{

“cells”: [

{

“cell_type”: “markdown”, “id”: “72a96953”, “metadata”: {}, “source”: [

“# Quick startn”, “n”, “In this quick tutorial we will learn how to create a simple model with one point source, with a power law spectrum. You can of course use any function instead of the power law. Use `list_models()` to obtain a list of available models.n”, “n”, “Let’s define the model:”

]

}, {

“cell_type”: “code”, “execution_count”: 1, “id”: “e1c28f11”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2021-08-17T13:12:07.342431Z”, “iopub.status.busy”: “2021-08-17T13:12:07.341453Z”, “iopub.status.idle”: “2021-08-17T13:12:09.612578Z”, “shell.execute_reply”: “2021-08-17T13:12:09.613166Z”

}

}, “outputs”: [], “source”: [

“%%capturen”, “from astromodels import *”

]

}, {

“cell_type”: “code”, “execution_count”: 2, “id”: “35e35f24”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2021-08-17T13:12:09.621327Z”, “iopub.status.busy”: “2021-08-17T13:12:09.620460Z”, “iopub.status.idle”: “2021-08-17T13:12:09.624210Z”, “shell.execute_reply”: “2021-08-17T13:12:09.624775Z”

}

}, “outputs”: [], “source”: [

“test_source = PointSource(‘test_source’,ra=123.22, dec=-13.56, spectral_shape=Powerlaw_flux())n”, “n”, “my_model = Model(test_source)”

]

}, {

“cell_type”: “markdown”, “id”: “fae6890b”, “metadata”: {}, “source”: [

“Now let’s use it:n”, “n”

]

}, {

“cell_type”: “code”, “execution_count”: 3, “id”: “91b8b4de”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2021-08-17T13:12:09.630391Z”, “iopub.status.busy”: “2021-08-17T13:12:09.629545Z”, “iopub.status.idle”: “2021-08-17T13:12:10.639603Z”, “shell.execute_reply”: “2021-08-17T13:12:10.640365Z”

}

}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“Differential flux @ 1 keV : 1.010 photons / ( cm2 s keV)n”

]

}

], “source”: [

“# Get and print the differential flux at 1 keV:n”, “n”, “differential_flux_at_1_keV = my_model.test_source(1.0)n”, “n”, “print("Differential flux @ 1 keV : %.3f photons / ( cm2 s keV)" % differential_flux_at_1_keV)n”

]

}, {

“cell_type”: “markdown”, “id”: “3694eb21”, “metadata”: {}, “source”: [

“Evaluate the model on an array of 100 energies logarithmically distributed between 1 and 100 keV and plot it”

]

}, {

“cell_type”: “code”, “execution_count”: 4, “id”: “e77e3771”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2021-08-17T13:12:10.650196Z”, “iopub.status.busy”: “2021-08-17T13:12:10.649237Z”, “iopub.status.idle”: “2021-08-17T13:12:13.135482Z”, “shell.execute_reply”: “2021-08-17T13:12:13.136155Z”

}

}, “outputs”: [

{

“data”: {

“text/plain”: [

“Text(0, 0.5, ‘Differential flux (ph./cm2/s/keV)’)”

]

}, “execution_count”: 4, “metadata”: {}, “output_type”: “execute_result”

}, {

“data”: {

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”, “text/plain”: [

“<Figure size 748.8x655.2 with 1 Axes>”

]

}, “metadata”: {}, “output_type”: “display_data”

}

], “source”: [

“import numpy as npn”, “import matplotlib.pyplot as pltn”, “%matplotlib inlinen”, “from jupyterthemes import jtplotn”, “jtplot.style(context="talk", fscale=1, ticks=True, grid=False)n”, “n”, “# Set up the energiesn”, “n”, “energies = np.logspace(0,2,100)n”, “n”, “# Get the differential fluxn”, “n”, “differential_flux = my_model.test_source(energies)n”, “n”, “n”, “fig, ax = plt.subplots()n”, “n”, “ax.loglog(energies, differential_flux)n”, “n”, “ax.set_xlabel("Energy (keV)")n”, “ax.set_ylabel("Differential flux (ph./cm2/s/keV)")”

]

}

], “metadata”: {

“jupytext”: {

“formats”: “ipynb,md”

}, “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”: “3.7.11”

}

}, “nbformat”: 4, “nbformat_minor”: 5

}