Python pylab.semilogy() Examples

The following are 3 code examples of pylab.semilogy(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module pylab , or try the search function .
Example #1
Source File: blackbody.py    From ColorPy with GNU Lesser General Public License v2.1 6 votes vote down vote up 
def blackbody_color_vs_temperature_plot (T_list, title, filename):
    '''Draw a color vs temperature plot for the given temperature range.'''
    num_T = len (T_list)
    rgb_list = numpy.empty ((num_T, 3))
    for i in range (0, num_T):
        T_i = T_list [i]
        xyz = blackbody_color (T_i)
        rgb_list [i] = colormodels.rgb_from_xyz (xyz)
    # Note that b and g become negative for low T.
    # MatPlotLib skips those on the semilog plot.
    plots.color_vs_param_plot (
        T_list,
        rgb_list,
        title,
        filename,
        plotfunc = pylab.semilogy,
        tight = True,
        xlabel = r'Temperature (K)',
        ylabel = r'RGB Color')
Example #2
Source File: radau_core.py    From Assimulo with GNU Lesser General Public License v3.0 5 votes vote down vote up 
def plot_stepsize(self):
        """
        Plots the step-size.
        """
        import pylab as P
        
        P.semilogy(N.diff(self.t),drawstyle='steps-post')
        P.title(self.problem.name)
        P.ylabel('Step length')
        P.xlabel('Number of steps')
        P.show()
Example #3
Source File: histfit.py    From fitter with GNU General Public License v3.0 4 votes vote down vote up 
def fit(self, error_rate=0.05, semilogy=False, Nfit=100,
            error_kwargs={"lw":1, "color":"black", "alpha":0.2},
            fit_kwargs={"lw":2, "color":"red"}):
        self.mus = []
        self.sigmas = []
        self.amplitudes = []
        self.fits = []

        pylab.figure(1)
        pylab.clf()
        pylab.bar(self.X, self.Y, width=0.85, ec="k")

        for x in range(Nfit):
            # 10% error on the data to add errors 
            self.E = [scipy.stats.norm.rvs(0, error_rate) for y in self.Y]
            #[scipy.stats.norm.rvs(0, self.std_data * error_rate) for x in range(self.N)]
            self.result = scipy.optimize.least_squares(self.func, 
                (self.guess_mean, self.guess_std, self.guess_amp))

            mu, sigma, amplitude = self.result['x']
            pylab.plot(self.X, amplitude * scipy.stats.norm.pdf(self.X, mu,sigma),
                **error_kwargs)
            self.sigmas.append(sigma)
            self.amplitudes.append(amplitude)
            self.mus.append(mu)


            self.fits.append(amplitude * scipy.stats.norm.pdf(self.X, mu,sigma))

        self.sigma = mean(self.sigmas)
        self.amplitude = mean(self.amplitudes)
        self.mu = mean(self.mus)


        pylab.plot(self.X, self.amplitude * scipy.stats.norm.pdf(self.X, self.mu, self.sigma), 
                   **fit_kwargs)
        if semilogy:
            pylab.semilogy() 
        pylab.grid()

        pylab.figure(2)
        pylab.clf()
        #pylab.bar(self.X, self.Y, width=0.85, ec="k", alpha=0.5)
        M = mean(self.fits, axis=0)
        S = pylab.std(self.fits, axis=0)
        pylab.fill_between(self.X, M-3*S, M+3*S, color="gray", alpha=0.5)
        pylab.fill_between(self.X, M-2*S, M+2*S, color="gray", alpha=0.5)
        pylab.fill_between(self.X, M-S, M+S, color="gray", alpha=0.5)
        #pylab.plot(self.X, M-S, color="k")
        #pylab.plot(self.X, M+S, color="k")
        pylab.plot(self.X, self.amplitude * scipy.stats.norm.pdf(self.X, self.mu, self.sigma), 
                   **fit_kwargs)
        pylab.grid()

        return self.mu, self.sigma, self.amplitude