random.normalvariate

All Samples(148) | Call(139) | Derive(0) | Import(9)

Normal distribution.

mu is the mean, and sigma is the standard deviation.

src/p/y/pygrace-HEAD/trunk/Examples/example_tools.py pygrace(Download)

def multiplot():
    import random
    m, b, sigma = 10, 60, 12
    x = [float(i) / 200 for i in range(0, 2000)]
    y0 = [m * x_i + b for x_i in x]
    r = [random.normalvariate(0, sigma) for i in y0]
    y1 = [y_i + r_i for y_i, r_i in zip(y0, r)]

def colorplot():
    from random import normalvariate
    from math import floor,ceil
 
    # generate some synthetic data from eliptical Gaussian
    data = []
    for i in range(10000):
        x = normalvariate(0,1.0)
        y = normalvariate(-x,1.0)

src/p/y/pygrace-HEAD/Examples/example_tools.py pygrace(Download)

def multiplot():
    import random
    m, b, sigma = 10, 60, 12
    x = [float(i) / 200 for i in range(0, 2000)]
    y0 = [m * x_i + b for x_i in x]
    r = [random.normalvariate(0, sigma) for i in y0]
    y1 = [y_i + r_i for y_i, r_i in zip(y0, r)]

def colorplot():
    from random import normalvariate
    from math import floor,ceil
 
    # generate some synthetic data from eliptical Gaussian
    data = []
    for i in range(10000):
        x = normalvariate(0,1.0)
        y = normalvariate(-x,1.0)

src/a/d/Adytum-PyMonitor-1.0.5/lib/math/precision.py Adytum-PyMonitor(Download)

    return ldexp(float(str(tmant)), texp)
 
 
from random import random, normalvariate, shuffle
 
def test(nvals):
    for j in xrange(1000):
        vals = [7, 1e100, -7, -1e100, -9e-20, 8e-20] * 10
        vals.extend([random() - 0.49995 for i in xrange(nvals)])        
        vals.extend([normalvariate(0, 1)**7 for i in xrange(nvals)])
        s = sum(vals)
        for i in xrange(nvals):
            v = normalvariate(-s, random())

src/s/i/simpy-HEAD/SimPy/SphinxStuff/Manuals/programs/levelinventory.py simpy(Download)

from SimPy.Simulation import *
from random import normalvariate,seed
 
class Deliver(Process):
   def deliver(self):          # an "offeror" PEM
       while True:
           lead = 10.0         # time between refills

   def demand(self):           # a "requester" PEM
       day = 1.0               # set time-step to one day
       while True:
           yield hold, self, day
           dd = normalvariate(1.20, 0.20)  # today's random demand
           ds = dd - stock.amount
               # excess of demand over current stock amount

src/s/i/simpy-HEAD/SimPy/SphinxInputs/Manuals/programs/levelinventory.py simpy(Download)

from SimPy.Simulation import *
from random import normalvariate,seed
 
class Deliver(Process):
   def deliver(self):          # an "offeror" PEM
       while True:
           lead = 10.0         # time between refills

   def demand(self):           # a "requester" PEM
       day = 1.0               # set time-step to one day
       while True:
           yield hold, self, day
           dd = normalvariate(1.20, 0.20)  # today's random demand
           ds = dd - stock.amount
               # excess of demand over current stock amount

src/p/y/pyrogue-HEAD/util.py pyrogue(Download)

 
import curses
from copy import deepcopy
from random import choice, randint, uniform as rnd, normalvariate as norm, seed
from math import ceil, sqrt
from sets import Set
from time import sleep

def int_range(mean, std_dev=None, max_std_dev=2):
    "Return an random integer normally distributed around mean, with the given std dev."
    if std_dev is None:
        std_dev = mean / 4.0
    mean += 0.5
    return int(min(mean+std_dev*max_std_dev, max(norm(mean, std_dev), mean-std_dev*max_std_dev)))

src/b/i/biopython-1.55/Bio/Graphics/GenomeDiagram/_Track.py biopython(Download)

    from Bio.SeqFeature import SeqFeature
    from _FeatureSet import FeatureSet
    from _GraphSet import GraphSet
    from random import normalvariate
 
    parser = GenBank.FeatureParser()
    fhandle = open('/data/genomes/Bacteria/Nanoarchaeum_equitans/NC_005213.gbk', 'r')

 
    graphdata = []
    for pos in xrange(1, len(genbank_entry.seq), 1000):
        graphdata.append((pos, normalvariate(0.5, 0.1)))
    gdgs = GraphSet(2, 'test data')
    gdgs.add_graph(graphdata, 'Test Data')
    gdt.add_set(gdgs)

src/p/y/pygrace-HEAD/trunk/Extensions/table.py pygrace(Download)

 
if __name__ == '__main__':
 
    from random import normalvariate as nv
 
    from PyGrace.grace import Grace

src/p/y/pygrace-HEAD/Extensions/table.py pygrace(Download)

 
if __name__ == '__main__':
 
    from random import normalvariate as nv
 
    from PyGrace.grace import Grace