generate pseudo-data events and check that they belong to their respective energy ranges." + "Now we can look at the true neutrino energy of the generated pseudo-data events and check that they belong to their respective energy ranges." ] }, { diff --git a/doc/sphinx/tutorials/sky_scan.ipynb b/doc/sphinx/tutorials/sky_scan.ipynb index 8a20e7fd63..c25c56336e 100644 --- a/doc/sphinx/tutorials/sky_scan.ipynb +++ b/doc/sphinx/tutorials/sky_scan.ipynb @@ -28,12 +28,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 33/33 [00:00<00:00, 516.49it/s]\n", - "100%|██████████| 33/33 [00:00<00:00, 468.00it/s]\n", - "100%|██████████| 33/33 [00:00<00:00, 474.53it/s]\n", - "100%|██████████| 33/33 [00:00<00:00, 520.21it/s]\n", - "100%|██████████| 4/4 [00:21<00:00, 5.38s/it]\n", - "100%|██████████| 136/136 [00:00<00:00, 9370.28it/s]\n" + "100%|██████████| 33/33 [00:00<00:00, 471.84it/s]\n", + "100%|██████████| 33/33 [00:00<00:00, 428.36it/s]\n", + "100%|██████████| 33/33 [00:00<00:00, 519.23it/s]\n", + "100%|██████████| 33/33 [00:00<00:00, 534.32it/s]\n", + "100%|██████████| 4/4 [00:23<00:00, 5.92s/it]\n", + "100%|██████████| 136/136 [00:00<00:00, 10206.58it/s]\n" ] } ], @@ -113,8 +113,9 @@ "source": [ "Here we scan a patch of the sky around the source coordinates. We therefore define a grid of points in R.A. and Dec. and unblind the data to get a TS, ns, and gamma value at each grid point.\n", "\n", - "The ``change_source`` property of the ``Analysis`` instance allows us to change the source hypothesis of the analysis without re-initializing the analysis instance itself.\n", + "The ``change_source`` method of the ``Analysis`` instance allows us to change the source hypothesis of the analysis without re-initializing the analysis instance itself.\n", "\n", + "By passing ``update_detsigyield_service=False``, the detector signal yield, i.e. the expected number of signal events for a given flux, is not recomputed for each new source position, but the one of the initial source position is used. This runtime optimization is a good approximation for a small patch of the sky like the one scanned here, because the detector signal yield changes only slowly with declination. For scans over larger declination ranges, use the default ``update_detsigyield_service=True``.\n", "\n", "> **Compute time note**: Despite this optimization, running the following cell can take 2-3 minutes on a laptop.\n" ] @@ -128,9 +129,6 @@ "source": [ "# Produce a little sky scan around the source location\n", "\n", - "src_ra = 40.67 # degrees\n", - "src_dec = -0.01 # degrees\n", - "\n", "ra_min = src_ra - 1.0\n", "ra_max = src_ra + 1.0\n", "dec_min = src_dec - 1.0\n", @@ -142,26 +140,23 @@ "centers_x = 0.5 * (edges_x[1:] + edges_x[:-1])\n", "centers_y = 0.5 * (edges_y[1:] + edges_y[:-1])\n", "\n", - "# Initialize the results arrays\n", + "# Initialize the results arrays. The first axis corresponds to Dec. (rows) and\n", + "# the second axis to R.A. (columns), as expected by matplotlib's pcolormesh.\n", "results_ts = np.zeros((nbins, nbins), dtype=float)\n", - "results_p = np.zeros((nbins, nbins), dtype=float)\n", "results_ns = np.zeros((nbins, nbins), dtype=float)\n", "results_g = np.zeros((nbins, nbins), dtype=float)\n", "\n", "for i, ra in enumerate(centers_x):\n", " for j, dec in enumerate(centers_y):\n", " # Change source position to evaluate the likelihood\n", - " src_dec = np.radians(dec)\n", - " src_ra = np.radians(ra)\n", - " source = PointLikeSource(src_ra, src_dec)\n", - " ana.change_source(source, update_detsigyield_service=False)\n", + " ana.change_source(PointLikeSource(ra=np.radians(ra), dec=np.radians(dec)), update_detsigyield_service=False)\n", "\n", " # Unblind the new position\n", " rss = RandomStateService(3090)\n", " ts, fitparam_values, status = ana.unblind(rss)\n", - " results_ts[i, j] = ts\n", - " results_ns[i, j] = fitparam_values['ns']\n", - " results_g[i, j] = fitparam_values['gamma']" + " results_ts[j, i] = ts\n", + " results_ns[j, i] = fitparam_values['ns']\n", + " results_g[j, i] = fitparam_values['gamma']" ] }, { @@ -169,7 +164,7 @@ "id": "7e2645c7", "metadata": {}, "source": [ - "The contours are computed assuming that Wilks' theorem holds true, that is the TS distribution follows a $\\chi^2$ distribution with 2 degrees of freedom in this example where we fit the flux normalization and spectral index of a powerlaw flux." + "The contours are computed assuming that Wilks' theorem holds true. At each grid point the flux normalization and the spectral index are re-fitted, hence the difference between the maximum TS value and the TS value at a given position follows a $\\chi^2$ distribution with 2 degrees of freedom, which correspond to the two scanned position parameters, R.A. and Dec." ] }, { @@ -180,7 +175,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -198,19 +193,14 @@ "chi2_90_quantile = ts_max - chi2.ppf(0.90, df=2)\n", "chi2_95_quantile = ts_max - chi2.ppf(0.95, df=2)\n", "\n", + "# The first index corresponds to Dec. and the second index to R.A.\n", "idx = np.unravel_index(results_ts.argmax(), results_ts.shape)\n", "best_dec = centers_y[idx[0]]\n", "best_ra = centers_x[idx[1]]\n", "\n", - "X, Y = np.meshgrid(centers_x, centers_y)\n", - "\n", - "src_ra = 40.67 # degrees\n", - "src_dec = -0.01\n", - "\n", "fig, ax = plt.subplots()\n", "\n", "im = ax.pcolormesh(centers_x, centers_y, results_ts, cmap='GnBu')\n", - "ax.invert_xaxis()\n", "contour_68 = ax.contour(\n", " centers_x, centers_y, results_ts, levels=[chi2_68_quantile], colors='w', linestyles='-', linewidths=2\n", ")\n", @@ -221,27 +211,27 @@ " centers_x, centers_y, results_ts, levels=[chi2_95_quantile], colors='w', linestyles=':', linewidths=2\n", ")\n", "ax.scatter([src_ra], [src_dec], marker='*', color='gold', s=120, label='Source position')\n", - "ax.scatter([best_ra], [best_dec], marker='x', color='w', s=120, label='best fit')\n", + "ax.scatter([best_ra], [best_dec], marker='X', color='w', edgecolor='k', s=120, label='best fit')\n", "\n", "ax.set_aspect('equal')\n", - "ax.set_xlim(40, 41.2)\n", - "ax.set_ylim(-0.5, 0.75)\n", + "# As usual for sky maps, the R.A. increases to the left.\n", + "ax.set_xlim(41.5, 40.25)\n", + "ax.set_ylim(-0.75, 0.5)\n", "cbar = plt.colorbar(im)\n", "cbar.set_label('Test Statistic')\n", "ax.set_xlabel('R.A. (Deg)')\n", "ax.set_ylabel('Dec. (Deg)')\n", "\n", "ax.clabel(\n", - " contour_68, fmt={chi2_68_quantile: '68% CL'}, fontsize=10, inline=False, inline_spacing=5, manual=[(40.5, 0.2)]\n", + " contour_68, fmt={chi2_68_quantile: '68% CL'}, fontsize=10, inline=False, inline_spacing=5, manual=[(41.0, -0.1)]\n", ")\n", "ax.clabel(\n", - " contour_90, fmt={chi2_90_quantile: '90% CL'}, fontsize=10, inline=False, inline_spacing=5, manual=[(40.1, -0.3)]\n", + " contour_90, fmt={chi2_90_quantile: '90% CL'}, fontsize=10, inline=False, inline_spacing=5, manual=[(41.05, -0.35)]\n", ")\n", "ax.clabel(\n", - " contour_95, fmt={chi2_95_quantile: '95% CL'}, fontsize=10, inline=False, inline_spacing=5, manual=[(40.6, 0.4)]\n", + " contour_95, fmt={chi2_95_quantile: '95% CL'}, fontsize=10, inline=False, inline_spacing=5, manual=[(41.1, 0.05)]\n", ")\n", "\n", - "ax.invert_xaxis()\n", "plt.legend()\n", "plt.show()" ] @@ -249,7 +239,7 @@ ], "metadata": { "kernelspec": { - "display_name": "skyllh (3.14.5)", + "display_name": "skyllh (3.14.x)", "language": "python", "name": "python3" }, diff --git a/doc/sphinx/tutorials/time_dependent_point_source.ipynb b/doc/sphinx/tutorials/time_dependent_point_source.ipynb index a425db89c3..41e24b4554 100644 --- a/doc/sphinx/tutorials/time_dependent_point_source.ipynb +++ b/doc/sphinx/tutorials/time_dependent_point_source.ipynb @@ -74,7 +74,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We create the ``Analysis`` instance for the TXS 0506+056 source." + "We create the ``Analysis`` instance for the TXS 0506+056 source.\n", + "\n", + "The ``gauss`` argument defines the Gaussian time profile of the signal hypothesis, with which the analysis is created, i.e. its mean ``mu`` (in MJD) and its width ``sigma`` (in days). This time profile is also used when generating signal events. Here, it is centered at MJD 57000 (December 2014), close to the neutrino flare of TXS 0506+056 observed by IceCube in 2014-2015. It does not constrain the flare search below: the EM algorithm fits the time profile using the entire livetime of the data." ] }, { @@ -86,9 +88,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 43/43 [00:00<00:00, 489.54it/s]\n", - "100%|██████████| 1/1 [01:04<00:00, 64.60s/it]\n", - "100%|██████████| 44/44 [00:00<00:00, 9419.63it/s]\n" + "100%|██████████| 43/43 [00:00<00:00, 407.11it/s]\n", + "100%|██████████| 1/1 [01:08<00:00, 68.91s/it]\n", + "100%|██████████| 44/44 [00:00<00:00, 7913.44it/s]\n" ] } ], @@ -107,7 +109,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 51/51 [00:03<00:00, 15.85it/s]\n" + "100%|██████████| 51/51 [00:02<00:00, 17.03it/s]\n" ] } ], @@ -133,11 +135,13 @@ } ], "source": [ + "best_fitparams = ana.pmm.create_global_floating_params_dict(best_fitparam_values)\n", + "\n", "print(f'best TS = {best_ts:g}')\n", "print(f'best em mu = {best_em_result[\"mu\"]:g}')\n", "print(f'best em sigma = {best_em_result[\"sigma\"]:g}')\n", - "print(f'best ns = {best_fitparam_values[0]:g}')\n", - "print(f'best gamma = {best_fitparam_values[1]:g}')" + "print(f'best ns = {best_fitparams[\"ns\"]:g}')\n", + "print(f'best gamma = {best_fitparams[\"gamma\"]:g}')" ] }, { @@ -157,7 +161,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 100/100 [01:49<00:00, 1.10s/it]\n" + "100%|██████████| 100/100 [02:00<00:00, 1.21s/it]\n" ] } ], @@ -172,7 +176,7 @@ "outputs": [ { "data": { - "image/png": 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" ] @@ -203,9 +207,7 @@ "- **`mu`** (MJD): the best-fit arrival time of the flare (centre of the Gaussian)\n", "- **`sigma`** (days): the best-fit duration of the flare (standard deviation of the Gaussian)\n", "\n", - "The known alert time for TXS 0506+056 is MJD 58018.87 (22 September 2017).\n", - "Converting `best_em_result[\"mu\"]` to a calendar date and comparing with this reference tells us how well\n", - "the EM algorithm recovered the timing of the known multi-messenger event." + "Note that the IceCube alert event IceCube-170922A (MJD 58018.87, 22 September 2017), which led to the identification of TXS 0506+056, was removed from the data by the ``remove_time`` argument of ``unblind_single_flare``. Hence, the EM algorithm finds the earlier neutrino flare of TXS 0506+056 in 2014-2015, which was found in IceCube's archival data. Converting `best_em_result[\"mu\"]` to a calendar date and comparing it with the alert time shows when this flare happened." ] }, { @@ -284,7 +286,7 @@ ], "metadata": { "kernelspec": { - "display_name": "skyllh (3.14.5)", + "display_name": "skyllh (3.14.x)", "language": "python", "name": "python3" }, diff --git a/skyllh/core/dataset.py b/skyllh/core/dataset.py index 0825be9dc1..0ad6e4df93 100644 --- a/skyllh/core/dataset.py +++ b/skyllh/core/dataset.py @@ -49,6 +49,7 @@ list_of_cast, module_class_method_name, str_cast, + typename, ) from skyllh.core.storage import ( DataFieldRecordArray, @@ -301,7 +302,11 @@ def __str__(self): s1 += 'set\n' else: s1 += 'not set\n' - s1 += f'transfer class = {classname(transfer_cls)}\n' + if transfer_cls is not None: + transfer_name = typename(transfer_cls) + else: + transfer_name = getattr(self.transfer_func, '__qualname__', classname(self.transfer_func)) + s1 += f'transfer class = {transfer_name}\n' s += display.add_leading_text_line_padding(display.INDENTATION_WIDTH, s1) s += '}' diff --git a/tests/core/test_dataset.py b/tests/core/test_dataset.py index 99babfdd6b..eed8d1aa43 100644 --- a/tests/core/test_dataset.py +++ b/tests/core/test_dataset.py @@ -31,6 +31,25 @@ ) +def transfer_function(origin, file_list, dst_base_path, username=None, password=None): + pass + + +class TestDatasetOrigin(unittest.TestCase): + def test_str_shows_transfer_class(self): + origin = DatasetOrigin( + host='data.mydomain.com', + base_path='/downloads/data', + sub_path='my_dataset', + transfer_func=WGETDatasetTransfer(protocol='https').transfer, + ) + self.assertIn('transfer class = WGETDatasetTransfer', str(origin)) + + def test_str_shows_transfer_function(self): + origin = DatasetOrigin(base_path='/downloads/data', sub_path='my_dataset', transfer_func=transfer_function) + self.assertIn('transfer class = transfer_function', str(origin)) + + class TestRSYNCDatasetTransfer(unittest.TestCase): def setUp(self): self.cfg = Config()