documents/tutorials/pure_absorption_rt.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "65a8505b",
"metadata": {},
"source": [
"We compare trans2E3 (2 E3) with a simple transmission. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "35e64659",
"metadata": {
"execution": {
"iopub.execute_input": "2022-10-20T05:48:24.207797Z",
"iopub.status.busy": "2022-10-20T05:48:24.207370Z",
"iopub.status.idle": "2022-10-20T05:48:26.190456Z",
"shell.execute_reply": "2022-10-20T05:48:26.190083Z"
}
},
"outputs": [],
"source": [
"from exojax.spec.rtransfer import trans2E3"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "b3cc56f2",
"metadata": {
"execution": {
"iopub.execute_input": "2022-10-20T05:48:26.192799Z",
"iopub.status.busy": "2022-10-20T05:48:26.192509Z",
"iopub.status.idle": "2022-10-20T05:48:26.194141Z",
"shell.execute_reply": "2022-10-20T05:48:26.193837Z"
}
},
"outputs": [],
"source": [
"def simple_trans(dtau, mu):\n",
" return jnp.exp(-dtau/mu)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "870b1568",
"metadata": {
"execution": {
"iopub.execute_input": "2022-10-20T05:48:26.207450Z",
"iopub.status.busy": "2022-10-20T05:48:26.207085Z",
"iopub.status.idle": "2022-10-20T05:48:26.269755Z",
"shell.execute_reply": "2022-10-20T05:48:26.269405Z"
}
},
"outputs": [],
"source": [
"import jax.numpy as jnp\n",
"dtau_arr=jnp.logspace(-3,1,100)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "3550f76e",
"metadata": {
"execution": {
"iopub.execute_input": "2022-10-20T05:48:26.303723Z",
"iopub.status.busy": "2022-10-20T05:48:26.296773Z",
"iopub.status.idle": "2022-10-20T05:48:27.368906Z",
"shell.execute_reply": "2022-10-20T05:48:27.368608Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"plt.plot(dtau_arr,trans2E3(dtau_arr),color=\"black\",label=\"$\\\\mathcal{T}= 2E_3 = 2 \\\\int_0^1 d \\\\mu \\\\, \\\\mu \\\\, e^{-\\\\Delta \\\\tau/\\\\mu}$\")\n",
"plt.plot(dtau_arr,simple_trans(dtau_arr,1),ls=\"dotted\",color=\"gray\", label=\"$\\\\mathcal{T}= e^{-\\\\Delta \\\\tau/\\\\mu} \\\\, \\\\, (\\\\mu=1)$\")\n",
"plt.plot(dtau_arr,simple_trans(dtau_arr,2.0/3.0),ls=\"dashed\",color=\"gray\", label=\"$\\\\mathcal{T}= e^{-\\\\Delta \\\\tau/\\\\mu} \\\\, \\\\, (\\\\mu=2/3)$\")\n",
"plt.plot(dtau_arr,simple_trans(dtau_arr,0.3),ls=\"dashdot\",color=\"gray\", label=\"$\\\\mathcal{T}= e^{-\\\\Delta \\\\tau/\\\\mu} \\\\, \\\\, (\\\\mu=0.3)$\")\n",
"#plt.yscale(\"log\")\n",
"plt.legend()\n",
"plt.xscale(\"log\")\n",
"plt.xlabel(\"$\\\\Delta \\\\tau$\")\n",
"plt.ylabel(\"transmission\")\n",
"plt.savefig(\"transrt.png\")\n",
"plt.savefig(\"transrt.pdf\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "01bfec42",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}