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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