random.normalvariate

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

Normal distribution.

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

src/a/s/astlib-HEAD/trunk/examples/CMRRestFrameConversion/CMRRestFrameConversion.py astlib(Download)

        bsMag={}
        bsCol={}
 
        bsCMR['slope']=random.normalvariate(sCMR, sCMRErr)
        bsCMR['intercept']=random.normalvariate(zpCMR, zpCMRErr)
        bsCMR['zeroMag']=obsCMR['zeroMag']
        bsMag['slope']=random.normalvariate(sMag, sMagErr)
        bsMag['intercept']=random.normalvariate(zpMag, zpMagErr)
        bsCol['slope']=random.normalvariate(sCol, sColErr)
        bsCol['intercept']=random.normalvariate(zpCol, zpColErr)
 
        bsCMR['scatter']=random.normalvariate(obsCMR['scatter'], obsCMR['scatterErr'])

src/a/s/astlib-HEAD/examples/CMRRestFrameConversion/CMRRestFrameConversion.py astlib(Download)

        bsMag={}
        bsCol={}
 
        bsCMR['slope']=random.normalvariate(sCMR, sCMRErr)
        bsCMR['intercept']=random.normalvariate(zpCMR, zpCMRErr)
        bsCMR['zeroMag']=obsCMR['zeroMag']
        bsMag['slope']=random.normalvariate(sMag, sMagErr)
        bsMag['intercept']=random.normalvariate(zpMag, zpMagErr)
        bsCol['slope']=random.normalvariate(sCol, sColErr)
        bsCol['intercept']=random.normalvariate(zpCol, zpColErr)
 
        bsCMR['scatter']=random.normalvariate(obsCMR['scatter'], obsCMR['scatterErr'])

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/s/c/scrapy-HEAD/profiling/priorityqueue/run.py scrapy(Download)

def normal_priority(priorities):
    sigma = priorities / 4.0
    dist = lambda: random.normalvariate(mu=0, sigma=sigma)
    return _distribution(priorities, dist)
 
def gauss_priority(priorities):
    sigma = priorities / 4.0

src/c/a/canada-HEAD/trunk/modules/spain.py canada(Download)

    def fill(self, dir = None):
        for i in range(0, self.width, 5):
            for j in range(0, self.height, 5):
                ox = random.normalvariate(0, 2)
                oy = random.normalvariate(0, 2)
                self.balls.append((complex(i + ox, j + oy), 0, 0.5, self.randpair()))

            # deletion conditions (other than "escape velocity" above: this ball
            # is too close to the black hole, or is too large.
            if ((self.black_hole is not None and abs(z - self.black_hole) < 6)
                    or m > max(4, random.normalvariate(max_mass_mean,
                        max_mass_sigma))):
                del self.balls[d]
                if d < self.smart_balls:

src/c/a/canada-HEAD/experimental/modules/spain.py canada(Download)

    def fill(self, dir = None):
        for i in range(0, self.width, 5):
            for j in range(0, self.height, 5):
                ox = random.normalvariate(0, 2)
                oy = random.normalvariate(0, 2)
                self.balls.append((complex(i + ox, j + oy), 0, 0.5, self.randpair()))

            # deletion conditions (other than "escape velocity" above: this ball
            # is too close to the black hole, or is too large.
            if ((self.black_hole is not None and abs(z - self.black_hole) < 6)
                    or m > max(4, random.normalvariate(max_mass_mean,
                        max_mass_sigma))):
                del self.balls[d]
                if d < self.smart_balls:

src/c/a/canada-HEAD/dropday-2008/modules/spain.py canada(Download)

    def fill(self, dir = None):
        for i in range(0, self.width, 5):
            for j in range(0, self.height, 5):
                ox = random.normalvariate(0, 2)
                oy = random.normalvariate(0, 2)
                self.balls.append((complex(i + ox, j + oy), 0, 0.5, self.randpair()))

                    continue
 
            if ((self.black_hole is not None and abs(z - self.black_hole) < 6)
                    or m > max(4, random.normalvariate(max_mass_mean,
                        max_mass_sigma))):
                del self.balls[d]
                if d < self.smart_balls:

src/c/a/canada-HEAD/modules/spain.py canada(Download)

    def fill(self, dir = None):
        for i in range(0, self.width, 5):
            for j in range(0, self.height, 5):
                ox = random.normalvariate(0, 2)
                oy = random.normalvariate(0, 2)
                self.balls.append((complex(i + ox, j + oy), 0, 0.5, self.randpair()))

            # deletion conditions (other than "escape velocity" above: this ball
            # is too close to the black hole, or is too large.
            if ((self.black_hole is not None and abs(z - self.black_hole) < 6)
                    or m > max(4, random.normalvariate(max_mass_mean,
                        max_mass_sigma))):
                del self.balls[d]
                if d < self.smart_balls:

src/s/h/shedskin-HEAD/univ/spirit.py shedskin(Download)

                continue
            elif syn_random and i == syn_double:
                if connection.fast:
                    connection.post_cell.syn_event_fast.synapse = connection.post_cell.syn_event_fast.synapse + (connection.s * self.trans_level*(random.normalvariate(1,self.syndeviance)))*layscale*2
                else:
                    connection.post_cell.syn_event_slow.synapse = connection.post_cell.syn_event_slow.synapse + (connection.s * self.trans_level*(random.normalvariate(1,self.syndeviance)))*layscale*2
            else:
                if connection.fast:
                    connection.post_cell.syn_event_fast.synapse = connection.post_cell.syn_event_fast.synapse + (connection.s * self.trans_level*(random.normalvariate(1,self.syndeviance)))*layscale
                else:
 
                    connection.post_cell.syn_event_slow.synapse = connection.post_cell.syn_event_slow.synapse + (connection.s * self.trans_level*(random.normalvariate(1,self.syndeviance)))*layscale

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