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- “cells”: [
- {
“cell_type”: “markdown”, “id”: “3e624a51”, “metadata”: {
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“duration”: 0.014379, “end_time”: “2021-08-17T08:11:19.115322”, “exception”: false, “start_time”: “2021-08-17T08:11:19.100943”, “status”: “completed”
}, “tags”: []
}, “source”: [
“# Exponential cutoff”
]
}, {
“cell_type”: “code”, “execution_count”: 1, “id”: “b8648420”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:11:19.260752Z”, “iopub.status.busy”: “2021-08-17T08:11:19.259722Z”, “iopub.status.idle”: “2021-08-17T08:11:24.277365Z”, “shell.execute_reply”: “2021-08-17T08:11:24.278048Z”
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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”: “8c3cc18b”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:11:24.308645Z”, “iopub.status.busy”: “2021-08-17T08:11:24.307669Z”, “iopub.status.idle”: “2021-08-17T08:11:24.314807Z”, “shell.execute_reply”: “2021-08-17T08:11:24.315428Z”
}, “nbsphinx”: “hidden”, “papermill”: {
“duration”: 0.026996, “end_time”: “2021-08-17T08:11:24.315747”, “exception”: false, “start_time”: “2021-08-17T08:11:24.288751”, “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”: “f771e8b2”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:11:24.340148Z”, “iopub.status.busy”: “2021-08-17T08:11:24.339167Z”, “iopub.status.idle”: “2021-08-17T08:11:24.347839Z”, “shell.execute_reply”: “2021-08-17T08:11:24.348461Z”
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“duration”: 0.024063, “end_time”: “2021-08-17T08:11:24.348769”, “exception”: false, “start_time”: “2021-08-17T08:11:24.324706”, “status”: “completed”
}, “tags”: [
“injected-parameters”
]
}, “outputs”: [], “source”: [
“# Parametersn”, “func_name = "Exponential_cutoff"n”, “wide_energy_range = Truen”, “x_scale = "log"n”, “y_scale = "log"n”, “linear_range = Falsen”
]
}, {
“cell_type”: “code”, “execution_count”: 4, “id”: “218d41fd”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:11:24.376698Z”, “iopub.status.busy”: “2021-08-17T08:11:24.375644Z”, “iopub.status.idle”: “2021-08-17T08:11:24.380643Z”, “shell.execute_reply”: “2021-08-17T08:11:24.381486Z”
}, “lines_to_next_cell”: 0, “nbsphinx”: “hidden”, “papermill”: {
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}, “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”: “75e198f9”, “metadata”: {
“lines_to_next_cell”: 0, “papermill”: {
“duration”: 0.011284, “end_time”: “2021-08-17T08:11:24.402958”, “exception”: false, “start_time”: “2021-08-17T08:11:24.391674”, “status”: “completed”
}, “tags”: []
}, “source”: [
“## Description”
]
}, {
“cell_type”: “code”, “execution_count”: 5, “id”: “a9dc6eab”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:11:24.435009Z”, “iopub.status.busy”: “2021-08-17T08:11:24.433216Z”, “iopub.status.idle”: “2021-08-17T08:11:24.446577Z”, “shell.execute_reply”: “2021-08-17T08:11:24.447423Z”
}, “papermill”: {
“duration”: 0.033867, “end_time”: “2021-08-17T08:11:24.447787”, “exception”: false, “start_time”: “2021-08-17T08:11:24.413920”, “status”: “completed”
}, “tags”: []
}, “outputs”: [
- {
- “data”: {
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“func.display()”
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}, {
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“duration”: 0.011259, “end_time”: “2021-08-17T08:11:24.469719”, “exception”: false, “start_time”: “2021-08-17T08:11:24.458460”, “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”
]
}, {
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“nbsphinx-thumbnail”
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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”: “89476f81”, “metadata”: {
“lines_to_next_cell”: 0, “papermill”: {
“duration”: 0.011568, “end_time”: “2021-08-17T08:11:25.381295”, “exception”: false, “start_time”: “2021-08-17T08:11:25.369727”, “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”: “035c2bd6”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:11:25.665774Z”, “iopub.status.busy”: “2021-08-17T08:11:25.469385Z”, “iopub.status.idle”: “2021-08-17T08:11:26.113063Z”, “shell.execute_reply”: “2021-08-17T08:11:26.114066Z”
}, “papermill”: {
“duration”: 0.721608, “end_time”: “2021-08-17T08:11:26.114387”, “exception”: false, “start_time”: “2021-08-17T08:11:25.392779”, “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”: “5fc62a73”, “metadata”: {
- “papermill”: {
“duration”: 0.012823, “end_time”: “2021-08-17T08:11:26.140238”, “exception”: false, “start_time”: “2021-08-17T08:11:26.127415”, “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”: “4fc566d2”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:11:26.228771Z”, “iopub.status.busy”: “2021-08-17T08:11:26.175561Z”, “iopub.status.idle”: “2021-08-17T08:11:26.797009Z”, “shell.execute_reply”: “2021-08-17T08:11:26.797918Z”
}, “papermill”: {
“duration”: 0.645159, “end_time”: “2021-08-17T08:11:26.798242”, “exception”: false, “start_time”: “2021-08-17T08:11:26.153083”, “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”: 9.38601, “end_time”: “2021-08-17T08:11:27.383577”, “environment_variables”: {}, “exception”: null, “input_path”: “Exponential_cutoff.ipynb”, “output_path”: “../docs/notebooks/Exponential_cutoff.ipynb”, “parameters”: {
“func_name”: “Exponential_cutoff”, “linear_range”: false, “wide_energy_range”: true, “x_scale”: “log”, “y_scale”: “log”
}, “start_time”: “2021-08-17T08:11:17.997567”, “version”: “2.3.3”
}
}, “nbformat”: 4, “nbformat_minor”: 5
}