Post-processing
Let's say you have correctly defined all simulation inputs and successfully performed a simulation run. The last part of your script may look like this:
from hamopy.algorithm import calcul
results = calcul(mesh, clim, init, time)
Now you want to display and analyse some results, which is why you use this program in the first place.
Simulation outcome
By default, the main algorithm of hamopy returns a dictionary containing all simulation results and everything one needs to interpret them. Here is a list of all keys and values stored within this dictionary.
'x': Mesh node coordinates; size:(N,)'t': All time coordinates of the simulation; size:(M,)'T': Temperature; size:(M,N)'PC': Capillary pressure; size:(M,N)'HR': Relative humidity; size:(M,N)'PV': Vapor pressure; size:(M,N)
All values are numpy arrays. This is for instance how to read the temperature of the i^th node at the j^th time of simulation:
results['T'][j,i]
As this data is a bit raw, two methods are available to easily extract data at user-defined times and locations without having to directly manipulate elements of the results dictionary.
evolution()
The evolution() method of the hamopy.postpro module helps extract the temporal evolution of a variable at a specific location.
from hamopy.postpro import evolution
import numpy as np
x_out = 0.05
t_out = np.array([0, 60, 120, 180, 240, 300, 360])
T_out = evolution(results, 'T', x_out, t_out)
This example returns the evolution of the temperature over time, at the point given by x_out, with the temporal discretisation given by t_out. The function may take 4 input arguments:
- the dictionary of results, provided by the simulation
- a string denoting which variable to extract (it must be one of the keys of
results) - the location of the point (preferably a single value)
- the time scale on which to extract the data (numpy array)
The last argument is optional: if not given, evolution() will take all time coordinates in results['t'] (this is not advised if the simulation time step size was adaptative).
distribution()
The distribution() method of the hamopy.postpro module helps extract the spatial distribution of a variable at a specific time.
from hamopy.postpro import distribution
import numpy as np
x_out = np.array([0, 0.02, 0.04, 0.06, 0.08, 0.10])
t_out = 3600
HR_out = distribution(results, 'HR', x_out, t_out)
This example returns the distribution of relative humidity, at the time given by t_out, over the spatial discretisation given by x_out. The function may take 4 input arguments:
- the dictionary of results, provided by the simulation
- a string denoting which variable to extract (it must be one of the keys of
results) - the coordinates on which the distribution spans (numpy array)
- the time of the distribution (preferably a single value)
The third argument is optional: if not given, distribution() will take all mesh node coordinates in results['x'].