tsbertalan

import time
from datetime import datetime
import matplotlib.pyplot as plt

#get the csv lines as strings
lines = list(open('hussieout.csv'))
lines = lines[:-1] #remove final newline
#split each line on commas (hopefully there's only one per line)
lines = [line.split(',') for line in lines]
#remove whitespace at the ends of the elements (mostly--only?--just \n characters at the ends)

tsbertalan

from matplotlib import pyplot as plt
from matplotlib import colors
import numpy as np
from tomIntegrate import randr
from time import sleep

NX = NY = 64
figsize=(16, 16)
ptake = 0.001
pgive = 0.0004

tsbertalan

# -*- coding: utf-8 -*-
from tomIntegrate import intor, randr
from matplotlib import pyplot as plt
from numpy import array, polyfit, log, exp

def dxdt(X, r=28.):
    s = 10.
    b = 8. / 3.
    x = X[0]
    y = X[1]

tsbertalan

returnList.py
_returnList.so
*_wrap.cxx

tsbertalan

# Really, when tuning, you have a reference note a fifth above or below the
# target note. But, just to show a beat-frequency effect, having the reference
# and target notes be about the same frequency is fine.

import numpy as np
import matplotlib.pylab as plt
length = 444  # make this larger if the waves shown are not very wave-like
resolution = 10000.0
xl = list(np.arange(1, length, length / resolution))
w1 = 1.0

tsbertalan

from math import exp, cos, sin

def print_answers(fs, fnames, W, tx=4.0):
    for (name, f) in zip(fnames, fs):
        print name+'(', tx, '):', f(tx)
    print " W(", tx, "):", W(tx)

def n32b():
    f1 = lambda t: exp(float(t)) * (3.0 / 4.0 - t / 2) - exp(-1.0 * t) / 4
    f1p = lambda t: exp(float(t))*(1./4-t/2)+exp(-t)/4

tsbertalan

#!/usr/bin/env python
#with open('/home/tsbertalan/bin/wordlists/nounsb.txt') as f:
with open('/home/tsbertalan/bin/wordlists/nouns/91K nouns.txt') as f:
    nouns = f.read().splitlines()
#with open('/home/tsbertalan/bin/wordlists/adjectivesb.txt') as f:
with open('/home/tsbertalan/bin/wordlists/adjectives/28K adjectives.txt') as f:
    adjs = f.read().splitlines()
nouns = list(set(nouns))
adjs = list(set(adjs))
from random import choice

tsbertalan

def n232nondiscriminant(a,b):
    return(b-a**2-1)

def n232discriminant(a,b):
    return((a**2-b+1)**2 - 4*a**2)

if __name__ == "__main__":
    stable_real_a = []
    stable_real_b = []
    unstable_real_a = []

tsbertalan

function [nlist, tlist] = birthdeath(l,K,numsteps)
    nlist =[0]; % list of numbers of transcripts at corresponding place in t
    tlist =[0]; % list of times. drawn from bernoulli distribution.
    evlist=[0]; % list of events. degradation is -1, transcription is 1,neither is 0.
    t  = 0;
    n  = 0;
    ev = 1;
    for j=[1:numsteps]
        t = t - log(1-rand(1))/(K*n+l);
        tlist = [tlist t];

tsbertalan

N = 10;
k = 1;
P0 = [zeros([N, 1]) ; 1];
A = irreversible(N);
f = @(t,P)mastereqn(P,A);
[tvec,P]=ode45(f, [0,25], P0);
tvecsize = size(tvec);
num_timesteps = tvecsize(1);

%  % Uncomment to check probability sums: