- {
- “cells”: [
- {
“cell_type”: “markdown”, “id”: “9b367d1d”, “metadata”: {
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“duration”: 0.012584, “end_time”: “2021-08-17T08:15:36.218882”, “exception”: false, “start_time”: “2021-08-17T08:15:36.206298”, “status”: “completed”
}, “tags”: []
}, “source”: [
“# Truncated gaussian”
]
}, {
“cell_type”: “code”, “execution_count”: 1, “id”: “8d7c43b6”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:15:36.251347Z”, “iopub.status.busy”: “2021-08-17T08:15:36.250016Z”, “iopub.status.idle”: “2021-08-17T08:15:39.649781Z”, “shell.execute_reply”: “2021-08-17T08:15:39.650795Z”
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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”: “12d6d462”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:15:39.684855Z”, “iopub.status.busy”: “2021-08-17T08:15:39.683681Z”, “iopub.status.idle”: “2021-08-17T08:15:39.686403Z”, “shell.execute_reply”: “2021-08-17T08:15:39.687433Z”
}, “nbsphinx”: “hidden”, “papermill”: {
“duration”: 0.023486, “end_time”: “2021-08-17T08:15:39.687802”, “exception”: false, “start_time”: “2021-08-17T08:15:39.664316”, “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”: “708d101e”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:15:39.713775Z”, “iopub.status.busy”: “2021-08-17T08:15:39.712212Z”, “iopub.status.idle”: “2021-08-17T08:15:39.720593Z”, “shell.execute_reply”: “2021-08-17T08:15:39.721786Z”
}, “papermill”: {
“duration”: 0.024867, “end_time”: “2021-08-17T08:15:39.722108”, “exception”: false, “start_time”: “2021-08-17T08:15:39.697241”, “status”: “completed”
}, “tags”: [
“injected-parameters”
]
}, “outputs”: [], “source”: [
“# Parametersn”, “func_name = "Truncated_gaussian"n”, “wide_energy_range = Truen”, “x_scale = "linear"n”, “y_scale = "linear"n”, “linear_range = Truen”
]
}, {
“cell_type”: “code”, “execution_count”: 4, “id”: “f651d9ae”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:15:39.750830Z”, “iopub.status.busy”: “2021-08-17T08:15:39.749784Z”, “iopub.status.idle”: “2021-08-17T08:15:39.757655Z”, “shell.execute_reply”: “2021-08-17T08:15:39.758543Z”
}, “lines_to_next_cell”: 0, “nbsphinx”: “hidden”, “papermill”: {
“duration”: 0.027205, “end_time”: “2021-08-17T08:15:39.758948”, “exception”: false, “start_time”: “2021-08-17T08:15:39.731743”, “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”: “d5f69e97”, “metadata”: {
“lines_to_next_cell”: 0, “papermill”: {
“duration”: 0.009637, “end_time”: “2021-08-17T08:15:39.779472”, “exception”: false, “start_time”: “2021-08-17T08:15:39.769835”, “status”: “completed”
}, “tags”: []
}, “source”: [
“## Description”
]
}, {
“cell_type”: “code”, “execution_count”: 5, “id”: “a200edc8”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:15:39.817042Z”, “iopub.status.busy”: “2021-08-17T08:15:39.814594Z”, “iopub.status.idle”: “2021-08-17T08:15:39.820325Z”, “shell.execute_reply”: “2021-08-17T08:15:39.821197Z”
}, “papermill”: {
“duration”: 0.03281, “end_time”: “2021-08-17T08:15:39.821824”, “exception”: false, “start_time”: “2021-08-17T08:15:39.789014”, “status”: “completed”
}, “tags”: []
}, “outputs”: [
- {
- “data”: {
- “text/html”: [
“<ul>n”, “n”, “<li>description: A truncated Gaussian function defined on the interval between the lower_bound (a) and upper_bound (b)</li>n”, “n”, “<li>formula: $\begin{split}f(x;\mu,\sigma,a,b)=\frac{\frac{1}{\sigma} \phi\left( \frac{x-\mu}{\sigma} \right)}{\Phi\left( \frac{b-\mu}{\sigma} \right) - \Phi\left( \frac{a-\mu}{\sigma} \right)}\\\phi\left(z\right)=\frac{1}{\sqrt{2 \pi}}\exp\left(-\frac{1}{2}z^2\right)\\\Phi\left(z\right)=\frac{1}{2}\left(1+erf\left(\frac{z}{\sqrt(2)}\right)\right)\end{split}$</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”, “<li>lower_bound: n”, “<ul>n”, “n”, “<li>value: -1.0</li>n”, “n”, “<li>desc: lower bound of gaussian, setting to -np.inf results in half 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>upper_bound: n”, “<ul>n”, “n”, “<li>value: 1.0</li>n”, “n”, “<li>desc: upper bound of gaussian setting to np.inf results in half 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”, “</ul>n”, “n”, “</li>n”, “n”, “</ul>n”
], “text/plain”: [
” * description: A truncated Gaussian function defined on the interval between the lower_boundn”, ” * (a) and upper_bound (b)n”, ” * formula: $\begin{split}f(x;\mu,\sigma,a,b)=\frac{\frac{1}{\sigma} \phi\left( \frac{x-\mu}{\sigma}n”, ” * \right)}{\Phi\left( \frac{b-\mu}{\sigma} \right) - \Phi\left( \frac{a-\mu}{\sigma}n”, ” * \right)}\\\phi\left(z\right)=\frac{1}{\sqrt{2 \pi}}\exp\left(-\frac{1}{2}z^2\right)\\\Phi\left(z\right)=\frac{1}{2}\left(1+erf\left(\frac{z}{\sqrt(2)}\right)\right)\end{split}$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: truen”, ” * lower_bound:n”, ” * value: -1.0n”, ” * desc: lower bound of gaussian, setting to -np.inf results in half normal distributionn”, ” * min_value: nulln”, ” * max_value: nulln”, ” * unit: ‘’n”, ” * is_normalization: falsen”, ” * delta: 0.1n”, ” * free: truen”, ” * upper_bound:n”, ” * value: 1.0n”, ” * desc: upper bound of gaussian setting to np.inf results in half normal distributionn”, ” * 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”: “0b86db27”, “metadata”: {
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“duration”: 0.010271, “end_time”: “2021-08-17T08:15:39.842595”, “exception”: false, “start_time”: “2021-08-17T08:15:39.832324”, “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”: “46f2f1f8”, “metadata”: {
- “execution”: {
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}, “tags”: [
“nbsphinx-thumbnail”
]
}, “outputs”: [
- {
- “data”: {
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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”: “96dc1ac3”, “metadata”: {
“lines_to_next_cell”: 0, “papermill”: {
“duration”: 0.01151, “end_time”: “2021-08-17T08:15:40.092088”, “exception”: false, “start_time”: “2021-08-17T08:15:40.080578”, “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”: “949acec0”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:15:40.135626Z”, “iopub.status.busy”: “2021-08-17T08:15:40.130177Z”, “iopub.status.idle”: “2021-08-17T08:15:40.552014Z”, “shell.execute_reply”: “2021-08-17T08:15:40.552711Z”
}, “papermill”: {
“duration”: 0.449592, “end_time”: “2021-08-17T08:15:40.553042”, “exception”: false, “start_time”: “2021-08-17T08:15:40.103450”, “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”: “717a176e”, “metadata”: {
- “papermill”: {
“duration”: 0.012306, “end_time”: “2021-08-17T08:15:40.577987”, “exception”: false, “start_time”: “2021-08-17T08:15:40.565681”, “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”: “750a83d6”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:15:40.636492Z”, “iopub.status.busy”: “2021-08-17T08:15:40.635347Z”, “iopub.status.idle”: “2021-08-17T08:15:40.814614Z”, “shell.execute_reply”: “2021-08-17T08:15:40.815695Z”
}, “papermill”: {
“duration”: 0.225328, “end_time”: “2021-08-17T08:15:40.816025”, “exception”: false, “start_time”: “2021-08-17T08:15:40.590697”, “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”: 7.415023, “end_time”: “2021-08-17T08:15:42.429753”, “environment_variables”: {}, “exception”: null, “input_path”: “Truncated_gaussian.ipynb”, “output_path”: “../docs/notebooks/Truncated_gaussian.ipynb”, “parameters”: {
“func_name”: “Truncated_gaussian”, “linear_range”: true, “wide_energy_range”: true, “x_scale”: “linear”, “y_scale”: “linear”
}, “start_time”: “2021-08-17T08:15:35.014730”, “version”: “2.3.3”
}
}, “nbformat”: 4, “nbformat_minor”: 5
}