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, 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, 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, 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 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_ORIG = np.array(z, copy=True).flatten() self.VALUES = np.array(val, 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.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 __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