Palpatineli

#!/usr/bin/python3.10
from pathlib import Path
import argparse
import SimpleITK as sitk

def main(folder: str, output: str | None):
    input_path = Path(folder)
    output_path = input_path if output is None else Path(output)
    output_path.mkdir(parents=True, exist_ok=True)
    for file in input_path.iterdir():

Palpatineli

#!/usr/bin/env python3
"""Update binaries from github release page. Check if there are new binaries and download the files
"""

from pathlib import Path
from datetime import datetime, timedelta
import json
import requests
import re
import subprocess

Palpatineli

# Real colorscheme for https://www.reddit.com/r/unixporn/comments/vkcasz/i3gaps_i_prefer_light_mode/
# Purportedly, the dotfile is from here: https://github.com/Swexti/dots
# But these are not the same as the screenshot in the post.
# The following colorscheme is colorpicked from the screenshot
colors:
  # Default colors
  primary:
    background: '#F4F5F8'
    foreground: '#464658'

Palpatineli

#!/bin/sh

HOST_NAME=`hostname`
HOST_IP=`nslookup $HOST_NAME | awk /Address:\ [-.0-9]/ | awk -F\  '{print $NF}'`
if [ $# -eq 0 ]
then
    PORT=54321
else
    PORT=$1
fi

Palpatineli

set-option -g visual-activity off
set-option -g visual-bell off
set-option -g visual-silence off
set-window-option -g monitor-activity off
set-option -g bell-action none

# The window modes {
setw -g mode-style bg="black",fg="red",bold
# }

Palpatineli

from collections import deque
import numpy as np

def cumulative_return(x: np.ndarray) -> np.ndarray:
    """Calculate the cumulative annual return from monthly fluctuations
    Args:
        x: an 1D array, each item is a float showing proportion of change of investment in that month
    Returns:
        an 1D array, each item is the annual rate of change (return) for the 12 previous months
    """

Palpatineli

import numpy as np
from scipy.linalg import eigh

def cpca(cov: np.ndarray, sample_n: np.ndarray, comp_n: int = 0, tol: float = 1E-6,
         max_iter: int = 1000) -> np.ndarray:
    """
    Args:
        cov: 3D array where the last 2 axes are covariance matrices.
        sample_n: for each covariance, how many samples were in there.
    """

Palpatineli

from typing import Optional, List
import numpy as np
from scipy.stats import gaussian_kde
from matplotlib.axes import Axes
from matplotlib.transforms import Affine2D
import matplotlib.pyplot as plt
from mpl_toolkits.axisartist.floating_axes import GridHelperCurveLinear, FloatingSubplot

HISTOGRAM_RATIO = 0.2  # length vs. height ratio of the histogram

Palpatineli

let hintcharacters = "asdfghjkl;"
map d closeTab
map u lastClosedTab
map <C-o> goBack
map <C-i> goForward
let searchalias w = "wikipedia"
let searchalias s = "scholar"