Function(s) related to the time function(s) estimation of the LOS GNSS and InSAR time-series, in the post-processing. This includes the following functions:
translate the model (time func) setup to design matrices
estimate the time function parameters given the time-series data, optimization param and model setup
1 - get_design_matrix4time_func()
Translate the input time function setup into the design matrix.
Inputs
date_list - list of str or datetime objects
polynomial - int, polynomial order, e.g. 1 (linear), 2 (quadratic), 3 (cubic), etc.
periodic - list of float, period(s) in years, e.g. 1.0 (annual), 0.5 (semi-annual), etc.
step - list of str or datetime objects, dates of the step functions
exp - dict with key for onset time in YYYYMMDD and values for char times in days
log - dict with key for onset time in YYYYMMDD and values for char times in days
Outputs
design_matrix - 2D np.ndarray in size of (num_date, num_param)
mintpy/timeseries2velocity.py --ts-std-file now supports linear propagation of covariance (STD) from time-series to time func parameters [update at Jun 22,2021]
Function(s) related to the time function(s) estimation of the LOS GNSS and InSAR time-series, in the post-processing. This includes the following functions:
1 -
get_design_matrix4time_func()
Translate the input time function setup into the design matrix.
Inputs
Outputs
Status
2 -
estimate_time_func()
For a 1D displacement time-series, estimate the time function parameters given the time-series data, time func setup and optimization setup.
Inputs
Outputs
G
andm
with inputdis_ts
]Status
mintpy/timeseries2velocity.py --ts-std-file
now supports linear propagation of covariance (STD) from time-series to time func parameters [update at Jun 22,2021]