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“cell_type”: “markdown”, “id”: “75e059f0”, “metadata”: {
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}, “tags”: []
}, “source”: [
“# Broken powerlaw”
]
}, {
“cell_type”: “code”, “execution_count”: 1, “id”: “abb92bb3”, “metadata”: {
- “execution”: {
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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”: “a4afe936”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:14:00.631565Z”, “iopub.status.busy”: “2021-08-17T08:14:00.630411Z”, “iopub.status.idle”: “2021-08-17T08:14:00.633432Z”, “shell.execute_reply”: “2021-08-17T08:14:00.634913Z”
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“duration”: 0.023972, “end_time”: “2021-08-17T08:14:00.635341”, “exception”: false, “start_time”: “2021-08-17T08:14:00.611369”, “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”: “95e56e11”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:14:00.663263Z”, “iopub.status.busy”: “2021-08-17T08:14:00.662120Z”, “iopub.status.idle”: “2021-08-17T08:14:00.665384Z”, “shell.execute_reply”: “2021-08-17T08:14:00.666052Z”
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“duration”: 0.021541, “end_time”: “2021-08-17T08:14:00.666451”, “exception”: false, “start_time”: “2021-08-17T08:14:00.644910”, “status”: “completed”
}, “tags”: [
“injected-parameters”
]
}, “outputs”: [], “source”: [
“# Parametersn”, “func_name = "Broken_powerlaw"n”, “wide_energy_range = Truen”, “x_scale = "log"n”, “y_scale = "log"n”, “linear_range = Falsen”
]
}, {
“cell_type”: “code”, “execution_count”: 4, “id”: “b353165b”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:14:00.703459Z”, “iopub.status.busy”: “2021-08-17T08:14:00.698681Z”, “iopub.status.idle”: “2021-08-17T08:14:00.705155Z”, “shell.execute_reply”: “2021-08-17T08:14:00.704401Z”
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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”: “28f4e004”, “metadata”: {
“lines_to_next_cell”: 0, “papermill”: {
“duration”: 0.010015, “end_time”: “2021-08-17T08:14:00.725906”, “exception”: false, “start_time”: “2021-08-17T08:14:00.715891”, “status”: “completed”
}, “tags”: []
}, “source”: [
“## Description”
]
}, {
“cell_type”: “code”, “execution_count”: 5, “id”: “c0250016”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:14:00.761447Z”, “iopub.status.busy”: “2021-08-17T08:14:00.760184Z”, “iopub.status.idle”: “2021-08-17T08:14:00.767527Z”, “shell.execute_reply”: “2021-08-17T08:14:00.768439Z”
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}, “outputs”: [
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- “data”: {
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“<ul>n”, “n”, “<li>description: A broken power law function</li>n”, “n”, “<li>formula: $ f(x)= K~\begin{cases}\left( \frac{x}{x_{b}} \right)^{\alpha} & x < x_{b} \\ \left( \frac{x}{x_{b}} \right)^{\beta} & x \ge x_{b} \end{cases} $</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 x_b)</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: True</li>n”, “n”, “<li>delta: 0.1</li>n”, “n”, “<li>free: True</li>n”, “n”, “</ul>n”, “n”, “</li>n”, “n”, “<li>xb: n”, “<ul>n”, “n”, “<li>value: 10.0</li>n”, “n”, “<li>desc: Break point</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”, “<li>alpha: n”, “<ul>n”, “n”, “<li>value: -1.5</li>n”, “n”, “<li>desc: Index before the break xb</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: 0.15000000000000002</li>n”, “n”, “<li>free: True</li>n”, “n”, “</ul>n”, “n”, “</li>n”, “n”, “<li>beta: n”, “<ul>n”, “n”, “<li>value: -2.5</li>n”, “n”, “<li>desc: Index after the break xb</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: 0.25</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 energy</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”, “</ul>n”, “n”, “</li>n”, “n”, “</ul>n”
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” * description: A broken power law functionn”, ” * formula: $ f(x)= K~\begin{cases}\left( \frac{x}{x_{b}} \right)^{\alpha} & x < x_{b}n”, ” * \\ \left( \frac{x}{x_{b}} \right)^{\beta} & x \ge x_{b} \end{cases} $n”, ” * parameters:n”, ” * K:n”, ” * value: 1.0n”, ” * desc: Normalization (differential flux at x_b)n”, ” * min_value: nulln”, ” * max_value: nulln”, ” * unit: ‘’n”, ” * is_normalization: truen”, ” * delta: 0.1n”, ” * free: truen”, ” * xb:n”, ” * value: 10.0n”, ” * desc: Break pointn”, ” * min_value: 1.0n”, ” * max_value: nulln”, ” * unit: ‘’n”, ” * is_normalization: falsen”, ” * delta: 1.0n”, ” * free: truen”, ” * alpha:n”, ” * value: -1.5n”, ” * desc: Index before the break xbn”, ” * min_value: -10.0n”, ” * max_value: 10.0n”, ” * unit: ‘’n”, ” * is_normalization: falsen”, ” * delta: 0.15000000000000002n”, ” * free: truen”, ” * beta:n”, ” * value: -2.5n”, ” * desc: Index after the break xbn”, ” * min_value: -10.0n”, ” * max_value: 10.0n”, ” * unit: ‘’n”, ” * is_normalization: falsen”, ” * delta: 0.25n”, ” * free: truen”, ” * piv:n”, ” * value: 1.0n”, ” * desc: Pivot energyn”, ” * min_value: nulln”, ” * max_value: nulln”, ” * unit: ‘’n”, ” * is_normalization: falsen”, ” * delta: 0.1n”, ” * free: false”
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}, “metadata”: {}, “output_type”: “display_data”
}
], “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”: “bc2a7bf0”, “metadata”: {
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“nbsphinx-thumbnail”
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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”: “c3499d79”, “metadata”: {
“lines_to_next_cell”: 0, “papermill”: {
“duration”: 0.012561, “end_time”: “2021-08-17T08:14:02.480587”, “exception”: false, “start_time”: “2021-08-17T08:14:02.468026”, “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”: “2434f978”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:14:02.568913Z”, “iopub.status.busy”: “2021-08-17T08:14:02.534734Z”, “iopub.status.idle”: “2021-08-17T08:14:03.351879Z”, “shell.execute_reply”: “2021-08-17T08:14:03.353200Z”
}, “papermill”: {
“duration”: 0.86121, “end_time”: “2021-08-17T08:14:03.353626”, “exception”: false, “start_time”: “2021-08-17T08:14:02.492416”, “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”: “1d7ea06e”, “metadata”: {
- “papermill”: {
“duration”: 0.013187, “end_time”: “2021-08-17T08:14:03.381221”, “exception”: false, “start_time”: “2021-08-17T08:14:03.368034”, “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”: “d658d993”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:14:03.436530Z”, “iopub.status.busy”: “2021-08-17T08:14:03.435455Z”, “iopub.status.idle”: “2021-08-17T08:14:04.099105Z”, “shell.execute_reply”: “2021-08-17T08:14:04.100732Z”
}, “papermill”: {
“duration”: 0.707058, “end_time”: “2021-08-17T08:14:04.101385”, “exception”: false, “start_time”: “2021-08-17T08:14:03.394327”, “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**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”: 9.530478, “end_time”: “2021-08-17T08:14:05.866921”, “environment_variables”: {}, “exception”: null, “input_path”: “Broken_powerlaw.ipynb”, “output_path”: “../docs/notebooks/Broken_powerlaw.ipynb”, “parameters”: {
“func_name”: “Broken_powerlaw”, “linear_range”: false, “wide_energy_range”: true, “x_scale”: “log”, “y_scale”: “log”
}, “start_time”: “2021-08-17T08:13:56.336443”, “version”: “2.3.3”
}
}, “nbformat”: 4, “nbformat_minor”: 5
}