jongbinjung

#NoEnv  ; Recommended for performance and compatibility with future AutoHotkey releases.
SendMode Input  ; Recommended for new scripts due to its superior speed and reliability.
SetWorkingDir %A_ScriptDir%  ; Ensures a consistent starting directory.

; HotKey to Initiate VI-mode with Double-tap of Alt
Shift::
If (A_PriorHotKey = "Shift" AND A_TimeSincePriorHotKey < 200)
{
; Set the flags for OSD
Gui, 99:+AlwaysOnTop -Caption +ToolWindow +Disabled -SysMenu +Owner

jongbinjung

library("ggplot2")

#' Draw Normal Distribution Density with an area shaded in.
#'
#' @param lb Lower bound of the shaded area. Use \code{-Inf} for a left tail.
#' @param ub Upper bound of the shaded area. Use \code{Inf} for a right tail.
#' @param mean Mean of the normal distribution
#' @param sd Standard deviation of the normal distribution
#' @param limits Lower and upper bounds on the x-axis of the area displayed.
#' @return ggplot object.

jongbinjung

# Assuming a (logistic) model with k features fit in R with the variable name "model", 
# generate a simple rule that uses integers in the range [-M, M]: 
# (note that M is set to 3 in this example)
M <- 3
model_coefs <- coef(model)
scaled_coefs <- (model_coefs / max(model_coefs)) * M
rounded_coefs <- round(scaled_coefs)

# The k features to be included in the initial model is best determined by domain expertise, 
# i.e., what are the minimal features that expert(s) in the field believes to be predictive 

jongbinjung

results <- foreach(i=1:1000, .combine = bind_rows) %:%
           foreach(method=c("binomial", "quasibinomial"), .combine = bind_rows) %dopar% {

  beta <- 0.1

  df <- data.frame(id = rep(1:1000, 2)) %>%
    mutate(m = rnorm(2000, 0, 1),
           x = rnorm(2000, m, 2),
           e = rnorm(2000, 0, beta / 2),
           y = inv_logit(1 + beta*x+e)) %>%

jongbinjung

#!/usr/bin/env python3
"""
Example of common pendulum use cases

Author: jongbin.jung
"""

import pandas as pd
import pendulum

jongbinjung

#!/usr/bin/env python
"""Collect and parse data from Gsheet

Read spreadsheet from https://docs.google.com/spreadsheets/d/{SHEET_ID}
and parse as a polars DataFrame.

This example is using a benign sheet of junk from
https://docs.google.com/spreadsheets/d/1cFVczEL7oznX_G-_VyJv5tgkEPmoVHRsGanzMj1O4nM
but should give you the idea.

jongbinjung

#!/usr/bin/env python3
"""Demonstration of why you shouldn't set numpy.random.seed, and what you should do instead"""

import numpy as np

# When setting a seed, you usually mean "make things deterministic".
# However, because the Global numpy seed is _shared_, it is not a reliable source to guarantee deterministic outcomes.


# For example, assume we want to use numpy's choice, to choose five random numbers from an array:

jongbinjung

# From https://github.com/ajfriend/h3_example_package
import numpy as np
from h3_example_package.geo import latlng_to_cell_vect
from h3 import api


N = 100000
lats, lngs = np.random.uniform(0, 90, N), np.random.uniform(0, 90, N)
res = 9