def validate_harmonics(self,
filename=[],
save_csv=False,
debug=False,
debug_plot=False):
"""
This method computes and store in a csv file the error in %
for each component of the harmonic analysis (i.e. *_error.csv).
Options:
filename: file name of the .csv file to be saved, string.
save_csv: will save both observed and modeled harmonic
coefficients into *.csv files (i.e. *_harmo_coef.csv)
"""
if not self._multi:
self.Variables = _load_validation(self._observed,
self._simulated,
flow=self._flow,
debug=self._debug)
self._validate_harmonics(filename, save_csv, debug, debug_plot)
else:
for i, meas in enumerate(self._observed):
self.Variables = _load_validation(meas,
self._simulated,
flow=self._flow,
debug=self._debug)
if filename == []:
filename = 'meas' + str(i)
else:
filename = filename + '_meas' + str(i)
self._validate_harmonics(filename, save_csv, debug, debug_plot)
def __init__(self,
observed,
simulated,
flow=[],
debug=False,
debug_plot=False):
self._debug = debug
self._flow = flow
if type(observed) in [tuple, list]:
self._multi = True
else:
self._multi = False
self._debug_plot = debug_plot
if debug: print '-Debug mode on-'
#Metadata
if not self._multi:
self.History = ['Created from ' + observed._origin_file +\
' and ' + simulated._origin_file]
else:
self.History = ['Created from multiple measurement sources' +\
' and ' + simulated._origin_file]
self._observed = observed
self._simulated = simulated
if not self._multi:
self.Variables = _load_validation(self._observed,
self._simulated,
flow=self._flow,
debug=self._debug)
return
def __init__(self, observed, simulated, flow=[], closefallback=False, debug=False, debug_plot=False):
self._debug = debug
self._flow = flow
if type(observed) in [tuple, list]:
self._multi = True
else:
self._multi = False
self._debug_plot = debug_plot
if debug: print '-Debug mode on-'
self._closefallback=closefallback
#Metadata
if not self._multi:
self.History = ['Created from ' + observed._origin_file +\
' and ' + simulated._origin_file]
else:
self.History = ['Created from multiple measurement sources' +\
' and ' + simulated._origin_file]
self._observed = observed
self._simulated = simulated
if not self._multi:
self.Variables = _load_validation(self._observed, self._simulated, flow=self._flow, closefallback=self._closefallback,debug=self._debug)
return
def __init__(self, observed, simulated, debug=False):
self._debug = debug
if debug: print '-Debug mode on-'
if debug: print 'Loading...'
#Metadata
self.History = ['Created from ' + observed._origin_file +\
' and ' + simulated._origin_file]
self.Variables = _load_validation(observed, simulated, debug=self._debug)
def validate_harmonics(self, filename=[], save_csv=False, debug=False, debug_plot=False):
"""
This method computes and store in a csv file the error in %
for each component of the harmonic analysis (i.e. *_error.csv).
Options:
filename: file name of the .csv file to be saved, string.
save_csv: will save both observed and modeled harmonic
coefficients into *.csv files (i.e. *_harmo_coef.csv)
"""
if not self._multi:
self.Variables = _load_validation(self._observed, self._simulated, flow=self._flow, debug=self._debug)
self._validate_harmonics(filename, save_csv, debug, debug_plot)
else:
for i, meas in enumerate(self._observed):
self.Variables = _load_validation(meas, self._simulated, flow=self._flow, debug=self._debug)
if filename == []:
filename = 'meas'+str(i)
else:
filename = filename + '_meas'+str(i)
self._validate_harmonics(filename, save_csv, debug, debug_plot)
def validate_data(self, filename=[], depth=[], plot=False, save_csv=False, debug=False, debug_plot=False):
"""
This method computes series of standard validation benchmarks.
Options:
- filename = file name of the .csv file to be saved, string.
- depth = depth at which the validation will be performed, float.
Only applicable for 3D simulations.
- plot = plot series of validation graphs, boolean.
- save_csv = will save benchmark values into *.csv file
as well as associated plots in specific folderssta
*References*
- NOAA. NOS standards for evaluating operational nowcast and
forecast hydrodynamic model systems, 2003.
- K. Gunn, C. Stock-Williams. On validating numerical hydrodynamic
models of complex tidal flow, International Journal of Marine Energy, 2013
- N. Georgas, A. Blumberg. Establishing Confidence in Marine Forecast
Systems: The design and skill assessment of the New York Harbor Observation
and Prediction System, version 3 (NYHOPS v3), 2009
- Liu, Y., P. MacCready, B. M. Hickey, E. P. Dever, P. M. Kosro, and
N. S. Banas (2009), Evaluation of a coastal ocean circulation model for
the Columbia River plume in summer 2004, J. Geophys. Res., 114
"""
if not self._multi:
self._validate_data(filename, depth, plot, save_csv, debug, debug_plot)
self.Benchmarks = self._Benchmarks
else:
I=0
for meas in self._observed:
try:
self.Variables = _load_validation(meas, self._simulated, flow=self._flow, debug=self._debug)
self._validate_data(filename, depth, plot, save_csv, debug, debug_plot)
if I == 0:
self.Benchmarks = self._Benchmarks
I += 1
else:
self.Benchmarks = pd.concat([self.Benchmarks, self._Benchmarks])
except PyseidonError:
pass
if save_csv:
try:
out_file = '{}_val.csv'.format(filename)
self.Benchmarks.to_csv(out_file)
except AttributeError:
raise PyseidonError("-No matching measurement-")
def validate_data(self,
filename=[],
depth=[],
plot=False,
save_csv=False,
debug=False,
debug_plot=False):
"""
This method computes series of standard validation benchmarks.
Options:
- filename = file name of the .csv file to be saved, string.
- depth = depth at which the validation will be performed, float.
Only applicable for 3D simulations.
- plot = plot series of validation graphs, boolean.
- save_csv = will save benchmark values into *.csv file
as well as associated plots in specific folderssta
*References*
- NOAA. NOS standards for evaluating operational nowcast and
forecast hydrodynamic model systems, 2003.
- K. Gunn, C. Stock-Williams. On validating numerical hydrodynamic
models of complex tidal flow, International Journal of Marine Energy, 2013
- N. Georgas, A. Blumberg. Establishing Confidence in Marine Forecast
Systems: The design and skill assessment of the New York Harbor Observation
and Prediction System, version 3 (NYHOPS v3), 2009
- Liu, Y., P. MacCready, B. M. Hickey, E. P. Dever, P. M. Kosro, and
N. S. Banas (2009), Evaluation of a coastal ocean circulation model for
the Columbia River plume in summer 2004, J. Geophys. Res., 114
"""
if not self._multi:
self._validate_data(filename, depth, plot, save_csv, debug,
debug_plot)
self.Benchmarks = self._Benchmarks
else:
I = 0
for meas in self._observed:
try:
self.Variables = _load_validation(meas,
self._simulated,
flow=self._flow,
debug=self._debug)
self._validate_data(filename, depth, plot, save_csv, debug,
debug_plot)
if I == 0:
self.Benchmarks = self._Benchmarks
I += 1
else:
self.Benchmarks = pd.concat(
[self.Benchmarks, self._Benchmarks])
except PyseidonError:
pass
if save_csv:
try:
out_file = '{}_val.csv'.format(filename)
self.Benchmarks.to_csv(out_file)
except AttributeError:
raise PyseidonError("-No matching measurement-")
def __init__(self, observed, simulated, debug=False, debug_plot=False,
find_adcp=False):
self._debug = debug
self._debug_plot = debug_plot
self.History = []
if debug: print '-Debug mode on-'
if debug: print 'Loading...'
# search predefined directoried for lined-up ADCP files if specified
if find_adcp:
if debug: print 'Finding relevant ADCP file...'
mod_time = simulated.Variables.matlabTime
mod_start, mod_end = mod_time[0], mod_time[-1]
mod_range = mod_end - mod_start
# iterate through ADCP directories
adcp_files = []
for adcp_dir in adcp_dirs:
files = [join(adcp_dir, f) for f in listdir(adcp_dir)
if isfile(join(adcp_dir, f))]
adcp_files.extend(files)
adcp_lineup = np.empty(len(adcp_files))
# check each file, see how much it lines up
for i, adcp in enumerate(adcp_files):
# ignore non-processed/non-ADCP files
if 'raw' in adcp.lower() or 'station' in adcp.lower() \
or '.mat' not in adcp or 'stn' in adcp.lower() \
or 'csv' in adcp.lower():
adcp_lineup[i] = 0
continue
try:
adcp = sio.loadmat(adcp)
times = adcp['time'][0][0][0][0]
if times.size == 1:
times = adcp['time'][0][0][0].flatten()
except NotImplementedError:
adcp = h5py.File(adcp, 'r')
times = np.rot90(adcp['time']['mtime'][:])[0]
obs_start, obs_end = times[0], times[-1]
# find lineup amount
if obs_start < mod_end or obs_end > mod_start:
val_start = max(obs_start, mod_start)
val_end = min(obs_end, mod_end)
lineup = val_end - val_start
adcp_lineup[i] = lineup
else:
adcp_lineup[i] = 0
# find maximally lined up adcp file, add metadata
max_ind = np.argmax(adcp_lineup)
max_adcp = adcp_files[max_ind]
# exit if none lined up
if adcp_lineup[max_ind] <= 0:
print 'No ADCPs line up with this simulated data!'
sys.exit(1)
if debug: print 'Detected ADCP: ' + max_adcp
self.History.append('ADCP matches %5.2f percent of the model' %
((adcp_lineup[max_ind] / mod_range) * 100.))
observed = ADCP(max_adcp)
# Metadata
self.History.append('Created from ' + observed._origin_file +
' and ' + simulated._origin_file)
self.Variables = _load_validation(observed, simulated,
debug=self._debug)