{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Analysing Spectrum" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Every simulation run requires [atomic data](io/configuration/components/atomic/atomic_data.rst) and a [configuration file](io/configuration/index.rst). \n", "\n", "## Atomic Data\n", "\n", "We recommend using the [kurucz_cd23_chianti_H_He.h5](https://github.com/tardis-sn/tardis-regression-data/raw/main/atom_data/kurucz_cd23_chianti_H_He.h5) dataset." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4766e33e30a9431d9289a30474a18896", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Iterations: 0/? [00:00\n", "\n", "**Note:**\n", "\n", "Get more information about the [progress bars](io/output/progress_bars.rst), [logging configuration](io/optional/tutorial_logging_configuration.ipynb), and [convergence plots](io/visualization/tutorial_convergence_plot.ipynb).\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/abhinav/workspace/code/tardis-main/tardis-code/tardis/tardis/transport/montecarlo/montecarlo_main_loop.py:123: NumbaTypeSafetyWarning:\n", "\n", "\u001b[1m\u001b[1m\u001b[1munsafe cast from uint64 to int64. Precision may be lost.\u001b[0m\u001b[0m\u001b[0m\n", "\n", " (\u001b[1mwarnings.py\u001b[0m:112)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ed0fece73ba1411f91f12ebbef971171", "version_major": 2, "version_minor": 0 }, "text/plain": [ "TqdmHBox(children=(HTML(value='Iterations:', layout=Layout(width='6%')), FloatProgress(value=0.0, layout=Layou…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9543c32ad24247aea16352c347944383", "version_major": 2, "version_minor": 0 }, "text/plain": [ "TqdmHBox(children=(HTML(value='Packets:\\u2007\\u2007\\u2007', layout=Layout(width='6%')), FloatProgress(value=0.…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from tardis import run_tardis\n", "\n", "sim = run_tardis(\n", " \"tardis_example.yml\",\n", " log_level=\"ERROR\"\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## HDF\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "TARDIS can save simulation data to HDF files for later analysis. The code below shows how to load a simulation from an HDF file. This is useful when you want to analyze simulation results without re-running the simulation.\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# import astropy.units as u\n", "# import pandas as pd\n", "\n", "# hdf_fpath = \"add_file_path_here\"\n", "# with pd.HDFStore(hdf_fpath, \"r\") as hdf:\n", "# sim = u.Quantity(hdf[\"/simulation\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calculate integrated spectrum" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "**Note:**\n", "\n", "It takes about a minute to calculate. Please be patient while it runs.\n", "\n", "
" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[\u001b[1mpy.warnings \u001b[0m][\u001b[1;33mWARNING\u001b[0m] \n", "\t/home/abhinav/workspace/code/tardis-main/tardis-code/tardis/tardis/spectrum/formal_integral.py:398: UserWarning:\n", "\n", "The number of interpolate_shells was not specified. The value was set to 80.\n", "\n", " (\u001b[1mwarnings.py\u001b[0m:112)\n" ] } ], "source": [ "spectrum_integrated = sim.spectrum_solver.spectrum_integrated" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting the Spectrum" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we will plot the spectrum using matplotlib and plotly. The plots show the luminosity density as a function of wavelength.\n", "\n", "- **Wavelength [$\\AA$]**: The x-axis represents the wavelength in Angstroms.\n", "- **Luminosity density [$ergs^{-1}\\AA^{-1}$]**: The y-axis represents the luminosity density in erg per second per Angstrom." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Matplotlib" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We use Matplotlib to create a static plot of the ***formal integral spectrum*** that was calculated in the previous step." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "# Create a new figure with specified dimensions\n", "plt.figure(figsize=(10, 6.5))\n", "\n", "spectrum_integrated.plot(label=\"Formal integral\") # Plot spectrum from formal integral solution\n", "\n", "# Set title, labels, and template\n", "plt.xlim(500, 9000)\n", "plt.title(\"TARDIS example model spectrum\")\n", "plt.xlabel(r\"Wavelength [$\\AA$]\")\n", "plt.ylabel(r\"Luminosity density [$ergs^{-1}\\AA^{-1}$]\")\n", "plt.legend()\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plotly\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here, we use Plotly to create an interactive plot of the ***virtual packet spectrum*** generated by the TARDIS simulation.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import plotly.graph_objects as go\n", "\n", "# Create a new figure for plotting the spectrum\n", "fig = go.Figure()\n", "\n", "# Plot the wavelength spectrum\n", "fig.add_trace(\n", " go.Scatter(\n", " x=sim.spectrum_solver.spectrum_virtual_packets.wavelength,\n", " y=sim.spectrum_solver.spectrum_virtual_packets.luminosity_density_lambda,\n", " mode=\"lines\",\n", " name=\"Spectrum\"\n", " )\n", ")\n", "\n", "# Set title, labels, and template\n", "fig.update_layout(\n", " title=\"TARDIS example model spectrum\",\n", " xaxis_title=r\"Wavelength [$\\overset{\\circ}{A}$]\",\n", " yaxis_title=r\"Luminosity density [$ergs^{-1}\\overset{\\circ}{A^{-1}}$]\", # Plotly doesnot support the Angstrom symbol so we are using a close approximation of it\n", " xaxis_range=[500, 9000],\n", " showlegend=True,\n", " template=\"plotly_white\"\n", ")\n", "\n", "# Display the plot\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "tardis", "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": "3.12.4" } }, "nbformat": 4, "nbformat_minor": 2 }