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}, “tags”: []
}, “source”: [
“# Log normal”
]
}, {
“cell_type”: “code”, “execution_count”: 1, “id”: “cdc4ee8f”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:15:55.731207Z”, “iopub.status.busy”: “2021-08-17T08:15:55.729976Z”, “iopub.status.idle”: “2021-08-17T08:15:58.724480Z”, “shell.execute_reply”: “2021-08-17T08:15:58.725373Z”
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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”: “5d6b7886”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:15:58.753278Z”, “iopub.status.busy”: “2021-08-17T08:15:58.751969Z”, “iopub.status.idle”: “2021-08-17T08:15:58.754793Z”, “shell.execute_reply”: “2021-08-17T08:15:58.755572Z”
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}, “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”: “bbee3448”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:15:58.784067Z”, “iopub.status.busy”: “2021-08-17T08:15:58.781506Z”, “iopub.status.idle”: “2021-08-17T08:15:58.785407Z”, “shell.execute_reply”: “2021-08-17T08:15:58.786066Z”
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“duration”: 0.021187, “end_time”: “2021-08-17T08:15:58.786423”, “exception”: false, “start_time”: “2021-08-17T08:15:58.765236”, “status”: “completed”
}, “tags”: [
“injected-parameters”
]
}, “outputs”: [], “source”: [
“# Parametersn”, “func_name = "Log_normal"n”, “wide_energy_range = Truen”, “x_scale = "linear"n”, “y_scale = "linear"n”, “linear_range = Truen”
]
}, {
“cell_type”: “code”, “execution_count”: 4, “id”: “e094c874”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:15:58.815312Z”, “iopub.status.busy”: “2021-08-17T08:15:58.814105Z”, “iopub.status.idle”: “2021-08-17T08:15:58.824164Z”, “shell.execute_reply”: “2021-08-17T08:15:58.825239Z”
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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”: “9563be71”, “metadata”: {
“lines_to_next_cell”: 0, “papermill”: {
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}, “tags”: []
}, “source”: [
“## Description”
]
}, {
“cell_type”: “code”, “execution_count”: 5, “id”: “f9d5660a”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:15:58.882005Z”, “iopub.status.busy”: “2021-08-17T08:15:58.880907Z”, “iopub.status.idle”: “2021-08-17T08:15:58.895526Z”, “shell.execute_reply”: “2021-08-17T08:15:58.896177Z”
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}, “outputs”: [
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- “data”: {
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“<ul>n”, “n”, “<li>description: A log normal function</li>n”, “n”, “<li>formula: $ K \frac{1}{ x \sigma \sqrt{2 \pi}}\exp{\frac{(\log x/piv - \mu/piv)^2}{2~(\sigma)^2}} $</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 0and +inf. Fix this to 1 to obtain a log Normal distribution</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: True</li>n”, “n”, “</ul>n”, “n”, “</li>n”, “n”, “<li>mu: n”, “<ul>n”, “n”, “<li>value: 0.0</li>n”, “n”, “<li>desc: Central 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: True</li>n”, “n”, “</ul>n”, “n”, “</li>n”, “n”, “<li>sigma: n”, “<ul>n”, “n”, “<li>value: 1.0</li>n”, “n”, “<li>desc: standard deviation</li>n”, “n”, “<li>min_value: 1e-12</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: 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. Leave this to 1 for a proper log normal distribution</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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}, “metadata”: {}, “output_type”: “display_data”
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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”
]
}, {
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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”: “a5c2c60e”, “metadata”: {
“lines_to_next_cell”: 0, “papermill”: {
“duration”: 0.011644, “end_time”: “2021-08-17T08:15:59.167021”, “exception”: false, “start_time”: “2021-08-17T08:15:59.155377”, “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”: “a9f3bdc7”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:15:59.250086Z”, “iopub.status.busy”: “2021-08-17T08:15:59.208315Z”, “iopub.status.idle”: “2021-08-17T08:15:59.633040Z”, “shell.execute_reply”: “2021-08-17T08:15:59.634042Z”
}, “papermill”: {
“duration”: 0.455587, “end_time”: “2021-08-17T08:15:59.634385”, “exception”: false, “start_time”: “2021-08-17T08:15:59.178798”, “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”: “a7f00a7b”, “metadata”: {
- “papermill”: {
“duration”: 0.012688, “end_time”: “2021-08-17T08:15:59.659635”, “exception”: false, “start_time”: “2021-08-17T08:15:59.646947”, “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”: “ca6ca3a9”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:15:59.728734Z”, “iopub.status.busy”: “2021-08-17T08:15:59.727563Z”, “iopub.status.idle”: “2021-08-17T08:15:59.891660Z”, “shell.execute_reply”: “2021-08-17T08:15:59.892493Z”
}, “papermill”: {
“duration”: 0.220254, “end_time”: “2021-08-17T08:15:59.892823”, “exception”: false, “start_time”: “2021-08-17T08:15:59.672569”, “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”: 6.393363, “end_time”: “2021-08-17T08:16:01.052840”, “environment_variables”: {}, “exception”: null, “input_path”: “Log_normal.ipynb”, “output_path”: “../docs/notebooks/Log_normal.ipynb”, “parameters”: {
“func_name”: “Log_normal”, “linear_range”: true, “wide_energy_range”: true, “x_scale”: “linear”, “y_scale”: “linear”
}, “start_time”: “2021-08-17T08:15:54.659477”, “version”: “2.3.3”
}
}, “nbformat”: 4, “nbformat_minor”: 5
}