Tradeoff: These guidelines bias toward caution over speed. For trivial tasks, use judgment.
Don't assume. Don't hide confusion. Surface tradeoffs.
Before implementing:
- State your assumptions explicitly. If uncertain, ask.
| archive/ | |
| # Python | |
| __pycache__/ | |
| *.py[cod] | |
| *$py.class | |
| # C extensions | |
| *.so |
| """ | |
| 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 |
| // 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)) |
| # 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 |
| # 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) { |
| # 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) %>% |
| # Download meta data using ffq. | |
| # Need input `SraRunTable.txt` | |
| # Author: Ji Huang | |
| # Date: 2022-09-21 | |
| import pandas as pd | |
| import subprocess | |
| import os |
| # 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 |
| 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"), |