{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "
\n", "\n", "**This is a fixed-text formatted version of a Jupyter notebook**\n", "\n", "- Try online[![Binder](https://static.mybinder.org/badge.svg)](https://mybinder.org/v2/gh/gammapy/gammapy-webpage/v0.19?urlpath=lab/tree/tutorials/analysis/3D/simulate_3d.ipynb)\n", "- You may download all the notebooks in the documentation as a\n", "[tar file](../../../_downloads/notebooks-0.19.tar).\n", "- **Source files:**\n", "[simulate_3d.ipynb](../../../_static/notebooks/simulate_3d.ipynb) |\n", "[simulate_3d.py](../../../_static/notebooks/simulate_3d.py)\n", "
\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3D map simulation\n", "\n", "## Prerequisites\n", "\n", "- Knowledge of 3D extraction and datasets used in gammapy, see for instance the [first analysis tutorial](../../starting/analysis_1.ipynb)\n", "\n", "## Context\n", "\n", "To simulate a specific observation, it is not always necessary to simulate the full photon list. For many uses cases, simulating directly a reduced binned dataset is enough: the IRFs reduced in the correct geometry are combined with a source model to predict an actual number of counts per bin. The latter is then used to simulate a reduced dataset using Poisson probability distribution.\n", "\n", "This can be done to check the feasibility of a measurement (performance / sensitivity study), to test whether fitted parameters really provide a good fit to the data etc.\n", "\n", "Here we will see how to perform a 3D simulation of a CTA observation, assuming both the spectral and spatial morphology of an observed source.\n", "\n", "**Objective: simulate a 3D observation of a source with CTA using the CTA 1DC response and fit it with the assumed source model.**\n", "\n", "## Proposed approach:\n", "\n", "Here we can't use the regular observation objects that are connected to a `DataStore`. Instead we will create a fake `~gammapy.data.Observation` that contain some pointing information and the CTA 1DC IRFs (that are loaded with `~gammapy.irf.load_cta_irfs`).\n", "\n", "Then we will create a `~gammapy.datasets.MapDataset` geometry and create it with the `~gammapy.makers.MapDatasetMaker`.\n", "\n", "Then we will be able to define a model consisting of a `~gammapy.modeling.models.PowerLawSpectralModel` and a `~gammapy.modeling.models.GaussianSpatialModel`. We will assign it to the dataset and fake the count data.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Imports and versions" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:04:42.096182Z", "iopub.status.busy": "2021-11-22T21:04:42.094537Z", "iopub.status.idle": "2021-11-22T21:04:42.258756Z", "shell.execute_reply": "2021-11-22T21:04:42.258934Z" } }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:04:42.261330Z", "iopub.status.busy": "2021-11-22T21:04:42.260999Z", "iopub.status.idle": "2021-11-22T21:04:42.774177Z", "shell.execute_reply": "2021-11-22T21:04:42.774351Z" } }, "outputs": [], "source": [ "import numpy as np\n", "import astropy.units as u\n", "from astropy.coordinates import SkyCoord\n", "from gammapy.irf import load_cta_irfs\n", "from gammapy.maps import WcsGeom, MapAxis\n", "from gammapy.modeling.models import (\n", " PowerLawSpectralModel,\n", " GaussianSpatialModel,\n", " SkyModel,\n", " Models,\n", " FoVBackgroundModel,\n", ")\n", "from gammapy.makers import MapDatasetMaker, SafeMaskMaker\n", "from gammapy.modeling import Fit\n", "from gammapy.data import Observation\n", "from gammapy.datasets import MapDataset" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:04:42.776381Z", "iopub.status.busy": "2021-11-22T21:04:42.776072Z", "iopub.status.idle": "2021-11-22T21:04:43.850839Z", "shell.execute_reply": "2021-11-22T21:04:43.851236Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\r\n", "Gammapy package:\r\n", "\r\n", "\tversion : 0.19 \r\n", "\tpath : /Users/adonath/github/adonath/gammapy/gammapy \r\n", "\r\n" ] } ], "source": [ "!gammapy info --no-envvar --no-dependencies --no-system" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simulation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will simulate using the CTA-1DC IRFs shipped with gammapy. Note that for dedictaed CTA simulations, you can simply use [`Observation.from_caldb()`]() without having to externally load the IRFs" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:04:43.856482Z", "iopub.status.busy": "2021-11-22T21:04:43.855968Z", "iopub.status.idle": "2021-11-22T21:04:43.920469Z", "shell.execute_reply": "2021-11-22T21:04:43.920675Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Invalid unit found in background table! Assuming (s-1 MeV-1 sr-1)\n" ] } ], "source": [ "# Loading IRFs\n", "irfs = load_cta_irfs(\n", " \"$GAMMAPY_DATA/cta-1dc/caldb/data/cta/1dc/bcf/South_z20_50h/irf_file.fits\"\n", ")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:04:43.923134Z", "iopub.status.busy": "2021-11-22T21:04:43.922823Z", "iopub.status.idle": "2021-11-22T21:04:43.924109Z", "shell.execute_reply": "2021-11-22T21:04:43.924292Z" } }, "outputs": [], "source": [ "# Define the observation parameters (typically the observation duration and the pointing position):\n", "livetime = 2.0 * u.hr\n", "pointing = SkyCoord(0, 0, unit=\"deg\", frame=\"galactic\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:04:43.926811Z", "iopub.status.busy": "2021-11-22T21:04:43.926501Z", "iopub.status.idle": "2021-11-22T21:04:43.936510Z", "shell.execute_reply": "2021-11-22T21:04:43.936685Z" } }, "outputs": [], "source": [ "# Define map geometry for binned simulation\n", "energy_reco = MapAxis.from_edges(\n", " np.logspace(-1.0, 1.0, 10), unit=\"TeV\", name=\"energy\", interp=\"log\"\n", ")\n", "geom = WcsGeom.create(\n", " skydir=(0, 0),\n", " binsz=0.02,\n", " width=(6, 6),\n", " frame=\"galactic\",\n", " axes=[energy_reco],\n", ")\n", "# It is usually useful to have a separate binning for the true energy axis\n", "energy_true = MapAxis.from_edges(\n", " np.logspace(-1.5, 1.5, 30), unit=\"TeV\", name=\"energy_true\", interp=\"log\"\n", ")\n", "\n", "empty = MapDataset.create(\n", " geom, name=\"dataset-simu\", energy_axis_true=energy_true\n", ")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:04:43.944292Z", "iopub.status.busy": "2021-11-22T21:04:43.943996Z", "iopub.status.idle": "2021-11-22T21:04:43.945651Z", "shell.execute_reply": "2021-11-22T21:04:43.945825Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Models\n", "\n", "Component 0: SkyModel\n", "\n", " Name : model-simu\n", " Datasets names : None\n", " Spectral model type : PowerLawSpectralModel\n", " Spatial model type : GaussianSpatialModel\n", " Temporal model type : \n", " Parameters:\n", " index : 3.000 +/- 0.00 \n", " amplitude : 1.00e-11 +/- 0.0e+00 1 / (cm2 s TeV)\n", " reference (frozen) : 1.000 TeV \n", " lon_0 : 0.200 +/- 0.00 deg \n", " lat_0 : 0.100 +/- 0.00 deg \n", " sigma : 0.300 +/- 0.00 deg \n", " e (frozen) : 0.000 \n", " phi (frozen) : 0.000 deg \n", "\n", "Component 1: FoVBackgroundModel\n", "\n", " Name : dataset-simu-bkg\n", " Datasets names : ['dataset-simu']\n", " Spectral model type : PowerLawNormSpectralModel\n", " Parameters:\n", " norm : 1.000 +/- 0.00 \n", " tilt (frozen) : 0.000 \n", " reference (frozen) : 1.000 TeV \n", "\n", "\n" ] } ], "source": [ "# Define sky model to used simulate the data.\n", "# Here we use a Gaussian spatial model and a Power Law spectral model.\n", "spatial_model = GaussianSpatialModel(\n", " lon_0=\"0.2 deg\", lat_0=\"0.1 deg\", sigma=\"0.3 deg\", frame=\"galactic\"\n", ")\n", "spectral_model = PowerLawSpectralModel(\n", " index=3, amplitude=\"1e-11 cm-2 s-1 TeV-1\", reference=\"1 TeV\"\n", ")\n", "model_simu = SkyModel(\n", " spatial_model=spatial_model,\n", " spectral_model=spectral_model,\n", " name=\"model-simu\",\n", ")\n", "\n", "bkg_model = FoVBackgroundModel(dataset_name=\"dataset-simu\")\n", "\n", "models = Models([model_simu, bkg_model])\n", "print(models)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, comes the main part of dataset simulation. We create an in-memory observation and an empty dataset. We then predict the number of counts for the given model, and Poission fluctuate it using `fake()` to make a simulated counts maps. Keep in mind that it is important to specify the `selection` of the maps that you want to produce " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:04:43.950935Z", "iopub.status.busy": "2021-11-22T21:04:43.950653Z", "iopub.status.idle": "2021-11-22T21:04:43.951923Z", "shell.execute_reply": "2021-11-22T21:04:43.952223Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Observation\n", "\n", "\tobs id : 0 \n", " \ttstart : 51544.00\n", "\ttstop : 51544.08\n", "\tduration : 7200.00 s\n", "\tpointing (icrs) : 266.4 deg, -28.9 deg\n", "\n", "\tdeadtime fraction : 0.0%\n", "\n" ] } ], "source": [ "# Create an in-memory observation\n", "obs = Observation.create(pointing=pointing, livetime=livetime, irfs=irfs)\n", "print(obs)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:04:43.955420Z", "iopub.status.busy": "2021-11-22T21:04:43.955137Z", "iopub.status.idle": "2021-11-22T21:04:47.299407Z", "shell.execute_reply": "2021-11-22T21:04:47.299637Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MapDataset\n", "----------\n", "\n", " Name : dataset-simu \n", "\n", " Total counts : 0 \n", " Total background counts : 161250.95\n", " Total excess counts : -161250.95\n", "\n", " Predicted counts : 161250.95\n", " Predicted background counts : 161250.95\n", " Predicted excess counts : nan\n", "\n", " Exposure min : 4.08e+02 m2 s\n", " Exposure max : 3.58e+10 m2 s\n", "\n", " Number of total bins : 810000 \n", " Number of fit bins : 804492 \n", "\n", " Fit statistic type : cash\n", " Fit statistic value (-2 log(L)) : nan\n", "\n", " Number of models : 0 \n", " Number of parameters : 0\n", " Number of free parameters : 0\n", "\n", "\n" ] } ], "source": [ "# Make the MapDataset\n", "maker = MapDatasetMaker(selection=[\"exposure\", \"background\", \"psf\", \"edisp\"])\n", "\n", "maker_safe_mask = SafeMaskMaker(methods=[\"offset-max\"], offset_max=4.0 * u.deg)\n", "\n", "dataset = maker.run(empty, obs)\n", "dataset = maker_safe_mask.run(dataset, obs)\n", "print(dataset)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:04:47.301942Z", "iopub.status.busy": "2021-11-22T21:04:47.301643Z", "iopub.status.idle": "2021-11-22T21:04:48.190029Z", "shell.execute_reply": "2021-11-22T21:04:48.190261Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MapDataset\n", "----------\n", "\n", " Name : dataset-simu \n", "\n", " Total counts : 169992 \n", " Total background counts : 161250.95\n", " Total excess counts : 8741.05\n", "\n", " Predicted counts : 169709.71\n", " Predicted background counts : 161250.95\n", " Predicted excess counts : 8458.76\n", "\n", " Exposure min : 4.08e+02 m2 s\n", " Exposure max : 3.58e+10 m2 s\n", "\n", " Number of total bins : 810000 \n", " Number of fit bins : 804492 \n", "\n", " Fit statistic type : cash\n", " Fit statistic value (-2 log(L)) : 562279.47\n", "\n", " Number of models : 2 \n", " Number of parameters : 11\n", " Number of free parameters : 6\n", "\n", " Component 0: SkyModel\n", " \n", " Name : model-simu\n", " Datasets names : None\n", " Spectral model type : PowerLawSpectralModel\n", " Spatial model type : GaussianSpatialModel\n", " Temporal model type : \n", " Parameters:\n", " index : 3.000 +/- 0.00 \n", " amplitude : 1.00e-11 +/- 0.0e+00 1 / (cm2 s TeV)\n", " reference (frozen) : 1.000 TeV \n", " lon_0 : 0.200 +/- 0.00 deg \n", " lat_0 : 0.100 +/- 0.00 deg \n", " sigma : 0.300 +/- 0.00 deg \n", " e (frozen) : 0.000 \n", " phi (frozen) : 0.000 deg \n", " \n", " Component 1: FoVBackgroundModel\n", " \n", " Name : dataset-simu-bkg\n", " Datasets names : ['dataset-simu']\n", " Spectral model type : PowerLawNormSpectralModel\n", " Parameters:\n", " norm : 1.000 +/- 0.00 \n", " tilt (frozen) : 0.000 \n", " reference (frozen) : 1.000 TeV \n", " \n", " \n" ] } ], "source": [ "# Add the model on the dataset and Poission fluctuate\n", "dataset.models = models\n", "dataset.fake()\n", "# Do a print on the dataset - there is now a counts maps\n", "print(dataset)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now use this dataset as you would in all standard analysis. You can plot the maps, or proceed with your custom analysis. \n", "In the next section, we show the standard 3D fitting as in [analysis_3d](analysis_3d.ipynb)." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:04:48.192758Z", "iopub.status.busy": "2021-11-22T21:04:48.192442Z", "iopub.status.idle": "2021-11-22T21:04:48.477430Z", "shell.execute_reply": "2021-11-22T21:04:48.477667Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d1abfad348ed499c8b85ceb51c8ceb40", "version_major": 2, "version_minor": 0 }, "text/plain": [ "interactive(children=(SelectionSlider(continuous_update=False, description='Select energy:', layout=Layout(wid…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# To plot, eg, counts:\n", "dataset.counts.smooth(0.05 * u.deg).plot_interactive(\n", " add_cbar=True, stretch=\"linear\"\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fit\n", "\n", "In this section, we do a usual 3D fit with the same model used to simulated the data and see the stability of the simulations. Often, it is useful to simulate many such datasets and look at the distribution of the reconstructed parameters." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:04:48.480493Z", "iopub.status.busy": "2021-11-22T21:04:48.480160Z", "iopub.status.idle": "2021-11-22T21:04:48.480981Z", "shell.execute_reply": "2021-11-22T21:04:48.481238Z" } }, "outputs": [], "source": [ "models_fit = models.copy()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:04:48.483168Z", "iopub.status.busy": "2021-11-22T21:04:48.482822Z", "iopub.status.idle": "2021-11-22T21:04:48.484091Z", "shell.execute_reply": "2021-11-22T21:04:48.484292Z" } }, "outputs": [], "source": [ "# We do not want to fit the background in this case, so we will freeze the parameters\n", "models_fit[\"dataset-simu-bkg\"].spectral_model.norm.frozen = True\n", "models_fit[\"dataset-simu-bkg\"].spectral_model.tilt.frozen = True" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:04:48.486473Z", "iopub.status.busy": "2021-11-22T21:04:48.486136Z", "iopub.status.idle": "2021-11-22T21:04:48.487377Z", "shell.execute_reply": "2021-11-22T21:04:48.487598Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "DatasetModels\n", "\n", "Component 0: SkyModel\n", "\n", " Name : model-simu\n", " Datasets names : None\n", " Spectral model type : PowerLawSpectralModel\n", " Spatial model type : GaussianSpatialModel\n", " Temporal model type : \n", " Parameters:\n", " index : 3.000 +/- 0.00 \n", " amplitude : 1.00e-11 +/- 0.0e+00 1 / (cm2 s TeV)\n", " reference (frozen) : 1.000 TeV \n", " lon_0 : 0.200 +/- 0.00 deg \n", " lat_0 : 0.100 +/- 0.00 deg \n", " sigma : 0.300 +/- 0.00 deg \n", " e (frozen) : 0.000 \n", " phi (frozen) : 0.000 deg \n", "\n", "Component 1: FoVBackgroundModel\n", "\n", " Name : dataset-simu-bkg\n", " Datasets names : ['dataset-simu']\n", " Spectral model type : PowerLawNormSpectralModel\n", " Parameters:\n", " norm (frozen) : 1.000 \n", " tilt (frozen) : 0.000 \n", " reference (frozen) : 1.000 TeV \n", "\n", "\n" ] } ], "source": [ "dataset.models = models_fit\n", "print(dataset.models)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:04:48.489880Z", "iopub.status.busy": "2021-11-22T21:04:48.489538Z", "iopub.status.idle": "2021-11-22T21:04:53.556181Z", "shell.execute_reply": "2021-11-22T21:04:53.556361Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 4.73 s, sys: 523 ms, total: 5.25 s\n", "Wall time: 5.07 s\n" ] } ], "source": [ "%%time\n", "fit = Fit(optimize_opts={\"print_level\": 1})\n", "result = fit.run(datasets=[dataset])" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:04:53.558422Z", "iopub.status.busy": "2021-11-22T21:04:53.558136Z", "iopub.status.idle": "2021-11-22T21:04:53.863115Z", "shell.execute_reply": "2021-11-22T21:04:53.863318Z" }, "nbsphinx-thumbnail": { "tooltip": "Simulate a 3D observation of a source with the CTA 1DC response and fit it with the assumed source model." } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/adonath/software/mambaforge/envs/gammapy-dev/lib/python3.9/site-packages/astropy/visualization/wcsaxes/core.py:211: MatplotlibDeprecationWarning: Passing parameters norm and vmin/vmax simultaneously is deprecated since 3.3 and will become an error two minor releases later. Please pass vmin/vmax directly to the norm when creating it.\n", " return super().imshow(X, *args, origin=origin, **kwargs)\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "dataset.plot_residuals_spatial(method=\"diff/sqrt(model)\", vmin=-0.5, vmax=0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compare the injected and fitted models: " ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:04:53.865552Z", "iopub.status.busy": "2021-11-22T21:04:53.865215Z", "iopub.status.idle": "2021-11-22T21:04:53.866457Z", "shell.execute_reply": "2021-11-22T21:04:53.866636Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True model: \n", " SkyModel\n", "\n", " Name : model-simu\n", " Datasets names : None\n", " Spectral model type : PowerLawSpectralModel\n", " Spatial model type : GaussianSpatialModel\n", " Temporal model type : \n", " Parameters:\n", " index : 3.000 +/- 0.00 \n", " amplitude : 1.00e-11 +/- 0.0e+00 1 / (cm2 s TeV)\n", " reference (frozen) : 1.000 TeV \n", " lon_0 : 0.200 +/- 0.00 deg \n", " lat_0 : 0.100 +/- 0.00 deg \n", " sigma : 0.300 +/- 0.00 deg \n", " e (frozen) : 0.000 \n", " phi (frozen) : 0.000 deg \n", "\n", " \n", "\n", " Fitted model: \n", " SkyModel\n", "\n", " Name : model-simu\n", " Datasets names : None\n", " Spectral model type : PowerLawSpectralModel\n", " Spatial model type : GaussianSpatialModel\n", " Temporal model type : \n", " Parameters:\n", " index : 3.017 +/- 0.02 \n", " amplitude : 9.62e-12 +/- 3.3e-13 1 / (cm2 s TeV)\n", " reference (frozen) : 1.000 TeV \n", " lon_0 : 0.191 +/- 0.01 deg \n", " lat_0 : 0.101 +/- 0.01 deg \n", " sigma : 0.298 +/- 0.00 deg \n", " e (frozen) : 0.000 \n", " phi (frozen) : 0.000 deg \n", "\n", "\n" ] } ], "source": [ "print(\n", " \"True model: \\n\",\n", " model_simu,\n", " \"\\n\\n Fitted model: \\n\",\n", " models_fit[\"model-simu\"],\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get the errors on the fitted parameters from the parameter table" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "execution": { "iopub.execute_input": "2021-11-22T21:04:53.870058Z", "iopub.status.busy": "2021-11-22T21:04:53.869691Z", "iopub.status.idle": "2021-11-22T21:04:53.871175Z", "shell.execute_reply": "2021-11-22T21:04:53.871443Z" } }, "outputs": [ { "data": { "text/html": [ "
Table length=11\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
typenamevalueuniterrorminmaxfrozenlink
str8str9float64str14float64float64float64boolstr1
spectralindex3.0174e+002.043e-02nannanFalse
spectralamplitude9.6162e-12cm-2 s-1 TeV-13.284e-13nannanFalse
spectralreference1.0000e+00TeV0.000e+00nannanTrue
spatiallon_01.9097e-01deg5.923e-03nannanFalse
spatiallat_01.0133e-01deg6.009e-03-9.000e+019.000e+01False
spatialsigma2.9806e-01deg4.154e-030.000e+00nanFalse
spatiale0.0000e+000.000e+000.000e+001.000e+00True
spatialphi0.0000e+00deg0.000e+00nannanTrue
spectralnorm1.0000e+000.000e+00nannanTrue
spectraltilt0.0000e+000.000e+00nannanTrue
spectralreference1.0000e+00TeV0.000e+00nannanTrue
" ], "text/plain": [ "\n", " type name value unit error min max frozen link\n", " str8 str9 float64 str14 float64 float64 float64 bool str1\n", "-------- --------- ---------- -------------- --------- ---------- --------- ------ ----\n", "spectral index 3.0174e+00 2.043e-02 nan nan False \n", "spectral amplitude 9.6162e-12 cm-2 s-1 TeV-1 3.284e-13 nan nan False \n", "spectral reference 1.0000e+00 TeV 0.000e+00 nan nan True \n", " spatial lon_0 1.9097e-01 deg 5.923e-03 nan nan False \n", " spatial lat_0 1.0133e-01 deg 6.009e-03 -9.000e+01 9.000e+01 False \n", " spatial sigma 2.9806e-01 deg 4.154e-03 0.000e+00 nan False \n", " spatial e 0.0000e+00 0.000e+00 0.000e+00 1.000e+00 True \n", " spatial phi 0.0000e+00 deg 0.000e+00 nan nan True \n", "spectral norm 1.0000e+00 0.000e+00 nan nan True \n", "spectral tilt 0.0000e+00 0.000e+00 nan nan True \n", "spectral reference 1.0000e+00 TeV 0.000e+00 nan nan True " ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result.parameters.to_table()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.0" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autoclose": false, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1.0, "eqLabelWithNumbers": true, "eqNumInitial": 1.0, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false }, "nbsphinx": { "orphan": true }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "2339ad8512b54d2e97a9d867247de0a5": 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