- {
- “cells”: [
- {
“cell_type”: “markdown”, “id”: “605ba343”, “metadata”: {}, “source”: [
“# Additional features for scripts and applicationsn”, “n”, “In this document we describe some features of the astromodels package which are useful for non-interactive environment such as scripts or applicationsn”, “n”, “First let’s import astromodels and let’s load a model from a file, which we will use as example:”
]
}, {
“cell_type”: “code”, “execution_count”: 1, “id”: “688e6b6b”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:08:58.755594Z”, “iopub.status.busy”: “2021-08-17T08:08:58.754085Z”, “iopub.status.idle”: “2021-08-17T08:09:20.643889Z”, “shell.execute_reply”: “2021-08-17T08:09:20.644471Z”
}
}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“[u001b[35mWARNING u001b[0m]u001b[35m The naima package is not available. Models that depend on it will not be availableu001b[0mn”
]
}, {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“[u001b[35mWARNING u001b[0m]u001b[35m The GSL library or the pygsl wrapper cannot be loaded. Models that depend on it will not be available.u001b[0mn”
]
}, {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“[u001b[35mWARNING u001b[0m]u001b[35m The ebltable package is not available. Models that depend on it will not be availableu001b[0mn”
]
}
], “source”: [
“from astromodels import *”
]
}, {
“cell_type”: “code”, “execution_count”: 2, “id”: “9fae1066”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:09:20.650324Z”, “iopub.status.busy”: “2021-08-17T08:09:20.649403Z”, “iopub.status.idle”: “2021-08-17T08:09:20.681811Z”, “shell.execute_reply”: “2021-08-17T08:09:20.682584Z”
}
}, “outputs”: [], “source”: [
“my_model = load_model("my_model.yml")”
]
}, {
“cell_type”: “markdown”, “id”: “434d53f1”, “metadata”: {}, “source”: [
“## Get dictionaries of point and extended sourcesn”, “n”, “If you don’t know the details (such as names) of the sources contained in the model, you can obtain dictionaries of point sources and extended sources like:”
]
}, {
“cell_type”: “code”, “execution_count”: 3, “id”: “16866268”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:09:20.689489Z”, “iopub.status.busy”: “2021-08-17T08:09:20.688539Z”, “iopub.status.idle”: “2021-08-17T08:09:20.692760Z”, “shell.execute_reply”: “2021-08-17T08:09:20.693403Z”
}
}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“odict_keys([‘source_1’, ‘source_2’])n”, “odict_keys([])n”
]
}
], “source”: [
“point_sources = my_model.point_sourcesn”, “extended_sources = my_model.extended_sourcesn”, “n”, “# Print the names of the point sourcesn”, “print(point_sources.keys())n”, “n”, “# Print the names of the extended sourcesn”, “print(extended_sources.keys())”
]
}, {
“cell_type”: “markdown”, “id”: “bacf1443”, “metadata”: {}, “source”: [
“You can use these dictionaries as usual. For example, you can loop over all point sources and print their position:”
]
}, {
“cell_type”: “code”, “execution_count”: 4, “id”: “a22c3051”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:09:20.700022Z”, “iopub.status.busy”: “2021-08-17T08:09:20.698944Z”, “iopub.status.idle”: “2021-08-17T08:09:20.702399Z”, “shell.execute_reply”: “2021-08-17T08:09:20.703454Z”
}
}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“The model contain point source source_1 at Sky direction (R.A., Dec.) = (125.60000, -75.30000) (J2000)n”, “The model contain point source source_2 at Sky direction (l, b) = (11.25000, -22.50000) (J2000)n”
]
}
], “source”: [
“for source_name, point_source in point_sources.items():n”, “n”, ” print("The model contain point source %s at %s" % (source_name, point_source.position))”
]
}, {
“cell_type”: “markdown”, “id”: “05267d4d”, “metadata”: {}, “source”: [
“## Accessing components and spectral shapes with no previous informationn”, “n”, “Similarly you can access components and their spectral shapes (i.e., functions) without knowing the names in advance. A dictionary containing the components of a given source can be obtained with:”
]
}, {
“cell_type”: “code”, “execution_count”: 5, “id”: “774960a0”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:09:20.709460Z”, “iopub.status.busy”: “2021-08-17T08:09:20.708423Z”, “iopub.status.idle”: “2021-08-17T08:09:20.712584Z”, “shell.execute_reply”: “2021-08-17T08:09:20.713223Z”
}
}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“odict_keys([‘synchrotron’, ‘IC’])n”
]
}
], “source”: [
“components = my_model.source_2.componentsn”, “print(components.keys())”
]
}, {
“cell_type”: “markdown”, “id”: “5742685c”, “metadata”: {}, “source”: [
“So now we can loop over all the sources and print their components:”
]
}, {
“cell_type”: “code”, “execution_count”: 6, “id”: “ac3dae3d”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:09:20.719388Z”, “iopub.status.busy”: “2021-08-17T08:09:20.718456Z”, “iopub.status.idle”: “2021-08-17T08:09:20.725730Z”, “shell.execute_reply”: “2021-08-17T08:09:20.726345Z”
}
}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“Point source source_1 has components odict_keys([‘main’])n”, “Point source source_2 has components odict_keys([‘synchrotron’, ‘IC’])n”
]
}
], “source”: [
“for source_name, point_source in my_model.point_sources.items():n”, “n”, ” print("Point source %s has components %s" % (source_name, point_source.components.keys()))”
]
}, {
“cell_type”: “markdown”, “id”: “f8a6d3d5”, “metadata”: {}, “source”: [
“With a fully-qualified path, you would need to know the name of the function to access its parameters. Instead, you can use the generic name “shape”. For example these two statements point to the same function instance:”
]
}, {
“cell_type”: “code”, “execution_count”: 7, “id”: “dd575db3”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:09:20.739387Z”, “iopub.status.busy”: “2021-08-17T08:09:20.738271Z”, “iopub.status.idle”: “2021-08-17T08:09:20.745456Z”, “shell.execute_reply”: “2021-08-17T08:09:20.746469Z”
}
}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“True”
]
}, “execution_count”: 7, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“my_model.source_1.spectrum.main.Powerlaw == my_model.source_1.spectrum.main.shapen”
]
}, {
“cell_type”: “markdown”, “id”: “568de1cd”, “metadata”: {}, “source”: [
“Once you have a function instance, you can obtain a dictionary of its parameters as:”
]
}, {
“cell_type”: “code”, “execution_count”: 8, “id”: “806e7740”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:09:20.752756Z”, “iopub.status.busy”: “2021-08-17T08:09:20.751728Z”, “iopub.status.idle”: “2021-08-17T08:09:20.755897Z”, “shell.execute_reply”: “2021-08-17T08:09:20.756526Z”
}
}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“odict_keys([‘K’, ‘piv’, ‘index’])n”
]
}
], “source”: [
“parameters = my_model.source_1.spectrum.main.Powerlaw.parametersn”, “print(parameters.keys())”
]
}, {
“cell_type”: “markdown”, “id”: “9a33667e”, “metadata”: {}, “source”: [
“Putting it all together, let’s loop over all sources in our model, then over each component in each source, then over each parameter in each component:”
]
}, {
“cell_type”: “code”, “execution_count”: 9, “id”: “74d66ef1”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:09:20.765109Z”, “iopub.status.busy”: “2021-08-17T08:09:20.763925Z”, “iopub.status.idle”: “2021-08-17T08:09:20.769508Z”, “shell.execute_reply”: “2021-08-17T08:09:20.770750Z”
}
}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“Found source source_1n”, ” Position of point source: Sky direction (R.A., Dec.) = (125.60000, -75.30000) (J2000)n”, ” Found component mainn”, ” Found parameter Kn”, ” Found parameter pivn”, ” Found parameter indexn”, “Found source source_2n”, ” Position of point source: Sky direction (l, b) = (11.25000, -22.50000) (J2000)n”, ” Found component synchrotronn”, ” Found parameter Kn”, ” Found parameter pivn”, ” Found parameter indexn”, ” Found component ICn”, ” Found parameter Kn”, ” Found parameter pivn”, ” Found parameter indexn”
]
}
], “source”: [
“for source_name, point_source in my_model.point_sources.items():n”, “n”, ” print("Found source %s" % source_name)n”, “n”, ” print(" Position of point source: %s" % point_source.position)n”, “n”, ” for component_name, component in point_source.components.items():n”, “n”, ” print(" Found component %s" % component_name)n”, “n”, ” for parameter_name, parameter in component.shape.parameters.items():n”, “n”, ” print(" Found parameter %s" % parameter_name)”
]
}, {
“cell_type”: “markdown”, “id”: “f5f57ceb”, “metadata”: {}, “source”: [
“Let’s now plot the differential flux between 1 and 100 keV of all components from all sources:n”, “n”
]
}, {
“cell_type”: “code”, “execution_count”: 10, “id”: “80070580”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:09:20.785581Z”, “iopub.status.busy”: “2021-08-17T08:09:20.784448Z”, “iopub.status.idle”: “2021-08-17T08:09:28.129037Z”, “shell.execute_reply”: “2021-08-17T08:09:28.130063Z”
}
}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“Text(0, 0.5, ‘Flux (ph cm$^{-2}$ s$^{-1}$ keV$^{-1}$’)”
]
}, “execution_count”: 10, “metadata”: {}, “output_type”: “execute_result”
}, {
- “data”: {
“image/png”: 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n”, “text/plain”: [
“<Figure size 748.8x655.2 with 1 Axes>”
]
}, “metadata”: {}, “output_type”: “display_data”
}
], “source”: [
“import numpy as npn”, “import matplotlib.pyplot as pltn”, “%matplotlib inlinen”, “from jupyterthemes import jtplotn”, “jtplot.style(context="talk", fscale=1, ticks=True, grid=False)n”, “n”, “fig, ax = plt.subplots()n”, “n”, “n”, “# Prepare 100 energies logarithmicall spaced between 1 and 100 keVn”, “energies = np.geomspace(1,100,100)n”, “n”, “# Now loop over all point sources and plot themn”, “for source_name, point_source in my_model.point_sources.items():n”, “n”, ” # Plot the sum of all components for this sourcen”, “n”, ” ax.loglog(energies, point_source(energies),label=source_name)n”, “n”, ” # If there is more than one component, plot them also separatelyn”, “n”, ” if len(point_source.components) > 1:n”, “n”, ” for component_name, component in point_source.components.items():n”, “n”, ” ax.loglog(energies,component.shape(energies),’–’,label="%s of %s" %(component_name, source_name))n”, “n”, “# Add a legendn”, “ax.legend(loc=0,frameon=False)n”, “n”, “ax.set_xlabel("Energy (keV)")n”, “ax.set_ylabel(r"Flux (ph cm$^{-2}$ s$^{-1}$ keV$^{-1}$")”
]
}, {
“cell_type”: “markdown”, “id”: “50b6160b”, “metadata”: {}, “source”: [
“## Getting the path of an element and using it programmaticallyn”, “n”, “Whenever you have an element from the model, you can get its fully-qualified path by using the .path property. This for example will print the path of all the parameters in the model:”
]
}, {
“cell_type”: “code”, “execution_count”: 11, “id”: “68116c0c”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:09:28.137844Z”, “iopub.status.busy”: “2021-08-17T08:09:28.136547Z”, “iopub.status.idle”: “2021-08-17T08:09:28.142223Z”, “shell.execute_reply”: “2021-08-17T08:09:28.143257Z”
}
}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“source_1.spectrum.main.Powerlaw.Kn”, “source_1.spectrum.main.Powerlaw.pivn”, “source_1.spectrum.main.Powerlaw.indexn”, “source_2.spectrum.synchrotron.Powerlaw.Kn”, “source_2.spectrum.synchrotron.Powerlaw.pivn”, “source_2.spectrum.synchrotron.Powerlaw.indexn”, “source_2.spectrum.IC.Powerlaw.Kn”, “source_2.spectrum.IC.Powerlaw.pivn”, “source_2.spectrum.IC.Powerlaw.indexn”
]
}
], “source”: [
“for source_name, point_source in my_model.point_sources.items():n”, “n”, ” for component_name, component in point_source.components.items():n”, “n”, ” for parameter_name, parameter in component.shape.parameters.items():n”, “n”, ” print(parameter.path)”
]
}, {
“cell_type”: “markdown”, “id”: “e4065a36”, “metadata”: {}, “source”: [
“If you have a path of an element in a string, you can use it to access the element by using the [] operator of the Model class like this:”
]
}, {
“cell_type”: “code”, “execution_count”: 12, “id”: “60750796”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:09:28.151571Z”, “iopub.status.busy”: “2021-08-17T08:09:28.150547Z”, “iopub.status.idle”: “2021-08-17T08:09:28.154017Z”, “shell.execute_reply”: “2021-08-17T08:09:28.155331Z”
}
}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“Parameter K = 0.8 [1 / (cm2 keV s)]n”, “(min_value = 1e-30, max_value = 1000.0, delta = 0.1, free = True)n”
]
}
], “source”: [
“my_path = ‘source_2.spectrum.IC.Powerlaw.K’n”, “n”, “K = my_model[my_path]n”, “n”, “print(K)”
]
}, {
“cell_type”: “markdown”, “id”: “ec251a2b”, “metadata”: {}, “source”: [
“## Alternative way of accessing the information in the modeln”, “n”, “We present here an alternative way to get information from the model without using dictionaries, and using instead source IDs. A source ID is just an ordinal number, separate for point sources and extended sources. Hence, the first point source has ID 0, the second point source has ID 1, and so on. Similarly, the first extended source has ID 0, the second has ID 1 and so on:”
]
}, {
“cell_type”: “code”, “execution_count”: 13, “id”: “b156c5c2”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:09:28.164123Z”, “iopub.status.busy”: “2021-08-17T08:09:28.162842Z”, “iopub.status.idle”: “2021-08-17T08:09:28.168309Z”, “shell.execute_reply”: “2021-08-17T08:09:28.169402Z”
}
}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“The first point source is called source_1n”, “The second point source is called source_2n”, “Point source ID 0 has name source_1n”, “Point source ID 1 has name source_2n”
]
}
], “source”: [
“# Get the number of point sources and of extended sourcesn”, “n”, “n_pts = my_model.get_number_of_point_sources()n”, “n_ext = my_model.get_number_of_extended_sources()n”, “n”, “# Get the name of the first point sourcen”, “n”, “print("The first point source is called %s" % my_model.get_point_source_name(0))n”, “print("The second point source is called %s" % my_model.get_point_source_name(1))n”, “n”, “# Of course you can achieve the same in a loopn”, “n”, “for id in range(n_pts):n”, “n”, ” print("Point source ID %s has name %s" % (id, my_model.get_point_source_name(id)))”
]
}, {
“cell_type”: “markdown”, “id”: “d36ed397”, “metadata”: {}, “source”: [
“Once you have the ID of a source, you can obtain information about it with these methods of the Model class:”
]
}, {
“cell_type”: “code”, “execution_count”: 14, “id”: “54d6b79c”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:09:28.195209Z”, “iopub.status.busy”: “2021-08-17T08:09:28.194250Z”, “iopub.status.idle”: “2021-08-17T08:09:28.197800Z”, “shell.execute_reply”: “2021-08-17T08:09:28.198565Z”
}
}, “outputs”: [], “source”: [
“src_id = 1n”, “n”, “src_name = my_model.get_point_source_name(src_id)n”, “n”, “ra, dec = my_model.get_point_source_position(src_id) # This will always return ra,decn”, “n”, “# Prepare 100 energies logarithmically spaced between 1 and 100 keVn”, “energies = np.logspace(0,2,100)n”, “n”, “differential_flux = my_model.get_point_source_fluxes(src_id, energies)n”, “n”, “# Similar methods exist for extended sources (to be completed)”
]
}, {
“cell_type”: “markdown”, “id”: “e8c7da01”, “metadata”: {}, “source”: [
“Depending on your application you might find these methods more convenient that interrogating the sources directly. Note however that some features are not available through this interface. For example, it is not possible to get information about different components through these methods.”
]
}, {
“cell_type”: “code”, “execution_count”: null, “id”: “3152631c”, “metadata”: {}, “outputs”: [], “source”: []
}
], “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”
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