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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.

Documentation

• Save markdown files under doc/.
• Sub-folders under doc/ are allowed.
• Prefix plan/summary filenames with YYYYMMDD (example: 20260219_implementation_summary.md).

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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"),