- {
- “cells”: [
- {
“cell_type”: “markdown”, “id”: “5fa367c0”, “metadata”: {
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“duration”: 0.010433, “end_time”: “2021-08-17T08:12:08.275578”, “exception”: false, “start_time”: “2021-08-17T08:12:08.265145”, “status”: “completed”
}, “tags”: []
}, “source”: [
“# WAbs”
]
}, {
“cell_type”: “code”, “execution_count”: 1, “id”: “0d5cd64c”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:12:08.307716Z”, “iopub.status.busy”: “2021-08-17T08:12:08.305640Z”, “iopub.status.idle”: “2021-08-17T08:12:11.378962Z”, “shell.execute_reply”: “2021-08-17T08:12:11.379921Z”
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}, “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”: “db1906f6”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:12:11.408787Z”, “iopub.status.busy”: “2021-08-17T08:12:11.407214Z”, “iopub.status.idle”: “2021-08-17T08:12:11.411948Z”, “shell.execute_reply”: “2021-08-17T08:12:11.413024Z”
}, “nbsphinx”: “hidden”, “papermill”: {
“duration”: 0.023066, “end_time”: “2021-08-17T08:12:11.413388”, “exception”: false, “start_time”: “2021-08-17T08:12:11.390322”, “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”: “667ff931”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:12:11.438813Z”, “iopub.status.busy”: “2021-08-17T08:12:11.437746Z”, “iopub.status.idle”: “2021-08-17T08:12:11.444760Z”, “shell.execute_reply”: “2021-08-17T08:12:11.446091Z”
}, “papermill”: {
“duration”: 0.02357, “end_time”: “2021-08-17T08:12:11.446413”, “exception”: false, “start_time”: “2021-08-17T08:12:11.422843”, “status”: “completed”
}, “tags”: [
“injected-parameters”
]
}, “outputs”: [], “source”: [
“# Parametersn”, “func_name = "WAbs"n”, “wide_energy_range = Falsen”, “x_scale = "log"n”, “y_scale = "log"n”, “linear_range = Falsen”
]
}, {
“cell_type”: “code”, “execution_count”: 4, “id”: “4abcf34b”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:12:11.474776Z”, “iopub.status.busy”: “2021-08-17T08:12:11.473698Z”, “iopub.status.idle”: “2021-08-17T08:12:11.489760Z”, “shell.execute_reply”: “2021-08-17T08:12:11.490885Z”
}, “lines_to_next_cell”: 0, “nbsphinx”: “hidden”, “papermill”: {
“duration”: 0.035099, “end_time”: “2021-08-17T08:12:11.491262”, “exception”: false, “start_time”: “2021-08-17T08:12:11.456163”, “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”: “012f6e17”, “metadata”: {
“lines_to_next_cell”: 0, “papermill”: {
“duration”: 0.009184, “end_time”: “2021-08-17T08:12:11.510501”, “exception”: false, “start_time”: “2021-08-17T08:12:11.501317”, “status”: “completed”
}, “tags”: []
}, “source”: [
“## Description”
]
}, {
“cell_type”: “code”, “execution_count”: 5, “id”: “e53f242f”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:12:11.542723Z”, “iopub.status.busy”: “2021-08-17T08:12:11.541429Z”, “iopub.status.idle”: “2021-08-17T08:12:11.546325Z”, “shell.execute_reply”: “2021-08-17T08:12:11.547179Z”
}, “papermill”: {
“duration”: 0.027234, “end_time”: “2021-08-17T08:12:11.547639”, “exception”: false, “start_time”: “2021-08-17T08:12:11.520405”, “status”: “completed”
}, “tags”: []
}, “outputs”: [
- {
- “data”: {
- “text/html”: [
“<ul>n”, “n”, “<li>description: Photometric absorption (Wabs implementation), f(E) = exp(- NH * sigma(E)) contributed by Dominique Eckert</li>n”, “n”, “<li>formula: $n.a.$</li>n”, “n”, “<li>parameters: n”, “<ul>n”, “n”, “<li>NH: n”, “<ul>n”, “n”, “<li>value: 1.0</li>n”, “n”, “<li>desc: absorbing column density in units of 1e22 particles per cm^2</li>n”, “n”, “<li>min_value: 0.0001</li>n”, “n”, “<li>max_value: 10000.0</li>n”, “n”, “<li>unit: </li>n”, “n”, “<li>is_normalization: True</li>n”, “n”, “<li>delta: 0.1</li>n”, “n”, “<li>free: True</li>n”, “n”, “</ul>n”, “n”, “</li>n”, “n”, “<li>redshift: n”, “<ul>n”, “n”, “<li>value: 0.0</li>n”, “n”, “<li>desc: the redshift of the source</li>n”, “n”, “<li>min_value: 0.0</li>n”, “n”, “<li>max_value: 15.0</li>n”, “n”, “<li>unit: </li>n”, “n”, “<li>is_normalization: False</li>n”, “n”, “<li>delta: 0.1</li>n”, “n”, “<li>free: False</li>n”, “n”, “</ul>n”, “n”, “</li>n”, “n”, “</ul>n”, “n”, “</li>n”, “n”, “</ul>n”
], “text/plain”: [
” * description: Photometric absorption (Wabs implementation), f(E) = exp(- NH * sigma(E))n”, ” * contributed by Dominique Eckertn”, ” * formula: $n.a.$n”, ” * parameters:n”, ” * NH:n”, ” * value: 1.0n”, ” * desc: absorbing column density in units of 1e22 particles per cm^2n”, ” * min_value: 0.0001n”, ” * max_value: 10000.0n”, ” * unit: ‘’n”, ” * is_normalization: truen”, ” * delta: 0.1n”, ” * free: truen”, ” * redshift:n”, ” * value: 0.0n”, ” * desc: the redshift of the sourcen”, ” * min_value: 0.0n”, ” * max_value: 15.0n”, ” * unit: ‘’n”, ” * is_normalization: falsen”, ” * delta: 0.1n”, ” * free: false”
]
}, “metadata”: {}, “output_type”: “display_data”
}
], “source”: [
“func.display()”
]
}, {
“cell_type”: “markdown”, “id”: “367dc399”, “metadata”: {
- “papermill”: {
“duration”: 0.0128, “end_time”: “2021-08-17T08:12:11.571529”, “exception”: false, “start_time”: “2021-08-17T08:12:11.558729”, “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”: “68946530”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:12:11.626002Z”, “iopub.status.busy”: “2021-08-17T08:12:11.624907Z”, “iopub.status.idle”: “2021-08-17T08:12:14.052835Z”, “shell.execute_reply”: “2021-08-17T08:12:14.054002Z”
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}, “tags”: [
“nbsphinx-thumbnail”
]
}, “outputs”: [
- {
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”, “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”: “efeabe42”, “metadata”: {
“lines_to_next_cell”: 0, “papermill”: {
“duration”: 0.011234, “end_time”: “2021-08-17T08:12:14.078634”, “exception”: false, “start_time”: “2021-08-17T08:12:14.067400”, “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”: “60b9f276”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:12:14.198752Z”, “iopub.status.busy”: “2021-08-17T08:12:14.163767Z”, “iopub.status.idle”: “2021-08-17T08:12:14.703545Z”, “shell.execute_reply”: “2021-08-17T08:12:14.704940Z”
}, “papermill”: {
“duration”: 0.614935, “end_time”: “2021-08-17T08:12:14.705261”, “exception”: false, “start_time”: “2021-08-17T08:12:14.090326”, “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”: “e8bc9a37”, “metadata”: {
- “papermill”: {
“duration”: 0.012871, “end_time”: “2021-08-17T08:12:14.731272”, “exception”: false, “start_time”: “2021-08-17T08:12:14.718401”, “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”: “0c09cd75”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:12:14.890369Z”, “iopub.status.busy”: “2021-08-17T08:12:14.889221Z”, “iopub.status.idle”: “2021-08-17T08:12:15.426280Z”, “shell.execute_reply”: “2021-08-17T08:12:15.427134Z”
}, “papermill”: {
“duration”: 0.6829, “end_time”: “2021-08-17T08:12:15.427508”, “exception”: false, “start_time”: “2021-08-17T08:12:14.744608”, “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”: 9.742723, “end_time”: “2021-08-17T08:12:17.005710”, “environment_variables”: {}, “exception”: null, “input_path”: “WAbs.ipynb”, “output_path”: “../docs/notebooks/WAbs.ipynb”, “parameters”: {
“func_name”: “WAbs”, “linear_range”: false, “wide_energy_range”: false, “x_scale”: “log”, “y_scale”: “log”
}, “start_time”: “2021-08-17T08:12:07.262987”, “version”: “2.3.3”
}
}, “nbformat”: 4, “nbformat_minor”: 5
}