explorefault class

class csi.explorefault(name, utmzone=None, ellps='WGS84', lon0=None, lat0=None, verbose=True)

Creates an object that will solve for the best fault details. The fault has only one patch and is embedded in an elastic medium.

Args:

• name : Name of the object

Kwargs:

• utmzone : UTM zone number

• ellps : Ellipsoid

• lon0/lat0 : Refernece of the zone

• verbose : Talk to me

Returns:

• None

Predict(theta, data, vertical=True)

Calculates a prediction of the measurement from the theta vector

Args:

• theta : model parameters [lon, lat, depth, dip, width, length, strike, strikeslip, dipslip]

• data : Data to test upon

Kwargs:

• vertical : True/False

Returns:

• None

plot(model='mean', show=True)

Plots the PDFs and the desired model predictions and residuals.

Kwargs:

• model : ‘mean’, ‘median’ or ‘rand’

• show : True/False

Returns:

• None

returnModel(model='mean')

Returns a fault corresponding to the desired model.

Kwargs:

• model : Can be ‘mean’, ‘median’, ‘rand’, an integer or a dictionary with the appropriate keys

Returns:

• fault instance

save2h5(filename)

Save the results to a h5 file.

Args:

• filename : Name of the input file

Returns:

• None

setLikelihood(datas, vertical=True)

Builds the data likelihood object from the list of geodetic data in datas.

Args:

• datas : csi geodetic data object (gps or insar) or list of csi geodetic objects. TODO: Add other types of data (opticorr)

Kwargs:

• vertical : Use the verticals for GPS?

Returns:

• None

setPriors(bounds, datas=None, initialSample=None)

Initializes the prior likelihood functions.

Args:

• boundsBounds is a dictionary that holds the following keys.

  • ‘lon’ : Longitude (tuple or float)

  • ‘lat’ : Latitude (tuple or float)

  • ‘depth’ : Depth in km of the top of the fault (tuple or float)

  • ‘dip’ : Dip in degree (tuple or float)

  • ‘width’ : Along-dip size in km (tuple or float)

  • ‘length’ : Along-strike length in km (tuple or float)

  • ‘strike’ : Azimuth of the strike (tuple or float)

  • ‘strikeslip’ : Strike Slip (tuple or float)

  • ‘dipslip’ : Dip slip (tuple or float)

    One bound should be a list with the name of a pymc distribution as first element. The following elements will be passed on to the function. example: bounds[0] = (‘Normal’, 0., 2.) will give a Normal distribution centered on 0. with a 2. standard deviation.

Kwargs:

• datas : Data sets that will be used. This is in case bounds has tuples or floats for reference of an InSAR data set

• initialSample : An array the size of the list of bounds default is None and will be randomly set from the prior PDFs

Returns:

• None

walk(niter=10000, nburn=5000, method='AdaptiveMetropolis')

March the MCMC.

Kwargs:

• niter : Number of steps to walk

• nburn : Numbero of steps to burn

• method : One of the stepmethods of PyMC2

Returns:

• None