{
“cells”: [
{

“cell_type”: “markdown”, “id”: “4936e430”, “metadata”: {

“papermill”: {

“duration”: 0.013657, “end_time”: “2021-08-17T08:12:18.129394”, “exception”: false, “start_time”: “2021-08-17T08:12:18.115737”, “status”: “completed”

}, “tags”: []

}, “source”: [

“# Constant”

]

}, {

“cell_type”: “code”, “execution_count”: 1, “id”: “f2320f66”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2021-08-17T08:12:18.161805Z”, “iopub.status.busy”: “2021-08-17T08:12:18.160677Z”, “iopub.status.idle”: “2021-08-17T08:12:21.523231Z”, “shell.execute_reply”: “2021-08-17T08:12:21.524233Z”

}, “nbsphinx”: “hidden”, “papermill”: {

“duration”: 3.385387, “end_time”: “2021-08-17T08:12:21.524986”, “exception”: false, “start_time”: “2021-08-17T08:12:18.139599”, “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”: “918cfa3e”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2021-08-17T08:12:21.551993Z”, “iopub.status.busy”: “2021-08-17T08:12:21.550996Z”, “iopub.status.idle”: “2021-08-17T08:12:21.558471Z”, “shell.execute_reply”: “2021-08-17T08:12:21.559770Z”

}, “nbsphinx”: “hidden”, “papermill”: {

“duration”: 0.024863, “end_time”: “2021-08-17T08:12:21.560167”, “exception”: false, “start_time”: “2021-08-17T08:12:21.535304”, “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”: “0453c10c”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2021-08-17T08:12:21.588307Z”, “iopub.status.busy”: “2021-08-17T08:12:21.586805Z”, “iopub.status.idle”: “2021-08-17T08:12:21.594326Z”, “shell.execute_reply”: “2021-08-17T08:12:21.595536Z”

}, “papermill”: {

“duration”: 0.025877, “end_time”: “2021-08-17T08:12:21.595862”, “exception”: false, “start_time”: “2021-08-17T08:12:21.569985”, “status”: “completed”

}, “tags”: [

“injected-parameters”

]

}, “outputs”: [], “source”: [

“# Parametersn”, “func_name = "Constant"n”, “wide_energy_range = Truen”, “x_scale = "linear"n”, “y_scale = "linear"n”, “linear_range = Truen”

]

}, {

“cell_type”: “code”, “execution_count”: 4, “id”: “8aaa2758”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2021-08-17T08:12:21.624742Z”, “iopub.status.busy”: “2021-08-17T08:12:21.623579Z”, “iopub.status.idle”: “2021-08-17T08:12:21.635031Z”, “shell.execute_reply”: “2021-08-17T08:12:21.635867Z”

}, “lines_to_next_cell”: 0, “nbsphinx”: “hidden”, “papermill”: {

“duration”: 0.030199, “end_time”: “2021-08-17T08:12:21.636194”, “exception”: false, “start_time”: “2021-08-17T08:12:21.605995”, “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”: “99ea8a6b”, “metadata”: {

“lines_to_next_cell”: 0, “papermill”: {

“duration”: 0.013527, “end_time”: “2021-08-17T08:12:21.659773”, “exception”: false, “start_time”: “2021-08-17T08:12:21.646246”, “status”: “completed”

}, “tags”: []

}, “source”: [

“## Description”

]

}, {

“cell_type”: “code”, “execution_count”: 5, “id”: “ec809d5f”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2021-08-17T08:12:21.690576Z”, “iopub.status.busy”: “2021-08-17T08:12:21.685176Z”, “iopub.status.idle”: “2021-08-17T08:12:21.700036Z”, “shell.execute_reply”: “2021-08-17T08:12:21.700959Z”

}, “papermill”: {

“duration”: 0.031644, “end_time”: “2021-08-17T08:12:21.701349”, “exception”: false, “start_time”: “2021-08-17T08:12:21.669705”, “status”: “completed”

}, “tags”: []

}, “outputs”: [

{
“data”: {
“text/html”: [

“<ul>n”, “n”, “<li>description: Return k</li>n”, “n”, “<li>formula: $ k $</li>n”, “n”, “<li>parameters: n”, “<ul>n”, “n”, “<li>k: n”, “<ul>n”, “n”, “<li>value: 0.0</li>n”, “n”, “<li>desc: Constant 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”, “</ul>n”, “n”, “</li>n”, “n”, “</ul>n”

], “text/plain”: [

” * description: Return kn”, ” * formula: $ k $n”, ” * parameters:n”, ” * k:n”, ” * value: 0.0n”, ” * desc: Constant valuen”, ” * 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”: “ac1d70b5”, “metadata”: {

“papermill”: {

“duration”: 0.010958, “end_time”: “2021-08-17T08:12:21.723283”, “exception”: false, “start_time”: “2021-08-17T08:12:21.712325”, “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”: “c6b4680f”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2021-08-17T08:12:21.808363Z”, “iopub.status.busy”: “2021-08-17T08:12:21.766926Z”, “iopub.status.idle”: “2021-08-17T08:12:21.944523Z”, “shell.execute_reply”: “2021-08-17T08:12:21.943752Z”

}, “papermill”: {

“duration”: 0.20984, “end_time”: “2021-08-17T08:12:21.944837”, “exception”: false, “start_time”: “2021-08-17T08:12:21.734997”, “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”: “071400d5”, “metadata”: {

“lines_to_next_cell”: 0, “papermill”: {

“duration”: 0.011891, “end_time”: “2021-08-17T08:12:21.968104”, “exception”: false, “start_time”: “2021-08-17T08:12:21.956213”, “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”: “e0663826”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2021-08-17T08:12:22.052125Z”, “iopub.status.busy”: “2021-08-17T08:12:22.012104Z”, “iopub.status.idle”: “2021-08-17T08:12:22.420055Z”, “shell.execute_reply”: “2021-08-17T08:12:22.421157Z”

}, “papermill”: {

“duration”: 0.440605, “end_time”: “2021-08-17T08:12:22.421473”, “exception”: false, “start_time”: “2021-08-17T08:12:21.980868”, “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”: “7d4e90fd”, “metadata”: {

“papermill”: {

“duration”: 0.011993, “end_time”: “2021-08-17T08:12:22.445809”, “exception”: false, “start_time”: “2021-08-17T08:12:22.433816”, “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”: “0351058c”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2021-08-17T08:12:22.508733Z”, “iopub.status.busy”: “2021-08-17T08:12:22.507530Z”, “iopub.status.idle”: “2021-08-17T08:12:22.661783Z”, “shell.execute_reply”: “2021-08-17T08:12:22.662636Z”

}, “papermill”: {

“duration”: 0.204816, “end_time”: “2021-08-17T08:12:22.663004”, “exception”: false, “start_time”: “2021-08-17T08:12:22.458188”, “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”: 6.234518, “end_time”: “2021-08-17T08:12:23.304613”, “environment_variables”: {}, “exception”: null, “input_path”: “Constant.ipynb”, “output_path”: “../docs/notebooks/Constant.ipynb”, “parameters”: {

“func_name”: “Constant”, “linear_range”: true, “wide_energy_range”: true, “x_scale”: “linear”, “y_scale”: “linear”

}, “start_time”: “2021-08-17T08:12:17.070095”, “version”: “2.3.3”

}

}, “nbformat”: 4, “nbformat_minor”: 5

}