def model_function2(h):
return model.exponential(h, 12.0, 1.4) + model.nugget(h, 1.3)
def main():
"""
Plots different variogram models and compares them to experimental data.
"""
lag = 0.1
nlag = 40
jura_data = np.genfromtxt('data.txt', names=True)
hc, gc = variogram.cloud(jura_data['X'], jura_data['Y'], jura_data['Co'])
he, ge = variogram.experimental(hc, gc, lag, nlag)
# Print one figure with 4 subplots, subplot syntax: (rows, columns, number)
# Each figure represents a different variogram model for the same data
plt.figure(1)
# Gaussian
plt.subplot(221)
x = np.linspace(0, nlag * lag, 1000)
y = model.gaussian(x, 10, 0.9) + 3.0
plt.scatter(he, ge, s=30)
variance = np.var(jura_data['Co'])
plt.axhline(variance, linestyle='--')
plt.xlim(xmin=0, xmax=lag * nlag)
plt.ylim(ymin=0)
plt.title('Gaussian')
plt.plot(x, y)
# Exponential
plt.subplot(222)
x = np.linspace(0, nlag * lag, 1000)
y = model.exponential(x, 12.5, 1.5)
plt.scatter(he, ge, s=30)
variance = np.var(jura_data['Co'])
plt.axhline(variance, linestyle='--')
plt.xlim(xmin=0, xmax=lag * nlag)
plt.ylim(ymin=0)
plt.title('Exponential')
plt.plot(x, y)
# Spherical
plt.subplot(223)
x = np.linspace(0, nlag * lag, 1000)
y = model.spherical(x, 13, 1.5)
plt.scatter(he, ge, s=30)
variance = np.var(jura_data['Co'])
plt.axhline(variance, linestyle='--')
plt.xlim(xmin=0, xmax=lag * nlag)
plt.ylim(ymin=0)
plt.xlabel('distance')
plt.ylabel('gamma')
plt.title('Spherical')
plt.plot(x, y)
# Pure nugget
plt.subplot(224)
x = np.linspace(0, nlag * lag, 1000)
y = model.nugget(x, 13)
plt.scatter(he, ge, s=30) # marker size 's' set to 30
variance = np.var(jura_data['Co'])
plt.axhline(variance, linestyle='--')
plt.xlim(xmin=0, xmax=lag * nlag)
plt.ylim(ymin=0)
plt.title('Nugget')
plt.plot(x, y)
plt.show()
def model_function(h):
"""
Model function used in the exercise 11
"""
return model.exponential(h, 7, 2) + model.nugget(h, 2)
def model_function(h):
return model.exponential(h, 14.5, 1.7) + model.nugget(h, 0.5)
def model_function2(h):
"""
Model function 2
"""
return model.gaussian(h, 10.0, 0.9) + model.nugget(h, 3.0)
def model_function3(h):
"""
Model function 3
"""
return model.exponential(h, 12.0, 1.4) + model.nugget(h, 1.3)
def model_function1(h):
"""
Model function 1
"""
return model.exponential(h, 14.5, 1.7) + model.nugget(h, 0.5)
def model_function5(h):
"""
Model function 5
"""
return model.exponential(h, 12.5, 1.5) + model.nugget(h, 1.0)
def model_function4(h):
"""
Model function 4
"""
return model.spherical(h, 10.3, 1.1) + model.nugget(h, 2.5)
def test_nugget():
h = 1
nugget = 2
assert model.nugget(h, nugget) == 2
def model_function(h):
return model.exponential(h, 0.65, 1.5) + model.nugget(h, 0.35)
