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import sys; from PIL import Image; import numpy as np | |
chars = np.asarray(list(' .,:;irsXA253hMHGS#9B&@')) | |
if len(sys.argv) != 4: print( 'Usage: ./asciinator.py image scale factor' ); sys.exit() | |
f, SC, GCF, WCF = sys.argv[1], float(sys.argv[2]), float(sys.argv[3]), 7/4 | |
img = Image.open(f) | |
S = ( round(img.size[0]*SC*WCF), round(img.size[1]*SC) ) | |
img = np.sum( np.asarray( img.resize(S) ), axis=2) |
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# This line imports the modules we will need. The first is the sys module used | |
# to read the command line arguments. Second the Python Imaging Library to read | |
# the image and third numpy, a linear algebra/vector/matrix module. | |
import sys; from PIL import Image; import numpy as np | |
# This is a list of characters from low to high "blackness" in order to map the | |
# intensities of the image to ascii characters | |
chars = np.asarray(list(' .,:;irsXA253hMHGS#9B&@')) | |
# Check whether all necessary command line arguments were given, if not exit and show a |
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a = "bla" | |
b = "so on" | |
class(a) = append("my_class", class(a)) | |
'+.my_class' = function(x,y) paste(x,y,sep=" and ") | |
print(a+b) |
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library(scales) | |
library(Hmisc) | |
emsa_plot = ggplot(emsa, aes(x=Name, y=intensity, shape=charge_ratio, col=charge_ratio, width=0.8)) + | |
geom_point(size=3, position=position_dodge(width=0.8)) + | |
stat_summary(fun.data=mean_sdl, mult=0, color="black", geom="errorbar", size=1, position=position_dodge(width=0.8)) + | |
scale_color_grey(start=0, end=0.5,name="charge ratio") + | |
scale_shape(name="charge ratio") + | |
scale_y_continuous(limits=c(-0.05,1), breaks=seq(0,1,by=0.2), label=percent) + |
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# Caching operator executes command if there is no saved version of the data | |
# just delete the saved file and code will be run again | |
'%c%' = function(ex, file) if(file.exists(file), env=parent.frame(4)) load(file) else evalq(ex)) | |
# Example | |
# first execution | |
{ x <- rnorm(1e6); save(x, file="cache.Rd") } %c% "cache.Rd" # executes the sampling |
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Rcpp::sourceCpp("matrix_reduce.cpp") | |
#' Reduces an ExpressionSet by an n-to-n map of features to groups. All entries | |
#' in \code{features} must exist in \code{eset}. \code{features} and | |
#' \code{groups} must have the same length. | |
#' | |
#' @param eset An ExpressionSet object. | |
#' @param features A character vector of features to be grouped. | |
#' @param groups A factor or character vector mapping the entries in | |
#' \code{features} to groups. |
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#/usr/bin/env python | |
from cobra.test import create_test_model | |
from cobra.flux_analysis import single_gene_deletion | |
cobra_model = create_test_model("textbook") | |
dels = {"b0008": 0.87, "b0114": 0.71, "b0116": 0.56, "b2276": 0.11, "b1779": 0.00} | |
rates, statuses = single_gene_deletion(cobra_model, gene_list=dels.keys(), | |
method="moma", solver="mosek") |
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ES <- function(p, w, pws, both=FALSE) { | |
n <- length(pws) | |
nr <- sum(abs(w[pws == p])) | |
nh <- sum(pws == p) | |
scores <- vector(length=n) | |
scores[pws == p] <- abs(w[pws == p])/nr | |
scores[pws != p] <- -1/(n - nh) | |
r <- range(cumsum(scores)) | |
i <- which.max(abs(r)) |
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import json | |
import pandas as pd | |
from sys import argv, exit | |
def benchmark_to_df(json_file): | |
with open(json_file) as jf: | |
content = json.load(jf) | |
df = pd.DataFrame(columns=("test", "time [ms]")) | |
for b in content["benchmarks"]: |
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{ | |
"workbench.colorTheme": "Sublime Material Theme - Dark", | |
"workbench.iconTheme": "material-icon-theme", | |
"editor.fontFamily": "'Fira Mono', monospace", | |
"editor.fontSize": 17, | |
"editor.rulers": [80], | |
"window.zoomLevel": 0, | |
"window.menuBarVisibility": "toggle", | |
// Settings for Python |
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