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}, “tags”: []
}, “source”: [
“# Cutoff powerlaw Ep”
]
}, {
“cell_type”: “code”, “execution_count”: 1, “id”: “5ae91f90”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:13:22.330035Z”, “iopub.status.busy”: “2021-08-17T08:13:22.328721Z”, “iopub.status.idle”: “2021-08-17T08:13:25.302187Z”, “shell.execute_reply”: “2021-08-17T08:13:25.302977Z”
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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”: “f1bb39d0”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:13:25.331796Z”, “iopub.status.busy”: “2021-08-17T08:13:25.330458Z”, “iopub.status.idle”: “2021-08-17T08:13:25.337397Z”, “shell.execute_reply”: “2021-08-17T08:13:25.338633Z”
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“duration”: 0.02387, “end_time”: “2021-08-17T08:13:25.339039”, “exception”: false, “start_time”: “2021-08-17T08:13:25.315169”, “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”: “c4a65b09”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:13:25.366973Z”, “iopub.status.busy”: “2021-08-17T08:13:25.364702Z”, “iopub.status.idle”: “2021-08-17T08:13:25.373421Z”, “shell.execute_reply”: “2021-08-17T08:13:25.374414Z”
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“duration”: 0.025946, “end_time”: “2021-08-17T08:13:25.374787”, “exception”: false, “start_time”: “2021-08-17T08:13:25.348841”, “status”: “completed”
}, “tags”: [
“injected-parameters”
]
}, “outputs”: [], “source”: [
“# Parametersn”, “func_name = "Cutoff_powerlaw_Ep"n”, “wide_energy_range = Truen”, “x_scale = "log"n”, “y_scale = "log"n”, “linear_range = Falsen”
]
}, {
“cell_type”: “code”, “execution_count”: 4, “id”: “a59a3b4e”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:13:25.407089Z”, “iopub.status.busy”: “2021-08-17T08:13:25.404772Z”, “iopub.status.idle”: “2021-08-17T08:13:25.410712Z”, “shell.execute_reply”: “2021-08-17T08:13:25.411418Z”
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“duration”: 0.027018, “end_time”: “2021-08-17T08:13:25.411789”, “exception”: false, “start_time”: “2021-08-17T08:13:25.384771”, “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”: “b8d90e84”, “metadata”: {
“lines_to_next_cell”: 0, “papermill”: {
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}, “tags”: []
}, “source”: [
“## Description”
]
}, {
“cell_type”: “code”, “execution_count”: 5, “id”: “75366e2f”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:13:25.468876Z”, “iopub.status.busy”: “2021-08-17T08:13:25.466846Z”, “iopub.status.idle”: “2021-08-17T08:13:25.476923Z”, “shell.execute_reply”: “2021-08-17T08:13:25.477798Z”
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}, “tags”: []
}, “outputs”: [
- {
- “data”: {
- “text/html”: [
“<ul>n”, “n”, “<li>description: A power law multiplied by an exponential cutoff parametrized with Ep</li>n”, “n”, “<li>formula: $ K~\frac{x}{piv}^{index}~\exp{-x(2+index)/xp} $</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>xp: n”, “<ul>n”, “n”, “<li>value: 499.99999999999994</li>n”, “n”, “<li>desc: peak in the x * x * N (nuFnu if x is a energy)</li>n”, “n”, “<li>min_value: 10.0</li>n”, “n”, “<li>max_value: 10000.0</li>n”, “n”, “<li>unit: </li>n”, “n”, “<li>is_normalization: False</li>n”, “n”, “<li>delta: 50.0</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 cutoff parametrized with Epn”, ” * formula: $ K~\frac{x}{piv}^{index}~\exp{-x(2+index)/xp} $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”, ” * xp:n”, ” * value: 499.99999999999994n”, ” * desc: peak in the x * x * N (nuFnu if x is a energy)n”, ” * min_value: 10.0n”, ” * max_value: 10000.0n”, ” * unit: ‘’n”, ” * is_normalization: falsen”, ” * delta: 50.0n”, ” * free: true”
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], “source”: [
“func.display()”
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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”: “6aabded4”, “metadata”: {
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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”: “9042c150”, “metadata”: {
“lines_to_next_cell”: 0, “papermill”: {
“duration”: 0.01227, “end_time”: “2021-08-17T08:13:26.850014”, “exception”: false, “start_time”: “2021-08-17T08:13:26.837744”, “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”: “ca0d47ad”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:13:26.963200Z”, “iopub.status.busy”: “2021-08-17T08:13:26.941442Z”, “iopub.status.idle”: “2021-08-17T08:13:27.650671Z”, “shell.execute_reply”: “2021-08-17T08:13:27.651817Z”
}, “papermill”: {
“duration”: 0.788794, “end_time”: “2021-08-17T08:13:27.652260”, “exception”: false, “start_time”: “2021-08-17T08:13:26.863466”, “status”: “completed”
}, “tags”: []
}, “outputs”: [
- {
- “data”: {
“image/png”: 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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”, “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”: “6091280f”, “metadata”: {
- “papermill”: {
“duration”: 0.012365, “end_time”: “2021-08-17T08:13:27.678955”, “exception”: false, “start_time”: “2021-08-17T08:13:27.666590”, “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”: “4b17e131”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:13:27.739985Z”, “iopub.status.busy”: “2021-08-17T08:13:27.716835Z”, “iopub.status.idle”: “2021-08-17T08:13:28.211393Z”, “shell.execute_reply”: “2021-08-17T08:13:28.210594Z”
}, “papermill”: {
“duration”: 0.520208, “end_time”: “2021-08-17T08:13:28.211792”, “exception”: false, “start_time”: “2021-08-17T08:13:27.691584”, “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
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