- {
- “cells”: [
- {
“cell_type”: “markdown”, “id”: “aa5f1657”, “metadata”: {
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“duration”: 0.010577, “end_time”: “2021-08-17T08:16:02.170587”, “exception”: false, “start_time”: “2021-08-17T08:16:02.160010”, “status”: “completed”
}, “tags”: []
}, “source”: [
“# Uniform prior”
]
}, {
“cell_type”: “code”, “execution_count”: 1, “id”: “53dae600”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:16:02.202130Z”, “iopub.status.busy”: “2021-08-17T08:16:02.201053Z”, “iopub.status.idle”: “2021-08-17T08:16:05.203661Z”, “shell.execute_reply”: “2021-08-17T08:16:05.202876Z”
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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”: “579cf931”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:16:05.230513Z”, “iopub.status.busy”: “2021-08-17T08:16:05.229468Z”, “iopub.status.idle”: “2021-08-17T08:16:05.236899Z”, “shell.execute_reply”: “2021-08-17T08:16:05.237770Z”
}, “nbsphinx”: “hidden”, “papermill”: {
“duration”: 0.023951, “end_time”: “2021-08-17T08:16:05.238143”, “exception”: false, “start_time”: “2021-08-17T08:16:05.214192”, “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”: “82897aaa”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:16:05.268868Z”, “iopub.status.busy”: “2021-08-17T08:16:05.267464Z”, “iopub.status.idle”: “2021-08-17T08:16:05.270384Z”, “shell.execute_reply”: “2021-08-17T08:16:05.271160Z”
}, “papermill”: {
“duration”: 0.023239, “end_time”: “2021-08-17T08:16:05.271890”, “exception”: false, “start_time”: “2021-08-17T08:16:05.248651”, “status”: “completed”
}, “tags”: [
“injected-parameters”
]
}, “outputs”: [], “source”: [
“# Parametersn”, “func_name = "Uniform_prior"n”, “wide_energy_range = Truen”, “x_scale = "linear"n”, “y_scale = "linear"n”, “linear_range = Truen”
]
}, {
“cell_type”: “code”, “execution_count”: 4, “id”: “6922a377”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:16:05.301541Z”, “iopub.status.busy”: “2021-08-17T08:16:05.296305Z”, “iopub.status.idle”: “2021-08-17T08:16:05.308946Z”, “shell.execute_reply”: “2021-08-17T08:16:05.310096Z”
}, “lines_to_next_cell”: 0, “nbsphinx”: “hidden”, “papermill”: {
“duration”: 0.027792, “end_time”: “2021-08-17T08:16:05.310460”, “exception”: false, “start_time”: “2021-08-17T08:16:05.282668”, “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”: “9530a1ee”, “metadata”: {
“lines_to_next_cell”: 0, “papermill”: {
“duration”: 0.009942, “end_time”: “2021-08-17T08:16:05.330385”, “exception”: false, “start_time”: “2021-08-17T08:16:05.320443”, “status”: “completed”
}, “tags”: []
}, “source”: [
“## Description”
]
}, {
“cell_type”: “code”, “execution_count”: 5, “id”: “47bc88ec”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:16:05.363640Z”, “iopub.status.busy”: “2021-08-17T08:16:05.362547Z”, “iopub.status.idle”: “2021-08-17T08:16:05.368201Z”, “shell.execute_reply”: “2021-08-17T08:16:05.369055Z”
}, “papermill”: {
“duration”: 0.02913, “end_time”: “2021-08-17T08:16:05.369397”, “exception”: false, “start_time”: “2021-08-17T08:16:05.340267”, “status”: “completed”
}, “tags”: []
}, “outputs”: [
- {
- “data”: {
- “text/html”: [
“<ul>n”, “n”, “<li>description: A function which is constant on the interval lower_bound - upper_bound and 0 outside the interval. The extremes of the interval are counted as part of the interval.</li>n”, “n”, “<li>formula: $ f(x)=\begin{cases}0 & x < \text{lower_bound} \\\text{value} & \text{lower_bound} \le x \le \text{upper_bound} \\ 0 & x > \text{upper_bound} \end{cases}$</li>n”, “n”, “<li>parameters: n”, “<ul>n”, “n”, “<li>lower_bound: n”, “<ul>n”, “n”, “<li>value: 0.0</li>n”, “n”, “<li>desc: Lower bound for the interval</li>n”, “n”, “<li>min_value: -inf</li>n”, “n”, “<li>max_value: inf</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>upper_bound: n”, “<ul>n”, “n”, “<li>value: 1.0</li>n”, “n”, “<li>desc: Upper bound for the interval</li>n”, “n”, “<li>min_value: -inf</li>n”, “n”, “<li>max_value: inf</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>value: n”, “<ul>n”, “n”, “<li>value: 1.0</li>n”, “n”, “<li>desc: Value in the interval</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”, “</ul>n”, “n”, “</li>n”, “n”, “</ul>n”
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” * description: A function which is constant on the interval lower_bound - upper_boundn”, ” * and 0 outside the interval. The extremes of the interval are counted as part ofn”, ” * the interval.n”, ” * formula: $ f(x)=\begin{cases}0 & x < \text{lower_bound} \\\text{value} & \text{lower_bound}n”, ” * \le x \le \text{upper_bound} \\ 0 & x > \text{upper_bound} \end{cases}$n”, ” * parameters:n”, ” * lower_bound:n”, ” * value: 0.0n”, ” * desc: Lower bound for the intervaln”, ” * min_value: -.infn”, ” * max_value: .infn”, ” * unit: ‘’n”, ” * is_normalization: falsen”, ” * delta: 0.1n”, ” * free: truen”, ” * upper_bound:n”, ” * value: 1.0n”, ” * desc: Upper bound for the intervaln”, ” * min_value: -.infn”, ” * max_value: .infn”, ” * unit: ‘’n”, ” * is_normalization: falsen”, ” * delta: 0.1n”, ” * free: truen”, ” * value:n”, ” * value: 1.0n”, ” * desc: Value in the intervaln”, ” * min_value: nulln”, ” * 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”: “812d386b”, “metadata”: {
- “papermill”: {
“duration”: 0.010856, “end_time”: “2021-08-17T08:16:05.391424”, “exception”: false, “start_time”: “2021-08-17T08:16:05.380568”, “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”: “80a9d69b”, “metadata”: {
- “execution”: {
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“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”: “9611cf4f”, “metadata”: {
“lines_to_next_cell”: 0, “papermill”: {
“duration”: 0.011634, “end_time”: “2021-08-17T08:16:05.645861”, “exception”: false, “start_time”: “2021-08-17T08:16:05.634227”, “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”: “065aacba”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:16:05.728125Z”, “iopub.status.busy”: “2021-08-17T08:16:05.714280Z”, “iopub.status.idle”: “2021-08-17T08:16:06.102248Z”, “shell.execute_reply”: “2021-08-17T08:16:06.102845Z”
}, “papermill”: {
“duration”: 0.446429, “end_time”: “2021-08-17T08:16:06.104042”, “exception”: false, “start_time”: “2021-08-17T08:16:05.657613”, “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”: “80b69a3d”, “metadata”: {
- “papermill”: {
“duration”: 0.01322, “end_time”: “2021-08-17T08:16:06.130092”, “exception”: false, “start_time”: “2021-08-17T08:16:06.116872”, “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”: “f80342c3”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:16:06.189257Z”, “iopub.status.busy”: “2021-08-17T08:16:06.188040Z”, “iopub.status.idle”: “2021-08-17T08:16:06.351096Z”, “shell.execute_reply”: “2021-08-17T08:16:06.351870Z”
}, “papermill”: {
“duration”: 0.209394, “end_time”: “2021-08-17T08:16:06.352259”, “exception”: false, “start_time”: “2021-08-17T08:16:06.142865”, “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”: 5.759483, “end_time”: “2021-08-17T08:16:06.882734”, “environment_variables”: {}, “exception”: null, “input_path”: “Uniform_prior.ipynb”, “output_path”: “../docs/notebooks/Uniform_prior.ipynb”, “parameters”: {
“func_name”: “Uniform_prior”, “linear_range”: true, “wide_energy_range”: true, “x_scale”: “linear”, “y_scale”: “linear”
}, “start_time”: “2021-08-17T08:16:01.123251”, “version”: “2.3.3”
}
}, “nbformat”: 4, “nbformat_minor”: 5
}