Source code for PVGeo._helpers.timeseries

__all__ = [
    '_calculate_time_range',
    'update_time_steps',
    'get_requested_time',
    'get_input_time_steps',
    'get_combined_input_time_steps',
]

import numpy as np
import collections


[docs]def _calculate_time_range(nt, dt=1.0): """Discretizes time range accoridng to step size ``dt`` in seconds """ return np.arange(0,nt*dt,dt, dtype=float)
[docs]def update_time_steps(algorithm, nt, dt=1.0, explicit=False): """Handles setting up the timesteps on on the pipeline for a file series reader. Args: algorithm (vtkDataObject): The data object (Proxy) on the pipeline (pass `self` from algorithm subclasses) nt (int or list): Number of timesteps (Pass a list to use length of that list) dt (float): The discrete value in seconds for the time step. explicit (boolean): if true, this will treat the nt argument as the exact timestep values to use Return: numpy.array : Returns the timesteps as an array """ if explicit and isinstance(nt, collections.Iterable): timesteps = nt else: if isinstance(nt, collections.Iterable): nt = len(nt) timesteps = _calculate_time_range(nt, dt=1.0) if len(timesteps) < 1: # NOTE: we may want to raise a warning here on the dev side. # if developing a new algorithm that uses this, you may want to # know exactly when this failse to update #'update_time_steps() is not updating because passed time step values are NULL.' return None executive = algorithm.GetExecutive() oi = executive.GetOutputInformation(0) #oi = outInfo.GetInformationObject(0) oi.Remove(executive.TIME_STEPS()) oi.Remove(executive.TIME_RANGE()) for t in timesteps: oi.Append(executive.TIME_STEPS(), t) oi.Append(executive.TIME_RANGE(), timesteps[0]) oi.Append(executive.TIME_RANGE(), timesteps[-1]) return timesteps
[docs]def get_requested_time(algorithm, outInfo, idx=0): """Handles setting up the timesteps on on the pipeline for a file series reader. Args: algorithm (vtkDataObject) : The data object (Proxy) on the pipeline (pass `self` from algorithm subclasses) outInfo (vtkInformationVector) : The output information for the algorithm idx (int) : the index for the output port Return: int : the index of the requested time Example: >>> # Get requested time index >>> i = _helpers.get_requested_time(self, outInfo) """ executive = algorithm.GetExecutive() timesteps = algorithm.get_time_step_values() outInfo = outInfo.GetInformationObject(idx) if timesteps is None or len(timesteps) == 0: return 0 elif outInfo.Has(executive.UPDATE_TIME_STEP()) and len(timesteps) > 0: utime = outInfo.Get(executive.UPDATE_TIME_STEP()) return np.argmin(np.abs(np.array(timesteps) - utime)) else: # if we cant match the time, give first if not len(timesteps) > 0: raise AssertionError('Number of timesteps must be greater than 0') return 0
[docs]def get_input_time_steps(algorithm, port=0, idx=0): """Get the timestep values for the algorithm's input Args: algorithm (vtkDataObject) : The data object (Proxy) on the pipeline (pass `self` from algorithm subclasses) port (int) : the input port idx (int) : optional : the connection index on the input port Return: list : the time step values of the input (if there arn't any, returns ``None``) """ executive = algorithm.GetExecutive() ii = executive.GetInputInformation(port, idx) return ii.Get(executive.TIME_STEPS())
[docs]def get_combined_input_time_steps(algorithm, idx=0): """This will iterate over all input ports and combine their unique timesteps for an output algorithm to have. Args: algorithm (vtkDataObject) : The data object (Proxy) on the pipeline (pass `self` from algorithm subclasses) Return: np.ndarray : a 1D array of all the unique timestep values (empty array if no time variance) """ executive = algorithm.GetExecutive() tsteps = [] for port in range(executive.GetNumberOfInputPorts()): ii = executive.GetInputInformation(port, idx) ti = ii.Get(executive.TIME_STEPS()) if ti is None: ti = np.array([]) tsteps.append(ti) return np.unique(np.concatenate(tsteps, 0))