- Expressivity: should be possible to express mathematical & programming concepts concisely & elegantly. This is important to reduce the mental overhead required for writing down and reading models and algorithms in the form of code.
- Speed: should be possible to convert code into software/program that gets the most out of modern computer hardware
- Functional Programming
- Parallel computing
- Extensibility: should have a powerful, unambiguous, easy to use package management system built right into the core of the language
- Visualisation: a large and very important part of scientific programming is generating side-effects: writing reports, generating plots, creating interactive documents
Conor Lawless CnrLwlss
CnrLwlss
/ Table S2_List of genetic interactions.csv
Last active
May 16, 2021 09:19
Data for interactive SGA plot on Observable
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| R package version: 0.0-39 | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Summary type: median | |||||||||||||
| Test type: wilcoxon | |||||||||||||
| x-axis treatment: 20 | |||||||||||||
| x-axis medium: SDM_rhlk_TGNH | |||||||||||||
| x-axis screen ID: SGA0188 | |||||||||||||
| x-axis screen name: CONTROL | |||||||||||||
| x-axis libraries: SDLV4_1536 | |||||||||||||
| x-axis client: MMA | |||||||||||||
| x-axis user: AAB |
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| from PIL import Image | |
| import numpy as np | |
| # Generate a 100x100 array of random floats between 0.0 and 1.0 | |
| random_floats = np.random.rand(100,100) | |
| # Scale these up to lie in the range of 8-bit integers (between 0.0 and 255.0) | |
| random_floats_8bit = random_floats * 255.0 | |
| # Round these float values to the nearest integer |
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| # Sampling noise | |
| simulateSample = function(mtDNAPerWell=1000, mutantFraction=0.95, fracVolumeSampled=1.0/3.0){ | |
| # Create a synthetic population of mtDNA molecules | |
| moleculeMutant = rbinom(n = round(mtDNAPerWell*fracVolumeSampled), size = 1, prob = mutantFraction) | |
| # Calculate mutant fraction in sampled molecules | |
| sum(moleculeMutant)/length(moleculeMutant) | |
| } | |
| simulateUncertainty = function(mtDNAPerWell=1000, mutantFraction=0.95, fracVolumeSampled=1.0/3.0, reps = 5000){ |
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| N = 10000 | |
| Ncurve = 1000 | |
| png("Walk.png",width=3000,height=1000,pointsize=12,type="cairo-png") | |
| plot(NULL,ylim=c(-200,200),axes=FALSE,xlim=c(N/2,N),xlab="",ylab="") | |
| for(i in 1:Ncurve) points(cumsum(rnorm(N,0,0.5)),type="l",col=rgb(0,0,0,0.1),lwd=2) | |
| dev.off() |
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| nA = 8 | |
| meanA = 48 | |
| sdA = 3 | |
| nB = 10 | |
| meanB = 53 | |
| sdB = 4 | |
| set.seed(42) | |
| nBoot = 100000 |
CnrLwlss
/ laser_neighbours.R
Last active
August 2, 2020 22:24
Calculates and visualises proportion of fibres that can be extracted/sampled, from a skeletal muscle section leaving all immediate neighbours of sampled fibres in place.
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| library(deldir) | |
| library(png) | |
| dat = read.delim("results.csv",sep=",",stringsAsFactors=FALSE) | |
| im = readPNG("AVE_mitocyto.png") | |
| h = dim(im)[1] | |
| w = dim(im)[2] | |
| td = data.frame( | |
| xCoord = dat$Value[dat$Channel=="xCoord"], |
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| #https://github.com/hrbrmstr/speedtest | |
| library(speedtest) | |
| fname = "speedtest_results2.txt" | |
| makeplots = FALSE | |
| if(!makeplots){ | |
| config = spd_config() | |
| servers = spd_servers(config = config) | |
| servers = spd_closest_servers(servers, config) | |
| best = spd_best_servers(servers, config, max = 3) |
CnrLwlss
/ StripchartOpacity.R
Last active
June 14, 2019 16:57
R script demonstrating a few things: 1) drawing boxplots with notches roughly indicating significance of differences 2) stripcharts using transparency to highlight values of high density 3) overlaying boxplot on stripcharts and 4) writing plots as multi-page .pdf reports
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| # Info about boxplot notches https://sites.google.com/site/davidsstatistics/home/notched-box-plots | |
| # Article about why not to use barplots https://doi.org/10.1371/journal.pbio.1002128 | |
| # Article about being wary of summary statistics and why raw data plots are better than boxplots https://www.autodeskresearch.com/publications/samestats | |
| # Generate some fake data | |
| concs = seq(0,10,1) | |
| concobs = rep(concs,each=500) | |
| mdel = function(x) -x^2+10*x+20 | |
| vals = mdel(concobs) + rnorm(length(concobs),0,12) | |
| dat = data.frame(conc=concobs,val=vals) |