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2.2

User Guide

  • Installation & Updates
    • Minimal installation
    • Full development installation
    • Updates
  • Tutorials
    • Tutorial 1: The first synthetic station
      • Building a Model collection
      • Creating Timeseries objects
      • Fitting the models
      • Plotting the fit and residuals
    • Tutorial 2: Advanced Models and Fitting
      • Creating more complex synthetic data
      • Spline models for transients
      • Building a Network
      • Fitting an entire network
      • Advanced plotting
      • Repeat with L2 regularization
      • Repeat with L1 regularization
      • Repeat with L0 regularization
      • Comparing specific parameters
    • Tutorial 3: Incorporating Spatial Coherence
      • Preparations
      • Dreaming up a network
      • Fantasizing data
      • Removing the Common Mode Error
      • Fitting the data using reweighted L1 regularization
      • Fitting the data using a spatially-aware L1 reweighting
      • Finding unmodeled jumps
      • Statistics of spatial reweighting
      • Model parameter correlations
      • Transient visualization with worm plots
      • Secular velocity comparisons
    • Tutorial 4: The use and estimation of covariance
      • Making a noisy network
      • Fitting the models with the spatial L0 solver
      • Quality of the fits
      • Correlation of parameters
      • Simple linear regression with restricted spline set
      • Empirical covariance estimation
    • Tutorial 5: Signal Recovery at Low SNR
      • Preparations
      • Defining the variables and hyperparameter space
      • Running test cases
      • Results
    • Tutorial 6: Spatially-variable Strain Field
      • Preparations
      • Average strain and rotation
      • Spatially-variable velocity, strain, and rotation
  • Examples
    • Example 1: Long Valley Caldera Transient Motions
      • Preparations
        • Getting data
        • Building the network
      • Cleaning the timeseries
        • Outlier and CME removal
        • First pass: major steps and noisy periods
        • Second pass: minor, catalog-based steps
      • Model parameter estimation
      • Modeled horizontal transient motion
      • Modeled vertical seasonal motion
      • Comparison of secular velocities
      • Final considerations
      • References
    • Example 2: Long Valley Caldera Comparisons
      • Preparations
      • Homogenous translation, rotation, and strain
      • Rotation about an Euler pole
      • Comparison of different methods
      • Loading other different datasets
      • North American reference frame
      • Estimate Euler poles for datasets
      • Map view comparison of datasets
      • Quantitative comparison of datasets
      • References
  • Frequently Asked Questions
    • 1. How do I know which penalties to use in lasso_regression() and ReweightingFunction?
    • 2. How can I save my Network object to save time?

API Documentation

  • DISSTANS
    • Configuration
      • config.defaults
    • Earthquakes
      • okada_displacement()
      • okada_prior()
      • empirical_prior()
    • Models
      • Model (Parent Class)
        • Model
        • check_model_dict()
      • Model Collection
        • ModelCollection
      • Fit Collection
        • FitCollection
      • Basic Models
        • Arctangent
        • Exponential
        • Hyperbolic Tangent
        • Logarithmic
        • Polynomial
        • Step
        • Sinusoid
      • Spline Models
        • BSpline
        • ISpline
        • BaseSplineSet
        • SplineSet
        • DecayingSplineSet
        • AmpPhModulatedSinusoid
    • Network
      • Network
        • Network.__contains__()
        • Network.__delitem__()
        • Network.__getitem__()
        • Network.__iter__()
        • Network.__len__()
        • Network.__setitem__()
        • Network.__str__()
        • Network.add_default_local_models()
        • Network.add_local_models()
        • Network.add_station()
        • Network.add_unused_local_models()
        • Network.ampphaseplot()
        • Network.analyze_residuals()
        • Network.call_func_no_return()
        • Network.call_func_ts_return()
        • Network.call_netwide_func()
        • Network.copy_timeseries()
        • Network.copy_uncertainties()
        • Network.create_station()
        • Network.decompose()
        • Network.default_local_models
        • Network.default_location_path
        • Network.euler_rot_field()
        • Network.evaluate()
        • Network.export_network_ts()
        • Network.fit()
        • Network.fitevalres()
        • Network.freeze()
        • Network.from_json()
        • Network.get_stations_with()
        • Network.get_trend_change()
        • Network.graphical_cme()
        • Network.gui()
        • Network.hom_velocity_field()
        • Network.import_network_ts()
        • Network.load_maintenance_dict()
        • Network.math()
        • Network.mean_longitude
        • Network.name
        • Network.num_stations
        • Network.plot_availability()
        • Network.remove_models()
        • Network.remove_station()
        • Network.remove_timeseries()
        • Network.spatialfit()
        • Network.station_locations
        • Network.station_names
        • Network.stations
        • Network.to_json()
        • Network.unfreeze()
        • Network.update_default_local_models()
        • Network.wormplot()
    • Processing
      • unwrap_dict_and_ts
        • unwrap_dict_and_ts()
      • Functions
        • clean
        • decompose
        • median
        • midas
      • Classes
        • StepDetector
    • Solvers
      • Solution Object
        • Solution
      • Solver Functions
        • lasso_regression
        • linear_regression
        • ridge_regression
      • Reweighting Functions
        • ReweightingFunction
        • InverseReweighting
        • InverseSquaredReweighting
        • LogarithmicReweighting
    • Station
      • Station
        • Station.__contains__()
        • Station.__delitem__()
        • Station.__getitem__()
        • Station.__iter__()
        • Station.__setitem__()
        • Station.__str__()
        • Station.add_fit()
        • Station.add_local_model()
        • Station.add_local_model_dict()
        • Station.add_local_model_kwargs()
        • Station.add_timeseries()
        • Station.analyze_residuals()
        • Station.fits
        • Station.get_arch()
        • Station.get_trend()
        • Station.get_trend_change()
        • Station.location
        • Station.models
        • Station.name
        • Station.remove_fit()
        • Station.remove_local_models()
        • Station.remove_timeseries()
        • Station.sum_fits()
        • Station.timeseries
        • Station.ts
        • Station.unused_models
    • Timeseries
      • Timeseries (Parent Class)
        • Timeseries
      • Specialized Classes
        • GipsyTimeseries
        • GipsyXTimeseries
        • UNRTimeseries
        • UNRHighRateTimeseries
        • F5File
        • F5Timeseries
    • Tools
      • Functions
        • best_utmzone
        • block_permutation
        • cov2corr
        • create_powerlaw_noise
        • date2decyear
        • download_unr_data
        • estimate_euler_pole
        • eulerpole2rotvec
        • full_cov_mat_to_columns
        • get_cov_dims
        • make_cov_index_map
        • get_cov_indices
        • get_field_vel_strain_rot
        • get_hom_vel_strain_rot
        • parallelize
        • parse_maintenance_table
        • parse_unr_steps
        • R_ecef2enu
        • R_enu2ecef
        • rotvec2eulerpole
        • selectpair
        • strain_rotation_invariants
        • tvec_to_numpycol
        • weighted_median
      • Classes
        • Click
        • RINEXDataHolding
        • Timedelta
DISSTANS
  • Python Module Index

Python Module Index

d
 
d
- disstans
    disstans.config
    disstans.earthquakes
    disstans.models
    disstans.network
    disstans.processing
    disstans.solvers
    disstans.station
    disstans.timeseries
    disstans.tools

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