def MakeTable(Obs_data,Obs_xml,filename):
head_amanzi = []
coordinates = []
mymodel = model_linear_materials_serial_1d.createFromXML(filename)
p_atm = 101325.0
rho = 998.2
g = 9.80665
for obs in Obs_data.observations.itervalues():
coordinates.append([obs.coordinate[0], obs.coordinate[2]])
# Convert from pressure to hydraulic head.
# This isn't really great, but it gets us there for the moment.
p = obs.data
h = [(p[i] - p_atm) / (rho*g) + obs.coordinate[2] for i in xrange(len(p))]
head_amanzi.append(str(h).rstrip(']').lstrip('['))
head_analytic = list(mymodel.head(numpy.array(coordinates)))
x = prettytable.PrettyTable(["x [m]", "z [m]", "Analytic [m]","Amanzi [m]"])
x.padding_width = 1
x.hrules = 1
for coords, h_analytic, h_amanzi in zip(coordinates,head_analytic,head_amanzi):
x.add_row([coords[0],coords[1],"%.4f" % float(h_analytic),"%.4f" % float(h_amanzi)])
if os.path.exists("table_values.txt"):
os.remove("table_values.txt")
table_file = open("table_values.txt", "w+")
table_file.write(x.get_string())
table_file.close()
def plotTestModel(filename, cmap, axes1, Obs_xml, Obs_data):
# Instantiate the analytic solution
mymodel = model_linear_materials_serial_1d.createFromXML(filename)
table_values = []
# Create a set of points to plot the solution
x = numpy.linspace(mymodel.x_0, mymodel.x_1, 11)
coords = numpy.zeros((11, 2))
coords[:, 0] = x
# Plot a line for each z-coordinate in the observations
for (z_val, color) in cmap.iteritems():
coords[:, 1] = z_val
head = mymodel.head(coords)
axes1.plot(x, head, color, label='$z = %0.2f $' % z_val)
axes1.legend(loc="upper right", fancybox=True, shadow=True)