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- “cells”: [
- {
“cell_type”: “markdown”, “id”: “c1b6ea98”, “metadata”: {
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}, “tags”: []
}, “source”: [
“# Cutoff powerlaw”
]
}, {
“cell_type”: “code”, “execution_count”: 1, “id”: “fd8493d9”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:13:14.083705Z”, “iopub.status.busy”: “2021-08-17T08:13:14.080444Z”, “iopub.status.idle”: “2021-08-17T08:13:17.096116Z”, “shell.execute_reply”: “2021-08-17T08:13:17.096989Z”
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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”: “2403455b”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:13:17.125924Z”, “iopub.status.busy”: “2021-08-17T08:13:17.124396Z”, “iopub.status.idle”: “2021-08-17T08:13:17.127233Z”, “shell.execute_reply”: “2021-08-17T08:13:17.128273Z”
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“duration”: 0.020845, “end_time”: “2021-08-17T08:13:17.128601”, “exception”: false, “start_time”: “2021-08-17T08:13:17.107756”, “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”: “2fb22217”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:13:17.155100Z”, “iopub.status.busy”: “2021-08-17T08:13:17.154089Z”, “iopub.status.idle”: “2021-08-17T08:13:17.157333Z”, “shell.execute_reply”: “2021-08-17T08:13:17.158182Z”
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“duration”: 0.020349, “end_time”: “2021-08-17T08:13:17.158507”, “exception”: false, “start_time”: “2021-08-17T08:13:17.138158”, “status”: “completed”
}, “tags”: [
“injected-parameters”
]
}, “outputs”: [], “source”: [
“# Parametersn”, “func_name = "Cutoff_powerlaw"n”, “wide_energy_range = Truen”, “x_scale = "log"n”, “y_scale = "log"n”, “linear_range = Falsen”
]
}, {
“cell_type”: “code”, “execution_count”: 4, “id”: “a1c03017”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:13:17.187584Z”, “iopub.status.busy”: “2021-08-17T08:13:17.186528Z”, “iopub.status.idle”: “2021-08-17T08:13:17.193513Z”, “shell.execute_reply”: “2021-08-17T08:13:17.194630Z”
}, “lines_to_next_cell”: 0, “nbsphinx”: “hidden”, “papermill”: {
“duration”: 0.026817, “end_time”: “2021-08-17T08:13:17.194997”, “exception”: false, “start_time”: “2021-08-17T08:13:17.168180”, “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”: “cafeca6c”, “metadata”: {
“lines_to_next_cell”: 0, “papermill”: {
“duration”: 0.00961, “end_time”: “2021-08-17T08:13:17.214218”, “exception”: false, “start_time”: “2021-08-17T08:13:17.204608”, “status”: “completed”
}, “tags”: []
}, “source”: [
“## Description”
]
}, {
“cell_type”: “code”, “execution_count”: 5, “id”: “cdda89d2”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:13:17.248081Z”, “iopub.status.busy”: “2021-08-17T08:13:17.246918Z”, “iopub.status.idle”: “2021-08-17T08:13:17.255647Z”, “shell.execute_reply”: “2021-08-17T08:13:17.256686Z”
}, “papermill”: {
“duration”: 0.032363, “end_time”: “2021-08-17T08:13:17.257004”, “exception”: false, “start_time”: “2021-08-17T08:13:17.224641”, “status”: “completed”
}, “tags”: []
}, “outputs”: [
- {
- “data”: {
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“<ul>n”, “n”, “<li>description: A power law multiplied by an exponential cutoff</li>n”, “n”, “<li>formula: $ K~\frac{x}{piv}^{index}~\exp{-x/xc} $</li>n”, “n”, “<li>parameters: n”, “<ul>n”, “n”, “<li>K: n”, “<ul>n”, “n”, “<li>value: 1.0</li>n”, “n”, “<li>desc: Normalization (differential flux at the pivot value)</li>n”, “n”, “<li>min_value: 1e-30</li>n”, “n”, “<li>max_value: 1000.0</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>piv: n”, “<ul>n”, “n”, “<li>value: 1.0</li>n”, “n”, “<li>desc: Pivot 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: False</li>n”, “n”, “</ul>n”, “n”, “</li>n”, “n”, “<li>index: n”, “<ul>n”, “n”, “<li>value: -2.0</li>n”, “n”, “<li>desc: Photon index</li>n”, “n”, “<li>min_value: -10.0</li>n”, “n”, “<li>max_value: 10.0</li>n”, “n”, “<li>unit: </li>n”, “n”, “<li>is_normalization: False</li>n”, “n”, “<li>delta: 0.2</li>n”, “n”, “<li>free: True</li>n”, “n”, “</ul>n”, “n”, “</li>n”, “n”, “<li>xc: n”, “<ul>n”, “n”, “<li>value: 10.0</li>n”, “n”, “<li>desc: Cutoff energy</li>n”, “n”, “<li>min_value: 1.0</li>n”, “n”, “<li>max_value: None</li>n”, “n”, “<li>unit: </li>n”, “n”, “<li>is_normalization: False</li>n”, “n”, “<li>delta: 1.0</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 power law multiplied by an exponential cutoffn”, ” * formula: $ K~\frac{x}{piv}^{index}~\exp{-x/xc} $n”, ” * parameters:n”, ” * K:n”, ” * value: 1.0n”, ” * desc: Normalization (differential flux at the pivot value)n”, ” * min_value: 1.0e-30n”, ” * max_value: 1000.0n”, ” * unit: ‘’n”, ” * is_normalization: truen”, ” * delta: 0.1n”, ” * free: truen”, ” * piv:n”, ” * value: 1.0n”, ” * desc: Pivot valuen”, ” * min_value: nulln”, ” * max_value: nulln”, ” * unit: ‘’n”, ” * is_normalization: falsen”, ” * delta: 0.1n”, ” * free: falsen”, ” * index:n”, ” * value: -2.0n”, ” * desc: Photon indexn”, ” * min_value: -10.0n”, ” * max_value: 10.0n”, ” * unit: ‘’n”, ” * is_normalization: falsen”, ” * delta: 0.2n”, ” * free: truen”, ” * xc:n”, ” * value: 10.0n”, ” * desc: Cutoff energyn”, ” * min_value: 1.0n”, ” * max_value: nulln”, ” * unit: ‘’n”, ” * is_normalization: falsen”, ” * delta: 1.0n”, ” * free: true”
]
}, “metadata”: {}, “output_type”: “display_data”
}
], “source”: [
“func.display()”
]
}, {
“cell_type”: “markdown”, “id”: “9740b338”, “metadata”: {
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}, “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”: “75ffd0c3”, “metadata”: {
- “execution”: {
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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”: “c063a73e”, “metadata”: {
“lines_to_next_cell”: 0, “papermill”: {
“duration”: 0.01182, “end_time”: “2021-08-17T08:13:18.571913”, “exception”: false, “start_time”: “2021-08-17T08:13:18.560093”, “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”: “8beb9f6c”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:13:18.617811Z”, “iopub.status.busy”: “2021-08-17T08:13:18.614544Z”, “iopub.status.idle”: “2021-08-17T08:13:19.256136Z”, “shell.execute_reply”: “2021-08-17T08:13:19.257035Z”
}, “papermill”: {
“duration”: 0.673087, “end_time”: “2021-08-17T08:13:19.257352”, “exception”: false, “start_time”: “2021-08-17T08:13:18.584265”, “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”: “011511fc”, “metadata”: {
- “papermill”: {
“duration”: 0.012615, “end_time”: “2021-08-17T08:13:19.282416”, “exception”: false, “start_time”: “2021-08-17T08:13:19.269801”, “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”: “1ba10e0c”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:13:19.373804Z”, “iopub.status.busy”: “2021-08-17T08:13:19.341861Z”, “iopub.status.idle”: “2021-08-17T08:13:19.963164Z”, “shell.execute_reply”: “2021-08-17T08:13:19.964067Z”
}, “papermill”: {
“duration”: 0.669182, “end_time”: “2021-08-17T08:13:19.964404”, “exception”: false, “start_time”: “2021-08-17T08:13:19.295222”, “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”: [
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