Python Gravitational Wave Interferometer Noise Calculator

pygwinc is a multi-faceted tool for processing and plotting noise budgets for ground-based gravitational wave detectors. It's primary feature is a collection of mostly analytic noise calculation functions for various sources of noise affecting detectors (gwinc.noise):

• quantum noise
• mirror coating thermal noise
• mirror substrate thermal noise
• suspension fiber thermal noise
• seismic noise
• Newtonian/gravity-gradient noise
• residual gas noise

pygwinc is also a generalized noise budgeting tool (gwinc.nb) that allows users to create arbitrary noise budgets (for any experiment, not just ground-based GW detectors) using measured or analytically calculated data. See the budget interface section below.

pygwinc includes canonical budgets for various well-known current and future GW detectors (gwinc.ifo):

• aLIGO
• A+
• Voyager
• Cosmic Explorer 1
• Cosmic Explorer 2

See IFO.md for the latest CI-generated plots and hdf5 cached data.

The inspiral_range package can be used to calculate various common "inspiral range" figures of merit for gravitational wave detector budgets. See figures of merit section below.

usage

command line interface

pygwinc provides a command line interface that can be used to calculate and plot noise budgets for generic noise budgets or the various canonical IFOs described above, save/plot hdf5 trace data, and dump budget IFO parameters:

$ python3 -m gwinc aLIGO

You can play with IFO parameters and see the effects on the budget by dumping the pre-defined parameters to a YAML-formatted parameter file, editing the parameter file, and re-calculating the noise budget:

$ python3 -m gwinc --yaml aLIGO > my_aLIGO.yaml
$ edit my_aLIGO.yaml
$ python3 -m gwinc -d my_aLIGO.yaml aLIGO
                             aLIGO    my_aLIGO.yaml
Materials.Coating.Philown    5e-05            3e-05
$ python3 -m gwinc my_aLIGO.yaml

You can also use the --ifo option to change parameters from the command line:

$ python3 -m gwinc aLIGO --ifo Optics.SRM.Tunephase=3.14

Stand-alone YAML files will always assume the nominal 'aLIGO' budget description.

Custom budgets may also be processed by providing the path to the budget module/package:

$ python3 -m gwinc path/to/mybudget

The command line interface also includes an "interactive" mode which provides an IPython shell for interacting with a processed budget:

$ python3 -m gwinc -i Aplus
GWINC interactive shell

The 'ifo' Struct and 'traces' data are available for inspection.
Use the 'whos' command to view the workspace.

You may interact with the plot using the 'plt' functions, e.g.:

In [.]: plt.title("foo")
In [.]: plt.savefig("foo.pdf")

In [1]: 

See command help for more info:

$ python3 -m gwinc -h

python library

For custom plotting, parameter optimization, etc. all functionality can be accessed directly through the gwinc library interface:

>>> import gwinc
>>> import numpy as np
>>> freq = np.logspace(1, 3, 1000)
>>> Budget = gwinc.load_budget('aLIGO')
>>> traces = Budget(freq).run()
>>> fig = gwinc.plot_noise(freq, traces)
>>> fig.show()

The load_budget() function takes most of the same inputs as the command line interface (e.g. IFO names, budget module paths, YAML parameter files), and returns the un-instantiated Budget class defined in the specified budget module (see budget interface below).

The budget run() method is a convenience method that calculates all budget noises and the noise total and returns a (possibly) nested dictionary of a noise data, in the form of a (data, style) tuple where 'data' is the PSD data and 'style' is a plot style dictionary for the trace. The dictionary will be nested if the budget includes any sub-budgets.

noise functions

pygwinc noise functions are available in the gwinc.noise package. This package includes multiple sub-modules for the different types of noises, e.g. suspensionthermal, coatingthermal, quantum, etc.)

The various noise functions need many different parameters to calculate their noise outputs. Many parameters are expected to be in the form of object attributes of a class-like container that is passed to the calculation function. The pygwinc Struct object is designed to hold such parameters.

For instance, the coating_brownian function expects a materials structure as input argument, that holds the various mirror materials parameters (e.g. materials.Substrate.MirrorY):

def coating_brownian(f, materials, wavelength, wBeam, dOpt):
    ...
    # extract substructures
    sub = materials.Substrate
    ...
    # substrate properties
    Ysub = sub.MirrorY

gwinc.Struct objects

pygwinc provides a Struct class that can hold parameters in attributes and additionally acts like a dictionary, for passing to the noise calculation functions. Structs can be created from dictionaries, or loaded from various file formats (see below).

YAML parameter files

The easiest way to store all budget parameters is in a YAML file. YAML files can be loaded directly into gwinc.Struct objects via the Struct.from_file() class method:

from gwinc import Struct
ifo = Struct.from_file('/path/to/ifo.yaml')

YAML parameter files can also be given to the load_budget() function as described above, in which case the base 'aLIGO' budget structure will be assumed and returned, with the YAML Struct inserted in the Budget.ifo class attribute.

Here are the included ifo.yaml files for all the canonical IFOs:

• aLIGO.yaml
• Aplus.yaml
• Voyager.yaml
• CE1.yaml
• CE2.yaml

The Struct.from_file() class method can also load MATLAB structs defined in .mat files, for compatibility with matgwinc, and MATLAB .m files, although the later requires the use of the python MATLAB engine.

budget interface

pygwinc provides a generic noise budget interface, gwinc.nb, that can be used to define custom noise budgets (it also underlies the "canonical" budgets included in gwinc.ifo). Budgets are defined in a "budget module" which includes BudgetItem definitions.

BudgetItem classes

The gwinc.nb package provides three BudgetItem classes that can be inherited to define the various components of a budget:

• nb.Noise: a noise source
• nb.Calibration: a noise calibration
• nb.Budget: a group of noises

The primary action of a BudgetItem happens in it's calc() method. For Noise classes, the calc method should return the noise PSD at the specified frequency points. For the Calibration class, calc should return a frequency response. Budget classes should not have a special calc method defined as they already know how to calculate the overall noise from their constituent noises and calibrations.

Additionally BudgetItems have two other methods, load and update, that can be overridden by the user to handle arbitrary data processing. These are useful for creating budgets from "live" dynamic noise measurements and the like. The three core methods therefore are:

• load(): initial loading of static data
• update(**kwargs): update data/attributes
• calc(): return final data array

See the built-in documentation for more info (e.g. pydoc3 gwinc.nb.BudgetItem)

budget module definition

A budget module is a standard python module (single .py file) or package (directory containing __inti__.py file) containing BudgetItem definitions describing the various noises and calibrations of a budget, as well as the overall budget calculation itself. Each budget module should include one nb.Budget class definition named after the module name.

Here's an example of a budget module named MyBudget. It defines two Noise classes and one Calibration class, as well as the overall Budget class (name MyBudget that puts them all together):

$ find MyBudget
MyBudget/
MyBudget/__init__.py
MyBudget/ifo.yaml
$

# MyBudget/__init__.py

import numpy as np
from gwinc import nb
from gwinc import noise


class SuspensionThermal(nb.Noise):
    """Suspension thermal noise"""
    style = dict(
        label='Suspension Thermal',
        color='#ad900d',
        linestyle='--',
    )

    def calc(self):
        n = noise.suspensionthermal.suspension_thermal(
            self.freq, self.ifo.Suspension)
        return 2 * n


class MeasuredNoise(nb.Noise):
    style = dict(
        label='Measured Noise',
        color='#838209',
        linestyle='-',
    )

    def load(self):
        psd, freq = np.loadtxt('/path/to/measured/psd.txt')
        self.data = self.interpolate(freq, psd)

    def calc(self):
        return self.data


class MyCalibration(nb.Calibration):
    def calc(self):
        return np.ones_like(self.freq) * 1234


class MyBudget(nb.Budget):
    noises = [
        SuspensionThermal,
        MeasuredNoise,
    ]
    
    calibrations = [
        MyCalibration,
    ]

The style attributes of the various Noise classes define plot style for the noise.

This budget can be loaded with the gwinc.load_budget() function, and processed with the Budget.run() method:

Budget = load_budget('/path/to/MyBudget')
budget = Budget(freq)
traces = budget.run()

Other than the necessary freq initialization argument that defines the frequency array, any additional keyword arguments are assigned as class attributes to the budget object, and to all of it's constituent sub noises/calibrations/budgets.

Note that the SuspensionThermal Noise class above uses the suspension_thermal analytic noise calculation function, which takes a "suspension" Struct as input argument. In this case, this suspension Struct is extracted from the self.ifo Struct at self.ifo.Suspension.

If a budget module defined as a package includes an ifo.yaml parameter file in the package directory, the load_budget() function will automatically load the YAML data into a gwinc.Struct and include it as an Budget.ifo attribute in the returned Budget class. This would provide the self.ifo needed in the SuspensionThermal Noise class above and is therefore a convenient way to provide parameter structures in budget packages. Otherwise it would need to be created/loaded in some other way and passed to the budget at instantiation, e.g.:

Budget = load_budget('/path/to/MyBudget')
ifo = Struct.from_file('/path/to/MyBudget.ifo')
budget = Budget(freq, ifo=ifo)
traces = budget.run()

The IFOs included in gwinc.ifo provide examples of the use of the budget interface (e.g. gwinc.ifo.aLIGO).

extracting single noise terms

There are various way to extract single noise terms from the Budget interface. The most straightforward way is to run the full budget, and extract the noise data the output traces dictionary:

Budget = load_budget('/path/to/MyBudget')
budget = Budget(freq)
traces = budget.calc_traces()
data, plot_style = traces['QuantumVacuum']

You can also calculate the final calibrated output noise for just a single term using the Budget calc_noise() method:

data = budget.calc_noise('QuantumVacuum')

You can also calculate a noise at it's source, without applying any calibrations, by using the Budget __getitem__ interface to extract the specific Noise BudgetItem for the noise you're interested in, and running it's calc() method directly:

data = budget['QuantumVacuum'].calc()

figures of merit

The inspiral_range package can be used to calculate various common "inspiral range" figures of merit for gravitational wave detector budgets. Here's an example of how to use it to calculate the inspiral range of the baseline 'Aplus' detector:

import gwinc
import inspiral_range
import numpy as np

freq = np.logspace(1, 3, 1000)
Budget = gwinc.load_budget('Aplus')
traces = Budget(freq).run()

range = inspiral_range.range(
    freq, traces['Total'][0],
    m1=30, m2=30,
)

Note you need to extract the zeroth element of the traces['Total'] tuple, which is the actual PSD data.

See the inspiral_range package for more details.