Welcome to popsynth’s documentation!

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This framework provides an abstract way to generate survey populations from arbitrary luminosity functions and redshift distributions. Additionally, auxiliary quantities can be sampled and stored.

Populations can be saved and restored via an HDF5 files for later use. Population synthesis routines can be created via classes or structured YAML files.

Users can construct their own classes for spatial, luminosity, etc. distributions which can all be connected to arbitrarily complex selection functions.

Note

This is not Synth Pop. If you were expecting that… I suggest you check out Depeche Mode. Though, it is possible to combine coding and good music.

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“# Installationn”, “n”, “`popsynth` can be installed withn”, “n”, “`bash\n", "pip install popsynth\n", "`n”, “Alternatively, one can install via gitn”, “n”, “`bash\n", "git clone https://github.com/grburgess/popsynth\n", "cd popsynth\n", "python setup.py install\n", "`n”, “n”, “n”, “In order to produce graphs of populations, one needs the optional [graphiz](https://graphviz.readthedocs.io/en/stable/) library.n”, “The 3D plots seen in the documentation require properly setting up the [ipyvolume](https://ipyvolume.readthedocs.io/en/latest/) package and configuring it for your local setup. n”

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“# 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.”

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“%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”)”

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“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”

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“## 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.”

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“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”

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“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)”

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“## Luminosity Function”

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“$\displaystyle \frac{\alpha L_{\rm min}^{\alpha}}{L^{\alpha+1}}$”

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” parameter valuen”, “0 Lmin 1.0n”, “1 alpha 2.0”

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“## Spatial Function”

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“If you have [networkx](https://networkx.org) andn”, “[graviz](https://graphviz.readthedocs.io/en/stable/), you can plot an”, “graph of the connections.”

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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”: “cfc30542”, “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”: “383c6581”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:23.868705Z”, “iopub.status.busy”: “2022-02-09T16:35:23.868206Z”, “iopub.status.idle”: “2022-02-09T16:35:23.871336Z”, “shell.execute_reply”: “2022-02-09T16:35:23.871796Z”

}, “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”: “9f87f5b6”, “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”: “a0a98a11”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:23.874143Z”, “iopub.status.busy”: “2022-02-09T16:35:23.873630Z”, “iopub.status.idle”: “2022-02-09T16:35:24.024367Z”, “shell.execute_reply”: “2022-02-09T16:35:24.023908Z”

}

}, “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”: “de6ac95ae4cc4db497db7984672a23a9”, “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”: “7763e518”, “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”: “9d83273a”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:24.032100Z”, “iopub.status.busy”: “2022-02-09T16:35:24.030575Z”, “iopub.status.idle”: “2022-02-09T16:35:24.180065Z”, “shell.execute_reply”: “2022-02-09T16:35:24.179642Z”

}

}, “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”: “03d6ce458a2a4f28997e1fdb1b981e32”, “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”: “3ebb03c5”, “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”: “b7c87a4e”, “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”: “079f386b”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:24.199010Z”, “iopub.status.busy”: “2022-02-09T16:35:24.195512Z”, “iopub.status.idle”: “2022-02-09T16:35:25.509621Z”, “shell.execute_reply”: “2022-02-09T16:35:25.510056Z”

}

}, “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”: “fac089f8”, “metadata”: {}, “source”: [

“For fun, we can display the fluxes on in a simulated universe in 3D”

]

}, {

“cell_type”: “code”, “execution_count”: 10, “id”: “d7c2c3e3”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:25.529123Z”, “iopub.status.busy”: “2022-02-09T16:35:25.528530Z”, “iopub.status.idle”: “2022-02-09T16:35:25.854623Z”, “shell.execute_reply”: “2022-02-09T16:35:25.847833Z”

}

}, “outputs”: [

{
“data”: {
“application/vnd.jupyter.widget-view+json”: {
“model_id”: “4577fe2d8ef24b08b9a3a7af31e3d90c”, “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”: “39bfc636”, “metadata”: {}, “source”: [

“The population object stores a lot of information. For example, an array of selection booleans:”

]

}, {

“cell_type”: “code”, “execution_count”: 11, “id”: “409e6a4c”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:25.859836Z”, “iopub.status.busy”: “2022-02-09T16:35:25.858823Z”, “iopub.status.idle”: “2022-02-09T16:35:25.862113Z”, “shell.execute_reply”: “2022-02-09T16:35:25.862552Z”

}

}, “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”: “e311faba”, “metadata”: {}, “source”: [

“We can retrieve selected and non-selected distances:”

]

}, {

“cell_type”: “code”, “execution_count”: 12, “id”: “ff383a60”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:25.867385Z”, “iopub.status.busy”: “2022-02-09T16:35:25.866136Z”, “iopub.status.idle”: “2022-02-09T16:35:25.868062Z”, “shell.execute_reply”: “2022-02-09T16:35:25.868479Z”

}

}, “outputs”: [], “source”: [

“distances = population.selected_distances”

]

}, {

“cell_type”: “code”, “execution_count”: 13, “id”: “6cc16a1e”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:25.872056Z”, “iopub.status.busy”: “2022-02-09T16:35:25.870704Z”, “iopub.status.idle”: “2022-02-09T16:35:25.874158Z”, “shell.execute_reply”: “2022-02-09T16:35:25.873673Z”

}

}, “outputs”: [], “source”: [

“hidden_distances = population.hidden_distances”

]

}, {

“cell_type”: “code”, “execution_count”: 14, “id”: “84da287f”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:25.879499Z”, “iopub.status.busy”: “2022-02-09T16:35:25.879004Z”, “iopub.status.idle”: “2022-02-09T16:35:26.083632Z”, “shell.execute_reply”: “2022-02-09T16:35:26.084065Z”

}

}, “outputs”: [

{
“data”: {
“text/plain”: [
“Text(0.5, 0, ‘z’)”

]

}, “execution_count”: 14, “metadata”: {}, “output_type”: “execute_result”

}, {

“data”: {

“image/png”: “iVBORw0KGgoAAAANSUhEUgAAAegAAAGtCAYAAADdxefTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAdKUlEQVR4nO3df2zV9b3H8RelbUBOb3axo+coDFp+HHV0kTHMrdzSpHAOnRhtoSRbV6DrIGHoGNGqNcgPTahiMGQmTh3EBZS6hZrKFrU91QZERoZUx9YpNdzJ1HLaUseEQzXHcs79w8u5Vgpt6TmcN+c8H/+d7/f0e97nD3j28/2efs+IcDgcFgAAMCUl3gMAAIALEWgAAAwi0AAAGESgAQAwiEADAGBQarwHkKRgMKgzZ84oPT1dKSn8zgAASHyhUEjBYFAZGRlKT0+/YP+QAv3www+rtrZWbW1tkqQdO3bohRdekCQtWbJES5culST19vZq3bp1Onz4sNLS0rRx40bdcsstFz3umTNndPz48aGMAgBAQpg0aZKuvfbaC7YPOtCHDx9WT09P5PHx48e1a9cu1dfXS5IWLlyogoICTZw4UfX19erp6VFTU5Pa2tq0atUqNTU1XXR1fP43h0mTJmnUqFFDemMAAFyNvvjiCx0/frzf1bM0yEAHg0Ft2bJFv/71r/Xyyy9Lknw+n4qKiuRwOCRJ8+fPl8/n04oVK9TY2KiKigpJktvtVmZmplpbW/W9732v3+OfD/eoUaM0ZsyYobw/AACuahdbvA7qgu9TTz2l0tJSjR07NrKts7NTTqcz8tjlcqmjoyOyz+Vy9dnn9/sva3AAAJLRgIE+evSojhw5okWLFvXZfqk7hHL3UAAAhmfAQL/zzjs6duyY5s6dq8LCQklSYWGhnE5nZMUsSX6/X1lZWZIkp9PZZ8Xs9/v7rLYBAMClDRjosrIyvfXWW2publZzc7Mkqbm5WR6PRw0NDQoEAgoEAmpoaJDH45Ekeb1e1dXVSZLa2tp08uRJ5ebmxvBtAACQWC7776Czs7NVVlam4uJiSVJ5ebmys7MlSSUlJWppadG8efOUlpammpoa/r4ZAIAhGGHh6ybPnj2ro0eP6oYbbuBT3ACApDBQ+1jWAgBgEIEGAMAgAg0AgEEEGgAAgwg0AAAGEWgAAAwi0AAAGESgAQAwiEADAGDQZd/qEwAQHXPy89R+oiuqx7z+unF6c//BqB4TVxaBBoA4az/RpdaWvKgec/pM4ny14xQ3AAAGEWgAAAwi0AAAGESgAQAwiEADAGAQgQYAwCACDQCAQQQaAACDCDQAAAYRaAAADCLQAAAYRKABADCIQAMAYBCBBgDAIAINAIBBBBoAAIMINAAABhFoAAAMSo33AACA6EtPH6HJkydH9ZjXXzdOb+4/GNVj4uIINAAkoGAwrNaWvKgec/pM4nwlcYobAACDCDQAAAYRaAAADCLQAAAYRKABADCIQAMAYBCBBgDAoEH9HXR5eblOnz6tcDis7Oxs1dTU6N///reKiooifwg/evRo/e53v5Mk9fb2at26dTp8+LDS0tK0ceNG3XLLLbF7FwAAJJhBBfrpp59WRkaGJOnRRx/V9u3bVVpaqnHjxmnPnj0XPL++vl49PT1qampSW1ubVq1apaamJqWksGAHAGAwBlXM83EOhUL6/PPPNWLEiEs+v7GxUYsXL5Ykud1uZWZmqrW1dZijAgCQPAa9pF2xYoVuvfVWffjhh1q+fLkkqbu7WwsXLlRpaWmflXRnZ6dcLlfkscvlkt/vj+LYAAAktkHfi3vbtm06d+6cnnjiCdXW1mrZsmXau3evxo4dq/b2dv30pz/VhAkT9P3vf1/hcDiWMwMAkPCGdFF45MiRKikp0Z49e5Senq6xY8dKkq6//nrNnTtXf/nLXyRJTqezz4rZ7/fL6XRGb2oAABLcgIH+7LPP1N3dHXnc2NioqVOn6tNPP1Vvb2/kOW+99Zbcbrckyev1qq6uTpLU1tamkydPKjc3NxbzAwCQkAY8xX369GmtWbNGwWBQkpSTk6N169appaVFTz75pFJSUhQKhbRw4ULNnj1bklRSUqKWlhbNmzdPaWlpqqmp4RPcAAAMwYCBnjBhgl566aULtnu9Xnm93n5/Ji0tTZs3bx7+dAAAJCmWtQAAGESgAQAwiEADAGAQgQYAwCACDQCAQQQaAACDCDQAAAYRaAAADCLQAAAYRKABADCIQAMAYBCBBgDAIAINAIBBBBoAAIMINAAABhFoAAAMItAAABhEoAEAMIhAAwBgEIEGAMAgAg0AgEEEGgAAgwg0AAAGEWgAAAwi0AAAGESgAQAwiEADAGAQgQYAwCACDQCAQQQaAACDCDQAAAYRaAAADCLQAAAYRKABADCIQAMAYBCBBgDAIAINAIBBBBoAAIMINAAABqUO5knl5eU6ffq0wuGwsrOzVVNTI4fDoVdffVW/+tWvFAqFVFRUpHvvvTfyM1u2bFFjY6NSUlL0y1/+UrfddlvM3gQAAIlmUCvop59+Wn/4wx/0xz/+US6XS9u3b9eZM2f02GOPaefOnXrttdf09ttv6+DBg5KkP/3pTzp8+LBee+017dy5U4899pgCgUBM3wgAAIlkUCvojIwMSVIoFNLnn3+ua665Rvv379esWbOUlZUlSSouLlZjY6Py8vLU2NiokpISpaamKisrSz/4wQ/05ptvsooGcFWbk5+n9hNd8R4DSWJQgZakFStW6G9/+5umTp2qBx54QL///e/ldDoj+10ul/bu3StJ6uzsVGFhYZ99HR0d0ZsaAOKg/USXWlvyon7c6TMPRv2YuPoN+kNi27Zt04EDB5Sbm6va2lqFw+GLPvdS+wAAwMCG9CnukSNHqqSkRHv27LlgVez3+yOnu51O5wX7vr7aBgAAlzZgoD/77DN1d3dHHjc2Nmrq1KnKz8/XoUOH1NnZqd7eXr388svyer2SJK/Xq/r6evX29qqzs1Nvv/228vPzY/cuAABIMANegz59+rTWrFmjYDAoScrJydG6deuUkZGh6upqLVmyRKFQSF6vV7Nnz5YkzZ49WwcOHFBRUZFSUlJUXV0d+aAZAAAY2ICBnjBhgl566aV+9y1YsEALFizod9/999+v+++/f3jTAQCQpLiTGAAABhFoAAAMItAAABhEoAEAMIhAAwBgEIEGAMAgAg0AgEEEGgAAgwg0AAAGEWgAAAwi0AAAGESgAQAwiEADAGAQgQYAwCACDQCAQQQaAACDCDQAAAYRaAAADCLQAAAYRKABADAoNd4DAEAszMnPU/uJrniPAVw2Ag0gIbWf6FJrS15Ujzl95sGoHg+4FE5xAwBgEIEGAMAgAg0AgEEEGgAAgwg0AAAGEWgAAAwi0AAAGESgAQAwiEADAGAQgQYAwCACDQCAQQQaAACDCDQAAAYRaAAADCLQAAAYRKABADAodaAn+P1+VVdXq6urSykpKSooKNB9992nQ4cOaeXKlfrOd74jSZo4caKefPJJSVIgEFBVVZX+8Y9/yOFw6LHHHtO0adNi+04AAEggAwZ65MiRqqqqUm5uroLBoCorK+Xz+fStb31L06dP1/PPP3/Bz2zbtk05OTl65plntG/fPm3cuFG1tbUxeQMAACSiAU9xjxs3Trm5uZKk9PR0ud1u+f3+S/6Mz+fT4sWLJUkFBQX66KOP1N3dHYVxAQBIDkO6Bn3q1Cm9/vrrys/PlyS9//77Ki4uVllZmfbv3x95XkdHh1wuV+Sxy+VSR0dHlEYGACDxDXiK+7xgMKjVq1dr2bJlmjx5srKysrR37145HA4dPXpUK1asUG1trSZMmBDLeQEASAqDWkGfO3dOVVVVuummm1RZWSlJcjgccjgckqQbbrhBM2bM0HvvvSdJcjqdfU6D+/1+OZ3OaM8OAEDCGlSg169frzFjxqi6ujqyraurS+FwWNJXp7SPHDmiKVOmSJK8Xq92794tSdq3b5/Gjx+vzMzMaM8OAEDCGvAUd0tLi+rq6jRt2jQVFxdLkhYtWqSUlBS9+OKLSk1NVTgc1j333KPJkydLkpYvX66qqip5vV5dc8012rx5c0zfBAAAiWbAQM+cOVNtbW397isvL+93e0ZGhp599tnhTQYAQBLjTmIAABhEoAEAMIhAAwBgEIEGAMAgAg0AgEEEGgAAgwg0AAAGEWgAAAwi0AAAGESgAQAwiEADAGAQgQYAwCACDQCAQQQaAACDCDQAAAYRaAAADCLQAAAYRKABADCIQAMAYBCBBgDAIAINAIBBBBoAAIMINAAABhFoAAAMItAAABhEoAEAMIhAAwBgEIEGAMAgAg0AgEEEGgAAgwg0AAAGEWgAAAwi0AAAGESgAQAwiEADAGAQgQYAwCACDQCAQQQaAACDBgy03+/XsmXL9MMf/lALFizQ448/rnA4LEnasWOHPB6PPB6Pdu7cGfmZ3t5ePfjgg/J4PLrtttt06NCh2L0DAAASUOpATxg5cqSqqqqUm5urYDCoyspK+Xw+ud1u7dq1S/X19ZKkhQsXqqCgQBMnTlR9fb16enrU1NSktrY2rVq1Sk1NTUpJYcEOAMBgDFjMcePGKTc3V5KUnp4ut9stv98vn8+noqIiORwOORwOzZ8/Xz6fT5LU2NioxYsXS5LcbrcyMzPV2toaw7cBAEBiGdKS9tSpU3r99deVn5+vzs5OOZ3OyD6Xy6WOjg5JUmdnp1wuV599fr8/SiMDAJD4Bh3oYDCo1atXa9myZZo8eXLkOnR/LrUPAAAMbFCBPnfunKqqqnTTTTepsrJSkuR0OiMrZumrD5NlZWVF9n19xez3+/ustgEAwKUNKtDr16/XmDFjVF1dHdnm8XjU0NCgQCCgQCCghoYGeTweSZLX61VdXZ0kqa2tTSdPnoxcxwYAAAMb8FPcLS0tqqur07Rp01RcXCxJWrRokZYuXaqysrLItvLycmVnZ0uSSkpK1NLSonnz5iktLU01NTV8ghvARc3Jz1P7ia54jwGYMmCgZ86cqba2tn73VVRUqKKi4oLtaWlp2rx587CHA5Ac2k90qbUlL6rHnD7zYFSPB1xpLGsBADCIQAMAYBCBBgDAIAINAIBBBBoAAIMINAAABhFoAAAMItAAABhEoAEAMIhAAwBgEIEGAMAgAg0AgEEEGgAAgwg0AAAGEWgAAAwi0AAAGESgAQAwiEADAGAQgQYAwCACDQCAQQQaAACDCDQAAAYRaAAADCLQAAAYRKABADAoNd4DALh6zMnPU/uJrniPASQFAg1g0NpPdKm1JS/qx50+82DUjwlc7TjFDQCAQQQaAACDCDQAAAYRaAAADCLQAAAYRKABADCIQAMAYBCBBgDAIAINAIBBBBoAAIMINAAABg0q0Bs2bFB+fr7cbndk25///GfNmDFDd955p+68806tXr06si8QCGjlypXyer1auHChPvjgg+hPDgBAAhvUl2Xcfvvt+sUvfqHZs2f32T59+nQ9//zzFzx/27ZtysnJ0TPPPKN9+/Zp48aNqq2tjc7EAAAkgUGtoGfNmqXMzMxBH9Tn82nx4sWSpIKCAn300Ufq7u6+vAkBAEhCw7oG/f7776u4uFhlZWXav39/ZHtHR4dcLlfkscvlUkdHx3BeCgCApHLZ3wf93e9+V3v37pXD4dDRo0e1YsUK1dbWasKECdGcDwCApHTZK2iHwyGHwyFJuuGGGzRjxgy99957kiSn0ym/3x95rt/vl9PpHOaoAAAkj8sOdFdXl8LhsKSvTmkfOXJEU6ZMkSR5vV7t3r1bkrRv3z6NHz9+SNewAQBIdoM6xb127drINeY5c+YoPz9fN954o1588UWlpqYqHA7rnnvu0eTJkyVJy5cvV1VVlbxer6655hpt3rw5du8AAIAENKhAb9q0qd/t5eXl/W7PyMjQs88+e/lTAQCQ5LiTGAAABhFoAAAMItAAABhEoAEAMIhAAwBgEIEGAMAgAg0AgEEEGgAAgwg0AAAGEWgAAAwi0AAAGESgAQAwiEADAGAQgQYAwCACDQCAQQQaAACDCDQAAAYRaAAADCLQAAAYRKABADCIQAMAYBCBBgDAIAINAIBBBBoAAIMINAAABhFoAAAMItAAABhEoAEAMIhAAwBgEIEGAMAgAg0AgEEEGgAAg1LjPQCA2JiTn6f2E13xHgPAZSLQQIJqP9Gl1pa8qB5z+syDUT0egIvjFDcAAAYRaAAADCLQAAAYRKABADBowEBv2LBB+fn5crvdfbbv2LFDHo9HHo9HO3fujGzv7e3Vgw8+KI/Ho9tuu02HDh2K/tQAACS4AQN9++23q76+vs+248ePa9euXaqvr1d9fb1eeOEF/fOf/5Qk1dfXq6enR01NTdq6dasefPBBhUKh2EwPAECCGjDQs2bNUmZmZp9tPp9PRUVFcjgccjgcmj9/vnw+nySpsbFRixcvliS53W5lZmaqtbU1BqMDAJC4LusadGdnp5xOZ+Sxy+VSR0dHZJ/L5eqzz+/3D3NMAACSy2UFOhwOX9Y+AAAwOJcVaKfTGVkxS5Lf71dWVlZk39dXzH6/v89qGwAADOyyAu3xeNTQ0KBAIKBAIKCGhgZ5PB5JktfrVV1dnSSpra1NJ0+eVG5ubvQmBgAgCQx4L+61a9dq//79kqQ5c+YoPz9fmzZtUllZmYqLiyVJ5eXlys7OliSVlJSopaVF8+bNU1pammpqapSSwp9bAwAwFAMGetOmTf1ur6ioUEVFxQXb09LStHnz5mEPBgBAMmNpCwCAQQQaAACDCDQAAAYRaAAADCLQAAAYRKABADCIQAMAYBCBBgDAIAINAIBBBBoAAIMINAAABhFoAAAMItAAABhEoAEAMIhAAwBgEIEGAMAgAg0AgEEEGgAAgwg0AAAGEWgAAAxKjfcAAKQ5+XlqP9EV7zEAGEKgAQPaT3SptSUvqsecPvNgVI8H4MriFDcAAAYRaAAADOIUNzAE//1fc+Q/2R7146YqPerHBHB1I9DAEPhPtuvX//k/UT/uqlOTo35MAFc3TnEDAGAQgQYAwCACDQCAQVyDRsKK1Qe6AOBKINBIWLH4QBcf5gJwpRBomMBqFwD6ItAwgdUuAPRFoIEElar0qN+PmxuqAFcOgQYSVK+CnJUArmL8mRUAAAaxggYMiMXpaABXNwINGMDpaADfxCluAAAMGvYKurCwUKNGjVJaWpok6YknntCUKVO0Y8cOvfDCC5KkJUuWaOnSpcN9KQAAkkZUTnH/5je/0fjx4yOPjx8/rl27dqm+vl6StHDhQhUUFGjixInReDkAABJeTE5x+3w+FRUVyeFwyOFwaP78+fL5fLF4KQAAElJUAn3XXXfpjjvu0NatW/Xll1+qs7NTTqczst/lcqmjoyMaLwUAQFIY9inu2tpaOZ1OnT17Vvfff7+ee+45hcPhaMwGAEDSGvYK+vxKecyYMSotLdW7774rp9PZZ8Xs9/uVlZU13JcCACBpDCvQPT09CgQCkqTe3l75fD653W55PB41NDQoEAgoEAiooaFBHo8nKgMDAJAMhnWK+9NPP9Xdd9+tUCikUCikm2++WStXrtTo0aNVVlam4uJiSVJ5ebmys7OjMS8AAElhWIGeMGGC9uzZ0+++iooKVVRUDOfwAAAkLe4kBgCAQQQaAACD+LIMAHEXi2/zSlV6VI8HXGkEGkDcJfu3efELCvpDoAEMGt9bHRvJ/gsK+kegAQxaLEIiEROgPwQaQELitDGudgQaQELitDGudgQaQ/Lf/zVH/pPt8R4DABIegcaQ+E+2cw0SAK4AblQCAIBBrKATGKejgejiz8xwJRHoBBaL09GcikYy48/McCVxihsAAIMINAAABhFoAAAM4ho0ACQg7qR29SPQAJCAuJPa1Y9T3AAAGESgAQAwiEADAGAQgQYAwCACDQCAQQQaAACDCDQAAAYRaAAADCLQAAAYxJ3EDOB7mwEA30SgDYjF9zZL3JYPAK5mnOIGAMAgAg0AgEEEGgAAgwg0AAAGEWgAAAwi0AAAGESgAQAwiEADAGAQNyoZIu76BQC4EmIW6A8++EAPPPCAzp49q5ycHG3ZskUOhyNWL3fFxOKuX9zxCwDwTTE7xb1hwwatWbNGPp9POTk52r59e6xeCgCAhBOTFXR3d7c++eQTFRQUSJJKS0t19913a82aNf0+PxQKSZK++OKLWIwTVdnZ2Rr5H2eje8xvRf+YsTpuss/K++f9J/v7P3s2+u8/WZ1v3vkGftOIcDgcjvaLtra26uGHH9bu3bsjQ+Tl5endd9/t9/mffvqpjh8/Hu0xAAAwb9KkSbr22msv2B6TFfRQm5+RkaFJkyYpPT1dKSl8sBwAkPhCoZCCwaAyMjL63R+TQDudTnV0dEQenzhxQk6n86LPT09P7/e3BwAAklVMlqvf/va3df3112vfvn2SpLq6Onm93li8FAAACSkm16Al6ejRo6qurlZPT4+ys7O1ZcuWiy7jAQBAXzELNAAAuHx8IgsAAIMINAAABiXkvbgT9Tajl2PDhg1qbm5WV1eX2tra4j1OXPn9flVXV6urq0spKSkqKCjQfffdpxEjRsR7tLgpLy/X6dOnFQ6HlZ2drZqamqT9t/J1Dz/8sGpra5P630xhYaFGjRqltLQ0SdITTzyhKVOmxHmq+Onp6dEjjzyid999V6mpqSorK9NPfvKTmL5mQl6D/vGPf6yVK1eqoKBAjz/+uNLT0y96F7NE9/bbbys7O1uzZ89O6v9sJKmrq0udnZ3Kzc1VMBhUZWWllixZovnz58d7tLg5c+ZM5MObjz76qEaPHp20/1bOO3z4sHbv3q2XX345qf/NFBYWaufOnRo/fny8RzFh/fr1uu6667Ry5UpJX91gK9Z/Hpxwp7j7u82oz+eL81TxM2vWLGVmZsZ7DBPGjRun3NxcSV/97b3b7Zbf74/zVPF1Ps6hUEiff/55Up9NkKRgMKgtW7bogQceiPcoMCQQCOiNN97Qz372s8i2K3HvjoQLdEdHR5+bolx33XVJ/58wLnTq1Cm9/vrrys/Pj/cocbdixQrdeuut+vDDD7V8+fJ4jxNXTz31lEpLSzV27Nh4j2LCXXfdpTvuuENbt27Vl19+Ge9x4uaTTz7R2LFjtWnTJpWUlOjnP/+5Pvnkk5i/bsIFOgHP2CPKgsGgVq9erWXLlmnyZL7qc9u2bTpw4IByc3NVW1sb73Hi5ujRozpy5IgWLVoU71FMqK2t1Z49e/Tiiy/q2LFjeu655+I9Utz09vbqgw8+0Ny5c1VfX6+5c+equro65q+bcIEe6m1GkVzOnTunqqoq3XTTTaqsrIz3OGaMHDlSJSUl2rNnT7xHiZt33nlHx44d09y5c1VYWCjpq+uw//rXv+I8WXyc/39zzJgxKi0tveiXHSUDp9OpjIyMyBm3BQsW6O9//3vMXzfhAs1tRnEp69ev15gxY67Ib7/WffbZZ+ru7o48bmxs1NSpU+M4UXyVlZXprbfeUnNzs5qbmyVJzc3NSXm6u6enR4FAQNJXq0efzye32x3nqeInMzNTbrdbf/3rXyVJBw4c0LRp02L+ugn5KW5uM/r/1q5dq/3796uzs1NZWVnKz8/Xpk2b4j1WXLS0tKisrEzTpk2LfGvaokWLtHTp0jhPFh8ff/yx1qxZo2AwKEnKycnRunXr+FDh/3G73Un7Ke6PP/5Yd999t0KhkEKhkG6++WY99NBDGj16dLxHi5tjx47poYceUk9PjzIyMvTII4/E/BJZQgYaAICrXcKd4gYAIBEQaAAADCLQAAAYRKABADCIQAMAYBCBBgDAIAINAIBBBBoAAINS4z0AgPh49dVXtXbt2sjjL7/8UjNmzNDzzz8fx6kAnMedxAAoEAho8eLFWrZsmX70ox/FexwA4hQ3kPRCoZDuvfde3XLLLcQZMIRAA0lu69atOnv2rB566KF4jwLga7gGDSSxV155Ra+88orq6uqUlpYW73EAfA3XoIEk9d5776myslK//e1vdeONN8Z7HADfwAoaSFJvvPGGTp8+rbKyssi2mTNnavv27XGcCsB5rKABADCID4kBAGAQgQYAwCACDQCAQQQaAACDCDQAAAYRaAAADCLQAAAYRKABADDofwHWOJs0yhh6tQAAAABJRU5ErkJggg==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”: “8033a889”, “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”: “5b61d912”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:26.088951Z”, “iopub.status.busy”: “2022-02-09T16:35:26.088424Z”, “iopub.status.idle”: “2022-02-09T16:35:26.093791Z”, “shell.execute_reply”: “2022-02-09T16:35:26.093310Z”

}

}, “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”: “eee2c2b2”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:26.098038Z”, “iopub.status.busy”: “2022-02-09T16:35:26.097521Z”, “iopub.status.idle”: “2022-02-09T16:35:26.114899Z”, “shell.execute_reply”: “2022-02-09T16:35:26.114415Z”

}

}, “outputs”: [], “source”: [

“population.writeto(“saved_pop.h5”)”

]

}, {

“cell_type”: “code”, “execution_count”: 17, “id”: “26704541”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:26.120134Z”, “iopub.status.busy”: “2022-02-09T16:35:26.118765Z”, “iopub.status.idle”: “2022-02-09T16:35:26.146691Z”, “shell.execute_reply”: “2022-02-09T16:35:26.145753Z”

}

}, “outputs”: [], “source”: [

“reloaded_population = popsynth.Population.from_file(“saved_pop.h5”)”

]

}, {

“cell_type”: “code”, “execution_count”: 18, “id”: “376152bc”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:26.151516Z”, “iopub.status.busy”: “2022-02-09T16:35:26.151002Z”, “iopub.status.idle”: “2022-02-09T16:35:26.155816Z”, “shell.execute_reply”: “2022-02-09T16:35:26.156515Z”

}

}, “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”: “8e9e745c”, “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”: “cda301ce”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:26.161223Z”, “iopub.status.busy”: “2022-02-09T16:35:26.160186Z”, “iopub.status.idle”: “2022-02-09T16:35:26.175245Z”, “shell.execute_reply”: “2022-02-09T16:35:26.174818Z”

}

}, “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”: “f386e16e”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:26.179541Z”, “iopub.status.busy”: “2022-02-09T16:35:26.178996Z”, “iopub.status.idle”: “2022-02-09T16:35:26.205022Z”, “shell.execute_reply”: “2022-02-09T16:35:26.204559Z”

}

}, “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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}, “metadata”: {}, “output_type”: “display_data”

}, {

“data”: {
“text/markdown”: [
“## Spatial Function”

], “text/plain”: [

“<IPython.core.display.Markdown object>”

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}, “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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“<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”, ” <tr>n”, ” <th>0</th>n”, ” <td>Lambda</td>n”, ” <td>0.5</td>n”, ” </tr>n”, ” <tr>n”, ” <th>1</th>n”, ” <td>delta</td>n”, ” <td>-2.0</td>n”, ” </tr>n”, ” <tr>n”, ” <th>2</th>n”, ” <td>r_max</td>n”, ” <td>5.0</td>n”, ” </tr>n”, ” </tbody>n”, “</table>n”, “</div>”

], “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”: [
“<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: []”

]

}, “metadata”: {}, “output_type”: “display_data”

}, {

“data”: {
“text/markdown”: [
“## stellar_mass”

], “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”, ” <tr>n”, ” <th>0</th>n”, ” <td>mu</td>n”, ” <td>0.0</td>n”, ” </tr>n”, ” <tr>n”, ” <th>1</th>n”, ” <td>sigma</td>n”, ” <td>1.0</td>n”, ” </tr>n”, ” </tbody>n”, “</table>n”, “</div>”

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” parameter valuen”, “0 mu 0.0n”, “1 sigma 1.0”

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}, “metadata”: {}, “output_type”: “display_data”

}

], “source”: [

“ps.display()”

]

}, {

“cell_type”: “code”, “execution_count”: 21, “id”: “ec0ab054”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:26.210190Z”, “iopub.status.busy”: “2022-02-09T16:35:26.209669Z”, “iopub.status.idle”: “2022-02-09T16:35:26.368584Z”, “shell.execute_reply”: “2022-02-09T16:35:26.369131Z”

}, “lines_to_next_cell”: 2

}, “outputs”: [

{
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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”: “62cbdaa6”, “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”: “9511465e”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:26.372173Z”, “iopub.status.busy”: “2022-02-09T16:35:26.371647Z”, “iopub.status.idle”: “2022-02-09T16:35:31.020672Z”, “shell.execute_reply”: “2022-02-09T16:35:31.014581Z”

}

}, “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”: “e985c5252bec4476b905b959f7ab8347”, “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”: “29b8646f64e64120b3262202e5a99c20”, “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”: “47ce5f84”, “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”: “00c27133”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:31.027427Z”, “iopub.status.busy”: “2022-02-09T16:35:31.026500Z”, “iopub.status.idle”: “2022-02-09T16:35:31.029596Z”, “shell.execute_reply”: “2022-02-09T16:35:31.029148Z”

}

}, “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”: “9bc3170b”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:31.033176Z”, “iopub.status.busy”: “2022-02-09T16:35:31.032638Z”, “iopub.status.idle”: “2022-02-09T16:35:31.036801Z”, “shell.execute_reply”: “2022-02-09T16:35:31.036347Z”

}

}, “outputs”: [], “source”: [

“ps.write_to(“/tmp/my_pop_synth.yml”)”

]

}, {

“cell_type”: “markdown”, “id”: “3e5ba309”, “metadata”: {}, “source”: [

“but our population synth is also serialized to our population!”

]

}, {

“cell_type”: “code”, “execution_count”: 25, “id”: “cc4a5e13”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:31.042479Z”, “iopub.status.busy”: “2022-02-09T16:35:31.041570Z”, “iopub.status.idle”: “2022-02-09T16:35:31.044126Z”, “shell.execute_reply”: “2022-02-09T16:35:31.044550Z”

}

}, “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”: “b5272ac3”, “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”: {

“codemirror_mode”: {
“name”: “ipython”, “version”: 3

}, “file_extension”: “.py”, “mimetype”: “text/x-python”, “name”: “python”, “nbconvert_exporter”: “python”, “pygments_lexer”: “ipython3”, “version”: “3.9.10”

}, “widgets”: {

“application/vnd.jupyter.widget-state+json”: {
“state”: {
“03d6ce458a2a4f28997e1fdb1b981e32”: {

“model_module”: “@jupyter-widgets/controls”, “model_module_version”: “1.5.0”, “model_name”: “HBoxModel”, “state”: {

“_dom_classes”: [], “_model_module”: “@jupyter-widgets/controls”, “_model_module_version”: “1.5.0”, “_model_name”: “HBoxModel”, “_view_count”: null, “_view_module”: “@jupyter-widgets/controls”, “_view_module_version”: “1.5.0”, “_view_name”: “HBoxView”, “box_style”: “”, “children”: [

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”, 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“cell_type”: “markdown”, “id”: “d3e2cbc8”, “metadata”: {}, “source”: [

“# Core Conceptn”, “popsynth core function is to create observed surveys from latent population models. n”, “n”, “First, let’s define what a population of objects is in terms of an”, “generative model. The two main ingredients are the objects’ spatialn”, “distribution ($\lambda(\vec{r}; \vec{\psi})$) and the distribution ofn”, “their inherent properties ($\pi(\vec{\phi} | \vec{\psi})$). Here,n”, “$\vec{\psi}$ are the latent population parameters, $\vec{r}$ are then”, “spatial locations of the objects, and $\vec{\phi}$ are the propertiesn”, “of the individual objects (luminosity, spin, viewing angle, mass,n”, “etc.). The spatial distribution is defined such that:n”, “n”, “$$\frac{d \Lambda}{dt}(\vec{\psi}) = \int d r \frac{dV}{dr} \lambda(\vec{r}; \vec{\psi})$$n”, “n”, “is the intensity of objects for a given set of populationn”, “parameters. With these definitions we can define the probability forn”, “an object to have position $\vec{r}$ and properties $\vec{\phi}$ asn”, “n”, “$$\pi(\vec{r}, \vec{\phi} | \vec{\psi}) = \frac{\lambda(\vec{r}; \vec{\psi}) \pi(\vec{\phi} | \vec{\psi})}{ \int d r \frac{dV}{dr} \lambda(\vec{r}; \vec{\psi})} $$n”, “n”, “popsynth allows you to specify these spatial and propertyn”, “distributions in an object-oriented way to create surveys. The finaln”, “ingredient to creating a sample for a survey is knowing how manyn”, “objects to sample from the population (before any selection effectsn”, “are applied). Often, we see this number in simulation frameworksn”, “presented as “we draw N objects to guarantee we have enough.” This isn”, “incorrect. A survey takes place over a given period of time ($\Deltan”, “t$) in which observed objects are counted. This is a description of an”, “Poisson process. Thus, the number of objects in a simulation of thisn”, “survey is a draw from a Poisson distribution:n”, “n”, “$$N \sim \mathrm{Poisson}\left(\Delta t \frac{d\Lambda}{dt}\right) \mathrm{.}$$n”, “n”, “Thus, popsynth first numerically integrates the spatialn”, “distribution to determine the Poisson rate parameter for the givenn”, “$\vec{\psi}$, then makes a Poisson draw for the number of objects inn”, “the population survey. For each object, positions and properties aren”, “drawn with arbitrary dependencies between them. Finally, selectionn”, “functions are applied to either latent or observed (with or withoutn”, “measurement error) properties.”

]

}, {

“cell_type”: “markdown”, “id”: “8ca44a40”, “metadata”: {}, “source”: [

Note: If instead we draw a preset number of objects, as is done inn”, “many astrophysical population simulation frameworks, it is equivalentn”, “to running a survey up until that specific number of objects isn”, “detected. This process is distributed as a negative binomial process,n”, “i.e, wait for a number of successes and requires a differentn”, “statistical framework to compare models to data.n”, “n”, “In the following, the process for constructing distributions andn”, “populations is described.”

]

}

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}

}, “nbformat”: 4, “nbformat_minor”: 5

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{

“cell_type”: “markdown”, “id”: “eed5217a”, “metadata”: {}, “source”: [

“# Distributions”

]

}, {

“cell_type”: “markdown”, “id”: “ad09d8ba”, “metadata”: {}, “source”: [

“The basic required object to create a population synth are a spatialn”, “and (optional if a derived luminosity sampler is create) luminosityn”, “distribution.n”

]

}, {

“cell_type”: “code”, “execution_count”: 1, “id”: “c10321c4”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:53.726211Z”, “iopub.status.busy”: “2022-02-09T16:34:53.725412Z”, “iopub.status.idle”: “2022-02-09T16:34:54.640524Z”, “shell.execute_reply”: “2022-02-09T16:34:54.641319Z”

}

}, “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 networkx as nxn”, “import numpy as npn”, “import warningsn”, “n”, “warnings.simplefilter(“ignore”)”

]

}, {

“cell_type”: “markdown”, “id”: “db6e064d”, “metadata”: {}, “source”: [

popsynth comes with several built in distributions included”

]

}, {

“cell_type”: “code”, “execution_count”: 2, “id”: “e6b565ab”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:54.645544Z”, “iopub.status.busy”: “2022-02-09T16:34:54.645007Z”, “iopub.status.idle”: “2022-02-09T16:34:57.318719Z”, “shell.execute_reply”: “2022-02-09T16:34:57.317494Z”

}

}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“Distributionn”, “BPLDistributionn”, “SFRDistributionn”, “ZPowerCosmoDistributionn”, “DeltaDistributionn”, “FlatlandDistributionn”, “Log10NormalDistributionn”, “LogNormalDistributionn”, “ParetoDistributionn”, “SchechterDistributionn”, “ConstantSphericalDistributionn”, “ZPowerSphericalDistributionn”, “SpiralGalaxyDistributionn”

]

}

], “source”: [

“import popsynthn”, “popsynth.update_logging_level(“INFO”)n”, “n”, “n”, “popsynth.list_available_distributions()”

]

}, {

“cell_type”: “markdown”, “id”: “6de3d322”, “metadata”: {}, “source”: [

“## Creating a simple population synthn”, “n”, “First we create a spatial distribution, in the case, a Spherical distribution with a power law density.n”

]

}, {

“cell_type”: “code”, “execution_count”: 3, “id”: “a9483dd8”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:57.323236Z”, “iopub.status.busy”: “2022-02-09T16:34:57.322709Z”, “iopub.status.idle”: “2022-02-09T16:34:57.326979Z”, “shell.execute_reply”: “2022-02-09T16:34:57.326530Z”

}

}, “outputs”: [], “source”: [

“spatial_distribution = popsynth.ZPowerSphericalDistribution()n”, “n”, “spatial_distribution.Lambda = 30n”, “spatial_distribution.delta = -2n”, “spatial_distribution.r_max = 10n”

]

}, {

“cell_type”: “markdown”, “id”: “d1019dfb”, “metadata”: {}, “source”: [

“And now we create a powerlaw luminosity distribution”

]

}, {

“cell_type”: “code”, “execution_count”: 4, “id”: “0b8f5629”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:57.330782Z”, “iopub.status.busy”: “2022-02-09T16:34:57.330265Z”, “iopub.status.idle”: “2022-02-09T16:34:57.333702Z”, “shell.execute_reply”: “2022-02-09T16:34:57.334106Z”

}

}, “outputs”: [], “source”: [

“luminosity_distribution = popsynth.ParetoDistribution()n”, “n”, “luminosity_distribution.alpha = 1.5n”, “luminosity_distribution.Lmin = 1n”

]

}, {

“cell_type”: “markdown”, “id”: “c159f1a6”, “metadata”: {}, “source”: [

“Combining these together with a random seed, we have a population synthesis object”

]

}, {

“cell_type”: “code”, “execution_count”: 5, “id”: “93fafd1c”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:57.337778Z”, “iopub.status.busy”: “2022-02-09T16:34:57.336304Z”, “iopub.status.idle”: “2022-02-09T16:34:57.340628Z”, “shell.execute_reply”: “2022-02-09T16:34:57.340169Z”

}

}, “outputs”: [], “source”: [

“pop_gen = popsynth.PopulationSynth(luminosity_distribution=luminosity_distribution, n”, ” spatial_distribution = spatial_distribution,n”, ” seed=1234n”, ” n”, ” n”, ” )”

]

}, {

“cell_type”: “code”, “execution_count”: 6, “id”: “3b17d596”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:57.346646Z”, “iopub.status.busy”: “2022-02-09T16:34:57.346136Z”, “iopub.status.idle”: “2022-02-09T16:34:57.372591Z”, “shell.execute_reply”: “2022-02-09T16:34:57.372163Z”

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“## Luminosity Function”

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“$\displaystyle \frac{\alpha L_{\rm min}^{\alpha}}{L^{\alpha+1}}$”

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“## Spatial Function”

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“data”: {
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“<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”, ” <tr>n”, ” <th>0</th>n”, ” <td>Lambda</td>n”, ” <td>30</td>n”, ” </tr>n”, ” <tr>n”, ” <th>1</th>n”, ” <td>delta</td>n”, ” <td>-2</td>n”, ” </tr>n”, ” <tr>n”, ” <th>2</th>n”, ” <td>r_max</td>n”, ” <td>10</td>n”, ” </tr>n”, ” </tbody>n”, “</table>n”, “</div>”

], “text/plain”: [

” parameter valuen”, “0 Lambda 30n”, “1 delta -2n”, “2 r_max 10”

]

}, “metadata”: {}, “output_type”: “display_data”

}

], “source”: [

“pop_gen.display()”

]

}, {

“cell_type”: “code”, “execution_count”: 7, “id”: “27c22d30”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:57.376729Z”, “iopub.status.busy”: “2022-02-09T16:34:57.376207Z”, “iopub.status.idle”: “2022-02-09T16:34:57.498698Z”, “shell.execute_reply”: “2022-02-09T16:34:57.497845Z”

}

}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m The volume integral is 2304.659941 u001b[0mn”

]

}, {

“data”: {
“application/vnd.jupyter.widget-view+json”: {
“model_id”: “002bafa414be4aae8d9168e1571ae062”, “version_major”: 2, “version_minor”: 0

}, “text/plain”: [

“Drawing distances: 0%| | 0/2257 [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 2257 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[33mu001b[2m WARNING u001b[0m| u001b[33mu001b[2m NO HIDDEN OBJECTS u001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m Detected 2257 distances u001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m Detected 2257 objects out to a distance of 10.00 u001b[0mn”

]

}

], “source”: [

“population = pop_gen.draw_survey()”

]

}, {

“cell_type”: “code”, “execution_count”: 8, “id”: “c26a3091”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:57.516832Z”, “iopub.status.busy”: “2022-02-09T16:34:57.502168Z”, “iopub.status.idle”: “2022-02-09T16:34:58.558869Z”, “shell.execute_reply”: “2022-02-09T16:34:58.556633Z”

}

}, “outputs”: [

{
“data”: {
“application/vnd.jupyter.widget-view+json”: {
“model_id”: “3a444709fa7145ad9ff547ce5a8c7ab9”, “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”,size=0.7);”

]

}, {

“cell_type”: “markdown”, “id”: “5302b112”, “metadata”: {}, “source”: [

“## Cosmological Distributionsn”, “n”, “If we want to create cosmological spatial distributions, we can usen”, “some of those that are built in.”

]

}, {

“cell_type”: “code”, “execution_count”: 9, “id”: “ad0b7f38”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:58.563123Z”, “iopub.status.busy”: “2022-02-09T16:34:58.561606Z”, “iopub.status.idle”: “2022-02-09T16:34:58.565562Z”, “shell.execute_reply”: “2022-02-09T16:34:58.565995Z”

}

}, “outputs”: [], “source”: [

“spatial_distribution = popsynth.ZPowerCosmoDistribution()n”, “spatial_distribution.Lambda = 100n”, “spatial_distribution.delta = -2n”, “spatial_distribution.r_max = 10n”

]

}, {

“cell_type”: “markdown”, “id”: “97a7a5be”, “metadata”: {}, “source”: [

“These distributions know about the cosmological Universe and haven”, “their fluxes computed using the luminosity distance rather than linearn”, “distace.”

]

}, {

“cell_type”: “code”, “execution_count”: 10, “id”: “6b4782df”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:58.570542Z”, “iopub.status.busy”: “2022-02-09T16:34:58.569600Z”, “iopub.status.idle”: “2022-02-09T16:34:58.573589Z”, “shell.execute_reply”: “2022-02-09T16:34:58.573126Z”

}

}, “outputs”: [], “source”: [

“luminosity_distribution = popsynth.SchechterDistribution()n”, “n”, “luminosity_distribution.alpha = 1.5n”, “luminosity_distribution.Lmin = 1n”, “n”

]

}, {

“cell_type”: “code”, “execution_count”: 11, “id”: “01b3bb5c”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:58.577797Z”, “iopub.status.busy”: “2022-02-09T16:34:58.577266Z”, “iopub.status.idle”: “2022-02-09T16:34:58.580351Z”, “shell.execute_reply”: “2022-02-09T16:34:58.580755Z”

}

}, “outputs”: [], “source”: [

“pop_gen = popsynth.PopulationSynth(luminosity_distribution=luminosity_distribution, n”, ” spatial_distribution = spatial_distribution,n”, ” seed=1234n”, ” n”, ” n”, ” )”

]

}, {

“cell_type”: “code”, “execution_count”: 12, “id”: “851b6674”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:58.584925Z”, “iopub.status.busy”: “2022-02-09T16:34:58.584410Z”, “iopub.status.idle”: “2022-02-09T16:34:58.599901Z”, “shell.execute_reply”: “2022-02-09T16:34:58.600301Z”

}

}, “outputs”: [

{
“data”: {
“text/markdown”: [
“## Luminosity Function”

], “text/plain”: [

“<IPython.core.display.Markdown object>”

]

}, “metadata”: {}, “output_type”: “display_data”

}, {

“data”: {
“text/latex”: [
“$\displaystyle \frac{1}{L_{\rm min}^{1+\alpha}\Gamma\left(1+\alpha\right)} L^{\alpha} \exp\left[ - \frac{L}{L_{\rm min}}\right]$”

], “text/plain”: [

“<IPython.core.display.Math 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”, ” <tr>n”, ” <th>0</th>n”, ” <td>alpha</td>n”, ” <td>1.5</td>n”, ” </tr>n”, ” <tr>n”, ” <th>1</th>n”, ” <td>Lmin</td>n”, ” <td>1.0</td>n”, ” </tr>n”, ” </tbody>n”, “</table>n”, “</div>”

], “text/plain”: [

” parameter valuen”, “0 alpha 1.5n”, “1 Lmin 1.0”

]

}, “metadata”: {}, “output_type”: “display_data”

}, {

“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”: {
“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”, ” <tr>n”, ” <th>0</th>n”, ” <td>Lambda</td>n”, ” <td>100</td>n”, ” </tr>n”, ” <tr>n”, ” <th>1</th>n”, ” <td>delta</td>n”, ” <td>-2</td>n”, ” </tr>n”, ” <tr>n”, ” <th>2</th>n”, ” <td>r_max</td>n”, ” <td>10</td>n”, ” </tr>n”, ” </tbody>n”, “</table>n”, “</div>”

], “text/plain”: [

” parameter valuen”, “0 Lambda 100n”, “1 delta -2n”, “2 r_max 10”

]

}, “metadata”: {}, “output_type”: “display_data”

}

], “source”: [

“pop_gen.display()”

]

}, {

“cell_type”: “code”, “execution_count”: 13, “id”: “58f07885”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:58.612902Z”, “iopub.status.busy”: “2022-02-09T16:34:58.607552Z”, “iopub.status.idle”: “2022-02-09T16:35:03.315375Z”, “shell.execute_reply”: “2022-02-09T16:35:03.315783Z”

}

}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m The volume integral is 742.019999 u001b[0mn”

]

}, {

“data”: {
“application/vnd.jupyter.widget-view+json”: {
“model_id”: “56eb7d38419941c5a99f982d28e14257”, “version_major”: 2, “version_minor”: 0

}, “text/plain”: [

“Drawing distances: 0%| | 0/715 [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 715 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[33mu001b[2m WARNING u001b[0m| u001b[33mu001b[2m NO HIDDEN OBJECTS u001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m Detected 715 distances u001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m Detected 715 objects out to a distance of 9.67 u001b[0mn”

]

}

], “source”: [

“population = pop_gen.draw_survey()”

]

}, {

“cell_type”: “code”, “execution_count”: 14, “id”: “4ef17318”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:03.334035Z”, “iopub.status.busy”: “2022-02-09T16:35:03.333487Z”, “iopub.status.idle”: “2022-02-09T16:35:03.722528Z”, “shell.execute_reply”: “2022-02-09T16:35:03.714795Z”

}

}, “outputs”: [

{
“data”: {
“application/vnd.jupyter.widget-view+json”: {
“model_id”: “0ad576d0f9a04ff7b8a31832c1e2f65d”, “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(cmap=”viridis”, background_color=”black”,size=0.7);”

]

}, {

“cell_type”: “markdown”, “id”: “369e5af9”, “metadata”: {}, “source”: [

“The cosmological parameters used when simulating are stored in the cosmology object:”

]

}, {

“cell_type”: “code”, “execution_count”: 15, “id”: “62923c33”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:03.727514Z”, “iopub.status.busy”: “2022-02-09T16:35:03.726971Z”, “iopub.status.idle”: “2022-02-09T16:35:03.731742Z”, “shell.execute_reply”: “2022-02-09T16:35:03.731312Z”

}

}, “outputs”: [

{
“data”: {
“text/plain”: [
“0.3070000112056732”

]

}, “execution_count”: 15, “metadata”: {}, “output_type”: “execute_result”

}

], “source”: [

“popsynth.cosmology.Om”

]

}, {

“cell_type”: “code”, “execution_count”: 16, “id”: “3311f206”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:03.736034Z”, “iopub.status.busy”: “2022-02-09T16:35:03.735521Z”, “iopub.status.idle”: “2022-02-09T16:35:03.740002Z”, “shell.execute_reply”: “2022-02-09T16:35:03.740399Z”

}

}, “outputs”: [

{
“data”: {
“text/plain”: [
“67.69999694824219”

]

}, “execution_count”: 16, “metadata”: {}, “output_type”: “execute_result”

}

], “source”: [

“popsynth.cosmology.h0”

]

}, {

“cell_type”: “code”, “execution_count”: 17, “id”: “d2fde9e7”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:03.744105Z”, “iopub.status.busy”: “2022-02-09T16:35:03.742513Z”, “iopub.status.idle”: “2022-02-09T16:35:03.748621Z”, “shell.execute_reply”: “2022-02-09T16:35:03.748186Z”

}, “lines_to_next_cell”: 2

}, “outputs”: [

{
“data”: {
“text/plain”: [
“0.6929130577203088”

]

}, “execution_count”: 17, “metadata”: {}, “output_type”: “execute_result”

}

], “source”: [

“popsynth.cosmology.Ode”

]

}, {

“cell_type”: “markdown”, “id”: “0c0a087a”, “metadata”: {}, “source”: [

“<div class=”alert alert-info”>n”, “n”, “Note: The values of Om and h0 can be changed and will change the values of all cosmological calculationsn”, “n”, “</div>n”, “n”, “n”

]

}, {

“cell_type”: “code”, “execution_count”: 18, “id”: “72acfc2f”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:03.752554Z”, “iopub.status.busy”: “2022-02-09T16:35:03.752043Z”, “iopub.status.idle”: “2022-02-09T16:35:03.800751Z”, “shell.execute_reply”: “2022-02-09T16:35:03.799754Z”

}

}, “outputs”: [], “source”: [

“popsynth.cosmology.Om=0.7”

]

}, {

“cell_type”: “code”, “execution_count”: 19, “id”: “4289ac13”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:03.806003Z”, “iopub.status.busy”: “2022-02-09T16:35:03.805454Z”, “iopub.status.idle”: “2022-02-09T16:35:03.808615Z”, “shell.execute_reply”: “2022-02-09T16:35:03.808069Z”

}

}, “outputs”: [

{
“data”: {
“text/plain”: [
“0.299913080846911”

]

}, “execution_count”: 19, “metadata”: {}, “output_type”: “execute_result”

}

], “source”: [

“popsynth.cosmology.Ode”

]

}, {

“cell_type”: “markdown”, “id”: “5d151ad5”, “metadata”: {}, “source”: [

“Let’s re run the last simulation to see how this changes things”

]

}, {

“cell_type”: “code”, “execution_count”: 20, “id”: “e6033c87”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:03.812770Z”, “iopub.status.busy”: “2022-02-09T16:35:03.812131Z”, “iopub.status.idle”: “2022-02-09T16:35:03.816579Z”, “shell.execute_reply”: “2022-02-09T16:35:03.817021Z”

}

}, “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”

]

}

], “source”: [

“pop_gen.clean()”

]

}, {

“cell_type”: “code”, “execution_count”: 21, “id”: “559b765d”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:03.821716Z”, “iopub.status.busy”: “2022-02-09T16:35:03.821194Z”, “iopub.status.idle”: “2022-02-09T16:35:03.887820Z”, “shell.execute_reply”: “2022-02-09T16:35:03.887366Z”

}

}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m The volume integral is 378.842250 u001b[0mn”

]

}, {

“data”: {
“application/vnd.jupyter.widget-view+json”: {
“model_id”: “111d92fdc34d44e9af458edf1da70ea4”, “version_major”: 2, “version_minor”: 0

}, “text/plain”: [

“Drawing distances: 0%| | 0/359 [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 359 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[33mu001b[2m WARNING u001b[0m| u001b[33mu001b[2m NO HIDDEN OBJECTS u001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m Detected 359 distances u001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m Detected 359 objects out to a distance of 7.12 u001b[0mn”

]

}

], “source”: [

“population = pop_gen.draw_survey()”

]

}, {

“cell_type”: “code”, “execution_count”: 22, “id”: “00116af6”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:03.914863Z”, “iopub.status.busy”: “2022-02-09T16:35:03.914269Z”, “iopub.status.idle”: “2022-02-09T16:35:04.111119Z”, “shell.execute_reply”: “2022-02-09T16:35:04.106394Z”

}

}, “outputs”: [

{
“data”: {
“application/vnd.jupyter.widget-view+json”: {
“model_id”: “14af51cfb3f3452c8d89c11f36664ad1”, “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”,size=0.7);”

]

}, {

“cell_type”: “code”, “execution_count”: null, “id”: “57c1d108”, “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”: “3.9.10”

}, “widgets”: {

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“encoding”: “base64”, “path”: [

“color”, 0, “data”

]

}, {

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“x”, 0, “data”

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iFj4BTSw/oUkCPwAAgD8Ulvg9YyYhP2IvCD8AAIA/PSkDPuHSJT+CAwY/AACAPwft9T3xRR8/OfIIPwAAgD/L8vU9aqAZPz/jCj8AAIA/wLSIPhO2nzsVqag+AACAP0DdMD7vx+U+jdEOPwAAgD9xHPg9vr0XP5htCz8AAIA/C7O4PvA0ST+Oj8Y+AACAP0omFz7vAAM/w5oOPwAAgD/Wjmo/OGZlP7dEzj0AAIA/huclPvHzLz/Wjf8+AACAPw==”, “encoding”: “base64”, “path”: [

“color”, 0, “data”

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}, {

“data”: “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”, “encoding”: “base64”, “path”: [

“x”, 0, “data”

]

}, {

“data”: “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”, “encoding”: “base64”, “path”: [

“y”, 0, “data”

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”, 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”, 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]

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“data”: 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”, “encoding”: “base64”, “path”: [

“y”, 0, “data”

]

}, {

“data”: 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{
“cells”: [
{

“cell_type”: “markdown”, “id”: “0434e05b”, “metadata”: {}, “source”: [

“# Custom distributions and populationsn”, “n”, “Custom populations can be created either by piecing together existing populations (spatial and luminosity populations) or building them from scratch with distributions.n”, “n”, “popsynth comes loaded with many combinations of typical population distributions. However, we demonstrate here how to create your own.”

]

}, {

“cell_type”: “markdown”, “id”: “b11f7a05”, “metadata”: {}, “source”: [

“## Creating distributionsn”, “n”, “The population samplers rely on distributions. Each population has an internal spatial and luminosity distribution. For example, lets look at a simple spatial distribution:n”

]

}, {

“cell_type”: “code”, “execution_count”: 1, “id”: “273b2f8f”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:48.334211Z”, “iopub.status.busy”: “2022-02-09T16:34:48.333690Z”, “iopub.status.idle”: “2022-02-09T16:34:51.884676Z”, “shell.execute_reply”: “2022-02-09T16:34:51.884085Z”

}

}, “outputs”: [], “source”: [

“%matplotlib inlinen”, “n”, “import numpy as npn”, “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”, “n”, “import popsynthn”, “n”, “popsynth.update_logging_level(“INFO”)n”, “import warningsn”, “n”, “warnings.simplefilter(“ignore”)”

]

}, {

“cell_type”: “code”, “execution_count”: 2, “id”: “c5cbdb64”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:51.891715Z”, “iopub.status.busy”: “2022-02-09T16:34:51.891174Z”, “iopub.status.idle”: “2022-02-09T16:34:51.895093Z”, “shell.execute_reply”: “2022-02-09T16:34:51.894668Z”

}

}, “outputs”: [], “source”: [

“from popsynth.distribution import SpatialDistributionn”, “n”, “n”, “class MySphericalDistribution(SpatialDistribution):n”, “n”, ” # we need this property to register the classn”, “n”, ” _distribution_name = “MySphericalDistribution”n”, “n”, ” def __init__(n”, ” self,n”, ” seed=1234,n”, ” form=None,n”, ” ):n”, “n”, ” # the latex formula for the ditributionn”, ” form = r”4 \pi r2”n”, “n”, ” # we do not need a “truth” dict here becausen”, ” # there are no parametersn”, “n”, ” super(MySphericalDistribution, self).__init__(n”, ” seed=seed,n”, ” name=”sphere”,n”, ” form=form,n”, ” )n”, “n”, ” def differential_volume(self, r):n”, “n”, ” # the differential volume of a spheren”, ” return 4 * np.pi * r * rn”, “n”, ” def transform(self, L, r):n”, “n”, ” # luminosity to fluxn”, ” return L / (4.0 * np.pi * r * r)n”, “n”, ” def dNdV(self, r):n”, “n”, ” # define some crazy change in the number/volume for funn”, “n”, ” return 10.0 / (r + 1) ** 2”

]

}, {

“cell_type”: “markdown”, “id”: “4681d619”, “metadata”: {}, “source”: [

“We simply define the differential volume and how luminosity is transformed to flux in the metric. Here, we have a simple sphere out to some r_max. We can of course subclass this object and add a normalization.n”, “n”, “n”, “Now we define a luminosity distribution.”

]

}, {

“cell_type”: “code”, “execution_count”: 3, “id”: “a4c7200f”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:51.903152Z”, “iopub.status.busy”: “2022-02-09T16:34:51.901950Z”, “iopub.status.idle”: “2022-02-09T16:34:51.903713Z”, “shell.execute_reply”: “2022-02-09T16:34:51.904151Z”

}, “lines_to_next_cell”: 2

}, “outputs”: [], “source”: [

“from popsynth.distribution import LuminosityDistribution, DistributionParametern”, “n”, “n”, “class MyParetoDistribution(LuminosityDistribution):n”, ” _distribution_name = “MyParetoDistribution”n”, “n”, ” Lmin = DistributionParameter(default=1, vmin=0)n”, ” alpha = DistributionParameter(default=2)n”, “n”, ” def __init__(self, seed=1234, name=”pareto”):n”, “n”, ” # the latex formula for the ditributionn”, ” lf_form = r”\frac{\alpha L_{\rm min}^{\alpha}}{L^{\alpha+1}}”n”, “n”, ” super(MyParetoDistribution, self).__init__(n”, ” seed=seed,n”, ” name=”pareto”,n”, ” form=lf_form,n”, ” )n”, “n”, ” def phi(self, L):n”, “n”, ” # the actual function, only for plottingn”, “n”, ” out = np.zeros_like(L)n”, “n”, ” idx = L >= self.Lminn”, “n”, ” out[idx] = self.alpha * self.Lmin ** self.alpha / L[idx] ** (self.alpha + 1)n”, “n”, ” return outn”, “n”, ” def draw_luminosity(self, size=1):n”, ” # how to sample the latent parametersn”, ” return (np.random.pareto(self.alpha, size) + 1) * self.Lmin”

]

}, {

“cell_type”: “markdown”, “id”: “172ff9c2”, “metadata”: {}, “source”: [

“<div class=”alert alert-info”>n”, “n”, “Note: If you want to create a cosmological distribution, inherit from from ComologicalDistribution class!n”, “n”, “</div>n”, “n”, “## Creating a population synthesizern”, “n”, “Now that we have defined our distributions, we can create a population synthesizer that encapsulated them”

]

}, {

“cell_type”: “code”, “execution_count”: 4, “id”: “46034aa0”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:51.910589Z”, “iopub.status.busy”: “2022-02-09T16:34:51.909327Z”, “iopub.status.idle”: “2022-02-09T16:34:51.911184Z”, “shell.execute_reply”: “2022-02-09T16:34:51.911612Z”

}

}, “outputs”: [], “source”: [

“from popsynth.population_synth import PopulationSynthn”, “n”, “n”, “class MyPopulation(PopulationSynth):n”, ” def __init__(self, Lmin, alpha, r_max=5, seed=1234):n”, “n”, ” # instantiate the distributionsn”, ” luminosity_distribution = MyParetoDistribution(seed=seed)n”, “n”, ” luminosity_distribution.alpha = alphan”, ” luminosity_distribution.Lmin = Lminn”, “n”, ” spatial_distribution = MySphericalDistribution(seed=seed)n”, ” spatial_distribution.r_max = r_maxn”, “n”, ” # pass to the super classn”, ” super(MyPopulation, self).__init__(n”, ” spatial_distribution=spatial_distribution,n”, ” luminosity_distribution=luminosity_distribution,n”, ” seed=seed,n”, ” )”

]

}, {

“cell_type”: “code”, “execution_count”: 5, “id”: “64db71b5”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:51.917101Z”, “iopub.status.busy”: “2022-02-09T16:34:51.916524Z”, “iopub.status.idle”: “2022-02-09T16:34:51.975570Z”, “shell.execute_reply”: “2022-02-09T16:34:51.975954Z”

}

}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m The volume integral is 768.219980 u001b[0mn”

]

}, {

“data”: {
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“model_id”: “0b8188e837d14e0a979d5710c2717bf1”, “version_major”: 2, “version_minor”: 0

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“Drawing distances: 0%| | 0/741 [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 741 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 258 distances u001b[0mn”

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}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m Detected 258 objects out to a distance of 9.92 u001b[0mn”

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}

], “source”: [

“my_pop_gen = MyPopulation(Lmin=1, alpha=1, r_max=10)n”, “n”, “flux_selector = popsynth.HardFluxSelection()n”, “flux_selector.boundary = 1e-2n”, “n”, “my_pop_gen.set_flux_selection(flux_selector)n”, “n”, “population = my_pop_gen.draw_survey()”

]

}, {

“cell_type”: “code”, “execution_count”: 6, “id”: “d02a5061”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:52.123727Z”, “iopub.status.busy”: “2022-02-09T16:34:51.995093Z”, “iopub.status.idle”: “2022-02-09T16:34:52.159234Z”, “shell.execute_reply”: “2022-02-09T16:34:52.149587Z”

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], “source”: [

“fig = population.display_obs_fluxes_sphere(cmap=”magma”, background_color=”black” ,s=50)”

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“# Selectionsn”, “n”, “Selections on parameters including flux, distance and any auxiliary variables, can be performed in arbitrarily complex way.n”, “We are familiar now with how to add selections onto fluxes and distances, now we will examine in more detail.n”, “n”

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“execution”: {
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“DummySelectionn”, “UnitySelectionn”, “BernoulliSelectionn”, “BoxSelectionn”, “LowerBoundn”, “UpperBoundn”, “SoftSelectionn”, “HardFluxSelectionn”, “SoftFluxSelectionn”, “GalacticPlaneSelectionn”, “DistanceSelectionn”

]

}

], “source”: [

“import matplotlib.pyplot as pltn”, “import numpy as npn”, “n”, “%matplotlib inlinen”, “n”, “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.loud_mode()n”, “popsynth.list_available_selection_functions()”

]

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“cell_type”: “markdown”, “id”: “9d7e84e4”, “metadata”: {}, “source”: [

“We can use these to set selections on parameters. Let’s add a dummy parameter that is sampled from a normal distribution:”

]

}, {

“cell_type”: “code”, “execution_count”: 2, “id”: “9e3dbf09”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:36.267153Z”, “iopub.status.busy”: “2022-02-09T16:35:36.266616Z”, “iopub.status.idle”: “2022-02-09T16:35:36.270152Z”, “shell.execute_reply”: “2022-02-09T16:35:36.269700Z”

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}, {

“cell_type”: “markdown”, “id”: “f5254d81”, “metadata”: {}, “source”: [

“Now we will use the built in Box selection function. Here, we will assign it to an auxiliary sampler, so we need to tell it to select on the observed value:”

]

}, {

“cell_type”: “code”, “execution_count”: 3, “id”: “7cc58948”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:36.274720Z”, “iopub.status.busy”: “2022-02-09T16:35:36.274198Z”, “iopub.status.idle”: “2022-02-09T16:35:36.277737Z”, “shell.execute_reply”: “2022-02-09T16:35:36.277294Z”

}

}, “outputs”: [], “source”: [

“box_select = popsynth.BoxSelection(name=”aux_selector”, use_obs_value=True)n”, “box_select.vmin = 0n”, “box_select.vmax = 0.5”

]

}, {

“cell_type”: “markdown”, “id”: “cdadb2b3”, “metadata”: {}, “source”: [

“We can also add on a selection function for the flux”

]

}, {

“cell_type”: “code”, “execution_count”: 4, “id”: “d93f1c55”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:36.281621Z”, “iopub.status.busy”: “2022-02-09T16:35:36.281101Z”, “iopub.status.idle”: “2022-02-09T16:35:36.283933Z”, “shell.execute_reply”: “2022-02-09T16:35:36.283507Z”

}

}, “outputs”: [], “source”: [

“flux_select = popsynth.HardFluxSelection()n”, “flux_select.boundary = 1e-6”

]

}, {

“cell_type”: “markdown”, “id”: “cfa11f5a”, “metadata”: {}, “source”: [

“Now, we can put it all together and create a survey:”

]

}, {

“cell_type”: “code”, “execution_count”: 5, “id”: “0a3b1725”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:36.289222Z”, “iopub.status.busy”: “2022-02-09T16:35:36.288669Z”, “iopub.status.idle”: “2022-02-09T16:35:40.929646Z”, “shell.execute_reply”: “2022-02-09T16:35:40.929101Z”

}, “lines_to_next_cell”: 2

}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m registering auxilary sampler: dummy u001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m The volume integral is 371.009999 u001b[0mn”

]

}, {

“data”: {
“application/vnd.jupyter.widget-view+json”: {
“model_id”: “136d1181452b459baa19010615f1dd94”, “version_major”: 2, “version_minor”: 0

}, “text/plain”: [

“Drawing distances: 0%| | 0/352 [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 352 total objects u001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m Sampling: dummy 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 dummy which selected 67 of 352 objects u001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m Before auxiliary selection there were 226 objects selected u001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m Detected 42 distances u001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m Detected 42 objects out to a distance of 1.84 u001b[0mn”

]

}

], “source”: [

“ps = popsynth.SchechterZPowerCosmoPopulation(n”, ” Lambda=50, delta=-2, Lmin=1e52, alpha=1.5, seed=1234n”, “)n”, “n”, “aux_parameter.set_selection_probability(box_select)n”, “n”, “ps.set_flux_selection(flux_select)n”, “n”, “ps.add_auxiliary_sampler(aux_parameter)n”, “n”, “pop = ps.draw_survey()”

]

}, {

“cell_type”: “code”, “execution_count”: 6, “id”: “ae9a8988”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:40.962098Z”, “iopub.status.busy”: “2022-02-09T16:35:40.946883Z”, “iopub.status.idle”: “2022-02-09T16:35:41.127699Z”, “shell.execute_reply”: “2022-02-09T16:35:41.128147Z”

}

}, “outputs”: [

{
“data”: {
“text/plain”: [
“<matplotlib.legend.Legend at 0x7f085097e8e0>”

]

}, “execution_count”: 6, “metadata”: {}, “output_type”: “execute_result”

}, {

“data”: {

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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”, “ax.scatter(n”, ” np.log10(pop.fluxes_observed), pop.dummy, color=”purple”, alpha=0.7, label=”total”n”, “)n”, “ax.scatter(n”, ” np.log10(pop.selected_fluxes_observed),n”, ” pop.dummy_selected,n”, ” color=”yellow”,n”, ” alpha=0.7,n”, ” label=”selected”,n”, “)n”, “n”, “ax.set(xlabel=”log10 fluxes”, ylabel=”dummy”)n”, “ax.legend()”

]

}, {

“cell_type”: “markdown”, “id”: “1ee3b768”, “metadata”: {}, “source”: [

“## custom selectionsn”, “n”, “we can also create our own custom selection functions.n”, “n”, “n”, “First, we will look at simply creating a selection. For simplicity, we will look at the Bernoulli selection class built in:”

]

}, {

“cell_type”: “code”, “execution_count”: 7, “id”: “c1d303cd”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:41.134977Z”, “iopub.status.busy”: “2022-02-09T16:35:41.134436Z”, “iopub.status.idle”: “2022-02-09T16:35:41.137963Z”, “shell.execute_reply”: “2022-02-09T16:35:41.137488Z”

}

}, “outputs”: [], “source”: [

“class BernoulliSelection(popsynth.SelectionProbability):n”, ” n”, ” # required to register class!n”, ” _selection_name = “BernoulliSelection”n”, “n”, ” # define the parameters to be usedn”, ” probability = popsynth.SelectionParameter(vmin=0, vmax=1, default=0.5)n”, “n”, ” def __init__(self) -> None:n”, “n”, ” super(BernoulliSelection, self).__init__(name=”Bernoulli”)n”, “n”, ” def draw(self, size: int) -> None:n”, ” “””n”, ” The draw function takes an integer for the size of the n”, ” samples and sets the private variable _selections which n”, ” should be an array of boolean valuesn”, ” n”, ” “””n”, ” n”, ” self._selection = stats.bernoulli.rvs(n”, ” self._probability, size=size).astype(bool) # type: np.ndarrayn”

]

}, {

“cell_type”: “markdown”, “id”: “299ab92a”, “metadata”: {}, “source”: [

“The procedure can become arbitraliy complex. It is important to note that selections will know about several private variables:n”, “n”, “`_observed_flux`n”, “`_observed_value`n”, “`_distance`n”, “`_luminosity`n”, “n”, “n”, “which enables you to use these values in your selection function.n”, “n”, “Because of this, several of the build in selections can be used to select on these variables (though some of this is done in the background for you.)n”, “n”, “n”, “`python\n", "my_box_selection = popsynth.BoxSelection(name=\"box_flux_selection\", use_flux=True)\n", "my_box_selection.vmin = 1E-4\n", "my_box_selection.vmax = 1E-2\n", "\n", "`n”, “n”, “Setting this as the flux selector will select only the fluxes above and below the limits”

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{
“cells”: [
{

“cell_type”: “markdown”, “id”: “475dbf8e”, “metadata”: {}, “source”: [

“# Auxiliary Samplersn”, “n”, “Along with sampling the spatial and luminosity distributions, auxiliary properties and be sampled that both depend on and/or influence the luminosity as well each other. This allows you to build up arbitrailiy complex dependencies between parameters which can lead to diverse populations.n”

]

}, {

“cell_type”: “code”, “execution_count”: 1, “id”: “8b7a73d5”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:04.586919Z”, “iopub.status.busy”: “2022-02-09T16:34:04.585103Z”, “iopub.status.idle”: “2022-02-09T16:34:12.054718Z”, “shell.execute_reply”: “2022-02-09T16:34:12.055483Z”

}, “lines_to_next_cell”: 2

}, “outputs”: [], “source”: [

“n”, “import networkx as nxn”, “import numpy as npn”, “import matplotlib.pyplot as pltn”, “n”, “%matplotlib inlinen”, “from jupyterthemes import jtplotn”, “n”, “jtplot.style(context=”notebook”, fscale=1, grid=False)n”, “n”, “purple = “#B833FF”n”, “yellow = “#F6EF5B”n”, “n”, “import warningsn”, “n”, “warnings.simplefilter(“ignore”)n”, “n”, “n”, “import popsynthn”, “n”, “popsynth.update_logging_level(“INFO”)”

]

}, {

“cell_type”: “markdown”, “id”: “6bd6d2ca”, “metadata”: {}, “source”: [

“## Built in auxiliary samplersn”, “n”, “There are several built in auxiliary samplers that allow you to quickly add on auxiliary parameters.n”

]

}, {

“cell_type”: “code”, “execution_count”: 2, “id”: “17d73e29”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:12.062171Z”, “iopub.status.busy”: “2022-02-09T16:34:12.061599Z”, “iopub.status.idle”: “2022-02-09T16:34:12.066563Z”, “shell.execute_reply”: “2022-02-09T16:34:12.066058Z”

}

}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“DeltaAuxSamplern”, “ViewingAngleSamplern”, “LogNormalAuxSamplern”, “Log10NormalAuxSamplern”, “NormalAuxSamplern”, “TruncatedNormalAuxSamplern”, “ParetoAuxSamplern”, “PowerLawAuxSamplern”, “BrokenPowerLawAuxSamplern”

]

}

], “source”: [

“popsynth.list_available_auxiliary_samplers()”

]

}, {

“cell_type”: “markdown”, “id”: “d925ad97”, “metadata”: {}, “source”: [

“We can add these on to the populations, but let’s have a look at how to use them.”

]

}, {

“cell_type”: “code”, “execution_count”: 3, “id”: “9e196e14”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:12.071235Z”, “iopub.status.busy”: “2022-02-09T16:34:12.070696Z”, “iopub.status.idle”: “2022-02-09T16:34:12.074398Z”, “shell.execute_reply”: “2022-02-09T16:34:12.073947Z”

}, “lines_to_next_cell”: 2

}, “outputs”: [], “source”: [

“x = popsynth.NormalAuxSampler(name=”aux_param”, observed=True)n”, “n”, “x.mu = 0n”, “x.sigma = 1n”, “n”, “# draws the observed values from normal distribution with std equal to taun”, “x.tau = 1”

]

}, {

“cell_type”: “markdown”, “id”: “293d4e59”, “metadata”: {}, “source”: [

“If value of x is observed (generates data), then we can set the width of the normal distribtuion from which the observed values are sampled from the latent values. Otherwise, only the latent values are stored. This applies to any of the built in auxiliary samplers. However, this can all be customized by adding our own:”

]

}, {

“cell_type”: “markdown”, “id”: “f15a2312”, “metadata”: {}, “source”: [

“## Creating a custom auxiliary samplern”, “Let’s create two auxiliary samplers that sample values from normal distributions with some dependency on each other.n”, “n”, “First, we specify the main population. This time, we will chose a SFR-like redshift distribution and a Schecter luminosity functionn”

]

}, {

“cell_type”: “code”, “execution_count”: 4, “id”: “2f753e93”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:12.079212Z”, “iopub.status.busy”: “2022-02-09T16:34:12.078675Z”, “iopub.status.idle”: “2022-02-09T16:34:12.081623Z”, “shell.execute_reply”: “2022-02-09T16:34:12.082029Z”

}

}, “outputs”: [], “source”: [

“pop_gen = popsynth.populations.SchechterSFRPopulation(n”, ” r0=100,a=0.0157, rise=1.0, decay=1.0, peak=1.0, Lmin=1e50, alpha=2.0n”, “)”

]

}, {

“cell_type”: “markdown”, “id”: “e4b66230”, “metadata”: {}, “source”: [

“Suppose we have a property “demo” that we want to sample as well. For this property, we do not observe it directly. We will get to that. This means that our property latent and could influence other parameters but we can not measure it directly. If you are familiar with Bayesian hierarchical models, this concept may be more familiar to you. As an example, this could be the temperature of a star, which influences its spectrum. The spectrum creates an observable, but the tempreature is imply a random latent variable sampled from a distribution. n”, “n”, “n”, “We create an `AuxiliarySampler` child class, and define the true_sampler for the latent values:”

]

}, {

“cell_type”: “code”, “execution_count”: 5, “id”: “45b19093”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:12.088055Z”, “iopub.status.busy”: “2022-02-09T16:34:12.087515Z”, “iopub.status.idle”: “2022-02-09T16:34:12.090933Z”, “shell.execute_reply”: “2022-02-09T16:34:12.090462Z”

}

}, “outputs”: [], “source”: [

“class DemoSampler(popsynth.AuxiliarySampler):n”, ” _auxiliary_sampler_name = “DemoSampler”n”, “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”, ” # pass up to the super classn”, ” super(DemoSampler, self).__init__(“demo”, observed=False)n”, “n”, ” def true_sampler(self, size):n”, “n”, ” # sample the latent values for this propertyn”, “n”, ” self._true_values = np.random.normal(self.mu, self.tau, size=size)”

]

}, {

“cell_type”: “markdown”, “id”: “f0d3d410”, “metadata”: {}, “source”: [

“Now we instantiate it and then assign it our pop_gen object. Then we draw out survey”

]

}, {

“cell_type”: “code”, “execution_count”: 6, “id”: “b12dd1d8”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:12.096705Z”, “iopub.status.busy”: “2022-02-09T16:34:12.096137Z”, “iopub.status.idle”: “2022-02-09T16:34:17.364594Z”, “shell.execute_reply”: “2022-02-09T16:34:17.365426Z”

}, “lines_to_next_cell”: 2

}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m registering auxilary sampler: demo u001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m The volume integral is 4760.754854 u001b[0mn”

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“data”: {
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“model_id”: “af86cf6e5f7b4888ac490a07c60f6170”, “version_major”: 2, “version_minor”: 0

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“Drawing distances: 0%| | 0/4693 [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 4693 total objects 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 applying selection to fluxes u001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m Detected 3223 distances u001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m Detected 3223 objects out to a distance of 8.44 u001b[0mn”

]

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“data”: {

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”, “text/plain”: [

“<Figure size 576x504 with 1 Axes>”

]

}, “metadata”: {}, “output_type”: “display_data”

}

], “source”: [

“demo1 = DemoSampler()n”, “n”, “pop_gen.add_observed_quantity(demo1)n”, “n”, “n”, “flux_selector = popsynth.HardFluxSelection()n”, “flux_selector.boundary = 1e-9n”, “n”, “pop_gen.set_flux_selection(flux_selector)n”, “n”, “population = pop_gen.draw_survey()n”, “n”, “n”, “n”, “## plot itn”, “options = {“node_color”: purple, “node_size”: 3000, “width”: 0.5}n”, “pos = nx.drawing.nx_agraph.graphviz_layout(population.graph, prog=”dot”)n”, “nx.draw(population.graph, with_labels=True, pos=pos, **options)”

]

}, {

“cell_type”: “code”, “execution_count”: 7, “id”: “bcfc14c0”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:17.369169Z”, “iopub.status.busy”: “2022-02-09T16:34:17.368050Z”, “iopub.status.idle”: “2022-02-09T16:34:20.743815Z”, “shell.execute_reply”: “2022-02-09T16:34:20.743354Z”

}

}, “outputs”: [

{
“data”: {

“image/png”: 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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, s=15)”

]

}, {

“cell_type”: “markdown”, “id”: “2e64ba57”, “metadata”: {}, “source”: [

“We can see that the population has stored out demo auxiliary property.”

]

}, {

“cell_type”: “code”, “execution_count”: 8, “id”: “cf441109”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:20.748009Z”, “iopub.status.busy”: “2022-02-09T16:34:20.747506Z”, “iopub.status.idle”: “2022-02-09T16:34:20.749975Z”, “shell.execute_reply”: “2022-02-09T16:34:20.749508Z”

}

}, “outputs”: [], “source”: [

“all_demo = population.demon”, “n”, “obs_demo = population.demo_obsn”, “n”, “selected_demo = population.demo_selected”

]

}, {

“cell_type”: “markdown”, “id”: “63c953bc”, “metadata”: {}, “source”: [

“We can also see that our demo sampler is now known which is important when creating populations from YAML files. This registering happens when we add the property `_auxiliary_sampler_name = \"DemoSampler\" ` which must be name of the class!”

]

}, {

“cell_type”: “code”, “execution_count”: 9, “id”: “a114ef60”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:20.754160Z”, “iopub.status.busy”: “2022-02-09T16:34:20.753597Z”, “iopub.status.idle”: “2022-02-09T16:34:20.756353Z”, “shell.execute_reply”: “2022-02-09T16:34:20.755919Z”

}

}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“DeltaAuxSamplern”, “ViewingAngleSamplern”, “LogNormalAuxSamplern”, “Log10NormalAuxSamplern”, “NormalAuxSamplern”, “TruncatedNormalAuxSamplern”, “ParetoAuxSamplern”, “PowerLawAuxSamplern”, “BrokenPowerLawAuxSamplern”, “DemoSamplern”

]

}

], “source”: [

“popsynth.list_available_auxiliary_samplers()”

]

}, {

“cell_type”: “markdown”, “id”: “fc4d9071”, “metadata”: {}, “source”: [

“## Observed auxiliary properties and dependent parametersn”, “n”, “Suppose now we want to simulate a property that is observed by an instrument but depends on latent parameters.n”, “n”, “We will create a second demo sampler and tell it what the observational error is as well as how to read from a secondary sampler:”

]

}, {

“cell_type”: “code”, “execution_count”: 10, “id”: “5748e394”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:20.763883Z”, “iopub.status.busy”: “2022-02-09T16:34:20.763348Z”, “iopub.status.idle”: “2022-02-09T16:34:20.764969Z”, “shell.execute_reply”: “2022-02-09T16:34:20.765442Z”

}

}, “outputs”: [], “source”: [

“class DemoSampler2(popsynth.AuxiliarySampler):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__(n”, ” self,n”, ” ):n”, “n”, ” # this time set observed=Truen”, ” super(DemoSampler2, self).__init__(“demo2”, observed=True, uses_distance=True)n”, “n”, ” def true_sampler(self, size):n”, “n”, ” # we access the secondary sampler dictionary. In thisn”, ” # case “demo”. This itself is a sampler withn”, ” # <>.true_values as a parametern”, ” secondary = self._secondary_samplers[‘demo’]n”, “n”, ” # now we sample the demo2 latent values and add on the dependence of “demo”n”, “n”, ” tmp = np.random.normal(self.mu, self.tau, size=size)n”, “n”, ” # for fun, we can substract the log of the distance as alln”, ” # auxiliary samples know about their distancesn”, “n”, ” self._true_values = tmp + secondary.true_values - np.log10(1 + self._distance)n”, “n”, ” def observation_sampler(self, size):n”, “n”, ” # here we define the “observed” values, i.e., the latened valuesn”, ” # with observational errorn”, “n”, ” self._obs_values = self._true_values + np.random.normal(n”, ” 0, self.sigma, size=sizen”, ” )”

]

}, {

“cell_type”: “markdown”, “id”: “b4cd0a28”, “metadata”: {}, “source”: [

“We recreate our base sampler:”

]

}, {

“cell_type”: “code”, “execution_count”: 11, “id”: “c374af0c”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:20.769606Z”, “iopub.status.busy”: “2022-02-09T16:34:20.769076Z”, “iopub.status.idle”: “2022-02-09T16:34:20.771072Z”, “shell.execute_reply”: “2022-02-09T16:34:20.771473Z”

}, “lines_to_next_cell”: 2

}, “outputs”: [], “source”: [

“pop_gen = popsynth.populations.SchechterSFRPopulation(n”, ” r0=100, a=0.0157, rise=1.0, decay=1.0, peak=1.0, Lmin=1e50, alpha=2.0n”, “)”

]

}, {

“cell_type”: “markdown”, “id”: “01605c71”, “metadata”: {}, “source”: [

“Now, make a new demo1, but this time we do not have to attach it to the base sampler. Instead, we will assign it as a secondary sampler to demo2 and popsynth is smart enough to search for it when it draws a survey.”

]

}, {

“cell_type”: “code”, “execution_count”: 12, “id”: “bfd5ae84”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:20.776124Z”, “iopub.status.busy”: “2022-02-09T16:34:20.775606Z”, “iopub.status.idle”: “2022-02-09T16:34:20.777621Z”, “shell.execute_reply”: “2022-02-09T16:34:20.778048Z”

}, “lines_to_next_cell”: 2

}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m registering auxilary sampler: demo2 u001b[0mn”

]

}

], “source”: [

“demo1 = DemoSampler()n”, “n”, “n”, “demo2 = DemoSampler2()n”, “n”, “demo2.set_secondary_sampler(demo1)n”, “n”, “# attach to the base samplern”, “pop_gen.add_observed_quantity(demo2)”

]

}, {

“cell_type”: “code”, “execution_count”: 13, “id”: “1291e73f”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:20.783873Z”, “iopub.status.busy”: “2022-02-09T16:34:20.783349Z”, “iopub.status.idle”: “2022-02-09T16:34:20.924932Z”, “shell.execute_reply”: “2022-02-09T16:34:20.925773Z”

}, “lines_to_next_cell”: 2

}, “outputs”: [

{
“data”: {

“image/png”: 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”, “text/plain”: [

“<Figure size 576x504 with 1 Axes>”

]

}, “metadata”: {}, “output_type”: “display_data”

}

], “source”: [

“pos = nx.drawing.nx_agraph.graphviz_layout(pop_gen.graph, prog=”dot”)n”, “n”, “n”, “fig, ax = plt.subplots()n”, “n”, “n”, “nx.draw(pop_gen.graph, with_labels=True, pos=pos, ax=ax, **options)”

]

}, {

“cell_type”: “code”, “execution_count”: 14, “id”: “985d388c”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:20.931020Z”, “iopub.status.busy”: “2022-02-09T16:34:20.930495Z”, “iopub.status.idle”: “2022-02-09T16:34:21.220912Z”, “shell.execute_reply”: “2022-02-09T16:34:21.220139Z”

}

}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m The volume integral is 4760.754854 u001b[0mn”

]

}, {

“data”: {
“application/vnd.jupyter.widget-view+json”: {
“model_id”: “c6a68f540bd341e38f4b7b21c1ad5701”, “version_major”: 2, “version_minor”: 0

}, “text/plain”: [

“Drawing distances: 0%| | 0/4693 [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 4693 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 applying selection to fluxes u001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m Detected 1124 distances u001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m Detected 1124 objects out to a distance of 3.18 u001b[0mn”

]

}

], “source”: [

“n”, “flux_selector = popsynth.HardFluxSelection()n”, “flux_selector.boundary = 1e-8n”, “n”, “pop_gen.set_flux_selection(flux_selector)n”, “population = pop_gen.draw_survey(flux_sigma=0.1)”

]

}, {

“cell_type”: “code”, “execution_count”: 15, “id”: “c8c4b43e”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:21.238021Z”, “iopub.status.busy”: “2022-02-09T16:34:21.223786Z”, “iopub.status.idle”: “2022-02-09T16:34:21.383669Z”, “shell.execute_reply”: “2022-02-09T16:34:21.384106Z”

}, “lines_to_next_cell”: 2

}, “outputs”: [

{
“data”: {
“text/plain”: [
“<matplotlib.collections.PathCollection at 0x7fc9e3f9e5e0>”

]

}, “execution_count”: 15, “metadata”: {}, “output_type”: “execute_result”

}, {

“data”: {

“image/png”: 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”, “text/plain”: [

“<Figure size 576x504 with 1 Axes>”

]

}, “metadata”: {}, “output_type”: “display_data”

}

], “source”: [

“fig, ax = plt.subplots()n”, “n”, “ax.scatter(population.demo2_selected, population.demo_selected, c=purple, s=40)n”, “n”, “ax.scatter(population.demo2, population.demo, c=yellow, s=20)”

]

}, {

“cell_type”: “markdown”, “id”: “3106018b”, “metadata”: {}, “source”: [

“## Derived Luminosity samplern”, “n”, “Sometimes, the luminosity does not come directly from a distribution. Rather, it is computed from other quantities. In these cases, we want to use the DerivedLumAuxSampler class.n”, “n”, “This allows you to sample auxiliary parameters and compute a luminosity from those.”

]

}, {

“cell_type”: “code”, “execution_count”: 16, “id”: “b4fc9707”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:21.391220Z”, “iopub.status.busy”: “2022-02-09T16:34:21.390709Z”, “iopub.status.idle”: “2022-02-09T16:34:21.394407Z”, “shell.execute_reply”: “2022-02-09T16:34:21.393436Z”

}

}, “outputs”: [], “source”: [

“class DemoSampler3(popsynth.DerivedLumAuxSampler):n”, ” _auxiliary_sampler_name = “DemoSampler3”n”, ” mu = popsynth.auxiliary_sampler.AuxiliaryParameter(default=1)n”, ” tau = popsynth.auxiliary_sampler.AuxiliaryParameter(default=1, vmin=0)n”, “n”, ” def __init__(self, mu=2, tau=1.0, sigma=1):n”, “n”, ” # this time set observed=Truen”, ” super(DemoSampler3, self).__init__(“demo3”, uses_distance=False)n”, “n”, ” def true_sampler(self, size):n”, “n”, ” # draw a random numbern”, ” tmp = np.random.normal(self.mu, self.tau, size=size)n”, “n”, ” self._true_values = tmpn”, “n”, ” def compute_luminosity(self):n”, “n”, ” # compute the luminosityn”, ” secondary = self._secondary_samplers[“demo”]n”, “n”, ” return (10 ** (self._true_values + 54)) + secondary.true_values”

]

}, {

“cell_type”: “code”, “execution_count”: 17, “id”: “efdcac8f”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:21.398851Z”, “iopub.status.busy”: “2022-02-09T16:34:21.398327Z”, “iopub.status.idle”: “2022-02-09T16:34:21.401522Z”, “shell.execute_reply”: “2022-02-09T16:34:21.401074Z”

}, “lines_to_next_cell”: 2

}, “outputs”: [], “source”: [

“pop_gen = popsynth.populations.SchechterSFRPopulation(n”, ” r0=100,a=0.0157, rise=1.0, decay=1.0, peak=1.0, Lmin=1e50, alpha=2.0n”, “)”

]

}, {

“cell_type”: “code”, “execution_count”: 18, “id”: “79f35484”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:21.406849Z”, “iopub.status.busy”: “2022-02-09T16:34:21.406112Z”, “iopub.status.idle”: “2022-02-09T16:34:21.527716Z”, “shell.execute_reply”: “2022-02-09T16:34:21.528129Z”

}

}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m registering derived luminosity sampler: demo3 u001b[0mn”

]

}, {

“data”: {

“image/png”: 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n”, “text/plain”: [

“<Figure size 576x504 with 1 Axes>”

]

}, “metadata”: {}, “output_type”: “display_data”

}

], “source”: [

“demo1 = DemoSampler()n”, “n”, “n”, “demo3 = DemoSampler3()n”, “n”, “demo3.set_secondary_sampler(demo1)n”, “n”, “# attach to the base samplern”, “pop_gen.add_observed_quantity(demo3)n”, “n”, “n”, “pos = nx.drawing.nx_agraph.graphviz_layout(pop_gen.graph, prog=”dot”)n”, “n”, “fig, ax = plt.subplots()n”, “n”, “nx.draw(pop_gen.graph, with_labels=True, pos=pos, **options, ax=ax)”

]

}, {

“cell_type”: “code”, “execution_count”: 19, “id”: “f013bac7”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:21.530931Z”, “iopub.status.busy”: “2022-02-09T16:34:21.530394Z”, “iopub.status.idle”: “2022-02-09T16:34:21.802158Z”, “shell.execute_reply”: “2022-02-09T16:34:21.802663Z”

}

}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m The volume integral is 4760.754854 u001b[0mn”

]

}, {

“data”: {
“application/vnd.jupyter.widget-view+json”: {
“model_id”: “3c317e5f78f84f25966f2d46d7c64053”, “version_major”: 2, “version_minor”: 0

}, “text/plain”: [

“Drawing distances: 0%| | 0/4693 [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 4693 total objects u001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m Sampling: demo3 u001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m demo3 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 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 Detected 3752 distances u001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m Detected 3752 objects out to a distance of 9.99 u001b[0mn”

]

}

], “source”: [

“flux_selector = popsynth.HardFluxSelection()n”, “flux_selector.boundary = 1e-5n”, “pop_gen.set_flux_selection(flux_selector)n”, “population = pop_gen.draw_survey(flux_sigma=0.1)”

]

}, {

“cell_type”: “code”, “execution_count”: 20, “id”: “f0f5b5a8”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:21.817710Z”, “iopub.status.busy”: “2022-02-09T16:34:21.805037Z”, “iopub.status.idle”: “2022-02-09T16:34:29.344950Z”, “shell.execute_reply”: “2022-02-09T16:34:29.345395Z”

}

}, “outputs”: [

{
“data”: {

“image/png”: 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”, “text/plain”: [

“<Figure size 576x504 with 1 Axes>”

]

}, “execution_count”: 20, “metadata”: {}, “output_type”: “execute_result”

}, {

“data”: {

“image/png”: 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”, “text/plain”: [

“<Figure size 576x504 with 1 Axes>”

]

}, “metadata”: {}, “output_type”: “display_data”

}

], “source”: [

“population.display_fluxes(obs_color=purple, true_color=yellow, s=15)”

]

}

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API

Here you can find the documentation of all classes and methods:

popsynth

popsynth package

Subpackages

popsynth.aux_samplers package
Submodules
popsynth.aux_samplers.delta_aux_sampler module
class popsynth.aux_samplers.delta_aux_sampler.DeltaAuxSampler(name: str, observed: bool = True)[source]

Bases: popsynth.auxiliary_sampler.AuxiliarySampler

__init__(name: str, observed: bool = True)[source]

A delta-function sampler for which the true value is fixed at xp. Assumes property is observed by default, in which case the observed value is sampled from the true value with some normally-distributed error, sigma.

Parameters:
  • name (str) – Name of the property
  • observed (bool) – True if the property is observed, False if it is latent. Defaults to True
  • xp (AuxiliaryParameter) – Value at which delta function is located
  • sigma (AuxiliaryParameter) – Standard deviation of normal distribution from which observed values are sampled, if observed is True
observation_sampler(size: int)[source]
sigma
true_sampler(size: int)[source]
xp
popsynth.aux_samplers.lognormal_aux_sampler module
class popsynth.aux_samplers.lognormal_aux_sampler.Log10NormalAuxSampler(name: str, observed: bool = True)[source]

Bases: popsynth.auxiliary_sampler.AuxiliarySampler

__init__(name: str, observed: bool = True)[source]

A Log10 normal sampler, where property ~ 10^N(mu, sigma).

Parameters:
  • name (str) – Name of the property
  • observed (bool) – True if the property is observed, False if it is latent. Defaults to True
  • mu (AuxiliaryParameter) – Mean of the log10normal
  • tau (AuxiliaryParameter) – Standard deviation of the log10normal
  • sigma (AuxiliaryParameter) – Standard deviation of normal distribution from which observed values are sampled, if observed is True
mu
observation_sampler(size: int)[source]
sigma
tau
true_sampler(size: int)[source]
class popsynth.aux_samplers.lognormal_aux_sampler.LogNormalAuxSampler(name: str, observed: bool = True)[source]

Bases: popsynth.auxiliary_sampler.AuxiliarySampler

__init__(name: str, observed: bool = True)[source]

A Log normal sampler, where property ~ e^N(mu, sigma).

Parameters:
  • name (str) – Name of the property
  • observed (bool) – True if the property is observed, False if it is latent. Defaults to True
  • mu (AuxiliaryParameter) – Mean of the lognormal
  • tau (AuxiliaryParameter) – Standard deviation of the lognormal
  • sigma (AuxiliaryParameter) – Standard deviation of normal distribution from which observed values are sampled, if observed is True
mu
observation_sampler(size: int)[source]
sigma
tau
true_sampler(size: int)[source]
popsynth.aux_samplers.normal_aux_sampler module
class popsynth.aux_samplers.normal_aux_sampler.NormalAuxSampler(name: str, observed: bool = True)[source]

Bases: popsynth.auxiliary_sampler.AuxiliarySampler

__init__(name: str, observed: bool = True)[source]

A normal distribution sampler, where property ~ N(mu, sigma).

Parameters:
  • name (str) – Name of the property
  • observed (bool) – True if the property is observed, False if it is latent. Defaults to True
  • mu (AuxiliaryParameter) – Mean of the normal
  • tau (AuxiliaryParameter) – Standard deviation of the normal
  • sigma (AuxiliaryParameter) – Standard deviation of normal distribution from which observed values are sampled, if observed is True
mu
observation_sampler(size: int)[source]
sigma
tau
true_sampler(size: int)[source]
popsynth.aux_samplers.plaw_aux_sampler module
class popsynth.aux_samplers.plaw_aux_sampler.BrokenPowerLawAuxSampler(name: str, observed: bool = True)[source]

Bases: popsynth.auxiliary_sampler.AuxiliarySampler

__init__(name: str, observed: bool = True)[source]

A broken power law distribution sampler, where property ~ x^``alpha`` for x < xbreak, and property ~ x^``beta`` for x > xbreak.

Parameters:
  • name (str) – Name of the property
  • observed (bool) – True if the property is observed, False if it is latent. Defaults to True
  • xmin (AuxiliaryParameter) – Minimum value of the broken power law
  • xmax (:class:``AuxiliaryParameter) – Maximum value of the broken power law
  • sigma (AuxiliaryParameter) – Standard deviation of normal distribution from which observed values are sampled, if observed is True
alpha
beta
observation_sampler(size: int)[source]
true_sampler(size: int)[source]
xbreak
xmax
xmin
class popsynth.aux_samplers.plaw_aux_sampler.ParetoAuxSampler(name: str, observed: bool = True)[source]

Bases: popsynth.auxiliary_sampler.AuxiliarySampler

__init__(name: str, observed: bool = True)[source]

A pareto distribution sampler, where property ~ 1 / x^(alpha + 1).

Parameters:
  • name (str) – Name of the property
  • observed (bool) – True if the property is observed, False if it is latent. Defaults to True
  • xmin (AuxiliaryParameter) – Minimum value of the pareto
  • alpha (AuxiliaryParameter) – Index of the pareto
  • sigma (AuxiliaryParameter) – Standard deviation of normal distribution from which observed values are sampled, if observed is True
alpha
observation_sampler(size: int)[source]
sigma
true_sampler(size: int)[source]
xmin
class popsynth.aux_samplers.plaw_aux_sampler.PowerLawAuxSampler(name: str, observed: bool = True)[source]

Bases: popsynth.auxiliary_sampler.AuxiliarySampler

__init__(name: str, observed: bool = True)[source]

A bounded power law distribution sampler, where property ~ x^``alpha``.

Parameters:
  • name (str) – Name of the property
  • observed (bool) – True if the property is observed, False if it is latent. Defaults to True
  • xmin (AuxiliaryParameter) – Minimum value of the power law
  • xmax (:class:``AuxiliaryParameter) – Maximum value of the power law
  • sigma (AuxiliaryParameter) – Standard deviation of normal distribution from which observed values are sampled, if observed is True
alpha
observation_sampler(size: int)[source]
sigma
true_sampler(size: int)[source]
xmax
xmin
popsynth.aux_samplers.sky_sampler module
class popsynth.aux_samplers.sky_sampler.DecSampler[source]

Bases: popsynth.auxiliary_sampler.NonObservedAuxSampler

__init__()[source]

Samples the declination (Dec) uniformly on the unit sphere.

Dec is in radians.

true_sampler(size)[source]
class popsynth.aux_samplers.sky_sampler.RASampler[source]

Bases: popsynth.auxiliary_sampler.NonObservedAuxSampler

__init__()[source]

Samples the right ascension (RA) uniformly on the unit sphere.

RA is in radians.

true_sampler(size)[source]
class popsynth.aux_samplers.sky_sampler.SkySampler(ra_sampler: popsynth.auxiliary_sampler.NonObservedAuxSampler = None, dec_sampler: popsynth.auxiliary_sampler.NonObservedAuxSampler = None)[source]

Bases: object

__init__(ra_sampler: popsynth.auxiliary_sampler.NonObservedAuxSampler = None, dec_sampler: popsynth.auxiliary_sampler.NonObservedAuxSampler = None)[source]

A sky sampler that samples angular positions in ra and dec. If no samplers are provided, then loads default samplers that sample uniformly on the unit sphere. RA and dec are in radians.

Parameters:
  • ra_sampler (NonObservedAuxSampler) – Right ascension (RA) sampler
  • dec_sampler (NonObservedAuxSampler) – Declination (Dec) sampler
dec_sampler
ra_sampler
popsynth.aux_samplers.trunc_normal_aux_sampler module
class popsynth.aux_samplers.trunc_normal_aux_sampler.TruncatedNormalAuxSampler(name: str, observed: bool = True)[source]

Bases: popsynth.auxiliary_sampler.AuxiliarySampler

__init__(name: str, observed: bool = True)[source]

A truncated normal sampler, where property ~ N(mu, sigma), between lower and upper.

Parameters:
  • name (str) – Name of the property
  • observed (bool) – True if the property is observed, False if it is latent. Defaults to True
  • mu (AuxiliaryParameter) – Mean of the normal
  • tau (AuxiliaryParameter) – Standard deviation of the normal
  • lower (AuxiliaryParameter) – Lower bound of the truncation
  • upper (AuxiliaryParameter) – Upper bound of the truncation
  • sigma (AuxiliaryParameter) – Standard deviation of normal distribution from which observed values are sampled, if observed is True
lower
mu
observation_sampler(size)[source]
sigma
tau
true_sampler(size)[source]
upper
popsynth.aux_samplers.viewing_angle_sampler module
class popsynth.aux_samplers.viewing_angle_sampler.ViewingAngleSampler[source]

Bases: popsynth.auxiliary_sampler.NonObservedAuxSampler

__init__()[source]

A viewing angle sampler that samples from 0 to max_angle. Unlike other samplers, it assumes that this is NOT an observed property

Parameters:max_angle (AuxiliaryParameter) – The maximum angle to which to sample in degrees
max_angle
true_sampler(size: int) → None[source]

Sample the viewing angle by inverse CDF

Parameters:size (int) – Number of samples
Module contents
class popsynth.aux_samplers.DeltaAuxSampler(name: str, observed: bool = True)[source]

Bases: popsynth.auxiliary_sampler.AuxiliarySampler

__init__(name: str, observed: bool = True)[source]

A delta-function sampler for which the true value is fixed at xp. Assumes property is observed by default, in which case the observed value is sampled from the true value with some normally-distributed error, sigma.

Parameters:
  • name (str) – Name of the property
  • observed (bool) – True if the property is observed, False if it is latent. Defaults to True
  • xp (AuxiliaryParameter) – Value at which delta function is located
  • sigma (AuxiliaryParameter) – Standard deviation of normal distribution from which observed values are sampled, if observed is True
observation_sampler(size: int)[source]
sigma
true_sampler(size: int)[source]
xp
class popsynth.aux_samplers.ViewingAngleSampler[source]

Bases: popsynth.auxiliary_sampler.NonObservedAuxSampler

__init__()[source]

A viewing angle sampler that samples from 0 to max_angle. Unlike other samplers, it assumes that this is NOT an observed property

Parameters:max_angle (AuxiliaryParameter) – The maximum angle to which to sample in degrees
max_angle
true_sampler(size: int) → None[source]

Sample the viewing angle by inverse CDF

Parameters:size (int) – Number of samples
class popsynth.aux_samplers.LogNormalAuxSampler(name: str, observed: bool = True)[source]

Bases: popsynth.auxiliary_sampler.AuxiliarySampler

__init__(name: str, observed: bool = True)[source]

A Log normal sampler, where property ~ e^N(mu, sigma).

Parameters:
  • name (str) – Name of the property
  • observed (bool) – True if the property is observed, False if it is latent. Defaults to True
  • mu (AuxiliaryParameter) – Mean of the lognormal
  • tau (AuxiliaryParameter) – Standard deviation of the lognormal
  • sigma (AuxiliaryParameter) – Standard deviation of normal distribution from which observed values are sampled, if observed is True
mu
observation_sampler(size: int)[source]
sigma
tau
true_sampler(size: int)[source]
class popsynth.aux_samplers.Log10NormalAuxSampler(name: str, observed: bool = True)[source]

Bases: popsynth.auxiliary_sampler.AuxiliarySampler

__init__(name: str, observed: bool = True)[source]

A Log10 normal sampler, where property ~ 10^N(mu, sigma).

Parameters:
  • name (str) – Name of the property
  • observed (bool) – True if the property is observed, False if it is latent. Defaults to True
  • mu (AuxiliaryParameter) – Mean of the log10normal
  • tau (AuxiliaryParameter) – Standard deviation of the log10normal
  • sigma (AuxiliaryParameter) – Standard deviation of normal distribution from which observed values are sampled, if observed is True
mu
observation_sampler(size: int)[source]
sigma
tau
true_sampler(size: int)[source]
class popsynth.aux_samplers.NormalAuxSampler(name: str, observed: bool = True)[source]

Bases: popsynth.auxiliary_sampler.AuxiliarySampler

__init__(name: str, observed: bool = True)[source]

A normal distribution sampler, where property ~ N(mu, sigma).

Parameters:
  • name (str) – Name of the property
  • observed (bool) – True if the property is observed, False if it is latent. Defaults to True
  • mu (AuxiliaryParameter) – Mean of the normal
  • tau (AuxiliaryParameter) – Standard deviation of the normal
  • sigma (AuxiliaryParameter) – Standard deviation of normal distribution from which observed values are sampled, if observed is True
mu
observation_sampler(size: int)[source]
sigma
tau
true_sampler(size: int)[source]
class popsynth.aux_samplers.TruncatedNormalAuxSampler(name: str, observed: bool = True)[source]

Bases: popsynth.auxiliary_sampler.AuxiliarySampler

__init__(name: str, observed: bool = True)[source]

A truncated normal sampler, where property ~ N(mu, sigma), between lower and upper.

Parameters:
  • name (str) – Name of the property
  • observed (bool) – True if the property is observed, False if it is latent. Defaults to True
  • mu (AuxiliaryParameter) – Mean of the normal
  • tau (AuxiliaryParameter) – Standard deviation of the normal
  • lower (AuxiliaryParameter) – Lower bound of the truncation
  • upper (AuxiliaryParameter) – Upper bound of the truncation
  • sigma (AuxiliaryParameter) – Standard deviation of normal distribution from which observed values are sampled, if observed is True
lower
mu
observation_sampler(size)[source]
sigma
tau
true_sampler(size)[source]
upper
class popsynth.aux_samplers.ParetoAuxSampler(name: str, observed: bool = True)[source]

Bases: popsynth.auxiliary_sampler.AuxiliarySampler

__init__(name: str, observed: bool = True)[source]

A pareto distribution sampler, where property ~ 1 / x^(alpha + 1).

Parameters:
  • name (str) – Name of the property
  • observed (bool) – True if the property is observed, False if it is latent. Defaults to True
  • xmin (AuxiliaryParameter) – Minimum value of the pareto
  • alpha (AuxiliaryParameter) – Index of the pareto
  • sigma (AuxiliaryParameter) – Standard deviation of normal distribution from which observed values are sampled, if observed is True
alpha
observation_sampler(size: int)[source]
sigma
true_sampler(size: int)[source]
xmin
class popsynth.aux_samplers.PowerLawAuxSampler(name: str, observed: bool = True)[source]

Bases: popsynth.auxiliary_sampler.AuxiliarySampler

__init__(name: str, observed: bool = True)[source]

A bounded power law distribution sampler, where property ~ x^``alpha``.

Parameters:
  • name (str) – Name of the property
  • observed (bool) – True if the property is observed, False if it is latent. Defaults to True
  • xmin (AuxiliaryParameter) – Minimum value of the power law
  • xmax (:class:``AuxiliaryParameter) – Maximum value of the power law
  • sigma (AuxiliaryParameter) – Standard deviation of normal distribution from which observed values are sampled, if observed is True
alpha
observation_sampler(size: int)[source]
sigma
true_sampler(size: int)[source]
xmax
xmin
class popsynth.aux_samplers.BrokenPowerLawAuxSampler(name: str, observed: bool = True)[source]

Bases: popsynth.auxiliary_sampler.AuxiliarySampler

__init__(name: str, observed: bool = True)[source]

A broken power law distribution sampler, where property ~ x^``alpha`` for x < xbreak, and property ~ x^``beta`` for x > xbreak.

Parameters:
  • name (str) – Name of the property
  • observed (bool) – True if the property is observed, False if it is latent. Defaults to True
  • xmin (AuxiliaryParameter) – Minimum value of the broken power law
  • xmax (:class:``AuxiliaryParameter) – Maximum value of the broken power law
  • sigma (AuxiliaryParameter) – Standard deviation of normal distribution from which observed values are sampled, if observed is True
alpha
beta
observation_sampler(size: int)[source]
true_sampler(size: int)[source]
xbreak
xmax
xmin
popsynth.distributions package
Submodules
popsynth.distributions.bpl_distribution module
class popsynth.distributions.bpl_distribution.BPLDistribution(seed: int = 1234, name: str = 'bpl')[source]

Bases: popsynth.distribution.LuminosityDistribution

Lbreak
Lmax
Lmin
__init__(seed: int = 1234, name: str = 'bpl')[source]

A broken power law luminosity distribution.

L ~ L^``alpha`` for L <= Lbreak L ~ L^``beta`` for L > Lbreak

Parameters:
  • seed (int) – Random seed
  • name (str) – Name of the distribution
  • Lmin (DistributionParameter) – Minimum value of the luminosity
  • alpha (DistributionParameter) – Index of the lower power law
  • Lbreak (DistributionParameter) – Luminosity of the power law break
  • beta (DistributionParameter) – Index of the upper power law
  • Lmax (DistributionParameter) – Maximum value of the luminosity
alpha
beta
draw_luminosity(size=1)[source]

function to draw the luminosity via an alternative method must be implemented in child class

Parameters:size
Returns:
phi(L)[source]

The functional form of the distribution. not required for sampling :param luminosity: Luminosity

popsynth.distributions.bpl_distribution.bpl(x, x0, x1, x2, a1, a2)[source]

Broken power law between bounds.

Parameters:
  • x – The domain of the function
  • x0 – Lower bound
  • x1 – Break point
  • x2 – Upper bound
  • a1 – Lower power law index
  • a2 – Upper power low index
popsynth.distributions.bpl_distribution.integrate_pl(x0, x1, x2, a1, a2)[source]

Integrate a broken power law between bounds.

Parameters:
  • x0 – Lower bound
  • x1 – Break point
  • x2 – Upper bound
  • a1 – Lower power law index
  • a2 – Upper power low index
popsynth.distributions.bpl_distribution.sample_bpl(u, x0, x1, x2, a1, a2)[source]

Sample from a broken power law between bounds.

Parameters:
  • u – Uniform random number on {0,1}
  • x0 – Lower bound
  • x1 – Break point
  • x2 – Upper bound
  • a1 – Lower power law index
  • a2 – Upper power low index
popsynth.distributions.cosmological_distribution module
class popsynth.distributions.cosmological_distribution.CosmologicalDistribution(seed: int = 1234, name: str = 'cosmo', form: str = None, truth: Dict[str, Any] = {}, is_rate: bool = True)[source]

Bases: popsynth.distribution.SpatialDistribution

__init__(seed: int = 1234, name: str = 'cosmo', form: str = None, truth: Dict[str, Any] = {}, is_rate: bool = True)[source]

Base class for cosmological spatial distributions.

Parameters:
  • seed (int) – Random seed
  • name (str) – Name of the distribution
  • form (str) – Mathematical description of distribution
  • truth (dict[str, Any]) – True values of parameters
  • is_rate (bool) – True if modelling a population of transient events, False if modelling a population of steady-state objects. Affects the time_adjustment method used in cosmo calculations. Default is True.
differential_volume(z)[source]

Differential comoving volume in Gpc^3 sr^-1.

dV/dzdOmega

Parameters:z – Redshift
Returns:The differential comoving volume in Gpc^-3 sr^-1.
time_adjustment(z)[source]

Time adjustment factor to handle both transient and steady-state populations.

Parameters:z – Redshift
Returns:Appropriate factor depending on is_rate
transform(L, z)[source]

Transformation from luminosity to energy flux.

L / 4 pi dL^2

dL is in cm. Therefore for L in erg s^-1 returns flux in erg cm^-2 s^-1.

Parameters:
  • L – Luminosity
  • z – Redshift
Returns:

Flux

class popsynth.distributions.cosmological_distribution.SFRDistribution(seed: int = 1234, name: str = 'sfr', is_rate: bool = True)[source]

Bases: popsynth.distributions.cosmological_distribution.CosmologicalDistribution

__init__(seed: int = 1234, name: str = 'sfr', is_rate: bool = True)[source]

A star-formation like distribution of the form presented in Cole et al. 2001.

r0``(``a``+``rise``z)/(1 + (z/``peak)^``decay``)

Parameters:
  • seed (int) – Random seed
  • name (str) – Name of the distribution
  • is_rate (bool) – True if modelling a population of transient events, False if modelling a population of steady-state objects. Affects the time_adjustment method used in cosmo calculations. Default is True.
  • r0 (DistributionParameter) – The local density in units of Gpc^-3
  • a (DistributionParameter) – Offset at z=0
  • rise (DistributionParameter) – Rise at low z
  • decay (DistributionParameter) – Decay at high z
  • peak (DistributionParameter) – Peak of z distribution
a
dNdV(z)[source]

The differential number of objects per volume element

Parameters:distance
Returns:
decay
peak
r0
rise
class popsynth.distributions.cosmological_distribution.ZPowerCosmoDistribution(seed: int = 1234, name: str = 'zpow_cosmo', is_rate: bool = True)[source]

Bases: popsynth.distributions.cosmological_distribution.CosmologicalDistribution

Lambda
__init__(seed: int = 1234, name: str = 'zpow_cosmo', is_rate: bool = True)[source]

A cosmological distribution where the density evolves as a power law.

Lambda (1+z)^``delta``

Parameters:
  • seed (int) – Random seed
  • name (str) – Name of the distribution
  • is_rate (bool) – True if modelling a population of transient events, False if modelling a population of steady-state objects. Affects the time_adjustment method used in cosmo calculations. Default is True.
  • Lambda (DistributionParameter) – The local density in units of Gpc^-3
  • delta (DistributionParameter) – The index of the power law
dNdV(distance)[source]

The differential number of objects per volume element

Parameters:distance
Returns:
delta
popsynth.distributions.delta_distribution module
class popsynth.distributions.delta_distribution.DeltaDistribution(seed: int = 1234, name: str = 'delta')[source]

Bases: popsynth.distribution.LuminosityDistribution

Lp
__init__(seed: int = 1234, name: str = 'delta')[source]

A delta function luminosity distribution, centred on Lp.

Parameters:
  • seed (int) – Random seed
  • name (str) – Name of the distribution
  • Lp (DistributionParameter) – The central value
draw_luminosity(size=1)[source]

function to draw the luminosity via an alternative method must be implemented in child class

Parameters:size
Returns:
phi(L)[source]

The functional form of the distribution. not required for sampling :param luminosity: Luminosity

popsynth.distributions.flatland_distribution module
class popsynth.distributions.flatland_distribution.FlatlandDistribution(seed: int = 1234, name: str = 'flatland', form: str = None)[source]

Bases: popsynth.distribution.SpatialDistribution

Lambda
__init__(seed: int = 1234, name: str = 'flatland', form: str = None)[source]

A flat spatial distribution with only length.

Parameters:
  • seed (int) – Random seed
  • name (str) – Name of the distribution
  • form (str) – Mathematical description of distribution
  • Lambda (DistributionParameter) – Length
dNdV(distance)[source]

The differential number of objects per volume element

Parameters:distance
Returns:
differential_volume(r)[source]

The differential volume

Parameters:distance – Distance
transform(L, r)[source]

The transform from luminosity to flux for the

Parameters:
  • flux
  • distance
Returns:

popsynth.distributions.log10_normal_distribution module
class popsynth.distributions.log10_normal_distribution.Log10NormalDistribution(seed: int = 1234, name: str = 'log10norm')[source]

Bases: popsynth.distribution.LuminosityDistribution

__init__(seed: int = 1234, name: str = 'log10norm')[source]

A log10-normal luminosity function

Log10Normal(mu, tau)

Parameters:
  • seed (int) – Random seed
  • name (str) – Name of the distribution
  • mu (DistributionParameter) – Mean of the log10 normal
  • tau (DistributionParameter) – Standard deviation of the log10 normal
draw_luminosity(size=1)[source]

function to draw the luminosity via an alternative method must be implemented in child class

Parameters:size
Returns:
mu
phi(L)[source]

The functional form of the distribution. not required for sampling :param luminosity: Luminosity

tau
popsynth.distributions.log_normal_distribution module
class popsynth.distributions.log_normal_distribution.LogNormalDistribution(seed: int = 1234, name: str = 'lognorm')[source]

Bases: popsynth.distribution.LuminosityDistribution

__init__(seed: int = 1234, name: str = 'lognorm')[source]

A log-normal luminosity distribution.

LogNormal(mu, tau)

Parameters:
  • seed (int) – Random seed
  • name (str) – Name of the distribution
  • mu (DistributionParameter) – Mean of the log normal
  • tau (DistributionParameter) – Standard deviation of the log normal
draw_luminosity(size=1)[source]

function to draw the luminosity via an alternative method must be implemented in child class

Parameters:size
Returns:
mu
phi(L)[source]

The functional form of the distribution. not required for sampling :param luminosity: Luminosity

tau
popsynth.distributions.pareto_distribution module
class popsynth.distributions.pareto_distribution.ParetoDistribution(seed: int = 1234, name: str = 'pareto')[source]

Bases: popsynth.distribution.LuminosityDistribution

Lmin
__init__(seed: int = 1234, name: str = 'pareto')[source]

A Pareto luminosity function.

alpha``*``Lmin``^``alpha / L^(``alpha``+1)

Parameters:
  • seed (int) – Random seed
  • name (str) – Name of the distribution
  • Lmin (DistributionParameter) – Minimum value of the luminosity
  • alpha (DistributionParameter) – Index of the pareto distribution
alpha
draw_luminosity(size: int = 1)[source]

function to draw the luminosity via an alternative method must be implemented in child class

Parameters:size
Returns:
phi(L)[source]

The functional form of the distribution. not required for sampling :param luminosity: Luminosity

popsynth.distributions.schechter_distribution module
class popsynth.distributions.schechter_distribution.SchechterDistribution(seed: int = 1234, name: str = 'schechter')[source]

Bases: popsynth.distribution.LuminosityDistribution

Lmin
__init__(seed: int = 1234, name: str = 'schechter')[source]

A Schechter luminosity function as in Schechter, Astrophysical Journal, Vol. 203, p. 297-306 (1976).

Parameters:
  • seed (int) – Random seed
  • name (str) – Name of the distribution
  • Lmin (DistributionParameter) – Minimum value of the luminosity
  • alpha (DistributionParameter) – Index of the distribution
alpha
draw_luminosity(size=1)[source]

function to draw the luminosity via an alternative method must be implemented in child class

Parameters:size
Returns:
phi(L)[source]

The functional form of the distribution. not required for sampling :param luminosity: Luminosity

popsynth.distributions.spherical_distribution module
class popsynth.distributions.spherical_distribution.ConstantSphericalDistribution(seed: int = 1234, name: str = 'cons_sphere', form: str = None)[source]

Bases: popsynth.distributions.spherical_distribution.SphericalDistribution

Lambda
__init__(seed: int = 1234, name: str = 'cons_sphere', form: str = None)[source]

A spherical distribution with constant density.

Parameters:
  • seed (int) – Random seed
  • name (str) – Name of the distribution
  • form (str) – Mathematical description of distribution
  • Lambda (DistributionParameter) – Density per unit volume
dNdV(distance)[source]

The differential number of objects per volume element

Parameters:distance
Returns:
class popsynth.distributions.spherical_distribution.SphericalDistribution(seed: int = 1234, name: str = 'sphere', form: str = None)[source]

Bases: popsynth.distribution.SpatialDistribution

__init__(seed: int = 1234, name: str = 'sphere', form: str = None)[source]

A generic spherical distribution. Can be inherited to form more complex spherical distributions

Parameters:
  • seed (int) – Random seed
  • name (str) – Name of the distribution
  • form (str) – Mathematical description of distribution
differential_volume(r)[source]

The differential volume

Parameters:distance – Distance
transform(L, r)[source]

The transform from luminosity to flux for the

Parameters:
  • flux
  • distance
Returns:

class popsynth.distributions.spherical_distribution.ZPowerSphericalDistribution(seed: int = 1234, name: str = 'zpow_sphere')[source]

Bases: popsynth.distributions.spherical_distribution.ConstantSphericalDistribution

__init__(seed: int = 1234, name: str = 'zpow_sphere')[source]

A spherical distribution with a power law density profile.

Lambda (1+r)^``delta``

Parameters:
  • seed (int) – Random seed
  • name (str) – Name of the distribution
  • form (str) – Mathematical description of distribution
  • delta (DistributionParameter) – Index of power law distribution
dNdV(distance)[source]

The differential number of objects per volume element

Parameters:distance
Returns:
delta
popsynth.distributions.spiral_galaxy_distribution module
class popsynth.distributions.spiral_galaxy_distribution.SpiralGalaxyDistribution(seed: int = 1234, name: str = 'spiral_galaxy', form: str = None)[source]

Bases: popsynth.distributions.spherical_distribution.SphericalDistribution

R0
R1
__init__(seed: int = 1234, name: str = 'spiral_galaxy', form: str = None)[source]

A spiral galaxy spatial distribution.

Parameters:
  • seed (int) – Random seed
  • name (str) – Name of the distribution
  • form (str) – Mathematical description of distribution
  • rho (DistributionParameter) – Local density
  • a (DistributionParameter) – Shape parameter
  • b (DistributionParameter) – Shape parameter
  • R1 (DistributionParameter) – Scale parameter
  • R0 (DistributionParameter) – Scale parameter
a
b
dNdV(r)[source]

The differential number of objects per volume element

Parameters:distance
Returns:
draw_sky_positions(size)[source]

Based on Wainscoat 1992 and Faucher-Giguere 2007.

Code thanks to Mortiz Pleintinger.

rho
Module contents
class popsynth.distributions.SphericalDistribution(seed: int = 1234, name: str = 'sphere', form: str = None)[source]

Bases: popsynth.distribution.SpatialDistribution

__init__(seed: int = 1234, name: str = 'sphere', form: str = None)[source]

A generic spherical distribution. Can be inherited to form more complex spherical distributions

Parameters:
  • seed (int) – Random seed
  • name (str) – Name of the distribution
  • form (str) – Mathematical description of distribution
differential_volume(r)[source]

The differential volume

Parameters:distance – Distance
transform(L, r)[source]

The transform from luminosity to flux for the

Parameters:
  • flux
  • distance
Returns:

class popsynth.distributions.CosmologicalDistribution(seed: int = 1234, name: str = 'cosmo', form: str = None, truth: Dict[str, Any] = {}, is_rate: bool = True)[source]

Bases: popsynth.distribution.SpatialDistribution

__init__(seed: int = 1234, name: str = 'cosmo', form: str = None, truth: Dict[str, Any] = {}, is_rate: bool = True)[source]

Base class for cosmological spatial distributions.

Parameters:
  • seed (int) – Random seed
  • name (str) – Name of the distribution
  • form (str) – Mathematical description of distribution
  • truth (dict[str, Any]) – True values of parameters
  • is_rate (bool) – True if modelling a population of transient events, False if modelling a population of steady-state objects. Affects the time_adjustment method used in cosmo calculations. Default is True.
differential_volume(z)[source]

Differential comoving volume in Gpc^3 sr^-1.

dV/dzdOmega

Parameters:z – Redshift
Returns:The differential comoving volume in Gpc^-3 sr^-1.
time_adjustment(z)[source]

Time adjustment factor to handle both transient and steady-state populations.

Parameters:z – Redshift
Returns:Appropriate factor depending on is_rate
transform(L, z)[source]

Transformation from luminosity to energy flux.

L / 4 pi dL^2

dL is in cm. Therefore for L in erg s^-1 returns flux in erg cm^-2 s^-1.

Parameters:
  • L – Luminosity
  • z – Redshift
Returns:

Flux

class popsynth.distributions.SFRDistribution(seed: int = 1234, name: str = 'sfr', is_rate: bool = True)[source]

Bases: popsynth.distributions.cosmological_distribution.CosmologicalDistribution

__init__(seed: int = 1234, name: str = 'sfr', is_rate: bool = True)[source]

A star-formation like distribution of the form presented in Cole et al. 2001.

r0``(``a``+``rise``z)/(1 + (z/``peak)^``decay``)

Parameters:
  • seed (int) – Random seed
  • name (str) – Name of the distribution
  • is_rate (bool) – True if modelling a population of transient events, False if modelling a population of steady-state objects. Affects the time_adjustment method used in cosmo calculations. Default is True.
  • r0 (DistributionParameter) – The local density in units of Gpc^-3
  • a (DistributionParameter) – Offset at z=0
  • rise (DistributionParameter) – Rise at low z
  • decay (DistributionParameter) – Decay at high z
  • peak (DistributionParameter) – Peak of z distribution
a
dNdV(z)[source]

The differential number of objects per volume element

Parameters:distance
Returns:
decay
peak
r0
rise
class popsynth.distributions.ZPowerCosmoDistribution(seed: int = 1234, name: str = 'zpow_cosmo', is_rate: bool = True)[source]

Bases: popsynth.distributions.cosmological_distribution.CosmologicalDistribution

Lambda
__init__(seed: int = 1234, name: str = 'zpow_cosmo', is_rate: bool = True)[source]

A cosmological distribution where the density evolves as a power law.

Lambda (1+z)^``delta``

Parameters:
  • seed (int) – Random seed
  • name (str) – Name of the distribution
  • is_rate (bool) – True if modelling a population of transient events, False if modelling a population of steady-state objects. Affects the time_adjustment method used in cosmo calculations. Default is True.
  • Lambda (DistributionParameter) – The local density in units of Gpc^-3
  • delta (DistributionParameter) – The index of the power law
dNdV(distance)[source]

The differential number of objects per volume element

Parameters:distance
Returns:
delta
class popsynth.distributions.ParetoDistribution(seed: int = 1234, name: str = 'pareto')[source]

Bases: popsynth.distribution.LuminosityDistribution

Lmin
__init__(seed: int = 1234, name: str = 'pareto')[source]

A Pareto luminosity function.

alpha``*``Lmin``^``alpha / L^(``alpha``+1)

Parameters:
  • seed (int) – Random seed
  • name (str) – Name of the distribution
  • Lmin (DistributionParameter) – Minimum value of the luminosity
  • alpha (DistributionParameter) – Index of the pareto distribution
alpha
draw_luminosity(size: int = 1)[source]

function to draw the luminosity via an alternative method must be implemented in child class

Parameters:size
Returns:
phi(L)[source]

The functional form of the distribution. not required for sampling :param luminosity: Luminosity

class popsynth.distributions.Log10NormalDistribution(seed: int = 1234, name: str = 'log10norm')[source]

Bases: popsynth.distribution.LuminosityDistribution

__init__(seed: int = 1234, name: str = 'log10norm')[source]

A log10-normal luminosity function

Log10Normal(mu, tau)

Parameters:
  • seed (int) – Random seed
  • name (str) – Name of the distribution
  • mu (DistributionParameter) – Mean of the log10 normal
  • tau (DistributionParameter) – Standard deviation of the log10 normal
draw_luminosity(size=1)[source]

function to draw the luminosity via an alternative method must be implemented in child class

Parameters:size
Returns:
mu
phi(L)[source]

The functional form of the distribution. not required for sampling :param luminosity: Luminosity

tau
class popsynth.distributions.LogNormalDistribution(seed: int = 1234, name: str = 'lognorm')[source]

Bases: popsynth.distribution.LuminosityDistribution

__init__(seed: int = 1234, name: str = 'lognorm')[source]

A log-normal luminosity distribution.

LogNormal(mu, tau)

Parameters:
  • seed (int) – Random seed
  • name (str) – Name of the distribution
  • mu (DistributionParameter) – Mean of the log normal
  • tau (DistributionParameter) – Standard deviation of the log normal
draw_luminosity(size=1)[source]

function to draw the luminosity via an alternative method must be implemented in child class

Parameters:size
Returns:
mu
phi(L)[source]

The functional form of the distribution. not required for sampling :param luminosity: Luminosity

tau
class popsynth.distributions.SchechterDistribution(seed: int = 1234, name: str = 'schechter')[source]

Bases: popsynth.distribution.LuminosityDistribution

Lmin
__init__(seed: int = 1234, name: str = 'schechter')[source]

A Schechter luminosity function as in Schechter, Astrophysical Journal, Vol. 203, p. 297-306 (1976).

Parameters:
  • seed (int) – Random seed
  • name (str) – Name of the distribution
  • Lmin (DistributionParameter) – Minimum value of the luminosity
  • alpha (DistributionParameter) – Index of the distribution
alpha
draw_luminosity(size=1)[source]

function to draw the luminosity via an alternative method must be implemented in child class

Parameters:size
Returns:
phi(L)[source]

The functional form of the distribution. not required for sampling :param luminosity: Luminosity

class popsynth.distributions.BPLDistribution(seed: int = 1234, name: str = 'bpl')[source]

Bases: popsynth.distribution.LuminosityDistribution

Lbreak
Lmax
Lmin
__init__(seed: int = 1234, name: str = 'bpl')[source]

A broken power law luminosity distribution.

L ~ L^``alpha`` for L <= Lbreak L ~ L^``beta`` for L > Lbreak

Parameters:
  • seed (int) – Random seed
  • name (str) – Name of the distribution
  • Lmin (DistributionParameter) – Minimum value of the luminosity
  • alpha (DistributionParameter) – Index of the lower power law
  • Lbreak (DistributionParameter) – Luminosity of the power law break
  • beta (DistributionParameter) – Index of the upper power law
  • Lmax (DistributionParameter) – Maximum value of the luminosity
alpha
beta
draw_luminosity(size=1)[source]

function to draw the luminosity via an alternative method must be implemented in child class

Parameters:size
Returns:
phi(L)[source]

The functional form of the distribution. not required for sampling :param luminosity: Luminosity

class popsynth.distributions.SphericalDistribution(seed: int = 1234, name: str = 'sphere', form: str = None)[source]

Bases: popsynth.distribution.SpatialDistribution

__init__(seed: int = 1234, name: str = 'sphere', form: str = None)[source]

A generic spherical distribution. Can be inherited to form more complex spherical distributions

Parameters:
  • seed (int) – Random seed
  • name (str) – Name of the distribution
  • form (str) – Mathematical description of distribution
differential_volume(r)[source]

The differential volume

Parameters:distance – Distance
transform(L, r)[source]

The transform from luminosity to flux for the

Parameters:
  • flux
  • distance
Returns:

class popsynth.distributions.ConstantSphericalDistribution(seed: int = 1234, name: str = 'cons_sphere', form: str = None)[source]

Bases: popsynth.distributions.spherical_distribution.SphericalDistribution

Lambda
__init__(seed: int = 1234, name: str = 'cons_sphere', form: str = None)[source]

A spherical distribution with constant density.

Parameters:
  • seed (int) – Random seed
  • name (str) – Name of the distribution
  • form (str) – Mathematical description of distribution
  • Lambda (DistributionParameter) – Density per unit volume
dNdV(distance)[source]

The differential number of objects per volume element

Parameters:distance
Returns:
class popsynth.distributions.ZPowerSphericalDistribution(seed: int = 1234, name: str = 'zpow_sphere')[source]

Bases: popsynth.distributions.spherical_distribution.ConstantSphericalDistribution

__init__(seed: int = 1234, name: str = 'zpow_sphere')[source]

A spherical distribution with a power law density profile.

Lambda (1+r)^``delta``

Parameters:
  • seed (int) – Random seed
  • name (str) – Name of the distribution
  • form (str) – Mathematical description of distribution
  • delta (DistributionParameter) – Index of power law distribution
dNdV(distance)[source]

The differential number of objects per volume element

Parameters:distance
Returns:
delta
class popsynth.distributions.DeltaDistribution(seed: int = 1234, name: str = 'delta')[source]

Bases: popsynth.distribution.LuminosityDistribution

Lp
__init__(seed: int = 1234, name: str = 'delta')[source]

A delta function luminosity distribution, centred on Lp.

Parameters:
  • seed (int) – Random seed
  • name (str) – Name of the distribution
  • Lp (DistributionParameter) – The central value
draw_luminosity(size=1)[source]

function to draw the luminosity via an alternative method must be implemented in child class

Parameters:size
Returns:
phi(L)[source]

The functional form of the distribution. not required for sampling :param luminosity: Luminosity

class popsynth.distributions.FlatlandDistribution(seed: int = 1234, name: str = 'flatland', form: str = None)[source]

Bases: popsynth.distribution.SpatialDistribution

Lambda
__init__(seed: int = 1234, name: str = 'flatland', form: str = None)[source]

A flat spatial distribution with only length.

Parameters:
  • seed (int) – Random seed
  • name (str) – Name of the distribution
  • form (str) – Mathematical description of distribution
  • Lambda (DistributionParameter) – Length
dNdV(distance)[source]

The differential number of objects per volume element

Parameters:distance
Returns:
differential_volume(r)[source]

The differential volume

Parameters:distance – Distance
transform(L, r)[source]

The transform from luminosity to flux for the

Parameters:
  • flux
  • distance
Returns:

class popsynth.distributions.SpiralGalaxyDistribution(seed: int = 1234, name: str = 'spiral_galaxy', form: str = None)[source]

Bases: popsynth.distributions.spherical_distribution.SphericalDistribution

R0
R1
__init__(seed: int = 1234, name: str = 'spiral_galaxy', form: str = None)[source]

A spiral galaxy spatial distribution.

Parameters:
  • seed (int) – Random seed
  • name (str) – Name of the distribution
  • form (str) – Mathematical description of distribution
  • rho (DistributionParameter) – Local density
  • a (DistributionParameter) – Shape parameter
  • b (DistributionParameter) – Shape parameter
  • R1 (DistributionParameter) – Scale parameter
  • R0 (DistributionParameter) – Scale parameter
a
b
dNdV(r)[source]

The differential number of objects per volume element

Parameters:distance
Returns:
draw_sky_positions(size)[source]

Based on Wainscoat 1992 and Faucher-Giguere 2007.

Code thanks to Mortiz Pleintinger.

rho
popsynth.populations package
Submodules
popsynth.populations.bpl_population module
class popsynth.populations.bpl_population.BPLHomogeneousSphericalPopulation(Lambda: float, Lmin: float, alpha: float, Lbreak: float, beta: float, Lmax: float, r_max: float = 5, seed: int = 1234)[source]

Bases: popsynth.populations.spatial_populations.SphericalPopulation

__init__(Lambda: float, Lmin: float, alpha: float, Lbreak: float, beta: float, Lmax: float, r_max: float = 5, seed: int = 1234)[source]

A population built on the ConstantSphericalDistribution spatial distribution and the BPLDistribution luminosity distribution.

Parameters:
  • Lambda (float) – Density per unit volume
  • Lmin (float) – Minimum value of the luminosity
  • alpha (float) – Lower luminosity index
  • Lbreak (float) – Break luminosity
  • beta (float) – Upper luminosity index
  • Lmax (float) – Maximum value of the luminosity
  • r_max (float) – Maximum distance
  • seed (int) – Random seed
class popsynth.populations.bpl_population.BPLSFRPopulation(r0: float, a: float, rise: float, decay: float, peak: float, Lmin: float, alpha: float, Lbreak: float, beta: float, Lmax: float, r_max: float = 5, seed: int = 1234, is_rate: bool = True)[source]

Bases: popsynth.populations.spatial_populations.SFRPopulation

__init__(r0: float, a: float, rise: float, decay: float, peak: float, Lmin: float, alpha: float, Lbreak: float, beta: float, Lmax: float, r_max: float = 5, seed: int = 1234, is_rate: bool = True)[source]

A population built on the SFRDistribution spatial distribution and the BPLDistribution luminosity distribution.

Parameters:
  • r0 (float) – Local density in units of Gpc^-3
  • a (float) – Offset at z=0
  • rise (float) – Rise at low z
  • decay (float) – Decay at high z
  • peak (float) – Peak of z distribution
  • Lmin (float) – Minimum value of the luminosity
  • alpha (float) – Lower luminosity index
  • Lbreak (float) – Break luminosity
  • beta (float) – Upper luminosity index
  • Lmax (float) – Maximum value of the luminosity
  • r_max (float) – Maximum redshift
  • seed (int) – Random seed
class popsynth.populations.bpl_population.BPLZPowerCosmoPopulation(Lambda: float, delta: float, Lmin: float, alpha: float, Lbreak: float, beta: float, Lmax: float, r_max: float = 5, seed: int = 1234, is_rate: bool = True)[source]

Bases: popsynth.populations.spatial_populations.ZPowerCosmoPopulation

__init__(Lambda: float, delta: float, Lmin: float, alpha: float, Lbreak: float, beta: float, Lmax: float, r_max: float = 5, seed: int = 1234, is_rate: bool = True)[source]

A population built on the ZPowerCosmoDistribution spatial distribution and the BPLDistribution luminosity distribution.

Parameters:
  • Lambda (float) – Density in Gpc^-3
  • delta (float) – Index of spatial distribution
  • Lmin (float) – Minimum value of the luminosity
  • alpha (float) – Lower luminosity index
  • Lbreak (float) – Break luminosity
  • beta (float) – Upper luminosity index
  • Lmax (float) – Maximum value of the luminosity
  • r_max (float) – Maximum distance
  • seed (int) – Random seed
class popsynth.populations.bpl_population.BPLZPowerSphericalPopulation(Lambda: float, delta: float, Lmin: float, alpha: float, Lbreak: float, beta: float, Lmax: float, r_max: float = 5, seed: int = 1234)[source]

Bases: popsynth.populations.spatial_populations.ZPowerSphericalPopulation

__init__(Lambda: float, delta: float, Lmin: float, alpha: float, Lbreak: float, beta: float, Lmax: float, r_max: float = 5, seed: int = 1234)[source]

A population built on the ZPowerSphericalDistribution spatial distribution and the BPLDistribution luminosity distribution.

Parameters:
  • Lambda (float) – Density per unit volume
  • delta (float) – Index of spatial distribution
  • Lmin (float) – Minimum value of the luminosity
  • alpha (float) – Lower luminosity index
  • Lbreak (float) – Break luminosity
  • beta (float) – Upper luminosity index
  • Lmax (float) – Maximum value of the luminosity
  • r_max (float) – Maximum distance
  • seed (int) – Random seed
popsynth.populations.lognormal_population module
class popsynth.populations.lognormal_population.Log10NormalHomogeneousSphericalPopulation(Lambda: float, mu: float, tau: float, r_max: float = 5, seed: int = 1234)[source]

Bases: popsynth.populations.spatial_populations.SphericalPopulation

__init__(Lambda: float, mu: float, tau: float, r_max: float = 5, seed: int = 1234)[source]

A population built on the ConstantSphericalDistribution spatial distribution and the Log10NormalDistribution luminosity distribution.

Parameters:
  • Lambda (float) – Density per unit volume
  • mu (float) – Mean of the luminosity distribution
  • tau (float) – Standard deviation of the luminosity distribution
  • r_max (float) – Maximum distance
  • seed (int) – Random seed
class popsynth.populations.lognormal_population.Log10NormalSFRPopulation(r0: float, a: float, rise: float, decay: float, peak: float, mu: float, tau: float, r_max: float = 5, seed: int = 1234, is_rate: bool = True)[source]

Bases: popsynth.populations.spatial_populations.SFRPopulation

__init__(r0: float, a: float, rise: float, decay: float, peak: float, mu: float, tau: float, r_max: float = 5, seed: int = 1234, is_rate: bool = True)[source]

A population built on the SFRDistribution spatial distribution and the Log10NormalDistribution luminosity distribution.

Parameters:
  • r0 (float) – Local density in units of Gpc^-3
  • a (float) – Offset at z=0
  • rise (float) – Rise at low z
  • decay (float) – Decay at high z
  • peak (float) – Peak of z distribution
  • mu (float) – Mean of the luminosity distribution
  • tau (float) – Standard deviation of the luminosity distribution
  • r_max (float) – Maximum redshift
  • seed (int) – Random seed
  • is_rate (bool) – True if modelling a population of transient events, False if modelling a population of steady-state objects. Affects the time_adjustment method used in cosmo calculations. Default is True.
class popsynth.populations.lognormal_population.Log10NormalZPowerCosmoPopulation(Lambda: float, delta: float, mu: float, tau: float, r_max: float = 5, seed: float = 1234, is_rate: float = True)[source]

Bases: popsynth.populations.spatial_populations.ZPowerCosmoPopulation

__init__(Lambda: float, delta: float, mu: float, tau: float, r_max: float = 5, seed: float = 1234, is_rate: float = True)[source]

A population built on the ZPowerCosmoDistribution spatial distribution and the Log10NormalDistribution luminosity distribution.

Parameters:
  • Lambda (float) – Density in units of Gpc^-3
  • delta (float) – Index of the spatial distribution
  • mu (float) – Mean of the luminosity distribution
  • tau (float) – Standard deviation of the luminosity distribution
  • r_max (float) – Maximum redshift
  • seed (int) – Random seed
  • is_rate (bool) – True if modelling a population of transient events, False if modelling a population of steady-state objects. Affects the time_adjustment method used in cosmo calculations. Default is True.
class popsynth.populations.lognormal_population.Log10NormalZPowerSphericalPopulation(Lambda: float, delta: float, mu: float, tau: float, r_max: float = 5, seed: int = 1234)[source]

Bases: popsynth.populations.spatial_populations.ZPowerSphericalPopulation

__init__(Lambda: float, delta: float, mu: float, tau: float, r_max: float = 5, seed: int = 1234)[source]

A population built on the ZPowerSphericalDistribution spatial distribution and the Log10NormalDistribution luminosity distribution.

Parameters:
  • Lambda (float) – Density per unit volume
  • delta (float) – Index of the spatial distribution
  • mu (float) – Mean of the luminosity distribution
  • tau (float) – Standard deviation of the luminosity distribution
  • r_max (float) – Maximum distance
  • seed (int) – Random seed
class popsynth.populations.lognormal_population.LogNormalHomogeneousSphericalPopulation(Lambda: float, mu: float, tau: float, r_max: float = 5, seed: int = 1234)[source]

Bases: popsynth.populations.spatial_populations.SphericalPopulation

__init__(Lambda: float, mu: float, tau: float, r_max: float = 5, seed: int = 1234)[source]

A population built on the ConstantSphericalDistribution spatial distribution and the LogNormalDistribution luminosity distribution.

Parameters:
  • Lambda (float) – Density per unit volume
  • mu (float) – Mean of the luminosity distribution
  • tau (float) – Standard deviation of the luminosity distribution
  • r_max (float) – Maximum distance
  • seed (int) – Random seed
class popsynth.populations.lognormal_population.LogNormalSFRPopulation(r0: float, a: float, rise: float, decay: float, peak: float, mu: float, tau: float, r_max: float = 5, seed: int = 1234, is_rate: bool = True)[source]

Bases: popsynth.populations.spatial_populations.SFRPopulation

__init__(r0: float, a: float, rise: float, decay: float, peak: float, mu: float, tau: float, r_max: float = 5, seed: int = 1234, is_rate: bool = True)[source]

A population built on the SFRDistribution spatial distribution and the LogNormalDistribution luminosity distribution.

Parameters:
  • r0 (float) – Local density in units of Gpc^-3
  • a (float) – Offset at z=0
  • rise (float) – Rise at low z
  • decay (float) – Decay at high z
  • peak (float) – Peak of z distribution
  • mu (float) – Mean of the luminosity distribution
  • tau (float) – Standard deviation of the luminosity distribution
  • r_max (float) – Maximum redshift
  • seed (int) – Random seed
  • is_rate (bool) – True if modelling a population of transient events, False if modelling a population of steady-state objects. Affects the time_adjustment method used in cosmo calculations. Default is True.
class popsynth.populations.lognormal_population.LogNormalZPowerCosmoPopulation(Lambda: float, delta: float, mu: float, tau: float, r_max: float = 5, seed: int = 1234, is_rate: bool = True)[source]

Bases: popsynth.populations.spatial_populations.ZPowerCosmoPopulation

__init__(Lambda: float, delta: float, mu: float, tau: float, r_max: float = 5, seed: int = 1234, is_rate: bool = True)[source]

A population built on the ZPowerCosmoDistribution spatial distribution and the LogNormalDistribution luminosity distribution.

Parameters:
  • Lambda (float) – Density in units of Gpc^-3
  • delta (float) – Index of the spatial distribution
  • mu (float) – Mean of the luminosity distribution
  • tau (float) – Standard deviation of the luminosity distribution
  • r_max (float) – Maximum redshift
  • seed (int) – Random seed
  • is_rate (bool) – True if modelling a population of transient events, False if modelling a population of steady-state objects. Affects the time_adjustment method used in cosmo calculations. Default is True.
class popsynth.populations.lognormal_population.LogNormalZPowerSphericalPopulation(Lambda: float, delta: float, mu: float, tau: float, r_max: float = 5, seed: int = 1234)[source]

Bases: popsynth.populations.spatial_populations.ZPowerSphericalPopulation

__init__(Lambda: float, delta: float, mu: float, tau: float, r_max: float = 5, seed: int = 1234)[source]

A population built on the ZPowerSphericalDistribution spatial distribution and the LogNormalDistribution luminosity distribution.

Parameters:
  • Lambda (float) – Density per unit volume
  • delta (float) – Index of the spatial distribution
  • mu (float) – Mean of the luminosity distribution
  • tau (float) – Standard deviation of the luminosity distribution
  • r_max (float) – Maximum distance
  • seed (int) – Random seed
popsynth.populations.pareto_populations module
class popsynth.populations.pareto_populations.ParetoHomogeneousSphericalPopulation(Lambda: float, Lmin: float, alpha: float, r_max: float = 5, seed: int = 1234)[source]

Bases: popsynth.populations.spatial_populations.SphericalPopulation

__init__(Lambda: float, Lmin: float, alpha: float, r_max: float = 5, seed: int = 1234)[source]

A population built on the ConstantSphericalDistribution spatial distribution and the ParetoDistribution luminosity distribution.

Parameters:
  • Lambda (float) – Density per unit volume
  • Lmin (float) – Minimum value of the luminosity
  • alpha (float) – Index of the luminosity distribution
  • r_max (float) – Maximum distance
  • seed (int) – Random seed
class popsynth.populations.pareto_populations.ParetoSFRPopulation(r0: float, a: float, rise: float, decay: float, peak: float, Lmin: float, alpha: float, r_max: float = 5, seed: int = 1234, is_rate: bool = True)[source]

Bases: popsynth.populations.spatial_populations.SFRPopulation

__init__(r0: float, a: float, rise: float, decay: float, peak: float, Lmin: float, alpha: float, r_max: float = 5, seed: int = 1234, is_rate: bool = True)[source]

A population built on the SFRDistribution spatial distribution and the ParetoDistribution luminosity distribution.

Parameters:
  • r0 (float) – Local density in units of Gpc^-3
  • a (float) – Offset at z=0
  • rise (float) – Rise at low z
  • decay (float) – Decay at high z
  • peak (float) – Peak of z distribution
  • Lmin (float) – Minimum value of the luminosity
  • alpha (float) – Index of the luminosity distribution
  • r_max (float) – Maximum redshift
  • seed (int) – Random seed
  • is_rate (bool) – True if modelling a population of transient events, False if modelling a population of steady-state objects. Affects the time_adjustment method used in cosmo calculations. Default is True.
class popsynth.populations.pareto_populations.ParetoZPowerCosmoPopulation(Lambda: float, delta: float, Lmin: float, alpha: float, r_max: float = 5, seed: int = 1234, is_rate: bool = True)[source]

Bases: popsynth.populations.spatial_populations.ZPowerCosmoPopulation

__init__(Lambda: float, delta: float, Lmin: float, alpha: float, r_max: float = 5, seed: int = 1234, is_rate: bool = True)[source]

A population built on the ZPowerCosmoDistribution spatial distribution and the ParetoDistribution luminosity distribution.

Parameters:
  • Lambda (float) – Density in units of Gpc^-3
  • delta (float) – Index of the spatial distribution
  • Lmin (float) – Minimum value of the luminosity
  • alpha (float) – Index of the luminosity distribution
  • r_max (float) – Maximum redshift
  • seed (int) – Random seed
  • is_rate (bool) – True if modelling a population of transient events, False if modelling a population of steady-state objects. Affects the time_adjustment method used in cosmo calculations. Default is True.
class popsynth.populations.pareto_populations.ParetoZPowerSphericalPopulation(Lambda: float, delta: float, Lmin: float, alpha: float, r_max: float = 5, seed: int = 1234)[source]

Bases: popsynth.populations.spatial_populations.ZPowerSphericalPopulation

__init__(Lambda: float, delta: float, Lmin: float, alpha: float, r_max: float = 5, seed: int = 1234)[source]

A population built on the ZPowerSphericalDistribution spatial distribution and the ParetoDistribution luminosity distribution.

Parameters:
  • Lambda (float) – Density per unit volume
  • delta (float) – Index of the spatial distribution
  • Lmin (float) – Minimum value of the luminosity
  • alpha (float) – Index of the luminosity distribution
  • r_max (float) – Maximum distance
  • seed (int) – Random seed
popsynth.populations.schechter_populations module
class popsynth.populations.schechter_populations.SchechterHomogeneousSphericalPopulation(Lambda: float, Lmin: float, alpha: float, r_max: float = 10, seed: int = 1234)[source]

Bases: popsynth.populations.spatial_populations.SphericalPopulation

__init__(Lambda: float, Lmin: float, alpha: float, r_max: float = 10, seed: int = 1234)[source]

A population built on the ConstantSphericalDistribution spatial distribution and the SchechterDistribution luminosity distribution.

Parameters:
  • Lambda (float) – Density per unit volume
  • Lmin (float) – Minimum value of the luminosity
  • alpha (float) – Index of the luminosity distribution
  • r_max (float) – Maximum distance
  • seed (int) – Random seed
class popsynth.populations.schechter_populations.SchechterSFRPopulation(r0: float, a: float, rise: float, decay: float, peak: float, Lmin: float, alpha: float, r_max: float = 10, seed: int = 1234, is_rate: bool = True)[source]

Bases: popsynth.populations.spatial_populations.SFRPopulation

__init__(r0: float, a: float, rise: float, decay: float, peak: float, Lmin: float, alpha: float, r_max: float = 10, seed: int = 1234, is_rate: bool = True)[source]

A population built on the SFRDistribution spatial distribution and the SchechterDistribution luminosity distribution.

Parameters:
  • r0 (float) – Local density in units of Gpc^-3
  • a (float) – Offset at z=0
  • rise (float) – Rise at low z
  • decay (float) – Decay at high z
  • peak (float) – Peak of z distribution
  • Lmin (float) – Minimum value of the luminosity
  • alpha (float) – Index of the luminosity distribution
  • r_max (float) – Maximum redshift
  • seed (int) – Random seed
  • is_rate (bool) – True if modelling a population of transient events, False if modelling a population of steady-state objects. Affects the time_adjustment method used in cosmo calculations. Default is True.
class popsynth.populations.schechter_populations.SchechterZPowerCosmoPopulation(Lambda: float, delta: float, Lmin: float, alpha: float, r_max: float = 10, seed: int = 1234, is_rate: bool = True)[source]

Bases: popsynth.populations.spatial_populations.ZPowerCosmoPopulation

__init__(Lambda: float, delta: float, Lmin: float, alpha: float, r_max: float = 10, seed: int = 1234, is_rate: bool = True)[source]

A population built on the ZPowerCosmoDistribution spatial distribution and the SchechterDistribution luminosity distribution.

Parameters:
  • Lambda (float) – Density in units of Gpc^-3
  • delta (float) – Index of the spatial distribution
  • Lmin (float) – Minimum value of the luminosity
  • alpha (float) – Index of the luminosity distribution
  • r_max (float) – Maximum redshift
  • seed (int) – Random seed
  • is_rate (bool) – True if modelling a population of transient events, False if modelling a population of steady-state objects. Affects the time_adjustment method used in cosmo calculations. Default is True.
class popsynth.populations.schechter_populations.SchechterZPowerSphericalPopulation(Lambda: float, delta: float, Lmin: float, alpha: float, r_max: float = 10, seed: int = 1234)[source]

Bases: popsynth.populations.spatial_populations.ZPowerSphericalPopulation

__init__(Lambda: float, delta: float, Lmin: float, alpha: float, r_max: float = 10, seed: int = 1234)[source]

A population built on the ZPowerSphericalDistribution spatial distribution and the SchechterDistribution luminosity distribution.

Parameters:
  • Lambda (float) – Density per unit volume
  • delta (float) – Index of the spatial distribution
  • Lmin (float) – Minimum value of the luminosity
  • alpha (float) – Index of the luminosity distribution
  • r_max (float) – Maximum distance
  • seed (int) – Random seed
popsynth.populations.spatial_populations module
class popsynth.populations.spatial_populations.MWRadialPopulation(rho: float, a: float = 1.64, b: float = 4.01, R1: float = 0.55, R0: float = 8.5, r_max: float = 20, seed: int = 1234, luminosity_distribution: popsynth.distribution.LuminosityDistribution = None)[source]

Bases: popsynth.population_synth.PopulationSynth

__init__(rho: float, a: float = 1.64, b: float = 4.01, R1: float = 0.55, R0: float = 8.5, r_max: float = 20, seed: int = 1234, luminosity_distribution: popsynth.distribution.LuminosityDistribution = None)[source]

A Milky-way like population based on SpiralGalaxyDistribution.

Parameters:
  • rho (float) – Local density
  • a (float) – Shape parameter
  • b (float) – Shape parameter
  • R1 (float) – Scale parameter
  • R0 (float) – Scale parameter
  • r_max (float) – Maximum distance
  • seed (int) – Random seed
  • luminosity_distribution (LuminosityDistribution, optional) – Luminosity distribution
  • is_rate (bool) – True if modelling a population of transient events, False if modelling a population of steady-state objects. Affects the time_adjustment method used in cosmo calculations. Default is True.
class popsynth.populations.spatial_populations.SFRPopulation(r0: float, a: float, rise: float, decay: float, peak: float, r_max: float = 5, seed: int = 1234, luminosity_distribution: popsynth.distribution.LuminosityDistribution = None, is_rate: bool = True)[source]

Bases: popsynth.population_synth.PopulationSynth

__init__(r0: float, a: float, rise: float, decay: float, peak: float, r_max: float = 5, seed: int = 1234, luminosity_distribution: popsynth.distribution.LuminosityDistribution = None, is_rate: bool = True)[source]

A cosmological population with a density that scales similarly to the star formation rate. Based on ZpowerCosmoDistribution.

Parameters:
  • r0 (float) – Local density in units of Gpc^-3
  • a (float) – Offset at z=0
  • rise (float) – Rise at low z
  • decay (float) – Decay at high z
  • peak (float) – Peak of z distribution
  • r_max (float) – Maximum redshift
  • seed (int) – Random seed
  • luminosity_distribution (LuminosityDistribution, optional) – Luminosity distribution
  • is_rate (bool) – True if modelling a population of transient events, False if modelling a population of steady-state objects. Affects the time_adjustment method used in cosmo calculations. Default is True.
class popsynth.populations.spatial_populations.SphericalPopulation(Lambda: float, r_max: float = 5.0, seed: int = 1234, luminosity_distribution: popsynth.distribution.LuminosityDistribution = None)[source]

Bases: popsynth.population_synth.PopulationSynth

__init__(Lambda: float, r_max: float = 5.0, seed: int = 1234, luminosity_distribution: popsynth.distribution.LuminosityDistribution = None)[source]

A generic spherical population based on ConstantSphericalDistribution.

Parameters:
  • Lambda (float) – Density per unit volume
  • r_max (float) – Maximum distance
  • seed (int) – Random seed
  • luminosity_distribution (LuminosityDistribution, optional) – Luminosity distribution
class popsynth.populations.spatial_populations.ZPowerCosmoPopulation(Lambda: float, delta: float, r_max: float = 5.0, seed: int = 1234, luminosity_distribution: popsynth.distribution.LuminosityDistribution = None, is_rate: bool = True)[source]

Bases: popsynth.population_synth.PopulationSynth

__init__(Lambda: float, delta: float, r_max: float = 5.0, seed: int = 1234, luminosity_distribution: popsynth.distribution.LuminosityDistribution = None, is_rate: bool = True)[source]

A cosmological population with a density that scales as (z+1)^delta. Based on ZpowerCosmoDistribution.

Parameters:
  • Lambda (float) – Local density in units of Gpc^-3
  • delta (float) – Index of spatial distribution
  • r_max (float) – Maximum redshift
  • seed (int) – Random seed
  • luminosity_distribution (LuminosityDistribution, optional) – Luminosity distribution
  • is_rate (bool) – True if modelling a population of transient events, False if modelling a population of steady-state objects. Affects the time_adjustment method used in cosmo calculations. Default is True.
class popsynth.populations.spatial_populations.ZPowerSphericalPopulation(Lambda: float, delta: float, r_max: float = 5.0, seed: int = 1234, luminosity_distribution: popsynth.distribution.LuminosityDistribution = None)[source]

Bases: popsynth.population_synth.PopulationSynth

__init__(Lambda: float, delta: float, r_max: float = 5.0, seed: int = 1234, luminosity_distribution: popsynth.distribution.LuminosityDistribution = None)[source]

A spherical population with a density that scales as (r+1)^delta. Based on ZpowerSphericalDistribution.

Parameters:
  • Lambda (float) – Local density per unit volume
  • delta (float) – Index of spatial distribution
  • r_max (float) – Maximum distance
  • seed (int) – Random seed
  • luminosity_distribution (LuminosityDistribution, optional) – Luminosity distribution
Module contents
class popsynth.populations.SphericalPopulation(Lambda: float, r_max: float = 5.0, seed: int = 1234, luminosity_distribution: popsynth.distribution.LuminosityDistribution = None)[source]

Bases: popsynth.population_synth.PopulationSynth

__init__(Lambda: float, r_max: float = 5.0, seed: int = 1234, luminosity_distribution: popsynth.distribution.LuminosityDistribution = None)[source]

A generic spherical population based on ConstantSphericalDistribution.

Parameters:
  • Lambda (float) – Density per unit volume
  • r_max (float) – Maximum distance
  • seed (int) – Random seed
  • luminosity_distribution (LuminosityDistribution, optional) – Luminosity distribution
class popsynth.populations.SFRPopulation(r0: float, a: float, rise: float, decay: float, peak: float, r_max: float = 5, seed: int = 1234, luminosity_distribution: popsynth.distribution.LuminosityDistribution = None, is_rate: bool = True)[source]

Bases: popsynth.population_synth.PopulationSynth

__init__(r0: float, a: float, rise: float, decay: float, peak: float, r_max: float = 5, seed: int = 1234, luminosity_distribution: popsynth.distribution.LuminosityDistribution = None, is_rate: bool = True)[source]

A cosmological population with a density that scales similarly to the star formation rate. Based on ZpowerCosmoDistribution.

Parameters:
  • r0 (float) – Local density in units of Gpc^-3
  • a (float) – Offset at z=0
  • rise (float) – Rise at low z
  • decay (float) – Decay at high z
  • peak (float) – Peak of z distribution
  • r_max (float) – Maximum redshift
  • seed (int) – Random seed
  • luminosity_distribution (LuminosityDistribution, optional) – Luminosity distribution
  • is_rate (bool) – True if modelling a population of transient events, False if modelling a population of steady-state objects. Affects the time_adjustment method used in cosmo calculations. Default is True.
class popsynth.populations.ZPowerSphericalPopulation(Lambda: float, delta: float, r_max: float = 5.0, seed: int = 1234, luminosity_distribution: popsynth.distribution.LuminosityDistribution = None)[source]

Bases: popsynth.population_synth.PopulationSynth

__init__(Lambda: float, delta: float, r_max: float = 5.0, seed: int = 1234, luminosity_distribution: popsynth.distribution.LuminosityDistribution = None)[source]

A spherical population with a density that scales as (r+1)^delta. Based on ZpowerSphericalDistribution.

Parameters:
  • Lambda (float) – Local density per unit volume
  • delta (float) – Index of spatial distribution
  • r_max (float) – Maximum distance
  • seed (int) – Random seed
  • luminosity_distribution (LuminosityDistribution, optional) – Luminosity distribution
class popsynth.populations.ZPowerCosmoPopulation(Lambda: float, delta: float, r_max: float = 5.0, seed: int = 1234, luminosity_distribution: popsynth.distribution.LuminosityDistribution = None, is_rate: bool = True)[source]

Bases: popsynth.population_synth.PopulationSynth

__init__(Lambda: float, delta: float, r_max: float = 5.0, seed: int = 1234, luminosity_distribution: popsynth.distribution.LuminosityDistribution = None, is_rate: bool = True)[source]

A cosmological population with a density that scales as (z+1)^delta. Based on ZpowerCosmoDistribution.

Parameters:
  • Lambda (float) – Local density in units of Gpc^-3
  • delta (float) – Index of spatial distribution
  • r_max (float) – Maximum redshift
  • seed (int) – Random seed
  • luminosity_distribution (LuminosityDistribution, optional) – Luminosity distribution
  • is_rate (bool) – True if modelling a population of transient events, False if modelling a population of steady-state objects. Affects the time_adjustment method used in cosmo calculations. Default is True.
class popsynth.populations.ParetoHomogeneousSphericalPopulation(Lambda: float, Lmin: float, alpha: float, r_max: float = 5, seed: int = 1234)[source]

Bases: popsynth.populations.spatial_populations.SphericalPopulation

__init__(Lambda: float, Lmin: float, alpha: float, r_max: float = 5, seed: int = 1234)[source]

A population built on the ConstantSphericalDistribution spatial distribution and the ParetoDistribution luminosity distribution.

Parameters:
  • Lambda (float) – Density per unit volume
  • Lmin (float) – Minimum value of the luminosity
  • alpha (float) – Index of the luminosity distribution
  • r_max (float) – Maximum distance
  • seed (int) – Random seed
class popsynth.populations.ParetoSFRPopulation(r0: float, a: float, rise: float, decay: float, peak: float, Lmin: float, alpha: float, r_max: float = 5, seed: int = 1234, is_rate: bool = True)[source]

Bases: popsynth.populations.spatial_populations.SFRPopulation

__init__(r0: float, a: float, rise: float, decay: float, peak: float, Lmin: float, alpha: float, r_max: float = 5, seed: int = 1234, is_rate: bool = True)[source]

A population built on the SFRDistribution spatial distribution and the ParetoDistribution luminosity distribution.

Parameters:
  • r0 (float) – Local density in units of Gpc^-3
  • a (float) – Offset at z=0
  • rise (float) – Rise at low z
  • decay (float) – Decay at high z
  • peak (float) – Peak of z distribution
  • Lmin (float) – Minimum value of the luminosity
  • alpha (float) – Index of the luminosity distribution
  • r_max (float) – Maximum redshift
  • seed (int) – Random seed
  • is_rate (bool) – True if modelling a population of transient events, False if modelling a population of steady-state objects. Affects the time_adjustment method used in cosmo calculations. Default is True.
class popsynth.populations.ParetoZPowerSphericalPopulation(Lambda: float, delta: float, Lmin: float, alpha: float, r_max: float = 5, seed: int = 1234)[source]

Bases: popsynth.populations.spatial_populations.ZPowerSphericalPopulation

__init__(Lambda: float, delta: float, Lmin: float, alpha: float, r_max: float = 5, seed: int = 1234)[source]

A population built on the ZPowerSphericalDistribution spatial distribution and the ParetoDistribution luminosity distribution.

Parameters:
  • Lambda (float) – Density per unit volume
  • delta (float) – Index of the spatial distribution
  • Lmin (float) – Minimum value of the luminosity
  • alpha (float) – Index of the luminosity distribution
  • r_max (float) – Maximum distance
  • seed (int) – Random seed
class popsynth.populations.ParetoZPowerCosmoPopulation(Lambda: float, delta: float, Lmin: float, alpha: float, r_max: float = 5, seed: int = 1234, is_rate: bool = True)[source]

Bases: popsynth.populations.spatial_populations.ZPowerCosmoPopulation

__init__(Lambda: float, delta: float, Lmin: float, alpha: float, r_max: float = 5, seed: int = 1234, is_rate: bool = True)[source]

A population built on the ZPowerCosmoDistribution spatial distribution and the ParetoDistribution luminosity distribution.

Parameters:
  • Lambda (float) – Density in units of Gpc^-3
  • delta (float) – Index of the spatial distribution
  • Lmin (float) – Minimum value of the luminosity
  • alpha (float) – Index of the luminosity distribution
  • r_max (float) – Maximum redshift
  • seed (int) – Random seed
  • is_rate (bool) – True if modelling a population of transient events, False if modelling a population of steady-state objects. Affects the time_adjustment method used in cosmo calculations. Default is True.
class popsynth.populations.BPLHomogeneousSphericalPopulation(Lambda: float, Lmin: float, alpha: float, Lbreak: float, beta: float, Lmax: float, r_max: float = 5, seed: int = 1234)[source]

Bases: popsynth.populations.spatial_populations.SphericalPopulation

__init__(Lambda: float, Lmin: float, alpha: float, Lbreak: float, beta: float, Lmax: float, r_max: float = 5, seed: int = 1234)[source]

A population built on the ConstantSphericalDistribution spatial distribution and the BPLDistribution luminosity distribution.

Parameters:
  • Lambda (float) – Density per unit volume
  • Lmin (float) – Minimum value of the luminosity
  • alpha (float) – Lower luminosity index
  • Lbreak (float) – Break luminosity
  • beta (float) – Upper luminosity index
  • Lmax (float) – Maximum value of the luminosity
  • r_max (float) – Maximum distance
  • seed (int) – Random seed
class popsynth.populations.BPLSFRPopulation(r0: float, a: float, rise: float, decay: float, peak: float, Lmin: float, alpha: float, Lbreak: float, beta: float, Lmax: float, r_max: float = 5, seed: int = 1234, is_rate: bool = True)[source]

Bases: popsynth.populations.spatial_populations.SFRPopulation

__init__(r0: float, a: float, rise: float, decay: float, peak: float, Lmin: float, alpha: float, Lbreak: float, beta: float, Lmax: float, r_max: float = 5, seed: int = 1234, is_rate: bool = True)[source]

A population built on the SFRDistribution spatial distribution and the BPLDistribution luminosity distribution.

Parameters:
  • r0 (float) – Local density in units of Gpc^-3
  • a (float) – Offset at z=0
  • rise (float) – Rise at low z
  • decay (float) – Decay at high z
  • peak (float) – Peak of z distribution
  • Lmin (float) – Minimum value of the luminosity
  • alpha (float) – Lower luminosity index
  • Lbreak (float) – Break luminosity
  • beta (float) – Upper luminosity index
  • Lmax (float) – Maximum value of the luminosity
  • r_max (float) – Maximum redshift
  • seed (int) – Random seed
class popsynth.populations.BPLZPowerSphericalPopulation(Lambda: float, delta: float, Lmin: float, alpha: float, Lbreak: float, beta: float, Lmax: float, r_max: float = 5, seed: int = 1234)[source]

Bases: popsynth.populations.spatial_populations.ZPowerSphericalPopulation

__init__(Lambda: float, delta: float, Lmin: float, alpha: float, Lbreak: float, beta: float, Lmax: float, r_max: float = 5, seed: int = 1234)[source]

A population built on the ZPowerSphericalDistribution spatial distribution and the BPLDistribution luminosity distribution.

Parameters:
  • Lambda (float) – Density per unit volume
  • delta (float) – Index of spatial distribution
  • Lmin (float) – Minimum value of the luminosity
  • alpha (float) – Lower luminosity index
  • Lbreak (float) – Break luminosity
  • beta (float) – Upper luminosity index
  • Lmax (float) – Maximum value of the luminosity
  • r_max (float) – Maximum distance
  • seed (int) – Random seed
class popsynth.populations.BPLZPowerCosmoPopulation(Lambda: float, delta: float, Lmin: float, alpha: float, Lbreak: float, beta: float, Lmax: float, r_max: float = 5, seed: int = 1234, is_rate: bool = True)[source]

Bases: popsynth.populations.spatial_populations.ZPowerCosmoPopulation

__init__(Lambda: float, delta: float, Lmin: float, alpha: float, Lbreak: float, beta: float, Lmax: float, r_max: float = 5, seed: int = 1234, is_rate: bool = True)[source]

A population built on the ZPowerCosmoDistribution spatial distribution and the BPLDistribution luminosity distribution.

Parameters:
  • Lambda (float) – Density in Gpc^-3
  • delta (float) – Index of spatial distribution
  • Lmin (float) – Minimum value of the luminosity
  • alpha (float) – Lower luminosity index
  • Lbreak (float) – Break luminosity
  • beta (float) – Upper luminosity index
  • Lmax (float) – Maximum value of the luminosity
  • r_max (float) – Maximum distance
  • seed (int) – Random seed
class popsynth.populations.SchechterHomogeneousSphericalPopulation(Lambda: float, Lmin: float, alpha: float, r_max: float = 10, seed: int = 1234)[source]

Bases: popsynth.populations.spatial_populations.SphericalPopulation

__init__(Lambda: float, Lmin: float, alpha: float, r_max: float = 10, seed: int = 1234)[source]

A population built on the ConstantSphericalDistribution spatial distribution and the SchechterDistribution luminosity distribution.

Parameters:
  • Lambda (float) – Density per unit volume
  • Lmin (float) – Minimum value of the luminosity
  • alpha (float) – Index of the luminosity distribution
  • r_max (float) – Maximum distance
  • seed (int) – Random seed
class popsynth.populations.SchechterSFRPopulation(r0: float, a: float, rise: float, decay: float, peak: float, Lmin: float, alpha: float, r_max: float = 10, seed: int = 1234, is_rate: bool = True)[source]

Bases: popsynth.populations.spatial_populations.SFRPopulation

__init__(r0: float, a: float, rise: float, decay: float, peak: float, Lmin: float, alpha: float, r_max: float = 10, seed: int = 1234, is_rate: bool = True)[source]

A population built on the SFRDistribution spatial distribution and the SchechterDistribution luminosity distribution.

Parameters:
  • r0 (float) – Local density in units of Gpc^-3
  • a (float) – Offset at z=0
  • rise (float) – Rise at low z
  • decay (float) – Decay at high z
  • peak (float) – Peak of z distribution
  • Lmin (float) – Minimum value of the luminosity
  • alpha (float) – Index of the luminosity distribution
  • r_max (float) – Maximum redshift
  • seed (int) – Random seed
  • is_rate (bool) – True if modelling a population of transient events, False if modelling a population of steady-state objects. Affects the time_adjustment method used in cosmo calculations. Default is True.
class popsynth.populations.SchechterZPowerSphericalPopulation(Lambda: float, delta: float, Lmin: float, alpha: float, r_max: float = 10, seed: int = 1234)[source]

Bases: popsynth.populations.spatial_populations.ZPowerSphericalPopulation

__init__(Lambda: float, delta: float, Lmin: float, alpha: float, r_max: float = 10, seed: int = 1234)[source]

A population built on the ZPowerSphericalDistribution spatial distribution and the SchechterDistribution luminosity distribution.

Parameters:
  • Lambda (float) – Density per unit volume
  • delta (float) – Index of the spatial distribution
  • Lmin (float) – Minimum value of the luminosity
  • alpha (float) – Index of the luminosity distribution
  • r_max (float) – Maximum distance
  • seed (int) – Random seed
class popsynth.populations.SchechterZPowerCosmoPopulation(Lambda: float, delta: float, Lmin: float, alpha: float, r_max: float = 10, seed: int = 1234, is_rate: bool = True)[source]

Bases: popsynth.populations.spatial_populations.ZPowerCosmoPopulation

__init__(Lambda: float, delta: float, Lmin: float, alpha: float, r_max: float = 10, seed: int = 1234, is_rate: bool = True)[source]

A population built on the ZPowerCosmoDistribution spatial distribution and the SchechterDistribution luminosity distribution.

Parameters:
  • Lambda (float) – Density in units of Gpc^-3
  • delta (float) – Index of the spatial distribution
  • Lmin (float) – Minimum value of the luminosity
  • alpha (float) – Index of the luminosity distribution
  • r_max (float) – Maximum redshift
  • seed (int) – Random seed
  • is_rate (bool) – True if modelling a population of transient events, False if modelling a population of steady-state objects. Affects the time_adjustment method used in cosmo calculations. Default is True.
class popsynth.populations.Log10NormalHomogeneousSphericalPopulation(Lambda: float, mu: float, tau: float, r_max: float = 5, seed: int = 1234)[source]

Bases: popsynth.populations.spatial_populations.SphericalPopulation

__init__(Lambda: float, mu: float, tau: float, r_max: float = 5, seed: int = 1234)[source]

A population built on the ConstantSphericalDistribution spatial distribution and the Log10NormalDistribution luminosity distribution.

Parameters:
  • Lambda (float) – Density per unit volume
  • mu (float) – Mean of the luminosity distribution
  • tau (float) – Standard deviation of the luminosity distribution
  • r_max (float) – Maximum distance
  • seed (int) – Random seed
class popsynth.populations.Log10NormalSFRPopulation(r0: float, a: float, rise: float, decay: float, peak: float, mu: float, tau: float, r_max: float = 5, seed: int = 1234, is_rate: bool = True)[source]

Bases: popsynth.populations.spatial_populations.SFRPopulation

__init__(r0: float, a: float, rise: float, decay: float, peak: float, mu: float, tau: float, r_max: float = 5, seed: int = 1234, is_rate: bool = True)[source]

A population built on the SFRDistribution spatial distribution and the Log10NormalDistribution luminosity distribution.

Parameters:
  • r0 (float) – Local density in units of Gpc^-3
  • a (float) – Offset at z=0
  • rise (float) – Rise at low z
  • decay (float) – Decay at high z
  • peak (float) – Peak of z distribution
  • mu (float) – Mean of the luminosity distribution
  • tau (float) – Standard deviation of the luminosity distribution
  • r_max (float) – Maximum redshift
  • seed (int) – Random seed
  • is_rate (bool) – True if modelling a population of transient events, False if modelling a population of steady-state objects. Affects the time_adjustment method used in cosmo calculations. Default is True.
class popsynth.populations.Log10NormalZPowerSphericalPopulation(Lambda: float, delta: float, mu: float, tau: float, r_max: float = 5, seed: int = 1234)[source]

Bases: popsynth.populations.spatial_populations.ZPowerSphericalPopulation

__init__(Lambda: float, delta: float, mu: float, tau: float, r_max: float = 5, seed: int = 1234)[source]

A population built on the ZPowerSphericalDistribution spatial distribution and the Log10NormalDistribution luminosity distribution.

Parameters:
  • Lambda (float) – Density per unit volume
  • delta (float) – Index of the spatial distribution
  • mu (float) – Mean of the luminosity distribution
  • tau (float) – Standard deviation of the luminosity distribution
  • r_max (float) – Maximum distance
  • seed (int) – Random seed
class popsynth.populations.Log10NormalZPowerCosmoPopulation(Lambda: float, delta: float, mu: float, tau: float, r_max: float = 5, seed: float = 1234, is_rate: float = True)[source]

Bases: popsynth.populations.spatial_populations.ZPowerCosmoPopulation

__init__(Lambda: float, delta: float, mu: float, tau: float, r_max: float = 5, seed: float = 1234, is_rate: float = True)[source]

A population built on the ZPowerCosmoDistribution spatial distribution and the Log10NormalDistribution luminosity distribution.

Parameters:
  • Lambda (float) – Density in units of Gpc^-3
  • delta (float) – Index of the spatial distribution
  • mu (float) – Mean of the luminosity distribution
  • tau (float) – Standard deviation of the luminosity distribution
  • r_max (float) – Maximum redshift
  • seed (int) – Random seed
  • is_rate (bool) – True if modelling a population of transient events, False if modelling a population of steady-state objects. Affects the time_adjustment method used in cosmo calculations. Default is True.
class popsynth.populations.LogNormalHomogeneousSphericalPopulation(Lambda: float, mu: float, tau: float, r_max: float = 5, seed: int = 1234)[source]

Bases: popsynth.populations.spatial_populations.SphericalPopulation

__init__(Lambda: float, mu: float, tau: float, r_max: float = 5, seed: int = 1234)[source]

A population built on the ConstantSphericalDistribution spatial distribution and the LogNormalDistribution luminosity distribution.

Parameters:
  • Lambda (float) – Density per unit volume
  • mu (float) – Mean of the luminosity distribution
  • tau (float) – Standard deviation of the luminosity distribution
  • r_max (float) – Maximum distance
  • seed (int) – Random seed
class popsynth.populations.LogNormalSFRPopulation(r0: float, a: float, rise: float, decay: float, peak: float, mu: float, tau: float, r_max: float = 5, seed: int = 1234, is_rate: bool = True)[source]

Bases: popsynth.populations.spatial_populations.SFRPopulation

__init__(r0: float, a: float, rise: float, decay: float, peak: float, mu: float, tau: float, r_max: float = 5, seed: int = 1234, is_rate: bool = True)[source]

A population built on the SFRDistribution spatial distribution and the LogNormalDistribution luminosity distribution.

Parameters:
  • r0 (float) – Local density in units of Gpc^-3
  • a (float) – Offset at z=0
  • rise (float) – Rise at low z
  • decay (float) – Decay at high z
  • peak (float) – Peak of z distribution
  • mu (float) – Mean of the luminosity distribution
  • tau (float) – Standard deviation of the luminosity distribution
  • r_max (float) – Maximum redshift
  • seed (int) – Random seed
  • is_rate (bool) – True if modelling a population of transient events, False if modelling a population of steady-state objects. Affects the time_adjustment method used in cosmo calculations. Default is True.
class popsynth.populations.LogNormalZPowerSphericalPopulation(Lambda: float, delta: float, mu: float, tau: float, r_max: float = 5, seed: int = 1234)[source]

Bases: popsynth.populations.spatial_populations.ZPowerSphericalPopulation

__init__(Lambda: float, delta: float, mu: float, tau: float, r_max: float = 5, seed: int = 1234)[source]

A population built on the ZPowerSphericalDistribution spatial distribution and the LogNormalDistribution luminosity distribution.

Parameters:
  • Lambda (float) – Density per unit volume
  • delta (float) – Index of the spatial distribution
  • mu (float) – Mean of the luminosity distribution
  • tau (float) – Standard deviation of the luminosity distribution
  • r_max (float) – Maximum distance
  • seed (int) – Random seed
class popsynth.populations.LogNormalZPowerCosmoPopulation(Lambda: float, delta: float, mu: float, tau: float, r_max: float = 5, seed: int = 1234, is_rate: bool = True)[source]

Bases: popsynth.populations.spatial_populations.ZPowerCosmoPopulation

__init__(Lambda: float, delta: float, mu: float, tau: float, r_max: float = 5, seed: int = 1234, is_rate: bool = True)[source]

A population built on the ZPowerCosmoDistribution spatial distribution and the LogNormalDistribution luminosity distribution.

Parameters:
  • Lambda (float) – Density in units of Gpc^-3
  • delta (float) – Index of the spatial distribution
  • mu (float) – Mean of the luminosity distribution
  • tau (float) – Standard deviation of the luminosity distribution
  • r_max (float) – Maximum redshift
  • seed (int) – Random seed
  • is_rate (bool) – True if modelling a population of transient events, False if modelling a population of steady-state objects. Affects the time_adjustment method used in cosmo calculations. Default is True.
popsynth.selection_probability package
Submodules
popsynth.selection_probability.flux_selectors module
class popsynth.selection_probability.flux_selectors.HardFluxSelection[source]

Bases: popsynth.selection_probability.generic_selectors.LowerBound

__init__() → None[source]

A hard selection on the observed flux.

Based on LowerBound.

draw(size: int)[source]
class popsynth.selection_probability.flux_selectors.SoftFluxSelection[source]

Bases: popsynth.selection_probability.generic_selectors.SoftSelection

__init__() → None[source]

A soft selection on the observed flux.

Based on SoftSelection.

draw(size: int)[source]
popsynth.selection_probability.generic_selectors module
class popsynth.selection_probability.generic_selectors.BernoulliSelection[source]

Bases: popsynth.selection_probability.selection_probability.SelectionProbability

__init__() → None[source]

A Bernoulli selection with probability as a parameter.

Parameters:probability (SelectionParameter) – Probability for each Bernoulli trial
draw(size: int) → None[source]
probability
class popsynth.selection_probability.generic_selectors.BoxSelection(name: str = 'box selection', use_obs_value: bool = False, use_distance: bool = False, use_luminosity: bool = False, use_flux: bool = False)[source]

Bases: popsynth.selection_probability.selection_probability.SelectionProbability

__init__(name: str = 'box selection', use_obs_value: bool = False, use_distance: bool = False, use_luminosity: bool = False, use_flux: bool = False)[source]

A box selection on observed_value, distance, luminosity or flux.

Parameters:
  • name (str) – Name of the selection
  • use_obs_value (bool) – If True, make selection on observed_value. False by default.
  • use_distance (bool) – If True make selection on distance. False by default.
  • use_luminosity (bool) – If True make selection on luminosity. False by default.
  • use_flux (bool) – If True make selection on flux. False by default.
  • vmin (SelectionParameter) – Minimum value of selection
  • vmax (SelectionParameter) – Maximum value of selection
draw(size: int) → numpy.ndarray[source]
vmax
vmin
class popsynth.selection_probability.generic_selectors.LowerBound(name: str = 'Hard selection', use_obs_value: bool = False, use_distance: bool = False, use_luminosity: bool = False, use_flux: bool = False)[source]

Bases: popsynth.selection_probability.selection_probability.SelectionProbability

__init__(name: str = 'Hard selection', use_obs_value: bool = False, use_distance: bool = False, use_luminosity: bool = False, use_flux: bool = False)[source]

A hard, lower bound selection on obs_value, distance, luminosity or flux.

Selects values >= boundary.

Parameters:
  • name (str) – Name of the selection
  • use_obs_value (bool) – If True, make selection on observed_value. False by default.
  • use_distance (bool) – If True make selection on distance. False by default.
  • use_luminosity (bool) – If True make selection on luminosity. False by default.
  • use_flux (bool) – If True make selection on flux. False by default.
  • boundary (SelectionParameter) – Value of the selection boundary
boundary
draw(size: int) → None[source]
class popsynth.selection_probability.generic_selectors.SoftSelection(name: str = 'Soft Selection', use_obs_value: bool = False, use_distance: bool = False, use_luminosity: bool = False, use_flux: bool = False)[source]

Bases: popsynth.selection_probability.selection_probability.SelectionProbability

__init__(name: str = 'Soft Selection', use_obs_value: bool = False, use_distance: bool = False, use_luminosity: bool = False, use_flux: bool = False) → None[source]

A soft selection using an inverse logit function either on the log or linear value of the observed_value, distance, luminosity or flux.

Parameters:
  • name (str) – Name of the selection
  • use_obs_value (bool) – If True, make selection on observed_value. False by default.
  • use_distance (bool) – If True make selection on distance. False by default.
  • use_luminosity (bool) – If True make selection on luminosity. False by default.
  • use_flux (bool) – If True make selection on flux. False by default.
  • boundary (SelectionParameter) – Center of the inverse logit
  • strength (SelectionParameter) – Width of the logit
boundary
draw(size: int, use_log=True) → None[source]
strength
class popsynth.selection_probability.generic_selectors.UnitySelection(name='unity')[source]

Bases: popsynth.selection_probability.selection_probability.SelectionProbability

__init__(name='unity')[source]

A selection that returns all selected.

Parameters:name – Name of the selection
draw(size: int) → None[source]
class popsynth.selection_probability.generic_selectors.UpperBound(name: str = 'Hard selection', use_obs_value: bool = False, use_distance: bool = False, use_luminosity: bool = False, use_flux: bool = False)[source]

Bases: popsynth.selection_probability.selection_probability.SelectionProbability

__init__(name: str = 'Hard selection', use_obs_value: bool = False, use_distance: bool = False, use_luminosity: bool = False, use_flux: bool = False)[source]

A hard, upper bound selection on obs_value, distance, luminosity or flux.

Selects values <= boundary.

Parameters:
  • name (str) – Name of the selection
  • use_obs_value (bool) – If True, make selection on observed_value. False by default.
  • use_distance (bool) – If True make selection on distance. False by default.
  • use_luminosity (bool) – If True make selection on luminosity. False by default.
  • use_flux (bool) – If True make selection on flux. False by default.
  • boundary (SelectionParameter) – Value of the selection boundary
boundary
draw(size: int) → None[source]
popsynth.selection_probability.selection_probability module
class popsynth.selection_probability.selection_probability.DummySelection[source]

Bases: popsynth.selection_probability.selection_probability.SelectionProbability

__init__()[source]

A Dummy selection for testing.

draw(size=1)[source]
class popsynth.selection_probability.selection_probability.SelectionParameter(default: Optional[float] = None, vmin: Optional[float] = None, vmax: Optional[float] = None, free: bool = True)[source]

Bases: popsynth.utils.meta.Parameter

class popsynth.selection_probability.selection_probability.SelectionProbability(name: str = 'name', use_obs_value: bool = False, use_distance: bool = False, use_luminosity: bool = False, use_flux: bool = False)[source]

Bases: object

__init__(name: str = 'name', use_obs_value: bool = False, use_distance: bool = False, use_luminosity: bool = False, use_flux: bool = False) → None[source]

Base class for selections on a population.

Parameters:
  • name (str) – Name of the selection
  • use_obs_value (bool) – If True, make selection on observed_value. False by default.
  • use_distance (bool) – If True make selection on distance. False by default.
  • use_luminosity (bool) – If True make selection on luminosity. False by default.
  • use_flux (bool) – If True make selection on flux. False by default.
classmethod draw(size: int) → None[source]
n_non_selected
n_objects
n_selected
name
non_selection_index
parameters
reset()[source]

Reset the selector.

select(size: int)[source]
selection
selection_index
set_distance(distance: numpy.ndarray) → None[source]

Set the distance of the selection.

set_luminosity(luminosity: numpy.ndarray) → None[source]

Set the luminosity of the selection.

set_observed_flux(observed_flux: numpy.ndarray) → None[source]

Set the observed flux of the selection.

set_observed_value(observed_value: numpy.ndarray) → None[source]

Set the observed value of the selection.

popsynth.selection_probability.spatial_selection module
class popsynth.selection_probability.spatial_selection.SpatialSelection(name: str)[source]

Bases: popsynth.selection_probability.selection_probability.SelectionProbability

__init__(name: str) → None[source]

A generic spatial selection.

Parameters:name (str) – Name of the selection
set_spatial_distribution(spatial_distribtuion: popsynth.distribution.SpatialDistribution) → None[source]

Set the spatial distribution for the selection.

Parameters:spatial_distribution (SpatialDistribution) – The spatial_distribution
class popsynth.selection_probability.spatial_selection.GalacticPlaneSelection(name: str = 'galactic plane selector')[source]

Bases: popsynth.selection_probability.spatial_selection.SpatialSelection

__init__(name: str = 'galactic plane selector')[source]

A selection that excludes objects near the galactic plane.

Parameters:
  • name (str) – Name of the selection
  • b_limit (SelectionParameter) – Limit around Galactic plane to exclude in Galactic latitude and in units of degrees
b_limit
draw(size: int)[source]
Module contents
class popsynth.selection_probability.HardFluxSelection[source]

Bases: popsynth.selection_probability.generic_selectors.LowerBound

__init__() → None[source]

A hard selection on the observed flux.

Based on LowerBound.

draw(size: int)[source]
class popsynth.selection_probability.SoftFluxSelection[source]

Bases: popsynth.selection_probability.generic_selectors.SoftSelection

__init__() → None[source]

A soft selection on the observed flux.

Based on SoftSelection.

draw(size: int)[source]
class popsynth.selection_probability.BernoulliSelection[source]

Bases: popsynth.selection_probability.selection_probability.SelectionProbability

__init__() → None[source]

A Bernoulli selection with probability as a parameter.

Parameters:probability (SelectionParameter) – Probability for each Bernoulli trial
draw(size: int) → None[source]
probability
class popsynth.selection_probability.LowerBound(name: str = 'Hard selection', use_obs_value: bool = False, use_distance: bool = False, use_luminosity: bool = False, use_flux: bool = False)[source]

Bases: popsynth.selection_probability.selection_probability.SelectionProbability

__init__(name: str = 'Hard selection', use_obs_value: bool = False, use_distance: bool = False, use_luminosity: bool = False, use_flux: bool = False)[source]

A hard, lower bound selection on obs_value, distance, luminosity or flux.

Selects values >= boundary.

Parameters:
  • name (str) – Name of the selection
  • use_obs_value (bool) – If True, make selection on observed_value. False by default.
  • use_distance (bool) – If True make selection on distance. False by default.
  • use_luminosity (bool) – If True make selection on luminosity. False by default.
  • use_flux (bool) – If True make selection on flux. False by default.
  • boundary (SelectionParameter) – Value of the selection boundary
boundary
draw(size: int) → None[source]
class popsynth.selection_probability.UpperBound(name: str = 'Hard selection', use_obs_value: bool = False, use_distance: bool = False, use_luminosity: bool = False, use_flux: bool = False)[source]

Bases: popsynth.selection_probability.selection_probability.SelectionProbability

__init__(name: str = 'Hard selection', use_obs_value: bool = False, use_distance: bool = False, use_luminosity: bool = False, use_flux: bool = False)[source]

A hard, upper bound selection on obs_value, distance, luminosity or flux.

Selects values <= boundary.

Parameters:
  • name (str) – Name of the selection
  • use_obs_value (bool) – If True, make selection on observed_value. False by default.
  • use_distance (bool) – If True make selection on distance. False by default.
  • use_luminosity (bool) – If True make selection on luminosity. False by default.
  • use_flux (bool) – If True make selection on flux. False by default.
  • boundary (SelectionParameter) – Value of the selection boundary
boundary
draw(size: int) → None[source]
class popsynth.selection_probability.SoftSelection(name: str = 'Soft Selection', use_obs_value: bool = False, use_distance: bool = False, use_luminosity: bool = False, use_flux: bool = False)[source]

Bases: popsynth.selection_probability.selection_probability.SelectionProbability

__init__(name: str = 'Soft Selection', use_obs_value: bool = False, use_distance: bool = False, use_luminosity: bool = False, use_flux: bool = False) → None[source]

A soft selection using an inverse logit function either on the log or linear value of the observed_value, distance, luminosity or flux.

Parameters:
  • name (str) – Name of the selection
  • use_obs_value (bool) – If True, make selection on observed_value. False by default.
  • use_distance (bool) – If True make selection on distance. False by default.
  • use_luminosity (bool) – If True make selection on luminosity. False by default.
  • use_flux (bool) – If True make selection on flux. False by default.
  • boundary (SelectionParameter) – Center of the inverse logit
  • strength (SelectionParameter) – Width of the logit
boundary
draw(size: int, use_log=True) → None[source]
strength
class popsynth.selection_probability.UnitySelection(name='unity')[source]

Bases: popsynth.selection_probability.selection_probability.SelectionProbability

__init__(name='unity')[source]

A selection that returns all selected.

Parameters:name – Name of the selection
draw(size: int) → None[source]
class popsynth.selection_probability.SelectionProbability(name: str = 'name', use_obs_value: bool = False, use_distance: bool = False, use_luminosity: bool = False, use_flux: bool = False)[source]

Bases: object

__init__(name: str = 'name', use_obs_value: bool = False, use_distance: bool = False, use_luminosity: bool = False, use_flux: bool = False) → None[source]

Base class for selections on a population.

Parameters:
  • name (str) – Name of the selection
  • use_obs_value (bool) – If True, make selection on observed_value. False by default.
  • use_distance (bool) – If True make selection on distance. False by default.
  • use_luminosity (bool) – If True make selection on luminosity. False by default.
  • use_flux (bool) – If True make selection on flux. False by default.
classmethod draw(size: int) → None[source]
n_non_selected
n_objects
n_selected
name
non_selection_index
parameters
reset()[source]

Reset the selector.

select(size: int)[source]
selection
selection_index
set_distance(distance: numpy.ndarray) → None[source]

Set the distance of the selection.

set_luminosity(luminosity: numpy.ndarray) → None[source]

Set the luminosity of the selection.

set_observed_flux(observed_flux: numpy.ndarray) → None[source]

Set the observed flux of the selection.

set_observed_value(observed_value: numpy.ndarray) → None[source]

Set the observed value of the selection.

class popsynth.selection_probability.SpatialSelection(name: str)[source]

Bases: popsynth.selection_probability.selection_probability.SelectionProbability

__init__(name: str) → None[source]

A generic spatial selection.

Parameters:name (str) – Name of the selection
set_spatial_distribution(spatial_distribtuion: popsynth.distribution.SpatialDistribution) → None[source]

Set the spatial distribution for the selection.

Parameters:spatial_distribution (SpatialDistribution) – The spatial_distribution
class popsynth.selection_probability.BoxSelection(name: str = 'box selection', use_obs_value: bool = False, use_distance: bool = False, use_luminosity: bool = False, use_flux: bool = False)[source]

Bases: popsynth.selection_probability.selection_probability.SelectionProbability

__init__(name: str = 'box selection', use_obs_value: bool = False, use_distance: bool = False, use_luminosity: bool = False, use_flux: bool = False)[source]

A box selection on observed_value, distance, luminosity or flux.

Parameters:
  • name (str) – Name of the selection
  • use_obs_value (bool) – If True, make selection on observed_value. False by default.
  • use_distance (bool) – If True make selection on distance. False by default.
  • use_luminosity (bool) – If True make selection on luminosity. False by default.
  • use_flux (bool) – If True make selection on flux. False by default.
  • vmin (SelectionParameter) – Minimum value of selection
  • vmax (SelectionParameter) – Maximum value of selection
draw(size: int) → numpy.ndarray[source]
vmax
vmin
class popsynth.selection_probability.DummySelection[source]

Bases: popsynth.selection_probability.selection_probability.SelectionProbability

__init__()[source]

A Dummy selection for testing.

draw(size=1)[source]
class popsynth.selection_probability.SelectionParameter(default: Optional[float] = None, vmin: Optional[float] = None, vmax: Optional[float] = None, free: bool = True)[source]

Bases: popsynth.utils.meta.Parameter

class popsynth.selection_probability.GalacticPlaneSelection(name: str = 'galactic plane selector')[source]

Bases: popsynth.selection_probability.spatial_selection.SpatialSelection

__init__(name: str = 'galactic plane selector')[source]

A selection that excludes objects near the galactic plane.

Parameters:
  • name (str) – Name of the selection
  • b_limit (SelectionParameter) – Limit around Galactic plane to exclude in Galactic latitude and in units of degrees
b_limit
draw(size: int)[source]
class popsynth.selection_probability.DistanceSelection(name: str = 'distance')[source]

Bases: popsynth.selection_probability.spatial_selection.SpatialSelection

__init__(name: str = 'distance')[source]

Select distances

Parameters:
  • name (str) – Name of the selection
  • min_distance – minimum distance to select
  • max_distance – maximum distance to select
draw(size: int)[source]
max_distance
min_distance
popsynth.utils package
Submodules
popsynth.utils.array_to_cmap module
popsynth.utils.array_to_cmap.array_to_cmap(values, cmap, use_log: bool = False)[source]

Generates a color map and color list that is normalized to the values in an array. Allows for adding a 3rd dimension onto a plot.

Parameters:
  • values – A list a values to map into a cmap
  • cmap – The mpl colormap to use
  • use_log (bool) – True if the mapping should be done in log space False by default.
Returns:

A color map and a normalized color list

popsynth.utils.configuration module
class popsynth.utils.configuration.Cosmology(Om: float = 0.307, h0: float = 67.7)[source]

Bases: object

Om = 0.307
__init__(Om: float = 0.307, h0: float = 67.7) → None
h0 = 67.7
class popsynth.utils.configuration.LogConsole(on: bool = True, level: str = 'WARNING')[source]

Bases: object

__init__(on: bool = True, level: str = 'WARNING') → None
level = 'WARNING'
on = True
class popsynth.utils.configuration.LogFile(on: bool = True, level: str = 'WARNING')[source]

Bases: object

__init__(on: bool = True, level: str = 'WARNING') → None
level = 'WARNING'
on = True
class popsynth.utils.configuration.Logging(debug: bool = False, console: popsynth.utils.configuration.LogConsole = LogConsole(on=True, level='WARNING'), file: popsynth.utils.configuration.LogFile = LogFile(on=True, level='WARNING'))[source]

Bases: object

__init__(debug: bool = False, console: popsynth.utils.configuration.LogConsole = LogConsole(on=True, level='WARNING'), file: popsynth.utils.configuration.LogFile = LogFile(on=True, level='WARNING')) → None
console = LogConsole(on=True, level='WARNING')
debug = False
file = LogFile(on=True, level='WARNING')
class popsynth.utils.configuration.PopSynthConfig(logging: popsynth.utils.configuration.Logging = Logging(debug=False, console=LogConsole(on=True, level='WARNING'), file=LogFile(on=True, level='WARNING')), cosmology: popsynth.utils.configuration.Cosmology = Cosmology(Om=0.307, h0=67.7), show_progress: bool = True)[source]

Bases: object

__init__(logging: popsynth.utils.configuration.Logging = Logging(debug=False, console=LogConsole(on=True, level='WARNING'), file=LogFile(on=True, level='WARNING')), cosmology: popsynth.utils.configuration.Cosmology = Cosmology(Om=0.307, h0=67.7), show_progress: bool = True) → None
cosmology = Cosmology(Om=0.307, h0=67.7)
logging = Logging(debug=False, console=LogConsole(on=True, level='WARNING'), file=LogFile(on=True, level='WARNING'))
show_progress = True
popsynth.utils.cosmology module
popsynth.utils.hdf5_utils module
popsynth.utils.hdf5_utils.clean_graph_dict(graph_dict)[source]

Clean networkx graph dict so that it can be stored in an HDF5 file.

popsynth.utils.hdf5_utils.fill_graph_dict(graph_dict, fill_value=1)[source]

Fill a networkx graph dict so that it can be stored in an HDF5 file.

popsynth.utils.hdf5_utils.recursively_load_dict_contents_from_group(h5file, path)[source]

Load files from hdf5.

Parameters:
  • h5file – HDF5 file
  • path – Path in file
Returns:

A dictionary

Return type:

Dict[Any, Any]

popsynth.utils.hdf5_utils.recursively_save_dict_contents_to_group(h5file, path, dic: Dict[Any, Any])[source]

Save dict to HDF5.

Parameters:
  • h5file – HDF5 file
  • path – Path inside file
  • dic (Dict[Any, Any]) – Dictionary to save
popsynth.utils.logging module
class popsynth.utils.logging.ColoredFormatter(*args, colors: Optional[Dict[str, str]] = None, **kwargs)[source]

Bases: logging.Formatter

Colored log formatter.

__init__(*args, colors: Optional[Dict[str, str]] = None, **kwargs) → None[source]

Initialize the formatter with specified format strings.

format(record) → str[source]

Format the specified record as text.

class popsynth.utils.logging.LoggingState(popsynth_usr_log_handler, popsynth_console_log_handler)[source]

Bases: object

__init__(popsynth_usr_log_handler, popsynth_console_log_handler)[source]

A container to store the state of the logs.

debug_logs()[source]
loud_logs()[source]
restore_last_state()[source]
silence_logs()[source]
class popsynth.utils.logging.MyFilter(level)[source]

Bases: object

__init__(level)[source]

Initialize self. See help(type(self)) for accurate signature.

filter(logRecord)[source]
popsynth.utils.logging.activate_logs()[source]

Re-activate silenced logs.

popsynth.utils.logging.activate_warnings()[source]

Supress warning messages in console and file usr logs.

popsynth.utils.logging.debug_mode()[source]

Activate debug in the console.

popsynth.utils.logging.loud_mode()[source]

Turn on all progress bars and logging.

popsynth.utils.logging.quiet_mode()[source]

Turn off all logging and progress bars.

popsynth.utils.logging.setup_logger(name)[source]

Set up a new logger.

Parameters:name – Name of the logger
popsynth.utils.logging.show_progress_bars()[source]
popsynth.utils.logging.silence_console_log()[source]
popsynth.utils.logging.silence_logs()[source]

Turn off all logging.

popsynth.utils.logging.silence_progress_bars()[source]
popsynth.utils.logging.silence_warnings()[source]

Supress warning messages in console and file usr logs.

popsynth.utils.logging.update_logging_level(level)[source]
popsynth.utils.meta module
class popsynth.utils.meta.Parameter(default: Optional[float] = None, vmin: Optional[float] = None, vmax: Optional[float] = None, free: bool = True)[source]

Bases: object

__init__(default: Optional[float] = None, vmin: Optional[float] = None, vmax: Optional[float] = None, free: bool = True)[source]

Parameter base class.

Parameters:
  • default (Optional[float]) – Default parameter value
  • vmin (Optional[float]) – Minimum parameter value
  • vmax (Optional[float]) – Maximum parameter value
  • free – If True, parameter is free
default
fix

‘p.fix = True’ or ‘p.fix = False’.

Type:Gets or sets whether the parameter is fixed or not. Use booleans, like
free

‘p.free = True’ or ‘p.free = False’.

Type:Gets or sets whether the parameter is free or not. Use booleans, like
class popsynth.utils.meta.ParameterMeta[source]

Bases: type

popsynth.utils.package_data module
popsynth.utils.package_data.get_path_of_data_file(data_file) → pathlib.Path[source]
popsynth.utils.package_data.get_path_of_log_dir() → pathlib.Path[source]
popsynth.utils.package_data.get_path_of_log_file(file_name: str) → pathlib.Path[source]
popsynth.utils.progress_bar module
popsynth.utils.progress_bar.progress_bar(itr, **kwargs)[source]
popsynth.utils.registry module
popsynth.utils.rejection_sample module
popsynth.utils.rejection_sample.rejection_sample(size, ymax, xmax, func)[source]

Rejection sample func up to ymax and xmax.

Parameters:
  • size – Number of samples
  • ymax – Maximum value of y
  • xmax – Maximum value of x
  • func – Function
popsynth.utils.spherical_geometry module
popsynth.utils.spherical_geometry.sample_theta_phi(size: int)[source]

Sample size samples uniformly on the surface of the unit sphere.

popsynth.utils.spherical_geometry.xyz(r, theta, phi)[source]

Convert spherical coordinates to Cartesian.

Module contents

Submodules

popsynth.auxiliary_sampler module
class popsynth.auxiliary_sampler.AuxiliaryParameter(default: Optional[float] = None, vmin: Optional[float] = None, vmax: Optional[float] = None, free: bool = True)[source]

Bases: popsynth.utils.meta.Parameter

class popsynth.auxiliary_sampler.AuxiliarySampler(name: str, observed: bool = True, uses_distance: bool = False, uses_luminosity: bool = False, uses_sky_position: bool = False)[source]

Bases: object

__init__(name: str, observed: bool = True, uses_distance: bool = False, uses_luminosity: bool = False, uses_sky_position: bool = False) → None[source]

Base class for auxiliary samplers.

Parameters:
  • name (str) – Name of the sampler
  • observed (bool) – True if the property is observed, False if it is latent. Defaults to True
  • uses_distance (bool) – True if sampler uses distance values
  • uses_luminosity (bool) – True if sampler uses luminosities
  • uses_sky_position (bool) – True if sampler uses sky positions
display()[source]
draw(size: int = 1)[source]

Draw the primary and secondary samplers. This is the main call.

Parameters:size (int) – The number of samples to draw
get_secondary_objects(recursive_secondaries: Optional[Dict[str, Any]] = None) → Dict[str, Any][source]

Get secondary objects.

Parameters:recursive_secondaries – Recursive dict of secondaries
Returns:Dict of objects
Return type:Dict[str, Any]
get_secondary_properties(graph=None, primary=None, spatial_distribution=None) → popsynth.auxiliary_sampler.SecondaryStorage[source]

Get properties of secondary samplers.

Parameters:
  • graph – Graph
  • primary – Primary sampler
  • spatial_distribution – Spatial Distribution
Returns:

Dict of samplers

Return type:

SamplerDict

has_secondary

if this sampler has a secondary :returns:

is_secondary

If another sampler depends on this

Returns:
luminosity_distance

luminosity distance if needed.

make_secondary(parent_name: str) → None[source]

sets this sampler as secondary for book keeping

Parameters:parent_name (str) –
Returns:
name

The name of the sampler

Returns:
obs_name
obs_values

The values obscured by measurement error.

Returns:
observation_sampler(size: int = 1) → numpy.ndarray[source]
observed

if this sampler is observed

Returns:
parents

The parents of this sampler

reset()[source]

Reset all the selections.

secondary_samplers

Secondary samplers. :returns: Dict of secondary samplers :rtype: SamplerDict

selection

The selection booleans on the values

Returns:
selector

The selection probability object

Returns:
set_luminosity(luminosity: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]]) → None[source]

Set the luminosity values.

Parameters:luminosity (ArrayLike) – Luminosity
set_secondary_sampler(sampler: popsynth.auxiliary_sampler.AuxiliarySampler) → None[source]

Add a secondary sampler upon which this sampler will depend. The sampled values can be accessed via an internal dictionary with the samplers ‘name’

self._secondary_sampler[‘name’].true_values self._secondary_sampler[‘name’].obs_values

Parameters:sampler ("AuxiliarySampler") – An auxiliary sampler
Returns:
set_selection_probability(selector: popsynth.selection_probability.selection_probability.SelectionProbability) → None[source]

Set a selection probabilty for this sampler.

Parameters:selector (SelectionProbability) – A selection probability oobject
Returns:
set_spatial_values(value: popsynth.distribution.SpatialContainer) → None[source]

Set the spatial values.

Parameters:value (SpatialContainer) – Spatial values
true_sampler(size: int = 1)[source]
true_values

The true or latent values

Returns:
truth

A dictionary containing true paramters used to simulate the distribution

uses_distance

If this uses distance

Returns:
uses_luminosity

If this uses luminosity

Returns:
uses_sky_position

If this uses sky position

Returns:
class popsynth.auxiliary_sampler.DerivedLumAuxSampler(name: str, uses_distance: bool = False)[source]

Bases: popsynth.auxiliary_sampler.AuxiliarySampler

__init__(name: str, uses_distance: bool = False)[source]

Base class for generating luminosity from other properties.

Parameters:
  • name (str) – Name of the sampler
  • uses_distance (bool) – True if sampler uses distance values
compute_luminosity()[source]
class popsynth.auxiliary_sampler.NonObservedAuxSampler(name: str, uses_distance: bool = False, uses_luminosity: bool = False)[source]

Bases: popsynth.auxiliary_sampler.AuxiliarySampler

__init__(name: str, uses_distance: bool = False, uses_luminosity: bool = False)[source]

Base class for auxiliary samplers.

Parameters:
  • name (str) – Name of the sampler
  • observed (bool) – True if the property is observed, False if it is latent. Defaults to True
  • uses_distance (bool) – True if sampler uses distance values
  • uses_luminosity (bool) – True if sampler uses luminosities
  • uses_sky_position (bool) – True if sampler uses sky positions
class popsynth.auxiliary_sampler.SecondaryContainer(name: str, true_values: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], obs_values: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], selection: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]])[source]

Bases: object

__init__(name: str, true_values: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], obs_values: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], selection: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]]) → None[source]

A container for secondary properties that adds dict and dictionary access

Parameters:
  • name (str) – the name of the secondary
  • true_values (ArrayLike) –
  • obs_values (ArrayLike) –
  • selection (ArrayLike) –
Returns:

name
obs_values

The observed values of the sampler

Returns:
selection

The the slection of the values

Returns:
true_values

The true (latent) values of the sampler

Returns:
class popsynth.auxiliary_sampler.SecondaryStorage[source]

Bases: dotmap.DotMap

__init__()[source]

A container for secondary samplers

Returns:
add_secondary(secondary_values: popsynth.auxiliary_sampler.SecondaryContainer) → None[source]

Add on a new secondary

Parameters:secondary_values (SecondaryContainer) –
Returns:
popsynth.distribution module
class popsynth.distribution.Distribution(name: str, seed: int, form: str)[source]

Bases: object

__init__(name: str, seed: int, form: str) → None[source]

A distribution base class.

Parameters:
  • name (str) – Name of the distribution
  • seed (int) – Random seed
  • form (str) – the LaTeX form
display()[source]

use ipython pretty display to display the functions

Returns:
form

The latex form of the distribution

Returns:
name

The name of the distribution

Returns:
params

The parameters of the distribution

truth

value of the parameters used to simulate

class popsynth.distribution.DistributionParameter(default: Optional[float] = None, vmin: Optional[float] = None, vmax: Optional[float] = None, free: bool = True)[source]

Bases: popsynth.utils.meta.Parameter

class popsynth.distribution.LuminosityDistribution(name: str, seed: int, form: Optional[str] = None)[source]

Bases: popsynth.distribution.Distribution

__init__(name: str, seed: int, form: Optional[str] = None)[source]

A base class for luminosity distributions.

Parameters:
  • name (str) – Name of the distribution
  • seed (int) – Random seed
  • form (Union[str, None]) – the LaTeX form
draw_luminosity(size)[source]

function to draw the luminosity via an alternative method must be implemented in child class

Parameters:size
Returns:
phi(luminosity)[source]

The functional form of the distribution. not required for sampling :param luminosity: Luminosity

class popsynth.distribution.SpatialContainer(distance: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], theta: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], phi: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]])[source]

Bases: object

Container for 3D spatial values.

__init__(distance: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], theta: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], phi: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]]) → None
dec
ra
class popsynth.distribution.SpatialDistribution(name: str, seed: int, form: Optional[str] = None)[source]

Bases: popsynth.distribution.Distribution

__init__(name: str, seed: int, form: Optional[str] = None)[source]

A base class for spatial distributions, such as redshift distributions.

Parameters:
  • name (str) – Name of the distribution
  • seed (int) – Random seed
  • form (Union[str, None]) – the LaTeX form
dNdV(distance)[source]

The differential number of objects per volume element

Parameters:distance
Returns:
dec

The declination of the objects

Returns:
differential_volume(distance)[source]

The differential volume

Parameters:distance – Distance
distances

the distances to the objects

Returns:
draw_distance(size: int) → None[source]

Draw the distances from the specified dN/dr model.

Parameters:size (int) – Number of distances to sample
draw_sky_positions(size: int) → None[source]

Draw teh sky positions of the objects

Parameters:size (int) –
Returns:
phi

the longitudinal coordinate fo the objects

Returns:
r_max
ra

the right acension of the objects

Returns:
spatial_values

All the spatial values of the objects :returns:

theta

the polar coordinate of the objects

Returns:
time_adjustment(distance)[source]

The time adjustment

Parameters:distance – Distance
transform(flux, distance)[source]

The transform from luminosity to flux for the

Parameters:
  • flux
  • distance
Returns:

popsynth.population module
class popsynth.population.Population(luminosities: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], distances: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], known_distances: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], known_distance_idx: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], unknown_distance_idx: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], fluxes: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], flux_obs: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], selection: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], flux_sigma: float, r_max: float, n_model: int, lf_params: Dict[str, Any], spatial_params: Optional[Dict[str, Any]] = None, model_spaces: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]], None] = None, seed: int = 1234, name: Optional[str] = None, spatial_form: Optional[Dict[str, Any]] = None, lf_form: Optional[Dict[str, Any]] = None, auxiliary_quantities: Optional[popsynth.auxiliary_sampler.SecondaryStorage] = None, truth: Dict[str, float] = {}, graph: Optional[Dict[str, Any]] = None, theta=None, phi=None, pop_synth: Optional[Dict[str, Any]] = None)[source]

Bases: object

__init__(luminosities: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], distances: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], known_distances: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], known_distance_idx: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], unknown_distance_idx: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], fluxes: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], flux_obs: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], selection: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], flux_sigma: float, r_max: float, n_model: int, lf_params: Dict[str, Any], spatial_params: Optional[Dict[str, Any]] = None, model_spaces: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]], None] = None, seed: int = 1234, name: Optional[str] = None, spatial_form: Optional[Dict[str, Any]] = None, lf_form: Optional[Dict[str, Any]] = None, auxiliary_quantities: Optional[popsynth.auxiliary_sampler.SecondaryStorage] = None, truth: Dict[str, float] = {}, graph: Optional[Dict[str, Any]] = None, theta=None, phi=None, pop_synth: Optional[Dict[str, Any]] = None) → None[source]

A population container for all the properties of the population.

Parameters:
  • luminosities (ArrayLike) – The luminosities
  • distances (ArrayLike) – The distances
  • known_distances (ArrayLike) – The known distances
  • known_distance_idx (ArrayLike) – The index of the known distances
  • unknown_distance_idx (ArrayLike) – The index of the unknown distances
  • fluxes (ArrayLike) – The latent fluxes
  • flux_obs (ArrayLike) – The observed fluxes
  • selection (ArrayLike) – The selection vector
  • flux_sigma (float) – The uncertainty on the observed flux
  • r_max (float) – The maximum distance of the survey
  • n_model (int) – Number of models
  • lf_params (Dict[str, Any]) – Luminosity function parameters
  • spatial_params (Optional[Dict[str, Any]]) – Spatial distribution parameters
  • model_spaces (ArrayLike) – Model spaces
  • seed (int) – Random seed
  • name (str) – Name of the population
  • spatial_form (Optional[Dict[str, Any]]) – Form of the spatial distribution
  • lf_form (Optional[Dict[str, Any]]) – Form of the luminosity function
  • auxiliary_quantities (Optional[Dict[str, Any]]) – Dict of auxiliary quantities
  • truth (Dict[str, float]) – Dict of true values
  • graph (Optional[Dict[str, Any]]) – Graph of generative model
  • theta – Theta
  • phi – Phi
  • pop_synth (Optional[Dict[str, Any]]) – Population synth
addto(file_name: str, group_name: str) → None[source]

Write population to a group in an existing HDF5 file.

Parameters:
  • file_name (str) – Name of the file
  • group_name (str) – Name of the group
dec

The declination of the objects

display()[source]

Display the simulation parameters.

display_distances(ax=None)[source]

Display the distances

Parameters:ax – Axis on which to plot
display_flux_sphere(seen_cmap='Reds', unseen_cmap='Blues', distance_transform=None, use_log=False, background_color='white', **kwargs)[source]
display_fluxes(ax=None, true_color='#dec3c3', obs_color='#8F2727', arrow_color='k', with_arrows=True, **kwargs)[source]

Display the fluxes.

Parameters:
  • ax – Axis on which to plot
  • true_color – Color of true values
  • obs_color – Color of obs values
  • arrow_color – Color of arrows
  • with_arrows – If True, display arrows
display_hidden_fluxes_sphere(cmap='magma', distance_transform=None, use_log=False, background_color='white', show=True, **kwargs)[source]
display_luminosities(ax=None, true_color='#dec3c3', obs_color='#8F2727', **kwargs)[source]

Display the luminosities

Parameters:
  • ax – Axis on which to plot
  • true_color – Color of true values
  • obs_color – Color of obs values
display_obs_fluxes(ax=None, flux_color='#8F2727', **kwargs)[source]

Display the observed fluxes.

Parameters:
  • ax – Axis on which to plot
  • flux_color – Color of fluxes
display_obs_fluxes_sphere(cmap='magma', distance_transform=None, use_log=False, background_color='white', show=True, **kwargs)[source]
display_true_fluxes(ax=None, flux_color='#8F2727', **kwargs)[source]

Display the fluxes.

Parameters:
  • ax – Axis on which to plot
  • flux_color – Color of fluxes
distance_probability
distances

The distances to the objects

flux_sigma

The simulated flux sigma

fluxes_latent

The latent fluxes of the objects

fluxes_observed

All of the observed fluxes, i.e., scattered with error

classmethod from_file(file_name: str)[source]

Load a population from a file.

Parameters:file_name (str) – Name of the file
classmethod from_group(file_name: str, group_name: str)[source]

Load a population from a group in a file.

Parameters:
  • file_name (str) – Name of the file
  • group_name (str) – Name of the group
graph

The networkx graph of the population

Returns:
hard_cut
has_detections

If the population has detections

hidden_distances

The distances that are hidden by the selection

hidden_fluxes_latent

The latent fluxes that are hidden by the selection

hidden_fluxes_observed

The observed fluxes that are hidden by the selection

known_distances

The observed distances

luminosities_latent

The true luminosities of the objects. These are always latent as one cannot directly observe them.

luminosity_parameters
n_detections

The number of DETECTED objects in the population

n_non_detections

The number of NON-DETECTED objects in the population

n_objects

The number of objects in the population

phi

The phi angle of the objects

pop_synth

Dictionary population synth used to create this population

ra

The right ascension of the objects

selected_distances

The selected distances. Note, this is different than the KNOWN distances.

selected_fluxes_latent

The selected latent fluxes

selected_fluxes_observed

The selected obs fluxes

selection

The selection vector

spatial_parameters

spatial parameters

Returns:
theta

The polar angle of the objects

to_stan_data() → dict[source]

Create Stan input

to_sub_population(observed: bool = True) → popsynth.population.Population[source]

Create a population that is down selected from either the observed or unobserved population

Parameters:observed (bool) – Extract the observed or unobserved object
Returns:A new population object
Return type:Population
truth

The simulated truth parameters

writeto(file_name: str) → None[source]

Write population to an HDF5 file

Parameters:file_name (str) – Name of the file
popsynth.population_synth module
class popsynth.population_synth.PopulationSynth(spatial_distribution: popsynth.distribution.SpatialDistribution, luminosity_distribution: Optional[popsynth.distribution.LuminosityDistribution] = None, seed: int = 1234)[source]

Bases: object

__init__(spatial_distribution: popsynth.distribution.SpatialDistribution, luminosity_distribution: Optional[popsynth.distribution.LuminosityDistribution] = None, seed: int = 1234)[source]

Basic and generic population synth. One specifies the spatial and luminosity distribution OR derived luminosity distribution and everything is setup.

Parameters:
  • spatial_distribution (SpatialDistribution) – The spatial distribution to sample locations from
  • luminosity_distribution (LuminosityDistribution) – The optional luminosity distribution
  • seed (int) – Random seed
add_auxiliary_sampler(auxiliary_sampler: Union[popsynth.auxiliary_sampler.DerivedLumAuxSampler, popsynth.auxiliary_sampler.AuxiliarySampler])[source]

Add an auxiliary sampler or derived luminosity sampler to the population synth.

Parameters:auxiliary_sampler (Union[DerivedLumAuxSampler, AuxiliarySampler]) – The auxiliary_sampler
add_model_space(name, start, stop, log=True)[source]

Add a model space for stan generated quantities

Parameters:
  • name – Name that Stan will use
  • start – Start of the grid
  • stop – Stop of the grid
  • log – Use log10 or not
add_observed_quantity(auxiliary_sampler: Union[popsynth.auxiliary_sampler.DerivedLumAuxSampler, popsynth.auxiliary_sampler.AuxiliarySampler])[source]

Add an auxiliary sampler or derived luminosity sampler to the population synth

Parameters:auxiliary_sampler (Union[DerivedLumAuxSampler, AuxiliarySampler]) – The auxiliary_sampler
add_spatial_selector(spatial_selector: popsynth.selection_probability.selection_probability.SelectionProbability) → None[source]

Add a spatial selector into the mix

Parameters:spatial_selector (SelectionProbability) – The spatial selector
clean(reset: bool = False)[source]

Clean the auxiliary samplers, selections, etc from the population synth

Parameters:reset (bool) – If True, reset any attached distributions and samplers
display() → None[source]

Display the simulation parameters.

draw_log10_fobs(f, f_sigma, size=1) → numpy.ndarray[source]

Draw the log10 of the the fluxes.

draw_log_fobs(f, f_sigma, size=1) → numpy.ndarray[source]

Draw the log10 of the the fluxes.

draw_survey(flux_sigma: Optional[float] = None, log10_flux_draw: bool = True) → popsynth.population.Population[source]

Draw the total survey and return a Population object.

This will sample all attached distributions and apply selection functions.

If a value of flux_sigma is given, the log10 observed fluxes are sampled with measurement error.

Parameters:
  • flux_sigma (Optional[float]) – The homoskedastic sigma for the flux in log10 space
  • log10_flux_draw (bool) – if True, fluxes are drawn in log space
Returns:

a Population object

Return type:

Population

classmethod from_dict(input: Dict[str, Any]) → popsynth.population_synth.PopulationSynth[source]

Build a PopulationSynth object from a dictionary

Parameters:input (Dict[str, Any]) – the dictionary from which to build
Returns:Popsynth object
Return type:PopulationSynth
classmethod from_file(file_name: str) → popsynth.population_synth.PopulationSynth[source]

read the population in from a yaml file

Parameters:file_name – the file name of the population synth
graph
luminosity_distribution
name
set_distance_selection(selector: popsynth.selection_probability.selection_probability.SelectionProbability) → None[source]

Set the selection type for the distance.

Parameters:selector (SelectionProbability) – The selector
set_flux_selection(selector: popsynth.selection_probability.selection_probability.SelectionProbability) → None[source]

Set the selection type for the flux

Parameters:selector (SelectionProbability) – The selector
spatial_distribution
to_dict() → Dict[str, Any][source]

Convert the population synth to a dictionary

Returns:Popsynth dict
Return type:Dict[str, Any]
write_to(file_name: str) → None[source]

Write the population synth to a YAML file.

Parameters:file_name (str) – the file name of the output YAML

Module contents

class popsynth.AuxiliarySampler(name: str, observed: bool = True, uses_distance: bool = False, uses_luminosity: bool = False, uses_sky_position: bool = False)[source]

Bases: object

__init__(name: str, observed: bool = True, uses_distance: bool = False, uses_luminosity: bool = False, uses_sky_position: bool = False) → None[source]

Base class for auxiliary samplers.

Parameters:
  • name (str) – Name of the sampler
  • observed (bool) – True if the property is observed, False if it is latent. Defaults to True
  • uses_distance (bool) – True if sampler uses distance values
  • uses_luminosity (bool) – True if sampler uses luminosities
  • uses_sky_position (bool) – True if sampler uses sky positions
display()[source]
draw(size: int = 1)[source]

Draw the primary and secondary samplers. This is the main call.

Parameters:size (int) – The number of samples to draw
get_secondary_objects(recursive_secondaries: Optional[Dict[str, Any]] = None) → Dict[str, Any][source]

Get secondary objects.

Parameters:recursive_secondaries – Recursive dict of secondaries
Returns:Dict of objects
Return type:Dict[str, Any]
get_secondary_properties(graph=None, primary=None, spatial_distribution=None) → popsynth.auxiliary_sampler.SecondaryStorage[source]

Get properties of secondary samplers.

Parameters:
  • graph – Graph
  • primary – Primary sampler
  • spatial_distribution – Spatial Distribution
Returns:

Dict of samplers

Return type:

SamplerDict

has_secondary

if this sampler has a secondary :returns:

is_secondary

If another sampler depends on this

Returns:
luminosity_distance

luminosity distance if needed.

make_secondary(parent_name: str) → None[source]

sets this sampler as secondary for book keeping

Parameters:parent_name (str) –
Returns:
name

The name of the sampler

Returns:
obs_name
obs_values

The values obscured by measurement error.

Returns:
observation_sampler(size: int = 1) → numpy.ndarray[source]
observed

if this sampler is observed

Returns:
parents

The parents of this sampler

reset()[source]

Reset all the selections.

secondary_samplers

Secondary samplers. :returns: Dict of secondary samplers :rtype: SamplerDict

selection

The selection booleans on the values

Returns:
selector

The selection probability object

Returns:
set_luminosity(luminosity: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]]) → None[source]

Set the luminosity values.

Parameters:luminosity (ArrayLike) – Luminosity
set_secondary_sampler(sampler: popsynth.auxiliary_sampler.AuxiliarySampler) → None[source]

Add a secondary sampler upon which this sampler will depend. The sampled values can be accessed via an internal dictionary with the samplers ‘name’

self._secondary_sampler[‘name’].true_values self._secondary_sampler[‘name’].obs_values

Parameters:sampler ("AuxiliarySampler") – An auxiliary sampler
Returns:
set_selection_probability(selector: popsynth.selection_probability.selection_probability.SelectionProbability) → None[source]

Set a selection probabilty for this sampler.

Parameters:selector (SelectionProbability) – A selection probability oobject
Returns:
set_spatial_values(value: popsynth.distribution.SpatialContainer) → None[source]

Set the spatial values.

Parameters:value (SpatialContainer) – Spatial values
true_sampler(size: int = 1)[source]
true_values

The true or latent values

Returns:
truth

A dictionary containing true paramters used to simulate the distribution

uses_distance

If this uses distance

Returns:
uses_luminosity

If this uses luminosity

Returns:
uses_sky_position

If this uses sky position

Returns:
class popsynth.DerivedLumAuxSampler(name: str, uses_distance: bool = False)[source]

Bases: popsynth.auxiliary_sampler.AuxiliarySampler

__init__(name: str, uses_distance: bool = False)[source]

Base class for generating luminosity from other properties.

Parameters:
  • name (str) – Name of the sampler
  • uses_distance (bool) – True if sampler uses distance values
compute_luminosity()[source]
class popsynth.Population(luminosities: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], distances: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], known_distances: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], known_distance_idx: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], unknown_distance_idx: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], fluxes: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], flux_obs: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], selection: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], flux_sigma: float, r_max: float, n_model: int, lf_params: Dict[str, Any], spatial_params: Optional[Dict[str, Any]] = None, model_spaces: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]], None] = None, seed: int = 1234, name: Optional[str] = None, spatial_form: Optional[Dict[str, Any]] = None, lf_form: Optional[Dict[str, Any]] = None, auxiliary_quantities: Optional[popsynth.auxiliary_sampler.SecondaryStorage] = None, truth: Dict[str, float] = {}, graph: Optional[Dict[str, Any]] = None, theta=None, phi=None, pop_synth: Optional[Dict[str, Any]] = None)[source]

Bases: object

__init__(luminosities: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], distances: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], known_distances: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], known_distance_idx: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], unknown_distance_idx: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], fluxes: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], flux_obs: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], selection: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], flux_sigma: float, r_max: float, n_model: int, lf_params: Dict[str, Any], spatial_params: Optional[Dict[str, Any]] = None, model_spaces: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]], None] = None, seed: int = 1234, name: Optional[str] = None, spatial_form: Optional[Dict[str, Any]] = None, lf_form: Optional[Dict[str, Any]] = None, auxiliary_quantities: Optional[popsynth.auxiliary_sampler.SecondaryStorage] = None, truth: Dict[str, float] = {}, graph: Optional[Dict[str, Any]] = None, theta=None, phi=None, pop_synth: Optional[Dict[str, Any]] = None) → None[source]

A population container for all the properties of the population.

Parameters:
  • luminosities (ArrayLike) – The luminosities
  • distances (ArrayLike) – The distances
  • known_distances (ArrayLike) – The known distances
  • known_distance_idx (ArrayLike) – The index of the known distances
  • unknown_distance_idx (ArrayLike) – The index of the unknown distances
  • fluxes (ArrayLike) – The latent fluxes
  • flux_obs (ArrayLike) – The observed fluxes
  • selection (ArrayLike) – The selection vector
  • flux_sigma (float) – The uncertainty on the observed flux
  • r_max (float) – The maximum distance of the survey
  • n_model (int) – Number of models
  • lf_params (Dict[str, Any]) – Luminosity function parameters
  • spatial_params (Optional[Dict[str, Any]]) – Spatial distribution parameters
  • model_spaces (ArrayLike) – Model spaces
  • seed (int) – Random seed
  • name (str) – Name of the population
  • spatial_form (Optional[Dict[str, Any]]) – Form of the spatial distribution
  • lf_form (Optional[Dict[str, Any]]) – Form of the luminosity function
  • auxiliary_quantities (Optional[Dict[str, Any]]) – Dict of auxiliary quantities
  • truth (Dict[str, float]) – Dict of true values
  • graph (Optional[Dict[str, Any]]) – Graph of generative model
  • theta – Theta
  • phi – Phi
  • pop_synth (Optional[Dict[str, Any]]) – Population synth
addto(file_name: str, group_name: str) → None[source]

Write population to a group in an existing HDF5 file.

Parameters:
  • file_name (str) – Name of the file
  • group_name (str) – Name of the group
dec

The declination of the objects

display()[source]

Display the simulation parameters.

display_distances(ax=None)[source]

Display the distances

Parameters:ax – Axis on which to plot
display_flux_sphere(seen_cmap='Reds', unseen_cmap='Blues', distance_transform=None, use_log=False, background_color='white', **kwargs)[source]
display_fluxes(ax=None, true_color='#dec3c3', obs_color='#8F2727', arrow_color='k', with_arrows=True, **kwargs)[source]

Display the fluxes.

Parameters:
  • ax – Axis on which to plot
  • true_color – Color of true values
  • obs_color – Color of obs values
  • arrow_color – Color of arrows
  • with_arrows – If True, display arrows
display_hidden_fluxes_sphere(cmap='magma', distance_transform=None, use_log=False, background_color='white', show=True, **kwargs)[source]
display_luminosities(ax=None, true_color='#dec3c3', obs_color='#8F2727', **kwargs)[source]

Display the luminosities

Parameters:
  • ax – Axis on which to plot
  • true_color – Color of true values
  • obs_color – Color of obs values
display_obs_fluxes(ax=None, flux_color='#8F2727', **kwargs)[source]

Display the observed fluxes.

Parameters:
  • ax – Axis on which to plot
  • flux_color – Color of fluxes
display_obs_fluxes_sphere(cmap='magma', distance_transform=None, use_log=False, background_color='white', show=True, **kwargs)[source]
display_true_fluxes(ax=None, flux_color='#8F2727', **kwargs)[source]

Display the fluxes.

Parameters:
  • ax – Axis on which to plot
  • flux_color – Color of fluxes
distance_probability
distances

The distances to the objects

flux_sigma

The simulated flux sigma

fluxes_latent

The latent fluxes of the objects

fluxes_observed

All of the observed fluxes, i.e., scattered with error

classmethod from_file(file_name: str)[source]

Load a population from a file.

Parameters:file_name (str) – Name of the file
classmethod from_group(file_name: str, group_name: str)[source]

Load a population from a group in a file.

Parameters:
  • file_name (str) – Name of the file
  • group_name (str) – Name of the group
graph

The networkx graph of the population

Returns:
hard_cut
has_detections

If the population has detections

hidden_distances

The distances that are hidden by the selection

hidden_fluxes_latent

The latent fluxes that are hidden by the selection

hidden_fluxes_observed

The observed fluxes that are hidden by the selection

known_distances

The observed distances

luminosities_latent

The true luminosities of the objects. These are always latent as one cannot directly observe them.

luminosity_parameters
n_detections

The number of DETECTED objects in the population

n_non_detections

The number of NON-DETECTED objects in the population

n_objects

The number of objects in the population

phi

The phi angle of the objects

pop_synth

Dictionary population synth used to create this population

ra

The right ascension of the objects

selected_distances

The selected distances. Note, this is different than the KNOWN distances.

selected_fluxes_latent

The selected latent fluxes

selected_fluxes_observed

The selected obs fluxes

selection

The selection vector

spatial_parameters

spatial parameters

Returns:
theta

The polar angle of the objects

to_stan_data() → dict[source]

Create Stan input

to_sub_population(observed: bool = True) → popsynth.population.Population[source]

Create a population that is down selected from either the observed or unobserved population

Parameters:observed (bool) – Extract the observed or unobserved object
Returns:A new population object
Return type:Population
truth

The simulated truth parameters

writeto(file_name: str) → None[source]

Write population to an HDF5 file

Parameters:file_name (str) – Name of the file
class popsynth.PopulationSynth(spatial_distribution: popsynth.distribution.SpatialDistribution, luminosity_distribution: Optional[popsynth.distribution.LuminosityDistribution] = None, seed: int = 1234)[source]

Bases: object

__init__(spatial_distribution: popsynth.distribution.SpatialDistribution, luminosity_distribution: Optional[popsynth.distribution.LuminosityDistribution] = None, seed: int = 1234)[source]

Basic and generic population synth. One specifies the spatial and luminosity distribution OR derived luminosity distribution and everything is setup.

Parameters:
  • spatial_distribution (SpatialDistribution) – The spatial distribution to sample locations from
  • luminosity_distribution (LuminosityDistribution) – The optional luminosity distribution
  • seed (int) – Random seed
add_auxiliary_sampler(auxiliary_sampler: Union[popsynth.auxiliary_sampler.DerivedLumAuxSampler, popsynth.auxiliary_sampler.AuxiliarySampler])[source]

Add an auxiliary sampler or derived luminosity sampler to the population synth.

Parameters:auxiliary_sampler (Union[DerivedLumAuxSampler, AuxiliarySampler]) – The auxiliary_sampler
add_model_space(name, start, stop, log=True)[source]

Add a model space for stan generated quantities

Parameters:
  • name – Name that Stan will use
  • start – Start of the grid
  • stop – Stop of the grid
  • log – Use log10 or not
add_observed_quantity(auxiliary_sampler: Union[popsynth.auxiliary_sampler.DerivedLumAuxSampler, popsynth.auxiliary_sampler.AuxiliarySampler])[source]

Add an auxiliary sampler or derived luminosity sampler to the population synth

Parameters:auxiliary_sampler (Union[DerivedLumAuxSampler, AuxiliarySampler]) – The auxiliary_sampler
add_spatial_selector(spatial_selector: popsynth.selection_probability.selection_probability.SelectionProbability) → None[source]

Add a spatial selector into the mix

Parameters:spatial_selector (SelectionProbability) – The spatial selector
clean(reset: bool = False)[source]

Clean the auxiliary samplers, selections, etc from the population synth

Parameters:reset (bool) – If True, reset any attached distributions and samplers
display() → None[source]

Display the simulation parameters.

draw_log10_fobs(f, f_sigma, size=1) → numpy.ndarray[source]

Draw the log10 of the the fluxes.

draw_log_fobs(f, f_sigma, size=1) → numpy.ndarray[source]

Draw the log10 of the the fluxes.

draw_survey(flux_sigma: Optional[float] = None, log10_flux_draw: bool = True) → popsynth.population.Population[source]

Draw the total survey and return a Population object.

This will sample all attached distributions and apply selection functions.

If a value of flux_sigma is given, the log10 observed fluxes are sampled with measurement error.

Parameters:
  • flux_sigma (Optional[float]) – The homoskedastic sigma for the flux in log10 space
  • log10_flux_draw (bool) – if True, fluxes are drawn in log space
Returns:

a Population object

Return type:

Population

classmethod from_dict(input: Dict[str, Any]) → popsynth.population_synth.PopulationSynth[source]

Build a PopulationSynth object from a dictionary

Parameters:input (Dict[str, Any]) – the dictionary from which to build
Returns:Popsynth object
Return type:PopulationSynth
classmethod from_file(file_name: str) → popsynth.population_synth.PopulationSynth[source]

read the population in from a yaml file

Parameters:file_name – the file name of the population synth
graph
luminosity_distribution
name
set_distance_selection(selector: popsynth.selection_probability.selection_probability.SelectionProbability) → None[source]

Set the selection type for the distance.

Parameters:selector (SelectionProbability) – The selector
set_flux_selection(selector: popsynth.selection_probability.selection_probability.SelectionProbability) → None[source]

Set the selection type for the flux

Parameters:selector (SelectionProbability) – The selector
spatial_distribution
to_dict() → Dict[str, Any][source]

Convert the population synth to a dictionary

Returns:Popsynth dict
Return type:Dict[str, Any]
write_to(file_name: str) → None[source]

Write the population synth to a YAML file.

Parameters:file_name (str) – the file name of the output YAML
{
“cells”: [
{

“cell_type”: “markdown”, “id”: “93b97ffb”, “metadata”: {}, “source”: [

“# Short GRBS n”, “n”, “In [Ghirlanda et al. 2016](https://arxiv.org/abs/1607.07875) a fitting algorithm was used to determine the redshift and luminosity of short GRBS. We can use the parameters to reproduce the population and the observed GBM survey.”

]

}, {

“cell_type”: “code”, “execution_count”: 1, “id”: “d2e7708e”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:42.942500Z”, “iopub.status.busy”: “2022-02-09T16:35:42.941986Z”, “iopub.status.idle”: “2022-02-09T16:35:46.560958Z”, “shell.execute_reply”: “2022-02-09T16:35:46.560007Z”

}

}, “outputs”: [], “source”: [

“from popsynth import SFRDistribution, BPLDistribution, PopulationSynth, NormalAuxSampler, AuxiliarySampler, HardFluxSelectionn”, “from popsynth import update_logging_leveln”, “update_logging_level(“INFO”)”

]

}, {

“cell_type”: “code”, “execution_count”: 2, “id”: “6f6da754”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:46.568188Z”, “iopub.status.busy”: “2022-02-09T16:35:46.567653Z”, “iopub.status.idle”: “2022-02-09T16:35:46.575135Z”, “shell.execute_reply”: “2022-02-09T16:35:46.574255Z”

}

}, “outputs”: [], “source”: [

“%matplotlib inlinen”, “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 networkx as nxn”, “import numpy as npn”, “import warningsn”, “n”, “warnings.simplefilter(“ignore”)”

]

}, {

“cell_type”: “markdown”, “id”: “72e244af”, “metadata”: {}, “source”: [

“In the work, the luminosity function of short GRBs is model as a broken power law.”

]

}, {

“cell_type”: “code”, “execution_count”: 3, “id”: “9dffdecb”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:46.579425Z”, “iopub.status.busy”: “2022-02-09T16:35:46.578991Z”, “iopub.status.idle”: “2022-02-09T16:35:46.582073Z”, “shell.execute_reply”: “2022-02-09T16:35:46.581627Z”

}

}, “outputs”: [], “source”: [

“bpl = BPLDistribution()n”, “n”, “bpl.alpha = -0.53n”, “bpl.beta = -3.4n”, “bpl.Lmin = 1e47 # erg/sn”, “bpl.Lbreak = 2.8e52n”, “bpl.Lmax = 1e55n”

]

}, {

“cell_type”: “markdown”, “id”: “c7c24217”, “metadata”: {}, “source”: [

“To model the redshift distribution, an empirical form from [Cole et al 2001](https://academic.oup.com/mnras/article/326/1/255/1026734?login=true) is used. In `popsynth` we call this the `SFRDistribution` (but perhaps a better name is needed).”

]

}, {

“cell_type”: “code”, “execution_count”: 4, “id”: “fbdc78ce”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:46.586096Z”, “iopub.status.busy”: “2022-02-09T16:35:46.585376Z”, “iopub.status.idle”: “2022-02-09T16:35:46.589014Z”, “shell.execute_reply”: “2022-02-09T16:35:46.588316Z”

}

}, “outputs”: [], “source”: [

“sfr = SFRDistribution()”

]

}, {

“cell_type”: “code”, “execution_count”: 5, “id”: “e81f3a83”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:46.592922Z”, “iopub.status.busy”: “2022-02-09T16:35:46.592444Z”, “iopub.status.idle”: “2022-02-09T16:35:46.595720Z”, “shell.execute_reply”: “2022-02-09T16:35:46.595268Z”

}

}, “outputs”: [], “source”: [

“sfr.r0 = 5.n”, “sfr.a = 1n”, “sfr.rise = 2.8n”, “sfr.decay = 3.5n”, “sfr.peak = 2.3”

]

}, {

“cell_type”: “markdown”, “id”: “18a87d23”, “metadata”: {}, “source”: [

“We can checkout how the rate changes with redshift”

]

}, {

“cell_type”: “code”, “execution_count”: 6, “id”: “84f30104”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:46.601294Z”, “iopub.status.busy”: “2022-02-09T16:35:46.599885Z”, “iopub.status.idle”: “2022-02-09T16:35:47.274250Z”, “shell.execute_reply”: “2022-02-09T16:35:47.273772Z”

}

}, “outputs”: [

{
“data”: {
“text/plain”: [
“Text(0, 0.5, ‘$\frac{\mathrm{d}N}{\mathrm{d}V}$’)”

]

}, “execution_count”: 6, “metadata”: {}, “output_type”: “execute_result”

}, {

“data”: {

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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”, “z = np.linspace(0,5,100)n”, “n”, “ax.plot(z, sfr.dNdV(z), color=purple)n”, “ax.set_xlabel(“z”)n”, “ax.set_ylabel(r”$\frac{\mathrm{d}N}{\mathrm{d}V}$”)”

]

}, {

“cell_type”: “markdown”, “id”: “5fb08e6b”, “metadata”: {}, “source”: [

“In their model, the authors also have some secondary parameters that are connected to the luminosity. These are the parameters for the spectrum of the GRB. It is proposed that the spectra peak energy (Ep) is linked to the luminosity by a power law relation:n”, “n”, “n”, “$$ \log E_{\mathrm{p}} \propto a + b \log L$$n”, “n”, “We can build an auxiliary sample to simulate this as well. But we will also add a bit of scatter to the intercept of the relation.”

]

}, {

“cell_type”: “code”, “execution_count”: 7, “id”: “41b70729”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:47.279088Z”, “iopub.status.busy”: “2022-02-09T16:35:47.278568Z”, “iopub.status.idle”: “2022-02-09T16:35:47.281530Z”, “shell.execute_reply”: “2022-02-09T16:35:47.281961Z”

}

}, “outputs”: [], “source”: [

“intercept =NormalAuxSampler(name=”intercept”, observed=False)n”, “n”, “intercept.mu = 0.034n”, “intercept.sigma = .005n”, “n”, “n”

]

}, {

“cell_type”: “code”, “execution_count”: 8, “id”: “febbec0f”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:47.289360Z”, “iopub.status.busy”: “2022-02-09T16:35:47.288148Z”, “iopub.status.idle”: “2022-02-09T16:35:47.289953Z”, “shell.execute_reply”: “2022-02-09T16:35:47.290366Z”

}

}, “outputs”: [], “source”: [

“class EpSampler(AuxiliarySampler):n”, ” n”, ” _auxiliary_sampler_name = “EpSampler”n”, “n”, ” def __init__(self):n”, “n”, ” # pass up to the super classn”, ” super(EpSampler, self).__init__(“Ep”, observed=True, uses_luminosity = True)n”, “n”, ” def true_sampler(self, size):n”, “n”, ” # we will get the intercept’s latent (true) valuen”, ” # from its samplern”, ” n”, ” intercept = self._secondary_samplers[“intercept”].true_valuesn”, ” n”, ” slope = 0.84n”, “n”, ” self._true_values = np.power(10., intercept + slope * np.log10(self._luminosity/1e52) + np.log10(670.))n”, ” n”, ” def observation_sampler(self, size):n”, ” n”, ” # we will also add some measurement error to Epn”, ” self._obs_values = self._true_values + np.random.normal(0., 10, size=size)n”, ” “

]

}, {

“cell_type”: “markdown”, “id”: “8875465e”, “metadata”: {}, “source”: [

“Now we can put it all together.”

]

}, {

“cell_type”: “code”, “execution_count”: 9, “id”: “403dacf5”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:47.295231Z”, “iopub.status.busy”: “2022-02-09T16:35:47.294043Z”, “iopub.status.idle”: “2022-02-09T16:35:47.295806Z”, “shell.execute_reply”: “2022-02-09T16:35:47.296188Z”

}

}, “outputs”: [], “source”: [

“pop_synth = PopulationSynth(spatial_distribution=sfr, luminosity_distribution=bpl)”

]

}, {

“cell_type”: “markdown”, “id”: “b7f24579”, “metadata”: {}, “source”: [

“We will have a hard flux selection which is Fermi-GBM’s fluz limit of ~ 1e-7 erg/s/cm2”

]

}, {

“cell_type”: “code”, “execution_count”: 10, “id”: “0125d67a”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:47.299807Z”, “iopub.status.busy”: “2022-02-09T16:35:47.298347Z”, “iopub.status.idle”: “2022-02-09T16:35:47.301761Z”, “shell.execute_reply”: “2022-02-09T16:35:47.301320Z”

}

}, “outputs”: [], “source”: [

“selection = HardFluxSelection()n”, “selection.boundary = 1e-7”

]

}, {

“cell_type”: “code”, “execution_count”: 11, “id”: “e7fcc399”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:47.306456Z”, “iopub.status.busy”: “2022-02-09T16:35:47.305271Z”, “iopub.status.idle”: “2022-02-09T16:35:47.307046Z”, “shell.execute_reply”: “2022-02-09T16:35:47.307465Z”

}

}, “outputs”: [], “source”: [

“pop_synth.set_flux_selection(selection)”

]

}, {

“cell_type”: “markdown”, “id”: “2d847487”, “metadata”: {}, “source”: [

“We need to add the Ep sampler. Once we set the intercept sampler as a secondary it will automatically be added to the population synth.”

]

}, {

“cell_type”: “code”, “execution_count”: 12, “id”: “65c597c6”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:47.312026Z”, “iopub.status.busy”: “2022-02-09T16:35:47.310901Z”, “iopub.status.idle”: “2022-02-09T16:35:47.312579Z”, “shell.execute_reply”: “2022-02-09T16:35:47.312963Z”

}

}, “outputs”: [], “source”: [

“ep = EpSampler()”

]

}, {

“cell_type”: “code”, “execution_count”: 13, “id”: “5357f043”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:47.317337Z”, “iopub.status.busy”: “2022-02-09T16:35:47.316198Z”, “iopub.status.idle”: “2022-02-09T16:35:47.317888Z”, “shell.execute_reply”: “2022-02-09T16:35:47.318271Z”

}

}, “outputs”: [], “source”: [

“ep.set_secondary_sampler(intercept)”

]

}, {

“cell_type”: “code”, “execution_count”: 14, “id”: “707d6588”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:47.322332Z”, “iopub.status.busy”: “2022-02-09T16:35:47.321850Z”, “iopub.status.idle”: “2022-02-09T16:35:47.325604Z”, “shell.execute_reply”: “2022-02-09T16:35:47.326003Z”

}

}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m registering auxilary sampler: Ep u001b[0mn”

]

}

], “source”: [

“pop_synth.add_auxiliary_sampler(ep)”

]

}, {

“cell_type”: “markdown”, “id”: “53a96453”, “metadata”: {}, “source”: [

“We are ready to sample our population. We will add some measurement uncertainty to the fluxes as well.”

]

}, {

“cell_type”: “code”, “execution_count”: 15, “id”: “adff1763”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:47.334242Z”, “iopub.status.busy”: “2022-02-09T16:35:47.329575Z”, “iopub.status.idle”: “2022-02-09T16:35:51.769098Z”, “shell.execute_reply”: “2022-02-09T16:35:51.768580Z”

}

}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m The volume integral is 797.550666 u001b[0mn”

]

}, {

“data”: {
“application/vnd.jupyter.widget-view+json”: {
“model_id”: “7e0d00e1a3c542b1a7e63b3144a2d3da”, “version_major”: 2, “version_minor”: 0

}, “text/plain”: [

“Drawing distances: 0%| | 0/770 [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 770 total objects u001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m Sampling: Ep u001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m Ep is sampling its secondary quantities u001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m Sampling: intercept 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 499 distances u001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“u001b[32mu001b[1m INFO u001b[0m| u001b[32mu001b[1m Detected 499 objects out to a distance of 5.31 u001b[0mn”

]

}

], “source”: [

“population = pop_synth.draw_survey(flux_sigma=0.2)”

]

}, {

“cell_type”: “code”, “execution_count”: 16, “id”: “3745e374”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:51.787385Z”, “iopub.status.busy”: “2022-02-09T16:35:51.779217Z”, “iopub.status.idle”: “2022-02-09T16:35:52.267790Z”, “shell.execute_reply”: “2022-02-09T16:35:52.268213Z”

}, “tags”: [

“nbsphinx-thumbnail”

]

}, “outputs”: [

{
“data”: {

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n”, “text/plain”: [

“<Figure size 576x504 with 1 Axes>”

]

}, “metadata”: {}, “output_type”: “display_data”

}

], “source”: [

“population.display_fluxes(true_color=purple, obs_color=yellow, with_arrows=False, s= 5);”

]

}, {

“cell_type”: “markdown”, “id”: “72668f22”, “metadata”: {}, “source”: [

“Let’s look at our distribution of Ep”

]

}, {

“cell_type”: “code”, “execution_count”: 17, “id”: “a89bd986”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:52.284799Z”, “iopub.status.busy”: “2022-02-09T16:35:52.274168Z”, “iopub.status.idle”: “2022-02-09T16:35:52.408468Z”, “shell.execute_reply”: “2022-02-09T16:35:52.409229Z”

}

}, “outputs”: [

{
“data”: {
“text/plain”: [
“<matplotlib.legend.Legend at 0x7f99e01bbf40>”

]

}, “execution_count”: 17, “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”, “ax.hist(np.log10(population.Ep_obs[population.selection]), histtype=”step”, color=yellow, lw=3, label=”Ep observed”)n”, “ax.hist(np.log10(population.Ep[~population.selection]), histtype=”step”, color=purple, lw=3, label=”Ep hidden”)n”, “ax.set_xlabel(“log Ep”)n”, “n”, “ax.legend()”

]

}, {

“cell_type”: “code”, “execution_count”: 18, “id”: “1907b103”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:52.412886Z”, “iopub.status.busy”: “2022-02-09T16:35:52.411787Z”, “iopub.status.idle”: “2022-02-09T16:35:53.283758Z”, “shell.execute_reply”: “2022-02-09T16:35:53.282956Z”

}

}, “outputs”: [

{
“data”: {
“text/plain”: [
“Text(0.5, 0, ‘log Flux’)”

]

}, “execution_count”: 18, “metadata”: {}, “output_type”: “execute_result”

}, {

“data”: {

“image/png”: 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tx1i8VtxOuytKK+WOWB9z8QlE6oTyDKoNROo4V7WZVcdea7r43qWilxKb3Y+1462KTNoZGRkbgmpEp7juEZ0A+z+z/OfXQ0i1kmMsVysWcYh7hLLxXdCbVOI2ZA9F/ZdwzoN7DvFPAwIa+0TE/dPFg/c335s2pkHEZvdb7XirIl/VjIyMdcdyIzoHTZWv23oW1GEXYuBasS7ymOBcgS8e77OGhenixVLOixlV3juL0cHsUe+n2vFWRY60MzIy1h1bZUTnctFzFeGqFstGkgAaz+OLg6geoNMeVeN5VE+2yGrQdPFq+pvvTSr6/jQq2Y7IpJ2RkbHu2MwRnUullhcjRROISYvMwRPDJZw7tIC4OyPJqp5s7yn6vMdS8YPU1Ffb37yZbUyd13a9jEq2ltXp9kMm7YyMjHWFBou0dz0JN3+ycSM6B4miY3jPysuUtEVjQmh+HxjHOYfgk+L7FopH/IGu83RGkiuNNpeuqVcpZ0enqM2+2/Kiso00T+l3bcU9giueSFmFlUf4W6+/fHsik3ZGRsa6oFct7kfBjxhRlzMWYe95ev1GdC6XWladJ5YfoHqbSjQmbhyRCWK4iivGUIZSH7fg/F5imELcRItEeiPJ9bTFVK2h8SYaJ7vX5w6sW8p5tVFtv2sbyzP42pP4oZfX7Zj32up0OyKTdkZGxrqgVy1OE0KEPR+3edqrHdHZD8ullsU/Tdl4I5EwWDQbiWESmIQ4SWyWiKu3idIdwGkAcSjNvpFkbz0ZhnHFQ4h/ZuXfIbyFxhLVkMgvomESVaU29Lk1pY7XEtUOlrZfWYS/la1OtxsyaWdkZKwZS6nFb5+G/Z9eP8IGqy3HeMciNinaaWUglu9TNv+/EM4A86ZUl92IjIFeB50GPOhNNI6gscQV4NwBpPgYvv5riJT0iyRFxGrJ7gSxfAuNHxHDWTR+hK4g1VuRmPMH0CioTnWMEC0WbAJWGjGvLardiF7wrWt1ut2QSTsjI2PN2Cy1eKtFqvE9tPwZiIKM4dxDiD+Y7EMvgdaBCKSQX28ncpgBPLALmDcCFxsqEmU/RXE8DfZYeGvsjF5j+QEx3u4wT1lpqjeRmFgNXbXtj44ERJqAX1XEvPaodiOU4ll9vl7I+YiMjAccK7UZ7YdKLd4P66kWj+UpyvnvovEKyJAV0uM0MXyAhivEcMPe6DzgzHpN6hh5g0VzwyA7QEYtFaAzqAZ8cWJJUVUVvao2UL2NiM3o1ngNMFIM5VlinBnATrTbr9vMW4r02Cax1fmMD9ZTvRg2ohf83vWX33/IkXZGxgOK1dqM9oN4+2xV026dY13V4pGyeRaNF4BqlxGwUHmWGG+CjAGlqcdlJEXSgkXdihH5COIcMAY6ikrAFU/jay8MFL2aEr0EXFKpTxHjftBJol4HnUHcjiUj4kFmPa8+Yl57VLsRveDZ6nR9kEk7I+MBxVptRntRqcI7NwHrqRaHBhreB72TdgYOqAitjriP4fwIsZwBokXSADqb3lcDt687pSACWscXjy8T7XXWZD1260zRtJZovIzqTUS8RfbrYpKyujrwIBuC5bARveD345jMe4FM2hkZ2wxVH7QfWn0Eu5zN6L5PrfzYIkb2+z5lNez1VIsDqBaozgAuRZFNLHoWICBuDOcfJYYrEK8bYckYGkdAxnHFMaAEvWZTuChR9fjaM9Z/rHEJEumY0Y0gbtxGb4qgeNA7CJgSPW0keiPiTjGZoYErnl2CxFYfMa9XVLsRveAb2V/+ICCTdkbGNsF6prM3UjgmfmMsSkVKxO20ejYNjKwFI+4SEW1FtaH5fROlAa44hK99BlecRMObxFBHZRykRnULLOf/1ZIir97oVZwZsMRwA2QM4S7i9oNMoJqMXERQnU394u8agcZZVG+lg+7GuZGBz1lhkIg5R7X3LzJpZ2RsE6xnOnszbUb7YXXZgjrijqJ8hBFRRdxDwC5UHaAU9efxtZOozgNK1wAL1yYyE3S9kwixnzlLd5tVZ/SKzuL8UXztbyH+Ucr5fwvxEhrPGGlLgcg44o8Qy3eI5Rkj8XidGK8BgnMB5MCSafS19oXnqPb+QybtjIxtgPVOZ2+GcKwf1pItEHH44mPE8qfAqC3WXsEVE4jMU9V5bdpU/3DfUtV1NL7fV+QVynOoarLr7G6zWix6DeII4ZoJ3KQycrmGc4fReB5EiOEKMbyDieccMcyBm8AtISyrImbxzxDLHxPDh4TyXWI4j/OP4WvPD9QXvpWRvchXhkzaGRnbABuRzt544dhCrDVb4IpP4PzfmPWnKN3WnzV667yLE8LiIq8YPkB1Fie1vsYkvdGrnUNxxUSrVg4FrtgHBDTOo3rDImwNaXeioHfQeBn84QXCst51a3iL0DwHOonqFKoloXmaUL5HbfhXERkamPD6XZN7QZzZi3x1yKSdkbENsBHp7I0WjvViPbIFznmK+mcpm28jEqmGbKgqrqPOuzwh9Bd5qSroNCKHU09xe/zm4m1WDWAe5w6grm2SIghRmyA1tOofx1L4Bm/e424/JGFZte5QnkV1BpFRnD+RUvKTxDiZ2s4E9A6x8W0a8UN88fAApiv9h4AAfbMKG02c2Yt8dciknZGxDbCR6eyNEo71Yr2yBa44aQ1XVZ23QxldRYyd9erFouX+Iq8SGEPDtR5r0XFw4/Rvs+pWlnfeVkVGUG2i4Ur1Ta1GQA3rL79GaDZxxVFi+SYxRkLjL4Cb1komBaH5PsQmyDzi0lp1puXmht5CtbEs4fUjyXL+uyDg/cFFiXMjovDsRb56ZNLOyNgmuBfp7PXESrMFi5FFP2U0SDuKjLOJEHZDx8QuBEJ5BvFP41zRvy2q9hhl4xYxXk0tY6k+HSdx4lks/S7ukZbYrP2aWkuYRnNg08pCtTKFiaA1pDiIyF7KxtvEeA60kZTnQLwKTGNk79EwBuwFZlOaXVomMyLFooTXjyQtk3DTMvUcWNCqJv4ZNLy1QenrreNFvt1q6pm0MzK2CTY7nb3eGDRbEEulnD0F/j2Qxcmis7Ycmm+0o2YBtEHUqwiKqzzJ4xSqpjj3xeN9hWV2rNcXrr3n995UMwyB1FEUmENkBOcfIYb38M4Ti2PE8lq7Q01vAHuQ4gDeH0zHLCFcQvy+dJIbwN109pj+TWEkXjN/dTeSWtd8OsZihNePJEPKJKSfO+hAddaEb+V7K0pfD06A996LfLvW1DNpZ2RsM2xWOnsjsPeTlia/8y6E2e5sQaUsn712itru07iaMDThGTm6NFl0W4yChuuW2iag5U1CnKLlXkaBxrCosMxu3rvBhy5RmfhxRHZREeKCVDNlqqs/jq89iZFOgxjeBryN/SywjQMN0IgU+3GJsNswUZtGxcjZjGNopd0rUxlAhoDRHkOXxQivH0nahLTWz10YJoYPB05fdxFgnAUpcP7xdH1XZ+O60diuNfVM2hkZGavCSnqtF7R6jcCe52HiF8Glu9Dk92Hqx5GdT7+HOEEDzF2y10aPLi0Eq6JIi6ivA8MpHV1CvEJLACbDaHwHZJxQ1vocr464EUQXispI6vSl6rEa3weea7WVtWvdIJVQTUtCOI9zh7oieJECZBeqYusm0o6yPTBKm7R3gUac39cyelmK8PqRpD3uAaFF+tVxXPEQMZxlIZn3j+ZjeYqyebrLbS403yaG89SGf6Mvcd9LL/LtXFPPpJ2RkbEiLNZrvfeTEBv9SXxBq1cJt9+0FP/+z7SV5a7WQIo50HQAgflrMHIEYLHUr5GjasMIQwRh1DTaOps+F0H2ITJGVaMWygXHW0huRfrOnYM85ohxNtGc74kU22vsS5QISoEvnulb3He1TxLLN0FvYcRd5dPr6ecaMAT+k8Ak4g/a+wYgvH4kWQz9kq07nu8iTvHPoPGjgdLXFQGi11rWrjZMRQnNUzj/MEX9432Ocy9d27ZOTX2lyKSdkXEfYD38yAdFLwHHefjoX8Gl16C+d6FhyiCtXi1leb2OlsOIb5NFbNoxXK1/6rcix7L5JtX0LSOOUXBHIbwLRERGWw4uIgI6jWpBbxC4VASoqiYYC2ctgpYaIjZTu3es5mLHcv5hxD+OhnfQ+H7XOWKMaLiIeoEQsfnfAYuuq+h7F05uIf5hiqFfR6RkEMJbiiRVTy58boApZEZ+0exZ04ap65yUxHAW1ecWXd+9cW279zX11SKTdkbGNsZ6+pEPdL4+BDxzAWYvgavD8IGFhimDtHq1leWOxtRxhvafppJ/uRrglp677IqTeFXKcN4WIAXOjYOME+OHoPOt49kXUcTtSITXexu02rT4p7sIUVVpzv0rQvMN0Nugc6gMo5Q4ADexID3dSZTmQf4OGs8Tw+lWn7RNGBsCIM5/E18cRPUAUY6j5U+BS8BcWueofadwDe+P4lzRZ/3VV1xMfb+QJPs9t9jmRfyzhOYbXSK8GG+g2kR6d4xSJPHf1opct0JNfbXIpJ2RsY2x3uM1l0MnASsw8yHcfMPS3YhF2mMPd0fRg7R6dSrLG1eMLOrj74GfZWjfCEV96dSviFDUnweU0PwZIkVqm1KQ3R2bjA5hmTtCd5q3VxFex/ljuOITiEBo/pTQPIUQzdyEZqqb3yaGBkXts4uuUcTZ0JDUFlYJn6rfXe05VOfaKVsR0PewSNvbxWXIfpY5nD8GaN/JZOulil4sMu9U6lciPDRCnAM/1rUO58ZxbpStGLlu1/nembQzMrYpNmK85nLoJODZCzB7MXUNpZbh+avgPIw+1G2YMkirV7sPXZi98BzNqWfZ/UyDXY/VcW6wyMe8uF37RuxG0vCQRso8mLAMBV+c6CK8Sk1sazMFemj+BOf/Bl/7DKF8B6G0mqfOmjBNaymALxAJi5LiIMKnzpSthitouAqt0aMFUAcZQWQMcRNY9L0wgl1vVXS3ur7/93DFIQJ30BgQCR2Zjv1LZkjuJbbrJLRM2hkZ2xTr7Uc+SF28iohv/BDmJu138UbAxZiR8twkjBztNkwZxBhmYR+6Q/zKUqrLGq/oLCJ1XE9E1UlGMVwlhmtYy7dD4ySh+SYxTmK3zNnuxm0N4DwxfIjqC31v/KpzaLwLUu9SattrJnyqIuKy+TaqN0AiLcdTamlnNJ9c20Lf2mtLFEbq+05CufVTRfcXcAng/SNI/VE0fohqA+dGWxH+VsZ2m4SWSTsjY5tivfzIV1oX3/dpI/epH7fJOkao7bTXY8PEY+OfaJP/Soxh1qMPvfdGvHxE1UjEKsTwITCDxmi7EB1CvCBMo+wAvYS1kM1gArEIUQlNwdd/Fe/bi+/0Eg/hHIJrDzhprbVNvlabb9IsT4NW88J30Ooz14g4j6rrW3tVnSeUH6Sae8eIUDfB4ur7lWAJAZcbpaj/PPDzbLXIdbu5ni2FTNoZGdsU6+VHvtK6uAgc+CUzSImzgDMh2vw1I2s/Ant/vr+96moJeT1uuhWRq8ZWzbp9LCOjGD8AvWMXozWNax7iDUR2oOwFuQx6je553h7iNcr5P8OP/metc8byFGXDhpsIe1G9DmHS1uMOLBA+WabgE8TwATFcsEEjIlY719n0RSYoak/1jWBN6HYLEaXLghVwxVHWWlseXMC1NSLX7ep6thQyaWdkbGOs1Y98tXVx8bDn2TbZjz0Eo0eMtPd+0kxT1gPredNd+lgOcQ+j8Q1M+JXy0gq4EVRv4opnqPnjNOdvQ7iC9UzXQOqWrpBRNLxNCA2ci8ToKRvfQ+MkqiWKB7wdOV5H3RF87USLfDs3Jr54FNUSEZ9aqUbQOIarPUNt+O/iXL/+4ojG8zi/t6NfOtXN4xTe/eK6RJnbScC1XV3PlsKmk3ZZlvzO7/wOn/3sZ/mH//AfbvbpMzLuK6zVj3wtdfF+G4bxF9Z3gMl63nSXO5YrnsD53cQwnaJtD86EX6pNnD9mJiHuIZp338VS42ppbBkFRtBwndD4ho0DCdfR8j1wO21T0FJ770P8boqhX8G5UVR1QQuVuKM4/zhoHZVxkNqStqCGVG+uHNI6Z3u7XbjiiZX/Afpguwi4trPr2VJYE2m/8sorfOtb3+Lq1aucPn269fyZM2f43d/9Xaanpzlx4gRf+cpX2LFjBwB/8Ad/wEsvvcSdO3fWtvKMjIwWVpt2XktdvN+GAcxTfD1MXlQjoTwLBFTbzmOruel2HitGR2sWd9exhnDFx8AdRONlVO8gamYtzh/AFZ8AwLndiN+PaiJtTOilcRprA5OU0p4C5s3dTcYARURRbuLkiNWG6VCti5hqPU6h+mOcP4yvfRpffwKRoQG+a0e92U0gMg4IiEek3uoFXy9sfQHX9nU9Wwpr2ma8/PLLfP3rX1/w/CuvvMIXv/hFXnvtNU6cOMFXv/pVAP7qr/6K8fFxHn744bWcNiMjY51Q1cU1dj+v0Z4fhHjFgx+G638N5/4lnP3n9jj5fcsarxQaoDmtlHM/IpSniOVpYjhDDFet75rOm+4Ax1MlNH9k/cWNHxKbf0lo/pBQ2jFjnMHU21avFQTvj+D9E7jiScQ9TlH/bCslbWM9nzGDFnHtfnCdQfyBRGYBKMGNgs6g8W4i5EkIl1MqXLqHncSrltYmprT4JKF5Bg3vDrQ5sRT/I4RwhVieSdfsLDFcRdwj2zKqXBvqrY1RL7a669lSWNNf8VOf+hT79+/vem5ycpILFy7w4osvAvCFL3yB1157DYDvfve7vPPOO/zhH/4h3/rWt/joo4/WcvqMjIx1wL5PW1pb6hCa9rhcmlsDlDP2CG0xmza6xWzXF065XPyYakR/7l/CR//6FFM/Pkd52yUFd0itWFeBld10Tb19jpYPuUTQOdA71salt+lSb9eetB5sCYgboagvFH0VQy/j658AitRa5cBN4HyVgvZY73YyG9HZtDNyIDvQWBLLU7RU62iPDaiak5iElDbv2VUteg2j1c+VdrvYKjZOWx2VmHCp61JtwrRn56iqiHuEyoJ1u2Hda9qXL1/m0KFDrd+PHDnCpUs2quef/JN/AsDrr7/OqVOnOHr06HqfPiMjY4VYri7e2b+NW9getutJuP322k1eWip2HxmeeA8NjvmrexD/AX5kDjSiYYoylhTDnx8ocmxFsu0Gq46HWWxyVvu90MAVzy5br3XOUR/5TWL8NWAa1RFi89+0WqEEQdw4MUzaRZNxy5grOD+B897W5Z/Ghp3MUvmmq04nkldieRbcbrQ+nzYqi31PTZmEf4Now0RvshdxB3HOmXVqfIZBfcq3KlYqTOwVzcEwpJJIDG9vSzX5upN2766mHz796U/z6U+vo1olIyNjzeiti/fr3xagnAPX0R524wcwexl29Kl6DWry0qliF98x6UuSO+awYiQaQD8kNF/HOT/AzbaRxF02lhPRFHFbhGWuXbsIzR+h8aMVK9TN+3u3RX7uKKE8h6vat9wBJJaozFgAnVzCrGeapLwuW4YqFrXfgTiT1juGiBLjbWJ5BtdnUlYFyya8ZdeoJXqbQtSjOkEM76P6f2KmLCsjqq3U47xSYWKvaC40TxPLd9L33p5q8nUn7UOHDnH58uXW7xcvXuyKvDMyMrYHFkzzmoOpn8DwhNmUVnA1aN5OXU89HDCoyUuXp3lIk76KBn74JmF+ByZuj8BQSi9fpWz8jILlbrbtsZ0iBTCWlN6KeZAfQuMNQvMczrkVK9Q7I78YZ0FvERVEdpnZyNCLhPIsQoPeMZ5Vit8VJymAZrwB4TIWmY9aC1ny77ZpYP0nZbXr4gVKQbUhsVr7FBoU1dvgD6/o+221Hue1qMGr+eYa39/2avJ1X+HExARHjx7l29/+NgBf+9rXeOmll9b7NBkZGRuIfv3bGsxka26yW2AmDoqd1qPddYwViNkqFbt90CZ9iZSIKxGnWCq7CcyAXoD4PrH8Ec35vyRGK6z3q3NWdU37eTxlAgXU0tdVZrDX27y6kS9X86zMUzTOInjE7UXZifjHKIZepqg/T1F7jIVzt9tTy6posDbyfwP/aFJ+jyDiEdmHTSqbaQ0UWbimSiVt36nzj6OxicbrOL+3yz51kO/XUrVrs4vsrRbfjUFqzIO8Z2lUWZN+xx5EmLjWz/fH2r/XyrCmSPtLX/oS3/nOdwD4/Oc/z+c+9zleffVVvvzlL/N7v/d7vPrqqxw/fpyvfOUr67LYjIyMzUG//m3xIIXZlKrNhGhh5+Ow+wm49RaEOSPxlZi89Lq72aQvpdh9ntqeOWAeq/mCJenTPOzyHcrGD/F+eNGIsKprhrKGUIJO21hOdwTnH6JsvtPl011hubagGEPLPCVqaeI2ABkixPcRqeFrzy8x4vKZLmc250YoasfR2DAxWbwB3CSW5r7WiLdwbg/iRnoi3nar14IebSkQt6v1fCeq76daZ+F87cGi2kGi8fWL2Nc6A3t9Z2jfq0zEmkj71Vdf7fv8U089xTe+8Y21HDoj477DIAM5tgo6+7c1JpL2MDQBc9e61x8DFHW4/Y7Vu/2widNWOtO726xFaE49D2Wk2PHvQS/SJm2HqbNNDR7m/ojoH8IVh/qmf3vrmqoFIiWqNWL5JsTzxDSDu/LptiEbS9/IY/ljYriEzZCexcZ0VrJtSSNCHb72XM/5a2h4i9D4kwU3e+ePE+Jp0ClUr9vNX9OAkngVlYjogZ7v120tKu4A6iZQLfH+KdAP+hIVDBOap1PqvXsdy/U422bDddWIF0u9r5dBzlpnYK/3DO175baWbUwzMjYYKx3IsRUgHnY9BRe/CfPXqWZPUN8L+xO5Vt+loC1OK4YAhZs/sYh5JTO9+6nYQ3A05z2EylpUaA3PwGFWotOoTqLRtSLKfnXKygxE0ijNWL5h86zd7lZ/dOXTjZto3cj7CbEsEr2ASA0Tx1WTv4xkVXbgpOhaQ3X+WL6x6M3eFSdRjZTz7yKiqFrLmzhTuWucQtOmovPY/aP5J3HFSWLp+xIVIosSrh13YVSqqqjeopz/t6BzKRrfnVL6C81vqjWtVx15rRaq62XBei/d1jJpZ2RsMFY6kGO12JBIvspEJ5GZONj1NOxPxOo8vPeHRthdH1vDTO9Kxa4a0eZ5vD9MiLdAP8Ki7TRZiyHsFqZphGYiNKq07OLp7a6brrRTyhaB38IXv4j4Z7vsRXsjUZhPbV1X03qqG3hAZHcSgnWvYZCbva89SSzfTtdd0fJMxzttLKcpzdvHXspatB9ROf/IsuvoF5XGcBUEHKUNIaPR2uiIb6fgO2vE6+lKtlYL1fWzYL13bmuZtDMyNhCrHcixonMMEMmvlNA1WO/12DFTilfpcRG4/TMj7WLEDFYG8S5f3YaiYYrseAMbg9lOPRsq8i5QncFukkZosFydsn3TFWillC1q1kScby4REadI1B3AqRLDzXSRHMhOxB1cZA2D3OzriBtBYwPSoBFpOaQUkD7b7/v1sxbtP2O8QQxvt47Vbx0Le5yHEFcgbl96Z2UgE02lrp3Rdntt61lHXup7bubn17s+vhJk0s7I2ECsZSDHoFgqkt/36dWl5jvXLdItOutctx+yxzBr7+k8ZjFm7WCT34ebb1pbWG2XTQcbrDRQtxaqcA2LqvcAt4G7GHkPA7vtrTptB1SHSomqo1iyTrnwpmsRegGuhmqxaCQayrOIfxRxj6DlGZw3go7xGqiYeYpzi9RKF7/Zw1BKxSuqEMozSTg3l2Z7j+KKfQgycB12YWq/iviXJ51eso+xJMz/MdXGqVKrW2mhMwPQvbb1rCNvFax3fXwlyKSdkbGBWMtAjkEQG3Dzp7SDz4QqktfYri+vJDU/yLpVzW98+gJMv2+fGd4PIw8Baab39b9eWBefPmefnfjsMt8tBiNsvQ6k+dC6k1ZNWw4noo52wDhHKN8GEVyaVa2qfZW8i910o0a8P0q/iFhV0XiNqNdNdCZjIHUURdxeXLWzkV0gtb610n7nVdXkD15Qzn8TjTeJsUTcHtCb2IZlDtwQkiZ+LVeHXU7ZvDLSsdp3KM8SwjkEh7hxM49pqdVv2XXos7btNMpzJbhX3yuTdkbGBqK3lamCJlJbbWq8SolP/QSufx/ccJswW8njO3Dr1OpS84Ose/L79vrIQSPk+Wsw8xFIDQ6/ZHO1T70Kc1fb9XCi/X7pNdj/C/3PXxFOc/67EC/RSourB2x0JQwZceps+pQDCsQdxzmr8cbyTCta7Ieum26cMQMSIJTvEsMFNN4E2dsm13iNGCdNLS51hNL6rYvHza+8lRJdulbae7NXvQVCSjurDRXRgMh+pHgCS9k7EMHX/1OcG102khtE2Two6XQey8k+YriG6lWcKs4fRGU/vv4pfM0mpfWubbuM8lwp7tX3yqSdkbHB6Dd3eiU9zJ2oasNTP7YIGsANAcGsRKHtVuaHUh25TzQ/SGp+qXX31urHHoLRI/a8G7ENQTkDsx8tTIOL2PPlLNR2LDxvLE+ZrafeSHl5TUq4EUR2oHoDtAAZMlW1CqrXU3/abSqP7uWUvJ03XZsCdq7DZKUkaonGq/jiYIrap1L9e7wldrPpXO8DnW5lS9dKO8+rOkc5/29xqZ1N03QwU61PgU6A3kjjOhuAg+LxJXuBB1U2D0I6XVPIUuYC5iBOE+MNot5GZJxIBL24ZJ/y1h/luTps9vfKpJ2RscFYbiDHIOgSm92F26ehPg6jR1Pv9GU7z9wkjBwFFPZ83N43aGq+Vyy21LrLfuYraSBXmLX3rwZtkoiJwEZSKlpA51HZAVoDFEmjMpEIMVq6mpuoHuhIPQ+m5NX40QJXNOcPEsMkUTX1JUfE719gVLJatbDd7B0wj5LGeaplDCCaCC1ehjT9S6QOGgfoBV5c7BZbGYVdLYJemnTax9J4zXrH3SiqIxBvQ5hFarutNLANfby3IzJpZ2RsEnoHcqwEnWIzESPGuXl7bTQNy5u/lgRhDvY8n8RebvnU/HLq837rHqhWX7eof+bD7mhbFUaPLXYtKqvJSpk8mnTTs5jQSZDiOBqbmCCtxMRjOxBGLE8/gIK8U6DVt34NaLhiqWrGEDeMygTi9oNq2lB4BjFhWQqqNTTeTOcpW+s24ZlAuIk4i3KdG29tRjr7oBdGyQtFZlU9XvUW5dyf9XFVWwzJuz02LNPQ9d4S8UVXq9128/HejsiknZGxxdGbipYCXB0IRtSjR9rpaTyc+AfpdQZLza+mj3zQWv2hX4GLf2JCtNiwdQ3vs+f7Zxs6LTlNmSxuDHQUFQH3OL72KBo/wlq+jDw1XjMVs7RbovqJqvoJtMQ9jNXJqxQ1xPI0Gq4Cmlqu9qIhEOLbRlyVqo49FEO/tGqC0vAWGktUQzpGRONd0HnQOjCLxh2Ie6hjOphN/qLxN6CX+gjN+ojd4jViuIYrJpAVRMXVscrwZvrO6XtqBKljCorujdJG9yk/6MiknZGxxdHbNiZiorPZyxZMVe3BAOPPtwm7eu9ys7IX6yO/+SbsfhaK0f4EO8iGYP9nbA233oTGbajvsmMuVs/vIpwOH23VBrgRnNwihrOgt1Atcf6gVZeTWlykAAkwgKiqEmjF8h1TgSeluYYribCrlHskllcAZ8I3v7Ot9lsiSI2xxLIBO9IIz25UpQDnD6AxTeSKt0DnQMYQ/3HQd9AYOyLsq+l9dyjD+zi3N5F5Nwl3iswsJX7LCLsjtT9oVOyKk3iNlPE8RsYF4veiepOqT76z33uj+5QfdGTSzsjY4uiXih5JYrP5KSvn+vrS4rbFUvP9+shVTQU+dwXKO1Db3b+3e5Ba/Wrq+Z2Eg9uLuCNAIIYS5zyKouxC43U0Xge3G5ERakOfQ/wziDRZTlTVvUZBUVzxRCK5SSzeHk3tZJdpDywZAY7g/AEQbz3T8TyqJ1vnK8sGofF1W38VBRfPUAy93FM3T2l58Yg/QNT9qJ4G2YGgOAeq+0AnaY/YvN66sDZru+1G1kvCbbHbbUuJS7+Nw/JRsYhQ1D8OCKF8C5ECQYhBUvS+r8OFbnv3X28HZNLOyNji6JeKFmDkCBz+OzD+wmBk2M+VrN+GYOajJGwrkv/3NNz4ob3WL10+SK1+JfX8fgM+QuNPcV6J8WprgpVQoLqPov4bqc2rIorFLsTiAi2Yw9eewBWPofEuEQ96F/QWloYHI/ImGj9CXYGr7E8T8cVYp5z/JmH+3wM3gQKb3b2P0PgxAPWR3+w4Z3ftWSQiEmkPRPGtyDiGSeAaIjVEdqFRW9eq042sl4TtmuxC3Ahrde+qhq+0WsT8kdTTrihz903/9VZHJu2MjG2ApVLRyzmLLSk069kQaLQ6OWobg6k32rXo+Unrve6T6d0QtFXNaY50vE4Mk8nSU0AiGi+j4WeI//kBjjiICxg4vxPVcbS8ikXXAi371JqtJ97oEF/ZZ42wfwjcwUhXQe/aJ91+YvkWMf5aK1W+sPZs4jvV0CU6E3cA80efQ2QIICnAk8lMh/huMWvT9XDvWqxFrN9AlYyNQybtjIxtgLW0jS0nNOvcEDRvGnG7GsSSRGJAgOnzcO0v4OCLG/AFl0QdG6P5Pugs2nJHGwHZSQwfovrCsoQxKHk5f5wQZkCSgUuLsEeBAjSgcR5NvuDeHyXGkli+lfi90x9dkgmMRaMwTct6lZ5SgM6aOj2WLdFZe32PWz94FZVX9qEtj1m/JAn3GskgNVzxxKqi4t4Wsfu1/3qrIpN2RsY2wkrbxgYdWFJtCMpZeP9/hxt/szCCd3W48y4c+KWV95mvFqb2ftOGW+g1Kucz69WeBtmJ1ZsHUysP4gLmipMUGmnG81ZPELBa9qgZvjADeoPYfAPcLouMw1k0XAd2sFCdFrHJX8PAWNcrC0sBNmu73/pi6dobDpkAF9BwE/E7EVfvmEC2ECLSGvupehbVJhrPE0tZtu0rR9JbC5m0MzLuY1RCM1e0u5Sq+3OvK5p4cyjb+Thc+0vwHXcHVRieaBunrHXIyaBou6NVozibGIEIyG4sCh5i0Lrsci5gFUFZG5RQzn8b1ZtWL47TmI2qx5TkdyEGkF2g+21tMgc6hqXIq+M6VAVfe6avitzW1THr2/VPQYt/DImRWL5OjFfNnc1P4Gs/j6+9gHNL76Rieao9P3uAtq/l/Msz7g0yaWdk3MdwdWjcgNlLHX3SyaN8sYElE38Lrvx7mLsEsWmp8uEJM3FxQ2sfcjIoOt3RRALqdkOcTq9KEnmVOH9sRRFgv8ixf//2I/j6540kw5UkSjNhF8yBqKXrw4fgJhB/AA1XgL3pTNNYlL0bX3+BYujlJdfQiYrEVbVrprcZsTTx/vG2ej2cR90QuMX7rQe1Nu3EIP7lGZuPTNoZGfcxbvwA4rxF2VVtevayRc5HX+6f5nYFHPlVS5Gj9p5KpLZ7DUNOBkGnwh3X6442Zi6fOmtvRHD+MK74xGDHViU0f0oM71qPd4crWCdBWWQ8SyxPU9Seohj9bUKYojnzz1Jrk1pdGEk17GmUEuefIGil/t4DegApHsPXf5OiaK9hJdFr57pUhRivWn1cXUu9Pli/9SBzvIc7nls5yWdsDjJpZ2Tch9BgAztuvmmWoThThVeRs9RMCb4YFqjVh1c/5GSg9fZVuNfZ9fFhhA53NBkDGUURxD1BUX962bSwHV9pzv0rQvOUzaiWghjHiWEer9FEXiJdLWVQUOoUNf8Mzo10uIGRftauc1g/8/O42n+KyCyqoxDfJobXaJZG0KqgcR7n3LLRa0Wc1bpiuA7xCognhllEJtqOZ8v2Wy+vnO/Gykg+Y/OQSTsj4z5CJ/k1b8Gtt2D4oKW2qylc4q0UG5uLt2+tx5CTlaDLW92Ziv36Dxxu5Dg7Hu91R2vi/GEj7CVnSrdT0KF5itB8AxHFIuFoxiQOYnCgJao32ors6j3hErH8caoZHyCGK0aUOtJWlssORIqWetsn4/XQfKMrvazaIDRP49xeYBBnskar1U3DZHoutZLFu2i8jPjD6RhL91uvvO1rpSS/Psiit+WRSTsj4z5CV3vXsBHtXBrZOfZQO1gshgerTa9lyMmgqBTuKrb2zvr77bdP8vH/F4i8BzKOFBNWa15CeNWbgoY6MVxCCLTFYYksdQoJO20gSJqm1QmRGjF8iK+9gK99Bo3fBW6CDGN92IK4w4jUcR0q9P7p5WC1+Q4zlPaa+0WvdWCotS4BVEZAp0EcqrdQDoEyUL/1oPOz7XuvT2/3oMiit8GRSTsj4z7BgsEiHR7l1WCRqja9Z4Nr0ytBpXC/dQqmPwQnaZ1NuPOu8P7/epLj/6DdqiR6EQ1DqCxfB7YId968xGUeYbT1PtVpiDOoNFCdSBO1draIu5qsVbWUiX8aV5smNH8CXMX8wQ8sot7ul1622nzvJDLoH70acT5EaP4Yqf5YMpr6wB2qTUCSk9vy/daDzM/uxEpIfq3IorfBkUk7I+M+QT8f8cqjfPYKlHNQ32OEvfeTVvPutDS9V/BD9m/2kum6wpwZu1SeJjOXTlHOv4Mvlm9V6h/hesTVjLyroSCxsigtQYdApzCinUUYBinMlcxNADUas/8GDW+nXuwGuAmcfxLn3CLq7YXpZUEQN04MU3RarS5tivIJnP8bG92pJSIFUjwMsh9xnmLoNxdtI1sMS5mh9KanV0Lyq0UWva0MmbQzMu4x+nmCrwb9fMQFm2k99gh87L+wlPmNH8B7/2tb8LXrSdjzcUuZ3wsCFw+jD5vKPZaWGq/u324oMnTgPWY/EHac6PjMMnVg6Zo6JYiMo7GB8+NmAaq3sZ7vHeB2IgIqw6CKFE8YOYoQYySGixCugnP2GQHiVVQcKk8AgVCe7VrLYullZAJfO2rnGyB6dc5T1D9L2Xgb8yX3SUluLmkrJezFsHR6eqMdz7LobSXIpJ2RcY+wpCf4Ksp4S864ftaMUya/337d1WD6HEy+DkN7zVRlLedfLVTBj9hjOWPtz3h7rtjRoBibo3HLo9q9rsXqwP0EVOImcOIRdxDindRPvQvcWItURcZQzOJTaaLxNqoK4W1AIA5BR11cw0cEVYSAisM1j+FrP9c6Xr/0ctEiaKUahiLSkVboA1ecpOg4Tu/Y0fUQb93b9PS9Eb1tV2TSzsi4R1jOE3yl0AC7nzOSvn164WCR3pr37AWrd4uYAUucW9v5VwMNcPW7Vs8e+5itu5OY43wdXx9Gy6Yp3zvuWCsZjgFQ1D+LK54lxls0+qRj7cSj+PqLaHiPGM7jpEEgpiL7LG3SNmc2oQlSIDhCeQ6Reovk+k0r6yToWL7TJZZz/pilw50fKE3da7yyWvHWStLTG6Hu3mzR23ZHJu2MjHuAQT3BBzpWn4h915Ow5xNQdKTcy46atyrMTbYJMjZtTa628vOvBq01v2mRvjioH4D6FWjesbXEEnY86nBDx3G104gf7Ia+lIBKRHBuN84fbLdvpeMRp0EKQuPfoeE8IruJshfwtMJ8lWTsUlKNz0QV8eM4cX1JTnWeWJ5B4/stcq36tRHQeAXVO4TmTxD3A5w/jBH7PBWZi38e5wKdZLl+0fHy6WnVoQ1Vd2+m6G27I5N2RsY9QD/RWIVeT/Dl0C9iv/kT+70zYvZDdswwaxwUG8kljWS4UpH7Cs+/FBar119/3WZ0V25tUkDjKgwfgaEmramTe56DxuWT7DwB4vrf0Hujv+VU0iIOV/wCMXwH1dvWCqZzgOL8kVQzbhDj+8CHGIHOYENKPNW4UBg1K1E33u4j70NysfyAGG+3hG2K9WsjAvGu1delABlBw/uEcAVx+1staaHxPeDriD+E88fwxQnEP7OO4q3l09MbnT5fqbL9QUYm7YyMe4B+orEKi3mC98OgEbsqXP9rmL4A0+/b8cMsyA4gDQNp9XCv4PyLrqs3+h+BHY+ZrznApdfa/djz161+Xdtpaxk5ZM9pNOHc7meF8eefA7rTzKrRZlQvEv31E1BVgiuN7+OcENWbc5nUcMWeRL5pjKbOAgKyzx41eYnLYZB9uOI4kvy/W9c9kVxovkEo30otZ7cRSWYugPi9RtSxEsORIvh5O4Y/jMYLKMMgs2lDMYeWBVGtF87p+om3lktPA5um7s5jPpdHJu2MjHuAJUVjK+ihbk3x8m23s+p4nRFzFY2PHLTIdv6abRjKO0amo0dXd/7FUJ0PB/NX4NYkXP0LG0Qyesg2Ds7bv2LEUuJgG4aRwzByBHaf7B4DqtpdB9Z40zzE/cGBoj/VSGj+iFCew4lD/GEEJcY50ClcK1pWuxBELMpWxO1H417E76U28o8gnjaSo5vknH+E0HyD5vy/TmTrQe8ibmeK4Kcg7gO9ixErtA1fmoDaqXXaprPEmfQ+Sz9ouJVWVSAyhKXpu7Ea8dbS6en5rO7eQsiknZFxj7DA33ts5f7erm5R6dyldpp5KE3kqiLm3mh87CEzWoklzF2HHcdsjvZqzt8Pneeb6RC7+cLWOfMRhBlwO+39xS57LGct2nYjpnbvVbF3DfVQl3qXAyrSSk/3F09ZdB3Ks4TyFIIjppS2IDgZJsZpVBWNV4nhA9ArmOjMvpCGccQ/hLi9OBfB9Se5GCPl/H+AeAkjfAc00CiI24FqicaLwCxGuEJr1GfLyzx2XM35jvc5IKDxGkjA13+RWL7XNzpeaeS7VHpaNau7txIyaWdk3COsh7/3jR/YvVRDIuWYbEsjHP0NO145s7B+Ls7ON7zf+rfFd59/Lb3jnTO8O8VuYMYpLfFbtNZnAWq7jKyP/edw+D9eeM6FCueQdinO/MjdRCvq7Y3+KrKHgGi0THfy8hZ3wI4pY8RwCY0fYaYrVQtWRaYhidhGMUJbSHIA5cxXMaOWNAEMtVqB3gbGLPqOMR27hhFyI52nZo+qIGPpPaH9XgFwRqY6jfjn8VJfF/FWty6gO2rO6u6thUzaGRn3GKv1964i2tFjRoRzk23PbjfUnuK1XP28GOlMQbdV3c07Fvnu7hP1LoXqfGG6W+wGVqNWNavS4QOp1SxNHht7BA79R4ttEnpruN5SxFqiBDptQTujv06yV/WpZyyCSBfZiztGDJdTXblShleRcgDmiOV1XK17NFpnDTbGGWK8ijiHxsojXNJammi0VDuuDnEXMNNxjmqTUEOKI7b2cBEjcrVHBWQknXcHzgXEr0281W4bewe0RDrGlXYSdFZ3bx1k0s7I2KboVKCPPgQjR9t17Vi2p3itpH4++X24+CeWcq82AHffM76YGLB3uzrfjR/a51tZ5iR4A5i7Zn3ZY8eoRmOz9+cXnzrWqXC2NPY10Luo3gEpzF7UH4QF0V8v2e9C4w3EOYycA6oeXxwDZohxl0XwSHq9krI3gNvEcIZy/tKi7U6t3yR5nOts+nwdX/9PCOE9nBRE3QX6Jt2EvQfxx/HFYVzt7xDLn1A2/h3EKVqk7HYmtfoRqo3JasVb7XGlbyBimx7RcWJsUNCtC8jq7q2DTNoZGdsUvRG0SNt8pFcBPkj9XANc/rcwd8WO5TwQ7PfLr8H+FfRuV8edn4Tp80bew6nWrhF2Pmok3usEtxg6U7QarxHjpEWdGgFHjNdwUiQDlc7ozyZlxXCRGG/YxVKrMVvUOowrHkH8Y1B+ADJEO8qupTavpv3sdpv4axHBm8gw4g6gVf+3jJkyXSO4g4gbQ8oLxDCfRGqViEyAIcQfxxWHUJ3FuYAf+iQi9ZYKnWRjalO9TgxMmosZooTmT22+eMe4Ug2TaXRrva8qPKu77z0yaWdkbFOsJIJeqn6uwerezbsw8+HCNLiICcrKWbNCHWht6Xx7PwnX/gLuvGstZm6og6Djymr5rjiJaqScfzcRTYHUHgaZsF5rGUpE09mC5awnvTyPibqM5NEhpHga54+j8TwxnAa9merPQ1gfNrQV5CM4v7dVN+8neLP+708T4n8A7gA24AO3D+cPo+FdxO1Gw3ksNQ6W/q4BI9ZjDtavrQUw1/o+7bR09wjQpbCUnzgoMbyLUNI5rrRVNpBxsip8ayKTdkbGNsZKFeid9XNVS4dfeg1mPzLynvkQ6nutlr0e/uOugIMvWutWOQsoFKPp2Cus5VuK9kli+XbKQ3f2SBdoGqHZSTSqEY2X+3wZQcNZgqi1f+HB7UNcE40e9BKm8Hb2z+1EZKLrCG0jlXqH69l5Iz51iOzG+Ydx/gQxvAdAjApU7V6pFi+7k7J8ihD24XyN0PjTLqL19V9HpMliaWlzXptL12kYEbekIYorHkerdoMutTpACVIjq8K3JjJpZ2RsY6xFgX79dbj4TZi7mjgtlVfnTVhNPbViqdqIz9U6pFXGLuszGKWOuJGB249U51C9lkhxDCPKdIHiJYSHAYcmhbgrDhJDgfKx1FoGGt+zoSB6DZEDHUcfJjSNqDtdz5w7lM5dGuHWniCGt9F4HeI1O39rLnYD24EYcYorUG2YOK6DaJ0qvvZEn2trQrLQ/D4ar5qszR3AFb+AxvcXNUQR/zTOjRDjODFO9qjCPc4/nmvWWxSZtDMy7gOsVIGuAW6+aYKz6n4tArW9pugOMxBHUy16Hxx+afWGK+s5GKWztm1BdiJhZfH2IwXVaWDWagfiUgo8mnVKvIrGKSrVuMa7uNoncEkgEDFio0NtrsmLPJZnrIVMbyEEYrhmsbk/gEgt+Y0/C9TNWEUcaNodiQDDIDtw/rFUTwdp9Wsnaxe9Rjn/LqH8Ga5H3R3LU5SN70K0P6QAMVwhxv/QMpBZcDl0FhHbUMQwj3PY2rREKfC1k5sw2StjtdhU0j5z5gz/2//2v9FsNpmYmOCf/JN/spmnz8jISAjz0LzdanVuob4LRMHvgJ1P28jOyuhkJaj6vF2xfoNRWp/1z0L5HrF8y1LCUsf5J8E9vfC9Mmwp4Jjar0Sg8hKnBvGGRcAp1aBamio9TEJxKK11wmRaet12HG4Hzj9MDOctFR4uofFq6q92xDAHsh/nXAdBHrOBIOJRSe1gCLgRRAKK4v0xYjgLHc5jGq+iYRLViLgSjQ1CrFLczxLKc1aL76rjC6p3iSqIP9Tl2mavW0aic+RnjONWL/eP4mvPA4qqDSzJEffWwppI+5VXXuFb3/oWV69e5fTp063nz5w5w+/+7u8yPT3NiRMn+MpXvsKOHTt44okn+G//2/8WgH/8j//x2laekZGxKmiwf8WOhSVNEajvs/arh/8v3T3cAx27x3Pc1cyydOxh6M2Er3YwiYY3UW0AO42w4g1iuESjPENt+Nfxted6xGjjqNzExGVJiCZjILuJ8UbrvarTyTa0ROO7xCCIP2g1YX8A4TDF0K8m+9AGMZxG43Vi7DVTuYPGK+AOdxDkJxD3A2tVY6iDZIeBOr54Blc8m8xdLPUfNRLLyUTw8yYckxoi44SyBu44qrdQLReqvClR2ZVea7vq9BqiLDSIkXWZ5rURIzwzDGu6mi+//DJf//rXFzz/yiuv8MUvfpHXXnuNEydO8NWvfrX12re//W3+6//6v+b5559fy6kzMjJWiEp4du5fwrn/JVmfajLo6njP0D7Yc9KU4iuNgqtUuDaSA1u0dPvshYXvHXQwSSWyskczS0GvofHD5F42a//iOZozf0xovtHx6Qbi9iD+YyD7QPaCO4DzDyPuGMguVB0abxthyyiwG7QkxmsWQVOR3aM4N5JIyFrJNE6l9q4R2lltj+otYoyIeziJ1E4l0guWfnBHEf8c4h6nGPo7FPXncc5byjpGYriaFO0fAZOAIqktK4ZrhOZPKef/DbH8AHQKjdOpRp4gBc4/gi+eAamhSVzma08uUJ7bZLRu8Rra7Kqpx/LUAH/9do29nP8mzbk/ppz/JqH5hpUTMtYFayLtT33qU+zfv7/rucnJSS5cuMCLL74IwBe+8AVee+211usvvvgi/+yf/TP++q//mkajj0VTRkbGhqCXUIcPmk+5G0q1U8xd7civr85/vN/EMXF2jtlr3Zyi0cRoS20K+hPAj4hhxmrQepvuXmcFrlI2vpciPVCtGbHrTSzKLhDZg/gDOL8jRc2a+qarQ42C7ARqaLye6rzdZGe19YfQFBWLjIEbTT4p6ZjiieE8zZk/oDn3J6gGkP22OQin0fBTRO6kjdMsqtEiWjdkPeWxpDJmgYhq1SY2C/EGoDi3DxgCnW69bgS5h6L2KEX9eYqhl6kN/12KoZcXZCG6r3evVWz1XauWs16V+cLPh+YPKRtvr5r0M5bHute0L1++zKFDh1q/HzlyhEuXLgHwve99jz//8z+n2WzyzDPPUK/nloKMjM1AX0LFhoXIcTj2hbaYbbWCs3IGGjehGO4+z2jl1OZSOnzAwSR9W5bKc6hOpfR4r4LcVNgaLhHjDN7vQMNbaCxRDa1IVcMkUQOu+BgmZCvbuXudBhRXPJxmX89TDP0Kzo2mSL9d53XFJ3D+b9LgkhKRnVAcQ9xeNN6yL01A9SYiau/DgwwjbthsVbW0zUhjJ654GOePp/M/geoM2pwB5tP6ZpNZyyzIEEIEN2HblfAh6Ayqozh/EF/7TGuTMbghirnHmVDOBH7t0sFsailz9Ka82wNZzhHLNwBHVJsdbiNS13+E54OMdSftpdIgn/3sZ/nsZz+73qfMyMhYBosRKlj/tKuvsaUr+ZXf/lk7uh492tZ+7Xwcjv89iGHptrSWgK3eP+pz4gg40N5osRqsMQd6ndD4M9Q/bsdwE2i4merJ5vet4QYh3sH5PZBeb09dcSD7E+HsBIaSP/d7xDiLSIHzj+Frz1PUP0vZeBtJbmUiQmxFpJPEcB0NV+wLax2YB7ff1qq3iOE2MI/GqwS9Q2i+D3oLcT5F8aklTK0lDYkgdZDdrfN5fwB1E6jOUQz/Os7tXpQcl6o1q9Zs3GnahCAFIuOWHeAW5fy/BeYX1LlbKXUJVAKJ9uzwatxpHuG5Xlh30j506BCXL19u/X7x4sWuyDsjI2PzsByhwgpqy4tM/ups6Ro5aKM459ItYOwhWg5trr54Pa5XwFbb1WDfL84xctQvELA5txt1+4mNq5i4LBEa5pIGhaVqy7cSATmIt9N7K1/x2VQLDojfD/4xNFw1okIRiagKvjiOhrdSyncyRfmlpXzD+xRDL1PUkwI7TKM4xB1Hw2UjQLC16TStVH5soLI7PVe0BWwEiBeB22jcldY7j2UUdph4jiKl8u+ieg3kAIKlsMXtWpSwF3NHE/9My7RFw1tJPd8eGRfjJOgNxI/jqkEqXSYtz7YHsuDT9Y9Us8NVq2g7j/BcL6w7aU9MTHD06FG+/e1v8+KLL/K1r32Nl156ab1Pk5GRMQAGJdSla8vdhNrlFR670+4jD9nj3KR5lo89AuPPLvQ47yX/3l7uOFdn5sIw0LT0ehVp4hE3SlH/dZp4YuPfY5ah1VzqIaAOcRLxE8R4B+IdLMLutOyMwAzKLkstM4ZF6Q2UIZQhitqjiH+G0PgT0Mm2CYk4hEhovoHzj5i9anmWGM9jU8Leg3gVZA8mkmvSzgRE+12n7OfKLB6XhGxz6fXpdFHr6XO3QQPidqHukPVUd4wX7TcmszOqjuWbXaUG1SbN+e8g8j3E7QHqaLyKcwfQZGXannY2i3NPdv030TZpebQ1kMWmpY3bukQsWm8NZMkjPNcLayLtL33pS3znO98B4POf/zyf+9znePXVV/nyl7/M7/3e7/Hqq69y/PhxvvKVr6zLYjMyMgZHbx17EELth6XMUfY83z2rW2hPHCtn4eH/ou1X3kv+fgR2PmYGKwt6udXRvHmc+aG3GTo4CXSbf4g4asO/QSNeRsNl0GsgNVNxM0qM13GyHxgBLqaDVnOsq1nWmiLwecQNg9tpRCd78MUJfO05VOeSSGyqj0ArEMM7xHCO0PipTQ6TAmigTNMaXwYY+QXam4Y0/Ly6MDKatHRpJBojGOFWQjQF9iP+cXtbvIzqHYjXUXcEXzvRqmH3RtUVIYvb17688ap9Vjwie7GWsks4H4y43QRGuJpsY9ujT1vH0FlAqKavgW0g7PhTVsGQIVPd5xGe64Y1kfarr77a9/mnnnqKb3zjG2s5dEZGxhrROboTlibUxdBL/Kqtcie3fgZ7f67/rG4RqO3ChNlpXGhLva7mcT5/DSa/C1f+HEITdjyczpGO37hyEr/jPTTcQHywNiY3jmojtXU1QVNURw1Iw7q5DhqMEnUPRpBVpF29t7r1eaxFy/q3Rfbj/AE0nkf1JFb7LYjaTKRd/QMoiHHe7EJdZ4SbnM5o0h696THyrQGjWAQeLNXBGK1RntVMbbcz/apmsRpvgCjEK2ilmlcPshNf/2W8H2udv1fAp5oImWBROWqRdEc0bPXxWteMcbtGaqTMwlSMyAgiQy2HOkmObOIOEGU/RXECX3thwyLsB7UXPNuYZmTcYyxWK14rekd3VhCB+p7BhGcV8bua9VrPTbbnbNfHIZQLJ41VpOyGrB+8GINdT8LNn8HdC3Djr6G8DaTxn3PXYeQQTN20z1fHHz4Q2Hmyias9gbgk9EqEGZrfRxMhm0X4CMR0AKljtdcI3MKIcwS73TUxEm2AjIMOWXuXVOn3m2gUlGrK1ZCpvONNVFKknKJ558dBjBSRAlRT21UlumoC47Sj685+7ipVXwK3rb1L9oAcBL3bFhwg1kgvYyANYuoLt41BQONdNLwL/uPp2vcT8HUTcksxT5UZ8KnuPE6MV9PrbWpwxTP2H1HHITvT8VUU3Z5ENkJRrNyQZVAsNb1sI8631ZBJOyPjHmGpWvF63HtWMrpzMVTEP33O6uGdc7YbN+Dmj2F/agipvsf8deOl+l7zEdEG3PgBTJ2yaWLl7fb30wCN69XCjLydV8aOn2LkY2cYOvgGGuvAeDv1ihLDVVwxhsY9aLyOMIpyC4tAA8guxO9H9SbEESDS7sVOaWv5GLjbEOdTpOapxFeCMwIs37DUr5uAeCUdZxbxY8B+nH8cDe8BpRG2zqRgvJ5ap6qLLO0+bqbSc7sxodk0VpcfwtV+AYBYvmljM6XA+X3EGEA/MiF5nLa2LwLITsrG9xH/GM5ZSr2qMUOnFmCPCddaA1MKVAPOjbeITtwETjwwjDKHyIgRs38WDW92kbLvGA9q09e6ndU2MvJdanrZg+CZnkk7I+MeYT0HaSyGlY7u7IV4i5InX+/eSKjC8ATcPg37P92eNNacgdP/byP0ucspYt4Pw4dh/orN7O6FRiPy4eTTNHriFGMnTlOMQgx10NgSXeEOWA07ThLLFBGKN+MSYvo3nNLNMeXah3BuX7L8bGIGK6P44b9HbPzvqF7p/mI6jYYPacz+MRrP42QHzj9OlD0QPwCdSWKr/Yg8iSueJjR+gkXY6Rgo+KN4/4Q5q6kDvWrpfCytnRi4lRoXNwo08MWT+OIRYjiLagPnRnFyhND4HhovpUjcAzuAiJZv0pj5A4raI4h7GMsONNF4jfYgEOsPN6HePMg+2u1nbRT1z/bYmgI0kOLZZUl58H7w1WM5A5gHoRc8k3ZGxj1AP7MTYE2DNPphLaM7K+z5uA0OadyA2LRU+XBqG+v0DxdvEfX0edOEVRH5zCUo5zGO6tRmdXxnDVDbA+MvRHY89R6+Zl7e4e44GicRnxTNqsRwA2QUEY8VzWNKFw/ZP7fbUubhBjCHuB02OEMPJQHXbZQZYvM1VOcRt88ickqUFI1Lzfqr4yWbBeYuIrIDZTRFzIpz48TyDOIfx9VKYuNbWOQsGKHuAcD5vRRDv4nqLI2Z/xPiOdACuNGxE1LQprWaxfMt97JO8tR4gdC8k2rMgjIDcTZdwLtobKDxHZA6MVxF9Xpb7a4RZCfiHkEkoPohxAtoOEuUHYg/1hKzWfp9qJWC7u1Nv7cp6O5MQicelF7wTNoZGfcAvSKxTqx2kMZSWOnozk4Uw2aOEufaojJxFlD6upGzKkx+Dz74P6zuLQ7cMBAhzlvULQ58ASGmAFPSsTyomK2qH2rgh+ZMZAVoYyJtMqZQnUe5hfMTqGoHKQF6HbtZS0e616FBgd0IQlQbwWkzp/dbA1a8jbjdOP84EIjhHEhE4wxmfVodf8aekxGzLE3pdJu5/T7OP0qUn4FYrVjEoXodjYIrjqJaIzT+DMIbWHo8ReNaA2ogQ61afEU+IsOo1qmIu5oURrVZiSmyZ8RS6YQkmosgPhFbCRSIt/JCLP8KGMc5B/5wuo6l+ZR3pJZt5Gf/3vTa8G/cQ+Kud6nVO7HaXvDtJmjLpJ2RcQ+wmEgMBjc7WS8sJ4TrrI27mtHNzAXzE6+Pw3t/aLzWuGMEXYxZVF7Vtn3N9E47TqThJLNt4bKqcdXuJ0zVrqGOlsOIb4LC0ITgiwOoTqCpBcm5mpFNrAw8GvYl/FGEArjZlqD7w/j6Z9B4IY3g9DgZNxc0vQY6TSyvIP4WIjYopDJ1FO/RWI3RrFzOZtE4gvP7WsQV4wwiZ3HFvnaPMillG6fw7hcJjT8lNH7acZyq1hwwFXzV6mVGJKo1YvkGoTyL6gwioxYlu0O2QdGGHUNGERlNf7jqjzeNcztTCjwpw5GkGr+K87upxHEmQKvmfj+XNhvVUJbFe9OL+mADn9abEDvnqXduHPr1qS+/tu0paMuknZFxD7AeIrG1YjnTlDLNpyhGu2vjd98xUq5S5HEOpn4CQ/uthl0du7MN2TkYPmD/bp6iNR60vgcO/sdw/P8BU38Nt37mmLt2nNGjpxmaEEaOttfri6fReD6lkW1cppF5SSzfw/sjKbV7AAgoDicOX/s48DRNnUaoJzK9mgh2BDSgGpPNaYm4Q2i8kc6aRGzMY5Gxpqh1orUukRpo2dWjXEW4zu0C9yhx/l+ntYFlBGZpwwEFGktiuEJt6HPE8k2a89/BTFiCnYOLKWrfixCJ4V1AbSMjHYIyGQMkRd+dt/iQHNqWSy03lu1NVz25JEEu7BUfwvmHzK/dre0/7n5q9U5h3KDYroK2TNoZGfcIaxWJrQSDuJBpA278EG69BdMfmtIbrLf70K9YbXzvz8HZfwFjx9qbjVhaYDt/3WrfsxfsPNqweLIYg/oue33840bcH/vPbR313W2ir2rv5fxJS79jN2U6bsqxlK4oy9LTBb72LBrnMWLyoDeI4QbidhMaf4q4hxEZsznTHX3KAmiVnxeBqFYflwKNd7tU2sgoRHD+cOv8qoorHkfj+7aZ6DAmsfR5HZGqDlsZqtRpu40FrP6NRbNSoPIkzbnfh3ARi6YdqiNASQyeov5ZNJ5H4m5ivG0K8LSJUFV87VFs4tbPECnSZkGJGhGZ6BtFdqeW65i6vJF0A53vL1JqeunacduPXGxSWpwiNH+M83+TxG6rj2bXQ62+nQVtmbQzMu4R1kMkthwWi6b3frK/EG72Etx5195X+YXMfAgX/8TWu+d566PurMWLt0x0bEB9vzmdlWaWhQgUO6DYZa9rgGKnRemLpeJrowI8h+rCm3IVTVnaeBqRMVxxwkTf8fsmwIp3rd3KHUVkX5dAS1Uxdy+rPdt7q7UUqAhItHpxpdKWHYiMojEixSHE1RdEeLF0HVGbGZMYoR9HZFe7DivO0ilWWacaToJ4nH8ScZFY/gDCRynNLvbe1Eqmeg1XPIbISbQ+TyzPpNS2bW6cf8QIOpwHnSTEu4lkC5zbiVIjlFdw/kDXxqNKLVuE/KZ5tscbqLh2Hb+VZRhlqdpxJyG2MxqCiEfjJGXjbQrWHs2uTa2+fQVtmbQzMu4x1iISWw6LtZX1E8JphLmrUN6B2lj3ceauWlq7nwNaNYRk7hoUQ9afzRSEWds0xDlr6artAWT5Odqt4y5xU7auKkv3xnAejfPW1iXjBD0Negf0IzRcR6tpVe4wvnjC6rU4tCJN17744mqIO0zUacQfoj2z2+GKfYg7iK//GiIly20mfO3RVkTpimcIjR9j6fZpWmTMDhCHc/twzqHUieEiCyX2YENORrG6t0NkBFf/OKptlXmXx7g/DOEKMV5F/D6cP5iu1xVUb4DsXpBariJk5/YR3CHrTddp1AnOHwOZsEEjS0ahacQnru281vrDlYjELRDNrr+gbbOQSTsj4z7FUm1ld9+1iLgaPFW9v0XGYsTYvAMh2WhPBlOR73rKTFU6jztyGHY+Sop4rR4+lKyuwyw0blsUvvfn1pb+D803KOe/SyU2Uykg3EH8MXAHLUrWO9jwD0FlxOq/5VVwMxRDv0ytdpLQfIiyeQZ4h4ogNbVx2ZjNeZw/ARyiW8w1h0iZ2q76oYq0u0m3GHrZ1l++leoJ5vCGm8C5vTY+VBVXPITqu6iMWaRf/THMcBzx+xEZ6j5j2tz0pnwVRfUm4grQm6hadO39QZSCYuhXEBluEWfv522Ds8cIHkXcEXxxYoDacT0p32fpHtIClQPbvY5m11PQttnIpJ2RcZ9iybayWdjzHNx+q92+pRGkBrWdFhw1bkN51/hCClOO3zoF4z8P4y90p9z3plq8Ni3VP/QhNCatr7u2x6LvHY+m/vNVCnOtTvv97v5jSpS7pg7XAxhBNFokRwwoN4FpiNCY+f/gi2fw9V+noEY5/z5ow6Jxt7M167ry2257cBsWi8I6RU3IEFB2iZqcc9RHfpMYfw2YJsZhiKeI4UNsRnW9NSozhgsgO5IILe2qtAbsxtd+cQlC6U35dtiVtjzGq+8yl6J1t+jnRQRfHET1AMo8xdCv4Nwoy6EixLL5NtWoTvv7aduBbQtEs+slaNtsZNLOyLhPsVxb2cTfMn/wS38GMx9gmeI6FLstGAzT7SRuMWIpcFfYXO4Tf79/LT6URtw7joEepauvO8ytrf/cJm5d7REPpWZvnUa1RJwAdUjpWbiJWYSaQlt0ntD4IapN6iO/hWqkbHwnpcGn0HAHlT2If5LKU1zoV/tttzIBA4uanCuA3Ukv8POovkBn3V41olqaAED2WC2bNMykON7VarWwnao35dueb11FuO219SPN/iljawvbuUR2YSFccZICKHWKGC4hUmsJ5rZKNLvZ9qvrhUzaGRn3KQZpK7vzM5i7iKmiazA0DjFA41bK4IpF3jses/Yu6DZ/6SXgzo2CuO7zrkf/eR/dc2rbmqlWgLjdaBTbbXCdirBB0HgFaBIbHzEXruH8IVCXVOikiVpTqDpESlTvgozh/BFc8QTinyU03+hyChP3MTT93Ivl0sDt1LYSmm8QynPE8mcgjfTdxm3j4Pbi/EGsxYtF+4u7J27ZfOtYXuvqK1+MNNczZVwRovhniOWPF2QUtlI0uxn2q+uJTNoZGfcxutrK7oAfNlvSfZ82B7Nrr4OoETbR5mwPH4K9L4A+Z1lVV6yMfHecgFtvJhvThFjC7qcGW/NiZi8iw+bqFa4gotCqHY+C320zsZlH3H5gDyo7oXw9ReLV1K3WWSBcIsb3cf5jiHsCc0S7jnId4QbiHkeDQ8MNooJIjXL+PDHMIVxvOYWpvg3O44vHF2wqBhU1tVukgtXUZRTViLhxazFL9XRoEMt3Fu0vXpDydUfw9aPYdqQ9BGQx0lzvlLFzHldfmFHIWD0yaWdk3McQSbXmaOrvcs6GfADcTAOsuonR5lwPTVjN+9YbdIW3i5m/dLaWNe+a+YqI1bMbSUB88w24e27xSWaVFeqtU0baxY7e9wriDkL5HlpF1jKGyFF8/ZfwtcfTYutoeIuy+S6xdYvr8Phukb21Uplr2QSWPr6ZoszSfMrjVDr3bTTOE8ozgKCEVkeWSETDXSKX8cWhju8zWITaKQDTjpS2iEsq+MPpPCOoFsum4vulfPs5k/V7bqNSxtstmt3KyKSdkXGf4/rrcPMnFi0XyfTkxg+sJ9vVMX1SB2IT5ifhzlkbx9m8DbVdsPMx2PNMf/V31VoG4ARGDqT0emGuZ37IiHexSWaq8N6/sMhfm+3pYGE+gjTY9wv11D/cwPmPEeMU0DCBlqtZZBjeBBlrKZzrxbM0RZN96GXas7QjUMPq3bMpHV6mFHkJ4tDogEvADBrNKD3KDqC0ud0ylNqarN4MdZRgBiYExI2uIEJtC8CqlHbLDjUJyFR92gCUA/UX95Jk5++D2HeuhGS3m3f3dkcm7YyM+xiLtX25urVzDe2zlHhn4BZmEncF2PFwEmE3rdWr38hQDXDzLZi5aFF6Zfut2MZgeJ+l2IcOwGhyUuudZDb5vTT+M6bnguJ2nGLkyHsEnaM5O4TqFUT2Iu5AcgALxDCJhvNovIaIqaNj+IhClaL+PMXQf2bvKT8iFbkxkrVo2si7gcYbiD+QFh7sgsQ7qTBvIbXGW5hD2kyL3FHBLE7vQLhDjFOIP4D3Ty3p+tUtZOsWgHXaoaoAMoQvHk0bAF1zf3EsT1E2306tbau379yu3t3bHZm0Mx5ILDckYytiNWterO1LxKLnoQP289xkEiwXUNsLYx/reK+zc1azs3vPHebhzjvW4pU6sWjcNDFbmMEiebURnY3rsOcTpkyvxGwaUqq+bG8uxh47xdjx04SGoPOeWM6hXMK5gCQ3L8Wj+pGlkGUYkk2oxuuUjb/AFc8g4vDFIUr9BIRz2ISt6gs0gb3gdwO3UfYibj8xNrEJX9XoT+z4Opt6wM0u1dqwAOZobQJEIE4Smn+Jc34BCS5GdOIesTGfyVpV3AGi7KcoTuBrL3REsLImsViMgbLxPTROotoAqSMuTQBboeFJP+/usvE2Xhs9a85YT2TSznigsNSQjK0aHKxlzUu1fe18zKLn26ethu2HYcfjVvvud9zFRoa6wkRu1WdUjZQpLUJXtZQ5atPB6vusZ7sSs4V5+ydVS69ERh56DxALegtw3hNjLYm/JtqpbJ3GokVQDUasMoeW12jO/Z/44hE0zuKLQwRi2kU0IfVfi38I5w8DJcXw3wF2EMsfU87/a1SGrHXMjRpx60z63DC2E0mTtlqRu2/V3tGbhPLcAhJcbEiFK57A157sEoAVRf+odS1isdD8EbE8i208IqhD422cV/D7GNTwZIGRiyoar6E6RTl/mhgutMoUOepeX2TSznigsJitJ/RP/W4FrGXNS7Z9PZO8zz9pYzXrO+390+cX7+3upxqPpUXt89cSccd2St3V7Gft0H7NXoEjv9aO2P2QtZUNTdjcbV9v4Ifn0OARbwNGxAnCODFcpcskpHIW0xsYEZWgBeb9PUvZOIvILZzsw/vDBO60U8vqEW+DNsSNpl5kh9ReIIYP0XgXjbdQvQV6xb6A7AJGgbuJxKerq4OlpqudSxPVW6jOp7T1QqJr/Y1E0HgeP/TyQAKw1YrFzJzmb2wjUm0uUNBZYryA90cZ3PCk24hF47X2GE8iMLctJmZtR2TSznhgsJStZ2+NdatgPda82DSxvb8Ak9+Hm2+2xWZ7nu1vU7rUyFA/ZFG785Zm15LWeGcnxpFlcu50Q6Yo3/OJ7mPsOGFrA5ifrFPODOOHm4wehZGHqu98AIc3ERjzICMgu0FvYdFuSaqkYwrys4jUiOpR9uCdzdKO8ZoRrji0PEOkwNdOAtIxMONqyxTEJnw1MP/xNE0rKMpoRyuZTe3SOJPWMI+WH1DO/1lHPXqQIRUrb2QfVAimOmfjR6tUf7V5ECDeRdzRFaS023V4VaV7jGdhfe4EQnm2b8o9i9dWj0zaGQ8MlrT1XCT1u1EYtD69HmtebJrYte/BxW/ayMxKPDZ9Dg6/vNCmdKmRoeJh9zMQ52HkqEXet34Kc28CqQe8dT9XmLtkm4K9n4LJvzQf9HI2tYkVsPMpR7HjOHuePc3IQ5UfGaCaxjpahGntT5fRUCaBWKUMr6xH0yjMMI/41E7lxkFvQ5xFZAikwLlxVBvE8pRd8+ZpRPbiXEB1CuJ1oMQVR1siMfsbfphSCIlopUi1cAX2mKEJoaOH+tlFRWQwTGieQeP5BaIuO15n+5bVxUN5Dg0fJgOYHTh/bNGUtJm3vAlxEstUhLR2b9dJRnHF00v/h9T5N+8wYjF1ezNlDLDxcOEd62EXh2sew9d+jtaI0CxeWxMyaWc8MFjO1nOtbl2DYKX16fVcc+c0MQ1w6TWb3lWJx0hTvi6/Bs/9P1c2MrQVzb9lM7lnL9O2/55P32XYvg8RPvoTeO8PbZiIH7L2rpGHbF27T8LEL540568+dVu7uQ8Dczi3B5WHieGGTaRiDrutFaAxtWXNEcvT+NpJnH8MGEaKyou78heHUJ7D1Nmp7ckfQHUCfLAWM9nX3kC4CQg3bJKWeDRegJgK+dTBHWvNuO7soV5MRIZIhxAtibqap5HyPev86iA4I74zqF5rTdHSOIXiCWlD0JuSjuUpYnkeZBQbOVqz/xhlBBjDFYdwbmU7VlecRFUpG9+ztjlRUAehDn4n5g3vCOU5ROr42nOL1vT7rTmjP3JeIuOBQVXf1dj9vMbBx0WuFVV9Os6nrOS8/X799f7v36g1lzMw+9HCjYKIPV/Otkl+sDGaFs3vfAJQI+H6HjNIqezBEftdS5i9CLMfJte0YCQ/e8F+v3u2Xbcthl6mNvx3KYZeNlvMrgXXETeCcwfwtSeR4lFg2AgJl9Te0+CGqSZ3xfAuGi+kWVxtX3G7qNNt05bW9xLMqnQPvngUpIZSAoK43Th/GO8P4ms/h6v/Arij4A7gi/09xGzpb1ecxNeebB9HarjicTo3C+0PXSM0T6Gx0aXODs3vm9atY+ylRbFTQLXRiR3nTqMwnUPcQ8BoirKx3aDbh699ZsVpars2Ym14/lHQvfbHZw7VaVA1Rzdxyfa1XNIYRnv/I8/oixxpZzxQ6Fff3f0k7H6O1nCLjYIGi0RnL7ZbrCoTETe0eH16sZr0WkZcrgd6U/wazL4Utd/FW5QemylhPWKkPT9pj7GJZbN9u+1s5Gi7HcwPL1337PXKdu4owV1JEW8dmAc3hsgYFp/4FLXeRbUPScoYgrLAbQZwbhRf+wTwCarUfGj8qdV002eEOiJV6qP7D1n1UPcTkUGDGE53fUZRc2OjezqXzaK+isguFoy9bBmxdPud26CVuwh1nD+AihDjDSo7vNrQZ1YV5XZuBlQOExE02BhPdA7xx9o95zoLTA9kDJOxNDJpZzxQ6KzvlvNWW7192lK6G93+FebhzrttlXVnlBnD4vXpxWrSa0ExCqMPwcyH3d9V1QxQlqqTL5bi3/1cd+tWMWrjPSWJlMXZo0sk71JAXCE2jPiLMQU5RTnf08vsn0GkSSeJd7Y/wSzOP0KUpg0NCWcR8Rbx+fFWVC2yIzmg1Tq+k+JrJwD6pq+df5j2BmLY2rHdI5SN76Y6dokJsBRkz7I91N0OZf2ma6Wxmj3TucC3LGI6x17aQe29tnGo99S+z6E4RGzSlk/mNMhQ6qle+X/wnZsBEcH5QwS9ncalKuLb5QRJafi1GsNkZNLOeEAh3sRSlb3nZrR/ucJU2v1S0s3b3QM2FlvzegnlxMOhX4GLf2JCtFbUv8+eX2pTsFgLmmp361ax094/X9pYzmInjBwBN2nkPHyErqDWpY6p8Z87RYztuqfGBs3mdxD3PcTtWSBe6oxcVWtoeItQniVEE1mJH+8SkIk/hvOPoPH9Rfucq1q6pduFGM4Tw+kegRhUDKqaRHAyjrgjKBHVpkXoy/RQ95+u5VH17fnTrfcKzh1A8V12p9WsasA2OOJsalhVP3b70DBJ1EkcWMStHl88uuK0+FKbAefGU+tXe7NRbVqcK9B1miL2ICOTdsYDiXvR/hVLI67KOay1lkR2MWyuyGT/Z2wdt960iLi+C3Y/u3Tafanrdvtt2PWkicvAMgp+DEZ3wcghGHvYXnP1pDR/yEoF89ds0zD2EOz9uciOJ97rqjXHcNValbARlf3ES1XkKgI4I3HXPGYGJx2LrSJqX3sO1efol37v3ASE5pmWQKzT8tPMRM7j/AFiALhhf2D5CI2X0OJZxNUR98hAyui+him159BKxde1/s8gIiac04DqXcSNIf5ISz3e2xPe2rTEKaJeRziMrz265GZiMXSJyXo2A+ImEFXEFVgkbxsi8c9Y1sQ/g+/9nmuYIvYgIpN2xgOJe9H+5Ydg5+MwXSSiSuYjwxMwdtwi7XJm86xVB0m799atl7tuez7R3vhULmt7TsL+z2LdVw37zjd+YO8ZPghjj1if98TfAvENmnNzWKSpRtjhHSCgUdCwG/zhLkX2YvVuazOqL0oQSw3FsNaqOhrPLyKcspYm4g1Ur5sQTOZN+AaQiLEi/OVqxv1r3dLRHrVQQd/Z+iZiqvX2BK/5rvpxZY2qbgK0QTH0qytWi9txl94MeDlMbfhzrVJGK/PR+JOuUoev//qCUkfGYMiknfFA4l60f4k3FzKdh9EjbeFbZfP53h+un7XqSnzK+6XdF6tb7/3k0tetGFpiI9BxnsXe01nf1XgtRdhpgAdCjFM48UZAS4iXKvMOVzy7Iuew7kEei5uhWF22aJmKqGqHYYmgehvl0LKbi8XOXb23InJrXSPV07sncdmvvbfyfnVyLIPhdiAytORaFkdjBZsBTyzf6Nvi5QGXW7xWhUzaGQ8klrT33MD2r14luB+2G185Z54Ua62tLz+TeoBjBLj6XUubO79wTYtdt91Pdm8UljV96fOeqr5bNt62FiZx1vurCm7ECDJOoW6ir3hpteYd/T6HfAwTfml3axgk29PDlOWbSewWaRmWuFHoUH0vp4zuPTfUcf4YrvgEIo5Yvrni79OvTq4oqiXeP76G6HbwzcBStq0rHU6S0UYm7YwHFmtppVrtlLDelLTzFmG7daitLzqT2oK0ZTcAVXR98017FN82PZGONR3/v9v7WxuPUfBiNe31UOG74iReG5TzpxFRVEaBmFq3AEojn+LJBTf91Zp3dH4OPDF8RAxvYEozwfk0EpQO4ZR/htD8IRreN8MSjHBtMFjycWV5ZXQsT1E23sZKADeAm4TmT3D+bxB3CNUGrst05W28NvG1T3SkwxdG6VUZoNc5LYb3CE1ZlQtZf9HcYmKyQWxbc4vXSpFJO+OBxWpaqdZrSlgVZZYz61dbXziT2trJAG4NL78BqFThGmh1ElWfH32ovabY7L5uUz9aXxW+1XdfIIYLmFGHgzhpkbeWIHV88cwC8dJqI7sFE6viVTRMprS3IDJODFM4DUjxse66sj9MCFcQP2y+4y1Dl51I8jJfShndNSoz3ALmQEYRGSWGaxCu4fxekAM2aCxeReMUzXCaGD7A+RNpzef7RuImuIuEOItzh9J3LNfkQjb4lLH+Ubn9Tdbe4vWg+pdn0s544LGSVqpBJm6tJApfr9p6v5nUYBuJuUkThS21AehVhUtHC/DsFRg5nK5Tx5oq85Tbp9dfhW9zsE9QNt5GJIKbQJiwCLv2NEX9+T6fWj6yU7VadfeNvoHGWUsnqOt2GiPg/D7gAIjD138N5+y2GWOJahPx+61XW0baF0IDil9WoR3LHxPDJaxvICnFddoMWyT1cadygMZrrRYv0Qg6n/rEwRcH+2YWVCMa38e57l3hWlLUg04ZW1lUPjgedP/yTNoZGQNiuTaxvZ9sq6IHjcKXq63DYIryBTOpOxAbyfd7iQ1ApypcHAzth9vvQJyx7z0FDB2AI7/evY6NUuHb5CgFpkxBDsmudHH3LlXrDe5Xg7aBHKdTb3anYcuzxPJ0MmexSV4a7yCyw/5oLcMSQWkiUqLqU5/yGWL5BiJ1kD2WQpdqZOjyCm2L8C8gUkNjk9Zo0eQohu4Dt9Oe09C1mUAKFJeMXUD1QIdArU3IG5miXkp9X2Ets78Xw4PuX55JOyNjQCxHUNf+Am6/tfI0cV9r1acsFX/uXw4+WKTT2KR6jyrgYNcz3WRbZQNcYe3FruiO+LX6f+nzuOrJhefdCBW+Dbg4g3P7ULcXs+d0VH7XneiMvGK4hMZbC2rQiBCaZyxqxwPpRl++B9pAZHcyBVHQeSNERrvMTaqUbkUaCEbYRDReNz9zn1qgBlJoN4D5ZJJyHiNssJA/pjoHqHrs4pttaWWkIhLNv1yha8Y4nYS8sSnq5bDa2d+LIYvbMmlnZAyMJQlqxCxKV5Mm7ldbv/7Xy6fhu46RIvbK2GTuqo26jPMwfAjunDFi3vsLcOOvzAP9zrvmxFbstP5xJ1A2jdDvvgtxzgxfarugvt/q2rffhv2/0P4uG6HCX9ALnEZtitD3xtwZeTl3CMV31aDFPWxDNvS69VZLgcg4yH40vIUrHjM3L0iK9SHQOaRjUleV0gV6+pTHO+rfU6ja+3vTv/3rr4lQZQLchxBrWLTtgDrIDogRX38uHaPaPOxrrQsK4/hFvc43JkW9UgwSlQ+GLG7LpJ2RMSCWIqidj1lNeU1zr1NtfbVubZ0Re2zYukYOmpe4Nm3dt39mavLKiUzEHNqmU7948xbMX7E6th+G+jjUdttzImaG0vtd1n+gyeA35gUELxbtipto1aBj+RM0XrbWLHFAIMarIE1LQxMQKVqjONUHNFzFFYeAJnSldM20BLXjIBPWax+nUG3YOYvHW+nfpeuvqb0tvonIMOoOmYhN50CGbb1uJ77+K2h4lxguouEqyE00SvqOe0BZkpBXkqLe+uKue5s52ArIpJ2RsQIsRlB7Pwl3zy2MwlUtcl7OV7wTq60TVxH73p+Dc/8CdhxfmEq//jdm8DLXYaUqYuMw/ZhF4/Vxi9hjxOrjll1m/pq5l/WmvFelwl9SrLeSG3N/gq9q0DZBy+rGELGRkbP2xeJNbEyltzQ0ChIQPK72ML7+a/Q6jcVYQ+NNNE4m1Z9F7eIfx4mjGPrNllBNNRKaP2pZqfarv1p7m1KG8wgNcLuBh0EsLS9u2Ai7PJMmdNkGIcarOPEU9V+yc8XzixLyICnq7SLu2iqZg3uJTNoZGSvAUgTVGYUrNh969pqR4Ht/OHhr2FrrxLFMa6stfL68Y5F2bLQ3EqrQvGNTuUKa842DcNfGZJZzUBszz5Cdjy1DyH3q3l0vD9Ayt7Ib80KCNwGbTbAyWN04lu8bYQvY/yuBYWK4grikGseEZja727eFZdWxw1uWYteQovZotXBViuHP2VCM1kCNs4TyFIIjuvFWjR0glGcQ/zTOFUkJr4TmzzB/80lU3yPGJqKH0HgVcfu63ccIwDC+ZhG76kmWi5CXSlFvJ3HXRojbthM2lbR/8IMf8Ed/9EeoKo888gj/1X/1X23m6TMy1g392sQ6o/C771hNeXgCRo+urHd5rXXiXtJvbSCu2kSvW29bJC07En/FRJipft68ZZzkau1xmeU0jO43f/BerKR3fZCWOeh/Y3b+EXAniHEGs/N0XQRv1+haqi03cf4wsXwXGEJkAuRDLKSOtgAZA/8EqufRskQkgBQ4N47GeWJ5qouwWvOj/UE0ObNBaal1VyD+GVtzJVQjWGuWgIZJVDX1bqdUOuCLJyzarj2PiKNsfI8YryJSw/kDIHuI5ds4Qsvnu6rxK3Ooztt3qjYvq4BqJJRnMbGf76tC30oR7HqL27Yb1kTar7zyCt/61re4evUqp0+fbj1/5swZfvd3f5fp6WlOnDjBV77yFXbs2MEnP/lJPvnJTwLwX/6X/2Um7Yz7Cp3p6bP/AsaOdZPuSnqX11In7iX9mQsmRCvTLIvZiyQigfpuUDG7U7BWr+Zte90PWV17aMJ+r4+zoJ0MBidiDWaNqoGkvF78unTemFXnCc3ThOb3ifN/3Io4q/aviuB7CU/cPmJ5BqSOUlrdmBHgbqobK6LvovEuUnwcl9zQKtJaSFjtVLy4A6jsB5pADSRiAzKkVWNX9bR68ETQcAFtbTbqWHTdmSp/FinP4d1uW0cyZ7GWsCk7X0v9Lmi8RTn/Z0Bj1elsVU0p/FNpg5HS/W4ifYetK+5aLHOw9evya8OavtHLL7/M17/+9QXPv/LKK3zxi1/ktdde48SJE3z1q1/tev2P/uiP+Nt/+2+v5dQZGVsWsbQItd/9oqpJL4dqA3Di78Oj/9Aeq1Gag2Dfp2H8BeOMO+9AmIHaDlOS1xJBlzP2+tAE7HjSRGbDh8CPGIkr4IYtKg8zMPOh1conv59ayVheNKdpXraq+ZlPvg43fgRTP7HNRJVNX+y6iDg0vEto/KWlsanOe4Wy8V1ieYpq4pW4g/jaU7jiiaSuLlMqQXH+KcxitCLsUUs1aDDxV7xu7+24vjHOoHo7kQBU0awCMV4lhneI5Rmb+BVvoVojxhlCuEmMMbWnjbd7znXa/n6qiBu3FrEUzbaJZh6haPWZiwjIOBpuEsNp6ykvzxCabxNjiRC60tmxPLXsfxuqEdU5yxykudiC60r326CW7SXusg3IG5Tz36Q598eU898kNN9I5ZL7B2uKtD/1qU8teG5ycpILFy7w4osvAvCFL3yBf/yP/zFf/OIXAfjDP/xDQgj8/b//99dy6oyMLYv17F1eiVtbFyLsed5aua79pfVwV4Tvdlmbl0Z4/r83kq7GZd5M9/z6Htj5KIRg6nKXerXDvPmLg20iKtGc87SmllUE3imau/66DTERl3gxdFukLnZd2qnbm90RpAjoTRN5tdKk80DRsvq0mnWByC6KoZdsTXP/KvVQV5alCng0vktoXrcatuzBUhG3KOf+DHEjrSjW+eM05/4DGj/EvMYjREGj0pj5AzReh3jR2s7cAcQ9hnMQ43Vsi1Igfrw90pJBeqrtc6oOIdpGJ04jxXjXu5a3a104mKSql5Na10h98KpTxLifor59xF3bqS6/Fqz7X+Py5cscOnSo9fuRI0e4dOkSAP/u3/07/uAP/oB3332Xf/pP/+l6nzojY0ugSk9rTypZoz2/kbOyVS0SPvcv4ew/h/f/DxOT9RqEiSSCrXw7Wi/A0L52JN1MJlyNW5Y2n/qxtbZdei2ZstStTj71E3tt6icwfcE+XxFxFY27FNVXx64sVmNY6ro0UJ0xpfYClBYldxBe5RtuefwqcrxFaL6JKx43wmxN5XK2KOqgZbpEES0/QMvziNttJN4RxYp/BuWuCdo0WuuXjIJeQcu/gnjZ1sVdiB+g4Qzi94J7HCmexhVP4DoEaXYdunuqOyNDRdE4hSuOpfr3kzh/AnHDoDdN8d719682AAvRqrVrM5HafDKjuWoiOb+/dc0g4msnto24aznTFe39H+M2xroL0ZZKRfzyL/8yv/zLv7zep8zI2HJY/97lwbCgvpzuVc1bVr+uEKPVqz/8GpSzbSOW0WOw41ETrs1cNtJ2KQIudrSj5Onz5gDnhyww7BBTM3fZHo/+hhFx51CU0aN2rPlrNnhEgN0nl7oudURG0X7+rBQmJkuEJ+4RYuONbsV5vAs4ysa/wYUPrZbsnkAI5noW3kHdDlCPUtiimE9jQCdax2lFse44QhN1+yFNALMTXcXq24VdICIwA/oOYT7gaocRd8S8yjt4Zbmeaihs85BqzHZ8TdejPf6zvc7+6ez+pOZb9XJxE7gFyvQXtlS719J4cExX1p20Dx06xOXLl1u/X7x4sSvyzshYb6x2TOZGYjW9y2tFv/qyONjxGNw5CzjjJFe352t7LIB1zkg6puBz7CFLWY8chqmfWmQsPXtxV4fbZ+yzo8c6ouaGveaGrHcdussFIun4R5JCfQQO/K3Fa/U2OORRYnkR1ettElEFtwdfnOggvMcRtxv0NmiZTFAwAtaI0CDEJsIlpDiciLsEBOfN/Ux1nhjeTbXkftagTaNqaRfAzTe8ygSUredbUavswrl9xNhA3JBl3gfsqVYtCI0/7UqZV7XyGG/Q6YS2dK/yQlJrH+cqFfmb+M3jaycWOc5WxYNjurLupD0xMcHRo0f59re/zYsvvsjXvvY1XnrppfU+TUbGilqN7hVWXZNeBRYzZRl9yCLusYct6q3tgNlLMHzAxGCzV4y0pbBU+sgRI2PxZmE6exWKjnueqrWylXftOhcjieSPtuvasUwbhGKhml01CdQc7Hl2+c2MK05SDKmpx+PV1Ed+AF/7+VabFYDIEM4/hMY5SxuHc4jTtGZPDNctpax3CeU0ImModVxXJDuEGbFAP2tQkZ2I7EfDFSO1qlfO3kGbsKusgAO9m/zCraDfz7SlF5UyWgS0T886sh9fO2qvD9Sr3J/UxE3gxAPDKHPbtuf5QTJdWRNpf+lLX+I73/kOAJ///Of53Oc+x6uvvsqXv/xlfu/3fo9XX32V48eP85WvfGVdFpuR0YlBW40eFCwmgBMxQdrxv2f1Yy3h3P8Csx+1xWDigWhp9JkPYMcj9vzOJ81UZf5yIuFau/dcakZRVbm5NRSLhcKyfZ+2oPfya7ZRgETymiL5JTZZIkJRfx5fO0mMs8TyZ2i8gMZzhMZFNE3r0vAmGq/aqEvxoHdQdrSO04rUZQTxj4Bi/uKUXb3JsMda0ui++Tv/CBp+hoXK06g2QerALmAUE6YBrUkrADWgiWppqWidRaRcUU91v571ol4RqzJIe9NipAZQ1D97X/Q8PyimK2si7VdffbXv80899RTf+MY31nLojIwlsVp/7vsZi5myxNKmhom3iFmDRce3OqxM/ViKnAuYn7KoHIXxZy0avvE39nulDtdoz8NgJjAiRvBD+2Bob/s4Uz+0lPqBX1r+7yXiIJ5Dw/lEPB0K4Wpal9uHIyQv8FkTivmHQDvV5wUihamkEZx/PI3sTIQ41N8a1FqKTuP8flQiMdywc/r9IM+gzW8DM3STdgAuEcuS6B4yK1ItVpQJWtpMRBi0VrsUqdm12d413wfFdCXbmGZsS2zUHOftjk4BXPOuCcxETPF991y7fLDjMbj6F+DTHaC2M5WJnU33EmctY50CsVYZYhh2P2njPsFI+vbppQV3nQpysHNNXzBB2tRPbKrY7meXLm0sphAGiGWa1gXgDoAqKndSD/YNS1G7HZbA9uMdUfQcvvYk8By9N/pOa1CAcv6b7YjcpaEkSbRVDL1M6Tyh8VPgJugtqlGaUIBOQZgi6lFC40/RVRihrHVS1oNCaus3UWxrIpN2xrbERs1x3gpYi7CuUwB39S+sN7rlMd5RPpj4W3Dl38PcpXbae9cTJj6TAk78g7ZqHNrHLOftGJf+rRmtgNWzD/0nsOcFKBZZc+8ma+aj9txvDeaHvnxpYzGFcEh9xyamsj7t64gbQ2NMOfxZUI8Ux7p6pDvbrXpv9J3PmRlJj5Crw05UpElt+DcQ9zHKuT/BesaTswwx/VwDHBobhLi2/uG1uH7d76R2vyOTdsa2xEbMcb7XWG9h3d2zC6eLdZYPjvxq/7T3+PPdhN36rIdbP4VLf2rzuqs1zXwIF/+1vb4Y4XZusjS2x4JCUrN7+33p0sZiCmGfasS+1ddMKyLejfjHIY4T4y1EJtquaisSKS2vTrba+1OE8k00WC+0GaFMpjdW9WcbBboaX+/tMo0rY+Nw/+VGMh4YtKw66xCSJmj8hY3vhd4oVMI6bbSFdTd+CFf/Ay070EFRRbZgm4HYbJuaVOWDfZ+GvT9vjmjlnEXY1fXTYErzzvNqgJtvmplK9XslIpu/3uEr3gedhjMa2uI1VRje3ybwpWxe+5mPVHDFM6mMHKjar8y3e9zGYvrDOLcbxFmbl9TwtSeXFCl12n0udm4TqHUSfx3nxqwfnGo9Smt0mtSpVOlLGaEshoUGKYPbl2bcH8iRdsa2xSC90Fuxh7sfeoV11WSuuUlzGrvzrgm/Bo26/ZCN2px+r7t/eng/jB3vLh9ox2PlqHb77YXRfpg3Z7TGdUtnV8Oy/Ji9r3F7aS1BtZm6+SYWLngY2Q8jD7Xfs1xpYzExVaUeD+W5dHCHc+Op7pyasYqPDdRutVg0K/5ZfJ9zd/dZO5w/QWi8CTqZruocqAd24dKcbHvvyvqHl3P92mrTuDI2Bpm0M+4Z1otQ+/VCb4ce7k6UM9Zu5YdtfbMXrB2rqvnG2ZW1s1Xp5plLJi5zHgj2+44T9vrk99vlhdooUMLFbwJiE8p62+j2fcqsTMMcFjyKPZZ37Rj1XYsTbvW33vcp+7fz8e56Owxa2tBkR/r0QvJ1JrIKzYcIzXPWF119KqXCnStY7ra3mIe1h8GFXG4PxGkTwiUXM9yu1iaiWo/9PLf0sVp4cFy/MhZHJu2MTcdmEOp26eGursXNN+HWW0ZYQ/tgvqMdy9XaNedB29k0QFQTllWWoa4GIwft+dhY2DKnsZ36Hj6Yzivt8+79OdsA+NG2sUrn99j1tP3cvGOPxSjg+v+tJ37JCH5Qm9elarmdEHH42s8hUl9Vv+7g0Wx/clSNaDxPURxCOYhqaSn1OAXcxiaPjVrPtyrl/DdXUJu+N65f9/uoy+2GTNoZm46NJtSN6uHeiFR757UYPmiK6tmLRor1PWYtOrwP6Kn5LtfOFuZtnGanZWhF/GHGiLW3ZS6WlvouZ82H3A1bOn3kIXNKa96B+l6Lku+eS3O31VzTRj8GZQPe+O/NtAVMVT76kK3FLfK3HtTmdSUTnNbW2rTWaLZj5jaCVA407iBR91IM/x1EdhLLN1c8kWqzXb+y6G1rIpN2xqZiM0xR1quHuyJpV7exleudGYgN61Gu0DlMI8xCM12H2QiNmwvr0UttIrr8vl339S7GrC+7t2Vu9oqlvsWZKK1zfObYifZnRlMNeu5qOv6Q9YRffg0aHarw6Q/g9s9g52Ptz8DCv/Vyf4ve6NfEYAHwS9ZyV9fatNZodvHPOzeKyE6AVdemN9P160EZdbndkEk7Y1OxGaYoa+3h7k3fd07AWo/MQCsl/lO4/n0brjGUrEGryDg2zXK06BAhV/VonNWjl9pELNcS5+o9fuDR5mb70fR5aT/OXoPDL1kkvutJuPgn7ZYvSQ5r87egecOIvYKoRedz18yytJOjVva3bqThHw6N11CdMvm5FBB34uvziVCXuuaDpXh7o9lqg6DqBpotPUg03K/nu/2+paP5zTJIyaK3rYtM2hmbis0wRVlrD3dnytoVNlxDk7nVWIoY15IZqI6PWAqakMZZko6fnh8eNyLtrUdPfg9u/mT58sJy40G73NNu2vXZ+bidv7MOrhFuvgU3fmJCucYt+87VxLCh/Ubo85NpfGfnkCtso6aBli85rPRvbdFrDB8R46QRSZoDqvE2sXwHV3++7ydXk+J1xclkWdoeUCLuAKqaSHxpv+/lo+E6MIQyBymNXmHQ2vTGG6Rk0dtWRSbtjE3FZpmirHae9YLWqzK1S3kjstEj7ddWkxnoPf7w/rZKvDp+bFqdeMcx0I7JWeKs1n3r1GDlheVa4jpfL2fhg/9fu3+6qoPPXDYyRm2DEOdMyT58BEYOtY83fyMdNNIajiVikbfrOe9K/9Y2K/thYjzVTbSqOL8veYSf7Eugg6Z4eyNx2xiM44vdVMQamqeJ4bwp+pfYACwVDdsm4k00XiGGS4jUEDduLm1baiLVgzPqcrshk3bGpmO1hLoSrHaedW/6XorkDhaSQUloE+ZqMgO9x696lOcmrY6Nh70fB/820FxYj/ZDqY7d57yLbSKWqxuLt3Gdu59tb6aqczYmYWSiO+0uNSPy0Y6U98gElLdAhQ7HMauF73zUNgNhDoqdq/tbu+IJnPsOqkmBTWEe4u7AopHfIClekAWRuLhH7HWxfu/2AScJzRv42pMD1Xj7RcPtTcRenAuoThHDVRw+TdvaGhOpHqRRl9sNmbQzNh2rJdRVnWuF86x70/ci7Wi4ar2C1WcGFhyf9ixqcW3Pb3GLZCM+bsM5NqK80LuZcjVTi4/0iMiGJsw7vDPlPXzERHLTH/aox49ZMrmcs9T6ridXJ+ATGcIVH0PVbEC70sqLRn4LU7yW4i5RZvE6h4azCyPx8i00TuL94Z7PTSESqDzObV2D13h7NxHiD6Caho7IUDrG1lFlPyijLrcbMmln3DOslFA3A/3S9yMPWdSIN/Kp7bKRlavJDCxWHkBtqlbl+b1UNmJRQl9jeaF3M+U8vPeHCzcIo0eNrP2wpdWLMdib1qZNU7r7Ies9r2rvxZB9x+r3lQr4uiO/9m0rasT7o4t8qp3iVVViuIrGC8nwRGio+bw6t6/nXAWqd5MNakWioS1+66nzqs6iOo9twZYShi3cRNjxC5R5tlqd+EGZCrbdkEk7I6MHCwhz1FK8MSRTkTUef+8nLcV999026fWmjJfKRmx0eaFzM7XYBuPIr3avbYGJyoiN3hw52HPsntp7q62uMDFb9divja0z8otxGo03ASGUEY0fLagvdxK9xmto+ACbdy3gRtB4GeI0WkTEd0z+QhDZgWppfdaADSMpzBq1478ABTTeopz/M6CxjNBte9aJ81SwrYVM2hkZPeglzKkfdUSMIxZwrablq7eVzI/AnudsTKZb5H+J/bIRm1leWHKDEKEyLu81zAmzMP2+Xatec5dy2kZ83vqpqdLvvAPlbdMMSM0yGTsfhz3P9LSxiSRld0TLv0TDFcTVISrKRN9xl9X7m3PvALOpYD+CyBigqDSJcQpxE91iMn/MXMvi+5YadiP42smUnm8jhivmUOosZb+06UuuE2esHZm0MzIWgXgjxNunO9TksU1AK2356iU2Srj9lkWVq+n33ozyQr8NwiBRdSXgu/uuqeKrzPLQhNW+b/7YNkIzF+318nY61k5Lx08XoPN2rM5rE8tThPIM6BTiCiBaG1hSkofybFd92VK8TxKap4jMI+Jp50osnW0natepVRVfO4ErnkX18XScYbpFa7PAMCIF4vb2XLPF69y5TpyxVmTSzshYApXa2xUmvuokoNq4RYy10eWPsxlOcKvBoNasnRuEzkEjvVF1q489qcgbN628IB6I1vO+43gS05FKBHeTJaqkdred7fa3rlS6RkJ5DqFB1NIIURV0Bo03CPEaOI9rHkv+4xU51xE3hsS6LaLze7ndIPtAhlDMpGUpX/DOGq9qpJz/Zle6vHVdk6JdtU5vK9n9UifOnuT3Bpm0MzKWQKX2vnvODFA6fD1oTFm0OPHZ7s/0I8LNcIJbCQYd2tL7XfptPqSw1zv72DVxY33cUt6VEcvoQfs5zMPcJZts1u4Rsz7w5q0UcYf2tfHDSmj+iFi+kdIdU0aIKsCsfVgEwRmxS72VnhZx+OJRYnkR1ettMlcFt4fa0C92kehyvuDtGm9ctEYNw6mW/n7fnu7tXCfOnuT3Fpm0MzKWgHhrU7r+eg+ZqfUm334b9v9CFQkuToQrcYLb6BngGuDqd9NozKK/q1rnd2netvft+TiMv7Bw81G1xbXawJw9xnlr+xp7uH0tRdp95vNTRvgaMeJWwJk4r743lSeG7b3W33wOxQHTKaV9J31oCKgZb/txnLgF6WlXnKQY6nE58wfxtc90kM3wiuw7l6pRI0Is31nW1GU7InuS31tk0s7IWAZ7Pm4k0phK7mh1GEnTrzqj5OWmly3nBLfRI0tbx38TJl9v91xXJimdqfrrfw3Xf2hisdlL9r0v/RmM/3wa/lF2H3vkIYuo3Yit/fZp+9zsFft59IhdR7AoeuwRmPwrS50371hPeGik6xbseoN9f1wkNt/DOUeM2JxqqaU+vFlgDhjC+b2IVPOquw1XRISi/nwSk80BgshQn7Tuyuw7+9WonX9kVb7d2yHdnD3J7z0yaWdkLINi2NTMsfLQ9m0SraLkQWrWy7VqbfTI0ur4GtL3oMfznKTsnrU13vqpmaU4sZ5tItz4a0BNdNb1XaMNFdn3KXj3DyzNXd+TZmsHm/gF1ou+52lre7v6H2wMaUzX1A2l7z5qg1H2nKyuTRoYQqphyxjoLHb7KoARQInxOqK3ky3oEfq1UFldeSkRwsrasvrVqKFBDG/T288N/Yl/e6Wbsyf5vUYm7YyMZdBpiNLZmtUZJZczg9WsF2vV2mihWtfxpW3N2ul5Ls42EqilxGcvGWF3H8jc4Q7/Mtx+Z+HmQ5s2vcx5SFMoKWdoTSl75P/azhwc+VW48Td2zCo9rslk5sAvdVwbNSK1SDaA/P/bO/fgqMr7/7+fczabhIDcciPRnwTReEEtLZRShUydMVNr6m9wcFrtl+pYR/BWmc5UcWhJYLjYSittrY5T7e33j1ZG1NppG6fOIPO15VvKt35BCylB8RtzgSSABHaT7J7n98dzzmbv13N29+y+XzNM2LNnz3mek8v7eT7XGkg5TX3AGFUXh2VjN2AETkH3Nme148s2LSvcR22N1xL+8FaiQosVfneZmzObG7EfijYhaZBql5yJzzpeqpbTgWrh1xcCqJyrdrlCaQRkUJ036yplstY8U41SwhGa2kVfdDVQuzx28TH+KRA0S6AKoXKuPTMAGEAwAFx0zZSVIt4zjecOsIQ0MHkE6k+WASEAaRgAqk3B95u7cA2aPheAhJRGVsKdKC1L6FebO/7E5mvLxC20SxGc7AHkcKiVqIQHesUiILw4Sw7m5kKY00Pfi4kjKedGnIGiTYoCp4OvciW6IxakmcqUon+1EQBmXpn6+k63LLWub0wAvj7V8CMwZj7zatUK1DJHC6H8zwN/RkSGlDTH4pmuGozEW3x4LwL0qHkIAUAHKqrV58KPp1skRvMsggfApDEKGTxu3sADYByQ0wB9IXS9FoAOIYTZ9jI7U220yVvKCsjgBwhO/CGh+TraxA1UQsrTkMERCM0AhKqmJuUEjMDhsB105ubmQpvTNc8iiMCHCE6OmpaPRHMjTkDRJgXF6eArO5FSBWglGmtEf+oxYGJUHT9zSKWMJZuX0y1Lret/8ibgH1Jj8M4CDEPtumdeHek3r7sBGFoCjPwXVFS2aTr31ABzPzdVIz0azaveP/mfgBa+eDGA+gSfS6dIjCWkhhFEwJgAhNnty1ARcar0qCfs/NxNtZbJ2wgcSmm+jjZxS0xCGqcg9Dnmzn+qwUnkDjrz0qaFN6dLCAHoFa2Ibt7CYDTn4ZMlBSUUHDURGXw1sr/QI4sl1VitneOCbwKzrweq6lTAVqJ5yaDy91qm6bnLVEqV8ALBSfV19mL7aorPWaKCroWugr+gAdXzwoqdBKfOFQK45nvAJbcD0/4P4J0LVNYqX/P8u5PfZ/7dpk/aq3bQwgvU35D6c6mQ0gDkx/BUNEH3tEL3XAnoCwFRrXa1UprnSWi6PWVBU5mvpTQSnBNUu1B5BpB6RAGWqR30lLnZGvvUOfHnkGo8hhGAlH7TdO4UlnVAQMCTcG7EGbjTJgWjWKuExSPTsY71xvcHn/2XEs/RA/F37E7WFDcm1K66uh4RtcCB+H5zTQMuuxdo+Q9lOaiYnniHHY5mthidf1dmn7NI7CqJNCVLYxTAWQAXAGMcwWANdE+LWYLUrrKgyc3XqrvXOKThi9jpq8hxDyADCC+RCsTuoDMrbRoWSY8gIDVAGJBSgwyegJSvqxx2UQPds8Ahk7k7G5+UChRtByl2P22hKbYqYcnIZKypzj31n6rmeKLULqdqiof7zaMXH8n85poXqJwT/71kZPq51K6SsFabxilVc1wIQJsBYCaE1gBNb7HZRBxfoMK7e0k5Dhn8EEKbCaHVq8ItEBDabBjB00BUP+/oKPRMSptKWQFpnIE0hiGNs5hKM9MBeQFB41O1w4cHRvATeKSEx3udjc8j+wh7Yg98ug4gparPfPy3QO8v1dfhv5m1IEgIS0TiYUfwlZ1kMtZ450qpynfqVcC5Y4l37OEmarux/NrRllNpqOOFXlimdj8osTAMA1KejihHKrTZ0DQvpPGRrabhROZrIzgEaQQgEIQmPBBiJozAKUjjJCQkJAIAaqFXLILQvOq1qIBe0ZrQCqByyKuSip4MfgBpBCCDn6qIeRgqZ12eggrKG4P6s24AxgiCk39zxFSu6rC3AqIirbkR++BO2wGcLpJRKjgdfGU30xeoamLhZu94Yw2fFzQVre0fVjvwac1KmGsujU2OyYd1wele3NGka21K1/2geRZBlxMIjB+FCm33QOizITTVD9uJAh/R5ut43b2EVqeyxAOfAGIYEAKaVg9N/4IZmDWJXFOzQv5srQ7S6AOkCFuBBaGKzPhUNL0QgBBqESHHTbO1fZRS4xO3QdG2GTf5aYuBGBGpBmYsVH7fYiCiBndYRLh3tkp9SiR41rGBblWTW/Mqwa6eN1V8ZNrFkZ9x2rpgCejcpc734s40KyBd94MSi8Uwgn1Q5Usjg7yc8KnGpoDFdvdSTUAACQ80fYFZIlXACPRACAHNFpO96V8XUhWbEaoSnGqgckGdIg1AWNVqYLY7z83Elywf3M2NT9wKRdtm3OSnLQasiOs5S5Sv99yx9FKknCDernBk/1TVLt0DTGs0c68XRVbtijevuUuBM+8rv2540FdlHeA7BVQ3T83NSetCIdLqMrU2ZVScRmjQPQvy7lNN1t1LQkIapyE0b0iw1WfsrMltVYabAIQHwqoCJwRksNIcpFnyDlYEeoMaaxYUOh+cxIeibTNOF8koVUYPJA/OcpJEojb7c0D/n1ULSauHttVgY+y4Eu1kBMdVr+noBZxlIheauYhz2ESdb3dNNtYmy6VgLZCsRU6ixUxmEdf2Ej8QKwgpJ6Fp9aq4S1h5T9XUJHeTfei+xlEIMXsqEE8C0JoA41PzfhKqyMws6BVfyHqxUPh8cBIPirbNuM1PWwwU2qWQSNRO/w9w/oTaYVs9tK0GG1UNqa0miRZwQqgGJC3fUPnSTpmogcI822ysTdKsOz4+AlzoU8eqm80mJHEWM4X2qcbzc2v6PACzYQRPhsp7QnggtFpIWWGLVcO6bzBQAYEAIM9DaNOh6ZcAEKYp+zyEqIHuuSzrRQy7eRUvFG0HyHewj9sppEshkagBwJl/mpaRsOBbq8FGzfzUVpNUCzjNO5W+4VR6YHBclSsVQlkKwv8GJxTQFGNJ9L51XPNkbm0a2a+ed3UDUGXmkZuW36Ril8yn6mRt7niLBiPwPib9+yDliBI7s7G4lAHI4AeAlvvuNNa/7oEQAVhztG/O7OZVrJSVaOcrbzqTmsqksC6FRAsGGVSm7ap5SqTDhcOYUMFy6XxPUy3gnPQ3Swmc/qeqdhb0qUVCldkHXCD22SYay5wlU33E4xWFmfN5YPS/Io8LAMFg6kh7IHbhZC0wrOeWqTUgtg64F5p+CTTPZ6BFV7zJkkhxNHt261dDaH8FDB2q4fhUZHsw0AuhX5YypStdrMWK+hnxxBzPHRZQKVbKQrQLVd/aqSIZpUYhXQoJTdi6anxRc4kSHv/wlHDVXKxqc6dDqgWck/7mkf3AmfdUpLt/HEBQtdUEgOqm2GcbPRZjQtUq7/+zqqQ2PqL+hk+7JHKsn/4LCPoj5xA0VEMVKc1FUTVwUYKsALstLZYvFgCkMQIpTyM4+R40/R/weJfnFEiVPDhrEkKbZaaCmf5sCcjgSRhyBJAXTFN28QdzsYBK8VIWT95N9a3LlVzrbkfX8U6XRAVHANX4AlCpWbOvB+Z8Fph1rfKzahkud60FXLQ5OZm/OZdCK+HXntYMVDUCMAO7xk+rLl7hzzbeWHx9qrmIf0AtPvwD6rWvL/JeI/8AopPONU0J9vy7VH9sAZUV8OH/iy00ZGeRnXBfrFU1TUV765DGMAKTR2AEDqd/wShCCwI5GRGcpa6pdqfhNblDldugqR/qiPOLG7cWUFH14J2uv1448rrTHhoawq5du3D8+HG8/PLLeblnoYOcSHpk61KItqLoZp533Q3pC2siE3a42TcYZaGxg+gdppRTUerBHH350f2zay4GpjWp3wfDMBdIIv751lj8qkYIjEm165YB9XvjH55KV5NBNVYZnDJpWwTOA8P7gU/fT25JsNfSYtbmllpk1TQ1IghhZB1IlU5wVvjuVEqpxgBAaLNDed1uCeYqdLBfppRLilpOot3Z2Ym3334bJ0+exNGjR0PHe3p68Pjjj+P8+fNYsGABdu7cienTp6OhoQE7duzA2rVrcx54ujBvuriJjjPI1KVgWVEg1C5w/BQw8i5wcu9U5HGq39dkCwYnYxOie1yHm+Cr5wFanJ/ZTK8d0ddaM8XTG7t7jT5fBsyx6GocmtcUZcMUcFOkLTdCvOeiVwNj/05vwWxf8KaVy+ybWmWE8ADQcwikSh2cFRlVfg6AAaHXhiq2RZ/vhmAutxRQKZcUtZyWTR0dHdizZ0/M8c7OTqxfvx7d3d1YsGABXnjhhVxukxNuqm9dTthRnz3cinLhEzMdy1BC4BsARg9m5gKJZ8JOdjxXrB3mhf81fc1m4JYMAMa4CvrK9drp1hmPPl94lFBLqXLTNTNHXUpTwMM+P/dziCm6JQ1l8Qj44o/PWjCH7h/W1vSyb6mvtV/IPOZkqla4Frn1N+uTq77b2QZSTQWdxd632nxf7U49lR2oqFoFzXOtyt1OcD6xh3RaqJYKOYn20qVLUVtbG3FseHgYfX19aGtrAwCsXr0a3d3dudwmJ4q9SUK5YkecgWVFkUb8CG9I55tw5Eq8HtdVjSrY6+y/1Dyy8dUDmccJhJ9vBNRuv6pe+cQB0zder44bganrzb87/n3qbshswRyyuuRo0dA8i+DxXmnmR6uHKrTZEKIup17bmfS+FkKDpk2D7lmQdq9skgtWy9JYSq3Ht+0+7cHBQTQ2NoZeNzU1YWBgAADg9/uxfft29PT0YNOmTejq6oKmOf+Dy7zp4sKuOAPLihI8H2sJtXaDxe4CSdTjWgI492+g99fqnGwyHjKNE4g+X6uITfFq/qqZAjaZnhshHV+13dkd1m4X2lUITryBYKAH0hiGkGPQPFdD6NdkflGTTCuxFbJyW3lRPilqtot29KoynKqqKmzZssXuW6aEedPFhV1xBuGlLy1/K6BEoLpWfd+L3QWSqMe1r081J6m5JHEAV7pkGicQfn6i35t4QX7x7pPOgtmxtDfjX4A0oOsLMZWCNQEZfD/rQieZBme5LZjLrZRTiprtot3Y2IjBwcHQ6/7+/oiddyFh3nRxYGcxFeuPv3/YLDlaqQS7+mLn8rztLNITL3JaStVQpKouUsgLlfGQy+9NqgWzU9kdsT7OqT91dkRuZxqc5ZZgLjdTLlYN20W7rq4Ozc3N2Lt3L9ra2rB79260t7fbfRuSBvmqAJcpdqb4RHQJe1dFKwfM6l92pWeFynMmqAiWa5Ge6N2o7gW8M81ynkbkMyp2c38iEgm/c9kdLMNZbpSLVSMn0d64cSP27dsHAFi5ciVWrFiBbdu2oaurCxs2bMC2bdvQ0tKCnTt32jJYkh6FqgCX1thMAbQqY9kVZ6B5gIaVQP0N2btAohc50c8xUUUwIDczbvhuNDAOnD6ompWceQ8RncXimfuLdWGWLs6VsC0fHyeJpNStGjmJ9rZt2+Iev/LKK/Haa6/lcmmSA/luxZgOiRYSLWtig5pyIRtTbqKxSamaWAjNLGU6YAaLCVUlDbDXZC104Oz/AGcPAZWzzTSwsM5i08JKjxbzwiwTnCphW04+TlJe8Ce3xHCyNGYuJErxGj3gTA50rmMb/Qcw+FaYnzloRqgL5T8Pj7eMzjnOlvDvXfXFQHUjVOyUAUxElR4tpdK8uZawTYRby3ASkoyyaBhSTmTrI3TSzFrMpWQTtuaUqq9zVb0SaqEjbkUwIHszbvQzjyg9CrWbr26eKiM66zqESocW6/PMBqeyO8rFx0nKC4p2iZGpjzAfZtZiLiWbaGyhHGKrXKemfMv+wciKYNmYcZO1wIwoJQrA94m5sw8CJ14GZl0DzLw28x7ZbsCp7I5UPk4n+24TYjcU7RIjUx9hPvzfheyXnYqErTk1tcsNrz85rRmAAWiVqiJYtsFzyZ55+PfO1zfVSrOqEUBAlWY9+376PbJJYsqlwQQpLSjaJUi6FeDyZWa1O9jI6Txpa2zz2tUuNp2KYJmMPdkzb1mjXp99H/ANqetb0eOAqqnuP6WEOp0e2eVKOrvncmkwQUoLinYJkq6PMJ9maztKyTplyk82NiHSrwiWDqmeuTGpvnczrwEmPgU8VWHBcGaNdTlp7rzF1Ovx08C8WzJ8ni5PF4tHurvndNps0lROihGKdgmTykeYT7O1HcFGTpnyU43NTl9rus/cMw3wzoo8z4pg17xq0ZCqR3YiSiVdLB7p755ZfIW4Ey4ly5hCdEDLts1lPlLZnGrBGX2PdJ55vPOsCPaq2ilxFZpq7FExI/1FVimli0lpQEq/+TWT9oyp22wSUoxwp13muKUDWjFHoGdKus883nm1y4BAVAfCTGIDSiVdLJ4ZXGjNkIYPQsT+WYvePbP4CnErFO0yxy0d0OKZlaVU5mJPtbsiptNpomH5mqPPgxZr2s5kkVUqi5/4ZvDjAM5CiLkx58fbPZdCgwmmq5UfFG0CwLkcWbsIj/KGmQ7lH1YiVHMpMPL34vHJphvgFf3Mk/maw8/LZZFVzOl36ZLIDK5pGgwDMKQBLUzAEu2e3Vx8helq5QtFm7gGazc50A1c+EQFZE1rBqobCl9bHcg9wCuTQLtsF1lO1frOL4mDyCAugu65DNLoS3v37MYGE0xXK18o2sQ1WOlXZ94HKueYgVmW8IjsfLJ2pj3lEt2eT1+zW+IYEpO4g5emTYNe8RkAn4Hbds/pwnS18oaiTVxFcFxVAsvVJ2t32lOuoptPX3Myn7obcrfTDyJz1+45fZiuVs5QtImrsMsna3fOd66iWwhfc7iJ3W2526UQRJY97BVeztCGQlxFJrnlMggELsTmcDuR822JbjzSEd1C5MyH47bcbSuIzFPZgYqq/wtPZQf0imvLIgjLsjTI8P6wUJYGTWe6WqnDnTZxHal8sql2jdnsilOZje0I8CqUr9nNudtuDCKzg/K2NJQ3FG3iOlLlOacyfWdiis7EbJyr6BYqZ75UcrfLCTenq5HcoGgT1xIv7SndXWO6u+KM0rBsEt1858zb4U93QwBbKVKuloZyhqJNXEcygUh315jOrjhbs3GxF6qJJhfTvtsC2AhxOxRt4hrSEYh0d43p7IrLyWycrWnfqc5rhJD4ULSJa0hHIDLdNSbbFZdCyc9ERFsr0jXth38OcG8AGyFuhaJNXEEmpmq7orBLo+RnJKmsFUJXgh3tfoj3uekLyscSQUixQNEmriATU7WdUdhuLvkZz/efzFoxd1liQY/3ubOHgYlRVfs9GrdbIggpVijaxBVkY6q2IyDMLa1Lw0m0m56zJEVRGQM4816soEsJfHok9nOaR71nBAEtvKiNTZYItp0kJBaKNnEFhTZVuykiPNFuOpm1YvKc2jnHFfRDQMAPeCpjP+edA8xcBIz12meJYNtJQhJD0Sauwc2m6nyRzPd/7phaeMhA7Of0StOUHsdiEfQDehUAGftexXSg/gb1zy5LBNtOEpIYijZxDW40VeebZLvpoA+YdR3w6ftxrBXXA58eTeB+mAFc1DplOo/4XJiVww5LBNtOEpIc/vQT12GZqktNsBM1OMmEVI1L6r4IzF4MCC8QnFRfZy8Gapcnb1hSuzz+5+y3cqi2k/GYajtJSPnCnTYhBcbOqmKpfP+aJ7G1Ipn7IX9WDradJCQZFG1CCozdVcXS8f3HC6xLR5idDsiz2k5aPm0LKSV0D9tOEkLRJqSAONEWM9ddcaEj5dl2kpDEULQJKSBO1jcvtPhmC9tOEpIY/iYQUkBSBY6Vc1UxITQIUUXBJiQM/jYQUkCswLFEUdulFiFPCMkNmsdJSZKs53axwaIxhJB0oWiTksLO9Kl8kSpwzE0LEEKIs1C0SUlhd/pUPokOHHPjAoQQ4iz0aRPXkKpiWKr0qVwqjdlBphXPrAWInIhcgIzsd3ac5YCUBqT0m53ECHEP3GmToifdHaeT6VO5EDH+MWXmnrlIlQZNtGN2In+bsIMYcT95FW2fz4euri5MmzYN9fX1eOCBB/J5e+JS0jV5Z9NzOx+M7AdG/xvw9wP+YcCYAEYOAud6gJa74wt3sS5A3A47iBG3k5N5vLOzEytWrEBra2vE8Z6eHqxatQrt7e1Yt24dxsbGAADd3d1oa2tDZ2cnPvzwQ5w5cyaX27seOxpElDqZmLyLMX3KGr+/H/ANAggCmg4IAxj+GzD81/ifY/62/aTqIEZTOXEDOYl2R0cH9uzZE3O8s7MT69evR3d3NxYsWIAXXngBADA4OIh58+YBAObNm4eTJ0/mcnvXIqX6g338t0DvL9XX4b+p4yQSa8cZD2vHGc7cZfnqRpUewXFlEvcPx+6ojUngzOH4i7ZCL0BKc0HJDmLE/eRkHl+6dGnMseHhYfT19aGtrQ0AsHr1ajz88MNYv349GhsbMTAwgMWLF2NwcBD19fW53N61uDnCOd9kavIutp7beqX6Z0yoHXY4WgUQ9Cc2dRcif7u0I9bZQYy4H9t92oODg2hsbAy9bmpqwsDAAACgvb0dmzdvxsGDBzF//nzMmjXL7tsXPQwwyoxUrSYTPatiqbstdBV0NnIQQNiuWUqgqg6omJHY1F2IBUj4glLTgeB5YPQf6j23LyjZQYyUAraLtkxi462ursaTTz5p9y1dBQOMMsftFcNql6ugs+G/KZO4VqEEu3peeqbufC1ArAUlBHC+Dxg/BcgAIDzKvD9nierH7WbYQYy4Hdt/BRsbGzE4OBh63d/fH7HzLneKNcK5mCk2k3emCKGixGdcoXzYQb/aYc8ssoWHtaD0DwH+QTVuoQEwgPMngFPvAg0rCz3K3GAHsfRQQXl8PsWI7aJdV1eH5uZm7N27F21tbdi9ezfa29vtvo1rydbcS4rH5J0NQgB1XwRqlxXvwkOvBPRqtcOO9l/rlcDYv4H6G4pv3NmghKiq0MMoOpjHXvzktITauHEjVq5US++VK1di48aNAICuri48/fTTaG9vR29vL+67777cR1pCFFuEM8kf1sKjGIVP6MCMhbFxWlICVbVAwBcbrU9KCyuPHXIyIo/dCBwu9NCIiZDJnNAF5vz58zhy5AiuvPJK1NQkSFp1MTJYvLsuUp4YAeD97YBvwIx49yrBrr5Y/X/BN/mzWqpIaSAw/mbc6HqICngqO2gqzwOpdM/lYSXuxs3mXlKaaB5gXjswehCAVD+jQrC/d3mg8tgFYr/JU3nsdCkUGoo2ISQCt0frk2xhHrsboGgTQiJwe7Q+yQ7msbsDijYhJC5035QfzGMvfijahBBCADCP3Q1QtAkhhETAPPbihUsoQgghxCVQtAkhhBCXQNEmhBBCXAJFmxBCCHEJFG1C0kAGgcAF9ZUQQgoFo8cJSYKUwMj+yOpgVktNNj0ihOQbijYhSRjZP9VGVa9QfdBP/7d6r/YLhR0bIaT8oHmckATIoNphR9eWEJo6TlM5ISTfULQJSUBwXJnE4xE4z97ShJD8Q9EmBPEDzfRK5cOOh6dGNdIghJB8Qp82KWuSBprp6v+WTzv0GUO1qmTnK0JIvqFok7ImVaAZe0u7FykNsOkFKTUo2qRsSRVoNnep2k2zt7S7kFLCCBw220v6IUQVNF21lxTM0wPABY2boWiTssUKNNMrYt+zAs2sftLsLe0ejMBhBCePQggBAR2QkwhOHgUA6BXXFnh0hYULGvfDJRYpWxhoVnpIacAIfhgjQEIIU6iMAo2sOLAWNJCTEQsaI3C40EMjaULRJmWLFWgW/XdcGuo4zeBuZAJS+uO+I6UPyiRcnnBBUxpQtElZM3cZMHsxILxAcFJ9nb2YgWbuxQshquK+I0Q1gHI2n3BBUwrQp03KGiEYaFZKCKFB01tCPm0LKSV0T0uZB12ZCxo5GfMOFzTuoZx/ggkJYQWaUbDdj+ZZBL2iFRAVkAgAogJ6RSs0z6JCD62gWAsaKWXEcSklNL3cFzTugTttQjJABlXUuV5JgS9WhBDQK66F5rkGTGuKxFq4KB+2D0JUQ/e0lP2Cxk1QtEnalLNgsUWn+1BCHd+/Xa5wQeN+KNokJRQstugkpQUXNO6FSyySEkuw5ESkYI3sL/TI8gNbdBJCigWKNkkKBYstOgkhxQNFmySFgsXKaYSQ4oGiTZJCwWLlNEJI8UDRJkmhYClYOY0QUgwwepykhD2lWTmNEFIcULRJSihYU7BFJyGkkFC0SdpQsAghpLDQp00IIYS4BIo2IYQQ4hIo2oQQQohLoGgTQgghLiGvoj00NIQnnngCX/va1/J5W0IIIaQkSEu0Ozs7sWLFCrS2tkYc7+npwapVq9De3o5169ZhbGws6XUaGhqwY8cOzJo1K+sBE0IIIeVKWqLd0dGBPXv2xBzv7OzE+vXr0d3djQULFuCFF14AABw7dgxr166N+PfXv/7V3pETQgghZUZaedpLly6NOTY8PIy+vj60tbUBAFavXo2HH34Y69evx8KFC/H888/bO1JCCCGkzMnapz04OIjGxsbQ66amJgwMDCT9jN/vx6ZNm9DT04NNmzbBMIyk5xNCCCFkiqwrokkpM/5MVVUVtmzZku0tCSGEkLIma9FubGzE4OBg6HV/f3/EztsOrJ243++39bqEEEJIMWLpXSJLdNaiXVdXh+bmZuzduxdtbW3YvXs32tvbs71cXCYmJgAAH330ka3XJYQQQooZS/+iETINO/fGjRuxb98+DA0NoaGhAStWrMC2bdtw5MgRbNiwARcuXEBLSwt27tyJGTNm2Droc+fOwev1QtNYB4YQQkhpYxgGJiYmMGPGDHi93pj30xJtQgghhBQebl8JIYQQl0DRJoQQQlwCRZsQQghxCRRtQgghxCVQtAkhhBCXQNEmhBBCXAJFmxBCCHEJWVdEI5kxNDSEXbt24fjx43j55ZcTHnML8cbu8/nQ1dWFadOmob6+Hg888ECBR5kdx48fx65duzBnzhy0tLTg7rvvLvSQcmJgYACbN29GbW0tNE0rifr/H3zwAV566SUAwNtvv40//elPmD59eoFHlT1SSvz0pz/FuXPnUF9fj/vvv7/QQ8qZ/fv348c//jFaW1uxePFirFq1qtBDyplAIIB169Zh+fLl+Na3vlWQMXCnnYLOzk6sWLECra2tEcd7enqwatUqtLe3Y926dRgbG0t6nYaGBuzYsQOzZs1KesxpnJxPd3c32tra0NnZiQ8//BBnzpxxYAaJsWtu77zzDu644w50dXXhvffew9mzZ50cdkLsmk9PTw9uuukmbN26FaOjoxgfH3dy2Emxa05XX301tmzZgvvuuw8rV64smGDbNZ+//OUv+Pjjj+H1elFXV+fkkFNi15yEEKipqYHP58Mll1zi5JCTYtd8AODFF1+0vVx3prAiWgr+/ve/o6WlBTfccAOOHj0aOn7nnXdi3bp1aGtrww9/+EN4vV6sX78ex44dw1NPPRVxjXvuuQfLly8HAKxduzam13i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“<Figure size 576x504 with 1 Axes>”

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{
“cells”: [
{

“cell_type”: “markdown”, “id”: “51cec9ad”, “metadata”: {}, “source”: [

“# BL Lac blazarsn”, “A model for the luminosity function and cosmic evolution of BL Lac type blazars is presented in [Ajello et al. 2014](https://arxiv.org/abs/1310.0006), based on observations in gamma-rays with the Fermi-LAT instrument.n”, “n”, “We can use the results of this paper to build a BL Lac population that is able to reproduce the results reported in the recent [4FGL Fermi-LAT catalog](https://arxiv.org/abs/1902.10045) reasonably well.”

]

}, {

“cell_type”: “code”, “execution_count”: 1, “id”: “784f9c48”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:31.369063Z”, “iopub.status.busy”: “2022-02-09T16:34:31.368489Z”, “iopub.status.idle”: “2022-02-09T16:34:34.958840Z”, “shell.execute_reply”: “2022-02-09T16:34:34.958014Z”

}

}, “outputs”: [], “source”: [

“from scipy import special as sfn”, “from astropy.coordinates import SkyCoordn”, “from popsynth import (ZPowerCosmoDistribution, SoftFluxSelection,n”, ” GalacticPlaneSelection)n”, “ttttt n”, “from popsynth import SFRDistribution, BPLDistribution, PopulationSynth, NormalAuxSampler, AuxiliarySampler, HardFluxSelectionn”, “n”, “%matplotlib inlinen”, “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 networkx as nxn”, “import numpy as npn”, “import warningsn”, “n”, “warnings.simplefilter(“ignore”)n”

]

}, {

“cell_type”: “markdown”, “id”: “4925c867”, “metadata”: {}, “source”: [

“The work mentioned above presents 3 models for the BL Lac luminosity function. Here, we focus on the case of pure density evolution (referred to as PDE in the paper). In this case, the BL Lac population is parametrised as having a broken power law luminosity distribution, with an independent density evolution following a cosmological power law distribution.n”, “n”, “We work with a luminosity range of $L_\mathrm{min} = 7\times 10^{43}$ erg $\mathrm{s}^{-1}$ and $L_\mathrm{max} = 10^{52}$ erg $\mathrm{s}^{-1}$ following Ajello et al. 2014. All luminosities are in units of erg $\mathrm{s}^{-1}$. Similarly, the maxium redshift considered in $z=6$.n”, “n”, “We start by setting up the broken power law distribution (BPLDistribution).”

]

}, {

“cell_type”: “code”, “execution_count”: 2, “id”: “f860fd46”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:34.978159Z”, “iopub.status.busy”: “2022-02-09T16:34:34.967571Z”, “iopub.status.idle”: “2022-02-09T16:34:35.403554Z”, “shell.execute_reply”: “2022-02-09T16:34:35.402488Z”

}

}, “outputs”: [

{
“data”: {
“text/plain”: [
“Text(0.5, 0, ‘L [erg $\mathrm{s}^{-1}$]’)”

]

}, “execution_count”: 2, “metadata”: {}, “output_type”: “execute_result”

}, {

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Hy6ZA1bawl0lSwJBL9dBxWHAhlnOvhb1LYMHlsOEZqK0Oe5mkZGfIpXpKSYNeVwaXSs0aAeufgpfGw+6/hr1MUjIz5NIpyugCg++DoY8HH78yGVbcBAd3hLtLUnIy5FIDnXkRXPIL6HM17FoAC8bCpp9AbU3YyyQlE0MunYaUdDj7WzDiP6FjPpQ/Bi9/FfYuDXuZpGRhyKVG0CoHLngYhkyD2oOwZBKsKoPqd8JeJinRGXKpEXUeCZf8B/T+ZvDa8wWXwdZf+1avkpqOIZcaWWoGnHNN8HK1zF6weiosmQiVm8JeJikRGXKpibQ5Gy58Gs67Ffavh5evgHVP+FavkhqXIZeaUCwFuv1T8NrzroWw8Vlfey6pcRlyqRm07AgDp8KwJ4O4vzIZ/vbLsFdJSgSGXGpGnfLhMz+HMy+B1++GLf8V9iJJUWfIpWaW2hIG3w9nfgZeuwu2/CrsRZKizJBLIUhtCYMfgE7D4bU7Yetvwl4kKaoMuRSS1JYw5EHodGHwErWtL4S9SFIUGXIpRKkZMOSh4O1dV98O234X9iJJUWPIpZClZgRv79pxaPC2rtt+H/YiSVFiyKU4kJoRvE97hyGwqgS2/yHsRZKiwpBLcSKtFVzwCHQYDCtvg+1zw14kKQoMuRRHPoh5+0Gw6jaoeDHsRZLinSGX4kxaaxj6KJwxAFYWQ8Ufw14kKZ4ZcikOpWXC0MfgjPNg5Q9gx5/DXiQpXhlyKU59EPN2/eHVm2Hn/4a9SFI8MuRSHEtrA0OnQ7t+sOJm2Dkv7EWS4o0hl+JcizYw9HFoey6suAl2zg97kaR4YsilCGjRBob98EjMb4RdC8JeJCleGHIpIlq0hWGPQ9uzYfkNsOulsBdJigeGXIqQFu1g6A+hTW9YcQPsXhj2IklhM+RSxKSfAcOegMyesPx62P3XsBdJCpMhlyLo4zHfsyjsRZLCYsiliEpvH8S8dXdYNgX2LA57kaQwGHIpwtLbQ/6T0LobLLsO9iwNe5Gk5mbIpYhL7wDDnoTWubDse7DXmEtJxZBLCaBlRxj2FLTKhWVFsHdZ2IskNRdDLiWIlh2D0+wZXYMj87eXh71IUnMw5FICadnpSMy7wCvXwtsrwl4kqakZcinBtDwT8p+ClllHYv5q2IskNSVDLiWglmdC/ozgf1+5Ft5ZGfYiSU3FkEsJKiPryJF5R3jlX+Cd1WEvktQUDLmUwDI6BzFv0QFeucaYS4nIkEsJLqPLkZi3h1cmw77Xw14kqTEZcikJtMoOHjNvcQYsvQbeXRP2IkmNxZBLSaJV9pEj87aw5LvGXEoUhlxKIq26BjFPa33kyHxt2IsknS5DLiWZVjnBafbUDFj6XXi3POxFkk6HIZeSUOvcj8T8ati/LuxFkhrKkEtJqnW3IOYpLWHJ1bB/fdiLJDWEIZeSWOtuwWPmKS2CI/PKDWEvknSqDLmU5DLPCmIeSw2OzCs3hr1I0qlo1pAvWrSIcePGUVJSwpw5cwDYsWMHt9xyC+PGjWvOKZI+IrNHEHNiR2K+OexFkuqrXiEvLS1l5MiR5OXlHXN7eXk5Y8aMobCwkEmTJlFZWXnC7xOLxcjMzKSqqoru3bsD0KVLF+655x7at2/fsJ9AUqPI7Hkk5sCSiXBgc5hrJNVXvUI+evToo0fQH1VaWkpRURFz586ld+/ePPPMMwCsX7+eiRMnHvNr4cKFDBs2jGeffZY77riD6dOnN+5PIum0tekVXM+cOlgyCQ68GfYiSSeTVp9Pys/P/8Rtu3fvZsuWLYwaNQqAsWPHMnnyZIqKiujTpw8zZsz41O+XkZFBSooPz0vxqE3vIOZLJgW/8mcEj6NLik8NrmlFRQXZ2dlHP87JyWH79u0n/Jq5c+dy22238f3vf5/Ro0cDcPDgQUpKSigvL6ekpITa2tqGTpLUSNqcDcOehNr3jxyZ/y3sRZI+Tb2OyI+nrq7ulL+msLCQwsLCY27LyMhg6tSpDZ0hqYm07fPhkfnSI0fmrbuFvUrSxzX4iDw7O5uKioqjH2/btu2YI3RJ0df2nODI/PDB4Alw720Je5Gkj2twyLOyssjNzWXevHkAzJ49+xNH25Kir925MOwJqKkKjs6rtoW9SNJH1SvkxcXFFBQUAFBQUEBxcTEAZWVlTJs2jcLCQjZs2MCECROabqmk0LTLg/wnoOYALJ4IVSd+OoykZhSra8iD3SE6cOAAa9asoW/fvmRmZoY9R0oq+94IrpjWoi3kPx1c41xS0zpZ93wNmKR6O6MfDHsc3n83eMy8quLkXyOpaRlySafkjPNg6OPw/jvGXIoHhlzSKWs/AIb+EKrfCZ4Ad3Bn2Iuk5GXIJTVI+wEwbDpUv30k5rvCXiQlJ0MuqcHaD4Khj8Gh3cFp9kO7w14kJR9DLum0dDj/SMx3GXMpDIZc0mnrMDiI+cGdwWn2Q3vCXiQlD0MuqVF0GAIXPAIHK2DJ1cZcai6GXFKj6Tg0iHnVVlh6NRzaG/YiKfEZckmNquOwIObvHYl59dthL5ISmyGX1Og65cMF04KrpS25Oni9uaSmYcglNYlOF8KQh+C9t4L3ZzfmUtMw5JKazJkXBTE/sPlIzPeFvUhKPIZcUpM682IY/CBUbjLmUlMw5JKaXNZnYMgDULkRXpkcXD1NUuMw5JKaRdaIIOb718HSyfD+/rAXSYnBkEtqNlkjYPD9sL/8yJF5ZdiLpOgz5JKaVecCGHwfvLs2iHmNMZdOiyGX1Ow6j4Lz74V334BXroWaA2EvkqLLkEsKRZfPBjHf95oxl06HIZcUmi5/B4Puhn2r4ZXvQc17YS+SoseQSwpV9t/DoLtg3ypYZsylU2bIJYUu+x9g4B3w9quwrAhqqsJeJEWHIZcUF7oWwqCp8PYKWH4dHD4Y9iIpGgy5pLjR9VIYOBX2LoNlxlyqF0MuKa7kXAoDy2DvUlg+xZhLJ2PIJcWdnC/CgFLYswSWX2/MpRMx5JLiUu5oGFACexbD8hvg8KGwF0nxyZBLilu5X4IBt8Gev8IKYy4dlyGXFNdyvwznFcPul2HFjVBbHfYiKb4Ycklxr9s/Qf9i2P2SMZc+zpBLioTuY6D/LbBrAay4CWrfD3uRFB8MuaTI6H4Z9LsZds2HV2825hIYckkRc9ZY6Hcj7JwHr94CtTVhL5LCZcglRc5ZX4G+34ed/2vMJUMuKZJ6jIe+18POP8PKYmOu5JUW9gBJaqgeV0BdLaydBqtSgiuopfinmpKM/8lLirSeXwPqYO0jQCy46IoxVzLxP3dJkdfz68GRefljBDG/3ZgrefifuqSE0OvKIObrHodYSnAFtVhq2KukpmfIJSWM3lcBtbDuCYjFgiuoGXMlOkMuKaH0/ufgyHz9U0BKcNEVY65EZsglJZyzJ0BdHWyYERyZn3dbcLpdSkSGXFJC6vNtoBY2/AhICa6gZsyViAy5pIR19neC0+wbZwIxOO8HxlyJx5BLSlixGPSZBNTCxh8HH/e/xZgrsRhySQktFoM+3w0eM9/0r0HE+90c3C4lAkMuKeHFYnDONcFp9s0/AVKCK6gZcyUCQy4pKcRicO6/AHWw+bngyLzv9425os+QS0oasRicey3UHYY3/w1Igb5TjLmirVmf8rFo0SLGjRtHSUkJc+bMOXp7TU0NEyZMYObMmc05R1ISisUg7zo46wp46+ew9uHg8XMpquoV8tLSUkaOHEleXt4xt5eXlzNmzBgKCwuZNGkSlZWVJ/w+sViMzMxMqqqq6N69+9HbZ86cSWFhYQPmS9Kpi8WCI/GzxsGbPw+unGbMFVX1Cvno0aOPOYL+QGlpKUVFRcydO5fevXvzzDPPALB+/XomTpx4zK+FCxcybNgwnn32We644w6mT58OwOLFi+nQoQM9evRoxB9Lkk4sFgseI+9+Obz5s+DKacZcUVSvx8jz8/M/cdvu3bvZsmULo0aNAmDs2LFMnjyZoqIi+vTpw4wZMz71+2VkZJCSEvwdYsGCBVRVVbFgwQL27NnDpZdeSm5ubkN+Fkk6JbFY8Oz1jz4B7pzJPmauaGnwk90qKirIzs4++nFOTg7bt28/4dfMnTuX+fPnU1VVxejRowGYMmUKEDx+vnr1aiMuqVl9NOabZgEpcM53jbmio8Ehr2vAOajCwsJPfSx8+PDhDB8+vKFzJKnBYinQ76bgdeabjrwDXJ+rjbmiocEhz87OpqKi4ujH27ZtO+YIXZKiJJYSvH1rXR1sfDa49GmfiWGvkk6uwS8/y8rKIjc3l3nz5gEwe/Zsn3kuKdJiKcGFVXK/HFw1bf3TYS+STq5eIS8uLqagoACAgoICiouLASgrK2PatGkUFhayYcMGJkyY0HRLJakZxFLgvFsh50uw4WlY/6OwF0knFqtryIPdITpw4ABr1qyhb9++ZGZmhj1HUoKqOwyr74BtLwSPl5/9rbAXKVmdrHu+RaskHUcsFQbcBtTC+ieDI/Xe3wx7lfRJhlySPkUsFQaUBk+AW/dDIAa9rwp7lXQsQy5JJxBLhYFlQB2sezw4Mu91ZdirpA8Zckk6iVgqDCgLXmde/lgQ855fD3uVFDDkklQPKWkwcGoQ87WPADHo+bWwV0mGXJLqLSUNBt0JK+tg7bTgyLzHFWGvUrIz5JJ0ClLSYNBd8GotrHkIiEGP8WGvUjJr8Du7SVKySkmD8++Bzn8Hax6Et/4j7EVKZoZckhogJQ3Ovxs6j4I37oe3Zoe9SMnKkEtSA6W0gPPvhayR8Ma98Lfnw16kZGTIJek0pLSAwfdB1gh4/R7425ywFynZGHJJOk0p6TD4fjjzEnj9LtjyX2EvUjIx5JLUCI7G/DPw2l2w9ddhL1KyMOSS1EhSW8LgB6DT8ODKaVtfCHuRkoEhl6RGlNoShjwInS6E1bfDtt+GvUiJzpBLUiNLzYAhD0HHfFhVBtt+F/YiJTJDLklNIDUDLngYOg4NYr7992EvUqIy5JLURFIzYMg06DAEVpbA9rlhL1IiMuSS1ITSWsEFj0CHwbDqNqj4n7AXKdEYcklqYh/EvP0gWHkrVLwY9iIlEkMuSc0grTVc8CicMQBWFkPFH8NepERhyCWpmaS1hqGPwRnnwcofwI4/h71IicCQS1IzSssMYt6uP7x6M+z837AXKeoMuSQ1s7Q2MHQ6tOsHK26GnfPCXqQoM+SSFIIWbWDo49AuD1bcBDvnh71IUWXIJSkkH8S87bmw4kbYtSDsRYoiQy5JIWrRFoY9Dm3PhuU3wK6Xwl6kqDHkkhSyFu1g6A+hTW9YcQPsXhj2IkWJIZekOJB+Bgx7AjJ7wvLrYfdfw16kqDDkkhQnPh7zPYvCXqQoMOSSFEfS2wcxb90dlk2BPYvDXqR4Z8glKc6kt4f8J6F1N1h2HexZGvYixTNDLklxKL0DDHsSWufCsu/BXmOuT2HIJSlOtewIw56CVrmwrAj2Lgt7keKRIZekONayY3CaPSM7ODJ/e0XYixRvDLkkxbmWnSD/KcjoDK9ca8x1LEMuSRHQ8kzInxH87yvXwtuvhr1I8cKQS1JEfDzm76wKe5HigSGXpAjJyApOs6d3gFcmwzurw16ksBlySYqYjM5BzFu0h1eugX2vhb1IYTLkkhRBrbKD0+wt2sPSa2Df62EvUlgMuSRFVKvsI0fm7YKYv7sm7EUKgyGXpAhr1TWIeVomLPmuMU9GhlySIq5VTnCaPa3VkSPztWEvUnMy5JKUAFrnBjFPzYCl34V3y8NepOZiyCUpQbTuFsQ8pSUsvRr2rw97kZqDIZekBHI05umwZJIxTwaGXJISTGb3IzFPC47MKzeEvUhNyZBLUgLKPCt4NnssFZZcDZUbw16kpmLIJSlBZfYMrmdO7EjMN4c8SE3CkEtSAmvTMzjNDrBkIhzYHOYaNYW05v4NFy1axMMPP0xeXh5DhgwhLy+PX/ziFwD86U9/4ve//z1t2rRp7lmSlLDa9IT8J4Mnvy2ZFIQ9s0fYq9RY6n1EXlpaysiRI8nLyzvm9vLycsaMGUNhYSGTJk2isrLyhN8nFouRmZlJVVUV3bt3p3///kydOpUJEyZQUFBgxCWpCbTpHcS87nAQ8wNvhb1IjSVWV1dXV59PXLJkCb169eKSSy5h7doP3zboiiuuYNKkSYwaNYr777+f9PR0ioqKWL9+PQ888MAx3+Oqq65i+PDhpKSkcPDgQSZOnMisWbMAuO+++/jCF77AoEGDTrjjwIEDrFmzhr59+5KZmXmqP68kJbX964OQp6QfOTLvHvYinczJulfvU+v5+fmfuG337t1s2bKFUaNGATB27FgmT55MUVERffr0YcaMGZ/6/TIyMkhJCU4IHDx4kPLycm666ab6zpEkNUDbPsGz2ZdMhKVHTrO37hb2Kp2O03qMvKKiguzs7KMf5+TksH379hN+zdy5c5k/fz5VVVWMHj0agBdeeIEvfvGLpzNFklRPbfvAsCeD15h/8Jh569ywV6mhTivk9Twrf4zCwkIKCwuPuW3s2LGnM0OSdIranQvDnghelrZkElw4I7j4iqLntF5+lp2dTUVFxdGPt23bdswRuiQpfrXLg/wnoOZAEPOqE59QVZw6rZBnZWWRm5vLvHnzAJg9e/YnjrYlSfGrXV8Y9kN4f/+RmFec/GsUX+od8uLiYgoKCgAoKCiguLgYgLKyMqZNm0ZhYSEbNmxgwoQJTbNUktQkzuh3JOb7gifBGfNoqffLz+KFLz+TpKax77XgWuYtOgSPmWd0CXuR4OTd8y1aJUkAnHEeDP0hVL8dnGY/uDPsRaoPQy5JOqr9ABg2HQ7tPRLzXWEv0skYcknSMdoPgqGPwaHdwZvGHNod9iKdiCGXJH1Ch/ODmB/cGRyZG/P4ZcglScfVYfCRmO8I3jjm0J6wF+l4DLkk6VN1GAIXPAoHtx+J+d6wF+njDLkk6YQ6XgAXPAJVW4PHzKvfDnuRPsqQS5JOquOwIObvbQ2OzI15/DDkkqR66ZQPF0yD9/4GS74L1e+EvUhgyCVJp6DThXDBw/DeW8G7wBnz8BlySdIp6TQchjwEBzYfifm+sBclN0MuSTplZ170YcxfuQbefzfsRcnLkEuSGuTMi2HwA7B/Ayy9JrgUqpqfIZckNVjWJTDkAdi/zpiHxZBLkk5L1ogjR+bl8MpkeL8y7EXJxZBLkk5b55Ew+D54d20Q8xpj3mwMuSSpUXQeBeffC+++Aa9ca8ybiyGXJDWaLp+F8++Bfa8difmBsBclPkMuSWpUXT4Hg+46EvPvQc17YS9KbIZcktTosv/hSMxXwTJj3qQMuSSpSWT/Awy8A95+FZYVQU1V2IsSkyGXJDWZroUwcCq8vSKI+eGDYS9KPIZcktSkci6FgbfD28th2XXGvLEZcklSk8v5Agwshb1LYfkUY96YDLkkqVnk/CMMKIU9S2D59ca8sRhySVKzyR0NA0pgz2JYfgMcPhT2ougz5JKkZpX7JTjvVtizEFYY89NmyCVJza7b/wlivvtlWHEj1FaHvSi6DLkkKRTd/gn6/wB2v2TMT4chlySFpvv/hf63wK4FsOImqH0/7EXRY8glSaHqfhn0uxl2zYdXbzbmp8qQS5JCd9ZY6Hcj7JwHr/4AamvCXhQdhlySFBfO+gr0/T7s/DOsNOb1ZsglSXGjx3jImwI7/gQri415faSFPUCSpI/q+VWgDtZOg1UpwRXUUqzVp/JfjSQp7vT8GkHMHwFiwRXUjPnx+a9FkhSXen4d6mqh/DGIpQRXUIulhr0q/hhySVLc6nVlEPN1jxMcmZcZ848z5JKkuNb7KqAW1j0BsVhwBTVj/iFDLkmKe73/OTgyX/8UkAIDbjPmHzDkkqRIOHsC1NXBhhnBkfl5twWPnSc7Qy5Jiow+3wZqYcOPgBQ4r9iYG3JJUqSc/Z3gyHzjM0HE+9+S3DE35JKkSInFoM9EoBY2PgvEoP/NyRtzQy5JipxYDPpcHTwBbtO/BhHvd1Nwe7Ix5JKkSIrF4Jxrgphv/gkQC66glmwxN+SSpMiKxeDcfwHqYPNzwZF53+8nV8wNuSQp0mIxOPfa4Mj8zZ8BMeh7ffLE3JBLkiIvFoO8IqAW3vz5kY+nJEfMDbkkKSF8EO+6uiMxT4Vzv5f4MW/WkNfV1fHYY4+xf/9+OnfuzHe+8x2qqqooKyujdevWdO7cmauvvro5J0mSEkjsyGl1amHzT4Ejp90TOeb1etVdaWkpI0eOJC8v75jby8vLGTNmDIWFhUyaNInKysoTfp8//vGPvPXWW6Snp5OVlQXA3LlzGTVqFKWlpWzatIl33nmnYT+JJEkcifkN0P3y4Alw6x4PjtITVb1CPnr0aObMmfOJ20tLSykqKmLu3Ln07t2bZ555BoD169czceLEY34tXLiQjRs30q9fP2688UYWLlzI7t27qaiooGvXrgB07dqVnTt3NuKPJ0lKRrEY9LsBul8Gm2YFV05L1JjX69R6fn7+J27bvXs3W7ZsYdSoUQCMHTuWyZMnU1RURJ8+fZgxY8YnvmbXrl0cOnQIgLZt21JVVUV2djbbt29nyJAhVFRU0Llz59P5eSRJAj58k5i6Wtj04w/fRCbRTrM3+DHyiooKsrOzj36ck5PD9u3bT/g1hYWF3H777axfv56WLVvSvXt3zjzzTG6//XaWLVtGz549ad++fUMnSZJ0jA/ei72uLng711jqkbd3TSANDnldA85RZGRkcM899xxzW6tWrbj33nsbOkOSpBOKpcB5P+DDq6bFoM93wl7VeBoc8uzsbCoqKo5+vG3btmOO0CVJihexFDjvVqAONjwdfHz2hLBXNY4GXysmKyuL3Nxc5s2bB8Ds2bMpLCxstGGSJDWmD2Ke84+w/inYMDPsRY2jXiEvLi6moKAAgIKCAoqLiwEoKytj2rRpFBYWsmHDBiZMSJC/3kiSElIsFQaUQNcvwPonYeOPw150+mJ1DXmwO0QHDhxgzZo19O3bl8zMzLDnSJIiqO4wrCqF7b+HcyZD76vCXvTpTtY936JVkpR0YqkwoCx4Nvu6x4PT7r2uDHtVwxhySVJSSkmDgbcDtVD+WBDznl8Pe9WpM+SSpKSVkgYD7wiOzNc+AsSg59fCXnVqDLkkKamlpMGgO2FlLaydFhyZ97gi7FX11+CXn0mSlChS0mDQ3dD572DNQ/Dmv4e9qP4MuSRJBDE//27o/FlY8wC89R9hL6ofQy5J0hEpLeD8eyCrAN64H96aHfaikzPkkiR9REoLGHwfZI2EN+6Fvz0f9qITM+SSJH3M0ZiPgNfvgb/NCXvRpzPkkiQdR0o6DL4fzrwEXr8Ltvwq7EXHZ8glSfoUR2P+GXjtTtj667AXfZIhlyTpBFJbwuAHoNNwWH0HbH0h7EXHMuSSJJ1EaksY8iB0yofVt8O234a96EOGXJKkekjNgCEPQ8d8WFUG234X9qKAIZckqZ5SM+CCh6Hj0CMx/33Yiwy5JEmnJDUDhkyDDkNgVQls/0O4ewy5JEmnKK0VXPAIdBgcxLzif8LbYsglSWqAD2LefhCsvBUqXgxnhyGXJKmB0loHMT9jAKwshoo/Nv8GQy5J0mlIy4Shj8EZ58HKH8COPzfv72/IJUk6TR/EvF1/ePVm2Pm/zfd7G3JJkhpBWhsYOh3a9YP965vx922+30qSpMTWog1c+HTwHu3NxSNySZIaUXNGHAy5JEmRZsglSYowQy5JUoQZckmSIsyQS5IUYYZckqQIM+SSJEWYIZckKcIMuSRJEWbIJUmKMEMuSVKEGXJJkiLMkEuSFGGRu4xpbW0tAAcPHgx5iSRJTe+D3n3Qv4+LXMirq6sB2Lx5c7hDJElqRh/07+NidXV1dc285bRUV1ezf/9+0tPTSUnxkQFJUmKrra2lurqatm3bkp7+yYudRy7kkiTpQx7SSpIUYYZckqQIM+SSJEWYIZckKcIMuSRJEWbIJUmKMEMuSVKEGfIkVFNTw4QJE5g5cyYAixYtYty4cZSUlDBnzpyQ1yWfj98fr7/+OiUlJZSUlDBixAgqKytDXpg8Pn5fbNy4kWuvvZaysjJmzZoV8rrkcrw/l3bs2MEtt9zCuHHjQl4XXwx5xJWWljJy5Ejy8vKOub28vJwxY8ZQWFjIpEmTjonBzJkzKSwsPPpxLBYjMzOTqqoqunfv3mzbE1Fj3B/9+/dn6tSpTJgwgYKCAtq0adNs+xNJY9wXf/nLX7j88sspKyvj1VdfZd++fc22P9Gc6v1xvD+XunTpwj333EP79u2be35cM+QRN3r06OMeRZeWllJUVMTcuXPp3bs3zzzzDACLFy+mQ4cO9OjR4+jnDhs2jGeffZY77riD6dOnN9v2RNQY98cHfv7znzN+/Pgm35yoGuO++PKXv8yf//xn7rvvPt555x127tzZbPsTzaneH/65VH+GPOLy8/M588wzj7lt9+7dbNmyhVGjRgEwduxY5s6dC8CCBQtYt24dP/vZz/jTn/7E1q1bj75nfUZGhu9ff5oa4/6A4GpH5eXlDBo0qHl/gATSGPdFx44dKSkp4aabbiI9PZ2cnJxm/zkSxaneH/65VH+Ru/qZTq6iooLs7OyjH+fk5LB9+3YApkyZAgSPP61evZrc3Fzmzp3L/PnzqaqqYvTo0aFsTmSnen8AvPDCC3zxi19s/rEJ7lTvi23btvHEE0/w/vvvM3r0aDIzM0PZnahOdH8c78+lgwcPcvfdd1NeXk5JSQllZWVGHkOekOpzHZzhw4czfPhwAAoLC495XFCN61TvDwiOTNT4TvW+yMnJ4c4772zqWUnrRPfH8f5cysjIYOrUqU09K3L8q0wCys7OpqKi4ujH27ZtO+ZvvWpe3h/xw/sivnh/NA5DnoCysrLIzc1l3rx5AMyePdsj7hB5f8QP74v44v3ROLweecQVFxczf/58duzYQZcuXRg5ciR33XUXa9as4eabb+a9996jV69ePPjgg7Rt2zbsuQnP+yN+eF/EF++PpmPIJUmKME+tS5IUYYZckqQIM+SSJEWYIZckKcIMuSRJEWbIJUmKMEMuKVT79+9n7NixDBkyhPLy8rDnSJFjyCWFKiMjg6effprPf/7zYU+RIsmQSwksLy+PwYMHM23atLCnfKoWLVrQsWPH4/6zK6+8koEDB3LFFVc08yopOgy5FEGf+9znePnll+v1ub/61a+47rrrmnhR0/jJT37C7bffHvYMKa55GVNJx1VTU0NaWuP8EbFr166j1/v+qIcffpisrKxG+T2kZGXIpSSzY8cO7rzzTpYsWULr1q256qqruPLKK4HgSH/8+PH85je/YdOmTaxYsYK1a9dSXFzMm2++yciRI0lJSaFHjx7HPcp/+umnee6556isrKRz586UlZVx8cUXk5WVxXPPPdfcP6qUFDy1LiWR2tparr76avLy8vjLX/7CrFmzmDVrFvPnzz/6Ob/97W95+umnWbp0KbW1tUyePJkxY8awePFiRo8ezYsvvnjc771x40Z+9rOfMXv2bJYvX87MmTPJzc2t165vf/vbLFiwgNtuu41f/vKXjfKzSsnCI3IpiaxatYq9e/cyefJkALp3785XvvIVfve73zFy5EgAvvGNb9C1a1cAlixZQk1NDVdeeSWxWIzCwkIGDhx43O+dmppKdXU1GzZsoGPHjnTr1q3eu370ox+d5k8mJS9DLiWRrVu3snPnToYNG3b0tsOHDx/z8QcRB9i5cyddunQhFosd959/VI8ePfjBD37A9OnTWb9+PSNGjODmm2+mS5cuTfCTSPqAIZeSSNeuXenWrRtz58791M/5aLSzsrLYsWMHdXV1R2/fvn073bt3P+7XfulLX+JLX/oSlZWVlJSU8OCDD/LAAw807g8h6Rg+Ri5F1Pvvv8+hQ4eO/qqpqTnp1wwaNIjMzEyefvppDh48yOHDhykvL2flypXH/fzBgweTmprKT3/6U2pqanjxxRdZtWrVcT9348aNLFy4kOrqatLT02nZsiUpKf4RIzU1/18mRdR3vvMdBg0adPTX9OnTT/o1qampPPXUU6xZs4a///u/56KLLuLWW2+lsrLyuJ+fnp7O9OnTmT17Nvn5+fz617/ms5/9LOnp6Z/43Orqah566CGGDx/OiBEj2Lt373FfciapccXq6urqwh4hqWkMHDiQ9PR0vvGNb1BUVNQo3/Pyyy9n/PjxXHbZZY3y/U7km9/8JitWrGDQoEHMmjWryX8/KYoMuaQTWrx4Mb169aJDhw785je/obS0lBdffJHOnTuHPU0SPtlN0kls2rSJoqIiqqqq6NatG4899pgRl+KIR+SSJEWYT3aTJCnCDLkkSRFmyCVJijBDLklShBlySZIizJBLkhRhhlySpAgz5JIkRZghlyQpwgy5JEkR9v8BC+iRpyiPAnAAAAAASUVORK5CYII=n”, “text/plain”: [

“<Figure size 576x504 with 1 Axes>”

]

}, “metadata”: {}, “output_type”: “display_data”

}

], “source”: [

“bpl = BPLDistribution()n”, “bpl.alpha = -1.5n”, “bpl.beta = -2.5n”, “bpl.Lmin = 7e43n”, “bpl.Lmax = 1e52n”, “bpl.Lbreak = 1e47n”, “n”, “fig, ax = plt.subplots()n”, “L = np.geomspace(bpl.Lmin, bpl.Lmax)n”, “ax.plot(L, bpl.phi(L), color=purple)n”, “ax.set_xscale(“log”)n”, “ax.set_yscale(“log”)n”, “ax.set_xlabel(r”L [erg $\mathrm{s}^{-1}$]”)”

]

}, {

“cell_type”: “markdown”, “id”: “9d59c1df”, “metadata”: {}, “source”: [

“We now move to the redshift distribution. Following the paper, we parametrize this as a negative power law in $z$. for the purpose of this example, we assume that Bl Lac blazars emit with a steady state. This means that we need to set the is_rate parameter to False when defining the ZPowerCosmoDistribution cosmological distribution. What we mean when we do this is that our local number density, Lambda is not per unit time. We also want to survey the whole sky, so we integrate over $4\pi$ sr in the value that we pass to the Lambda. “

]

}, {

“cell_type”: “code”, “execution_count”: 3, “id”: “d2ec27b3”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:35.420830Z”, “iopub.status.busy”: “2022-02-09T16:34:35.420284Z”, “iopub.status.idle”: “2022-02-09T16:34:36.212917Z”, “shell.execute_reply”: “2022-02-09T16:34:36.212113Z”

}

}, “outputs”: [

{
“data”: {
“text/plain”: [
“Text(0, 0.5, ‘$\frac{\mathrm{d}N}{\mathrm{d}V}$’)”

]

}, “execution_count”: 3, “metadata”: {}, “output_type”: “execute_result”

}, {

“data”: {

“image/png”: 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”, “text/plain”: [

“<Figure size 576x504 with 1 Axes>”

]

}, “metadata”: {}, “output_type”: “display_data”

}

], “source”: [

“zpow = ZPowerCosmoDistribution(is_rate=False)n”, “zpow.Lambda = 9000 # Gpc^-3 sr n”, “zpow.delta = -6n”, “n”, “fig, ax = plt.subplots()n”, “z = np.linspace(0.01, 6)n”, “ax.plot(z, zpow.dNdV(z), color=purple)n”, “ax.set_yscale(“log”)n”, “ax.set_xlabel(“z”)n”, “ax.set_ylabel(r”$\frac{\mathrm{d}N}{\mathrm{d}V}$”)”

]

}, {

“cell_type”: “markdown”, “id”: “15f3c415”, “metadata”: {}, “source”: [

“Apart from their redshifts and luminosities, BL Lacs also have other properties. As a simple example, we can consider their spectral index, assuming that the gamma-ray emission is well modelled in the energy range of interest (0.1 to 100 GeV) by a simple power law. n”, “n”, “We assume these true values of these indices are normally distributed with mean, $\mu$, and standard deviation, $\tau$. Additionally, we recognise that these are reconstructed quantities in real surveys, with uncertain values. This is reflected in the error, $\sigma$, on the observed values.”

]

}, {

“cell_type”: “code”, “execution_count”: 4, “id”: “24d49186”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:36.216404Z”, “iopub.status.busy”: “2022-02-09T16:34:36.215856Z”, “iopub.status.idle”: “2022-02-09T16:34:36.219685Z”, “shell.execute_reply”: “2022-02-09T16:34:36.219246Z”

}

}, “outputs”: [], “source”: [

“index = NormalAuxSampler(name=”index”)n”, “index.mu = 2.1n”, “index.tau = 0.25n”, “index.sigma = 0.1”

]

}, {

“cell_type”: “markdown”, “id”: “7863b6b3”, “metadata”: {}, “source”: [

“We know that the Fermi-LAT detector cannot detect all objects in the Universe, and it is necessary to model some kind of selection function. In general, brighter and spectrally harder objects are easier to detect. We take this into acount by selecting on the flux, $F=L/4\pi d_L^2(z)$, where $d_L$ is the luminosity distance in cm.n”, “n”, “This selection effect will not really be a hard boundary, although we could approximate it as such. In reality, the probability to detect an object increases as a function of its flux. To capture this effect, we consider a SoftFluxSelection as follows.”

]

}, {

“cell_type”: “code”, “execution_count”: 5, “id”: “6c226665”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:36.243283Z”, “iopub.status.busy”: “2022-02-09T16:34:36.242683Z”, “iopub.status.idle”: “2022-02-09T16:34:36.732382Z”, “shell.execute_reply”: “2022-02-09T16:34:36.731863Z”

}

}, “outputs”: [

{
“data”: {
“text/plain”: [
“Text(0, 0.5, ‘Detection prob.’)”

]

}, “execution_count”: 5, “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”: [

“flux_selector = SoftFluxSelection()n”, “flux_selector.boundary = 4e-12 # erg cm^-2 s^-1n”, “flux_selector.strength = 2n”, “n”, “# This is what is happening under the hoodn”, “fig, ax = plt.subplots()n”, “F = np.geomspace(1e-15, 1e-8)n”, “ax.plot(F, sf.expit(flux_selector.strength * (np.log10(F) - np.log10(flux_selector.boundary))), color=purple)n”, “ax.set_xscale(“log”)n”, “ax.set_xlabel(“F [erg $\mathrm{cm}^{-2}$ $\mathrm{s}^{-1}$]”)n”, “ax.set_ylabel(“Detection prob.”)”

]

}, {

“cell_type”: “markdown”, “id”: “cf736b9d”, “metadata”: {}, “source”: [

“Finally, sometimes it is harder to detect objects near the bright Galactic plane. We take this into account by excluding $10^\circ$ either side of the plane in Galactic longitude using the GalacticPlaneSelector.”

]

}, {

“cell_type”: “code”, “execution_count”: 6, “id”: “96d00d68”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:36.736811Z”, “iopub.status.busy”: “2022-02-09T16:34:36.736311Z”, “iopub.status.idle”: “2022-02-09T16:34:36.739655Z”, “shell.execute_reply”: “2022-02-09T16:34:36.739219Z”

}

}, “outputs”: [], “source”: [

“gp = GalacticPlaneSelection()n”, “gp.b_limit = 10”

]

}, {

“cell_type”: “markdown”, “id”: “c325223a”, “metadata”: {}, “source”: [

“Now, lets finally bring all this together to make a simulated population. Here, we defined our luminosity and spatial distributions already, so we can use them directly in PopulationSynth, but there is also the BPLZPowerCosmoPopulation available as a quick interface.”

]

}, {

“cell_type”: “code”, “execution_count”: 7, “id”: “290fdead”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:36.745064Z”, “iopub.status.busy”: “2022-02-09T16:34:36.744002Z”, “iopub.status.idle”: “2022-02-09T16:34:36.745624Z”, “shell.execute_reply”: “2022-02-09T16:34:36.746053Z”

}

}, “outputs”: [], “source”: [

“# Main pop synthn”, “pop_synth = PopulationSynth(spatial_distribution=zpow, luminosity_distribution=bpl)n”, “n”, “# Add our selection effectsn”, “pop_synth.set_flux_selection(flux_selector)n”, “pop_synth.add_spatial_selector(gp)n”, “n”, “# Add our auxiliary param - spectral indexn”, “pop_synth.add_observed_quantity(index)”

]

}, {

“cell_type”: “markdown”, “id”: “ab59c26a”, “metadata”: {}, “source”: [

“Lets run it! The last parameter to set is adding some uncertainty to our observed flux values.”

]

}, {

“cell_type”: “code”, “execution_count”: 8, “id”: “c2a2d85c”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:36.753460Z”, “iopub.status.busy”: “2022-02-09T16:34:36.749548Z”, “iopub.status.idle”: “2022-02-09T16:34:42.372390Z”, “shell.execute_reply”: “2022-02-09T16:34:42.371576Z”

}

}, “outputs”: [

{
“data”: {
“application/vnd.jupyter.widget-view+json”: {
“model_id”: “4fc1841452714214bd9d5a4ddf79b602”, “version_major”: 2, “version_minor”: 0

}, “text/plain”: [

“Drawing distances: 0%| | 0/9254 [00:00<?, ?it/s]”

]

}, “metadata”: {}, “output_type”: “display_data”

}

], “source”: [

“population = pop_synth.draw_survey(flux_sigma=0.1)”

]

}, {

“cell_type”: “markdown”, “id”: “4a6c298c”, “metadata”: {}, “source”: [

“We can now have a look at the properties of this simulated population, such as the detected and undetected fluxes and distances.”

]

}, {

“cell_type”: “code”, “execution_count”: 9, “id”: “039fff88”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:42.391845Z”, “iopub.status.busy”: “2022-02-09T16:34:42.390424Z”, “iopub.status.idle”: “2022-02-09T16:34:43.083270Z”, “shell.execute_reply”: “2022-02-09T16:34:43.082221Z”

}

}, “outputs”: [

{
“data”: {

“image/png”: 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”, “text/plain”: [

“<Figure size 576x504 with 1 Axes>”

]

}, “metadata”: {}, “output_type”: “display_data”

}

], “source”: [

“population.display_fluxes(true_color=purple, obs_color=yellow, with_arrows=False, s=5);”

]

}, {

“cell_type”: “code”, “execution_count”: 10, “id”: “d2d95d8c”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:43.102735Z”, “iopub.status.busy”: “2022-02-09T16:34:43.099297Z”, “iopub.status.idle”: “2022-02-09T16:34:43.231110Z”, “shell.execute_reply”: “2022-02-09T16:34:43.231872Z”

}

}, “outputs”: [

{
“data”: {
“text/plain”: [
“<matplotlib.legend.Legend at 0x7f64650ee670>”

]

}, “execution_count”: 10, “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”, “ax.hist(population.distances, color=purple, histtype=”step”, lw=3, label=”All”)n”, “ax.hist(population.distances[population.selection], histtype=”step”, lw=3, n”, ” color=yellow, label=”Detected”)n”, “ax.set_xlabel(“$z$”)n”, “ax.legend()”

]

}, {

“cell_type”: “markdown”, “id”: “e320e3e5”, “metadata”: {}, “source”: [

“We can also check out the spectral index distribution.”

]

}, {

“cell_type”: “code”, “execution_count”: 11, “id”: “29cf5448”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:43.234838Z”, “iopub.status.busy”: “2022-02-09T16:34:43.234294Z”, “iopub.status.idle”: “2022-02-09T16:34:43.378306Z”, “shell.execute_reply”: “2022-02-09T16:34:43.378719Z”

}

}, “outputs”: [

{
“data”: {
“text/plain”: [
“<matplotlib.legend.Legend at 0x7f6465229910>”

]

}, “execution_count”: 11, “metadata”: {}, “output_type”: “execute_result”

}, {

“data”: {

“image/png”: 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”, “text/plain”: [

“<Figure size 576x504 with 1 Axes>”

]

}, “metadata”: {}, “output_type”: “display_data”

}

], “source”: [

“fig, ax = plt.subplots()n”, “ax.hist(population.index, color=purple, histtype=”step”, lw=3, label=”All”)n”, “ax.hist(population.index[population.selection], histtype=”step”, lw=3, n”, ” color=yellow, label=”Detected”)n”, “ax.set_xlabel(“Spectral index”)n”, “ax.legend()”

]

}, {

“cell_type”: “markdown”, “id”: “538d3d7a”, “metadata”: {}, “source”: [

“Let’s see the distribution of objects on the sky in Galactic coordinates:”

]

}, {

“cell_type”: “code”, “execution_count”: 12, “id”: “64ac3564”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:34:43.381427Z”, “iopub.status.busy”: “2022-02-09T16:34:43.380877Z”, “iopub.status.idle”: “2022-02-09T16:34:44.142463Z”, “shell.execute_reply”: “2022-02-09T16:34:44.142923Z”

}, “tags”: [

“nbsphinx-thumbnail”

]

}, “outputs”: [

{
“data”: {
“text/plain”: [
“<matplotlib.legend.Legend at 0x7f646530c670>”

]

}, “execution_count”: 12, “metadata”: {}, “output_type”: “execute_result”

}, {

“data”: {

“image/png”: 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“text/plain”: [

“<Figure size 576x504 with 1 Axes>”

]

}, “metadata”: {}, “output_type”: “display_data”

}

], “source”: [

“c_all = SkyCoord(population.ra, population.dec, unit=”deg”, frame=”icrs”)n”, “c_sel = SkyCoord(population.ra[population.selection], n”, ” population.dec[population.selection], unit=”deg”, frame=”icrs”,)n”, “n”, “fig, ax = plt.subplots(subplot_kw={“projection”: “hammer”})n”, “ax.scatter(c_all.galactic.l.rad-np.pi, c_all.galactic.b.rad, alpha=0.1, n”, ” color=purple, label=”All”)n”, “ax.scatter(c_sel.galactic.l.rad-np.pi, c_sel.galactic.b.rad, alpha=0.8, n”, ” color=yellow, label=”Detected”)n”, “ax.axhline(0, color=”k”)n”, “ax.legend()”

]

}, {

“cell_type”: “markdown”, “id”: “1a8215d4”, “metadata”: {}, “source”: [

“We can now imagine that by changing the input parameters, we can fit our model to the observations in order to have an optimal representation of the true BL Lac blazar population with this parameterizations.”

]

}, {

“cell_type”: “code”, “execution_count”: null, “id”: “959b5a1e”, “metadata”: {}, “outputs”: [], “source”: []

}

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{
“cells”: [
{

“cell_type”: “markdown”, “id”: “14da46a1”, “metadata”: {}, “source”: [

“# Stellar Mass-Luminosity Biasn”, “n”, “Suppose that stars have a mass-luminosity relationship such that $Ln”, “\propto M^3$. If we have a flux-limited survey, it will bias usn”, “towards observing more massive stars which are not representative ofn”, “the full mass distribution. Let’s see how to set this up in popsynth. n”, “n”, “## Setup the problemn”, “First, we will assume that we have some initial mass function (IMF)n”, “for our stars that describes how there masses are distributed. Forn”, “simplicity, we will assume that this IMF is just a log-normal distribution:”

]

}, {

“cell_type”: “code”, “execution_count”: 1, “id”: “96b057c3”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:55.142933Z”, “iopub.status.busy”: “2022-02-09T16:35:55.142389Z”, “iopub.status.idle”: “2022-02-09T16:35:58.757978Z”, “shell.execute_reply”: “2022-02-09T16:35:58.756978Z”

}

}, “outputs”: [], “source”: [

“n”, “%matplotlib inlinen”, “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 networkx as nxn”, “import numpy as npn”, “import warningsn”, “n”, “warnings.simplefilter(“ignore”)n”, “n”, “import popsynthn”, “n”, “# create a sampler for massn”, “# we do not directly observe the mass as it is a latent quantityn”, “n”, “initial_mass_function = popsynth.LogNormalAuxSampler(name=”mass”, observed = False)n”

]

}, {

“cell_type”: “markdown”, “id”: “b898004d”, “metadata”: {}, “source”: [

“We now assume the dependent variable is the luminosity, so we need an”, “DerivedLumAuxSampler that generates luminosities given a mass:”

]

}, {

“cell_type”: “code”, “execution_count”: 2, “id”: “0178dfb9”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:58.765707Z”, “iopub.status.busy”: “2022-02-09T16:35:58.764378Z”, “iopub.status.idle”: “2022-02-09T16:35:58.766304Z”, “shell.execute_reply”: “2022-02-09T16:35:58.766719Z”

}

}, “outputs”: [], “source”: [

“class MassLuminosityRelation(popsynth.DerivedLumAuxSampler):n”, ” _auxiliary_sampler_name = “MassLuminosityRelation”n”, ” n”, ” def __init__(self, mu=0.0, tau=1.0, sigma=1):n”, ” # this time set observed=Truen”, ” super(MassLuminosityRelation, self).__init__(“mass_lum_relation”, uses_distance=False)n”, “n”, ” def true_sampler(self, size):n”, ” n”, “tt# the secondary quantity is mass n”, “ttn”, ” mass = self._secondary_samplers[“mass”].true_valuesn”, ” n”, “tt# we will store the log of mass cubedn”, ” self._true_values = 3 * np.log10(mass)n”, “n”, ” def compute_luminosity(self):n”, ” # compute the luminosityn”, “tt# from the relationn”, ” return np.power(10., self._true_values)n”, “n”, “n”, “luminosity = MassLuminosityRelation()n”, “n”

]

}, {

“cell_type”: “markdown”, “id”: “1e8cceab”, “metadata”: {}, “source”: [

“Now we can put everything together. First, we need to assignn”, “mass as a secondary quantity to the luminosity”

]

}, {

“cell_type”: “code”, “execution_count”: 3, “id”: “02a53fd1”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:58.771731Z”, “iopub.status.busy”: “2022-02-09T16:35:58.770476Z”, “iopub.status.idle”: “2022-02-09T16:35:58.772317Z”, “shell.execute_reply”: “2022-02-09T16:35:58.772712Z”

}

}, “outputs”: [], “source”: [

“luminosity.set_secondary_sampler(initial_mass_function)”

]

}, {

“cell_type”: “markdown”, “id”: “95e82cb2”, “metadata”: {}, “source”: [

“Finally, we will use a simple spherical geometry to hold our stars. Wen”, “will also put a hard flux limit on our survey to simulate an”, “flux-limited catalog.”

]

}, {

“cell_type”: “code”, “execution_count”: 4, “id”: “42049cda”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:58.778413Z”, “iopub.status.busy”: “2022-02-09T16:35:58.777153Z”, “iopub.status.idle”: “2022-02-09T16:35:58.779007Z”, “shell.execute_reply”: “2022-02-09T16:35:58.779420Z”

}

}, “outputs”: [], “source”: [

“pop_gen = popsynth.populations.SphericalPopulation(1, r_max=10)n”, “n”, “# create the flux selectionn”, “n”, “flux_selector = popsynth.HardFluxSelection()n”, “flux_selector.boundary = 1e-2n”, “pop_gen.set_flux_selection(flux_selector)n”, “n”, “# now add the luminisity samplern”, “n”, “pop_gen.add_observed_quantity(luminosity)n”

]

}, {

“cell_type”: “markdown”, “id”: “c5b2be44”, “metadata”: {}, “source”: [

“Now let’s draw our survey.”

]

}, {

“cell_type”: “code”, “execution_count”: 5, “id”: “b476212e”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:58.783320Z”, “iopub.status.busy”: “2022-02-09T16:35:58.781515Z”, “iopub.status.idle”: “2022-02-09T16:35:58.996430Z”, “shell.execute_reply”: “2022-02-09T16:35:58.995920Z”

}

}, “outputs”: [

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“Drawing distances: 0%| | 0/4125 [00:00<?, ?it/s]”

]

}, “metadata”: {}, “output_type”: “display_data”

}

], “source”: [

“n”, “pop = pop_gen.draw_survey(flux_sigma=0.5)n”

]

}, {

“cell_type”: “markdown”, “id”: “56364fd7”, “metadata”: {}, “source”: [

“We can now look at the distribution of the masses:n”

]

}, {

“cell_type”: “code”, “execution_count”: 6, “id”: “e686650f”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:59.005642Z”, “iopub.status.busy”: “2022-02-09T16:35:59.005130Z”, “iopub.status.idle”: “2022-02-09T16:35:59.184017Z”, “shell.execute_reply”: “2022-02-09T16:35:59.183562Z”

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}, “outputs”: [

{
“data”: {
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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(0,20,50)n”, “n”, “ax.hist(pop.mass,n”, ” bins=bins,n”, ” label=’all’, n”, ” color=purple,n”, ” histtype=”step”,n”, ” lw=2n”, ” n”, ” )n”, “n”, “ax.hist(pop.mass[pop.selection],n”, ” bins=bins,n”, ” label=’selected’,n”, ” color=yellow,n”, ” histtype=”step”,n”, ” lw=2n”, ” )n”, “n”, “ax.set_xlabel(‘stellar mass’)n”, “ax.legend()n”

]

}, {

“cell_type”: “markdown”, “id”: “85e52d32”, “metadata”: {}, “source”: [

“We can see that indeed our selected masses are biased towards higher values. n”, “n”, “Let’s look in the mass-luminostiy plane:”

]

}, {

“cell_type”: “code”, “execution_count”: 7, “id”: “d21b8176”, “metadata”: {

“execution”: {
“iopub.execute_input”: “2022-02-09T16:35:59.202219Z”, “iopub.status.busy”: “2022-02-09T16:35:59.190673Z”, “iopub.status.idle”: “2022-02-09T16:35:59.783442Z”, “shell.execute_reply”: “2022-02-09T16:35:59.782274Z”

}, “lines_to_next_cell”: 0

}, “outputs”: [

{
“data”: {
“text/plain”: [
“<matplotlib.legend.Legend at 0x7f26c0821bb0>”

]

}, “execution_count”: 7, “metadata”: {}, “output_type”: “execute_result”

}, {

“data”: {

“image/png”: 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“text/plain”: [

“<Figure size 576x504 with 1 Axes>”

]

}, “metadata”: {}, “output_type”: “display_data”

}

], “source”: [

“fig, ax = plt.subplots()n”, “n”, “bins = np.linspace(0,20,50)n”, “n”, “ax.scatter(pop.mass[~pop.selection],n”, ” pop.luminosities_latent[~pop.selection], n”, ” label=’not selected’, n”, ” color=purple,n”, ” alpha=0.5,n”, ” s=10n”, ” n”, ” n”, ” n”, ” )n”, “n”, “ax.scatter(pop.mass[pop.selection],n”, ” pop.luminosities_latent[pop.selection], n”, ” label=’selected’, n”, ” color=yellow,n”, ” alpha=0.5,n”, ” s=5n”, ” )n”, “n”, “n”, “n”, “ax.set_xlabel(‘stellar mass’)n”, “ax.set_ylabel(‘luminosity’)n”, “ax.set_yscale(‘log’)n”, “n”, “n”, “ax.legend()n”, “n”

]

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”, 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”, “encoding”: “base64”, “path”: [

“x”, 0, “data”

]

}, {

“data”: 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”, “encoding”: “base64”, “path”: [

“y”, 0, “data”

]

}, {

“data”: 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DnEDUTW5BO1E7QTCxpD994AjAey3PQGWJMMC6EvA/KzxZvq9gkEBTBoPASfBmwUwFjkAuuBJAIn62v0Nk6cBz+VpAZNGoQEsSp0GeqhbAJE6mwYUKisDCodi/0dbDQF46kMCsB2vAY/d3P5If/77nLug/SaSewI3q/cBOfSFB62LCP2RVuEDhxhfBBHEAwTZdPcD2KWo/vwYZwAKuucDiErlAlK/nwJWwZsDyCFFAkTcJPuRQhb/3JLbAh0SIwCKc1ECc9fI+8PA5QS6ZpsBBkDlB6NPcv7c6n0CScoY/9bOdwFbUAT9Dr5BAs50dwPwjHkEqfDbBrECKwBjjDcDLnQDBzHBNQZvMkkBLO81AxtNbwEDWf8CmuwJBNanvwNqgYkHm42fAnq1mQTdeR0FvxhtB8qjQQNOoIEFA15fA9z4ywVQvhsCfLG7AMiziQIA8N8DNW5lANYXbwKFZ7j8lI6ZA+eApQYFZZ8Fgz5XARlAJwEqyDUFxIBzBbyRGQWV/qb9mIMLAHAM/QHDSpkBGCoBBapwVwbiAQsBZE6LABQXoQGmsKkEjn2hBdr8zwfabLMGVFhc/aGwBQTzpXkCgTc3A93v+v9HE00B0PX7BiZeXwI0d1j/eBe/AGAtbQWfgisCSELnAvrWtwBYy8T+Zv4jAKW6/QE+cDr9tUGXA//SqwAr3AEGFSMW+Cdx1v7GPA78MAqnASC/3wDZqEsBRSQzBlFVwQLPpe0D55uNA0HAdQQFFzT/LDmHAIpwpwUw118DDRuFAdYSAQLqMir83PerAfP+uPxvOQkHNyc9AkwI5wJtwuT95oY5AHia1wFOp8kAMR8vAGoh7QIEK30BJcKe/HBeDPrpQrsBsBVVBkJDqv10Qvj9+Oy9BXtcgwRmpCEAJKZ7A6MiBQBfrqT+leY3Ap5nYwCATmD8MMytACPAQvklJVsD90M7ArPkrwJhWYT8UNKpAhTmdwHLVRkDoJuI/rM0aQWJW9sCaFqK/e5QGQdhEiUCIILnA4kobvjDjmcDfJU1AGbsEwR3P2j+OzkRBc1WcwFcsScHTOKDBa2hkQI6z1cBZgW/B8JIQQWArM0AtAetAbR7ZQGepA0AsBH4+mP6GwPlgNUFctJpAn0bTP7GftMDBWjlAdhKoQI/fSMCQymPBO3/ewFW5AcG2zZ3APjPdwAngpUD/xldAzmCVwJ6uxMA8y3FAPDIcQMUjTcD+1efA9FPWQAFMncGntca/OiQqQHpyEUG/Qfe/pTHlv225AkGc4kHA122aP+CWBkBrZuLAsIVHQMvfKb8pLh3APoHSvHCrgcAXEnpAqSypwD74gkB9a+Q/DZj/v6MZ5z8yd05BiWPhv14ojMAJpy5BZuEgQQvyBcHJjgLBS3fEwBKNj0CX+9LADrk1QXnEf0An+CpB36UawYsu+sCZ24XAx3sNwdgQZ0GTGHHBLsTFQM8248AHc8HATNWrPzNpkT7UWA5BXPCBwCKM1EAQpCpBGrSpwP1I2sDcKjHB82XxwL9ywj/bvQXBkfU7P83xXsGSURM/L49sQUowBEA4dR7BQVUbPhA/HkCD0a1AJiGzvxOPjMD3On7B43+DPoqp6UAs3QVAVQzZP5EMDsFknJM/xIjqQNZepUD6LilALcw0wdYCOUBPIPTACaHuwE8LHUEjNQNAXXgBwZe8lsC+dTLBBD2Ev0wFOsFUYzrAt8ghQYohpcABpBRANbOkv4cRDUEaTTHBbKJFQHkBab+YnBhBAWW3QOSiWcALL5TAN9xYwC6610DTFKLAkut/QXexQUCqMQPAiMhYv8gZGsHEw8tAKGfZP7TobkFdGo6/SpbVwFZHbUEc58BA2ruAwZ2I5cAMHxfBgHjtwKUoAsHwqjbBynQtwYWClkD9ysPAjIcRwd7tY8B89yDAq0woQSFQu8BoSWTApziLwG9rIEEljxfB7YbCwMIgW8Cas5lA8PcJQXLBWD/ExDXAh0fCQHbj4sBmFBnBOQSBQLEPIMFPnJ/ABeyEwXEWP0G7jQ/Bf4KSQK+P3cCGhwRBCg36wO6+MEHYPXdBidJJQDBr8sC42MZAEqBnQV+dTED8oZzAW9SxwA7PzcDUKPxAKd8+QGl8z8C0GpBAicdOQDAInsAwaq+/MnXLwG8lO8BOu0vAutIAwcy4z8BZJnxACvhFwOnEnsBX/RVBPeyiv8wtjcCW+Fi/zp0IQSaUlcCsja1A24ZrQEInHMEdcmG/+p1cP/PspECZNN9A2E9uQWeWhcF0FJhA39JHQOa9JMDA8qxAAQaoPy8s68CZeE5B03tGQb3uhcC6bFvB9dudwBtAz8AahFlBboZRQL8kMb6vJaVAX8S2P0uZuUDlxQ/B7cChQE+2pkAIZ2tARgFHQUWBiEBAtrPAp6Azwe4AZEB+Ku9AmSkxQQG/kEAvIuo+/o7QwCPE20Dcc2fAzAJQwPqSaMEaC/VAY6Z4wXuVwz/m0xnB/k4YQJk/fECrOKPAe1UuQQhrDj+3OppAqOsZwLToJ0HO99PAjx/dvxwJ6sCwy0LBU6jDwE6xt8BJj/BACIERwXi6pUC2GfdANcXjP9AJHMHFNJ0/Z2VgwGBGJ0EDMjnBBcAfP5hTlEAnCZ5AdJgiwVe9l0Eg9bZAklkwv5gigEDzXoTASLiGwHnv4cAFNMfAo3RjQLEZqD8awudA8JalQCjl9EBSELlAfGgvwVw3PMAqaNZA6Sbsv3CxFEE8Q1BBgFrmvH3PmEAcQ8G+ylLfwH/4n0AKw6FAdJPwQKtXE8F40TrAmapKQfECasGqnwRAi/yaQIabmsDZ71bBib4fPTLqkr9zJPG/MUUWvombHcFuPSTBtX+xwMtyI0EdrRxB5iMCwfliZMD/9I0+0yFuwbTO3MCRf+xADSlJvwbsksHngYLAZJUQwYUGDcHC1AG/RvESQa95ID/jhINBzA9QwSl/lUDFRcBA2KpCQX2OMT/DAaJA07VaQBOykEB4z8JA87uVQEuodMCHm4i/TCUqQRc000C29yzBcbp6QCR+JcHfb67Ao0KKwXHhoECyX6jA+DxrQHNHo8APzL6/RNBvwAc/W8AD/7pAr/sWQZ59lcHRhV9B9vSJwIO2PUCugjhBATEDwEaXhcBJuHJABFz0QMqXB0HEMPxA4NZTwOblhMHEoCzBzqDbQBK6OEHv55NAJ0c0wF+XQsDo7eg/vP8bQSQhcMF9Jma/4UOBQCi5Xz+XVFPAF1V2QI6qkEDWZRpBX/l4PysTS0C1FrM/Xj9yQCOpIz9innxAogsiQN3Y28Dr4B++o18+QZd/BkEzNCtB13NXQdDWUMDNGum/fkkmwdJg175PL1PBvqr7P7AZ+74AQhfAhInBQP60hsGYzqVAx0Q5wdYEv0DXIxHBJ6sXQWwU1cAm/ZdAHyT4v9lcr8Dv+qfA0aCVPyxCIUCCzDlBV3OkwEO3ikDfPlnB7H4rwXf7lr89PJ1BowhoQFaDtEBpYanAtNRnQeaqB0GXZKjAjn6kQH4ZK8HOEJi/Le2qQcxiosB3KPg/K1j4v47b2kA7Q+JA7YrwQPyWuUAEghNA1TXpv87lnT+O6SxAcXkLvyYSA0HU3H/AR/1eQCxd2UAaGps/BuZvPilLJsFe3VDAGJM4vgM1fsG8ZRzA/xSVQOwbN0GjQr0/u5SAQO/G7sDtkb9AfqE+QHWpEUErnKi+ER/OQEqaMz/FSSJB/l6VQJX8ekAT7dZAxskXQKTeJUHBRMlA8o4Bwaw2BUD3ClzB+FEnwXtHuEAcoqpA5BfbP5Ns2b/UrSTBtzeOQBvcd8F65QS+MW8lQcwrFEGRcqLAi8iDv9W2Cr/q1tS9j3RmwPG81UBxP99A0eWzQN71dUGJ08xAnh6lQAO2iEAK8kTApSXHQHI8GUGevgnAbHWFwSst6UCXP/a/tvEov9HfZr/zEqVATP3XwKAis0BhITTBk9gLwQfjH8GZwApBixTpv+1gicAiwEU/CcjpPlJVsMDDgmFBl3S8QKvOu8D7C7NA+WpFQMwF1MCNXw1Bh/WQQD4r90DIAdpAzEi1wHMijsBnw3zB1d3xQMQfUkHpQBtAkHJHwFaqT8FZu2fAkmhmwIz4IcEmmci/PFOEQQeFokBBcQI/t50MwIIresEtXo5ADBUYwcCYKMF4Vtm/Ah7ZwOvVt0CxQqo/PjW5wKXqcsApoetAeEp8Qb9FPj8H6enAig7CQCXn2sDaQNJAI50oQcwpL8ESPjdAAIvMwK4oxsA2ToHApiGsQBND3UBf1E/Beh/NwHGcqED9SULBHNNYQf5TeEG1jsRAqe0iwaEY7ECFa5c/T6P1wHEURUBM2bs/z9M4wEjPH8E0RZNB1C0XwR+k2sAteJdAOR/OwDVfDcHwvAvBydsDQdrcDEHgmIy/JWhJwNiW3sBloSnAM9zPQPzGkD4WMefAWLZkwXd7A0EqOxxBQII3wenzlcC+Y/NAVz9HQTprq0DUxuPAq5sXweMY8740nqNATl1/QdM2ssAFHWE+MTeYP9EFxsAA8FrALGvWwAw/CsG901DA4sPAPwzz+sD5BbXA8/EOQTB1BcDKflS/vdanQPf5z75ufK/AloA8P0S0HUEOUclAYoNzQH2HHED+2tO/Pi7bwPINCsFtmZ1AyMleP/HVOMDeQsE/qa7cwLXftUDj8ZrARc5CQIfP6UDry7RAMXyPQJpXwL+BM09Bs263PRIyJkGM64bBrLzgv/ugjEB2FxvBhleBQGkfrkDUmwhB8pExwL/G0UBRu9fAUVvowJ8IZ8CydoJAFOw8QEUMIMGOo0bAbs0lQZKekr9U+31AhPnav2/MFMEKJhBBExLYwKJiAEF/irnAiyZGP0uuij5vzu0+qs7RQB1zzz4Mc5y/1HYyQZrdlsCU2mnAmxHrwNpwQT5oObvAp0Bhvj+1pUBM9ZnA0ui0wM/eXUE1VS/A+I0cQQBb3sDTWtPAHpbsQNyLrUC1HfhAJHHRv/3W2L3t9ZLAxAopwRk8h0AwkYM/q7IfwZeVesFuQ2c/C9o8v3coI0EMdiA+cpfNvw/Mv8BO4kLAxlv3vvMmn8Aj+jdAG6jfQMiAmEB/KyNBHKANQSYkDsAmE7M+5YYbwaoNccDfAjjBowr/QJPiIUEOh3hAInqMPx13qUCvffPAf506wCZjUMD1Yt/Ag9/aQFzJ1sCIOopAJ6fbQAggfT8dpQ5Aq4ppwAgcmEAPt5lA6yzDv9xPiMAcHCNBHFHUwEYBd0Ei4Ym+0gScv35Enb81A8jAjlSPQOZbjsClk5/AqkLXwGFEx8DOcwPBkNgtQcbN8cCmh2lBoD89wC3yqEDugbtAgrebwKH/ib/HeHpBBcNfwaIJJsBTXTjBHZ8lwNvUmr55jnTAnmq+wJTRGUGIaBu+e0QEwWajWMAH9sFAX760QKFbEUDfDg9AeMu9QGTbAMEoHyg/b0xMwfgjk0BCOGDBMVyawFxbSMBbEg9BwYYCwGlFJ0FXhH/A2Hkhwf4uzkCF1inBCnBZwEeqKsHPkmE/wwmxQIdLbUC1b6q/AyXLwEqqWEE2ewhBTevJv/G13cChB8fAJacMwb6VLsDX5yFBypqBwMJcBUBf9NtAxaqFQCigF0HiwpHAsx5Qv7u+f0AOeJxAnEBIPgGTzcBnn75AHHHSvwgjDEFUAthAbKMTwFfPML8/L9G+2i5owVD7rkCmFQfBBHdgwMU4KUGbbde/yUqIQPnPur/1KQlBduq/P+fKlcDsBn3Azo63v6BLhUDxhR5AruYrQTUiLkGdL7TANKFaQalvU0G7HitBa8YeQDDe7EBUBxPAVywUwQpHeMC2OmY/pWCSv4HDzkB5He5A3kn3wK49bcDu8zTBjq78v8jBZ0E2JKHA6Rf+v1imQUGVC6xAb2aSQCX0G8E7y2a/XXCgwBrmM0EUt0vB7dfcQCLXIcF4NrfAM+DUQAmlwEDIKG/Bj+IaQeSmhUFHJyzBLUQ3wfAMg0BZJYNAVowTQJzZLkFRZPdAaninv8o2lsBB2wLApVoQQa43JMHAZn5B5hxMwAowVsFLJMDAZBLGv9Wvsb9EhMpA4M1yvz2ppkBqRNS/o2GPv/L6KcG0EZS/OkB3waJd4b9QZndAl8HNwNDyjUCbn5nACNEXwCdYsMDx60lBAx9dQNauK0CnpCdBKl13P2PE3MCHKxLBdazeQH8vpECBY4S+Bq+CwNhD1kBDgzzBSKMiwYYSM0FNIitAqsNeQTRdJ0CT+ftA0HQBwF6BWEGlAD1BUIUFwHUpM8C3DHnABhG3wAJ4P8G9PSJBSon2v/9GrUDO0jHAhiX6v10zSkAO0I3AXjZTQU9A6MBS3/e/onCEQNQYcD5O+V1A1aC9wG0yy7+KVIzAKQ4nwGQx/sArlaxAgk3FwPEo17//7gxBGxO/vTqo1kAf6KZAU0/yP4+/6EBW2ufAeuF9wSfnhkCBya9A4XmyQAEHAkF2e9nAMQCWQOv+t8CSsZpAXXOwwOCYiEA39/pAHTdRv29woMAeZRlBgMKKQQ8kq0AKOQk/ymr2QNTK6cCg4oNA3PrUQDbn/T+B0/xAXRyCwBhAKcBT5OnASni7wHbVJcHVsJLB4y+HP+VXKUAeFSdAm5i0QNsJHEELwya8QKAoQYjf+cD6/WpBEOwawaF1gMHAMLNA7X4qQKNEYcBp+sW/DSckwW2VosB1oxJBpx6RQOaMEkHQBn5AizEewLdW6MA12M1AE5hTwLipucAUSIpAxBjGwCPGjMAaZJi/7oN6QH+ZikBR2lfASTYZwZqbI8GKoYM/ROx2vwuAQ0ARtYvAYgY0QQtZpsArLL9A5s9jPwFOr0BO8GPBFbX4v35FIEAUzkbADGoqwVNsA8DG3MlAWf35QI8CU0Ado1BB7+G5wLVSGb8ekFjABVANwCF7BUCboNRAWHM1Qe+Fo0Amrz/BFFuhwDQq6r2yGipAGmTHwDkdh8FLKp2+8k7lwEqN0kAhucXAwYLQwI+iacDTz17A9H1zQLBmmMAQ+N5AgEfuQEV++MBCLFu/MihsQQoXbsD8oo1As6+7Px91BcAhKT4/hnZNwKXIIMGvfa/Bg6yaQLVbKcApxEy+xWCFQANBgUBG5ARBqV8TQY4sZMDONwBAwnDrQMe43D4u/DVBSTq5wEvS4EC7yGJAUhlwwcJwv0BqaXzBa/oJP7thdEF+ZMdA0+lOP1onAkEqmI3B4I/twOQLxz/NBNvAW8c5wVDjLkHSGIXAxi2DwMk3ssCDTirBah0ewfUQGsDxH7tAze5GQESOJEHKOR7BrVPLQPHWwz6OO4/AUPOLv/W2c0F39bm9F2W9wDtrk8Bze2pAgVcIwW07aT9iioNAQlTtwBPhCsD2Aw/AXgpRQfpe30DrshpAlRcwwUiBlsCTzgrBtEihQStmQcGsw4RAft7SwAaMLMEOiGfBKRkgQTqd+0C1mmrBY3cRQPjFmMB+7jXB3xpvQWB5IsFjaVbAKPK+wI+/w0DTpbLAciiswIbLDkAc7SfBSvRwwesULUA8WHvB8D6Gv7t5tMAbXEhBQSM/QDBvpMCmeiNAkJVlQKv0GkEwXcjA5Zj6wDgyhUF4nmrBCVw3wP24q0BzvDVBzVbtQLPVmUHEFzLB67CxwMGZir+W0ybBMBghQGuhscB4JJHAJ0YEQaRO+L9D/8FA2j5TQS9+hL/hSvE/zY9wwDVIWEHjEC9A76QWQMTdu0C0KIHAPAeVQJMslcCCK4NA0ropwDH/Z0GIlDm/gfI4QcrZrsCxF9TAz3bCQCNNhz/Hy4/BFoAIwXMuaMCTJXpBw25bwHN3ekAiDNJA+lMNQYaxhkEd20nBzca9wM0gUkGG5zZA0c0ZwfOW/cAK2KrAWaAJvwtP+cCD5B5Apx6YQC/28cABduHAZTQQwL020sBMSXNAtSqVQBRxlEC4GoNAH0s1wV97EMGpVDbB0d0hwex+jMDxsDXBDbT2wMT0K8GtZ9I/RYXGv0aYUkCm+JhA3LaNwAiYG8FMe6xAjiXMwFlAtD9Q94nBCjtJv71aAkENeLBAGIJHQH4uIsFHHIdAtDjxQLV8vcBtGkrBwl+RQLsQ0kBblDFBvqzrQBBJgEBA8edAVMRiwEn0mMCD8c++hp01vM8qdcBV9iPB4s0VvcdLKD7c3yfBBdhLP3r91cC0Y+DAlVwsQKKfSUAm+eFAsY6wwEZ9ssBC7+5AtLspwXoIVcAu0StBhLs+wP3o4EBlTZhAMQLzQEEHdEDB1WU/oJsvQbBoJj653XPBhLeHQfWt1UDgpKhA6BatQNsj2sDQ98zAGnUnP+55XUFlMQ6/FJfvwMp65kCABgHBL7pcwDfG0ECttbJA6g58wYfy0kD9tyVBV5U+QZQVBUHbJeVAFRI5vyrn1EAbbp3AFOoBQQwpgUD5Lf29npLYQFYd3sCGozXAPQ4UP2WzLsGCAXjATp+nQGAxkkCxegHAawAWQMWjfUFq83PBgaRQQBEpBkHY1Pq9mgbXvgIO0MAL1V5AupazQEOt+EDPh2BAh8etwHKrIMHTwxvAcfRewbXkH8H0Edq/lB0nPmb+msAd7Oo+Gu0awSUXJEHAeodAuUPgP97mt0AG6Im/SUdjwIGkFsFKji/B5OejwHcTNUGu/h9BOelQwOzXDUFpvGbBpW3pwL3bo79RUoZAjhTVQKlczr+JmAhB6fjOwPs3qUBphmJBpkEpwF3LOcB1YrXAUt7UwGChX0EhGxZBr45QwOVzdECWQgpA8a+BwPCS8L9OHQZAy+CyPw5Szj8UPahAnBb7PnKQFMHgcOhADvCwwPibE8C3auJAh0w2wPafIkBrVsbAWOIaQSCCf0HvyYFA9HoUQBO3AcFzIPTAJMEwQOK2l0Aym0PAUru9QGfC6j790FdAnzFUPdZfC0HYwF5BOhtHwWVT6T2x/y9BKJn7v/5XCMGLH1A+Bs5gQOlvjkAg9tpAz6lZQeCdG8BCTnFAzCw+wAhVy0CCoUdB6SSkwCF72ECRTyDA+D57wLhBWj/mo9rAVgyKwGZSO0EW62pATgVjv4Vr9r8zmBfBlJm/P22N8MAmEMrAeV3awI2rnsDAbhpAse68P/tVez+ddzdB+jswQVAlakDrBOBAvRs6wIxQA8BhXvZALleBQbuq/MCiHZPAllXQv/YWNsCjN4vA7XrLwPfi3MCRggJBQJ6PwHwVE0EqHh5AjhBJQQFABcGjdi7A16CBvxS6HED0HOJAHpUqwWNuK8F3UlBBqW6Jv++kBkHvkU3AjNW8wDP2M8GzIONA2xsRQIBe38BoZPW+rrgsQbj13cD7xMY9FPT3QCwB2z+Q1zfB/E/kwPVf1MB84oE/MRJDQCN4SkEQ4R9B8wHcwBmsnsFIANTAlcMcQWGgB8B0FkjBAi0bQTIuU8DPBMRAFKFdwHpA2L3p7B9BvaI1vy1OJMGxpAjBq5QDvqySrcBZdOfA1JEMQN2wIUGkk5LAU1mJwKbkHb/bkWrAcNwqwP9Fw8D+EpTBxu4swRyiqcCY49u/fbZSQUg+j0H3qstA7y8QQcXpn8CJlpK/SZuJQO06PsEWDT3AXyHqwAIOmL8EvRq/lLcvQWzJx8CvdclA7KM3wPMpkMESvMhAoccVwBjC7MB6+lZAwgpXQROuuUAYO4M/Mp1kP0lJdT/dDxhBNN6eQHBSbcALYlQ+CBl6vziyF0GstU7BDLSCQRw1TMGmtUBAKM6pwACfHMEjHTxAS5XhwD1+AEFRUkdA+B8Sv15CekA7wJG/0Tr3QHwVvT6vpwg/wxt8QFeEmMASmwrBcrLRP+sd2cCgHBRAAIMuQCqFpEAgq1RAFNGlwOxWJ0Hz/1RAg8UEQTUmJsE6QErAbMwcwXeLCMGTs2jBhZcJwB6Rxb/kGVvA1ULMwF1vgkF/bdHAVyQ/wUnmfcAh5AZASVlcQHGVdUEBzv1AiYj7v+7Kk8CIUBHBDCJzwNcYDEGXTqJANJX8PrgYDUBg6N3AED2TwBaNI0EgGSBASfgTwPdpIMHAa7hA1MdQQfoPL0F/qUvAxjf8wAL0I8GN/Nw/ZMrfQPzThb7zVWzA5w7FP5f1PcFPOtg/MoyaQPPi2T5W6J2/mDoqwRkLrsBZrDRBnUuXvWqRnz+L+7lAPVDAP6qFvsChZWjAooBpQTihp8Bdus7A9aNCwVV2gkCEOJo/NAIcwYibLkBNiDlAh2Zlwe7sH8HOX3xBfs+SQJmGgsHiuQdAvICoQC3FBcATjQO/pBNrwcHEocA5EJbAVSBJPqiRN8EpzVZAePjfwE9LokBUYlrAF0ggwcF4m8DJYYhAWAePQNDDUT9T1vu/CbUdQZp37D/ZISZAA5MXQeeUxsAkXZjAx/0+wQDaz0CxB39AZSkowJ5z7cC/6+FAoEQZQVH9F0FYhRlB8dwXQf/jOr8kIXzBw19aQcEBWEC2idzA2nOmwJc2CcGBYhW/yh4DwVjM+MBV1y7BZH68PahTQ8F20B3ACG0vPy4vBkF5giXBW14ywVvZyD+hNe2/oXiGwMHVkb8XtrLAZkvlQJG9nz9a/C6+DfNuQGgfx8ARz7LAOYi0P2idEcBb8/vAWiEVwfsmucCJ/hbAJd/+QPsBT0A0sk++bR+rQExbskCaTC5A8WMiwN7h5kANkhlBKemtv/mlOb+gmwTBqu0pwTmdK8A+sQs/7XzPv+9QGMDWmWzBGpcbQDahn7/d9R9AZ3owwAUBDkF4Hy++0C8eQRd3R8BkGs7AfwTzwLHAgL+OZaFAMTYNQGHUk8Dtkak/SZxnQe1UOMHYEodAwYwpQQ6Ds0BK/d3AWZGAwGbgAcGtWypBw70hwSmrX8DBFBrBWIYLQInJAMFykJPAgUAdwejINUAviWtBljvMP7CDUEFm8f9AR2SkwOUfjkBfepjBdw6VQE/3i775qYzBNTnkQJGBAL9j0tVAZF6fQCxjQkCX3y/APs4QwC0+ukBkTXdA9SkXQHqAhj8lo+bAPQV0QWKeCsDCa8RAaCrXP+SzdsFqv4rAopcWwEMl5sCg/M5AIKjeQKPlM8HWiic/DzrvQO+K675YMBDBolYQwF105kCyTYhAYL+OwC8GEMFHm13BVOp0wUBSYUFvwZfAMTdMwQhZuT966PxAaBgjwHj3mEAMmKRAURYPQc0uqj9zALPAJNYdQUiIvMA09aBAVGZ5P8iPGUF2hi9ApgBgQEKzlkAuNWpA9GH/vxujKUFtqwzA1WUXQWRxbsC4fiPBRsyiwDHBA78WQO49FnuKwV4LAsAXP4/AhZ5GwHlXgMBwLdE+NKJLQR4/U8Fc/8RAHzKsQCYW2r7qHDnAl1MMwf/PEEEN2rnAFM3yQGiv/j889hBBpgYJP2n9acC654PAmaH2QMq8LsHxeHHBptQlwf+RqEDa/tTANvAYwRl+zkBrJ8NAnjR/v3fEDsEkSkBBW1goQP0TisFuy5NAsv46PdzPzr8SHFTBPJj+v/S6bcAflb3AQYkHwbDkOUGsxY7AVB3dwCMriEHWEBG/sv3fPi+7W0AScdvAg1bhv7/kZz2hxBrAQ/a0QIFHW8HIZTW/rJ+DwC+SNMHK0TNByjzpwPbk1r8CY9/AiTbvQCot3cAOhI3AEWadQJYR8cB+rShB3PrmwHgt7sC/BftAQjMawYkJq0C19PFAHQkFwe0tjcD5ByNB7im3wByaZ8EvaJBAVaNUwbraLkG41HlBjl36wHAYgsE8k+S/L7kkQTZonj93hAW/QKfovxPLt0DGXo5A59g7waIqFMApeLZASH1fQFv2CMFKYtFA8kOEPsr+E0GQmLnAJCgUwcp9rcDrHbVALpSCwA1eN8DMhDxBiWsKQAkOur86ZTPBeLM1QXgahUAFZzTB6CZVv/EKT8ERejjAKaKuwCAgmsCtiJXAz+/owIgQV0CvGTlBPZyQwOKYOcGLNEbB6fGkQLScM8DpuRZBRK4kwV0i50BaShdBlEIdwXV3LEGHhBfBLei0QA2jkT+96DxAxc0tQUN5Q0AXQffAYTZ1v8SVKsGtpZG+sE2kvhLHGUGq/+LAP1QnQYeWysBFhBjBEqdZQSw3v8DT2bLAkW3CP2+kJcGQCTC+J8ohwdEl8b/vmdnAbwLnwMI/DMH4f29A+JB4wIHSwD5HIojAUCe2QLe4wT+rpR/BXbCFQCzgwz8TQ19BFz6XQOWG9MCbmTzAnMZJwV2tJsBDOVHBPb6oQInt4kAuaLo/u9u8QC/5pMCGXzzAvdWBQfxeS8DSp3xBwggrQYbOY0DteKXAcA+JwdWZ9j5epyHA/GMpwQ5LgEF0MypAdbcPwfFpVcC0r68/9yQdwUtXOMEYqExBPz+/wIttJMGP/vXA0WmwwCOZJMHhalDBVpIWwMDmMMGMECFBHFjCP48BE0EVbW1BV49KP3ZTIMAdS8FA4hQeQONejD90JW7AtnkJwd2izkBGTEXAdimYwPO/ikB7XL++yD6ewL9Ocb/sxaZA5BynQFp2vb+aHHXAaD8xQfnxfUAVOtu+DbK9wNYAJMF9xzRBy1MewRWD5MBIp4JAimTFQGQkxkC5b4y/4gygQIZyy8AQRgrBCNkgPyaAsUAl8rtAqxt2QUx8sT/B4njAn6uzQKQ7Ab9xKhhAwrtYvXp7Rj/CWLVANieMwVzDk0DF8uHAXrylwMFPlcG264zAgEwSweYIVsHgA4TAGE8SQZGLMcA2lA7BseK6wMzi8MCcpVdB20snQZscREFS8tQ+lV0lQS2CIEGTRBfBvRGpPwpqL0HdARNAdYvvwHHHvj/kKihBZDJOQPFvMMBuEJPAq/mOwGtBiUHyRTxAZLaSQHvDk8CKKxFBc1cTQITfzkDZRWXA0/66QB48HEGJNYe+bMmTwFC7j78s5M/AXBWEQIXdMEABIMe/r6zqv8zRSL/FgOPAISh2wNSqAkFG9s1AWOiXvlaDBcFvLqlA8ILoQLm6Ib4mu5nAPXO4wBZRLEC6XKE/kXEFwKqkC78MbNVA/hsmQdI6AkGKJvlAlu8aQbh7w7+VTVVAOvklQVmUj8C5VyHB1E+jwArQJ8G0xhhBvhAcQee8lEHsx7q/ormVwIEBqUE+7Uq/yDPYQNZJRz8PaUxBmKhAQaDKfcEeF99AbU+hQCDoAEBH3XvATT16QGz5pEEa/ApAySTAvzPYCMFgUHvBLWo3wfL62ECf0CHBZr8XwSyjE8G6ATzAiHWOQVPOekCsuxBBG49KQHkB7UAOgTfAOi5PQMbtGEGUOX5AlngGPoezGcAnRZ+/ThbSwA1B4MBdWUnAzVklQPP0vT8M/+BALlwWwXr7asAKlS9BvydtQBN7A8D0ddnAnDsMwJlAgkDeKNZAWIENwV3LvEByUw1A4+mPQUzpMkFFBfTAvICfQP1pGz9AkZPAS/7KwPd53EBZsMJAulL7wEG3JUAtDBlByuPoQBEMW78IBdPA1KK7wNTkRMBJAQu/gJagP9LGpsDB82vBhnIpwUm6cUCZsXtBGCyBwGMwi8CRxpW/7Ke8QGplw0DfceVA8uVwQS41MEEmcoXAF+9qQc/MpkH/bVVB5L12P9VtAcF5Bl1AUUc4QRZlIkG5VZ1A1PUUwSSWQEHaSK/AuksRQZ3DPcEAyoBA/adewacNFEH3IwPAEmobwXS4YL5l7jXA5W2NwKDNdMEQoT7B1O2cv+9NIsEVJapA5PKLv+PUikDXEVlBfllwPhcEZ0A4GnjAmq+pQFDmwEDlYbK/UoV3vzpu4sDAgHLAsT1rwcmbqb8AcsnAwMEgQaByuMAIVVxBFgOOwBP0G8H9sZtAI0PgQGC0JsEqGk3AHSoIwQNl+r+PbQZBXooVQTGFesFeOTdACjbeQKHYRcF9O9c/BWdjQVXUhEHvVbNAbrAjwWZjkcCVkZRAMiIiwVXT4b3NOodAXC5jQagGRr7B6DNB9N73wDHU7cBvxCDA3ue9wK6hoMDqzx1BAhOSQC6zp8HeZTHAbPfJQAgB0cB71iPB/QITQRMyY0AVVCVBl50HwWxQS0A2zzlBhS5KvEcPIEBOb0G/gMQdQfwGU8GHPDU/iL2HQLN94ECkEWtBfgRqwbM1D0BTkko/qcfmQCyqoD/kgbZAV75LQFUgzUCvbnJBeA31P4ChgcE16JC/ajs9wPQT6MDw2w9BvW3yP1BzAkCNpEnAM30JQYccM0E2Y44/nUPCwAab+L/w6otAAxttQfwIgUBJDpA/NhQXwQ0clEAPNSbB9uatQL6qykDB/2hAgFcpQQ6CiMFllMjAoq9eQA7UtsC5GBxBNjsdQH77YkHt27jAMyxDQC+HPUB7tT2/viDfQEU3DMCezSZBZrdCQVEhVEGyYZRArCcTwYju0UDEjMPAtxTWvyrroUA9Ot9A0d2YwFeVnUD46iFBXk2gQEn+4cAZl0NBblCAwXvNI0CZh6LAtImQwPxdJkH+9sxALUEkwV9Zn0DGDZ1AA8cIQOa5tkCK+dXAkPcxQW5PnMATlNDAurUTQK3TrUC73JnA7MPDwGKcDj+GGee/hmAjwHCrpMDwbGVAFZxGwLOKgEBP1xu/F6u1wEMl1kCSkbnAQuOOQJanMsAk7TLAb/XTQLzUREDxqoQ9fthEQYxU/0BEglNAM1grQVues0Dh9gzBpm3QwOCK+b9mAGXAm2KFQOCf+cBkPspA96YdwP9zosAz2hxBDbslvk77OUHBo+k/GzPSv811gsB63zzB2WH/wI3h4sAIZrm+PBfLP1RMlMCnBvHAVEHyQMGqpEBqtdBAWBXlQLPcDUGkebnAREa2wI6Kd0GY5ejAH8a7wFDIsz/pRvdAqPhgQLdXbMHMLh1BOByOwJHSgEHxEO1ADuaCwGbDbkFbpJA/cgCDQHkLbEHepS7BTvmfvzI1VcHJ24VBbzogwUbWCsAsspfAXhJiwfKO6MC6JifBjjjJQF0TusAgrkTAcCk4wCsdDMHPifHAThwIQcPTq0A7eNRA6/mEQPgl38BdKOjAxRrEQGw+DEArCabA3JPuwNeShkB39VXBzye2vyT+qsCoiV8/R9Mnwd0KNcB4zjNBntk3wFpWuD//5OjAy9sTwVS3LcGQDp7AEu2aQKSQh8Flqu1AhcpFvgCWAsECIw5BoTgrQU6ObUGrkjNBzOhdQCj1ukAYHMRAw3w4wM8MF0GV1YfA+SU/wZ9RR79o891Ak9mCQckOJr+AKMM/TreWwJaloEEK+aJAlaAUQPQrjUBbkA7BTsqUQNJRi0A9XvLAlYqbQCNCJUHjabNAF8ciQXUGmUCoYidAsSWTQNglB0E+X8HAYg+WQC/n60Be3BPB3mHav80WVMGma1tAprc/wTymFcGKN7hA/wgYwGNcQ8ECYV5Az9ykwLmjQEAgxWtAiACIQD5YFEH/GOJANuX1P4/wMsDZt6I/sVn2wFmBG8EvNA3ATSyhwJ6RtkBCmf3AkzATwfzaH8G1yCfAWep+QLcAZEGQ/2jAWhYjQeHqlMAWXQtBMLNjwOQAt0CnBRTA1T+SwBfDaMDLahFB3j4KQIbYLMGO4RdBv8FSwMv/vcD8FrxAb7/QwDGhE0G0fXxAyV7BvoZNJsDs3dQ/FSABwVU8oMDEZyJBQGgEwGMymb+Rx8zAibGuvZJZtkC5s19BrVOPQMYS/cBJwK3A6LQFQM20Ez+BKKXAnqo7wT6GYr5yqFBBIQRsQLeU0UBxKZhA6t7oQA/B5z/ZvmhBRPEMQFbMFUCq/ce+HenZP2Xks0D4a+bAjaZcQe9LsEB+iFTAV8YQQQpVLUEIGDXAD0cRwMuiQ0Cld1LA7KJbv5f8hL2w/U7BCGNmwc8ZhEBPXdnAlZCfQOuBhL/vp5RA2IiuwGPYiEAmoCrBmWIpQXecCr8eyObAE6kkQR8locDpO13AfPAHQWIVUMHy7JO+7wvbQJfgpT+h68vAiytpQXsJ3D8K/OVAmPC0wPLNmMDj1RvBAXPDvrOWXj7MhuJAwriXwF12k7760QJBDP5QQBQZvcBgxlBBWRGaQIKCOcE+WA1AErENQT3Os0D8qLlArCCdwAM4m780WNVA38GPQGOvFsDh2RfBCcAPP9j0C7/rOo3AgBozv6lbJkBxMS3A0p6EQBs3r0BnulTBwaocwd/xPj919afAuqzuwHV8AkAq2QvATLDewOfTUUCN/97AfxgkQam64EANxWtB8ZrDwLK9EsAPhBDBV0/YQCLnRsFZKrXABJH3wDkUYj8HkKnAais+QKP3QMBRUUHBEN1lQDuqjsB/DCVBi5AdwVwX1cDAXSRBcsHKvyl/KUEbg9jA71D4v6/UpUBQEIVAT7bzQNStF8C43wBBlgSEQKMWoUANvmtAAp1xQG2rqkAJk/BA1LbyP+JAvcAE15VAC/DDwDjNnsDonZTAU/uKwEuL4UAzVFFBoCeXwF2KdMAsp4PAN8NbQdY/lz4TsTNBbyHQQKwiaMFfS24/RfQIQIwlTEBjRN7A0X4yQb6EJUGyNehAQ0nIv2NWFECn7pvBvr/2QFBjFUCH0NrACYyCwQApLMGp1INBOf7bwKCUfMHlWcNAvnGHP1uppkBGjuXAetCZQHGprkAmY4HB7HtQQfLoxMB5dnPAlQygQYjCsT+JUHFBAIyaQIIyj0HhzFLBrQGqQIgfE0G1K9LACqbiQLC8SkDl/pdAefl9QZKzMUDa80o/wJ8LQRMKscC+Zpw/pe02wRa3MkHDKdxAIKDOQPoKpsAe73NBvwgzwZyzB8E8sjG/Jjk7v6SRdMHXwJXA8Dh3QXSva0HKyBDBmqUqQSH6b8A8otjATa4kQYG0v8DXugJA9MskwU5uQzxF0lNBOSQwweSdv7+YrG+/DPlavoqKpUFewSm+05A4wJjCFUHTkQnBoMaiP3wJg8CLPWxBYkubQISdakGR5cdASLOBQBln/EBczOjARkgmQdklEz9queQ+cEbiPfOv9D+PLSJBoJIYwOHcwj+91iO/yr5UwbmmfkArwTJBx27AP07SA8Bg6IxAThAPQHaE4kAbzD/BB0GTP/zjgMBPL8a9dZaSwKScWEFygi1BqUozQORWJkA+MUnA62u2QKwdt0CzdXFBIDk+QaMJ3T/IdgTAsxk0wFnPIEFq8cjAYVItwF0IXcF18edAvOJrQcrzvEAvUqXA6SbsPlm9n0D/Lw5BonxwQC9VoEAaZ/LAhVviwMflJ8G5/N/AWOUeQE21rMBwBvRA0XvEQDYtTsFxSJDAWO8Uwe03HUHtA0HBG8F9wKn6g0FO5WVBkskvQJxMX0CFActA0QPpwD9ZG8EVLWZBTcaHQHO2JEGeztTAQm8pwQPJEsBbcHlBNtEAP81Ng8BgGEHBdy03wM5GjUDHW37B49mDwX3c2L9Y2YPAzxvVwMvORz/1+wU/OyYMQX8xgkDsfe/Am2TyvwsrnkDirFDBYm+MvoJS2cCpy/Q+GGUwQHKukUCxeb++JlhxPtjHjj/AxYrAImBRQO9oBEDB6oY/TLhGQKe4tEDFb03AkEfqwIR5IMHEBTdBsSDSQKoNB0EZnLnAWgjJPhGl0cBsjQ5BLZfjwMEG2cDuNotAEHfIPz4TPUBg4W3ADi6XwOoQEcCZ/au/Q7/TwLFnhcC1TnxBJnQ7QHidBMFedRVB/6H6QDxDbcF99gZBcv1gwVRBcD4Zb3XAmTktQUedH8GAoyhBMGW1wKckMUC8T/3AAOWUQNiokkED52JBxxn5QIovdEHZNBnAVFNcwDxz5MDuNeA+uySRQAE2CkGFQCw/9qXlwBoEX0AIQnlBlpaqQE1mKMEjRYtAd9wPwcHMR0FdlkLA/JHcQMYr6r9gdZFAfZYzQBuBskA4AlO+GGyzwP9QFsFEht1AHfK8QAGLM0ABMTBBDBkzQQ15LEG1HNvA5i2NP6VxZUGGO61AHEDpQE+om0Dych1BZd24QOrwIMHjrYbAdoYKwd2yAcGv8ppAWe+ewMF188DhV9C+/lvcQOr/N0GQs5DAhT6GQHlLr8CECzjBaOKowLpEPcCmE0vAagY0QWMm9cCSmZhAaNMtwGt+EsBNSrVAI3dtQPigHcHbsEDB4sNawapVkEDTDyPB+iSUwK1HzMAEBCFBw7D6wMDaEUHMD+5AHyzfQN2XIT7NkFvB4y0mwCfSMMEdKpxA/Xo2wAvgs0CgFwE/jho8wEQK98AqBW3AK0awwLWRcsE8Y1/BEjMMQdD/DkB3I1dAWFpvQD8ZhsCPyYFAQlOKQY3K3cA8TMVA1fAOwYBQoUC6TAxA673bQLnyTkHaDdi/HUAOwIo/CUGc/8zAPYclv+rtQcDUoBlBwpFfwIojBcC4ak3BmVrNwE6EIb/kc1fBzTsuQfm9X8EsSkA/cwmQQDrt5b5vu6dAwtdoQaeZF0Gk2W7BECoewEiNcr9D5qK98VhdwFBmAUCj1NK+jX9awRaOgMAnvSDA3TMUQNLSqcDooVPASENqwfWrGMEIE9HA10ADwVL5fUBIz5vAQVEQQfJT1L+TikfBaEeUQArm40AehYVAmMbgP5MVecHiWBDBTwoNwHNNk0B4ezzAEprvQOLWSkCkn39BnJL4QFCqgL3vlHrAvhTMvk5gC0FbOvtAjXwhwbWDXMCzuBjAAmsNwU8bw78eLuvA4DYlQQq+nD8RrCbANCmawEmQKcBQV6PA6MJ8wMHRkECdAInAaeHDQCkMFcEBTyVAlGiswPS24sAesXHAVsykwGzjm0Dw2TBBKsW6QLBvN8GcMsPAhAguvx5SdEDb0SBAHwgVwaMGuMCw0OHA/BGDwLvEC0F/tjJBuLzTQAAp9D2PF2lBdDeKwOKCCMHUDN+/abDtv1/fAb+PwEBBgHUzv7jDHkFQOdBAw7ngwHEbrsDsD77A6n1fwczERkHtCivARmvivn3hocAR28nAK1ewQBMYjL+gAnRBgTykQNBrYUDXlZHAUMWdwBQUI0EH2adAjbwRwO4ZN8B3v34/cbi9wPTuGD+xzldAxo6cwRVFAkHCTynByzEawR+DqD9Us75AzV80QQiFIMBdf8DAjIR1QFKTPsGW7HO/2AXcwDjr6ECTDqvAQ2E0QVDxpMBYO2rAltLowLuvMkBMW4HBVUsaQS3kpcDbnxZAnIgOQNhBS78PSxvBdlI0QawwLkGgAAZBxkxZwU6b8ED4i1tBsUAhQH7JJMEcIjtBI7crQQmS2ECSUiFBhtB/QHzdusCtnLBA/owewSDWvEDAro5ADMbkPncKyUBLYcJA6j6xwKXFH8GOLA1B5xilwPB3tL5pEpC/Y7t/vyka8EBwywg+RMl8QZbLucAAhhvB7agcwLS8a8GDlH/AWdZfwWvubEEH79VAjQOowBzFKD9wxClA1OdSQGfAukD5NEHB2hpeQWAw0cCXz+K/EBvlQM7grz8CVodAJ0vOQDbUFcGj/eFAOZt4QXSF3UBjzPc/Ojm2QFPr3EDyPipAQ+qqQPLgtT8m+23Ax9b5PSOzEkE8m4w/s8lAQMAukcCuQCtBvcXOQJE4nsGn8a9APV0EwYbwacCs0TNAtW/Av4LWc0GkwglA3tlAQZcG7EDHPGZA3Yysvz7YNMH1yNNA+EOHwIiwuMBrg93AdevqP7BjzEANSRJBouH5v+0xLsBvuOHAwQOewIOfE8F2jMZA1zYTPXHtuUDoAjNBBgebQMKJTz6L04FBw57ZQH3tHEG2a1lAq6ELQCDCD0FJPO0/lZpJQCcRhkBOKs1A3mBhwUAXWr8022lAqqoSwWGefsC7RH5A71YEwHAzjsCgUiPB9ElywOU7LUEn+0nBnKETQTkewMDRZIFAigPlP6Xw9sAA6xbBo5YbwSJK38CH0nhBNHlev4qtxb5Kv7+/rmflQBWnAUHppW7A3cf6P7EpB0DCV74/A6MCQerwiECCgNjAhJQqQCs8j0CqkkRA5DoAQWhnqUCcbC1A28X5vK3dvEB6NEpAwc6DQE+p70DIBjbAQ4davyCNFEG6qxJAPTxVwQACnL+axNnApz+TwOm6GcFw6J4/nDNgQVivPEDrcHO9Hw9LwY/cJ0E6wDbBK47eQMl+wcBODZJA40b2QLVD/MDV9YvAuvJ+PyEK7D+YjYDAEVwJQVNK6L/BFkA/k0/LwH29MUEPIlRBcFcIQc5QvMD5kevAFI4JwZ8PrsA1gMJABOU3wKFRk0Cst4dAXPYlwaJKI0FqlrbAJ7GAwamOKcGRP+hAnZ6bwHMwe0Ea02FB+XarQDyyJ8CMTo5A74kqwZo3hMAsVB7BqYrIv2djvcB564XB2SjVPkrLlkDzQoDAIDonwAIGrr9BQBrBn7rnwKVJG8EU2Li/LcyMwTFX8ECvztrAQbA6QGN3EUHHPATBkvgbQWpX1ECC/+lAmdqoQPyRmcAxwUDAB8XjwCXR2UCY6IDBq+cWQWXOgEFKkrk+GzX8P0Oi00DL35VA3v1lvyx3HUDgcxHB32UlQcteyj9JFC7A6HoYP5wQi0BRSFvBn3nMwNexk8DqiTq//XApQSKlHMG0f5ZA2CytwD5PJz+PnixBDvs+P43+QkCQ7IPAdi9ZwNV4fMBOcci/p2mmwDr0/MBGrVfA/HbKv4zlHkB/i53AqcuhQODJoj6szTHBCso3wY5EscA2xwRAHhCcPrnZzUDma6FAzcftP2VLGUGs5R5A4MRLvz0YgUEJsmfB2jiSv/2uhUAU5pg/nz3OQCZGaMChFfZAmn0mwZNVXEHEzl5AwXZrwAGO+8Czat8/AlkvPxrXrkCgiwfBXRFvwcpDWUD4qoXBoKU3wHS95kBTldTAAByLQehplUBwmyVBSVUcvwvN6MDqNo7AaRgawbpRPsERsRRB4t5kQQmKBD4cXh9AUf8kwIm6QcDio4lA8eURwWM3REBoYyJB+B4nwYXr3MCaVJBAZkbUwJwLcMAf1QpAbsdlQJT3nkBvGiFB3AlewNlTJMEmmHlAZA6BwQbFgkBfHh/Bbtu2QHRwP8Ap4dFA9630P2uNsMCwbhzBusyDPClhKUD1NjBB8YKVwK6I98CaWy1AOf7awIxkvsDZxghB1E6owL+jdUF4nj0/tk+YPu+AL0HkepZASUowQM3recCxCEdBJXDbQBnlDMBtjBVBtX74P7yNQkG/r0PARkiDwL2x6cCUHo3A7vXVQDHhJMFsa1XB9m6CQdQfMsFtL+rAuTmcv1YYwj8NMGvAq5DmwB8OVb+QPmdBzjfbwBki+cCBYgfAkqXXQE6C4UBTILZAHvSTwIRUkMD9PSFBelyEQCO0WEBUnoXABAaIPnnTEUAVJ3nAH8PiwI9ZY0HKW5LAK0htP9ietECoDmXAoPbCQO/eiMCzxA5AooMDwCuz9r/QE9/A514EQQYnLsCneJ1AVgI0QSmSbL8weKFAFAS6wJ/zGUHtJdBAx77eQEqyGkDzLyDBd/SRwIMJKcHstq/AG3UXQR1KKUCEEOZA6+y2QNXp4kAyua5A3/6qwGUOmj/lY4vAO7H0QAKNs8CBuhRBIVUCwXwZL0DGKDxBwjruwLD5yUAQPQlABRqGwF86EsGT1pZALT3mvlklqkBaHPFADzY7wYvU1MBW9kZAuUtnQUvTScAAtLY/jQYHQRZFlMDqhTjATNKhwLaQyUAvpenAdZ2CQKX5Wj7WERm/ZyOPv/OPjj2TuGdBC9HRQOCxZEDCg6TA9eZPwdwWjcA5+BLBOjDaQExhFcGLYsDAZGZQwdGLFMHSmgVBVFyUQCWTh0FYOQpBdKzJQKQYIUE7xsPAIacxQVgp6sBP07PAXceYQK7YBD58vczAYPuZwayp4sBVcWk/bydiwB3ALsEDNZFBYW6UQAx0PkAPeXLAl91Hv0d/L8H2/ctAKiN/P9DRV0GISy5BZmFgQRyfSUF+NSNBtS0/wFSEyj6+J15B2jIVwW3A0ECA0FZA13SIQJSNvMCvQNxAsDRBwW+L+sBHe1VBQCmRQDUV9L77dIjA0PQhwOI0xUCd3ozAzTgVQY9KMkGNgAtBxDGFQQr+NMGmnNi/OOA5webim8CE8//ApWJBwQZVKcF+eINBLUR7wLHSGMGIs+5ARdPNQMSf5L5+KYbBzvtEQVJFqsAqOxhB91xYQOa/hT/hxVi/bC6mPw6SIsHkkYo/CjiCwZGqmD8/zfRAX8NYv2HadUDWSlZBiZtQwOVO/kCXYR3Aj2lfwFpoNUE+58xAuf6aQCbe7MCx7SvA99wxwcAYlUA/JXq+Z2UgwXInOD8+foDAIfcKwOGRAEG0JBpBahiDQD6B98BfuhXBpdu8wL4LXEC7v7C/gcLkv/U12cAJNZS+pIPcQNyjp8BRDoNBLEYMwRg1DUBjQ+w/99r1v2+N8kALvTdAPoewwM+PiMEky9++gJ0fQWMEhj66VRDB9gfZwKtL8UChi909r8JWwPW6L0AFN5hB+MOQP3WGAL5tUfXAKg2BQfRzBUFG4J1AHiR9QHzrmcBvMsZAPnFvQKkZpUDOSAtA+QWowHImED5gJ0PBWu80QX23OcHZvcBAnmSiQEduMkESv/1AZHUUwdesp8Db1d/ASf2jwOo2Q0DmrhzBe+SWvremUr786WLBzCobwU60JkG/CvnAMTXMwFVtEsHolVZArWV9vTQcfsEDLR9BFv8/wRNJH8HDlaVAdTN9PrMp3b+bsM5AQMVOQFL/ksBv2kM8ZQXrwJQK9D0O4j7BIq+VP5HEFcFlfuc8MFvCwMcnB0HNldDAVruEwa2tuUCzJa9AUaTwQAWCIcGhisJAl2vxP1xhzr8OpqJAJiNgQDZH1kAlctK/rvFfQdfaFEDLB649raiGPx/zR0FJebc/Pr35QG8XxUAbL4tAlHXRQF/bKkFw8uhAKsBxP1UpgkGNKBJArZSuvsS/qMBwS71AcHAbP0C5WkDvDtRADc4UwWI2Xr9MLx1Bw4zKvxD8pj89GEhBxwjpQNdlCEBmZblAaYUWQTomHsEqDmrBKqi/QEgJVcDOfoS/2+obQfYsisE+AuZAoB9BQE8fbsDeIs9AeS0oQRMAjEDZgGjB41xvwZB6JEB3Ox9B17VXwDFI6sAzvS3AdNAFwdOmEMGxfv3Adr/2wCh8dcGeyZXAUN8sQH7nGTwo786/n8SnwAVsIkHms24/GAM6QdW8mECJZ97A50xlQYD9cEAXDxnBly9LP5rJnEBR6WVBvJACQU/QkMAYwnFA9E3OQFLp6UB7MfY/iKqDveJVf8AwAZDAfskcQUzi50AkeCzBTJOxvxSsfkDHxARAVxmHvzvicUC4P4vB8MDMQBQMJsHOozJB0cDswLvvKkChosVAWvOhQK6cSEEJaOO+tJviQAzqo0B3V5fA5l4JQcZlO0F/OdDANxnZP6sBcMD8co8/AWjVwDs3bkEfZzlArkxjwTimT0HWZxTBjh9WQanPe0D7bxHBC5CQQfIugUE2Ar/AuGURQW+uO8EiyrhAX8K1wJRTkcAx+45AdsKewEW5RsDM0eNA1xcdQXQYKcEi6oRAIpjmQKpK2kDiEx7BtWFwQAh5LEG6ZTxBRsCOwHxXGcECEv49VFcjwTlEDUAWRijAZocWvx9Z/UDNsBzB8AvhQGbIIUC2KRzBUy3dQMTu7T9jt9LA57HiQLUxzEDCDHZAkONKQEVms0CP63jAv/z4QNI0wkBkPd3Ai5r1QDJYk8BEM9JAqv4Cvzq2SDt4CvC+i/wkwSPunb/ZjQ1AZwS1wFg87ECrymXAREj8v1GaPEGGMMrA/GEwwEn5nkCknalAqFj5P+Pae0GHnzXBvWWNwOcH7b9XuH7ASQcCwBu5wEAuwQxBDDa0QF9qFcE4Mp5AZKbCP/SJCcEB8/jA3sRcwYfBWECe4qJB2yhXQVRTmMA0zNVAln5CQK5e2MCfOIVAcN47QJdclsAKJqNA6v3dwBMXUMBcXcE/Bt8owfIz8L9m28BARQeewO47NcEGph9BCyjsP490CEEoqjFB+IIfPm3xu8DzrITAXPuhwHytp8BonQjBcooCwNubRsBYkl3AnTtCQNZm4EBARxXBOyJnPzbPQ0FQQQfA2fbqP6g0q8AoIDbBrUkqwOcy5kDufCBAnSa9wFMntMDIr8U9Wj1qQPfdH8DLQXzBSQ3fQH7aLkEY/yxBpdkZwMiBXcDURbHA48G1wMSKOsH6QRxBEt0pQWJiIcAle+DAiLcNQYPDQMDqLPjAySJwQGjao0BnQ7o/K6u7wMPVSUBxXuZAv4GVwIsI0MAe3AvBtQPhwHGRlkCRMlNBXMPZQFUIFUHM+j3AWLg+QCEAasHSALfAE+m3P9Qtk8DyEJbAxABqwEBdjcBEQnPALJsXQZYRFEFO3OVADRmIvrS+JMFj2hXB2vYYwTOXpsAdByHAcrCiQFxi/EBJ/zbBRyWHwcluDcH4Q65ATdS2QCAG6sAUMZbALuJrwC04475hBjnBZ8W5QBiiI8HsO2nBvhy4QHW1C8H64KRAbQCLvyu5EEHA+SJBDjQVwExL+8Bu5S7AWXoNwT3kEkHfXLPA8NWbvjdv1j/xNyDBWiyQwF8sn0GHYDvBe/Z9wFX0dMGPbeVA9Xd/v/q6yUAU1UdAjV19wDC4hMCysG3AaLhlQFbKYcCLDqxAY6mlvZLAG8HRBQZBhPMpQeaVcMDEGNpAoOLNQEtmO8G8LQxBYqSXv7DDSMG7PEg/wMFQwZZCK8GPsSPBYdE5wGq3IMCgcVq/MhNvQWyX2kDeHnJBFPjOwAZkZUFJXc5AfO9UwULJC8HulR7B5fwQwdJDMkBVQBPA4sC8wCvziD9f3YNAmt6XQCtZhkBNgp6//nuxQLfcxMDOpNG/mV1PwPxgjkDVgF9A2gI5QLcDhcCyyCVBzMK4PzltpMCT5g5B0J+OQHSNMUHGn7nAleqEQQrUI0ER85XAp9kDwe+rw0AsdgbA3LgFwZ1zJD9i3XDAd59eQXq5gDx2o9dAPy9hwBetvMCYO8jAGJsXQd/xhcFdXAZBjUsUwdnXIkF4gM8/eB3vQFZE6MAqbpRACOURwRYLWcDdKBpBwLVSwP6tn8BsE4NBOu8TwOfFAMCeo0NBaZJuwbpFp0CpuJhALDJLwfT8s8BN5uU9ViqkwPJ1TsCFvh9AjQJovwd89MAOI5nAUOiNv/t9ob4eeCbBYRXtwJknZMBkNMhA/xsaQbhJPj27icNA7ooWwBurC0G8hca+1alOQD1qkkAL+KLAja4WQbO4gkAmBRJBHIDrwHnh38CBl8PAOSsXQYW3vL/jLBVBwFG5wIGLJkD5eSpBTFAxwP/wir+SqFDAEqLuvz1HW8HUbwtBB/AVQZ2GZ77UdQZBAD1ewWSl3cAKlf1AL3C9wI0dQsCZgTXAFcWMQDqwL0AwiSBB17OtP2VsFcFzoZdAXN5DwVdIk0FUFQpBa4woQFShmkBGXrG/r5PfQPb3BUFHkDVByBZgQRm5GUGiDbLA7JL5QMk9r8AhHsTAMl3jwLDvfMHNeexAPWWoQISCosAA2tVAGonwQOEG38CR56G/qT3LQNu8asCa84LAJ8YWv5IrmD97IRk+G8ytvy+eJEE5OzJBb+TIwGSjRcC4RIXA2xmhQPXmUkCN0slAMqA+wLjHB0E3tSLBkucEwTb68j9Pv03AR2DCP9yNr8CKWcpAgrSRQFb8vz5UHxBB4RgLQBe0rz84rLxA/pcWwSaGTUCs4RfAfZDgwIoRXkHhYQPAA7DKwI/dQcC9tPJAJaZUQM2DvMAzECzBygd2wOuFakEZ12dBlxYnwGOK4EArlZxAbJIcv9so20DjJJLBkHzzQIIv20C8nS7BAWBjQKDipkBJ7aNA71Fzv0CbZ78sKgNAieXAQNG5a8FT4/PAYSEwweeLjMB02obAh2oRQDG957/Or1BAHEHgPlwOF7/EBhvBUDabQNEfHb+8JWLA4Zagvpj+H0Fg/RHBI/ZBQCkldUBCOqjApDIUwdQoxUDKkJhAnaeoQMgM6MCRg4BBUr12P4qoh0CtF8TAq2s7wG0ZYkFk95LAJ2bdQBHeFj4+1I/B7pvKwPbmE0EPlfvAEcqgv3X+oL6Ze8NAD5GuQAiJoUDVpHTAUMTWQBrpJMD4QBVBEq29wGb/or91jd5AFyMOQbNpfcC30BhA0nGgv+787MA7EU7AffCXwBS718CW06zA0ZheQOMY70DJULJAAzSEQOJi4UBLnL4+2DdAwEqqmsCnMQY/+TkrwcTIikCtFYxA4jW8PxHKo0BHfC1AeolAwEsvpT+0Cp4/IfTKwNjMfsEWNlE+PXd5v/MND7/iiZLAW6Xpvy0VZ0HDz3LB+wMawW2PXcAgBKdAmw5dQVhjs8Ga8NA/l/1oQe5Xi7+KCMRAG0JcQdeHZUGSrCRAh1KAv6U9f8EI4LpAfJVHQDrN+ECwAiNBYPyeQBhZuz9rLiLBmCV7wDwNekEfgHBBqgw6QQ/gBUAc6ufA+ec4wFpyy0AxUiFBmmaAwfcdEr/6rtBAFl2twOVvFUD6D1rAmdXOwJMIncDo4wDAjNCdwNxJA8HjG3i/dBoHwETIAkH9G67AP2aZQNJd5T4gQ2VBkln+QHoq5EC55yNBSlCcwOO1L8Bwn47AQyuTwFHg38AJ1r/AjysEwYTVo0Dq16BA4iNZwTtmTMHFwBbBJ+GiwAaTREC1hgxA8KU/QVdXwz+JJhxBzGiNQMZlEEDir9BA7SKjwAUBlUAfyTjB9CPxQHSBlkB44NBAXzJEwO7/Nz9xqA7BnKkuwSe+8MAqJ6TAe2kuv8bSncBY5hZBZJOOwKpS3b6uKg09SMU8QFICM0EVechAQXVDwRzB/r8Lru5APcIRwa5wIsFgkC9AfrLavxA0iz/a2pfAv2aHwQr+xMC/4NK/bdawQC+u/kBZlto/O9ZgwWHWpkGPcbM/arvNP7TGjsCDEnTASuy7QOKiNMHIdMhA4pH5PxhIQz/1lS8/3xJwQCzDp8AxbjxBsc4EwfSTaEDscBTBW8JjQCVt5b3uO9ZAo5PTQMoNJUGWeDdB7POVQLXHjz2BMlNBoAS/QMsi3T4SOonBrvy+QIriA8A5hTRBE5ypQKspuMD/0uc/N4uBQUMzKMADr+FA5PYyQFfaM0GXtTJB5t9DwKUKgsCaX/LAmCvFQLjGGj8diOK/UK5hwNa5W796R0LBv90pQYRtA0BmoRRA7CDhQPTf/ECSEUNAEiGHQOVCpsBXLr9ArELUv3ARrcDWoJJAXZzZQHQQV0E8BPfAY96KveKkzsCcixDAUurrviK3FsAEHE3B78PkQNwdoED9r0K/HxThv2pGNcAndKDAskg8wK9O2T9e/LI/+JPPwJzvJcGI8/rAOH0qwXRKCMBk29zAYKv0QJJCiEAqYhbAI8ZDwfD6ZsF6BNk9VP0OQAZovEDDNk5AlBV5QUaMT8GqXgVBKdLvP0SUNUG8MobAPGOMv2XLiMAfuEnAp6fqwAsyqMBSrec/F/tBQCV03sA+3KrAM2bCPxzdzkBHEqPA5Xywv9gKo8ER7K5APrIMQOW/yMCwvAHBKQC3P6jBLz+Ot8w9nvMswJYngsEseLy/yNqbvRb5a8DTEhPBC4sowf6nYsA2sP7AKH9DQEYH2EDF1qbA0OefwN/A0MBPSAs/554qwaLd/j84JJPAJTAfQbcMAj+n/pFAtBFtv7c2O78h5XJAi/NIQfGRgkCqBznBc7QZwXd/QcH4MB+/S6D+wLA6BcG1GSPBxCRBQKI8eMHd5BxBjq52Qcw2GUGDn2fBDB6qQPrkIcHj+EfAoo23vz5n9sDtL8nA8PqIQDC448BqwnrAgQRVQGVwhcDWv6xAcubqQFVMG0GwEWZARtb6QMKv98BR0AHB0smrQKE7FMCckh1BfrxvwFTLJ0FJoBvBCd0jQGDeSMDtJttAPxgGwUByh74OGdm/d4QnQBltqD/c9EDAHqc7wMI/4z0mCQvA24gsQTm+u8Dm2qG/LpoXQfsAmEDGyINBmsc9wJinJcHsB4nBL0MXQXW/wj/qEx9Bk5OXP/VR3j5F4YPBfmcBQdk4X79oUly/JL3uQAijSMAiqfU/byCnwDDCAcFu9aI+G8GgQIdPocDeFfpAKJPjPmC+QsGouWHAuxuePzrUJ8EyvwjAGhnKQPUKM0BQjCnB2jeoQCHssMBPvQ8+WJCvwMOFY0DkP+TAJiwxQNFMycDZ32pAnF1iQdDbAcD+RzJBpWV6QV7dBkFm1nk/ClBkwJQugb+TG5NAgHEgQPOc7UA3yXdBQLNQQfgkScBba8FAFh3mQF4qbMDf4fHASHdWwYF9xMApQTdArxrKQK6ku8Dg0WM/sop7QEwz+7/iyYTAcxjNQHUv4kAahnnAlW6XQBcOkUBaM2VBvhawQOCmxr/Ef+dAqe3bvxJSdMB/JZu/hPNqwTRh1UBbnjPAmw1UP1YBYkBRKCjBlVYGwXehg0Az37XAsZULQd30psCRzazAZ095wXteTME1PKlAEYqIQNRLyL+JSmtAg6O4wIzFg8E+sDBBl8xtQW8p1UCalL7AmQzaQGX8WsAT81XAwt/MwHZasUB/2E/BXYYEQbmn/T8p/ve/tydPQMnVFEDDT5zAt/+iwP3qDsEbbuTAFZTqP67GxUAtGDbBMpFVwOGzG8HH1v+/wRq8QMPFasF1LADB1L1xwK6Esb85Xv/AuBxswdvki8EF17O/Xp5tQA1lqD8r3B9B9fA1P7VkSsGD86DAaaNWQTHYOsAMZRtBcmdMwZYw0z5BwpG+00YLwXHmBUGgVSzBir/fQMFaVEEh1bVAnsC2wKgj8cDR2qZAAQESQFfvij/VFqA/XTAGQQvcFsF4k1fAQ9XKQPja08BrQOs/t2lcQM2BAr9Lc4pAjqTHQNC7mcHp6fDAfPDdQIhhnMCqkA6/osv1v2RbF8GPGaZARROjQMxpO0Ca4IzAT5wdv2ozHkAoGO/AR03TwCtTE8HzIdLA/n6FQHX0H8Gq4RXBiZkBQRnOsT6DXeRAiVR5wNmNT0A5EZFAEiNzwVcBNL/9MM++PLUVQBTQ5z86C4BA4CEIQdI6ikC6jG/AYBK6wBRitcB99lDAmIiHwFX7/UBMmSTBvbYqwVdOw0CA2fNA0kmcwF4UK8F7HgC/8IdUwEhqYEDkmoRApvoEQf8SbsDNUhbBhqofQduwYkAoJRTA24N4PuLWGUCMuCPBdWF0QYnROUB3wM/AVi9LwVJq6kAre6vBKEpXQPf+48Blno5B2RcXQLf/DT+kEC/A2iNtwZIPJcFYLl1BcFvcQNlwcsH3hMC/S0T1vmRayECa390/p66pwGA9PkCxqGlA+/nhwJRIxMAWkjPAY5eoQHw39MDfmf1AqGKXQBh6HEFysMfAm4wFv2Jc6L8qofm9SAWOwM1iJ8F02MBAKnbGQILYoUDGfP1Ao5kNwI10DcE+nxNBNhJRwbM0oEB39cy/vs0wQW7DWkDvhR7BlVQeQOu29j5I/8zAd7ePwEZLNEBnrPm/h1n4wNA108AVrWHAD+I0wBnMsD5vu17BCas+wZppS0AvbeK//XiDvxp/zcDtNafAkflFQK6/WkFCptlAC2qNwGYh9r8ITStAvGP5v3JNzMATAB/BF2glQY13gUHFtzdAyAAOwUmEkj6f/7k/M/IRQPQJ3UAWNrXAKjtRwHMHOr84C94/ggViQfDEtEDUFvRAh4o+wfeDgEFVnYnAhSFAQQRr1T4MBlnBphR4P7JfE8F2R9fAQQZWQUWkgkCDKgu/WCPWQG3eLUFzzclAPLuMQOWWmEBuV2TBqk7lQLE1K8AnpJHArA8+QcqQN0Fi1GdBMkBbvxguq75yOeJAewaZQOWLOUEpwgY/vZwewVaYG0FmDcdAa64sQEbAqcCCV5LAJ6fXPySw2UC130rAjb+eQBM9qcDxwYVAhwBowHdMzEDaj7rA1TtIwIWWZMA2ESXBJ4sowKIEP8EqFnVB4+U8QUy7LsAZXRXBJokjwH0OgkBcsSHBpHcswc/c08Cqv3xBRfIJQK3iXcDrHw9A1iNawUVLZsHRBA/AjcloP130FUFRUlZBuWpywUCerT/qI3dBPKEJQagBMkAHGRzBL0qNwV7blMCVIIvBGt1jQdtcpMBtbm7AvSEAwBHo2D+snnDA1mRrwPpO5T5OiWXBk97CwD7PacFbJ1LAqFQewB/E5T5vK8xAndxBQDGuzkCDdK5A2QGgQP6oJ8GDO67AUK1ePzj8gEEvZ0rBQSeRv7DkYz+/yLzAOIyYwAjgecGdsZRADkbZwCZmPEBpejNAY7KwwHY0nj9YN8fAvtMcQGIlLkD/d7c/Bl4mQB+SHEFuJcvAZoe2QPeSycACjoBAtbCRQJ9Zz73FjgjAvT0LwfnOG0HF1tI/LyOswEYGLMGAGExBpeNLwXQe50AyUhzBuV7aQO3lMb4Gpz3BMgcqQZWvcECdMpO/VDMIQMqKuEDAPkdAiC2YQMl02MAxwP4/JN/sv5Z9NcC6oyXAIsofwb3DiUBoqPi/p2bWwP64xsAJRU9BWEFiwFhC1UDUZUlAMEQWQbGplEA8g4tAUA8Dv4emMUHQ6eu/mKJ3QVu7nsA7zjpBZZBvP2//HEFuJ1tB4IMDQaw28z+4wWW/t0dnQG940sDt0WtBpQn/wB7E18Alq9FA0A8GQRs3wsB2ff8/yNfFPz1HO8GiFhfApfw1wFnKvz91+HfAxPElQTLpgz4FlujAZrILv+6fAkCCjoDBarb0wMkgBMD+9ELAFQYcwSX9E8GXnBpAMWGCwJfXikBVs99AB1enwF/uAUHcseE81cHHQGUE6EAJAspA8XylQMrYvEDxT/rAWNIFP5TShEGKdRrBXhsjwf2MhsFNsue/uHQCPzvk9sDFayBBpbRqwBfpkEAcEiLAYewEQWQf6MCfaHRAah+UQJAvLEHGz1VBI13ZQOEpksAO5gJB5q4lwXj6n8BT2tNAMHFMQYYjhEA96SHAAwsYwUaAmL9XufxAsy3ev+iZxECOTQhAC2ZQwdq2+L76C2LBZctQwaJ72j/ug9ZAf2pmwOSaA0GbnBpACYq4QHDrGEE180JBUN7LwEcH1sC2LUnBuzbmQFEPosAMQrPAqDDBQFUDcMEswI1Ar0XVwLX+GcCex0xAoWH2vhv6DcERowNBoVcvwYN0/T8S5Rw/eUv5QBDS2EAJfJPAxVMYQAl5rMBKV4pAsEE3vk5JXL9tyA9BhWd2v6mFj8Cj+otBDCYhQayT2L44al/Amtj7wKGbEcHdMTJBuTUEwTsfwD43J2/A6Ze+wPC2YsCpcuC/NQtgwY4NI0CFz9A/ptSFQbCjMEGj7gLBZR0CwNNOFsECkE0+fy38vlUFlcCtom7AIki2wHdJMkFViQhBX/BGwYxgpcBuCqrAE+ODwLG0LEEm0bBAJHeBwDQp2EBj9K9AXifFQJV7ckDkXiDARWZ+vtO2f8Gm7YLAwgKDQEt7l7/k967An2AhQeaMNUEiw8VAtlS6wObZn0DpFKXANoEgQQEGVMCJlK1AhrFNwPYGikChE/tAqQ8vQYh0BMBPzNTAfLmYPzh7CEHo+uA/ltIiQQVvucDzvqdAi+qTv0+vEkDZ/sHAf1iXwCzvicCLuXrB4P5TQIEoBUGFOZZAG93SP9WbMcBRGWRAXaYFQWkSBEFa57tA86fKv+6PZ0EBJFm/r789wPJ3mMBy4YBA9jaWwLpYZ0B8jhxBLbCnQAshNMCU0y/BdB/cwP1RvMAxixzBgC5AwXG4/UD3ex3BlsotQTP8uj+5HIZAV5TOwFKUJMGtciVBnXR3wAKgvsCZbXbAGWEqQdiHU78Do7A/glYdQbplWcA8eenAaeZdwTElBL+rrMBAiMkPwasfm8Av2bvAsYC3QA74MkFqnQjAj0SrQI+5ocCKBZVAsXIhwJl6XT/xaI9AFFeEQZBMa0CzwsA+WKRAwH+D8sADgJ9AS9QbQN9BmkBEW11A61jfQIQQIMGbFahA57OWQJCbAUFHor9AATyowEgyF8FBXBzBO5k1wSDIPEBwexvBLJDLwMJsW0HkV7M/aMO8P9R5HEFSEEs/17rNQKo2uz/do86/un2/QApLj8D3EaJAM27MPyRZF8FsnRRAfPTvv5NbEME6aUNATS01QQKTUMARLS/B+7qGwa/xv0BIxH1Ad2pOwK6oIcEUhAvB2AGCwcDuOz/8D8S/N7YpQOW5BcESN5e/59Ccvx+Ym7+rERNBB34mwBd4AUFhZN9AYOg1v65Ni0CHYPFA3pJ4QI9mwz1nSZPA4pu4QKAvpEHWzN1AQLsMQLjU68BVeyG/GAzeQMQf5z99rwNB9/LjwH/vDr8Wn8FAcQk9P5zQD8FgqTJAUk1pQJeuzUDrBkfB9gV/wDGzcsE3k+fA0AGJwPM9gsAssItA4sTswP8vo0BQbR1B1YNjv1SmgcAIyz3AuFuOQK4jLkGP3opAr9XBQFX9t8AEXQRBDH0xwVuvIr/7nP7AXX5lQfggj79bLCBBBn1dwdqX2MDVf3jBAMwtwTApKkH6zPjAYPmtP0r/J8HiHwlB2LaYv2aCXr9mFc3ADo/RQJu9L0G3UQTB/1KIwC+zi0BQeuY/vXBvv6kMQMF77nTA/BvJQATPi0BbXA3BQ11fwBrF0MCkbp7A5lUpQDt5k8BvfI7BTvfav2cg3sAweYpAIYuIwK47s0COqAs+hBe+QE8Fc0DpYZBAgIeJQFOvyj8eYgfBtbGawNSkNEDlCC7BRY85wIPTU0CHSYBA6Y4lwJbxpECAOhvBPrIZQU+XwUBOEQnB2cUbQWoGREE0iExAkQd5QCFqNEGj2iw+wr+fP7MvC8G8abrAKawawP6PAcE8qy1AkrCDwc4RasF59RNBniAgwT22hUC0ijZBylU0QO9gGsEUPffA9rqawCwDIEESj5y/mKe4QGtnCcAbyKVAO3sDQYV7JEE//DtAwQGbvn2IWsF7rnE/l62lv+FsFEGfB39AN0AqwHXLn8Ab7YlAseqdQOu07kCXZC7Aad2EwGgZFcGOBwZBG/iGQLUvPL9ADydA/hgCQZcThEGB2UTBxjWnwEKq+sBKefpAm5CEwKUYKMFOoURB1Ie+wPY8o0DU2Iq+82HKvw3sOz6kI2fBvgmswOAUtMCQLnTA/7CGQa75TsFl31VBgf5tweBz2UB0nsDAepu/QIkKAsGPkuXAeN46QBl6CMDGuYY/YG7PQEWA2EAieso/JsKHvxnDpED4mUtBHgMQwY6II0BzEKNAGE7MwL4ZhsCT97jAM90oQD9KjkDzyADAhtyBP5beGUBV+wDBgTmLv1iizkBQkJ9AkoybPwBpscAWqhXBStgBQYRYMEDw9+hAqIgnQFeFy8DEv4HAk4gZP4dRuUDdqdLAaISYQDqamkAZOjXBfrV6wMfGgUB4v8TAUkXKwIq/2kB72XdBRUYWQYCKjcDdxx9BQyC8wCHV3L+f5kpAixUFwECHV0Gsn+7AupsdQXSfFEFBWPc/snGPwNUBpUD9GAnBJuFcwAdiqUBo0shAAPFWweFgU8ACGdi/eucewepkzsCLia1B1stUQYQM1MDZxbDAmq9+QXvTFkAIK4dB5Ar2v4b+vsD8mxhBVdLJwIK+wUBLiyXA1gmQQFEHosAeW5/ArecSwDlpccBpESvBKEALQNyeccEi0wHAobkbQAXdvMDw9R5BmXsKQW0KnUA09Js/I1LpwLTlB0EDnNpAQEYmQLTQ/ED6T/A/ZEKzwBl/F8EeXSpAeeB7v28ltkBWSO/ATwkNwUKKhr+ovw5Bg/vCv8X8h8Bs9qFB93JKQGKLpECeWuRAHQygwMuWocCdvAI/JOVDwBo+JEFfp05AscQhQBPqob5xRcI/swlJvoH120BqMQDBw0vOwFoqLsBsRBlBrz7QP0OMnsCijTFBHGt8vjvq2sCUR4zAi8kEv6F12UAuRzTARJvYQHSJ90AzJKxAfCCSQDV/wUAycSXA8gwpQIAkdEEo5oRBFhDwQLmdnED2TQU+DQKVQFDlH8FCrb9AzKGJwJ0zJr+C9wW/rD7dwFmZe8AAIcZA8FLLwK70akDj9hfB7x2GQVaK1MCEg+6/ltBnwchs6z53rg7BJexWv/rAM0CrW8e/GrUoQeAaiz862ZRAbh4iQWUBkcHm1r1AlKI4wEbP9MAHqJ1A+B9oQW19/L+HV4TAZJltwXJlrkCAMzdB2oYxQYkdE0H6lp5AC+Hcv1BGAMEuPErAKfXiQDrcCEBXRWvBJoE4QZxsnUBOcB7BAnCRQIQdn78RSjHBB4BeP2GtHsHs+cjABJITQD8abkHbXX1AvEKWQMmoSEDeXk2/ng9LQEzTq0CPtVVA44tJwDhMTsCRcGjBgHFwPyNDy8CpbnbBQCrrwMx4xsAOEQG/DaiEQd3UDb8oWTRBPKptwDvrSEGxRBHASiD9wCjDJUBJaxHBBD7BQKo7yEARYh9B97TZQC39uMB+9s5A30dQwQsZ9r9jnrhABl36wCT+v8C40GxA7iKfvQD+Q0AXhwTB8N38v9Sy5T6UhwPBIDUQwYiQz8DVLsw/VqMVQMjClMCPGATByQiKwHna0cDd5SdBwjREQPuh2MCW3eXA39iawE7GckFjNQdB4ZAbQA6UYcG6h7fAp5zKQOAuA8Gqa6fAOsqpQKvAdMBtCCbBa+9vQMHvar96MIpACGGbQFtudb+N3IxBUCsxQDAVoj8na9dAHpBmQaAJakHK0QjBOvYkv0ZQxUAYuc5ABGx/wSuHTcBW8ei/N5AJwEwRE8F/GUZAABD7P5weMMGdmsm/nqxnwMliH0FPeK5A3c4lwaiM5j8vPIDBuPsKwcEiQkFaC9PATeBWQPxAgcDxGOS/KCAfwEcYbr/QPBdBSNDKQHF3O0G7erVAuzozwclBhMDMIyDBBGBkQKj5R0EfhyFBc7EZQIZyZUHvvgPB4Ry1QBGxicAZKs3AHS/IwEnVTEARQojBq6FqwXbGyL+VGBbBBPkKP8ZzZsD17o9AuXPowOLzhEBxha3AhcT3wKB2zMAHDIfATrFIQS8alEAAgCHBgUwrQQwxAkCjgoG/W/oZQVcTJsHVDRRBEiGsQAvCmUAdEPo/4a8QQB/2zkD5L89AyhpwwKqdikDtzoZBJKKIwGm/y0DHuZrA/ruPwDXn3L/o5ynACEaIQYc4ekBfNfZA2Qh1wVqrA8FVbRNAghXAQEf2i0AswrG+CQDyQEbcPkDZ3XZBic/GwOyGAkG0Dx9Apz8TQHqEo8CCqp29G6IxQamAur/BUmVBFBxgwXcSakCA5HXBDbmSwDzf8kBFeIDAjFyDQAO8G0HBVItARq2rv8RWJEENkSzBKlzywN5xX0EJ5yZBffUTQFiqmEHg0fa/duXWQGxoDcAbgFlABXNVwKm757+VWxfBvaWKvXQIR0ADuey/mwEuQbZOXsFWT6hAd5BlQb3GKUHD9uxA1PJKQG2nHUDSYqfAXDPHQG6XF0EBoe2/z8C4wObVecHeBRvAXPUWwUg3lMHMO9zAmHq5wEclmkBpQUBAOUN1QePmRkAvzRXAhHbSv5bvoEDkM5xAdwBzQHUpc8Bb7FVBTdsyQEN+b7+3KslAT7csQYeFzkCgSxJBF+EgQJyihEAIk6HAy8LlwBe8u8AnFN9AdMhGQV5lLUA1QSrBZGFPQInNysDKwG1B2UIyv36g6D//FtrA+XtrwRhvN8HpsI7B47PJvwCVvcCIpiTB3oLPQAbTfUEEDMTAZIRbvid8xT+pmWI/n6aewZDLbD8lN5nBdIPRQIjKtL8ur7m/+AgWvpZvdkDTX43Axf5CQM0xwz9Gzp7ALsMkQde8uz8hQZZAUJMPwWWBycBSEIRByjuuQK30vj8QKO6/ggpZQajUbT/d0LU+yAuEQGnKrcDxve1AHeqLwZr51MAN3TFAgF/tv7Z7mUAyNv/A5w35Pof8gUAVgJdAmQRmwaayHUEfhDHA8A1wQEZGiMBSPzNBcmQGQYpVr0AvF+jA2JaVQPc/M0DKosbAmkdGQE60PkA6iYdAB6EXQfpsh0G+HdPANPM8wQ6aRD8jT51AC7/Ev/cJfb/gIJ/A4vdUQeVQzMAoXK7ALAWuP7C9nMCfarZA5sj/v2E6iMActnNBEIEOQId4hMFlfm/A0mPJwN3MuUBdX7VAbnOlPyIltcA00gNAexcxwDqPpr9CfDNBh9rYQA3YAL8QxgpB60q3wArFb0F11vNAqgstQJYtU8HstG1B2DIfwaYFksHP0lrAoaomwcf/N0FoXejAUovjwPwVnkFXJVPB7t9/wTL5J8A2ibo/0OZuwFByo0COGKa9Xqc/wft42cBCt2LBqLoGvymFn0BFC4RAODhZwCyOvcDQ/TtAltjTQJG/3kBnKo5ADl9DQPGEucCFYQ9B4IktP+Bchr/sJOS/nEEeQC7M0EBWumpAwayeQEt/AsF4mg1BupmRQY/7m0DmTuNA1i8OwR5vTUAv/W8/9zmrP3WvMMFSSyfBCt+FwcJZlkBUJIhAMGp+QF8wgsEeanJAuUI1QN3RQUAMr5g/TEVewY1+M8Etoxu/0Gmjv+379j7K+oNBGbVgQepfCcA8vi7AB4QowARZ+EA7cMbAA60YwaKcbcBW4ytAxIvrwB+Fk78gvtrA/ePnQE7I/8BCCmK/PH3KQGDKasH0lCDBEOwiQWVwKEHdtbJA5HUQwT7178Bu19dA2zfIP+g1E0DP0/JAoTuTQMNWHkHPJpHANTN7wH/txj/CPA9Bw0gbwe+ezUDjJOVA58raQML7gsEWfk/AtRx/wYO2WL/RMCnBJzGdwPQmp8CTQiDA4VZSQKds20DhDphAmkVvwYwV3kDT68FAzpNMP2neAEFDdbnA6AgOQQpousBkaX4/fmrcwMqeTT/UjnDBQ+kYwd6jcMC3uAc/8govQCT2nEDG9JdA1YkSwS4i+8AEWjhAv+4RQR4SekC5lvjAbFyhP6js38DSIdDApnxkQVPfJ8FLesFArTNZwDTILkH2ywJBZLsPQMnWJ7+OPE5AJAOEwQ0N0cDRdx69NKYRQd1J70DndgW+3sKlvo3dw0AOGoVA5q2PPsOXLEFeAJxB9ZWoP/jMI8HZ+zK+fzAWQaC5zUBP/um+/qHfQN+WbMCa8CVAkBPqQKLztcD0uOa/NcbJQGnD4kA2XAo+W9PUQOu9AcFmdg3A2YwwQQZJA8A9dxFBa5LIwL69DEAS+eO/pNQfQNmDacDOzVrB1Et+QYguc8Dx+YbAn1XiwEGuOkCGDTfAq9y0QPcW+D/rhTHBdI9vv/uptkDXUpJARTFSQJW1LUDDwiLADOEBvx1z1T6Es43B8w+dP2ikiz7VeIe/483fwIYUicC54A4/MZ4fwBBYpUC5SnHBkwADwYq77MBF/TbAQb/3wHX9vb7xzyTB+KYGwZ3FG0FKS0lAf803QQ5T2UBlOHY/5LyBwMimq0BFdJ/AdYKrwFTvJMCipuFAce+GQHgrlMCOaQBBvpvaQE2eO8B2ZZNAS6Q5QIRyJUDHZ6Q/zonlP4ufFEG8nVjB9WMmwW/zI0HacYM95XLWwF2elMBLvXBB/gvuQLbzH0EcXK/APEJ+vjngAkF20wxB3OnhQOsxnkDPqrc/4GUZwIJlKUFEUKHAplGWwH4wzb9J7Ve/XeAiQahvVkBr3YpAiKnzv1/hvEDio6lAus9DQAf7JkHWaGBBML8uwVLsLMFbifhAmgTWwE4Ym8BznHHBFSsyQVqxGEF1Mvc9fLohQOaL7j4xlr9A4Y5mQfbZNkBphsY/avkYQWOLSsAlwNC/n+zxv33m0D514DU/LUgMQQW5yL//+RnB0e23P4l61UAltVy/nNSzQPlq2sA0xzDB+7wVQenUkMBdgmZAvlwpwWuTTUF2X5G/OPppQX69V8FY7bg9cE0jQUEUHcEi4KFAdClFwe/nRsBWK0VAqA73QH75ML+ZcTfAxU5LQLlSFMAM0opAgkGOwFBGLUBhyHXBxXvbwJk3O8DMhqLARuqFP7Q/FcHZ6vc+ZOh8PkXPB0HdZgNAhXrnQOa7mcAG+zzA61M3QCXJPsBerW3B+vvowHIj9sDDY5FAAZYlQcNdjr8WUAc/eTbRQCRGt7/2ZQtAgmdvwYwBhMDcrpJAkBhkwNMw2b/jQMO+3v7/QNhIQ0BmO5DAe7UFQbCBj8AOrr/AROdZQJb2C0H+dl0/DQ2/PpKLgcA0FT+/IZgEwTu7gkDhWzI/bW5rwGyBA8GFtQXB9A0uQI3LcsADb86/mdZhwN/Qi8B5liVBzR/iv2jIbMHQ7fzAMSHOwAqaNME0IWrBqjmTwHjsycBAzpVA56j+wFJ4GUGwX4tARkh/QPzA8z4XrpU/1wwsQQ+hvL72mRRAGK2zwMawRkCvlC1BCoWHwBKGwMClsrXA279hQIo9HcHgfynBHiuVQBqywkA2GgPA1vj2QGd9MUD2f61A8MyTwOQenMA9hCdBWVNJQMt+JsD8LP1AAKa+wMM2h8DlIJLA5LQwwG5sED/C4AbAGRqSvAzopMDQeGVAycvLwHnOvr9ROyJAmLRowD4jfcDkY9jAIB8swD46kcCtemXBkeE3wWbKXsAlwdg/FnuVQA9DUkAnSLrA32qEwH2dLkBa59dA7j1mwOE5BcFE2xlB8emaQKahmr/EBY6/Riq+QPMYLkGkwN9AvCMaQbv8t0DEcQPBsbfAwL/kVMA++ac/R1oRQNdtSEC4j9jAUtkGwf1M+kDPw7FAPqCXP/S9uEBuNqnAw2g/wP2EhMDW7BpBp1KhwEz8rcANrFTAsmc3wd2j1UCRmyBBU/MwQAE6PkEr8tXAG3xRwBx3YkHnZwjB67opQEaUQUAAtDu/UmnJwKy6NcBATZlAZhEUQVxjAcBl3idBQeT2PwadFMFajwZADXIIwdE6fEEFba3AHi+LvxGQR8HpV5vA9QtQP/NWHsAigJZAn45GwGY0dcCvQPDAqeRtv6lQVcEI5YI/X5qIQFjlur5ivlXAthciwX5TpMApj/M/qLjSwHYAokAEmTZBrjQswcHkGEEPda1AzBOWQKFcB7/tUIzA4fJwwPv8mcCOLAPBC5DUwJdOLkFVVIvA3vMiQeSWm8BoTj3B6X/qwKQ4H0HX+Ek/XKIbQHBYOMES+iHAAlKmwNiLNEE3GLRAmbPaQEtqqEAv+5XAdOJVwDFxIkHHURnBLUi9wF0sRkFiherAQHWLQC0QWkGLi9DAHNDbv64ulsD1Vp9Afev7wLzCW8BQjSbBvRM4wOI+gkAFP9hAIZnuQHBo9r8fyY3AwP3jwPSu4sAXlUBByhUNwJwXjD8FsDbBmzylQGeTecC5sMxAL2y1QPuwEkGWE7pA0KKCQZm1gMBsXq2/JXbAwGHBAEHvykbALdgRvofbnECghzFBR3HmwGyysT8bXU5AXu+CwIGYI0ApBVs/T1lOwf0jwj+PkXFBVQmGQU7EK8BzxY3A7mOYwIn8IcG/q7LAKYIcQbOzI0HlQhXBj8jfP70/IkFoOfpA/m/oQJ/+VEGpbglBFJ6wv7B4IkFK7OVA6iy9wGb2isD5xC9B+p0BQLBSFkHXHv2/gX38PwGe/cDNGTTATkp9QfhacDwLot1A1H26wKwuQMFK+ahAMLn2QI85yMDCcxNA7J50QQYmqcC8/fG/EkAcwSeVykAcla/A/Cirv9ME9L+wOjZBXAjTv0z0BsH0TsXAGbyewGE11D/fZxPACR9jQDkqzsC+FYbAj7HpwJ1hwcBuiw5ACTdnQcqAeECnq3RBhK4SwBoXZECucHzAFZ7UP0bBTEGMawvBQdU5QKXsRD/o/rtAW/KkP4cOA0BKWQTBqV7xPiGv2z/teWBAfwgZwQuKm8C/KGvAxCi5wGLBHT9ii9fA+N5ewRIQ28A7kIrAi1rDP3x6KkFNCpfAu7NYwXs9LEFcu2xAjsKxwEQAGEGAflC+KDYCQaP1MEGRDpdA1DpIwfFHkMB5zSjBJuUpQVgaJMGILv7ASnqmQLAGS0Gj24BA+NatwePD2UDNXMjA2Tv5QNP+x0D3WSTBbOFhwKFW0MBiZ5G/wr8PweiH779IgQ/BTcKMQZADiUAMG9hAc+iBvjLijMFQqeLAo1X1wAPTUEG7a/HAfBa5QFWBMcDTIidBN4gBwde4wkDkeyhAtTlPwXNgiD9G8pDAnmGGwFQIrUDIG7hAyd0gQQk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