- {
- “cells”: [
- {
“cell_type”: “markdown”, “id”: “7185a1f0”, “metadata”: {
- “papermill”: {
“duration”: 0.011279, “end_time”: “2021-08-17T08:11:57.192329”, “exception”: false, “start_time”: “2021-08-17T08:11:57.181050”, “status”: “completed”
}, “tags”: []
}, “source”: [
“# TbAbs”
]
}, {
“cell_type”: “code”, “execution_count”: 1, “id”: “1525c78f”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:11:57.227172Z”, “iopub.status.busy”: “2021-08-17T08:11:57.223689Z”, “iopub.status.idle”: “2021-08-17T08:12:02.332785Z”, “shell.execute_reply”: “2021-08-17T08:12:02.333471Z”
}, “nbsphinx”: “hidden”, “papermill”: {
“duration”: 5.131379, “end_time”: “2021-08-17T08:12:02.333927”, “exception”: false, “start_time”: “2021-08-17T08:11:57.202548”, “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”: “e0b9d12b”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:12:02.360537Z”, “iopub.status.busy”: “2021-08-17T08:12:02.359410Z”, “iopub.status.idle”: “2021-08-17T08:12:02.362593Z”, “shell.execute_reply”: “2021-08-17T08:12:02.363669Z”
}, “nbsphinx”: “hidden”, “papermill”: {
“duration”: 0.020182, “end_time”: “2021-08-17T08:12:02.364090”, “exception”: false, “start_time”: “2021-08-17T08:12:02.343908”, “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”: “b3dad3b9”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:12:02.389450Z”, “iopub.status.busy”: “2021-08-17T08:12:02.388382Z”, “iopub.status.idle”: “2021-08-17T08:12:02.391707Z”, “shell.execute_reply”: “2021-08-17T08:12:02.392741Z”
}, “papermill”: {
“duration”: 0.019542, “end_time”: “2021-08-17T08:12:02.393056”, “exception”: false, “start_time”: “2021-08-17T08:12:02.373514”, “status”: “completed”
}, “tags”: [
“injected-parameters”
]
}, “outputs”: [], “source”: [
“# Parametersn”, “func_name = "TbAbs"n”, “wide_energy_range = Falsen”, “x_scale = "log"n”, “y_scale = "log"n”, “linear_range = Falsen”
]
}, {
“cell_type”: “code”, “execution_count”: 4, “id”: “a6ef0bb4”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:12:02.424028Z”, “iopub.status.busy”: “2021-08-17T08:12:02.422778Z”, “iopub.status.idle”: “2021-08-17T08:12:02.437181Z”, “shell.execute_reply”: “2021-08-17T08:12:02.437986Z”
}, “lines_to_next_cell”: 0, “nbsphinx”: “hidden”, “papermill”: {
“duration”: 0.035351, “end_time”: “2021-08-17T08:12:02.438332”, “exception”: false, “start_time”: “2021-08-17T08:12:02.402981”, “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”: “199a9b51”, “metadata”: {
“lines_to_next_cell”: 0, “papermill”: {
“duration”: 0.009558, “end_time”: “2021-08-17T08:12:02.458286”, “exception”: false, “start_time”: “2021-08-17T08:12:02.448728”, “status”: “completed”
}, “tags”: []
}, “source”: [
“## Description”
]
}, {
“cell_type”: “code”, “execution_count”: 5, “id”: “774b0304”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:12:02.490297Z”, “iopub.status.busy”: “2021-08-17T08:12:02.489296Z”, “iopub.status.idle”: “2021-08-17T08:12:02.495566Z”, “shell.execute_reply”: “2021-08-17T08:12:02.496430Z”
}, “papermill”: {
“duration”: 0.028881, “end_time”: “2021-08-17T08:12:02.496749”, “exception”: false, “start_time”: “2021-08-17T08:12:02.467868”, “status”: “completed”
}, “tags”: []
}, “outputs”: [
- {
- “data”: {
- “text/html”: [
“<ul>n”, “n”, “<li>description: Photometric absorption (Tbabs 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”
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” * description: Photometric absorption (Tbabs 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”
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}, “metadata”: {}, “output_type”: “display_data”
}
], “source”: [
“func.display()”
]
}, {
“cell_type”: “markdown”, “id”: “a39dbe7c”, “metadata”: {
- “papermill”: {
“duration”: 0.011781, “end_time”: “2021-08-17T08:12:02.519553”, “exception”: false, “start_time”: “2021-08-17T08:12:02.507772”, “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”: “7dc7cfbc”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:12:02.577816Z”, “iopub.status.busy”: “2021-08-17T08:12:02.576677Z”, “iopub.status.idle”: “2021-08-17T08:12:05.037032Z”, “shell.execute_reply”: “2021-08-17T08:12:05.038027Z”
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}, “tags”: [
“nbsphinx-thumbnail”
]
}, “outputs”: [
- {
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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”: “8d7027b7”, “metadata”: {
“lines_to_next_cell”: 0, “papermill”: {
“duration”: 0.012085, “end_time”: “2021-08-17T08:12:05.062221”, “exception”: false, “start_time”: “2021-08-17T08:12:05.050136”, “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”: “ae861873”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:12:05.127348Z”, “iopub.status.busy”: “2021-08-17T08:12:05.121068Z”, “iopub.status.idle”: “2021-08-17T08:12:05.684202Z”, “shell.execute_reply”: “2021-08-17T08:12:05.683388Z”
}, “papermill”: {
“duration”: 0.610368, “end_time”: “2021-08-17T08:12:05.684497”, “exception”: false, “start_time”: “2021-08-17T08:12:05.074129”, “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”: “729174ee”, “metadata”: {
- “papermill”: {
“duration”: 0.013124, “end_time”: “2021-08-17T08:12:05.711125”, “exception”: false, “start_time”: “2021-08-17T08:12:05.698001”, “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”: “422eef6c”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:12:05.780007Z”, “iopub.status.busy”: “2021-08-17T08:12:05.767226Z”, “iopub.status.idle”: “2021-08-17T08:12:06.537116Z”, “shell.execute_reply”: “2021-08-17T08:12:06.538217Z”
}, “papermill”: {
“duration”: 0.81525, “end_time”: “2021-08-17T08:12:06.538584”, “exception”: false, “start_time”: “2021-08-17T08:12:05.723334”, “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”: 11.353862, “end_time”: “2021-08-17T08:12:07.188331”, “environment_variables”: {}, “exception”: null, “input_path”: “TbAbs.ipynb”, “output_path”: “../docs/notebooks/TbAbs.ipynb”, “parameters”: {
“func_name”: “TbAbs”, “linear_range”: false, “wide_energy_range”: false, “x_scale”: “log”, “y_scale”: “log”
}, “start_time”: “2021-08-17T08:11:55.834469”, “version”: “2.3.3”
}
}, “nbformat”: 4, “nbformat_minor”: 5
}