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postprocess.py
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postprocess.py
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from utilities.TimeSeries import TimeSeries
import dolfin as df
from common import info, parse_command_line, \
info_cyan, info_split, info_on_red, info_red, info_yellow
import os
import glob
import numpy as np
from mpi4py import MPI
from utilities import get_methods, get_help
"""
BERNAISE: Post-processing tool.
This module is used to read and analyze simulated data.
"""
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
size = comm.Get_size()
def compute_norms(err, vector_norms=["l2", "linf"],
function_norms=["L2", "H1"], show=True,
tablefmt="simple", save=False):
""" Compute norms, output to terminal, etc. """
info_split("Vector norms:", ", ".join(vector_norms))
info_split("Function norms:", ", ".join(function_norms))
headers = ["Fields"] + vector_norms + function_norms
table = []
for field in err.keys():
row = [field]
for norm_type in vector_norms:
row.append(df.norm(err[field].vector(), norm_type=norm_type))
for norm_type in function_norms:
row.append(df.norm(err[field], norm_type=norm_type))
table.append(row)
from tabulate import tabulate
tab_string = tabulate(table, headers, tablefmt=tablefmt, floatfmt="e")
if show:
info("\n" + tab_string + "\n")
if save and rank == 0:
info_split("Saving to file:", save)
with open(save, "w") as outfile:
outfile.write(tab_string)
def path_length(paths, total_length=True):
lengths = []
for x in paths:
dim = x.shape[1]
if dim == 2:
dx = x[:-1, :]-x[1:, :]
length = np.sum(np.sqrt(dx[:, 0]**2 + dx[:, 1]**2))
if dim == 3:
# FIXME: This is actually an area...
# Heron's formula
a = np.linalg.norm(x[0, :] - x[1, :])
b = np.linalg.norm(x[1, :] - x[2, :])
c = np.linalg.norm(x[2, :] - x[0, :])
s = (a + b + c)/2.0
length = np.sqrt(s*(s-a)*(s-b)*(s-c))
lengths.append(length)
if total_length:
return sum(lengths)
else:
return lengths
def index2letter(index):
return ("x", "y", "z")[index]
def get_steps(ts, dt=None, time=None):
""" Get steps sampled at equidistant times. """
if time is not None:
step, _ = get_step_and_info(ts, time)
steps = [step]
elif dt is not None and dt > 0.:
steps = []
time_max = ts.times[-1]
time = ts.times[0]
while time <= time_max:
step, _ = get_step_and_info(ts, time)
steps.append(step)
time += dt
else:
steps = range(len(ts))
return steps
def get_step_and_info(ts, time, step=0):
if time is not None:
step, time = ts.get_nearest_step_and_time(time)
else:
time = ts.get_time(step)
info("Time = {}, timestep = {}.".format(time, step))
return step, time
def call_method(method, methods, scripts_folder, ts, cmd_kwargs):
# Call the specified method
if method[-1] == "?" and method[:-1] in methods:
m = __import__("{}.{}".format(scripts_folder,
method[:-1])).__dict__[method[:-1]]
m.description(ts, **cmd_kwargs)
elif method in methods:
m = __import__("{}.{}".format(scripts_folder, method)).__dict__[method]
m.method(ts, **cmd_kwargs)
else:
info_on_red("The specified analysis method doesn't exist.")
def main():
info_yellow("BERNAISE: Post-processing tool")
cmd_kwargs = parse_command_line()
folder = cmd_kwargs.get("folder", False)
scripts_folder = "analysis_scripts"
methods = get_methods(scripts_folder)
# Get help if it was called for.
if cmd_kwargs.get("help", False):
get_help(methods, scripts_folder, __file__, skip=1)
# Get sought fields
sought_fields = cmd_kwargs.get("fields", False)
if not sought_fields:
sought_fields = None
elif not isinstance(sought_fields, list):
sought_fields = [sought_fields]
if not folder:
info("No folder(=[...]) specified.")
exit()
sought_fields_str = (", ".join(sought_fields)
if sought_fields is not None else "All")
info_split("Sought fields:", sought_fields_str)
ts = TimeSeries(folder, sought_fields=sought_fields)
info_split("Found fields:", ", ".join(ts.fields))
method = cmd_kwargs.get("method", "geometry_in_time")
if len(ts.fields) == 0:
info_on_red("Found no data.")
exit()
call_method(method, methods, scripts_folder, ts, cmd_kwargs)
if __name__ == "__main__":
main()