pylag.processing.ensemble module

Tools to assist with processing the results of ensemble simulations

pylag.processing.ensemble.get_probability_density_1D(file_names, dates, depths, depth_bnds, pylag_time_rounding)[source]

Compute the ensemble mean concentration in 1D

Particle concentrations are computed on the dates and at the depth levels given in the arrays dates and depth. Each member of the ensemble is a separate realisation, with particles starting at the sames locations and at the same time in each run. A different method should be used to compute probability densities for ensembles in which particles are released at different times.

To compute particle concentrations a gaussian kernel density estimator is used. Boundaries are treated as being reflecting, thus there is no loss of density.

Parameters

  • file_names (list[str]) – List of sorted PyLag output files. Each output file corresponds to one member of the ensemble.

  • dates (1D NumPy array ([t], datetime)) – Dates on which to compute the ensemble mean concentration.

  • depths (2D Numpy array ([t, z], float)) – Depths at which to compute the ensemble mean concentration. The array is 2D, since it may be desirable to have the depths at which concentrations are calculated vary in time (e.g. if the model has a moving free surface). NB dates.shape[0] must equal depths.shape[0].

  • depths_bnds (2D Numpy array ([t, 2], float)) – These are the lower and upper depth bands which are required by the kernel method.

  • pylag_time_rounding (int) – The number of seconds PyLag outputs should be rounded to.

Returns

conc – The concentration at the specified times and depths

Return type

2D Numpy array (float)

pylag.processing.ensemble.get_probability_density_2D(file_names, time_deltas, x_points, y_points, pylag_time_rounding, group_id=None)[source]

Compute the probability density in 2D

Probability densities are computed at the x and y points provided, and at time time points after the start of the simulation, as listed in the time_deltas array. Each member of the ensemble is a separate realisation. Typically, particles will have been released from the same points in space in each simulation. Particles may or may not have been released at different times.

To compute the probability density a gaussian kernel density estimator is used.

Parameters

  • file_names (list[str]) – List of sorted PyLag output files. Each output file corresponds to one member of the ensemble.

  • time_deltas (1D NumPy array, timedelta) – Time deltas corresponding to times after the start of each ensemble simulation at which the probability density should be computed. For example, if time_deltas = [1 (day), 2 (days)], then the probability density will be computed one and two days after the start of each simulation. NB - it is important that data has been saved at the specified time point. If it hasn’t, an index error will be thrown. For example, if the data has been saved at 15 minute time intervals, and time_delta = 30 minutes, there should be no problems. However, if time_delta = 40 minutes, which cannot be divided exactly by 15, an error will be generated.

  • x_points (1D NumPy array, float) – x points at which to compute the ensemble mean concentration.

  • y_points (1D NumPy array, float) – y points at which to compute the ensemble mean concentration.

  • pylag_time_rounding (int) – The number of seconds PyLag outputs should be rounded to.

  • group_id (int, optional) – Calculate the probability density for a single group.

Returns

dens – The concentration at the specified times and x/y positions.

Return type

2D Numpy array (float)