Ejemplo n.º 1
0
def plot(x, y, f, title, plt=None):
    if plt is None:
        #import matplotlib.pylab as plt
        import scitools.easyviz as plt
    plt.figure()
    plt.plot(x, y, x, f)
    plt.legend(["approximation", "exact"])
    plt.title(title)
    return plt
Ejemplo n.º 2
0
def plot(x, y, f, title, plt=None):
    if plt is None:
        # import matplotlib.pylab as plt
        import scitools.easyviz as plt
    plt.figure()
    plt.plot(x, y, x, f)
    plt.legend(["approximation", "exact"])
    plt.title(title)
    return plt
Ejemplo n.º 3
0
 def plotConvergence(cls, scale, error4scale, title, legend, regression = False):
     p.figure()
     p.loglog(scale, error4scale, "k-d", xlabel = "samples", ylabel = "error",)  # semilogy
     p.title(title)
     p.legend(legend)
     if regression and len(error4scale) > 1:
         p.hold('on')
         lineSpace = np.array((scale[0], scale[-1]))
         slope, intercept, r_value, p_value, std_err = stats.linregress(scale, np.log10(error4scale))
         line = np.power(10, slope * lineSpace + intercept)
         p.plot(lineSpace, line, "k:", legend = "{slope:.2}x + c" \
                .format(slope = slope))
     p.hardcopy(cls.folder + title + "  " + t.strftime('%Y-%m-%d_%H-%M') + ".pdf")
Ejemplo n.º 4
0
    
    n = 0
    while n<N and T[n]>=temp_after_death-0.0001 :
        T[n+1] = (T[n] - k*dt*((1-theta)*T[n] - T_s))/(1.0 + theta*dt*k)
        n = n + 1

    t = linspace(0,t_end,N+1)
    return T,t
    
    
def exact_sol(T0,k,T_s,t_end,dt):
    N = int(round(t_end/float(dt)))
    t = linspace(0,t_end,N+1)
    exact = (T0-T_s)*exp(-t*k) + T_s
    return exact,t
    

if __name__ == "__main__":
    T,t = cooling(37.5, 0.1, 14.0, 60.0, 5, 0.5)
    plot(t,T,"r--o")
    T_exact,t = exact_sol(37.5, 0.1, 14.0, 60.0, 5)
    #figure()
    hold("on")
    plot(t,T_exact)
    legend(["discrete sol","exact sol"])

    print "maximum error :" ,max(abs(T-T_exact))
    raw_input("press enter")