print(
" " + sys.argv[0] +
" [jobdata mule tag for y axis] [output_filename.pdf] [jobdir1] [jobdir2] ... [jobdirN]"
)
print("")
sys.exit(1)
if len(sys.argv) > 3:
# Load Jobs specified via program parameters
jd = JobsData(job_dirs=sys.argv[3:])
else:
# Load all Jobs
jd = JobsData()
# Consolidate data...
jdc = JobsDataConsolidate(jd)
# ... which belongs to the same time integration method
jdc_groups = jdc.create_groups(['runtime.timestepping_method'])
#
# Filter to exclude data which indicates instabilities
#
def data_filter(x, y, jd):
if y == None:
return True
if 'runtime.max_simulation_time' in jd:
if jd['runtime.max_simulation_time'] <= 24 * 60 * 60:
if y > 100:
from mule.plotting.Plotting import *
from mule.postprocessing.JobsData import *
from mule.postprocessing.JobsDataConsolidate import *
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
groups = ['runtime.timestepping_method', 'runtime.timestepping_order']
tagnames_y = [
'plane_data_diff_prog_u.res_norm_linf',
]
j = JobsData(verbosity=0)
c = JobsDataConsolidate(j)
print("")
print("Groups:")
job_groups = c.create_groups(groups)
for key, g in job_groups.items():
print(" + " + key)
# Filter out errors beyond this value!
def data_filter(x, y, jobdata):
if y == None:
return True
# Filter out NaNs for wallclock time studies
# NaNs require significantly more computation time
for j in range(len(data))]
for j in range(len(data)):
for i in range(len(data[0]) - 1):
data_new[j][i] = data[j][i]
return data_new
num_ensembles = 10
ensemble_data = []
for ensemble_id in range(num_ensembles):
j = JobsData('job_bench_*ensemble' + str(ensemble_id).zfill(2) + '*',
verbosity=100)
c = JobsDataConsolidate(j)
d = JobsData_DataTable(j, 'parallelization.num_ranks', bar_data)
data = d.get_data_float()
if True:
#if False:
"""
Add last column 'nl_timestepping'
"""
data_new = [[None for i in range(len(data[0]) + 1)]
for j in range(len(data))]
for j in range(len(data)):
for i in range(len(data[0])):
data_new[j][i] = data[j][i]