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archive/

# Python
__pycache__/
*.py[cod]
*$py.class

# C extensions
*.so

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Repository Guidelines

Scope & Precedence

• Apply these rules unless directly overridden by user instruction.
• Conflict order: (1) user instruction, (2) this guide, (3) generic assistant defaults.

Change Scope & Simplicity

• Avoid over-engineering. Only make changes that are directly requested or clearly necessary. Keep solutions simple and focused.
• Don't add features, refactor code, or make "improvements" beyond what was asked. A bug fix doesn't need surrounding code cleaned up. A simple feature doesn't need extra configurability. Don't add docstrings, comments, or type annotations to code you didn't change. Only add comments where the logic isn't self-evident.
• Don't add error handling, fallbacks, or validation for scenarios that can't happen. Trust internal code and framework guarantees. Only validate at system boundaries (user input, external APIs). Don't use feature flags or backwards-compatibility shims when you can just change the code.

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"""
The most atomic way to train and inference a GPT in pure, dependency-free Python.
This file is the complete algorithm.
Everything else is just efficiency.

@karpathy
"""

import os       # os.path.exists
import math     # math.log, math.exp

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// save to windows-user directory

linters: with_defaults(object_name_linter = NULL,
                       object_length_linter(50),
                       commented_code_linter = NULL,
                       object_usage_linter = NULL,
                       line_length_linter(120),
                       cyclocomp_linter = cyclocomp_linter(50))

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# example of bar plot with individual subject points for Anya -04/24/2019
# added error bars - 04/25/2019

library(tidyverse) #will need to install this first (run: install.packages("tidyverse"))


## fake data
data = tibble( # creating a dataframe (aka "tibble") called data
  subject = rep(1:10,times = 2 ), # making a column/vector of subject numbers (1-10) x2
  condition = rep(c("hard", "easy"), each = 10), # making a column/vector of condition names

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# Thanks for the suggestion from Joseph Elsherbini.

library(tidyverse)
library(ggforce)

n_pages(p) # use this to get the number of pages to print
# then run a for loop to loop over pages

pdf("test.pdf")
for (i in 1:5) {

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# 0. Prep -----------------------------------------------------------------

library(tidyverse)
library(here)

# 1. Load and clean EggNOG output -----------------------------------------

## The output was downloaded from the eggnog-mapper online run.
eggnog_go <- read_tsv(here("data", "misc", "out.emapper.annotations.gz"), 
                      skip = 4) %>% 

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# Download meta data using ffq.
# Need input `SraRunTable.txt`

# Author: Ji Huang
# Date: 2022-09-21

import pandas as pd
import subprocess
import os

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# Test on GENIE3 whether
# (Q1) if using more genes as regulator, for the same regulator-target edge 
# do I get the same order? No. The edge order will be different.

# (Q2) if using more genes as targets, for the same regulator-target edges,
# do I get the same order? Yes. The same edge will have the exact same weight.

# Author: Ji Huang
# Date: 2021-01-06

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library(ggplot2)
theme_jh <- function (base_size = 11, base_family = "Arial", 
                      base_line_size = base_size/22, 
                      base_rect_size = base_size/22) {
    theme_grey(base_size = base_size, base_family = base_family, 
               base_line_size = base_line_size, 
               base_rect_size = base_rect_size) +
        theme(panel.background = element_rect(fill = "white", colour = NA),
              panel.border = element_rect(fill = NA, colour = "grey20"), 
              panel.grid = element_line(colour = "grey92"),