Source code for openquake.hmtk.strain.geodetic_strain

# -*- coding: utf-8 -*-
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"""
:class:`openquake.hmtk.strain.geodectic_strain.GeodeticStain` is a
core class for storage and implementation of a geodetic strain rate
model
"""
import numpy as np
from copy import deepcopy

DATA_VARIABLES = ["longitude", "latitude", "exx", "eyy", "exy"]


[docs]class GeodeticStrain(object): """ :class:`openquake.hmtk.strain.geodetic_strain.GeodeticStrain` describes the geodetic strain model :param dict data: Strain data in the form of a dictionary where is vector of attributes is stored under the correponding dictionary key (i.e. - longitude - Longitude of point - latitude - Latitiude of point - exx - xx-component of strain tensor - eyy - yy-component of strain tensor - exy - xy-component of strain tensor :param numpy.ndarray seismicity_rate: Seismicity rate at each point associated with the strain model :param numpy.ndarray target_magnitudes: Magnitudes for the corresponding activity rates :param list data_variables: List of strain data attributes in the current class """ def __init__(self): """Instantiates""" self.data = None self.regions = None self.seismicity_rate = None self.regionalisation = None self.target_magnitudes = None self.data_variables = []
[docs] def get_secondary_strain_data(self, strain_data=None): """ Calculate the following and add to data dictionary: 1) 2nd invarient of strain 2) Dilatation rate 3) e1h and e2h 4) err :param dict strain_data: Strain data dictionary (as described) - will overwrite current data if input """ if strain_data: self.data = strain_data if not isinstance(self.data, dict): raise ValueError("Strain data not input or incorrectly formatted") # Check to ensure essential attributes are in data dictionary for essential_key in DATA_VARIABLES: if essential_key not in self.data: print(self.data) raise ValueError( "Essential strain information %s missing!" % essential_key ) self.data_variables = deepcopy(DATA_VARIABLES) # Second Invarient self.data["2nd_inv"] = np.sqrt( (self.data["exx"] ** 2.0) + (self.data["eyy"] ** 2.0) + 2.0 * (self.data["exy"] ** 2.0) ) # Dilatation self.data["dilatation"] = self.data["exx"] + self.data["eyy"] # err self.data["err"] = -1.0 * self.data["dilatation"] center_normal_rate = (self.data["exx"] + self.data["eyy"]) / 2.0 radius_rate = np.sqrt( (self.data["exx"] - center_normal_rate) ** 2.0 + (self.data["exy"] ** 2.0) ) # e1h and e2h self.data["e1h"] = center_normal_rate - radius_rate self.data["e2h"] = center_normal_rate + radius_rate self.data["area"] = np.zeros(self.get_number_observations()) self.data_variables.extend( ["2nd_inv", "dilatation", "err", "e1h", "e2h"] )
[docs] def get_number_observations(self): """ Returns the number of observations in the data file """ if isinstance(self.data, dict) and ("exx" in self.data.keys()): return len(self.data["exx"]) else: return 0