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}, “tags”: []
}, “source”: [
“# Quartic”
]
}, {
“cell_type”: “code”, “execution_count”: 1, “id”: “0b6fd481”, “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”: “e31621a9”, “metadata”: {
- “execution”: {
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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”: “16c8c4f2”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:12:46.103675Z”, “iopub.status.busy”: “2021-08-17T08:12:46.102700Z”, “iopub.status.idle”: “2021-08-17T08:12:46.110129Z”, “shell.execute_reply”: “2021-08-17T08:12:46.110763Z”
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}, “tags”: [
“injected-parameters”
]
}, “outputs”: [], “source”: [
“# Parametersn”, “func_name = "Quartic"n”, “wide_energy_range = Truen”, “x_scale = "linear"n”, “y_scale = "linear"n”, “linear_range = Truen”
]
}, {
“cell_type”: “code”, “execution_count”: 4, “id”: “a60cbb7d”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:12:46.139529Z”, “iopub.status.busy”: “2021-08-17T08:12:46.138512Z”, “iopub.status.idle”: “2021-08-17T08:12:46.145887Z”, “shell.execute_reply”: “2021-08-17T08:12:46.146797Z”
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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”: “53f9d6f3”, “metadata”: {
“lines_to_next_cell”: 0, “papermill”: {
“duration”: 0.009202, “end_time”: “2021-08-17T08:12:46.166229”, “exception”: false, “start_time”: “2021-08-17T08:12:46.157027”, “status”: “completed”
}, “tags”: []
}, “source”: [
“## Description”
]
}, {
“cell_type”: “code”, “execution_count”: 5, “id”: “409c6333”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:12:46.199460Z”, “iopub.status.busy”: “2021-08-17T08:12:46.198442Z”, “iopub.status.idle”: “2021-08-17T08:12:46.202831Z”, “shell.execute_reply”: “2021-08-17T08:12:46.203456Z”
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}, “outputs”: [
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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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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”: “001f8600”, “metadata”: {
“lines_to_next_cell”: 0, “papermill”: {
“duration”: 0.01167, “end_time”: “2021-08-17T08:12:46.487022”, “exception”: false, “start_time”: “2021-08-17T08:12:46.475352”, “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”: “78d73068”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:12:46.559998Z”, “iopub.status.busy”: “2021-08-17T08:12:46.519768Z”, “iopub.status.idle”: “2021-08-17T08:12:46.929187Z”, “shell.execute_reply”: “2021-08-17T08:12:46.929817Z”
}, “papermill”: {
“duration”: 0.431513, “end_time”: “2021-08-17T08:12:46.930132”, “exception”: false, “start_time”: “2021-08-17T08:12:46.498619”, “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”: “cfb72cb4”, “metadata”: {
- “papermill”: {
“duration”: 0.012328, “end_time”: “2021-08-17T08:12:46.954780”, “exception”: false, “start_time”: “2021-08-17T08:12:46.942452”, “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”: “17b4aecf”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:12:47.010680Z”, “iopub.status.busy”: “2021-08-17T08:12:46.988173Z”, “iopub.status.idle”: “2021-08-17T08:12:47.189306Z”, “shell.execute_reply”: “2021-08-17T08:12:47.190437Z”
}, “papermill”: {
“duration”: 0.223365, “end_time”: “2021-08-17T08:12:47.190763”, “exception”: false, “start_time”: “2021-08-17T08:12:46.967398”, “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”: 5.931019, “end_time”: “2021-08-17T08:12:47.727742”, “environment_variables”: {}, “exception”: null, “input_path”: “Quartic.ipynb”, “output_path”: “../docs/notebooks/Quartic.ipynb”, “parameters”: {
“func_name”: “Quartic”, “linear_range”: true, “wide_energy_range”: true, “x_scale”: “linear”, “y_scale”: “linear”
}, “start_time”: “2021-08-17T08:12:41.796723”, “version”: “2.3.3”
}
}, “nbformat”: 4, “nbformat_minor”: 5
}