def __load_job_raw_data(self, job_dirs=[]):
"""
Parse all output.out files and extract all kind of job output information
Return a dictionary with content from the job directories
{
[name of job directory] :
{
#
# Dictionary with data from job generation
# (read from [jobdir]/jobgeneration.pickle)
#
'jobgeneration':
{
'compile': [...],
'runtime': [...],
'parallelization': [...],
'platforms_platform': [...],
'platform_resources': [...],
},
'output':
{
'SimulationBenchmarkTimings.main': [value],
'SimulationBenchmarkTimings.main_simulationLoop': [value],
[...]
}
}
"""
self.__jobs_data = {}
for job_dir in job_dirs:
if self.verbosity > 5:
self.info("")
self.info("Processing '" + job_dir + "'")
job = JobData(job_dir, verbosity=self.verbosity)
if 'jobgeneration.job_unique_id' not in job.get_flattened_data():
# Be backward compatible
self.__jobs_data[job_dir] = job
else:
job_unique_id = job.get_flattened_data(
)['jobgeneration.job_unique_id']
self.__jobs_data[job_unique_id] = job
def compute_spectrum(
self,
filename_phys,
params,
):
#
# Get meta information about job
#
self.job_metadata_available = False
try:
# Step 1) Determine job directory name
jobdir = os.path.dirname(filename_phys)
print("Loading data from job directory '" + jobdir + "'")
# Step 2) Load job meta information
j = JobData(jobdir=jobdir)
self.job_metadata_available = True
except Exception as e:
print(str(e))
self.job_metadata_available = False
if self.job_metadata_available:
data = j.get_flattened_data()
if 'runtime.plane_domain_size' not in data:
raise Exception(
"Physical domain size must be specified via runtime parameters in MULE"
)
domain_size = data['runtime.plane_domain_size']
if isinstance(domain_size, list):
domain_size = domain_size[0]
#
# The following code is converted from a development of Pedro Peixoto to fit into the MULE framework
#
# some parameter
mmin = 0
# Load file
udata_ = PlaneDataPhysical(filename_phys)
udata = udata_.data
#Calculate spectrum
#-----------------------------------
print("Physical shape")
print(udata.shape)
uspec = np.fft.fft2(udata) / (udata.shape[0] * udata.shape[1])
print("Spectral shape")
print(uspec.shape)
# Calculate amplitude spectrum
data = np.multiply(uspec, np.conjugate(uspec))
data = data.real
n = data.shape[0]
# Since data u,v data is real, the spectrum has a symmetry and all that matters is the 1st quadrant
# we multiply by 2 to account for the symmetry
data = 2 * data[0:int(n / 2) - 1, 0:int(n / 2) - 1]
# Adjust data size
n = data.shape[0]
# m=int(n/2)+1
m = int(2 * n / 3) + 1 #anti-aliasing cut
if mmin == 0:
mmin = m
else:
if m > mmin:
m = mmin
print("Anti-aliased spectrum region:", m)
#Calculate energy per shell
# TODO: convert to linspace
r = np.arange(0, m + 1, 1) # radius
energy = np.zeros(m + 1)
shell_pattern = np.zeros((m + 1, m + 1))
print("Generating energy in shells (Each . is 1/", m, ")")
for i in range(0, m):
for j in range(0, m):
k = np.sqrt(pow(float(i), 2) + pow(float(j), 2))
intk = int(k)
if intk < m:
energy[intk] = energy[intk] + data[i, j]
shell_pattern[i, j] = intk
print(".", end='', flush=True)
#print(i, j, k, intk, data[i,j], energy[intk], data.shape, energy.shape)
print(".")
#Quick check to see if things match
#print("Energy in shells: ", energy[0:10])
#print("Energy in first column of data: ", data[0:10,0])
#print("Shell modes: ", r[0:10])
#print("Pattern:\n", shell_pattern[0:10,0:10])
self.spectrum = energy[:]
self.spectrum_mode = r[:]
if self.job_metadata_available:
# Convert wavenumber to wavelength
self.spectrum_wavelength = np.zeros(m + 1)
self.spectrum_wavelength[1:] = domain_size * 1000 / r[1:]
else:
self.spectrum_wavelength = None
#Figure definitions
fontsize = 18
figsize = (9, 7)
if len(sys.argv) <= 1:
print("")
print("Usage:")
print("")
print(" $ " + sys.argv[0] + " [physdata1] [physdata2] [...]")
print("")
sys.exit(1)
for filename in sys.argv[1:]:
jd = JobData(os.path.split(filename)[0])
jobdata = jd.get_flattened_data()
#Load data
print(filename)
data = np.loadtxt(filename)
if 'prog_h' in filename:
depth = jobdata['runtime.h0']
print("Summing full depth")
print(depth)
data = data + depth
#Define in km
from mule.postprocessing.JobData import *
if len(sys.argv) > 2:
output_filename = sys.argv[1]
else:
print("")
print("Usage:")
print("")
print(" "+sys.argv[0]+" [output_filename.pdf] [jobdir]")
print("")
sys.exit(1)
#List all parameters of job
jd = JobData(sys.argv[2])
jd_flat = jd.get_flattened_data()
#for key in jd_flat:
#print(key, '->', jd_flat[key])
jd_raw = jd.get_job_raw_data()
output=jd_raw['output']
runtime=jd_raw['jobgeneration']
runtime=runtime['runtime']
nm_name=output['simulation_benchmark_normal_modes.case']
nwaves=int(output['simulation_benchmark_normal_modes.nwaves'])
k0=[]
k1=[]
d0=[]