- {
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“cell_type”: “markdown”, “id”: “89282e75”, “metadata”: {
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}, “tags”: []
}, “source”: [
“# Cosine Prior”
]
}, {
“cell_type”: “code”, “execution_count”: 1, “id”: “a16af376”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:15:49.912322Z”, “iopub.status.busy”: “2021-08-17T08:15:49.911132Z”, “iopub.status.idle”: “2021-08-17T08:15:52.943700Z”, “shell.execute_reply”: “2021-08-17T08:15:52.944828Z”
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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”: “53034986”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:15:52.972268Z”, “iopub.status.busy”: “2021-08-17T08:15:52.971165Z”, “iopub.status.idle”: “2021-08-17T08:15:52.979454Z”, “shell.execute_reply”: “2021-08-17T08:15:52.980126Z”
}, “nbsphinx”: “hidden”, “papermill”: {
“duration”: 0.025033, “end_time”: “2021-08-17T08:15:52.980458”, “exception”: false, “start_time”: “2021-08-17T08:15:52.955425”, “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”: “b0eb3273”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:15:53.007884Z”, “iopub.status.busy”: “2021-08-17T08:15:53.006134Z”, “iopub.status.idle”: “2021-08-17T08:15:53.013828Z”, “shell.execute_reply”: “2021-08-17T08:15:53.014473Z”
}, “papermill”: {
“duration”: 0.024357, “end_time”: “2021-08-17T08:15:53.014790”, “exception”: false, “start_time”: “2021-08-17T08:15:52.990433”, “status”: “completed”
}, “tags”: [
“injected-parameters”
]
}, “outputs”: [], “source”: [
“# Parametersn”, “func_name = "Cosine_Prior"n”, “wide_energy_range = Truen”, “x_scale = "linear"n”, “y_scale = "linear"n”, “linear_range = Truen”
]
}, {
“cell_type”: “code”, “execution_count”: 4, “id”: “57686260”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:15:53.047997Z”, “iopub.status.busy”: “2021-08-17T08:15:53.046753Z”, “iopub.status.idle”: “2021-08-17T08:15:53.054808Z”, “shell.execute_reply”: “2021-08-17T08:15:53.056382Z”
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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”: “61c3ff7f”, “metadata”: {
“lines_to_next_cell”: 0, “papermill”: {
“duration”: 0.009876, “end_time”: “2021-08-17T08:15:53.076513”, “exception”: false, “start_time”: “2021-08-17T08:15:53.066637”, “status”: “completed”
}, “tags”: []
}, “source”: [
“## Description”
]
}, {
“cell_type”: “code”, “execution_count”: 5, “id”: “f85f5bfa”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:15:53.109805Z”, “iopub.status.busy”: “2021-08-17T08:15:53.108493Z”, “iopub.status.idle”: “2021-08-17T08:15:53.113258Z”, “shell.execute_reply”: “2021-08-17T08:15:53.113928Z”
}, “papermill”: {
“duration”: 0.027537, “end_time”: “2021-08-17T08:15:53.114293”, “exception”: false, “start_time”: “2021-08-17T08:15:53.086756”, “status”: “completed”
}, “tags”: []
}, “outputs”: [
- {
- “data”: {
- “text/html”: [
“<ul>n”, “n”, “<li>description: A function which is constant on the interval angular interval of cosine</li>n”, “n”, “<li>formula: $\cos(x)$</li>n”, “n”, “<li>parameters: n”, “<ul>n”, “n”, “<li>lower_bound: n”, “<ul>n”, “n”, “<li>value: -90.0</li>n”, “n”, “<li>desc: Lower bound for the interval</li>n”, “n”, “<li>min_value: -inf</li>n”, “n”, “<li>max_value: inf</li>n”, “n”, “<li>unit: </li>n”, “n”, “<li>is_normalization: False</li>n”, “n”, “<li>delta: 9.0</li>n”, “n”, “<li>free: True</li>n”, “n”, “</ul>n”, “n”, “</li>n”, “n”, “<li>upper_bound: n”, “<ul>n”, “n”, “<li>value: 90.0</li>n”, “n”, “<li>desc: Upper bound for the interval</li>n”, “n”, “<li>min_value: -inf</li>n”, “n”, “<li>max_value: inf</li>n”, “n”, “<li>unit: </li>n”, “n”, “<li>is_normalization: False</li>n”, “n”, “<li>delta: 9.0</li>n”, “n”, “<li>free: True</li>n”, “n”, “</ul>n”, “n”, “</li>n”, “n”, “<li>value: n”, “<ul>n”, “n”, “<li>value: 1.0</li>n”, “n”, “<li>desc: Value in the interval</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”, “</ul>n”, “n”, “</li>n”, “n”, “</ul>n”
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” * description: A function which is constant on the interval angular interval of cosinen”, ” * formula: $\cos(x)$n”, ” * parameters:n”, ” * lower_bound:n”, ” * value: -90.0n”, ” * desc: Lower bound for the intervaln”, ” * min_value: -.infn”, ” * max_value: .infn”, ” * unit: ‘’n”, ” * is_normalization: falsen”, ” * delta: 9.0n”, ” * free: truen”, ” * upper_bound:n”, ” * value: 90.0n”, ” * desc: Upper bound for the intervaln”, ” * min_value: -.infn”, ” * max_value: .infn”, ” * unit: ‘’n”, ” * is_normalization: falsen”, ” * delta: 9.0n”, ” * free: truen”, ” * value:n”, ” * value: 1.0n”, ” * desc: Value in the intervaln”, ” * min_value: nulln”, ” * max_value: nulln”, ” * unit: ‘’n”, ” * is_normalization: falsen”, ” * delta: 0.1n”, ” * free: true”
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}, “metadata”: {}, “output_type”: “display_data”
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], “source”: [
“func.display()”
]
}, {
“cell_type”: “markdown”, “id”: “f76956c2”, “metadata”: {
- “papermill”: {
“duration”: 0.014677, “end_time”: “2021-08-17T08:15:53.141338”, “exception”: false, “start_time”: “2021-08-17T08:15:53.126661”, “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”: “0e2a099f”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:15:53.233575Z”, “iopub.status.busy”: “2021-08-17T08:15:53.224648Z”, “iopub.status.idle”: “2021-08-17T08:15:53.375678Z”, “shell.execute_reply”: “2021-08-17T08:15:53.376583Z”
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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”: “50f42284”, “metadata”: {
“lines_to_next_cell”: 0, “papermill”: {
“duration”: 0.01152, “end_time”: “2021-08-17T08:15:53.399998”, “exception”: false, “start_time”: “2021-08-17T08:15:53.388478”, “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”: “8d7e7d30”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:15:53.480184Z”, “iopub.status.busy”: “2021-08-17T08:15:53.475027Z”, “iopub.status.idle”: “2021-08-17T08:15:53.839859Z”, “shell.execute_reply”: “2021-08-17T08:15:53.840593Z”
}, “papermill”: {
“duration”: 0.429401, “end_time”: “2021-08-17T08:15:53.840928”, “exception”: false, “start_time”: “2021-08-17T08:15:53.411527”, “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”: “35041f0a”, “metadata”: {
- “papermill”: {
“duration”: 0.012391, “end_time”: “2021-08-17T08:15:53.866743”, “exception”: false, “start_time”: “2021-08-17T08:15:53.854352”, “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”: “87404fa6”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:15:53.947116Z”, “iopub.status.busy”: “2021-08-17T08:15:53.926007Z”, “iopub.status.idle”: “2021-08-17T08:15:54.079866Z”, “shell.execute_reply”: “2021-08-17T08:15:54.080504Z”
}, “papermill”: {
“duration”: 0.20153, “end_time”: “2021-08-17T08:15:54.080829”, “exception”: false, “start_time”: “2021-08-17T08:15:53.879299”, “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”: 5.822169, “end_time”: “2021-08-17T08:15:54.595816”, “environment_variables”: {}, “exception”: null, “input_path”: “Cosine_Prior.ipynb”, “output_path”: “../docs/notebooks/Cosine_Prior.ipynb”, “parameters”: {
“func_name”: “Cosine_Prior”, “linear_range”: true, “wide_energy_range”: true, “x_scale”: “linear”, “y_scale”: “linear”
}, “start_time”: “2021-08-17T08:15:48.773647”, “version”: “2.3.3”
}
}, “nbformat”: 4, “nbformat_minor”: 5
}