Skip to content

Instantly share code, notes, and snippets.

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 / updater
Last active October 5, 2023 16:38
#!/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
# Real colorscheme for
# Purportedly, the dotfile is from here:
# But these are not the same as the screenshot in the post.
# The following colorscheme is colorpicked from the screenshot
# Default colors
background: '#F4F5F8'
foreground: '#464658'
Palpatineli / serve
Created January 18, 2022 17:27
get host ip and start server
HOST_IP=`nslookup $HOST_NAME | awk /Address:\ [-.0-9]/ | awk -F\ '{print $NF}'`
if [ $# -eq 0 ]
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 /
Created July 29, 2020 18:33
cumulative annual return
from collections import deque
import numpy as np
def cumulative_return(x: np.ndarray) -> np.ndarray:
"""Calculate the cumulative annual return from monthly fluctuations
x: an 1D array, each item is a float showing proportion of change of investment in that month
an 1D array, each item is the annual rate of change (return) for the 12 previous months
Palpatineli /
Created April 22, 2020 21:43
common pca: stepwise algorithm to find the nth common principle components
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:
cov: 3D array where the last 2 axes are covariance matrices.
sample_n: for each covariance, how many samples were in there.
Palpatineli /
Created July 25, 2019 22:11
Diagonal Distribution for a scatterplot in matplotlib
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
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"