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
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
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")
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")
