Read Rapid-PE

This is a package to read Rapid-PE outputs.

Install

Install from PyPI

This read-rapidpe package is available on PyPI: https://pypi.org/project/read-rapidpe/

pip install read-rapidpe

Install in dev mode

git clone git@git.ligo.org:yu-kuang.chu/read-rapidpe.git
cd read-rapidpe
pip install -e .

Example Usage

Reading files

from read_rapidpe import RapidPE_result

run_dir = "path/to/run_dir"
result = RapidPE_result.from_run_dir(run_dir)

There are three optional arguments:

use_ligolw ( default = True ) : whether to use ligo.lw to read XML files.
extrinsic_table ( default = True ) : whether to load extrinsic parameter as well.
parallel_n ( default = 1 ) : number of parallel jobs when reading XML files.

For example, one can do the following to speed up the reading process:

result = RapidPE_result.from_run_dir(run_dir, use_ligolw=False, extrinsic_table=False, parallel_n=4)

Plot marginalized log-likelihood on m1-m2 grid points

import matplotlib.pyplot as plt

# Plot marginalized-log-likelihood over intrinsic parameter (mass_1/mass_2) grid points
plt.scatter(result.mass_1, result.mass_2, c=result.marg_log_likelihood )
plt.xlabel("$m_1$")
plt.ylabel("$m_2$")
plt.colorbar(label="$\ln(L_{marg})$")

Plot interpolated likelihood

import matplotlib.pyplot as plt
import numpy as np


# Create Random m1, m2 samples
m1 = np.random.random(10000)*5
m2 = np.random.random(10000)*5


# After calling result.do_interpolate_marg_log_likelihood_m1m2(), 
# the method result.log_likelihood(m1, m2) will be avalible.
result.do_interpolate_marg_log_likelihood_m1m2()

# Calculate interpolated log_likelihood
log_likelihood = result.log_likelihood(m1, m2)


# =============== Plotting ===============
# Plot interpolated likelihood 
plt.scatter(m1, m2, c=np.exp(log_likelihood), marker=".", s=3, alpha=0.1)

# Plot marginalized likelihood on grid points
plt.scatter(result.mass_1, result.mass_2, c=np.exp(result.marg_log_likelihood), marker="+", vmin=0)

plt.xlabel("$m_1$")
plt.ylabel("$m_2$")
plt.colorbar(label=r"$\mathcal{L}$")

Convert to Pandas DataFrame

import pandas as pd
from read_rapidpe import RapidPE_grid_point


grid_point = RapidPE_grid_point.from_xml("ILE_iteration_xxxxxxxxxx.samples.xml.gz")
pd.DataFrame(grid_point.extrinsic_table)

Metadata

Release files for read-rapidpe 0.6.3

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for read-rapidpe 0.6.3
File	Size	Uploaded
read_rapidpe-0.6.3.tar.gz	21.6 kB	Jul 17, 2023	Details

Built distribution (wheel)

Table of built distributions (wheels) for read-rapidpe 0.6.3
File	Interpreter	ABI	Platform	Reset
read_rapidpe-0.6.3-py3-none-any.whl 24.7 kB Jul 17, 2023	Python 3	none	any	Details

Total release size: 46.3 kB

Release files / read_rapidpe-0.6.3.tar.gz

Download URL	read_rapidpe-0.6.3.tar.gz
Size	21.6 kB
Tags	Source
SHA-256 checksum How to use checksums	`9b0cb416861f95434d10c43b3d4c0a5daed42a1d84fae96c2552a5a717130394`
BLAKE2b-256 checksum How to use checksums	`6f9b09a40faebab2019172a0e3b028d2c517ff59fbfc90808ff5c62aa7ec7d10`
Upload date	Jul 17, 2023
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`poetry/1.4.2 CPython/3.10.6 Darwin/21.6.0`

Release files / read_rapidpe-0.6.3-py3-none-any.whl

Download URL	read_rapidpe-0.6.3-py3-none-any.whl
Size	24.7 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`1d9aa2081f3fb5ce284925ec02739055b30e718a297ba5e4fbe117beac63ddbf`
BLAKE2b-256 checksum How to use checksums	`44e964dea6a7c73485990ae536f35feb65c40073c52e7d208b21dce7df821678`
Upload date	Jul 17, 2023
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`poetry/1.4.2 CPython/3.10.6 Darwin/21.6.0`