hearsay¶

The purpose of this project is to compute simulations of the causal contacts between emiters in the Galaxy.

Project by Marcelo Lares (CONICET, UNC, Argentina)

A python virtual environment is suggested to work with this project. Requirements are listed in the project home directory file: requirements.txt.

Science case¶

Proposal¶

The abundance of intelligent civilisations in the Galaxy is a: longstanding question related to the expected probability of success for SETI programs. Traditional approaches to quantify this problem use either frequentist inferences or numerical simulations. The key challenges in both cases are the proper treatment of the time variable and the large uncertainties on the intervening factors. Here we present a three-parametric statistical model of the network of causally connected locations in a simplified Galaxy, with the restriction of a maximum distance range for any signal. We implement Monte Carlo simulations of this model to explore the parameter space and analyse the probabilities of causal contacts for random locations in the Galaxy.
We find: that, given the enormous distances involved, causal contacts between civilisations would be very rare. The odds to make a contact in a few years of monitoring are low for most models, except for those of a galaxy densely populated with ancient civilisations. The probability of causal contacts increases with the lifetime of civilisations more significantly than with the number of active civilisations. We show that the maximum probability of making a contact occurs when a civilization discovers the required communication technology.

Methods and working hypothesis¶

Simulations are suitable tools to analyze systems that evolve with time and involve randomness.

An advantage of simulations compared to theoretical approaches, is that the former usually require less assumptions and simplifications, and can be applied to systems where a theoretical model can not be found.

In particular, many complex stochastic processes that can be described by the evolution of the state of a system, can be efficiently modeled with the discrete–event (hereafter, DE) simulation approach.

A system described with the DE paradigm is characterized by a set of actors and events, where actors interact causally through a series of events on a timeline and process these events in chronological order

Space-time diagrams showing the causal contact connections.¶

All possible cases for the generation of events.¶

References¶

Abstract¶

The abundance of intelligent civilizations in the Galaxy is a longstanding question, often conceptualized as the problem of the lack of received communication. Estimates of the number of civilizations are generally guided by the Drake equation, despite the large uncertainties in its factors and its lack of a temporal nature. Alternative approaches use detailed numerical simulations of the galaxy and recipes for the stars and planets formation rates and for the origin of life, and rely on uncertain parameters. We present a statistical model for the abundance and duration of civilizations implemented through Monte Carlo simulations. We explore the hypothesis space of the model by a suite of simulations to analyze the emergence of communicating nodes and their causal connections. We based this on minimal assumptions and three free parameters, with focus on the proposed statistical properties of empirical models. Our analysis of the dependence of the rate of causal contacts on the mean number of civilizations, the mean lifespan distribution and the maximum distance a civilization can send signals, is considered to discuss the spatial and temporal structure of a populated galaxy within several scenarios. We find that, given the enormous distances involved, causal contacts between civilizations are very rare. The odds to make a contact in a few years of monitoring are low for most models, except for those of a galaxy densely populated with long-lived civilizations. The probability of causal contacts increases with the lifetime of civilizations much more significantly than with the number of active civilizations for a time window. We show that the maximum probability of making a contact occurs when a civilization gains the required communication technology.

Context¶

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API¶

Getting started¶

Hearsay for python¶

Hearsay has been tested for python 3.7

More testing is currently under development.

Preparing a virtual environment¶

It is recommended to install a virtual environment for a clean python ecosystem.

virtualenv MyVE
source MyVE/bin/activate

or

mkvirtualenv -p $(which python3)

Downloading hearsay¶

Hearsay is publically available from a GitHub repository. It can be downloaded with:

git clone https://github.com/mlares/hearsay.git

The code can be explored using GitHub, including development activity and documentation.

Installing hearsay¶

Once the virtualenvironment has been set (recommended), then install the hearsay package:

pip install -r requirements.txt

It is convenient to save the root directory of the hearsay installation. In bash, for example,

export hearsat_rootdir=”$(pwd)”

Hearsay module can be used anywhere provided the following command is executed within the environment in the directory $hearsay_rootdir:

pip install .

Testing hearsay¶

We first need to create an output directory, as set in the .ini file:

dir_output = ../out/

So, from a bash prompt:

mkdir $hearsay_root/out

In order to run a test experiment, go to the src directory and run:

python run_experiment.py ../set/experiment.ini

Configuration¶

Experiment section¶

In this section we must give a unique ID for an experiment. It can include numbers and characters, for example “01” or “first_experiment”

[experiment]

# experiment ID
exp_ID =  ID_001

The files resulting from the simulations will be stored in a directory with this name under the directory indicated in the dir_output varible (output section). In this case, it will create the directory ../out/ID_001 if it does not exist.

Simulation section¶

[simu]

# internal GHZ radius, lyr
GHZ_inner = 20000.
# external GHZ radius, lyr
GHZ_outer = 60000.

# time span to simulate
t_max = 2.e6

#tau_awakeningS
tau_a_min = 2000
tau_a_max = 80000
tau_a_nbins = 10

# tau_surviveS
tau_s_min = 2000
tau_s_max = 80000
tau_s_nbins = 10

# Separate data in directories according to D_max
D_max_min = 20000
D_max_max = 20000
D_max_nbins = 1

# Number of realizations for each simulation
Nran = 10

# Parallel run
run_parallel = Y
Njobs = 10

These variables are used to set:

the inner and outer radii of the Galactic Habitable Zone (GHZ_inner and GHZ_outer)
the maximum time span for a simulation (t_max), in years
the minimum and maximum values for the tau_awakening parameter, in kpc, and the number of values in that range. For examples, the values: tau_a_min = 3, tau_a_max = 8 and tau_a_nbins = 10 will generate ten values between 3 and 8 using the same criteria as numpy.linspace, i.e., tau_survive_values = numpy.linspace(tau_a_min, tau_a_max, tau_a_nbins)
the minimum and maximum values for the tau_survive parameter, in years, and the number of values in that range.
the minimum and maximum values for the D_max paramter, in kpc, and the number of values in that range.
A flag indicating whether the run will be made in parallel (run_parallel). The values ‘y’, ‘Y’, ‘yes’, ‘YES’, ‘true’, ‘True’, or ‘TRUE’ can be used to set a parallel run, and the values ‘n’, ‘N’, ‘no’, ‘NO’, ‘No’, False’, ‘false’, or ‘FALSE’ can be used to ser a serial run.
If a parallel run has been set, the parameter Njobs can be used to set the number of threads. If run_parallel has been set to False, Njobs will be ignored.

Output section¶

[output]

dir_output = ../out/
dir_plots = ../plt/
pars_root = params
progress_root = progress

plot_fname = plot
plot_ftype = PNG
clobber = N

These are the names of the directories for output and plots (fir_output and dir_plots, resp.). pars_root is used as the root name for the file that stores the parameters. For example, this configuration will generate a file named params.csv in the directory ../out/ID_001/.

If the sme experiment ID is used twice, it will ignore the files with the same names. The clobber variable allows to chose if these files will be overwritten (True, Yes) or not (False, No).

Verbose section¶

[UX]

show_progress = Y
verbose = Y

show_progress can be set to True/False or Y/N (similarly to the run_parallel variable), used to show a progress bar for the experiment.
verbose will print on STDOUT several messages indicating the steps of the simulation.

API Usage¶

This tools can be used as an API, from a python prompt or from a command line.

Steps:

Complete the configuration of the experiment
All the settings of the experimets are parsed from the configuration files using configparser.
run an experiment
process the experiment
show results

Configuration files¶

All parameters for the simulation are stored in a .ini file, for example:

[experiment]

# experiment ID
exp_ID =  PHLX_02


[simu]

# internal GHZ radius, lyr
GHZ_inner = 20000.
# external GHZ radius, lyr
GHZ_outer = 60000.

# time span to simulate
t_max = 2.e6

#tau_awakeningS
tau_a_min = 2000
tau_a_max = 80000
tau_a_nbins = 10

# tau_surviveS
tau_s_min = 2000
tau_s_max = 80000
tau_s_nbins = 10

# Separate data in directories according to D_max
D_max_min = 20000
D_max_max = 20000
D_max_nbins = 1

# Number of realizations for each simulation
Nran = 10

# Parallel run
run_parallel = Y
Njobs = 10


[output]

dir_output = ../out/
dir_plots = ../plt/
pars_root = params
progress_root = progress

plot_fname = plot
plot_ftype = PNG
clobber = N

[UX]

show_progress = Y
verbose = Y

It is not possible to add, remove or change fields from this file, if so it will trigger failures in the testing process. The fields are organized in four categories:

experiment: for the experiment ID. Each time a new ID is used, it will generate a new directory in the dir_output directory.
simu: for simulation parameters
output: for the names of directories and files that store simulation results.
UX: parameters related to the user experience

An example file is provided in the set directory.

The details about the configuration parameters are given in Configuration.

Loading configuration¶

There are two approaches to loading the configuration parameters, that depend on how the code is run:

1. Command line call

The configiration file can be given throught the command line:

python run_experiment.py config_file.ini

In order for this to work, the script run_experiment.py must contain:

from hearsay import hearsay
from sys import argv
conf = hearsay.parser(argv)

2. Call from a python environment

When used from a python environment, it is possible to pass a filename as a string.

from hearsay import hearsay
config_file = 'config_file.ini'
conf = hearsay.parser(config_file)

If the parser method is called without arguments, it will try to load the file ../set/experiment.ini which is distributed with the package.

Command line usage¶

For a simple test, go to src and run:

$ python run_experiment.py ../set/experiment.ini
$ python process_experiment.py ../set/experiment.ini
$ python plot_experiment.py ../set/experiment.ini

It is important to use the same configuration file on these three steps, since parameters are used to build filenames.

If no name is given for a configuration file, it will use a dafault file in ../set/experiment.ini.

API usage¶

To use functionalities, import the hearsay module:

from hearsay import hearsay

First, we must parse the configuration parameters from the .ini file.

All parameters with an assigned value must be read with the configparser module. The ConfigParser class is inherited in hearsay.parser.

Variables can be accessed using the names of the sections and the names of the fields. For example, conf[‘simu’][‘t_max’].

There are several posibilities for loading the configuration parameters.

From the command line it is possible to give the name of the file containing the parameter settings:

python run_experiment.py < ../set/experiment.ini

In this case, the file must contain the following:

from sys import argv
conf = hearsay.parser(argv)

From the python interface, it is possible to give the filename as a string:

from hearsay import hearsay
conf = hearsay.parser('../set/experiment.ini')

Also, in the default case, the function hearsay.parser can be called without arguments, and the default configuration file will be loaded:

from hearsay import hearsay
conf = hearsay.parser()

After the instantiation of a parser object without arguments, the default parameters can be overwritten with the specific methods:

from hearsay import hearsay

conf = hearsay.parser()
conf.check_file('../set/experiment.ini')
conf.read_config_file()
conf.load_filenames()
conf.load_parameters()

Finally, the simulation is made with the hearsay.GalacticNetwork class, where the function hearsay.GalacticNetwork.run_experiment() makes the computations.

G = hearsay.GalacticNetwork(conf)
G.run_experiment()

This function accepts the parallel flag which indicates a parallel version of the code will run:

G.run_experiment(parallel=True)

The analysis and visualization of the results can be done as follows:

R = hearsay.results(conf)
R.load()
res = R.redux_2d()

The method hearsay.results.redux_2d computes the matrices.

A complete example of visualization is provided in the src directory.

Tutorials¶

Contents of objects¶

C3Net.params¶

A pandas dataframe with the columns:

tau_awakening
tau_survive
d_max
filename

C3Net.config¶

This is a parser object, which is part of the hearsay.hearsay.Parser method.

Contains all configuration parameters in hearsay.C3Net.config.p:

G.config.p
   Out: pars(ghz_inner=20000.0, ghz_outer=60000.0, t_max=2000000.0,
   tau_a_min=2000.0, tau_a_max=80000.0, tau_a_nbins=10,
   tau_s_min=2000.0, tau_s_max=80000.0, tau_s_nbins=10,
   d_max_min=20000.0, d_max_max=20000.0, d_max_nbins=1,
   nran=3, run_parallel=True, njobs=10, exp_id='PHLX_02',
   dir_plots='../plt/', dir_output='../out/', pars_root='params',
   plot_fname='plot', plot_ftype='PNG', fname='../plt/plot_PHLX_02PNG',
   showp=True, overwrite=False, verbose=True)

hearsay.C3Net.config.filenames:

G.config.filenames
pars(exp_id='PHLX_02', dir_plots='../plt/', dir_output='../out/',
pars_root='params', progress_root='progress', plot_fname='plot',
plot_ftype='PNG', fname='../plt/plot_PHLX_02PNG')

Output of a simulation¶

The output of a single simulation is a dictionary. The length of this object is the number of nodes in the simulation run. Each entry has a list which contains the node itself and the nodes that reach contact to that node.

The first entry of this list contains:

index of the node
index of the node (repeated)
position X
position Y
time of the A event
time of the D event

The next entries of the list, if any, contain the contacts.

index of the receiver node
index of the emiter node
position X of the emiter node
position Y of the emiter node
time of the C event for the receiver node
time of the B event for the receiver node

Runnig and analyzing experiments¶

In this section we show how to use hearsay to run experiments and analyze the results.

First, we import the required modules:

import hearsay
import pandas as pd
from matplotlib import pyplot as plt
import numpy as np

# TUTORIAL 1: experiment from ini file

Now, we use the configuration file to load an experiment setup:

conf = hearsay.parser('experiment.ini')
G = hearsay.C3Net(conf)
G.set_parameters()
net = G.run(interactive=True)
R = hearsay.results(conf)
R.load()
res = R.redux_1d()
plt.hist(res['A'])
plt.show()

# TUTORIAL 2: CORRER UNA SIMULACION

It is possible to run a limited number of parameters of the experiment, or even an entirely new parameter set. For example, if we want the parameters:

tau_awakening = 20000 tau_survive = 20000 D_max = 20000 Nran = 7

we can just update the parameters:

conf.load_config(['nran'], ['7'])
tau_awakening = 20000
tau_survive = 20000
D_max = 20000
directory = ''.join([G.config.filenames.dir_output, G.config.filenames.exp_id])
filename = ''.join([directory, 'test.pk'])
pars = [[tau_awakening, tau_survive, D_max, filename]]
df = pd.DataFrame(pars, columns=['tau_awakening', 'tau_survive',
                                 'D_max', 'filename'])
G.set_parameters(df)

And then we can analyze them using:

res = G.run(interactive=True)
G.show_single_ccns(res[0])

hearsay package¶

Submodules¶

hearsay.hearsay module¶

HEARSAY.

This module contains tools to compute and analyze numerical simulations of a Galaxy with constrained causally connected nodes. It simulates a 2D simplified version of a disk galaxy and perform discrete event simulations to explore three parameters: 1. the mean time for the appeareance of new nodes, 2. the mean lifetime of the nodes, and 3. the maximum reach of signals.

A simulation is a realization of the Constrained Causally Connected Network (C3Net) model. The details of this model are explained in Lares, Funes & Gramajo (under review).

Classes in this module: - Parser - C3Net - Results

Additionally, it contains the function unwrap_run which is used for parallel runs with the joblib library.

class hearsay.hearsay.C3Net(conf=None)

Bases: object

C3Net: Constrained Causally Connected Network model.

init()¶: creates a node

__len__()¶: None

__repr__()¶: None

__str__()¶: None

run()¶: Run a suite of simulations for the full parametet set in the configuration file.

run_suite()¶: Run a suite of simulations for a given parameter set.

run_suite_II()¶: Run a suite of simulations for a given parameter set, to be run in parallel.

run_simulation()¶: Run a simulation for a point in parameter space.

show_single_ccns()¶: Show the contents of a simulation run.

prepare_dirs(filenames)

Prepare directories for experiments from dataframes.

Takes a list of paths and filenames and check if all paths exist.

Parameters: filenames (list) – A list with all filenames
Returns
Return type: None

run(parallel=False, njobs=None, interactive=False)

Run an experiment.

An experiment requires a set of at least three parameters, which are taken from the configuration file.

Parameters

parallel (Boolean) – Flag to indicate if run is made using the parallelized version. Default: False.
njobs (int) – Number of concurrent jobs for the parallel version. If parallel is False njobs is ignored.
interactive (boolean) – Flag to indicate if the result of the simulation suite is returned as a variable.

Returns

res – Only returned if interactive=True. Contains the results from the simulations. The size of the list is the number of simulations in the experiment, i.e., the number of lines in self.params. Each element of the list is a dictionary containing the complete list of CCNs and their contacts.

Return type

list

hearsay.olists module¶

OLISTS.

Module for the manipulation of ordered lists. This module is internal to hearsay.

class hearsay.olists.Node(data)

Bases: object

Node and linked list classes.

This class contains tools to manipulate nodes. A node is a point in the Galaxy that a acquires the ability to emit and receive messages at a given time. A set of nodes make a linked list.

getData(): Get data in a node.

getNext(): Get the next node, if exists.

setNext(newnext): Set the next node.

class hearsay.olists.OrderedList

Bases: object

Ordered list class.

Tools to make ordered lists. This structure is useful because it can be traversed and a new node can be added at any stage. # based on http://interactivepython.org/courselib/static/pythonds/ # BasicDS/ImplementinganOrderedList.html

add(data): Add an element to an ordered list.

isEmpty(): Ask if list is empty.

remove_first(): Remove first element.

show(): Print an ordered list.

size(): Retrieve the size of the list.

Module contents¶

Software for the Constrained Causally Connected Network Model.

This model is used to perform a statistical analysis of the hypothesis space for the contacts between nodes in a simplified galaxy.

Indices and tables¶

Minimal example¶

The following lines show how to install and run an example simulation suite. We assume that the package is downloaded in $hearsay_dir and the working directory is $working_dir

Clone hearsay from GitHub

cd $hearsay_dir
git clone https://github.com/mlares/hearsay.git

Create a virtual environment for python

virtualenv -p $(which python3) MyVE
source MyVE/bin/activate

Install the hearsay package
```
cd $hearsay_dir
pip install .
```
Create a configuration file. A template can be found in $hearsay_dir/set/experiment.ini
```
cd $working_dir
cp $hearsay_dir/set/experiment.ini $working_dir
```

Edit the configuration file. Set the following values:

experiment_ID = run_001
dir_output = out
dir_plots = plt

Create directories for output and plots, using the same values than the variables dir_output0 and dir_plots in the configuration file, for example:
```
cd $working_dir
mkdir out
mkdir plt
```
create a file experiment.py that contains the following:

conf = hearsay.parser('hearsay_dir/set/experiment.ini')
G = hearsay.C3Net(conf)
G.set_parameters()
net = G.run(interactive=True)
R = hearsay.results(conf)
R.load()
res = R.redux_1d()
plt.hist(res['A'])
plt.show()

A file with the name entered in the variable plot_fname of the configuration file will be saved in the directory plt.

hearsay¶

Science case¶

Proposal¶

Methods and working hypothesis¶

References¶

Abstract¶

Context¶

API¶

Getting started¶

Hearsay for python¶

Preparing a virtual environment¶

Downloading hearsay¶

Installing hearsay¶

Testing hearsay¶

Configuration¶

Experiment section¶

Simulation section¶

Output section¶

Verbose section¶

API Usage¶

Configuration files¶

Loading configuration¶

Command line usage¶

API usage¶

Tutorials¶

Contents of objects¶

C3Net.params¶

C3Net.config¶

Output of a simulation¶

Runnig and analyzing experiments¶

hearsay package¶

Submodules¶

hearsay.hearsay module¶

hearsay.olists module¶

Module contents¶

Indices and tables¶

Minimal example¶

hearsay

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