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}, “tags”: []
}, “source”: [
“# Powerlaw flux”
]
}, {
“cell_type”: “code”, “execution_count”: 1, “id”: “731cdc17”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:12:57.204797Z”, “iopub.status.busy”: “2021-08-17T08:12:57.203706Z”, “iopub.status.idle”: “2021-08-17T08:13:00.058916Z”, “shell.execute_reply”: “2021-08-17T08:13:00.059913Z”
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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”: “76c02298”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:13:00.085892Z”, “iopub.status.busy”: “2021-08-17T08:13:00.084831Z”, “iopub.status.idle”: “2021-08-17T08:13:00.092496Z”, “shell.execute_reply”: “2021-08-17T08:13:00.093651Z”
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“duration”: 0.024436, “end_time”: “2021-08-17T08:13:00.094333”, “exception”: false, “start_time”: “2021-08-17T08:13:00.069897”, “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”: “ee71e0ad”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:13:00.120437Z”, “iopub.status.busy”: “2021-08-17T08:13:00.119053Z”, “iopub.status.idle”: “2021-08-17T08:13:00.122036Z”, “shell.execute_reply”: “2021-08-17T08:13:00.123035Z”
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“duration”: 0.019225, “end_time”: “2021-08-17T08:13:00.123515”, “exception”: false, “start_time”: “2021-08-17T08:13:00.104290”, “status”: “completed”
}, “tags”: [
“injected-parameters”
]
}, “outputs”: [], “source”: [
“# Parametersn”, “func_name = "Powerlaw_flux"n”, “wide_energy_range = Truen”, “x_scale = "log"n”, “y_scale = "log"n”, “linear_range = Falsen”
]
}, {
“cell_type”: “code”, “execution_count”: 4, “id”: “9e9f3b37”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:13:00.153503Z”, “iopub.status.busy”: “2021-08-17T08:13:00.152452Z”, “iopub.status.idle”: “2021-08-17T08:13:00.159432Z”, “shell.execute_reply”: “2021-08-17T08:13:00.160431Z”
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“duration”: 0.026425, “end_time”: “2021-08-17T08:13:00.160779”, “exception”: false, “start_time”: “2021-08-17T08:13:00.134354”, “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”: “ea9369f7”, “metadata”: {
“lines_to_next_cell”: 0, “papermill”: {
“duration”: 0.010058, “end_time”: “2021-08-17T08:13:00.181319”, “exception”: false, “start_time”: “2021-08-17T08:13:00.171261”, “status”: “completed”
}, “tags”: []
}, “source”: [
“## Description”
]
}, {
“cell_type”: “code”, “execution_count”: 5, “id”: “5eb9229b”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:13:00.214799Z”, “iopub.status.busy”: “2021-08-17T08:13:00.213534Z”, “iopub.status.idle”: “2021-08-17T08:13:00.224410Z”, “shell.execute_reply”: “2021-08-17T08:13:00.225203Z”
}, “papermill”: {
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}, “tags”: []
}, “outputs”: [
- {
- “data”: {
- “text/html”: [
“<ul>n”, “n”, “<li>description: A simple power-law with the photon flux in a band used as normalization. This will reduce the correlation between the index and the normalization.</li>n”, “n”, “<li>formula: $ \frac{F(\gamma+1)} {b^{\gamma+1} - a^{\gamma+1}} (x)^{\gamma}$</li>n”, “n”, “<li>parameters: n”, “<ul>n”, “n”, “<li>F: n”, “<ul>n”, “n”, “<li>value: 1.0</li>n”, “n”, “<li>desc: Integral between a and b</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>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>a: n”, “<ul>n”, “n”, “<li>value: 1.0</li>n”, “n”, “<li>desc: lower bound for the band in which computing the integral F</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>b: n”, “<ul>n”, “n”, “<li>value: 100.0</li>n”, “n”, “<li>desc: upper bound for the band in which computing the integral F</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: 10.0</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 simple power-law with the photon flux in a band used as normalization.n”, ” * This will reduce the correlation between the index and the normalization.n”, ” * formula: $ \frac{F(\gamma+1)} {b^{\gamma+1} - a^{\gamma+1}} (x)^{\gamma}$n”, ” * parameters:n”, ” * F:n”, ” * value: 1.0n”, ” * desc: Integral between a and bn”, ” * min_value: 1.0e-30n”, ” * max_value: 1000.0n”, ” * unit: ‘’n”, ” * is_normalization: truen”, ” * delta: 0.1n”, ” * free: truen”, ” * 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”, ” * a:n”, ” * value: 1.0n”, ” * desc: lower bound for the band in which computing the integral Fn”, ” * min_value: nulln”, ” * max_value: nulln”, ” * unit: ‘’n”, ” * is_normalization: falsen”, ” * delta: 0.1n”, ” * free: falsen”, ” * b:n”, ” * value: 100.0n”, ” * desc: upper bound for the band in which computing the integral Fn”, ” * min_value: nulln”, ” * max_value: nulln”, ” * unit: ‘’n”, ” * is_normalization: falsen”, ” * delta: 10.0n”, ” * free: false”
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“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”: “a65209f3”, “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”: “2d3889fe”, “metadata”: {
“lines_to_next_cell”: 0, “papermill”: {
“duration”: 0.011782, “end_time”: “2021-08-17T08:13:01.625905”, “exception”: false, “start_time”: “2021-08-17T08:13:01.614123”, “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”: “a2eb9238”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:13:01.710208Z”, “iopub.status.busy”: “2021-08-17T08:13:01.667972Z”, “iopub.status.idle”: “2021-08-17T08:13:02.436465Z”, “shell.execute_reply”: “2021-08-17T08:13:02.437221Z”
}, “papermill”: {
“duration”: 0.800092, “end_time”: “2021-08-17T08:13:02.437546”, “exception”: false, “start_time”: “2021-08-17T08:13:01.637454”, “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”: “00383576”, “metadata”: {
- “papermill”: {
“duration”: 0.012959, “end_time”: “2021-08-17T08:13:02.462956”, “exception”: false, “start_time”: “2021-08-17T08:13:02.449997”, “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”: “790fe2ff”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:13:02.585307Z”, “iopub.status.busy”: “2021-08-17T08:13:02.579830Z”, “iopub.status.idle”: “2021-08-17T08:13:03.021705Z”, “shell.execute_reply”: “2021-08-17T08:13:03.022525Z”
}, “papermill”: {
“duration”: 0.546819, “end_time”: “2021-08-17T08:13:03.022845”, “exception”: false, “start_time”: “2021-08-17T08:13:02.476026”, “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”: {
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