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import numpy as np
import scipy.signal as signal
from scipy.signal.windows import tukey
from scipy.signal import savgol_filter

def pipeline_v2(strain_h1: np.ndarray, strain_l1: np.ndarray, times: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
    """
    The pipeline function processes gravitational wave data from the H1 and L1 detectors to identify potential gravitational wave signals.
    It takes strain_h1 and strain_l1 numpy arrays containing detector data, and times array with corresponding time points.
    The function returns a tuple of three numpy arrays: peak_times containing GPS times of identified events,

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# bash/zsh git prompt support
#
# Copyright (C) 2006,2007 Shawn O. Pearce <spearce@spearce.org>
# Distributed under the GNU General Public License, version 2.0.
#
# This script allows you to see repository status in your prompt.
#
# To enable:
#
#    1) Copy this file to somewhere (e.g. ~/.git-prompt.sh).

iphysresearch

#!/bin/bash

# automatically activates conda environments when entering directories
# with a conda environment file which must be named one of
#   - env(ironment).y(a)ml
#   - requirements.y(a)ml
# if env doesn't exist yet, create it; deactivate env when exciting folder
# installation: copy chpwd() to .bashrc or save the whole script as
# file and source it in .bashrc, e.g. by placing it in /usr/local/bin
# or by symlinking conda_auto_env there

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import lineax as lx
import jax.numpy as jnp
import jax
from jaxtyping import Float, Array


class CubicSpline:
    x_grid: Float[Array, str("batch")]  # input x data
    y_grid: Float[Array, str("n")]  # input y data

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gist - Manage your gist like a pro

All your notes, scripts, config files and snippets deserve version control and tagging!
gist is a simple bash script for gist management. It is lightweight(~700LOC) and dependency-free! Helps you to boost coding workflow.

• Getting Started
• Dependencies
• Basic Commands
• Update and clone gists from Github

iphysresearch

#!/bin/bash
# Ensure the script uses bash

# REF: https://albertotb.com/Git-prompt-with-conda-and-conda-auto-env/
# add the following to the end of your ~/.bashrc file:
GREEN="\[\033[38;5;155m\]"
DARK_GREEN="\[\033[00;32m\]"
GRAY="\[\033[38;5;8m\]"
ORANGE="\[\033[38;5;220m\]"
BLUE="\[\033[38;5;117m\]"

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How to organize your Python data science project

Having done a number of data projects over the years, and having seen a number of them up on GitHub, I've come to see that there's a wide range in terms of how "readable" a project is. I'd like to share some practices that I have come to adopt in my projects, which I hope will bring some organization to your projects.

Disclaimer: I'm hoping nobody takes this to be "the definitive guide" to organizing a data project; rather, I hope you, the reader, find useful tips that you can adapt to your own projects.

Disclaimer 2: What I’m writing below is primarily geared towards Python language users. Some ideas may be transferable to other languages; others may not be so. Please feel free to remix whatever you see here!

Disclaimer 3: I found the Cookiecutter Data Science page after finishing this blog post. Many ideas overlap here, though some directories are irrelevant in my work -- which is to

iphysresearch

import numpy as np
import datetime
import matplotlib.pyplot as plt

gw_event = [20150914,20151012,20151226, # O1 events
    20170104,20170608,20170729,20170809,20170814,20170817,20170818,20170823,
    20190408,20190412,20190413,20190413,20190421,20190424,20190425,20190426,
    20190503,20190512,20190513,20190514,20190517,20190519,20190521,20190521,
    20190527,20190602,20190620,20190630,20190701,20190706,20190707,20190708,
    20190719,20190720,20190727,20190728,20190731,20190803,20190814,20190828,

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import mxnet as mx
from mxnet import nd, gluon
from mxnet.gluon.nn import Dense, ELU, LeakyReLU, LayerNorm, Conv2D, MaxPool2D, Flatten, Activation, Dropout

import os, sys, datetime
from loguru import logger
#### REF #### https://loguru.readthedocs.io/en/stable/api/logger.html
# DEBUG 10  # INFO 20  # WARNING 30  # ERROR 40  # CRITICAL 50
config = {
    "handlers": [

iphysresearch

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(context='paper',
        style='ticks',
        font_scale=1,
        rc={'figure.figsize': (8, 5),
            'figure.dpi': 100,  # need fixed
            'xtick.direction': 'in',