- {
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}, “tags”: []
}, “source”: [
“# Sin”
]
}, {
“cell_type”: “code”, “execution_count”: 1, “id”: “f2ac3790”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:10:55.425535Z”, “iopub.status.busy”: “2021-08-17T08:10:55.424180Z”, “iopub.status.idle”: “2021-08-17T08:10:58.589377Z”, “shell.execute_reply”: “2021-08-17T08:10:58.590181Z”
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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”: “8f4306d6”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:10:58.617562Z”, “iopub.status.busy”: “2021-08-17T08:10:58.616329Z”, “iopub.status.idle”: “2021-08-17T08:10:58.623589Z”, “shell.execute_reply”: “2021-08-17T08:10:58.624420Z”
}, “nbsphinx”: “hidden”, “papermill”: {
“duration”: 0.023575, “end_time”: “2021-08-17T08:10:58.624771”, “exception”: false, “start_time”: “2021-08-17T08:10:58.601196”, “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”: “40db2d25”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:10:58.651535Z”, “iopub.status.busy”: “2021-08-17T08:10:58.649760Z”, “iopub.status.idle”: “2021-08-17T08:10:58.655865Z”, “shell.execute_reply”: “2021-08-17T08:10:58.659196Z”
}, “papermill”: {
“duration”: 0.025598, “end_time”: “2021-08-17T08:10:58.659695”, “exception”: false, “start_time”: “2021-08-17T08:10:58.634097”, “status”: “completed”
}, “tags”: [
“injected-parameters”
]
}, “outputs”: [], “source”: [
“# Parametersn”, “func_name = "Sin"n”, “wide_energy_range = Truen”, “x_scale = "linear"n”, “y_scale = "linear"n”, “linear_range = Truen”
]
}, {
“cell_type”: “code”, “execution_count”: 4, “id”: “1dfaafe6”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:10:59.011477Z”, “iopub.status.busy”: “2021-08-17T08:10:58.767611Z”, “iopub.status.idle”: “2021-08-17T08:10:59.013783Z”, “shell.execute_reply”: “2021-08-17T08:10:59.014799Z”
}, “lines_to_next_cell”: 0, “nbsphinx”: “hidden”, “papermill”: {
“duration”: 0.310901, “end_time”: “2021-08-17T08:10:59.050720”, “exception”: false, “start_time”: “2021-08-17T08:10:58.739819”, “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”: “c92f10df”, “metadata”: {
“lines_to_next_cell”: 0, “papermill”: {
“duration”: 0.110441, “end_time”: “2021-08-17T08:10:59.256749”, “exception”: false, “start_time”: “2021-08-17T08:10:59.146308”, “status”: “completed”
}, “tags”: []
}, “source”: [
“## Description”
]
}, {
“cell_type”: “code”, “execution_count”: 5, “id”: “5c7a427b”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:10:59.306851Z”, “iopub.status.busy”: “2021-08-17T08:10:59.305667Z”, “iopub.status.idle”: “2021-08-17T08:10:59.309959Z”, “shell.execute_reply”: “2021-08-17T08:10:59.310950Z”
}, “papermill”: {
“duration”: 0.04422, “end_time”: “2021-08-17T08:10:59.311306”, “exception”: false, “start_time”: “2021-08-17T08:10:59.267086”, “status”: “completed”
}, “tags”: []
}, “outputs”: [
- {
- “data”: {
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}, “metadata”: {}, “output_type”: “display_data”
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], “source”: [
“func.display()”
]
}, {
“cell_type”: “markdown”, “id”: “b70b7ec6”, “metadata”: {
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“duration”: 0.010361, “end_time”: “2021-08-17T08:10:59.332157”, “exception”: false, “start_time”: “2021-08-17T08:10:59.321796”, “status”: “completed”
}, “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”: “2aa27bd3”, “metadata”: {
- “execution”: {
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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”: “1b2743d3”, “metadata”: {
“lines_to_next_cell”: 0, “papermill”: {
“duration”: 0.012657, “end_time”: “2021-08-17T08:10:59.595060”, “exception”: false, “start_time”: “2021-08-17T08:10:59.582403”, “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”: “58e4a633”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:10:59.758938Z”, “iopub.status.busy”: “2021-08-17T08:10:59.655058Z”, “iopub.status.idle”: “2021-08-17T08:11:00.454406Z”, “shell.execute_reply”: “2021-08-17T08:11:00.455058Z”
}, “papermill”: {
“duration”: 0.848458, “end_time”: “2021-08-17T08:11:00.455468”, “exception”: false, “start_time”: “2021-08-17T08:10:59.607010”, “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”: “eb7da57c”, “metadata”: {
- “papermill”: {
“duration”: 0.012652, “end_time”: “2021-08-17T08:11:00.481402”, “exception”: false, “start_time”: “2021-08-17T08:11:00.468750”, “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”: “102314e4”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:11:00.563898Z”, “iopub.status.busy”: “2021-08-17T08:11:00.539894Z”, “iopub.status.idle”: “2021-08-17T08:11:00.775019Z”, “shell.execute_reply”: “2021-08-17T08:11:00.776254Z”
}, “papermill”: {
“duration”: 0.282818, “end_time”: “2021-08-17T08:11:00.777025”, “exception”: false, “start_time”: “2021-08-17T08:11:00.494207”, “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
}, “file_extension”: “.py”, “mimetype”: “text/x-python”, “name”: “python”, “nbconvert_exporter”: “python”, “pygments_lexer”: “ipython3”, “version”: “3.7.11”
}, “papermill”: {
“default_parameters”: {}, “duration”: 7.230741, “end_time”: “2021-08-17T08:11:01.465188”, “environment_variables”: {}, “exception”: null, “input_path”: “Sin.ipynb”, “output_path”: “../docs/notebooks/Sin.ipynb”, “parameters”: {
“func_name”: “Sin”, “linear_range”: true, “wide_energy_range”: true, “x_scale”: “linear”, “y_scale”: “linear”
}, “start_time”: “2021-08-17T08:10:54.234447”, “version”: “2.3.3”
}
}, “nbformat”: 4, “nbformat_minor”: 5
}