- {
- “cells”: [
- {
“cell_type”: “markdown”, “id”: “f7f1c995”, “metadata”: {
- “papermill”: {
“duration”: 0.010658, “end_time”: “2021-08-17T08:15:29.451316”, “exception”: false, “start_time”: “2021-08-17T08:15:29.440658”, “status”: “completed”
}, “tags”: []
}, “source”: [
“# Gaussian”
]
}, {
“cell_type”: “code”, “execution_count”: 1, “id”: “9f531548”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:15:29.490448Z”, “iopub.status.busy”: “2021-08-17T08:15:29.487259Z”, “iopub.status.idle”: “2021-08-17T08:15:32.511653Z”, “shell.execute_reply”: “2021-08-17T08:15:32.516720Z”
}, “nbsphinx”: “hidden”, “papermill”: {
“duration”: 3.056204, “end_time”: “2021-08-17T08:15:32.517236”, “exception”: false, “start_time”: “2021-08-17T08:15:29.461032”, “status”: “completed”
}, “tags”: []
}, “outputs”: [], “source”: [
“%%capturen”, “n”, “import numpy as npn”, “n”, “import matplotlib.pyplot as pltn”, “n”, “import warningsn”, “warnings.simplefilter("ignore")n”, “n”, “from astromodels.functions.function import _known_functionsn”, “n”, “n”, “from jupyterthemes import jtplotn”, “jtplot.style(context="talk", fscale=1, ticks=True, grid=False)n”, “%matplotlib inline”
]
}, {
“cell_type”: “code”, “execution_count”: 2, “id”: “f92a8ac3”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:15:32.548112Z”, “iopub.status.busy”: “2021-08-17T08:15:32.546957Z”, “iopub.status.idle”: “2021-08-17T08:15:32.554881Z”, “shell.execute_reply”: “2021-08-17T08:15:32.556191Z”
}, “nbsphinx”: “hidden”, “papermill”: {
“duration”: 0.029111, “end_time”: “2021-08-17T08:15:32.556576”, “exception”: false, “start_time”: “2021-08-17T08:15:32.527465”, “status”: “completed”
}, “tags”: [
“parameters”
]
}, “outputs”: [], “source”: [
“func_name = "TbAbs"n”, “n”, “x_scale="log"n”, “y_scale="log"n”, “n”, “linear_range = Falsen”, “n”, “wide_energy_range = False”
]
}, {
“cell_type”: “code”, “execution_count”: 3, “id”: “cf8ff6ae”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:15:32.588434Z”, “iopub.status.busy”: “2021-08-17T08:15:32.586890Z”, “iopub.status.idle”: “2021-08-17T08:15:32.590142Z”, “shell.execute_reply”: “2021-08-17T08:15:32.589388Z”
}, “papermill”: {
“duration”: 0.023513, “end_time”: “2021-08-17T08:15:32.590477”, “exception”: false, “start_time”: “2021-08-17T08:15:32.566964”, “status”: “completed”
}, “tags”: [
“injected-parameters”
]
}, “outputs”: [], “source”: [
“# Parametersn”, “func_name = "Gaussian"n”, “wide_energy_range = Truen”, “x_scale = "linear"n”, “y_scale = "linear"n”, “linear_range = Truen”
]
}, {
“cell_type”: “code”, “execution_count”: 4, “id”: “6678b68c”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:15:32.621026Z”, “iopub.status.busy”: “2021-08-17T08:15:32.619816Z”, “iopub.status.idle”: “2021-08-17T08:15:32.630158Z”, “shell.execute_reply”: “2021-08-17T08:15:32.631263Z”
}, “lines_to_next_cell”: 0, “nbsphinx”: “hidden”, “papermill”: {
“duration”: 0.03168, “end_time”: “2021-08-17T08:15:32.631635”, “exception”: false, “start_time”: “2021-08-17T08:15:32.599955”, “status”: “completed”
}, “tags”: []
}, “outputs”: [], “source”: [
“func = _known_functions[func_name]()n”, “n”, “if wide_energy_range:n”, “n”, ” energy_grid = np.geomspace(1e2,1e4,500)n”, ” n”, “else:n”, ” n”, ” energy_grid = np.geomspace(2e-1,1e1,1000)n”, “n”, “if linear_range:n”, “n”, “tenergy_grid = np.linspace(-5,5,1000)n”, “n”, ” n”, “blue = "#4152E3"n”, “red = "#E3414B"n”, “green = "#41E39E"”
]
}, {
“cell_type”: “markdown”, “id”: “4578e46f”, “metadata”: {
“lines_to_next_cell”: 0, “papermill”: {
“duration”: 0.0106, “end_time”: “2021-08-17T08:15:32.652379”, “exception”: false, “start_time”: “2021-08-17T08:15:32.641779”, “status”: “completed”
}, “tags”: []
}, “source”: [
“## Description”
]
}, {
“cell_type”: “code”, “execution_count”: 5, “id”: “2eb3e798”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:15:32.685568Z”, “iopub.status.busy”: “2021-08-17T08:15:32.684374Z”, “iopub.status.idle”: “2021-08-17T08:15:32.689145Z”, “shell.execute_reply”: “2021-08-17T08:15:32.690233Z”
}, “papermill”: {
“duration”: 0.027478, “end_time”: “2021-08-17T08:15:32.690592”, “exception”: false, “start_time”: “2021-08-17T08:15:32.663114”, “status”: “completed”
}, “tags”: []
}, “outputs”: [
- {
- “data”: {
- “text/html”: [
“<ul>n”, “n”, “<li>description: A Gaussian function</li>n”, “n”, “<li>formula: $ K \frac{1}{\sigma \sqrt{2 \pi}}\exp{\frac{(x-\mu)^2}{2~(\sigma)^2}} $</li>n”, “n”, “<li>parameters: n”, “<ul>n”, “n”, “<li>F: n”, “<ul>n”, “n”, “<li>value: 1.0</li>n”, “n”, “<li>desc: Integral between -inf and +inf. Fix this to 1 to obtain a Normal distribution</li>n”, “n”, “<li>min_value: None</li>n”, “n”, “<li>max_value: None</li>n”, “n”, “<li>unit: </li>n”, “n”, “<li>is_normalization: False</li>n”, “n”, “<li>delta: 0.1</li>n”, “n”, “<li>free: True</li>n”, “n”, “</ul>n”, “n”, “</li>n”, “n”, “<li>mu: n”, “<ul>n”, “n”, “<li>value: 0.0</li>n”, “n”, “<li>desc: Central value</li>n”, “n”, “<li>min_value: None</li>n”, “n”, “<li>max_value: None</li>n”, “n”, “<li>unit: </li>n”, “n”, “<li>is_normalization: False</li>n”, “n”, “<li>delta: 0.1</li>n”, “n”, “<li>free: True</li>n”, “n”, “</ul>n”, “n”, “</li>n”, “n”, “<li>sigma: n”, “<ul>n”, “n”, “<li>value: 1.0</li>n”, “n”, “<li>desc: standard deviation</li>n”, “n”, “<li>min_value: 1e-12</li>n”, “n”, “<li>max_value: None</li>n”, “n”, “<li>unit: </li>n”, “n”, “<li>is_normalization: False</li>n”, “n”, “<li>delta: 0.1</li>n”, “n”, “<li>free: True</li>n”, “n”, “</ul>n”, “n”, “</li>n”, “n”, “</ul>n”, “n”, “</li>n”, “n”, “</ul>n”
], “text/plain”: [
” * description: A Gaussian functionn”, ” * formula: $ K \frac{1}{\sigma \sqrt{2 \pi}}\exp{\frac{(x-\mu)^2}{2~(\sigma)^2}} $n”, ” * parameters:n”, ” * F:n”, ” * value: 1.0n”, ” * desc: Integral between -inf and +inf. Fix this to 1 to obtain a Normal distributionn”, ” * min_value: nulln”, ” * max_value: nulln”, ” * unit: ‘’n”, ” * is_normalization: falsen”, ” * delta: 0.1n”, ” * free: truen”, ” * mu:n”, ” * value: 0.0n”, ” * desc: Central valuen”, ” * min_value: nulln”, ” * max_value: nulln”, ” * unit: ‘’n”, ” * is_normalization: falsen”, ” * delta: 0.1n”, ” * free: truen”, ” * sigma:n”, ” * value: 1.0n”, ” * desc: standard deviationn”, ” * min_value: 1.0e-12n”, ” * max_value: nulln”, ” * unit: ‘’n”, ” * is_normalization: falsen”, ” * delta: 0.1n”, ” * free: true”
]
}, “metadata”: {}, “output_type”: “display_data”
}
], “source”: [
“func.display()”
]
}, {
“cell_type”: “markdown”, “id”: “1c495665”, “metadata”: {
- “papermill”: {
“duration”: 0.01085, “end_time”: “2021-08-17T08:15:32.712128”, “exception”: false, “start_time”: “2021-08-17T08:15:32.701278”, “status”: “completed”
}, “tags”: []
}, “source”: [
“## Shape n”, “n”, “The shape of the function. n”, “n”, “If this is not a photon model but a prior or linear function then ignore the units as these docs are auto-generated”
]
}, {
“cell_type”: “code”, “execution_count”: 6, “id”: “9a6bf6bc”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:15:32.799316Z”, “iopub.status.busy”: “2021-08-17T08:15:32.775622Z”, “iopub.status.idle”: “2021-08-17T08:15:32.946129Z”, “shell.execute_reply”: “2021-08-17T08:15:32.947374Z”
}, “papermill”: {
“duration”: 0.224487, “end_time”: “2021-08-17T08:15:32.947780”, “exception”: false, “start_time”: “2021-08-17T08:15:32.723293”, “status”: “completed”
}, “tags”: [
“nbsphinx-thumbnail”
]
}, “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”: [
“fig, ax = plt.subplots()n”, “n”, “n”, “ax.plot(energy_grid, func(energy_grid), color=blue)n”, “n”, “ax.set_xlabel("energy (keV)")n”, “ax.set_ylabel("photon flux")n”, “ax.set_xscale(x_scale)n”, “ax.set_yscale(y_scale)n”
]
}, {
“cell_type”: “markdown”, “id”: “b586b0e0”, “metadata”: {
“lines_to_next_cell”: 0, “papermill”: {
“duration”: 0.011426, “end_time”: “2021-08-17T08:15:32.971277”, “exception”: false, “start_time”: “2021-08-17T08:15:32.959851”, “status”: “completed”
}, “tags”: []
}, “source”: [
“## F$_{\nu}$n”, “n”, “The F$_{\nu}$ shape of the photon modeln”, “if this is not a photon model, please ignore this auto-generated plot”
]
}, {
“cell_type”: “code”, “execution_count”: 7, “id”: “0705c8fe”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:15:33.041430Z”, “iopub.status.busy”: “2021-08-17T08:15:33.040318Z”, “iopub.status.idle”: “2021-08-17T08:15:33.415991Z”, “shell.execute_reply”: “2021-08-17T08:15:33.416671Z”
}, “papermill”: {
“duration”: 0.433905, “end_time”: “2021-08-17T08:15:33.417037”, “exception”: false, “start_time”: “2021-08-17T08:15:32.983132”, “status”: “completed”
}, “tags”: []
}, “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”: [
“fig, ax = plt.subplots()n”, “n”, “ax.plot(energy_grid, energy_grid * func(energy_grid), red)n”, “n”, “n”, “ax.set_xlabel("energy (keV)")n”, “ax.set_ylabel(r"energy flux (F$_{\nu}$)")n”, “ax.set_xscale(x_scale)n”, “ax.set_yscale(y_scale)n”, “n”
]
}, {
“cell_type”: “markdown”, “id”: “73e5a628”, “metadata”: {
- “papermill”: {
“duration”: 0.012132, “end_time”: “2021-08-17T08:15:33.441788”, “exception”: false, “start_time”: “2021-08-17T08:15:33.429656”, “status”: “completed”
}, “tags”: []
}, “source”: [
“## $\nu$F$_{\nu}$n”, “n”, “The $\nu$F$_{\nu}$ shape of the photon modeln”, “if this is not a photon model, please ignore this auto-generated plot”
]
}, {
“cell_type”: “code”, “execution_count”: 8, “id”: “3d793436”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:15:33.487428Z”, “iopub.status.busy”: “2021-08-17T08:15:33.486115Z”, “iopub.status.idle”: “2021-08-17T08:15:33.691031Z”, “shell.execute_reply”: “2021-08-17T08:15:33.691895Z”
}, “papermill”: {
“duration”: 0.234839, “end_time”: “2021-08-17T08:15:33.692492”, “exception”: false, “start_time”: “2021-08-17T08:15:33.457653”, “status”: “completed”
}, “tags”: []
}, “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”: [
“fig, ax = plt.subplots()n”, “n”, “ax.plot(energy_grid, energy_grid**2 * func(energy_grid), color=green)n”, “n”, “n”, “ax.set_xlabel("energy (keV)")n”, “ax.set_ylabel(r"$\nu$F$_{\nu}$")n”, “ax.set_xscale(x_scale)n”, “ax.set_yscale(y_scale)n”
]
}
], “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”
}, “papermill”: {
“default_parameters”: {}, “duration”: 6.480253, “end_time”: “2021-08-17T08:15:34.946399”, “environment_variables”: {}, “exception”: null, “input_path”: “Gaussian.ipynb”, “output_path”: “../docs/notebooks/Gaussian.ipynb”, “parameters”: {
“func_name”: “Gaussian”, “linear_range”: true, “wide_energy_range”: true, “x_scale”: “linear”, “y_scale”: “linear”
}, “start_time”: “2021-08-17T08:15:28.466146”, “version”: “2.3.3”
}
}, “nbformat”: 4, “nbformat_minor”: 5
}