def __init__(self,
x,
y,
z,
val,
variogram_model='linear',
variogram_parameters=None,
variogram_function=None,
nlags=6,
weight=False,
anisotropy_scaling_y=1.0,
anisotropy_scaling_z=1.0,
anisotropy_angle_x=0.0,
anisotropy_angle_y=0.0,
anisotropy_angle_z=0.0,
verbose=False,
enable_plotting=False):
# Code assumes 1D input arrays. Ensures that any extraneous dimensions
# don't get in the way. Copies are created to avoid any problems with
# referencing the original passed arguments.
self.X_ORIG = np.atleast_1d(np.squeeze(np.array(x, copy=True)))
self.Y_ORIG = np.atleast_1d(np.squeeze(np.array(y, copy=True)))
self.Z_ORIG = np.atleast_1d(np.squeeze(np.array(z, copy=True)))
self.VALUES = np.atleast_1d(np.squeeze(np.array(val, copy=True)))
self.verbose = verbose
self.enable_plotting = enable_plotting
if self.enable_plotting and self.verbose:
print "Plotting Enabled\n"
self.XCENTER = (np.amax(self.X_ORIG) + np.amin(self.X_ORIG)) / 2.0
self.YCENTER = (np.amax(self.Y_ORIG) + np.amin(self.Y_ORIG)) / 2.0
self.ZCENTER = (np.amax(self.Z_ORIG) + np.amin(self.Z_ORIG)) / 2.0
self.anisotropy_scaling_y = anisotropy_scaling_y
self.anisotropy_scaling_z = anisotropy_scaling_z
self.anisotropy_angle_x = anisotropy_angle_x
self.anisotropy_angle_y = anisotropy_angle_y
self.anisotropy_angle_z = anisotropy_angle_z
if self.verbose:
print "Adjusting data for anisotropy..."
self.X_ADJUSTED, self.Y_ADJUSTED, self.Z_ADJUSTED = \
core.adjust_for_anisotropy_3d(np.copy(self.X_ORIG), np.copy(self.Y_ORIG), np.copy(self.Z_ORIG),
self.XCENTER, self.YCENTER, self.ZCENTER, self.anisotropy_scaling_y,
self.anisotropy_scaling_z, self.anisotropy_angle_x, self.anisotropy_angle_y,
self.anisotropy_angle_z)
self.variogram_model = variogram_model
if self.variogram_model not in self.variogram_dict.keys(
) and self.variogram_model != 'custom':
raise ValueError(
"Specified variogram model '%s' is not supported." %
variogram_model)
elif self.variogram_model == 'custom':
if variogram_function is None or not callable(variogram_function):
raise ValueError(
"Must specify callable function for custom variogram model."
)
else:
self.variogram_function = variogram_function
else:
self.variogram_function = self.variogram_dict[self.variogram_model]
if self.verbose:
print "Initializing variogram model..."
self.lags, self.semivariance, self.variogram_model_parameters = \
core.initialize_variogram_model_3d(self.X_ADJUSTED, self.Y_ADJUSTED, self.Z_ADJUSTED, self.VALUES,
self.variogram_model, variogram_parameters, self.variogram_function,
nlags, weight)
if self.verbose:
if self.variogram_model == 'linear':
print "Using '%s' Variogram Model" % 'linear'
print "Slope:", self.variogram_model_parameters[0]
print "Nugget:", self.variogram_model_parameters[1], '\n'
elif self.variogram_model == 'power':
print "Using '%s' Variogram Model" % 'power'
print "Scale:", self.variogram_model_parameters[0]
print "Exponent:", self.variogram_model_parameters[1]
print "Nugget:", self.variogram_model_parameters[2], '\n'
elif self.variogram_model == 'custom':
print "Using Custom Variogram Model"
else:
print "Using '%s' Variogram Model" % self.variogram_model
print "Sill:", self.variogram_model_parameters[0]
print "Range:", self.variogram_model_parameters[1]
print "Nugget:", self.variogram_model_parameters[2], '\n'
if self.enable_plotting:
self.display_variogram_model()
if self.verbose:
print "Calculating statistics on variogram model fit..."
self.delta, self.sigma, self.epsilon = core.find_statistics_3d(
self.X_ADJUSTED, self.Y_ADJUSTED, self.Z_ADJUSTED, self.VALUES,
self.variogram_function, self.variogram_model_parameters)
self.Q1 = core.calcQ1(self.epsilon)
self.Q2 = core.calcQ2(self.epsilon)
self.cR = core.calc_cR(self.Q2, self.sigma)
if self.verbose:
print "Q1 =", self.Q1
print "Q2 =", self.Q2
print "cR =", self.cR, '\n'
def update_variogram_model(self,
variogram_model,
variogram_parameters=None,
variogram_function=None,
nlags=6,
weight=False,
anisotropy_scaling_y=1.0,
anisotropy_scaling_z=1.0,
anisotropy_angle_x=0.0,
anisotropy_angle_y=0.0,
anisotropy_angle_z=0.0):
"""Allows user to update variogram type and/or variogram model parameters."""
if anisotropy_scaling_y != self.anisotropy_scaling_y or anisotropy_scaling_z != self.anisotropy_scaling_z or \
anisotropy_angle_x != self.anisotropy_angle_x or anisotropy_angle_y != self.anisotropy_angle_y or \
anisotropy_angle_z != self.anisotropy_angle_z:
if self.verbose:
print "Adjusting data for anisotropy..."
self.anisotropy_scaling_y = anisotropy_scaling_y
self.anisotropy_scaling_z = anisotropy_scaling_z
self.anisotropy_angle_x = anisotropy_angle_x
self.anisotropy_angle_y = anisotropy_angle_y
self.anisotropy_angle_z = anisotropy_angle_z
self.X_ADJUSTED, self.Y_ADJUSTED, self.Z_ADJUSTED = \
core.adjust_for_anisotropy_3d(np.copy(self.X_ORIG), np.copy(self.Y_ORIG), np.copy(self.Z_ORIG),
self.XCENTER, self.YCENTER, self.ZCENTER, self.anisotropy_scaling_y,
self.anisotropy_scaling_z, self.anisotropy_angle_x,
self.anisotropy_angle_y, self.anisotropy_angle_z)
self.variogram_model = variogram_model
if self.variogram_model not in self.variogram_dict.keys(
) and self.variogram_model != 'custom':
raise ValueError(
"Specified variogram model '%s' is not supported." %
variogram_model)
elif self.variogram_model == 'custom':
if variogram_function is None or not callable(variogram_function):
raise ValueError(
"Must specify callable function for custom variogram model."
)
else:
self.variogram_function = variogram_function
else:
self.variogram_function = self.variogram_dict[self.variogram_model]
if self.verbose:
print "Updating variogram mode..."
self.lags, self.semivariance, self.variogram_model_parameters = \
core.initialize_variogram_model_3d(self.X_ADJUSTED, self.Y_ADJUSTED, self.Z_ADJUSTED, self.VALUES,
self.variogram_model, variogram_parameters, self.variogram_function,
nlags, weight)
if self.verbose:
if self.variogram_model == 'linear':
print "Using '%s' Variogram Model" % 'linear'
print "Slope:", self.variogram_model_parameters[0]
print "Nugget:", self.variogram_model_parameters[1], '\n'
elif self.variogram_model == 'power':
print "Using '%s' Variogram Model" % 'power'
print "Scale:", self.variogram_model_parameters[0]
print "Exponent:", self.variogram_model_parameters[1]
print "Nugget:", self.variogram_model_parameters[2], '\n'
elif self.variogram_model == 'custom':
print "Using Custom Variogram Model"
else:
print "Using '%s' Variogram Model" % self.variogram_model
print "Sill:", self.variogram_model_parameters[0]
print "Range:", self.variogram_model_parameters[1]
print "Nugget:", self.variogram_model_parameters[2], '\n'
if self.enable_plotting:
self.display_variogram_model()
if self.verbose:
print "Calculating statistics on variogram model fit..."
self.delta, self.sigma, self.epsilon = core.find_statistics_3d(
self.X_ADJUSTED, self.Y_ADJUSTED, self.Z_ADJUSTED, self.VALUES,
self.variogram_function, self.variogram_model_parameters)
self.Q1 = core.calcQ1(self.epsilon)
self.Q2 = core.calcQ2(self.epsilon)
self.cR = core.calc_cR(self.Q2, self.sigma)
if self.verbose:
print "Q1 =", self.Q1
print "Q2 =", self.Q2
print "cR =", self.cR, '\n'
def __init__(self, x, y, z, variogram_model='linear', variogram_parameters=None,
variogram_function=None, nlags=6, weight=False, anisotropy_scaling=1.0,
anisotropy_angle=0.0, verbose=False, enable_plotting=False,
enable_statistics=False):
# Code assumes 1D input arrays. Ensures that any extraneous dimensions
# don't get in the way. Copies are created to avoid any problems with
# referencing the original passed arguments.
self.X_ORIG = np.atleast_1d(np.squeeze(np.array(x, copy=True)))
self.Y_ORIG = np.atleast_1d(np.squeeze(np.array(y, copy=True)))
self.Z = np.atleast_1d(np.squeeze(np.array(z, copy=True)))
self.verbose = verbose
self.enable_plotting = enable_plotting
if self.enable_plotting and self.verbose:
print "Plotting Enabled\n"
self.XCENTER = (np.amax(self.X_ORIG) + np.amin(self.X_ORIG))/2.0
self.YCENTER = (np.amax(self.Y_ORIG) + np.amin(self.Y_ORIG))/2.0
self.anisotropy_scaling = anisotropy_scaling
self.anisotropy_angle = anisotropy_angle
if self.verbose:
print "Adjusting data for anisotropy..."
self.X_ADJUSTED, self.Y_ADJUSTED = \
core.adjust_for_anisotropy(np.copy(self.X_ORIG), np.copy(self.Y_ORIG),
self.XCENTER, self.YCENTER,
self.anisotropy_scaling, self.anisotropy_angle)
self.variogram_model = variogram_model
if self.variogram_model not in self.variogram_dict.keys() and self.variogram_model != 'custom':
raise ValueError("Specified variogram model '%s' is not supported." % variogram_model)
elif self.variogram_model == 'custom':
if variogram_function is None or not callable(variogram_function):
raise ValueError("Must specify callable function for custom variogram model.")
else:
self.variogram_function = variogram_function
else:
self.variogram_function = self.variogram_dict[self.variogram_model]
if self.verbose:
print "Initializing variogram model..."
self.lags, self.semivariance, self.variogram_model_parameters = \
core.initialize_variogram_model(self.X_ADJUSTED, self.Y_ADJUSTED, self.Z,
self.variogram_model, variogram_parameters,
self.variogram_function, nlags, weight)
if self.verbose:
if self.variogram_model == 'linear':
print "Using '%s' Variogram Model" % 'linear'
print "Slope:", self.variogram_model_parameters[0]
print "Nugget:", self.variogram_model_parameters[1], '\n'
elif self.variogram_model == 'power':
print "Using '%s' Variogram Model" % 'power'
print "Scale:", self.variogram_model_parameters[0]
print "Exponent:", self.variogram_model_parameters[1]
print "Nugget:", self.variogram_model_parameters[2], '\n'
elif self.variogram_model == 'custom':
print "Using Custom Variogram Model"
else:
print "Using '%s' Variogram Model" % self.variogram_model
print "Sill:", self.variogram_model_parameters[0]
print "Range:", self.variogram_model_parameters[1]
print "Nugget:", self.variogram_model_parameters[2], '\n'
if self.enable_plotting:
self.display_variogram_model()
if self.verbose:
print "Calculating statistics on variogram model fit..."
if enable_statistics:
self.delta, self.sigma, self.epsilon = core.find_statistics(self.X_ADJUSTED, self.Y_ADJUSTED,
self.Z, self.variogram_function,
self.variogram_model_parameters)
self.Q1 = core.calcQ1(self.epsilon)
self.Q2 = core.calcQ2(self.epsilon)
self.cR = core.calc_cR(self.Q2, self.sigma)
if self.verbose:
print "Q1 =", self.Q1
print "Q2 =", self.Q2
print "cR =", self.cR, '\n'
else:
self.delta, self.sigma, self.epsilon, self.Q1, self.Q2, self.cR = [None]*6
def update_variogram_model(self, variogram_model, variogram_parameters=None,
variogram_function=None, nlags=6, weight=False,
anisotropy_scaling=1.0, anisotropy_angle=0.0):
"""Allows user to update variogram type and/or variogram model parameters."""
if anisotropy_scaling != self.anisotropy_scaling or \
anisotropy_angle != self.anisotropy_angle:
if self.verbose:
print "Adjusting data for anisotropy..."
self.anisotropy_scaling = anisotropy_scaling
self.anisotropy_angle = anisotropy_angle
self.X_ADJUSTED, self.Y_ADJUSTED = \
core.adjust_for_anisotropy(np.copy(self.X_ORIG),
np.copy(self.Y_ORIG),
self.XCENTER, self.YCENTER,
self.anisotropy_scaling,
self.anisotropy_angle)
self.variogram_model = variogram_model
if self.variogram_model not in self.variogram_dict.keys() and self.variogram_model != 'custom':
raise ValueError("Specified variogram model '%s' is not supported." % variogram_model)
elif self.variogram_model == 'custom':
if variogram_function is None or not callable(variogram_function):
raise ValueError("Must specify callable function for custom variogram model.")
else:
self.variogram_function = variogram_function
else:
self.variogram_function = self.variogram_dict[self.variogram_model]
if self.verbose:
print "Updating variogram mode..."
self.lags, self.semivariance, self.variogram_model_parameters = \
core.initialize_variogram_model(self.X_ADJUSTED, self.Y_ADJUSTED, self.Z,
self.variogram_model, variogram_parameters,
self.variogram_function, nlags, weight)
if self.verbose:
if self.variogram_model == 'linear':
print "Using '%s' Variogram Model" % 'linear'
print "Slope:", self.variogram_model_parameters[0]
print "Nugget:", self.variogram_model_parameters[1], '\n'
elif self.variogram_model == 'power':
print "Using '%s' Variogram Model" % 'power'
print "Scale:", self.variogram_model_parameters[0]
print "Exponent:", self.variogram_model_parameters[1]
print "Nugget:", self.variogram_model_parameters[2], '\n'
elif self.variogram_model == 'custom':
print "Using Custom Variogram Model"
else:
print "Using '%s' Variogram Model" % self.variogram_model
print "Sill:", self.variogram_model_parameters[0]
print "Range:", self.variogram_model_parameters[1]
print "Nugget:", self.variogram_model_parameters[2], '\n'
if self.enable_plotting:
self.display_variogram_model()
if self.verbose:
print "Calculating statistics on variogram model fit..."
self.delta, self.sigma, self.epsilon = core.find_statistics(self.X_ADJUSTED, self.Y_ADJUSTED,
self.Z, self.variogram_function,
self.variogram_model_parameters)
self.Q1 = core.calcQ1(self.epsilon)
self.Q2 = core.calcQ2(self.epsilon)
self.cR = core.calc_cR(self.Q2, self.sigma)
if self.verbose:
print "Q1 =", self.Q1
print "Q2 =", self.Q2
print "cR =", self.cR, '\n'
def update_variogram_model(self, variogram_model,
variogram_parameters=None,
nlags=6, anisotropy_scaling=1.0,
anisotropy_angle=0.0):
"""Allows user to update variogram type and/or variogram model parameters."""
if anisotropy_scaling != self.anisotropy_scaling or \
anisotropy_angle != self.anisotropy_angle:
if self.verbose:
print "Adjusting data for anisotropy..."
self.anisotropy_scaling = anisotropy_scaling
self.anisotropy_angle = anisotropy_angle
self.X_ADJUSTED, self.Y_ADJUSTED = \
core.adjust_for_anisotropy(np.copy(self.X_ORIG),
np.copy(self.Y_ORIG),
self.XCENTER, self.YCENTER,
self.anisotropy_scaling,
self.anisotropy_angle)
self.variogram_model = variogram_model
if self.variogram_model == 'linear':
self.variogram_function = variogram_models.linear_variogram_model
if self.variogram_model == 'power':
self.variogram_function = variogram_models.power_variogram_model
if self.variogram_model == 'gaussian':
self.variogram_function = variogram_models.gaussian_variogram_model
if self.variogram_model == 'spherical':
self.variogram_function = variogram_models.spherical_variogram_model
if self.variogram_model == 'exponential':
self.variogram_function = variogram_models.exponential_variogram_model
if self.verbose:
print "Updating variogram mode..."
self.lags, self.semivariance, self.variogram_model_parameters = \
core.initialize_variogram_model(self.X_ADJUSTED, self.Y_ADJUSTED, self.Z,
self.variogram_model, variogram_parameters,
self.variogram_function, nlags)
if self.verbose:
if self.variogram_model == 'linear':
print "Using '%s' Variogram Model" % 'linear'
print "Slope:", self.variogram_model_parameters[0]
print "Nugget:", self.variogram_model_parameters[1], '\n'
elif self.variogram_model == 'power':
print "Using '%s' Variogram Model" % 'power'
print "Scale:", self.variogram_model_parameters[0]
print "Exponent:", self.variogram_model_parameters[1]
print "Nugget:", self.variogram_model_parameters[2], '\n'
else:
print "Using '%s' Variogram Model" % self.variogram_model
print "Sill:", self.variogram_model_parameters[0]
print "Range:", self.variogram_model_parameters[1]
print "Nugget:", self.variogram_model_parameters[2], '\n'
if self.enable_plotting:
self.display_variogram_model()
if self.verbose:
print "Calculating statistics on variogram model fit..."
self.delta, self.sigma, self.epsilon = core.find_statistics(self.X_ADJUSTED,
self.Y_ADJUSTED,
self.Z,
self.variogram_function,
self.variogram_model_parameters)
self.Q1 = core.calcQ1(self.epsilon)
self.Q2 = core.calcQ2(self.epsilon)
self.cR = core.calc_cR(self.Q2, self.sigma)
if self.verbose:
print "Q1 =", self.Q1
print "Q2 =", self.Q2
print "cR =", self.cR, '\n'
def __init__(self, x, y, z, val, variogram_model='linear', variogram_parameters=None,
variogram_function=None, nlags=6, weight=False, anisotropy_scaling_y=1.0,
anisotropy_scaling_z=1.0, anisotropy_angle_x=0.0, anisotropy_angle_y=0.0,
anisotropy_angle_z=0.0, drift_terms=None, specified_drift=None,
functional_drift=None, verbose=False, enable_plotting=False):
# Deal with mutable default argument
if drift_terms is None:
drift_terms = []
if specified_drift is None:
specified_drift = []
if functional_drift is None:
functional_drift = []
# Code assumes 1D input arrays. Ensures that any extraneous dimensions
# don't get in the way. Copies are created to avoid any problems with
# referencing the original passed arguments.
self.X_ORIG = np.atleast_1d(np.squeeze(np.array(x, copy=True)))
self.Y_ORIG = np.atleast_1d(np.squeeze(np.array(y, copy=True)))
self.Z_ORIG = np.atleast_1d(np.squeeze(np.array(z, copy=True)))
self.VALUES = np.atleast_1d(np.squeeze(np.array(val, copy=True)))
self.verbose = verbose
self.enable_plotting = enable_plotting
if self.enable_plotting and self.verbose:
print "Plotting Enabled\n"
self.XCENTER = (np.amax(self.X_ORIG) + np.amin(self.X_ORIG))/2.0
self.YCENTER = (np.amax(self.Y_ORIG) + np.amin(self.Y_ORIG))/2.0
self.ZCENTER = (np.amax(self.Z_ORIG) + np.amin(self.Z_ORIG))/2.0
self.anisotropy_scaling_y = anisotropy_scaling_y
self.anisotropy_scaling_z = anisotropy_scaling_z
self.anisotropy_angle_x = anisotropy_angle_x
self.anisotropy_angle_y = anisotropy_angle_y
self.anisotropy_angle_z = anisotropy_angle_z
if self.verbose:
print "Adjusting data for anisotropy..."
self.X_ADJUSTED, self.Y_ADJUSTED, self.Z_ADJUSTED = \
core.adjust_for_anisotropy_3d(np.copy(self.X_ORIG), np.copy(self.Y_ORIG), np.copy(self.Z_ORIG),
self.XCENTER, self.YCENTER, self.ZCENTER, self.anisotropy_scaling_y,
self.anisotropy_scaling_z, self.anisotropy_angle_x, self.anisotropy_angle_y,
self.anisotropy_angle_z)
self.variogram_model = variogram_model
if self.variogram_model not in self.variogram_dict.keys() and self.variogram_model != 'custom':
raise ValueError("Specified variogram model '%s' is not supported." % variogram_model)
elif self.variogram_model == 'custom':
if variogram_function is None or not callable(variogram_function):
raise ValueError("Must specify callable function for custom variogram model.")
else:
self.variogram_function = variogram_function
else:
self.variogram_function = self.variogram_dict[self.variogram_model]
if self.verbose:
print "Initializing variogram model..."
self.lags, self.semivariance, self.variogram_model_parameters = \
core.initialize_variogram_model_3d(self.X_ADJUSTED, self.Y_ADJUSTED, self.Z_ADJUSTED, self.VALUES,
self.variogram_model, variogram_parameters, self.variogram_function,
nlags, weight)
if self.verbose:
if self.variogram_model == 'linear':
print "Using '%s' Variogram Model" % 'linear'
print "Slope:", self.variogram_model_parameters[0]
print "Nugget:", self.variogram_model_parameters[1], '\n'
elif self.variogram_model == 'power':
print "Using '%s' Variogram Model" % 'power'
print "Scale:", self.variogram_model_parameters[0]
print "Exponent:", self.variogram_model_parameters[1]
print "Nugget:", self.variogram_model_parameters[2], '\n'
elif self.variogram_model == 'custom':
print "Using Custom Variogram Model"
else:
print "Using '%s' Variogram Model" % self.variogram_model
print "Sill:", self.variogram_model_parameters[0]
print "Range:", self.variogram_model_parameters[1]
print "Nugget:", self.variogram_model_parameters[2], '\n'
if self.enable_plotting:
self.display_variogram_model()
if self.verbose:
print "Calculating statistics on variogram model fit..."
self.delta, self.sigma, self.epsilon = core.find_statistics_3d(self.X_ADJUSTED, self.Y_ADJUSTED,
self.Z_ADJUSTED, self.VALUES,
self.variogram_function,
self.variogram_model_parameters)
self.Q1 = core.calcQ1(self.epsilon)
self.Q2 = core.calcQ2(self.epsilon)
self.cR = core.calc_cR(self.Q2, self.sigma)
if self.verbose:
print "Q1 =", self.Q1
print "Q2 =", self.Q2
print "cR =", self.cR, '\n'
if self.verbose:
print "Initializing drift terms..."
# Note that the regional linear drift values will be based on the adjusted coordinate system.
# Really, it doesn't actually matter which coordinate system is used here.
if 'regional_linear' in drift_terms:
self.regional_linear_drift = True
if self.verbose:
print "Implementing regional linear drift."
else:
self.regional_linear_drift = False
if 'specified' in drift_terms:
if type(specified_drift) is not list:
raise TypeError("Arrays for specified drift terms must be encapsulated in a list.")
if len(specified_drift) == 0:
raise ValueError("Must provide at least one drift-value array when using the "
"'specified' drift capability.")
self.specified_drift = True
self.specified_drift_data_arrays = []
for term in specified_drift:
specified = np.squeeze(np.array(term, copy=True))
if specified.size != self.X_ORIG.size:
raise ValueError("Must specify the drift values for each data point when using the "
"'specified' drift capability.")
self.specified_drift_data_arrays.append(specified)
else:
self.specified_drift = False
# The provided callable functions will be evaluated using the adjusted coordinates.
if 'functional' in drift_terms:
if type(functional_drift) is not list:
raise TypeError("Callables for functional drift terms must be encapsulated in a list.")
if len(functional_drift) == 0:
raise ValueError("Must provide at least one callable object when using the "
"'functional' drift capability.")
self.functional_drift = True
self.functional_drift_terms = functional_drift
else:
self.functional_drift = False
def __init__(self, x, y, z, variogram_model='linear',
variogram_parameters=None, nlags=6,
anisotropy_scaling=1.0, anisotropy_angle=0.0,
verbose=False, enable_plotting=False):
# Code assumes 1D input arrays. Ensures that this is the case.
self.X_ORIG = np.array(x).flatten()
self.Y_ORIG = np.array(y).flatten()
self.Z = np.array(z).flatten()
self.verbose = verbose
self.enable_plotting = enable_plotting
if self.enable_plotting and self.verbose:
print "Plotting Enabled\n"
self.XCENTER = (np.amax(self.X_ORIG) + np.amin(self.X_ORIG))/2.0
self.YCENTER = (np.amax(self.Y_ORIG) + np.amin(self.Y_ORIG))/2.0
self.anisotropy_scaling = anisotropy_scaling
self.anisotropy_angle = anisotropy_angle
if self.verbose:
print "Adjusting data for anisotropy..."
self.X_ADJUSTED, self.Y_ADJUSTED = \
core.adjust_for_anisotropy(np.copy(self.X_ORIG), np.copy(self.Y_ORIG),
self.XCENTER, self.YCENTER,
self.anisotropy_scaling, self.anisotropy_angle)
self.variogram_model = variogram_model
if self.variogram_model == 'linear':
self.variogram_function = variogram_models.linear_variogram_model
if self.variogram_model == 'power':
self.variogram_function = variogram_models.power_variogram_model
if self.variogram_model == 'gaussian':
self.variogram_function = variogram_models.gaussian_variogram_model
if self.variogram_model == 'spherical':
self.variogram_function = variogram_models.spherical_variogram_model
if self.variogram_model == 'exponential':
self.variogram_function = variogram_models.exponential_variogram_model
if self.verbose:
print "Initializing variogram model..."
self.lags, self.semivariance, self.variogram_model_parameters = \
core.initialize_variogram_model(self.X_ADJUSTED, self.Y_ADJUSTED, self.Z,
self.variogram_model, variogram_parameters,
self.variogram_function, nlags)
if self.verbose:
if self.variogram_model == 'linear':
print "Using '%s' Variogram Model" % 'linear'
print "Slope:", self.variogram_model_parameters[0]
print "Nugget:", self.variogram_model_parameters[1], '\n'
elif self.variogram_model == 'power':
print "Using '%s' Variogram Model" % 'power'
print "Scale:", self.variogram_model_parameters[0]
print "Exponent:", self.variogram_model_parameters[1]
print "Nugget:", self.variogram_model_parameters[2], '\n'
else:
print "Using '%s' Variogram Model" % self.variogram_model
print "Sill:", self.variogram_model_parameters[0]
print "Range:", self.variogram_model_parameters[1]
print "Nugget:", self.variogram_model_parameters[2], '\n'
if self.enable_plotting:
self.display_variogram_model()
if self.verbose:
print "Calculating statistics on variogram model fit..."
self.delta, self.sigma, self.epsilon = core.find_statistics(self.X_ADJUSTED,
self.Y_ADJUSTED,
self.Z,
self.variogram_function,
self.variogram_model_parameters)
self.Q1 = core.calcQ1(self.epsilon)
self.Q2 = core.calcQ2(self.epsilon)
self.cR = core.calc_cR(self.Q2, self.sigma)
if self.verbose:
print "Q1 =", self.Q1
print "Q2 =", self.Q2
print "cR =", self.cR, '\n'
def __init__(self, x, y, z, variogram_model='linear',
variogram_parameters=None, nlags=6,
anisotropy_scaling=1.0, anisotropy_angle=0.0,
drift_terms=[None], point_drift=None, external_drift=None,
external_drift_x=None, external_drift_y=None,
external_drift_xspacing=None, external_drift_yspacing=None,
verbose=False, enable_plotting=False):
# Code assumes 1D input arrays. Ensures that this is the case.
self.X_ORIG = np.array(x).flatten()
self.Y_ORIG = np.array(y).flatten()
self.Z = np.array(z).flatten()
self.verbose = verbose
self.enable_plotting = enable_plotting
if self.enable_plotting and self.verbose:
print "Plotting Enabled\n"
self.XCENTER = (np.amax(self.X_ORIG) + np.amin(self.X_ORIG))/2.0
self.YCENTER = (np.amax(self.Y_ORIG) + np.amin(self.Y_ORIG))/2.0
self.anisotropy_scaling = anisotropy_scaling
self.anisotropy_angle = anisotropy_angle
if self.verbose:
print "Adjusting data for anisotropy..."
self.X_ADJUSTED, self.Y_ADJUSTED = \
core.adjust_for_anisotropy(np.copy(self.X_ORIG), np.copy(self.Y_ORIG),
self.XCENTER, self.YCENTER,
self.anisotropy_scaling, self.anisotropy_angle)
self.variogram_model = variogram_model
if self.variogram_model == 'linear':
self.variogram_function = variogram_models.linear_variogram_model
if self.variogram_model == 'power':
self.variogram_function = variogram_models.power_variogram_model
if self.variogram_model == 'gaussian':
self.variogram_function = variogram_models.gaussian_variogram_model
if self.variogram_model == 'spherical':
self.variogram_function = variogram_models.spherical_variogram_model
if self.variogram_model == 'exponential':
self.variogram_function = variogram_models.exponential_variogram_model
if self.verbose:
print "Initializing variogram model..."
self.lags, self.semivariance, self.variogram_model_parameters = \
core.initialize_variogram_model(self.X_ADJUSTED, self.Y_ADJUSTED, self.Z,
self.variogram_model, variogram_parameters,
self.variogram_function, nlags)
if self.verbose:
if self.variogram_model == 'linear':
print "Using '%s' Variogram Model" % 'linear'
print "Slope:", self.variogram_model_parameters[0]
print "Nugget:", self.variogram_model_parameters[1], '\n'
elif self.variogram_model == 'power':
print "Using '%s' Variogram Model" % 'power'
print "Scale:", self.variogram_model_parameters[0]
print "Exponent:", self.variogram_model_parameters[1]
print "Nugget:", self.variogram_model_parameters[2], '\n'
else:
print "Using '%s' Variogram Model" % self.variogram_model
print "Sill:", self.variogram_model_parameters[0]
print "Range:", self.variogram_model_parameters[1]
print "Nugget:", self.variogram_model_parameters[2]
if self.enable_plotting:
self.display_variogram_model()
if self.verbose:
print "Calculating statistics on variogram model fit..."
self.delta, self.sigma, self.epsilon = core.find_statistics(self.X_ADJUSTED,
self.Y_ADJUSTED,
self.Z,
self.variogram_function,
self.variogram_model_parameters)
self.Q1 = core.calcQ1(self.epsilon)
self.Q2 = core.calcQ2(self.epsilon)
self.cR = core.calc_cR(self.Q2, self.sigma)
if self.verbose:
print "Q1 =", self.Q1
print "Q2 =", self.Q2
print "cR =", self.cR, '\n'
if self.verbose:
print "Initializing drift terms..."
if 'regional_linear' in drift_terms:
self.regional_linear_drift = True
if self.verbose:
print "Implementing regional linear drift."
else:
self.regional_linear_drift = False
if 'external_Z' in drift_terms:
if external_drift is None:
raise ValueError("Must specify external Z drift terms.")
if external_drift_x is None or external_drift_y is None:
raise ValueError("Must specify coordinates of external Z drift terms.")
self.external_Z_drift = True
self.external_Z_array = np.array(external_drift)
self.external_Z_array_x = np.array(external_drift_x).flatten()
self.external_Z_array_y = np.array(external_drift_y).flatten()
if np.unique(self.external_Z_array_x[1:] - self.external_Z_array_x[:-1]).size != 1:
if external_drift_xspacing is None:
raise ValueError("X-coordinate spacing is not constant. "
"Must provide X-coordinate grid size.")
else:
self.external_Z_array_x_spacing = np.array(external_drift_xspacing).flatten()
else:
self.external_Z_array_x_spacing = np.zeros(self.external_Z_array_x.shape)
self.external_Z_array_x_spacing.fill(
np.unique(self.external_Z_array_x[1:] - self.external_Z_array_x[:-1])[0])
if np.unique(self.external_Z_array_y[1:] - self.external_Z_array_y[:-1]).size != 1:
if external_drift_yspacing is None:
raise ValueError("Y-coordinate spacing is not constant. "
"Must provide Y-coordinate grid size.")
else:
self.external_Z_array_y_spacing = np.array(external_drift_yspacing).flatten()
else:
self.external_Z_array_y_spacing = np.zeros(self.external_Z_array_y.shape)
self.external_Z_array_y_spacing.fill(
np.unique(self.external_Z_array_y[1:] - self.external_Z_array_y[:-1])[0])
self.z_scalars = self._calculate_data_point_zscalars(self.X_ORIG,
self.Y_ORIG)
if self.verbose:
print "Implementing external Z drift."
else:
self.external_Z_drift = False
if 'point_log' in drift_terms:
if point_drift is None:
raise ValueError("Must specify location(s) and strength(s) of point drift terms.")
self.point_log_drift = True
self.point_log_array = np.atleast_2d(np.array(point_drift))
if self.verbose:
print "Implementing external point-logarithmic drift; " \
"number of points =", self.point_log_array.shape[0], '\n'
else:
self.point_log_drift = False
def __init__(self,
x,
y,
z,
val,
variogram_model='linear',
variogram_parameters=None,
variogram_function=None,
nlags=6,
weight=False,
anisotropy_scaling_y=1.0,
anisotropy_scaling_z=1.0,
anisotropy_angle_x=0.0,
anisotropy_angle_y=0.0,
anisotropy_angle_z=0.0,
drift_terms=None,
specified_drift=None,
functional_drift=None,
verbose=False,
enable_plotting=False):
# Deal with mutable default argument
if drift_terms is None:
drift_terms = []
if specified_drift is None:
specified_drift = []
if functional_drift is None:
functional_drift = []
# Code assumes 1D input arrays. Ensures that any extraneous dimensions
# don't get in the way. Copies are created to avoid any problems with
# referencing the original passed arguments.
self.X_ORIG = np.atleast_1d(np.squeeze(np.array(x, copy=True)))
self.Y_ORIG = np.atleast_1d(np.squeeze(np.array(y, copy=True)))
self.Z_ORIG = np.atleast_1d(np.squeeze(np.array(z, copy=True)))
self.VALUES = np.atleast_1d(np.squeeze(np.array(val, copy=True)))
self.verbose = verbose
self.enable_plotting = enable_plotting
if self.enable_plotting and self.verbose:
print "Plotting Enabled\n"
self.XCENTER = (np.amax(self.X_ORIG) + np.amin(self.X_ORIG)) / 2.0
self.YCENTER = (np.amax(self.Y_ORIG) + np.amin(self.Y_ORIG)) / 2.0
self.ZCENTER = (np.amax(self.Z_ORIG) + np.amin(self.Z_ORIG)) / 2.0
self.anisotropy_scaling_y = anisotropy_scaling_y
self.anisotropy_scaling_z = anisotropy_scaling_z
self.anisotropy_angle_x = anisotropy_angle_x
self.anisotropy_angle_y = anisotropy_angle_y
self.anisotropy_angle_z = anisotropy_angle_z
if self.verbose:
print "Adjusting data for anisotropy..."
self.X_ADJUSTED, self.Y_ADJUSTED, self.Z_ADJUSTED = \
core.adjust_for_anisotropy_3d(np.copy(self.X_ORIG), np.copy(self.Y_ORIG), np.copy(self.Z_ORIG),
self.XCENTER, self.YCENTER, self.ZCENTER, self.anisotropy_scaling_y,
self.anisotropy_scaling_z, self.anisotropy_angle_x, self.anisotropy_angle_y,
self.anisotropy_angle_z)
self.variogram_model = variogram_model
if self.variogram_model not in self.variogram_dict.keys(
) and self.variogram_model != 'custom':
raise ValueError(
"Specified variogram model '%s' is not supported." %
variogram_model)
elif self.variogram_model == 'custom':
if variogram_function is None or not callable(variogram_function):
raise ValueError(
"Must specify callable function for custom variogram model."
)
else:
self.variogram_function = variogram_function
else:
self.variogram_function = self.variogram_dict[self.variogram_model]
if self.verbose:
print "Initializing variogram model..."
self.lags, self.semivariance, self.variogram_model_parameters = \
core.initialize_variogram_model_3d(self.X_ADJUSTED, self.Y_ADJUSTED, self.Z_ADJUSTED, self.VALUES,
self.variogram_model, variogram_parameters, self.variogram_function,
nlags, weight)
if self.verbose:
if self.variogram_model == 'linear':
print "Using '%s' Variogram Model" % 'linear'
print "Slope:", self.variogram_model_parameters[0]
print "Nugget:", self.variogram_model_parameters[1], '\n'
elif self.variogram_model == 'power':
print "Using '%s' Variogram Model" % 'power'
print "Scale:", self.variogram_model_parameters[0]
print "Exponent:", self.variogram_model_parameters[1]
print "Nugget:", self.variogram_model_parameters[2], '\n'
elif self.variogram_model == 'custom':
print "Using Custom Variogram Model"
else:
print "Using '%s' Variogram Model" % self.variogram_model
print "Sill:", self.variogram_model_parameters[0]
print "Range:", self.variogram_model_parameters[1]
print "Nugget:", self.variogram_model_parameters[2], '\n'
if self.enable_plotting:
self.display_variogram_model()
if self.verbose:
print "Calculating statistics on variogram model fit..."
self.delta, self.sigma, self.epsilon = core.find_statistics_3d(
self.X_ADJUSTED, self.Y_ADJUSTED, self.Z_ADJUSTED, self.VALUES,
self.variogram_function, self.variogram_model_parameters)
self.Q1 = core.calcQ1(self.epsilon)
self.Q2 = core.calcQ2(self.epsilon)
self.cR = core.calc_cR(self.Q2, self.sigma)
if self.verbose:
print "Q1 =", self.Q1
print "Q2 =", self.Q2
print "cR =", self.cR, '\n'
if self.verbose:
print "Initializing drift terms..."
# Note that the regional linear drift values will be based on the adjusted coordinate system.
# Really, it doesn't actually matter which coordinate system is used here.
if 'regional_linear' in drift_terms:
self.regional_linear_drift = True
if self.verbose:
print "Implementing regional linear drift."
else:
self.regional_linear_drift = False
if 'specified' in drift_terms:
if type(specified_drift) is not list:
raise TypeError(
"Arrays for specified drift terms must be encapsulated in a list."
)
if len(specified_drift) == 0:
raise ValueError(
"Must provide at least one drift-value array when using the "
"'specified' drift capability.")
self.specified_drift = True
self.specified_drift_data_arrays = []
for term in specified_drift:
specified = np.squeeze(np.array(term, copy=True))
if specified.size != self.X_ORIG.size:
raise ValueError(
"Must specify the drift values for each data point when using the "
"'specified' drift capability.")
self.specified_drift_data_arrays.append(specified)
else:
self.specified_drift = False
# The provided callable functions will be evaluated using the adjusted coordinates.
if 'functional' in drift_terms:
if type(functional_drift) is not list:
raise TypeError(
"Callables for functional drift terms must be encapsulated in a list."
)
if len(functional_drift) == 0:
raise ValueError(
"Must provide at least one callable object when using the "
"'functional' drift capability.")
self.functional_drift = True
self.functional_drift_terms = functional_drift
else:
self.functional_drift = False
def update_variogram_model(self,
variogram_model,
variogram_parameters=None,
nlags=6,
weight=False,
anisotropy_scaling=1.0,
anisotropy_angle=0.0):
"""Allows user to update variogram type and/or variogram model parameters."""
if anisotropy_scaling != self.anisotropy_scaling or \
anisotropy_angle != self.anisotropy_angle:
if self.verbose:
print "Adjusting data for anisotropy..."
self.anisotropy_scaling = anisotropy_scaling
self.anisotropy_angle = anisotropy_angle
self.X_ADJUSTED, self.Y_ADJUSTED = \
core.adjust_for_anisotropy(np.copy(self.X_ORIG),
np.copy(self.Y_ORIG),
self.XCENTER, self.YCENTER,
self.anisotropy_scaling,
self.anisotropy_angle)
self.variogram_model = variogram_model
if self.variogram_model == 'linear':
self.variogram_function = variogram_models.linear_variogram_model
elif self.variogram_model == 'power':
self.variogram_function = variogram_models.power_variogram_model
elif self.variogram_model == 'gaussian':
self.variogram_function = variogram_models.gaussian_variogram_model
elif self.variogram_model == 'spherical':
self.variogram_function = variogram_models.spherical_variogram_model
elif self.variogram_model == 'exponential':
self.variogram_function = variogram_models.exponential_variogram_model
else:
raise ValueError(
"Specified variogram model '%s' is not supported." %
variogram_model)
if self.verbose:
print "Updating variogram mode..."
self.lags, self.semivariance, self.variogram_model_parameters = \
core.initialize_variogram_model(self.X_ADJUSTED, self.Y_ADJUSTED, self.Z,
self.variogram_model, variogram_parameters,
self.variogram_function, nlags, weight)
if self.verbose:
if self.variogram_model == 'linear':
print "Using '%s' Variogram Model" % 'linear'
print "Slope:", self.variogram_model_parameters[0]
print "Nugget:", self.variogram_model_parameters[1], '\n'
elif self.variogram_model == 'power':
print "Using '%s' Variogram Model" % 'power'
print "Scale:", self.variogram_model_parameters[0]
print "Exponent:", self.variogram_model_parameters[1]
print "Nugget:", self.variogram_model_parameters[2], '\n'
else:
print "Using '%s' Variogram Model" % self.variogram_model
print "Sill:", self.variogram_model_parameters[0]
print "Range:", self.variogram_model_parameters[1]
print "Nugget:", self.variogram_model_parameters[2], '\n'
if self.enable_plotting:
self.display_variogram_model()
if self.verbose:
print "Calculating statistics on variogram model fit..."
self.delta, self.sigma, self.epsilon = core.find_statistics(
self.X_ADJUSTED, self.Y_ADJUSTED, self.Z, self.variogram_function,
self.variogram_model_parameters)
self.Q1 = core.calcQ1(self.epsilon)
self.Q2 = core.calcQ2(self.epsilon)
self.cR = core.calc_cR(self.Q2, self.sigma)
if self.verbose:
print "Q1 =", self.Q1
print "Q2 =", self.Q2
print "cR =", self.cR, '\n'
def __init__(self,
x,
y,
z,
variogram_model='linear',
variogram_parameters=None,
nlags=6,
weight=False,
anisotropy_scaling=1.0,
anisotropy_angle=0.0,
verbose=False,
enable_plotting=False):
# Code assumes 1D input arrays. Ensures that this is the case.
# Copies are created to avoid any problems with referencing
# the original passed arguments.
self.X_ORIG = np.array(x, copy=True).flatten()
self.Y_ORIG = np.array(y, copy=True).flatten()
self.Z = np.array(z, copy=True).flatten()
self.verbose = verbose
self.enable_plotting = enable_plotting
if self.enable_plotting and self.verbose:
print "Plotting Enabled\n"
self.XCENTER = (np.amax(self.X_ORIG) + np.amin(self.X_ORIG)) / 2.0
self.YCENTER = (np.amax(self.Y_ORIG) + np.amin(self.Y_ORIG)) / 2.0
self.anisotropy_scaling = anisotropy_scaling
self.anisotropy_angle = anisotropy_angle
if self.verbose:
print "Adjusting data for anisotropy..."
self.X_ADJUSTED, self.Y_ADJUSTED = \
core.adjust_for_anisotropy(np.copy(self.X_ORIG), np.copy(self.Y_ORIG),
self.XCENTER, self.YCENTER,
self.anisotropy_scaling, self.anisotropy_angle)
self.variogram_model = variogram_model
if self.variogram_model == 'linear':
self.variogram_function = variogram_models.linear_variogram_model
elif self.variogram_model == 'power':
self.variogram_function = variogram_models.power_variogram_model
elif self.variogram_model == 'gaussian':
self.variogram_function = variogram_models.gaussian_variogram_model
elif self.variogram_model == 'spherical':
self.variogram_function = variogram_models.spherical_variogram_model
elif self.variogram_model == 'exponential':
self.variogram_function = variogram_models.exponential_variogram_model
else:
raise ValueError(
"Specified variogram model '%s' is not supported." %
variogram_model)
if self.verbose:
print "Initializing variogram model..."
self.lags, self.semivariance, self.variogram_model_parameters = \
core.initialize_variogram_model(self.X_ADJUSTED, self.Y_ADJUSTED, self.Z,
self.variogram_model, variogram_parameters,
self.variogram_function, nlags, weight)
if self.verbose:
if self.variogram_model == 'linear':
print "Using '%s' Variogram Model" % 'linear'
print "Slope:", self.variogram_model_parameters[0]
print "Nugget:", self.variogram_model_parameters[1], '\n'
elif self.variogram_model == 'power':
print "Using '%s' Variogram Model" % 'power'
print "Scale:", self.variogram_model_parameters[0]
print "Exponent:", self.variogram_model_parameters[1]
print "Nugget:", self.variogram_model_parameters[2], '\n'
else:
print "Using '%s' Variogram Model" % self.variogram_model
print "Sill:", self.variogram_model_parameters[0]
print "Range:", self.variogram_model_parameters[1]
print "Nugget:", self.variogram_model_parameters[2], '\n'
if self.enable_plotting:
self.display_variogram_model()
if self.verbose:
print "Calculating statistics on variogram model fit..."
self.delta, self.sigma, self.epsilon = core.find_statistics(
self.X_ADJUSTED, self.Y_ADJUSTED, self.Z, self.variogram_function,
self.variogram_model_parameters)
self.Q1 = core.calcQ1(self.epsilon)
self.Q2 = core.calcQ2(self.epsilon)
self.cR = core.calc_cR(self.Q2, self.sigma)
if self.verbose:
print "Q1 =", self.Q1
print "Q2 =", self.Q2
print "cR =", self.cR, '\n'