- {
- “cells”: [
- {
“cell_type”: “markdown”, “id”: “b72f08dc”, “metadata”: {}, “source”: [
“# Quick startn”, “n”, “First, lets just run through some examples to see where we are goingn”, “by simulating a simple example population which we observe as an”, “survey. Let’s say we are in a giant sphere surrounded by fire fliesn”, “that fill the volume homogeneously. Furthermore, the light they emitn”, “follows a Pareto distribution (power law) in luminosity. Of course,n”, “this population can be anything; active galactic nuclei (AGN),n”, “gamma-ray bursts (GRBs), etc. The framework provided in popsynth isn”, “intended to be generic.”]
}, {
“cell_type”: “code”, “execution_count”: 1, “id”: “5fc4d55a”, “metadata”: {
- “execution”: {
- “iopub.execute_input”: “2022-02-09T16:33:03.168687Z”, “iopub.status.busy”: “2022-02-09T16:33:03.168165Z”, “iopub.status.idle”: “2022-02-09T16:33:06.922734Z”, “shell.execute_reply”: “2022-02-09T16:33:06.921798Z”
}
}, “outputs”: [], “source”: [
“%matplotlib inlinen”, “n”, “n”, “import matplotlib.pyplot as pltn”, “from jupyterthemes import jtplotn”, “n”, “jtplot.style(context=”notebook”, fscale=1, grid=False)n”, “purple = “#B833FF”n”, “yellow = “#F6EF5B”n”, “n”, “import popsynthn”, “n”, “popsynth.update_logging_level(“INFO”)n”, “n”, “import networkx as nxn”, “import numpy as npn”, “import warningsn”, “n”, “warnings.simplefilter(“ignore”)”]
}, {
“cell_type”: “code”, “execution_count”: 2, “id”: “04e45359”, “metadata”: {
- “execution”: {
- “iopub.execute_input”: “2022-02-09T16:33:06.932965Z”, “iopub.status.busy”: “2022-02-09T16:33:06.931846Z”, “iopub.status.idle”: “2022-02-09T16:33:06.933456Z”, “shell.execute_reply”: “2022-02-09T16:33:06.933789Z”
}, “lines_to_next_cell”: 0, “nbsphinx”: “hidden”
}, “outputs”: [], “source”: [
“class DemoSampler(popsynth.AuxiliarySampler):n”, ” _auxiliary_sampler_name = “DemoSampler”n”, ” mu = popsynth.auxiliary_sampler.AuxiliaryParameter(default=2)n”, ” tau = popsynth.auxiliary_sampler.AuxiliaryParameter(default=1, vmin=0)n”, “n”, ” def __init__(self):n”, “n”, ” super(DemoSampler, self).__init__(“demo”, observed=False)n”, “n”, ” def true_sampler(self, size):n”, “n”, ” self._true_values = np.random.normal(self.mu, self.tau, size=size)n”, “n”, “n”, “class DemoSampler2(popsynth.DerivedLumAuxSampler):n”, ” _auxiliary_sampler_name = “DemoSampler2”n”, ” mu = popsynth.auxiliary_sampler.AuxiliaryParameter(default=2)n”, ” tau = popsynth.auxiliary_sampler.AuxiliaryParameter(default=1, vmin=0)n”, ” sigma = popsynth.auxiliary_sampler.AuxiliaryParameter(default=1, vmin=0)n”, “n”, ” def __init__(self):n”, “n”, ” super(DemoSampler2, self).__init__(“demo2”)n”, “n”, ” def true_sampler(self, size):n”, “n”, ” secondary = self._secondary_samplers[“demo”]n”, “n”, ” self._true_values = (n”, ” (np.random.normal(self.mu, self.tau, size=size))n”, ” + secondary.true_valuesn”, ” - np.log10(1 + self._distance)n”, ” )n”, “n”, ” def observation_sampler(self, size):n”, “n”, ” self._obs_values = self._true_values + np.random.normal(n”, ” 0, self.sigma, size=sizen”, ” )n”, “n”, ” def compute_luminosity(self):n”, “n”, ” secondary = self._secondary_samplers[“demo”]n”, “n”, ” return (10 ** (self._true_values + 54)) / secondary.true_values”]
}, {
“cell_type”: “markdown”, “id”: “e93ee411”, “metadata”: {}, “source”: []}, {
“cell_type”: “markdown”, “id”: “d01c83c2”, “metadata”: {}, “source”: [
“## A spherically homogenous population of fire flies with a pareto luminosity functionn”, “n”, “popsynth comes with several types of populations included, thoughn”, “you can easily [construct yourn”, “own](https://popsynth.readthedocs.io/en/latest/notebooks/custom.html). Ton”, “access the built in population synthesizers, one simply instantiatesn”, “the population from the popsynth.populations module. Here, we willn”, “simulate a survey that has a homogenous spherical spatial distributionn”, “and a pareto distributed luminosity.”]
}, {
“cell_type”: “code”, “execution_count”: 3, “id”: “8b747bdf”, “metadata”: {
- “execution”: {
- “iopub.execute_input”: “2022-02-09T16:33:06.939912Z”, “iopub.status.busy”: “2022-02-09T16:33:06.938426Z”, “iopub.status.idle”: “2022-02-09T16:33:06.941745Z”, “shell.execute_reply”: “2022-02-09T16:33:06.941280Z”
}
}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“Luminosity Functionn”, “pareton”, “\frac{\alpha L_{\rm min}^{\alpha}}{L^{\alpha+1}}n”, “Lmin: 1n”, “alpha: 2.0n”, “Spatial Functionn”, “cons_spheren”, “\Lambdan”, “Lambda: 5n”, “r_max: 5n”, “n”]
}
], “source”: [
“homogeneous_pareto_synth = popsynth.populations.ParetoHomogeneousSphericalPopulation(n”, ” Lambda=5, Lmin=1, alpha=2.0 # the density normalization # lower bound on the LFn”, “) # index of the LFn”, “n”, “print(homogeneous_pareto_synth)”]
}, {
“cell_type”: “code”, “execution_count”: 4, “id”: “7ff330f2”, “metadata”: {
- “execution”: {
- “iopub.execute_input”: “2022-02-09T16:33:06.947215Z”, “iopub.status.busy”: “2022-02-09T16:33:06.943710Z”, “iopub.status.idle”: “2022-02-09T16:33:06.962506Z”, “shell.execute_reply”: “2022-02-09T16:33:06.962923Z”
}, “lines_to_next_cell”: 2
}, “outputs”: [
- {
- “data”: {
- “text/markdown”: [
- “## Luminosity Function”
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“<IPython.core.display.Markdown object>”]
}, “metadata”: {}, “output_type”: “display_data”
}, {
- “data”: {
- “text/latex”: [
- “$\displaystyle \frac{\alpha L_{\rm min}^{\alpha}}{L^{\alpha+1}}$”
], “text/plain”: [
“<IPython.core.display.Math object>”]
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” parameter valuen”, “0 Lmin 1.0n”, “1 alpha 2.0”]
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- “data”: {
- “text/markdown”: [
- “## Spatial Function”
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}, {
“cell_type”: “markdown”, “id”: “e6288f87”, “metadata”: {}, “source”: [
“If you have [networkx](https://networkx.org) andn”, “[graviz](https://graphviz.readthedocs.io/en/stable/), you can plot an”, “graph of the connections.”]
}, {
“cell_type”: “code”, “execution_count”: 5, “id”: “70f255e3”, “metadata”: {
- “execution”: {
- “iopub.execute_input”: “2022-02-09T16:33:06.967613Z”, “iopub.status.busy”: “2022-02-09T16:33:06.967087Z”, “iopub.status.idle”: “2022-02-09T16:33:07.129935Z”, “shell.execute_reply”: “2022-02-09T16:33:07.130317Z”
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n”, “text/plain”: [
“<Figure size 576x504 with 1 Axes>”]
}, “metadata”: {}, “output_type”: “display_data”
}
], “source”: [
“# we can also display a graph of the objectn”, “n”, “n”, “options = {“node_color”: purple, “node_size”: 3000, “width”: 0.5}n”, “n”, “pos = nx.drawing.nx_agraph.graphviz_layout(homogeneous_pareto_synth.graph, prog=”dot”)n”, “n”, “nx.draw(homogeneous_pareto_synth.graph, with_labels=True, pos=pos, **options)”]
}, {
“cell_type”: “markdown”, “id”: “dc8663c5”, “metadata”: {}, “source”: [
“## Creating a surveyn”, “n”, “We can now sample from this population with the draw_surveyn”, “function, but first we need specify how the flux is selected by addingn”, “a flux selection function. Here, we will use a hard selection functionn”, “in this example, but you [can make yourn”, “own](https://popsynth.readthedocs.io/en/latest/notebooks/selections.html#custom-selections). Then”, “selection function will mark objects with observed fluxes belown”, “the selection boundary as “hidden”, but we will still have access ton”, “them in our population. “]
}, {
“cell_type”: “code”, “execution_count”: 6, “id”: “978b9636”, “metadata”: {
- “execution”: {
- “iopub.execute_input”: “2022-02-09T16:33:07.133570Z”, “iopub.status.busy”: “2022-02-09T16:33:07.132844Z”, “iopub.status.idle”: “2022-02-09T16:33:07.135833Z”, “shell.execute_reply”: “2022-02-09T16:33:07.136331Z”
}, “lines_to_next_cell”: 0
}, “outputs”: [], “source”: [
“flux_selector = popsynth.HardFluxSelection()n”, “flux_selector.boundary = 1e-2n”, “n”, “homogeneous_pareto_synth.set_flux_selection(flux_selector)”]
}, {
“cell_type”: “markdown”, “id”: “c4ec1d9c”, “metadata”: {}, “source”: [
“And by observed fluxes, we mean those where the latent flux is obscured by observational error, here we sample the observational error from a log normal distribution with $\sigma=1$. In the future,`popsynth`will have more options.”]
}, {
“cell_type”: “code”, “execution_count”: 7, “id”: “83f38ae0”, “metadata”: {
- “execution”: {
- “iopub.execute_input”: “2022-02-09T16:33:07.139018Z”, “iopub.status.busy”: “2022-02-09T16:33:07.138487Z”, “iopub.status.idle”: “2022-02-09T16:33:07.304289Z”, “shell.execute_reply”: “2022-02-09T16:33:07.304726Z”
}
}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m The volume integral is 2617.993878 u001b[0mn”]
}, {
- “data”: {
- “application/vnd.jupyter.widget-view+json”: {
- “model_id”: “ea2a40b5d43d44ff9db79ac5089ed47c”, “version_major”: 2, “version_minor”: 0
}, “text/plain”: [
“Drawing distances: 0%| | 0/2567 [00:00<?, ?it/s]”]
}, “metadata”: {}, “output_type”: “display_data”
}, {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m Expecting 2567 total objects u001b[0mn”]
}, {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m applying selection to fluxes u001b[0mn”]
}, {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m Detected 573 distances u001b[0mn”]
}, {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m Detected 573 objects out to a distance of 4.99 u001b[0mn”]
}
], “source”: [
“population = homogeneous_pareto_synth.draw_survey(flux_sigma=0.1)”]
}, {
“cell_type”: “markdown”, “id”: “3f95c77d”, “metadata”: {}, “source”: [
“We now have created a population survey. How did we get here?n”, “n”, “* Once the spatial and luminosity functions are specified, we can integrate out to a given distance and compute the number of expected objects.n”, “n”, “* A Poisson draw with this mean is made to determine the number of total objects in the survey.n”, “n”, “* Next all quantities are sampled (distance, luminosity)n”, “n”, “* If needed, the luminosity is converted to a flux with a given observational errorn”, “n”, “* The selection function (in this case a hard cutoff) is appliedn”, “n”, “* A population object is createdn”, “n”, “We could have specified a soft cutoff (an inverse logit) with logarithmic with as well:”]
}, {
“cell_type”: “code”, “execution_count”: 8, “id”: “bead40c9”, “metadata”: {
- “execution”: {
- “iopub.execute_input”: “2022-02-09T16:33:07.312666Z”, “iopub.status.busy”: “2022-02-09T16:33:07.311576Z”, “iopub.status.idle”: “2022-02-09T16:33:07.459983Z”, “shell.execute_reply”: “2022-02-09T16:33:07.459528Z”
}
}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“u001b[33mu001b[2m WARNING u001b[0m| u001b[33mu001b[2m removing all registered Auxiliary Samplers u001b[0mn”]
}, {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“u001b[33mu001b[2m WARNING u001b[0m| u001b[33mu001b[2m removing flux selector u001b[0mn”]
}, {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“u001b[33mu001b[2m WARNING u001b[0m| u001b[33mu001b[2m removing distance selector u001b[0mn”]
}, {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“u001b[33mu001b[2m WARNING u001b[0m| u001b[33mu001b[2m removing spatial selector u001b[0mn”]
}, {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m The volume integral is 2617.993878 u001b[0mn”]
}, {
- “data”: {
- “application/vnd.jupyter.widget-view+json”: {
- “model_id”: “819edd0b0df942a0859432ed17440fc4”, “version_major”: 2, “version_minor”: 0
}, “text/plain”: [
“Drawing distances: 0%| | 0/2567 [00:00<?, ?it/s]”]
}, “metadata”: {}, “output_type”: “display_data”
}, {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m Expecting 2567 total objects u001b[0mn”]
}, {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m applying selection to fluxes u001b[0mn”]
}, {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m Detected 609 distances u001b[0mn”]
}, {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m Detected 609 objects out to a distance of 4.99 u001b[0mn”]
}
], “source”: [
“homogeneous_pareto_synth.clean()n”, “flux_selector = popsynth.SoftFluxSelection()n”, “flux_selector.boundary = 1e-2n”, “flux_selector.strength = 20n”, “n”, “n”, “homogeneous_pareto_synth.set_flux_selection(flux_selector)n”, “n”, “population = homogeneous_pareto_synth.draw_survey(flux_sigma=0.1)”]
}, {
“cell_type”: “markdown”, “id”: “0211c947”, “metadata”: {}, “source”: [
“More detail on the [process behind then”, “simulation](https://popsynth.readthedocs.io/en/latest/notebooks/distributions.html#Core-Concept)n”, “can be found deeper in the documentationn”, “n”, “## The Population Objectn”, “n”, “The population object stores all the information about the sampledn”, “survey. This includes information on the latent parameters, measuredn”, “parameters, and distances for both the selected and non-selectedn”, “objects.”]
}, {
“cell_type”: “markdown”, “id”: “9e8f4534”, “metadata”: {}, “source”: [
“We can have a look at the flux-distance distribution from then”, “survey. Here, yellow dots are the latent flux value, i.e., withoutn”, “observational noise, and purple dots are the measured values for then”, “*selected objects. Arrows point from the latent to measured values.”]
}, {
“cell_type”: “code”, “execution_count”: 9, “id”: “4bb73f3d”, “metadata”: {
- “execution”: {
- “iopub.execute_input”: “2022-02-09T16:33:07.478995Z”, “iopub.status.busy”: “2022-02-09T16:33:07.475454Z”, “iopub.status.idle”: “2022-02-09T16:33:08.840154Z”, “shell.execute_reply”: “2022-02-09T16:33:08.840597Z”
}
}, “outputs”: [
- {
- “data”: {
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n”, “text/plain”: [
“<Figure size 576x504 with 1 Axes>”]
}, “metadata”: {}, “output_type”: “display_data”
}
], “source”: [
“fig = population.display_fluxes(obs_color=purple, true_color=yellow)”]
}, {
“cell_type”: “markdown”, “id”: “0b1165dc”, “metadata”: {}, “source”: [
“For fun, we can display the fluxes on in a simulated universe in 3D”]
}, {
“cell_type”: “code”, “execution_count”: 10, “id”: “eca2d629”, “metadata”: {
- “execution”: {
- “iopub.execute_input”: “2022-02-09T16:33:08.858956Z”, “iopub.status.busy”: “2022-02-09T16:33:08.844214Z”, “iopub.status.idle”: “2022-02-09T16:33:09.201069Z”, “shell.execute_reply”: “2022-02-09T16:33:09.197266Z”
}
}, “outputs”: [
- {
- “data”: {
- “application/vnd.jupyter.widget-view+json”: {
- “model_id”: “d807a556116f44b29320fda46df123f2”, “version_major”: 2, “version_minor”: 0
}, “text/plain”: [
“VBox(children=(Figure(camera=PerspectiveCamera(fov=46.0, position=(0.0, 0.0, 2.0), projectionMatrix=(1.0, 0.0,…”]
}, “metadata”: {}, “output_type”: “display_data”
}
], “source”: [
“fig = population.display_obs_fluxes_sphere(background_color=”black”)”]
}, {
“cell_type”: “markdown”, “id”: “5665b20f”, “metadata”: {}, “source”: [
“The population object stores a lot of information. For example, an array of selection booleans:”]
}, {
“cell_type”: “code”, “execution_count”: 11, “id”: “e6e5a3d6”, “metadata”: {
- “execution”: {
- “iopub.execute_input”: “2022-02-09T16:33:09.206976Z”, “iopub.status.busy”: “2022-02-09T16:33:09.206220Z”, “iopub.status.idle”: “2022-02-09T16:33:09.208816Z”, “shell.execute_reply”: “2022-02-09T16:33:09.209266Z”
}
}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
- “array([False, False, False, …, False, False, False])”
]
}, “execution_count”: 11, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“population.selection”]
}, {
“cell_type”: “markdown”, “id”: “f3b1b226”, “metadata”: {}, “source”: [
“We can retrieve selected and non-selected distances:”]
}, {
“cell_type”: “code”, “execution_count”: 12, “id”: “e1e7650a”, “metadata”: {
- “execution”: {
- “iopub.execute_input”: “2022-02-09T16:33:09.214075Z”, “iopub.status.busy”: “2022-02-09T16:33:09.212948Z”, “iopub.status.idle”: “2022-02-09T16:33:09.214667Z”, “shell.execute_reply”: “2022-02-09T16:33:09.215082Z”
}
}, “outputs”: [], “source”: [
“distances = population.selected_distances”]
}, {
“cell_type”: “code”, “execution_count”: 13, “id”: “28fd7d27”, “metadata”: {
- “execution”: {
- “iopub.execute_input”: “2022-02-09T16:33:09.218521Z”, “iopub.status.busy”: “2022-02-09T16:33:09.217248Z”, “iopub.status.idle”: “2022-02-09T16:33:09.220591Z”, “shell.execute_reply”: “2022-02-09T16:33:09.220108Z”
}
}, “outputs”: [], “source”: [
“hidden_distances = population.hidden_distances”]
}, {
“cell_type”: “code”, “execution_count”: 14, “id”: “6a23dd8a”, “metadata”: {
- “execution”: {
- “iopub.execute_input”: “2022-02-09T16:33:09.236621Z”, “iopub.status.busy”: “2022-02-09T16:33:09.223105Z”, “iopub.status.idle”: “2022-02-09T16:33:09.436707Z”, “shell.execute_reply”: “2022-02-09T16:33:09.435585Z”
}
}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
- “Text(0.5, 0, ‘z’)”
]
}, “execution_count”: 14, “metadata”: {}, “output_type”: “execute_result”
}, {
- “data”: {
“image/png”: “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n”, “text/plain”: [
“<Figure size 576x504 with 1 Axes>”]
}, “metadata”: {}, “output_type”: “display_data”
}
], “source”: [
“fig, ax = plt.subplots()n”, “n”, “bins = np.linspace(0, 6, 20)n”, “n”, “n”, “ax.hist(hidden_distances, bins=bins, fc=yellow, ec=”k”,lw=1)n”, “ax.hist(distances, bins=bins, fc=purple, ec=”k”,lw=1)n”, “ax.set_xlabel(“z”)n”]
}, {
“cell_type”: “markdown”, “id”: “c14586fa”, “metadata”: {}, “source”: [
“## Saving the populationn”, “We can record the results of a population synth to an HDF5 file thatn”, “maintains all the information from the run. The true values of then”, “population parameters are always stored in the truth dictionary:n”]
}, {
“cell_type”: “code”, “execution_count”: 15, “id”: “26b4b362”, “metadata”: {
- “execution”: {
- “iopub.execute_input”: “2022-02-09T16:33:09.442332Z”, “iopub.status.busy”: “2022-02-09T16:33:09.441821Z”, “iopub.status.idle”: “2022-02-09T16:33:09.446872Z”, “shell.execute_reply”: “2022-02-09T16:33:09.446407Z”
}
}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
- “{‘cons_sphere’: {‘Lambda’: 5, ‘r_max’: 5}, ‘pareto’: {‘Lmin’: 1, ‘alpha’: 2.0}}”
]
}, “execution_count”: 15, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“population.truth”]
}, {
“cell_type”: “code”, “execution_count”: 16, “id”: “f857f4a9”, “metadata”: {
- “execution”: {
- “iopub.execute_input”: “2022-02-09T16:33:09.451650Z”, “iopub.status.busy”: “2022-02-09T16:33:09.451145Z”, “iopub.status.idle”: “2022-02-09T16:33:09.469096Z”, “shell.execute_reply”: “2022-02-09T16:33:09.468573Z”
}
}, “outputs”: [], “source”: [
“population.writeto(“saved_pop.h5”)”]
}, {
“cell_type”: “code”, “execution_count”: 17, “id”: “f7e6ddd1”, “metadata”: {
- “execution”: {
- “iopub.execute_input”: “2022-02-09T16:33:09.474599Z”, “iopub.status.busy”: “2022-02-09T16:33:09.473445Z”, “iopub.status.idle”: “2022-02-09T16:33:09.504341Z”, “shell.execute_reply”: “2022-02-09T16:33:09.503836Z”
}
}, “outputs”: [], “source”: [
“reloaded_population = popsynth.Population.from_file(“saved_pop.h5”)”]
}, {
“cell_type”: “code”, “execution_count”: 18, “id”: “f6492e36”, “metadata”: {
- “execution”: {
- “iopub.execute_input”: “2022-02-09T16:33:09.510682Z”, “iopub.status.busy”: “2022-02-09T16:33:09.509857Z”, “iopub.status.idle”: “2022-02-09T16:33:09.518683Z”, “shell.execute_reply”: “2022-02-09T16:33:09.520195Z”
}
}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
- “{‘cons_sphere’: {‘Lambda’: 5, ‘r_max’: 5}, ‘pareto’: {‘Lmin’: 1, ‘alpha’: 2.0}}”
]
}, “execution_count”: 18, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“reloaded_population.truth”]
}, {
“cell_type”: “markdown”, “id”: “c76d96d0”, “metadata”: {}, “source”: [
“## Creating populations from YAML filesn”, “n”, “It is sometimes easier to quickly write down population in a YAML filen”, “without having to create all the objects in python. Let’s a take an”, “look at the format:n”, “n”, “`yaml\n", "\n", "# the seed\n", "seed: 1234\n", "\n", "# specifiy the luminosity distribution\n", "# and it's parmeters\n", "luminosity distribution:\n", " ParetoDistribution:\n", " Lmin: 1e51\n", " alpha: 2\n", "\n", "# specifiy the flux selection function\n", "# and it's parmeters\n", "flux selection:\n", " HardFluxSelection:\n", " boundary: 1e-6\n", "\n", "# specifiy the spatial distribution\n", "# and it's parmeters\n", "\n", "spatial distribution:\n", " ZPowerCosmoDistribution:\n", " Lambda: .5\n", " delta: -2\n", " r_max: 5\n", "\n", "# specify the distance selection function\n", "# and it's parmeters\n", "distance selection:\n", " BernoulliSelection:\n", " probability: 0.5\n", "\n", "# a spatial selection if needed\n", "spatial selection:\n", " # None\n", "\n", "\n", "# all the auxiliary functions\n", "# these must be known to the\n", "# registry at run time if\n", "# the are custom!\n", "\n", "auxiliary samplers:\n", " stellar_mass\n", " type: NormalAuxSampler\n", " observed: False\n", " mu: 0\n", " sigma: 1\n", " selection:\n", " secondary:\n", " init variables:\n", "\n", " demo:\n", " type: DemoSampler\n", " observed: False\n", " selection:\n", " UpperBound:\n", " boundary: 20\n", "\n", " demo2:\n", " type: DemoSampler2\n", " observed: True\n", " selection:\n", " secondary: [demo, stellar_mass] # other samplers that this sampler depends on\n", "\n", "\n", "`n”, “n”, “We can load this yaml file into a population synth. We use a saved file to demonstrate:”]
}, {
“cell_type”: “code”, “execution_count”: 19, “id”: “04ad010c”, “metadata”: {
- “execution”: {
- “iopub.execute_input”: “2022-02-09T16:33:09.525401Z”, “iopub.status.busy”: “2022-02-09T16:33:09.524016Z”, “iopub.status.idle”: “2022-02-09T16:33:09.539187Z”, “shell.execute_reply”: “2022-02-09T16:33:09.538724Z”
}
}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m registering derived luminosity sampler: demo2 u001b[0mn”]
}, {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“Luminosity Functionn”, “demo2n”, “observed: Truen”, “demon”, “stellar_massn”, “Spatial Functionn”, “zpow_cosmon”, “\Lambda (z+1)^{\delta}n”, “Lambda: 0.5n”, “delta: -2.0n”, “r_max: 5.0n”, “demon”, “observed: Falsen”, “parents: [‘demo2’]n”, “stellar_massn”, “observed: Falsen”, “mu: 0.0n”, “sigma: 1.0n”, “parents: [‘demo2’]n”, “n”]
}
], “source”: [
“my_file = popsynth.utils.package_data.get_path_of_data_file(“pop.yml”)n”, “n”, “ps = popsynth.PopulationSynth.from_file(my_file)n”, “n”, “print(ps)”]
}, {
“cell_type”: “code”, “execution_count”: 20, “id”: “7b309ad2”, “metadata”: {
- “execution”: {
- “iopub.execute_input”: “2022-02-09T16:33:09.543672Z”, “iopub.status.busy”: “2022-02-09T16:33:09.543155Z”, “iopub.status.idle”: “2022-02-09T16:33:09.572640Z”, “shell.execute_reply”: “2022-02-09T16:33:09.571941Z”
}
}, “outputs”: [
- {
- “data”: {
- “text/markdown”: [
- “## Luminosity Function”
], “text/plain”: [
“<IPython.core.display.Markdown object>”]
}, “metadata”: {}, “output_type”: “display_data”
}, {
- “data”: {
- “text/html”: [
- “<div>n”, “<style scoped>n”, ” .dataframe tbody tr th:only-of-type {n”, ” vertical-align: middle;n”, ” }n”, “n”, ” .dataframe tbody tr th {n”, ” vertical-align: top;n”, ” }n”, “n”, ” .dataframe thead th {n”, ” text-align: right;n”, ” }n”, “</style>n”, “<table border=”1” class=”dataframe”>n”, ” <thead>n”, ” <tr style=”text-align: right;”>n”, ” <th></th>n”, ” <th>parameter</th>n”, ” <th>value</th>n”, ” </tr>n”, ” </thead>n”, ” <tbody>n”, ” </tbody>n”, “</table>n”, “</div>”
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“Empty DataFramen”, “Columns: [parameter, value]n”, “Index: []”]
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- “data”: {
- “text/markdown”: [
- “## Spatial Function”
], “text/plain”: [
“<IPython.core.display.Markdown object>”]
}, “metadata”: {}, “output_type”: “display_data”
}, {
- “data”: {
- “text/latex”: [
- “$\displaystyle \Lambda (z+1)^{\delta}$”
], “text/plain”: [
“<IPython.core.display.Math object>”]
}, “metadata”: {}, “output_type”: “display_data”
}, {
- “data”: {
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], “text/plain”: [
” parameter valuen”, “0 Lambda 0.5n”, “1 delta -2.0n”, “2 r_max 5.0”]
}, “metadata”: {}, “output_type”: “display_data”
}, {
- “data”: {
- “text/markdown”: [
- “## demo”
], “text/plain”: [
“<IPython.core.display.Markdown object>”]
}, “metadata”: {}, “output_type”: “display_data”
}, {
- “data”: {
- “text/html”: [
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}, “metadata”: {}, “output_type”: “display_data”
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- “data”: {
- “text/markdown”: [
- “## stellar_mass”
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“<IPython.core.display.Markdown object>”]
}, “metadata”: {}, “output_type”: “display_data”
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- “data”: {
- “text/html”: [
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” parameter valuen”, “0 mu 0.0n”, “1 sigma 1.0”]
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], “source”: [
“ps.display()”]
}, {
“cell_type”: “code”, “execution_count”: 21, “id”: “7c45a7ed”, “metadata”: {
- “execution”: {
- “iopub.execute_input”: “2022-02-09T16:33:09.578328Z”, “iopub.status.busy”: “2022-02-09T16:33:09.577615Z”, “iopub.status.idle”: “2022-02-09T16:33:09.740908Z”, “shell.execute_reply”: “2022-02-09T16:33:09.741422Z”
}, “lines_to_next_cell”: 2
}, “outputs”: [
- {
- “data”: {
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n”, “text/plain”: [
“<Figure size 576x504 with 1 Axes>”]
}, “metadata”: {}, “output_type”: “display_data”
}
], “source”: [
“options = {“node_color”: purple, “node_size”: 3000, “width”: 0.5}n”, “n”, “pos = nx.drawing.nx_agraph.graphviz_layout(ps.graph, prog=”dot”)n”, “n”, “nx.draw(ps.graph, with_labels=True, pos=pos, **options)”]
}, {
“cell_type”: “markdown”, “id”: “dd235266”, “metadata”: {}, “source”: [
“We can see that our population was created correctly for us.n”, “n”, “n”, “Now, this means we can easily pass populations around to our collaborators for testing”]
}, {
“cell_type”: “code”, “execution_count”: 22, “id”: “751f88b7”, “metadata”: {
- “execution”: {
- “iopub.execute_input”: “2022-02-09T16:33:09.749622Z”, “iopub.status.busy”: “2022-02-09T16:33:09.749099Z”, “iopub.status.idle”: “2022-02-09T16:33:14.589634Z”, “shell.execute_reply”: “2022-02-09T16:33:14.582570Z”
}
}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m The volume integral is 3.570283 u001b[0mn”]
}, {
- “data”: {
- “application/vnd.jupyter.widget-view+json”: {
- “model_id”: “51bbe68194154b2b91232d234ec8c490”, “version_major”: 2, “version_minor”: 0
}, “text/plain”: [
“Drawing distances: 0%| | 0/5 [00:00<?, ?it/s]”]
}, “metadata”: {}, “output_type”: “display_data”
}, {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m Expecting 5 total objects u001b[0mn”]
}, {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m Sampling: demo2 u001b[0mn”]
}, {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m demo2 is sampling its secondary quantities u001b[0mn”]
}, {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m Sampling: demo u001b[0mn”]
}, {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m Sampling: stellar_mass u001b[0mn”]
}, {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m Getting luminosity from derived sampler u001b[0mn”]
}, {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m applying selection to fluxes u001b[0mn”]
}, {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m Applying selection from demo which selected 5 of 5 objects u001b[0mn”]
}, {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m Before auxiliary selection there were 5 objects selected u001b[0mn”]
}, {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“u001b[33mu001b[2m WARNING u001b[0m| u001b[33mu001b[2m NO HIDDEN OBJECTS u001b[0mn”]
}, {
- “data”: {
- “application/vnd.jupyter.widget-view+json”: {
- “model_id”: “364dcf679f1f49a08b6c7dd5061de5e6”, “version_major”: 2, “version_minor”: 0
}, “text/plain”: [
“Selecting Bernoulli: 0%| | 0/5 [00:00<?, ?it/s]”]
}, “metadata”: {}, “output_type”: “display_data”
}, {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m Detected 3 distances u001b[0mn”]
}, {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m Detected 5 objects out to a distance of 3.37 u001b[0mn”]
}
], “source”: [
“pop = ps.draw_survey(flux_sigma=0.5)”]
}, {
“cell_type”: “markdown”, “id”: “77761813”, “metadata”: {}, “source”: [
“Now, since we can read the population synth from a file, we can also write one we have created with classes to a file:”]
}, {
“cell_type”: “code”, “execution_count”: 23, “id”: “8a75da82”, “metadata”: {
- “execution”: {
- “iopub.execute_input”: “2022-02-09T16:33:14.595364Z”, “iopub.status.busy”: “2022-02-09T16:33:14.592124Z”, “iopub.status.idle”: “2022-02-09T16:33:14.597478Z”, “shell.execute_reply”: “2022-02-09T16:33:14.597875Z”
}
}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
- “{‘seed’: 1234,n”, ” ‘spatial distribution’: {‘ZPowerCosmoDistribution’: {‘Lambda’: 0.5,n”, ” ‘delta’: -2.0,n”, ” ‘r_max’: 5.0},n”, ” ‘is_rate’: True},n”, ” ‘luminosity distribution’: {‘ParetoDistribution’: {‘Lmin’: 1e+51,n”, ” ‘alpha’: 2.0}},n”, ” ‘flux selection’: {‘HardFluxSelection’: {‘boundary’: 1e-06}},n”, ” ‘distance selection’: {‘BernoulliSelection’: {‘probability’: 0.5}},n”, ” ‘auxiliary samplers’: {}}”
]
}, “execution_count”: 23, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“ps.to_dict()”]
}, {
“cell_type”: “code”, “execution_count”: 24, “id”: “98b09690”, “metadata”: {
- “execution”: {
- “iopub.execute_input”: “2022-02-09T16:33:14.603744Z”, “iopub.status.busy”: “2022-02-09T16:33:14.603232Z”, “iopub.status.idle”: “2022-02-09T16:33:14.606626Z”, “shell.execute_reply”: “2022-02-09T16:33:14.606194Z”
}
}, “outputs”: [], “source”: [
“ps.write_to(“/tmp/my_pop_synth.yml”)”]
}, {
“cell_type”: “markdown”, “id”: “ad144506”, “metadata”: {}, “source”: [
“but our population synth is also serialized to our population!”]
}, {
“cell_type”: “code”, “execution_count”: 25, “id”: “78d6cce4”, “metadata”: {
- “execution”: {
- “iopub.execute_input”: “2022-02-09T16:33:14.611502Z”, “iopub.status.busy”: “2022-02-09T16:33:14.610934Z”, “iopub.status.idle”: “2022-02-09T16:33:14.614576Z”, “shell.execute_reply”: “2022-02-09T16:33:14.614152Z”
}
}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
- “{‘seed’: 1234,n”, ” ‘spatial distribution’: {‘ZPowerCosmoDistribution’: {‘Lambda’: 0.5,n”, ” ‘delta’: -2.0,n”, ” ‘r_max’: 5.0},n”, ” ‘is_rate’: True},n”, ” ‘luminosity distribution’: {‘ParetoDistribution’: {‘Lmin’: 1e+51,n”, ” ‘alpha’: 2.0}},n”, ” ‘flux selection’: {‘HardFluxSelection’: {‘boundary’: 1e-06}},n”, ” ‘distance selection’: {‘BernoulliSelection’: {‘probability’: 0.5}},n”, ” ‘auxiliary samplers’: {}}”
]
}, “execution_count”: 25, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“pop.pop_synth”]
}, {
“cell_type”: “markdown”, “id”: “2b945908”, “metadata”: {}, “source”: [
“Therefore we always know exactly how we simulated our data.”]
}
], “metadata”: {
- “jupytext”: {
- “formats”: “ipynb,md”
}, “kernelspec”: {
“display_name”: “Python 3”, “language”: “python”, “name”: “python3”}, “language_info”: {
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}, “file_extension”: “.py”, “mimetype”: “text/x-python”, “name”: “python”, “nbconvert_exporter”: “python”, “pygments_lexer”: “ipython3”, “version”: “3.9.10”
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”, “encoding”: “base64”, “path”: [
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