push changes properly
"\n",
"We will simulate 10 spectral datasets within given time intervals (Good Time Intervals) following a given spectral (a power law) and temporal profile (an exponential decay, with a decay time of 6 hr ). These are then analysed using the light curve estimator to obtain flux points. Then, we re-fit the simulated datasets to reconstruct back the injected profiles.\n",
"\n",
- "In summary, step are:\n",
+ "In summary, necessary steps are:\n",
"\n",
"- Choose observation parameters including a list of `gammapy.data.GTI`\n",
"- Define temporal and spectral models from :ref:model-gallery as per science case\n",
"- Perform the simulation (in 1D or 3D)\n",
"- Extract the light curve from the reduced dataset as shown in [light curve notebook](light_curve.ipynb)\n",
- "- Fit the simulated datasets using a source model \n",
+ "- Optionaly, we show here how to fit the simulated datasets using a source model \n",
"\n",
"\n",
"## Setup \n",
"source": [
"## Extract the lightcurve\n",
"\n",
- "This section uses standard light curve estimation tools for a 1D extraction. Only a spectral model needs to be defined in this case."
+ "This section uses standard light curve estimation tools for a 1D extraction. Only a spectral model needs to be defined in this case. Since the estimator returns the integrated flux separately for each time bin, the temporal model need not be accounted for at this stage."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "We have the reconstructed lightcurve at this point. Further standard analyis might involve modeling the temporal profiles with an analytical or theoretical model. You may do this using your favourite fiting package, one possible option being `curve_fit` inside `scipy.optimize`.\n",
+ "We have the reconstructed lightcurve at this point. Further standard analyis might involve modeling the temporal profiles with an analytical or theoretical model. You may do this using your favourite fitting package, one possible option being `curve_fit` inside `scipy.optimize`.\n",
"\n",
- "In the next section, we show how to simulatenously fit the all datasets using a given temporal model. We will fit the amplitude, spectral index and the decay time scale. Note that `t_ref` should be fixed by default for the `ExpDecayTemporalModel`. For modelling and fitting more complex flares, you should attach the relevant model to each dataset. For more details on joint fiiting in gammapy, see [here](modeling.ipynb)."
+ "In the next section, we show how to simulatenously fit the all datasets using a given temporal model. We will fit the amplitude, spectral index and the decay time scale. Note that `t_ref` should be fixed by default for the `ExpDecayTemporalModel`. For modelling and fitting more complex flares, you should attach the relevant model to each dataset. For more details on joint fitting in gammapy, see [here](modeling.ipynb)."
]
},
{
