pytomoatt.model

Module for handling model data.

class pytomoatt.model.ATTModel(para_fname='input_params.yml')[source]

Bases: object

Create initial model from external models

calc_dv(ref_mod_fname: str)[source]

calculate anomalies relative to another model

Parameters:

ref_mod_fname (str) – Path to reference model

calc_dv_avg()[source]

calculate anomalies relative to average velocity at each depth

copy()[source]

Copy the model

grid_data_ascii(model_fname: str, **kwargs)[source]

Grid data from custom model file in ASCII format

Parameters:

  • model_fname (str) – Path to model file

  • usecols (list or tuple) – Columns order by longitude, latitude, depth and velocity, defaults to [0, 1, 2, 3]

grid_data_crust1(type='vp')[source]

Grid data from CRUST1.0 model

Parameters:

type (str, optional) – Specify velocity type of vp or vs, defaults to ‘vp’

classmethod read(model_fname: str, para_fname='input_params.yml')[source]

Read an exists model

Parameters:

  • model_fname (str) – Path to the exists model

  • para_fname (str, optional) – Path to parameter file, defaults to ‘input_params.yml’

read_param()[source]

Read n_rtp, min_max_dep, min_max_lat and min_max_lon from para_fname

smooth(sigma=5.0, unit_deg=False, smooth_ani=False, **kwargs)[source]

Gaussian smooth the 3D velocity model

Parameters:

  • sigma (scalar or sequence of scalars) – Standard deviation for Gaussian kernel. If scalar, apply to all dimensions. If sequence of 3, apply to [depth, lat, lon]. Depth is always in km. Horizontal dimensions depend on unit_deg.

  • unit_deg (bool) – If True, horizontal sigma is in degrees. If False, horizontal sigma is in km. Defaults to False.

  • smooth_ani (bool) – If True, also smooth anisotropic parameters (xi, eta, zeta). Defaults to False.

  • kwargs – Additional arguments passed to scipy.ndimage.gaussian_filter

Examples

To smooth with 5 km in depth and 0.2 degrees in horizontal directions:

>>> model.smooth(sigma=[5.0, 0.2, 0.2], unit_deg=True)

To smooth with 5 km in depth and 20 km in horizontal directions:

>>> model.smooth(sigma=[5.0, 20.0, 20.0], unit_deg=False)
to_ani()[source]

Convert to anisotropic strength (epsilon) and azimuth (phi)

to_xarray()[source]

Convert to xarray

Returns:

attarray dataset

Return type:

pytomoatt.attarray.Dataset

write(out_fname=None)[source]

Write to h5 file with TomoATT format.

Parameters:

fname (str, optional) – file name of output model, defaults to ‘model_crust1.0.h5’