def compareTG(data):
'''
Does a comprehensive comparison between tide gauge height data and
modeled data, much like the above function.
Input is a dictionary containing all necessary tide gauge and model data.
Outputs a dictionary of useful statistics.
'''
# load data
mod_elev = data['mod_timeseries']['elev']
obs_elev = data['obs_timeseries']['elev']
obs_datenums = data['obs_time']
mod_datenums = data['mod_time']
mod_harm = data['elev_mod_harmonics']
# convert times and grab values
obs_time, mod_time = [], []
for i, v in enumerate(obs_datenums):
obs_time.append(dn2dt(v))
for j, w in enumerate(mod_datenums):
mod_time.append(dn2dt(w))
# check if they line up in the time domain
if (mod_time[-1] < obs_time[0] or obs_time[-1] < mod_time[0]):
# use ut_reconstr to create a new timeseries
mod_elev_int = ut_reconstr(obs_datenums, mod_harm)[0]
obs_elev_int = obs_elev
step_int = obs_time[1] - obs_time[0]
start_int = obs_time[0]
else:
# interpolate timeseries onto a common timestep
(obs_elev_int, mod_elev_int, step_int, start_int) = \
smooth(mod_elev, mod_time, obs_elev, obs_time)
# get validation statistics
stats = TidalStats(mod_elev_int,
obs_elev_int,
step_int,
start_int,
debug=True,
type='height')
elev_suite = stats.getStats()
elev_suite['r_squared'] = stats.linReg()['r_2']
elev_suite['phase'] = stats.getPhase(debug=False)
return elev_suite
def compareTG(data):
'''
Does a comprehensive comparison between tide gauge height data and
modeled data, much like the above function.
Input is a dictionary containing all necessary tide gauge and model data.
Outputs a dictionary of useful statistics.
'''
# load data
mod_elev = data['mod_timeseries']['elev']
obs_elev = data['obs_timeseries']['elev']
obs_datenums = data['obs_time']
mod_datenums = data['mod_time']
mod_harm = data['elev_mod_harmonics']
print data['name']
# convert times and grab values
obs_time, mod_time = [], []
for i, v in enumerate(obs_datenums):
obs_time.append(dn2dt(v))
for j, w in enumerate(mod_datenums):
mod_time.append(dn2dt(w))
# check if they line up in the time domain
if (mod_time[-1] < obs_time[0] or obs_time[-1] < mod_time[0]):
# use ut_reconstr to create a new timeseries
mod_elev_int = ut_reconstr(obs_datenums, mod_harm)[0]
obs_elev_int = obs_elev
step_int = obs_time[1] - obs_time[0]
start_int = obs_time[0]
else:
# interpolate timeseries onto a common timestep
(obs_elev_int, mod_elev_int, step_int, start_int) = \
smooth(mod_elev, mod_time, obs_elev, obs_time)
# get validation statistics
stats = TidalStats(mod_elev_int, obs_elev_int, step_int, start_int,
debug=True, type='height')
elev_suite = stats.getStats()
elev_suite['r_squared'] = stats.linReg()['r_2']
elev_suite['phase'] = stats.getPhase(debug=False)
return elev_suite
def compareTG(data, plot=False, save_csv=False, debug=False, debug_plot=False):
'''
Does a comprehensive comparison between tide gauge height data and
modeled data, much like the above function.
Input is a dictionary containing all necessary tide gauge and model data.
Outputs a dictionary of useful statistics.
'''
if debug: print "CompareTG..."
# load data
mod_elev = data['mod_timeseries']['elev']
obs_elev = data['obs_timeseries']['elev']
obs_datenums = data['obs_time']
mod_datenums = data['mod_time']
#TR: comment out
#mod_harm = data['elev_mod_harmonics']
# convert times and grab values
obs_time, mod_time = [], []
for i, v in enumerate(obs_datenums):
obs_time.append(dn2dt(v))
for j, w in enumerate(mod_datenums):
mod_time.append(dn2dt(w))
if debug: print "...check if they line up in the time domain..."
if (mod_time[-1] < obs_time[0] or obs_time[-1] < mod_time[0]):
print "---time periods do not match up---"
sys.exit()
else:
if debug: print "...interpolate timeseries onto a common timestep..."
(mod_elev_int, obs_elev_int, step_int, start_int) = \
smooth(mod_elev, mod_time, obs_elev, obs_time,
debug=debug, debug_plot=debug_plot)
if debug: print "...get validation statistics..."
stats = TidalStats(mod_elev_int,
obs_elev_int,
step_int,
start_int,
type='elevation',
debug=debug,
debug_plot=debug_plot)
elev_suite = tidalSuite(mod_elev_int,
obs_elev_int,
step_int,
start_int,
type='elevation',
plot=plot,
save_csv=save_csv,
debug=debug,
debug_plot=debug_plot)
if debug: print "...CompareTG done."
return elev_suite
def tidalSuite(model, observed, step, start, type, plot=False):
'''
Create stats classes for a given tidal variable.
Accepts interpolated model and observed data, the timestep, and start
time. Type is a string representing the type of data. If plot is set
to true, a time plot and regression plot will be produced.
Returns a dictionary containing all the stats.
'''
stats = TidalStats(model, observed, step, start, type=type)
stats_suite = stats.getStats()
stats_suite['r_squared'] = stats.linReg()['r_2']
stats_suite['phase'] = stats.getPhase()
if plot:
stats.plotData()
stats.plotRegression(stats.linReg())
return stats_suite
def tidalSuite(gear, model, observed, step, start,
model_u, observed_u, model_v, observed_v,
model_time, observed_time,
kind='', plot=False, save_csv=False, save_path='./',
debug=False, debug_plot=False):
"""
Create stats classes for a given tidal variable.
Accepts interpolated model and observed data, the timestep, and start
time. kind is a string representing the kind of data. If plot is set
to true, a time plot and regression plot will be produced.
Returns a dictionary containing all the stats.
"""
if debug: print "tidalSuite..."
stats = TidalStats(gear, model, observed, step, start,
model_u = model_u, observed_u = observed_u, model_v = model_v, observed_v = observed_v,
model_time = model_time, observed_time = observed_time,
kind=kind, debug=debug, debug_plot=debug_plot)
stats_suite = stats.getStats()
stats_suite['r_squared'] = stats.linReg()['r_2']
# calling special methods
if kind == 'direction':
rmse, nrmse = stats.statsForDirection(debug=debug)
stats_suite['RMSE'] = rmse
stats_suite['NRMSE'] = nrmse
try: #Fix for Drifter's data
stats_suite['phase'] = stats.getPhase(debug=debug)
except:
stats_suite['phase'] = 0.0
if plot or debug_plot:
plotData(stats)
plotRegression(stats, stats.linReg())
if save_csv:
stats.save_data(path=save_path)
plotData(stats, savepath=save_path, fname=kind+"_"+gear+"_time_series.png")
plotRegression(stats, stats.linReg(), savepath=save_path, fname=kind+"_"+gear+"_linear_regression.png")
if debug: print "...tidalSuite done."
return stats_suite
def tidalSuite(model, observed, step, start, type,
plot=False):
'''
Create stats classes for a given tidal variable.
Accepts interpolated model and observed data, the timestep, and start
time. Type is a string representing the type of data. If plot is set
to true, a time plot and regression plot will be produced.
Returns a dictionary containing all the stats.
'''
stats = TidalStats(model, observed, step, start, type=type)
stats_suite = stats.getStats()
stats_suite['r_squared'] = stats.linReg()['r_2']
stats_suite['phase'] = stats.getPhase()
if plot:
stats.plotData()
stats.plotRegression(stats.linReg())
return stats_suite
import numpy as np
from tidalStats import TidalStats
from datetime import datetime, timedelta
# test of the tidalStats class with known data
n = 100
data_1, data_2 = np.zeros(n), np.zeros(n)
for i in np.arange(n):
data_1[i] = i**2
data_2[i] = i ** 3
start = datetime(1994, 04, 20)
step = timedelta(minutes=10)
stats = TidalStats(data_1, data_2, step, start)
print stats.getStats()
stats.plotRegression(stats.linReg())
''' Test of loading in previously saved pickle data, and running it through interpolation and smoothing functions. ''' # grab the data filename_1 = '/array/home/116822s/tidal_data/stats_test/ADCP_data1.pkl' ADCP_f = open(filename_1, 'rb') filename_2 = '/array/home/116822s/tidal_data/stats_test/FVCOM_data1.pkl' FVC_f = open(filename_2, 'rb') filename_3 = '/array/home/116822s/tidal_data/stats_test/hindcast_1.pkl' hind_f = open(filename_3, 'rb') ADCP = pickle.load(ADCP_f) FVCOM = pickle.load(FVC_f) hind = pickle.load(hind_f) # plot the data #plt.scatter(ADCP[0]['pts'], FVCOM[0]['pts'], c='b') #plt.show() # first test (ADCP_i, FVCOM_i, step, start) = interpolate.interpol(ADCP[0], hind[0], timedelta(minutes=20)) speed_stats = TidalStats(ADCP_i, FVCOM_i, step, start) #lr = speed_stats.linReg() #speed_stats.plotRegression(lr) #speed_stats.plotData(graph='scatter') speed_stats.plotData()
def tidalSuite(gear,
model,
observed,
step,
start,
model_u,
observed_u,
model_v,
observed_v,
model_time,
observed_time,
kind='',
plot=False,
save_csv=False,
save_path='./',
debug=False,
debug_plot=False):
"""
Create stats classes for a given tidal variable.
Accepts interpolated model and observed data, the timestep, and start
time. kind is a string representing the kind of data. If plot is set
to true, a time plot and regression plot will be produced.
Returns a dictionary containing all the stats.
"""
if debug: print "tidalSuite..."
stats = TidalStats(gear,
model,
observed,
step,
start,
model_u=model_u,
observed_u=observed_u,
model_v=model_v,
observed_v=observed_v,
model_time=model_time,
observed_time=observed_time,
kind=kind,
debug=debug,
debug_plot=debug_plot)
stats_suite = stats.getStats()
stats_suite['r_squared'] = stats.linReg()['r_2']
# calling special methods
if kind == 'direction':
rmse, nrmse = stats.statsForDirection(debug=debug)
stats_suite['RMSE'] = rmse
stats_suite['NRMSE'] = nrmse
try: #Fix for Drifter's data
stats_suite['phase'] = stats.getPhase(debug=debug)
except:
stats_suite['phase'] = 0.0
if plot or debug_plot:
plotData(stats)
plotRegression(stats, stats.linReg())
if save_csv:
stats.save_data(path=save_path)
plotData(stats,
savepath=save_path,
fname=kind + "_" + gear + "_time_series.png")
plotRegression(stats,
stats.linReg(),
savepath=save_path,
fname=kind + "_" + gear + "_linear_regression.png")
if debug: print "...tidalSuite done."
return stats_suite
