def compareUV(data, threeDim, depth=5, plot=False):
'''
Does a comprehensive validation process between modeled and observed
data on the following:
Current speed
Current direction
Harmonic constituents (for height and speed)
Outputs a list of important statistics for each variable, calculated
using the TidalStats class
'''
# take data from input dictionary
mod_time = data['mod_time']
obs_time = data['obs_time']
mod_el = data['mod_timeseries']['elev']
obs_el = data['obs_timeseries']['elev']
v_mod_harm = data['vel_mod_harmonics']
v_obs_harm = data['vel_obs_harmonics']
el_mod_harm = data['elev_mod_harmonics']
el_obs_harm = data['elev_mod_harmonics']
#Check if 3D simulation
if threeDim:
obs_u_all = data['obs_timeseries']['u']
obs_v_all = data['obs_timeseries']['v']
mod_u_all = data['mod_timeseries']['u']
mod_v_all = data['mod_timeseries']['v']
bins = data['obs_timeseries']['bins']
siglay = data['mod_timeseries']['siglay']
# use depth interpolation to get a single timeseries
mod_depth = mod_el + np.mean(obs_el)
(mod_u, obs_u) = depthFromSurf(mod_u_all,
mod_depth,
siglay,
obs_u_all,
obs_el,
bins,
depth=depth)
(mod_v, obs_v) = depthFromSurf(mod_v_all,
mod_depth,
siglay,
obs_v_all,
obs_el,
bins,
depth=depth)
else:
obs_u = data['obs_timeseries']['ua']
obs_v = data['obs_timeseries']['va']
mod_u = data['mod_timeseries']['ua']
mod_v = data['mod_timeseries']['va']
# convert times to datetime
mod_dt, obs_dt = [], []
for i in mod_time:
mod_dt.append(dn2dt(i))
for j in obs_time:
obs_dt.append(dn2dt(j))
# put data into a useful format
mod_spd = np.sqrt(mod_u**2 + mod_v**2)
obs_spd = np.sqrt(obs_u**2 + obs_v**2)
mod_dir = np.arctan2(mod_v, mod_u) * 180 / np.pi
obs_dir = np.arctan2(obs_v, obs_u) * 180 / np.pi
obs_el = obs_el - np.mean(obs_el)
# check if the modeled data lines up with the observed data
if (mod_time[-1] < obs_time[0] or obs_time[-1] < mod_time[0]):
pred_uv = ut_reconstr(obs_time, v_mod_harm)
pred_uv = np.asarray(pred_uv)
pred_h = ut_reconstr(obs_time, el_mod_harm)
pred_h = np.asarray(pred_h)
# redo speed and direction and set interpolated variables
mod_sp_int = np.sqrt(pred_uv[0]**2 + pred_uv[1]**2)
mod_ve_int = mod_sp_int * np.sign(pred_uv[1])
mod_dr_int = np.arctan2(pred_uv[1], pred_uv[0]) * 180 / np.pi
mod_el_int = pred_h[0]
mod_u_int = pred_uv[0]
mod_v_int = pred_uv[1]
obs_sp_int = obs_spd
obs_ve_int = obs_spd * np.sign(obs_v)
obs_dr_int = obs_dir
obs_el_int = obs_el
obs_u_int = obs_u
obs_v_int = obs_v
step_int = obs_dt[1] - obs_dt[0]
start_int = obs_dt[0]
else:
# interpolate the data onto a common time step for each data type
# elevation
(mod_el_int, obs_el_int, step_int, start_int) = \
smooth(mod_el, mod_dt, obs_el, obs_dt)
# speed
(mod_sp_int, obs_sp_int, step_int, start_int) = \
smooth(mod_spd, mod_dt, obs_spd, obs_dt)
# direction
(mod_dr_int, obs_dr_int, step_int, start_int) = \
smooth(mod_dir, mod_dt, obs_dir, obs_dt)
# u velocity
(mod_u_int, obs_u_int, step_int, start_int) = \
smooth(mod_u, mod_dt, obs_u, obs_dt)
# v velocity
(mod_v_int, obs_v_int, step_int, start_int) = \
smooth(mod_v, mod_dt, obs_v, obs_dt)
# velocity i.e. signed speed
(mod_ve_int, obs_ve_int, step_int, start_int) = \
smooth(mod_spd * np.sign(mod_v), mod_dt,
obs_spd * np.sign(obs_v), obs_dt)
'''
# separate into ebb and flow
mod_dir_n = get_DirFromN(mod_u_int, mod_v_int)
obs_dir_n = get_DirFromN(obs_u_int, mod_v_int)
mod_signed_s, mod_PA = sign_speed(mod_u_int, mod_v_int, mod_sp_int,
mod_dr_int, 0)
obs_signed_s, obs_PA = sign_speed(obs_u_int, obs_v_int, obs_sp_int,
obs_dr_int, 0)
print mod_signed_s[:20], mod_PA[:20]
print obs_signed_s[:20], obs_PA[:20]
'''
# remove directions where velocities are small
MIN_VEL = 0.5
for i in np.arange(obs_sp_int.size):
if (obs_sp_int[i] < MIN_VEL):
obs_dr_int[i] = np.nan
if (mod_sp_int[i] < MIN_VEL):
mod_dr_int[i] = np.nan
# get stats for each tidal variable
elev_suite = tidalSuite(mod_el_int,
obs_el_int,
step_int,
start_int,
type='elevation',
plot=plot)
speed_suite = tidalSuite(mod_sp_int,
obs_sp_int,
step_int,
start_int,
type='speed',
plot=plot)
dir_suite = tidalSuite(mod_dr_int,
obs_dr_int,
step_int,
start_int,
type='direction',
plot=plot)
u_suite = tidalSuite(mod_u_int,
obs_u_int,
step_int,
start_int,
type='u velocity',
plot=plot)
v_suite = tidalSuite(mod_v_int,
obs_v_int,
step_int,
start_int,
type='v velocity',
plot=plot)
vel_suite = tidalSuite(mod_ve_int,
obs_ve_int,
step_int,
start_int,
type='velocity',
plot=plot)
#ebb_suite = tidalSuite(mod_ebb, obs_ebb, step_int, start_int,
# type='ebb', plot=True)
#flo_suite = tidalSuite(mod_flo, obs_flo, step_int, start_int,
# type='flow', plot=True)
# output statistics in useful format
return (elev_suite, speed_suite, dir_suite, u_suite, v_suite, vel_suite)
def compareUV(data, threeDim, depth=5, plot=False, save_csv=False,
debug=False, debug_plot=False):
'''
Does a comprehensive validation process between modeled and observed
data on the following:
Current speed
Current direction
Harmonic constituents (for height and speed)
Outputs a list of important statistics for each variable, calculated
using the TidalStats class
'''
if debug: print "CompareUV..."
# take data from input dictionary
mod_time = data['mod_time']
obs_time = data['obs_time']
mod_el = data['mod_timeseries']['elev']
obs_el = data['obs_timeseries']['elev']
# Check if 3D simulation
if threeDim:
obs_u_all = data['obs_timeseries']['u']
obs_v_all = data['obs_timeseries']['v']
mod_u_all = data['mod_timeseries']['u']
mod_v_all = data['mod_timeseries']['v']
bins = data['obs_timeseries']['bins']
siglay = data['mod_timeseries']['siglay']
# use depth interpolation to get a single timeseries
mod_depth = mod_el + np.mean(obs_el[~np.isnan(obs_el)])
(mod_u, obs_u) = depthFromSurf(mod_u_all, mod_depth, siglay,
obs_u_all, obs_el, bins, depth=depth,
debug=debug, debug_plot=debug_plot)
(mod_v, obs_v) = depthFromSurf(mod_v_all, mod_depth, siglay,
obs_v_all, obs_el, bins, depth=depth,
debug=debug, debug_plot=debug_plot)
else:
obs_u = data['obs_timeseries']['ua']
obs_v = data['obs_timeseries']['va']
mod_u = data['mod_timeseries']['ua']
mod_v = data['mod_timeseries']['va']
if debug: print "...convert times to datetime..."
mod_dt, obs_dt = [], []
for i in mod_time:
mod_dt.append(dn2dt(i))
for j in obs_time:
obs_dt.append(dn2dt(j))
if debug: print "...put data into a useful format..."
mod_spd = np.sqrt(mod_u**2.0 + mod_v**2.0)
obs_spd = np.sqrt(obs_u**2.0 + obs_v**2.0)
mod_dir = np.arctan2(mod_v, mod_u) * 180.0 / np.pi
obs_dir = np.arctan2(obs_v, obs_u) * 180.0 / np.pi
obs_el = obs_el - np.mean(obs_el[~np.isnan(obs_el)])
mod_pow = 0.5 * rho**3 * mod_spd**3
obs_pow = 0.5 * rho**3 * obs_spd**3
if debug: print "...check if the modeled data lines up with the observed data..."
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 the data onto a common time step for each data type..."
# elevation
(mod_el_int, obs_el_int, step_el_int, start_el_int) = \
smooth(mod_el, mod_dt, obs_el, obs_dt,
debug=debug, debug_plot=debug_plot)
# speed
(mod_sp_int, obs_sp_int, step_sp_int, start_sp_int) = \
smooth(mod_spd, mod_dt, obs_spd, obs_dt,
debug=debug, debug_plot=debug_plot)
# direction
(mod_dr_int, obs_dr_int, step_dr_int, start_dr_int) = \
smooth(mod_dir, mod_dt, obs_dir, obs_dt,
debug=debug, debug_plot=debug_plot)
# u velocity
(mod_u_int, obs_u_int, step_u_int, start_u_int) = \
smooth(mod_u, mod_dt, obs_u, obs_dt,
debug=debug, debug_plot=debug_plot)
# v velocity
(mod_v_int, obs_v_int, step_v_int, start_v_int) = \
smooth(mod_v, mod_dt, obs_v, obs_dt,
debug=debug, debug_plot=debug_plot)
# velocity i.e. signed speed
(mod_ve_int, obs_ve_int, step_ve_int, start_ve_int) = \
smooth(mod_spd * np.sign(mod_v), mod_dt,
obs_spd * np.sign(obs_v), obs_dt,
debug=debug, debug_plot=debug_plot)
# power (or power density? not sure)
(mod_pw_int, obs_pw_int, step_pw_int, start_pw_int) = \
smooth(mod_pow, mod_dt, obs_pow, obs_dt,
debug=debug, debug_plot=debug_plot)
if debug: print "...remove directions where velocities are small..."
MIN_VEL = 0.1
for i in np.arange(obs_sp_int.size):
if (obs_sp_int[i] < MIN_VEL):
obs_dr_int[i] = np.nan
if (mod_sp_int[i] < MIN_VEL):
mod_dr_int[i] = np.nan
if debug: print "...get stats for each tidal variable..."
elev_suite = tidalSuite(mod_el_int, obs_el_int, step_el_int, start_el_int,
type='elevation', plot=plot, save_csv=save_csv,
debug=debug, debug_plot=debug_plot)
speed_suite = tidalSuite(mod_sp_int, obs_sp_int, step_sp_int, start_sp_int,
type='speed', plot=plot, save_csv=save_csv,
debug=debug, debug_plot=debug_plot)
dir_suite = tidalSuite(mod_dr_int, obs_dr_int, step_dr_int, start_dr_int,
type='direction', plot=plot, save_csv=save_csv,
debug=debug, debug_plot=debug_plot)
u_suite = tidalSuite(mod_u_int, obs_u_int, step_u_int, start_u_int,
type='u velocity', plot=plot, save_csv=save_csv,
debug=debug, debug_plot=debug_plot)
v_suite = tidalSuite(mod_v_int, obs_v_int, step_v_int, start_v_int,
type='v velocity', plot=plot, save_csv=save_csv,
debug=debug, debug_plot=debug_plot)
vel_suite = tidalSuite(mod_ve_int, obs_ve_int, step_ve_int, start_ve_int,
type='velocity', plot=plot, save_csv=save_csv,
debug=debug, debug_plot=debug_plot)
pow_suite = tidalSuite(mod_pw_int, obs_pw_int, step_pw_int, start_pw_int,
type='power', plot=plot, save_csv=save_csv,
debug=debug, debug_plot=debug_plot)
# output statistics in useful format
if debug: print "...CompareUV done."
return (elev_suite, speed_suite, dir_suite, u_suite, v_suite, vel_suite, pow_suite)
def compareUV(data, threeDim, depth=5, plot=False, save_csv=False,
debug=False, debug_plot=False):
"""
Does a comprehensive validation process between modeled and observed.
Outputs a list of important statistics for each variable, calculated
using the TidalStats class
Inputs:
- data = dictionary containing all necessary observed and model data
- threeDim = boolean flag, 3D or not
Outputs:
- elev_suite = dictionary of useful statistics for sea elevation
- speed_suite = dictionary of useful statistics for flow speed
- dir_suite = dictionary of useful statistics for flow direction
- u_suite = dictionary of useful statistics for u velocity component
- v_suite = dictionary of useful statistics for v velocity component
- vel_suite = dictionary of useful statistics for signed flow velocity
- csp_suite = dictionary of useful statistics for cubic flow speed
Options:
- depth = interpolation depth from surface, float
- plot = boolean flag for plotting results
- save_csv = boolean flag for saving statistical benchmarks in csv file
"""
if debug: print "CompareUV..."
# take data from input dictionary
mod_time = data['mod_time']
if not data['type'] == 'Drifter':
obs_time = data['obs_time']
mod_el = data['mod_timeseries']['elev']
obs_el = data['obs_timeseries']['elev']
else:
obs_time = data['mod_time']
# Save path & create folder
name = data['name']
save_path = name.split('/')[-1].split('.')[0]+'/'
while exists(save_path):
save_path = save_path[:-1] + '_bis/'
mkdir(save_path)
# Check if 3D simulation
if threeDim:
obs_u_all = data['obs_timeseries']['u']
obs_v_all = data['obs_timeseries']['v']
mod_u_all = data['mod_timeseries']['u']
mod_v_all = data['mod_timeseries']['v']
bins = data['obs_timeseries']['bins']
siglay = data['mod_timeseries']['siglay']
# use depth interpolation to get a single timeseries
mod_depth = mod_el + np.mean(obs_el[~np.isnan(obs_el)])
(mod_u, obs_u) = depthFromSurf(mod_u_all, mod_depth, siglay,
obs_u_all, obs_el, bins, depth=depth,
debug=debug, debug_plot=debug_plot)
(mod_v, obs_v) = depthFromSurf(mod_v_all, mod_depth, siglay,
obs_v_all, obs_el, bins, depth=depth,
debug=debug, debug_plot=debug_plot)
else:
if not data['type'] == 'Drifter':
obs_u = data['obs_timeseries']['ua']
obs_v = data['obs_timeseries']['va']
mod_u = data['mod_timeseries']['ua']
mod_v = data['mod_timeseries']['va']
else:
obs_u = data['obs_timeseries']['u']
obs_v = data['obs_timeseries']['v']
mod_u = data['mod_timeseries']['u']
mod_v = data['mod_timeseries']['v']
if debug: print "...convert times to datetime..."
mod_dt, obs_dt = [], []
for i in mod_time:
mod_dt.append(dn2dt(i))
for j in obs_time:
obs_dt.append(dn2dt(j))
if debug: print "...put data into a useful format..."
mod_spd = np.sqrt(mod_u**2.0 + mod_v**2.0)
obs_spd = np.sqrt(obs_u**2.0 + obs_v**2.0)
mod_dir = np.arctan2(mod_v, mod_u) * 180.0 / np.pi
obs_dir = np.arctan2(obs_v, obs_u) * 180.0 / np.pi
if not data['type'] == 'Drifter':
obs_el = obs_el - np.mean(obs_el[~np.isnan(obs_el)])
# Chose the component with the biggest variance as sign reference
if np.var(mod_v) > np.var(mod_u):
mod_signed = np.sign(mod_v)
obs_signed = np.sign(obs_v)
else:
mod_signed = np.sign(mod_u)
obs_signed = np.sign(obs_u)
if debug: print "...check if the modeled data lines up with the observed data..."
if (mod_time[-1] < obs_time[0] or obs_time[-1] < mod_time[0]):
raise PyseidonError("---time periods do not match up---")
else:
if debug: print "...interpolate the data onto a common time step for each data type..."
if not data['type'] == 'Drifter':
# elevation
(mod_el_int, obs_el_int, step_el_int, start_el_int) = smooth(mod_el, mod_dt, obs_el, obs_dt,
debug=debug, debug_plot=debug_plot)
# speed
(mod_sp_int, obs_sp_int, step_sp_int, start_sp_int) = smooth(mod_spd, mod_dt, obs_spd, obs_dt,
debug=debug, debug_plot=debug_plot)
# direction
(mod_dr_int, obs_dr_int, step_dr_int, start_dr_int) = smooth(mod_dir, mod_dt, obs_dir, obs_dt,
debug=debug, debug_plot=debug_plot)
# u velocity
(mod_u_int, obs_u_int, step_u_int, start_u_int) = smooth(mod_u, mod_dt, obs_u, obs_dt,
debug=debug, debug_plot=debug_plot)
# v velocity
(mod_v_int, obs_v_int, step_v_int, start_v_int) = smooth(mod_v, mod_dt, obs_v, obs_dt,
debug=debug, debug_plot=debug_plot)
# velocity i.e. signed speed
(mod_ve_int, obs_ve_int, step_ve_int, start_ve_int) = smooth(mod_spd * mod_signed, mod_dt,
obs_spd * obs_signed, obs_dt,
debug=debug, debug_plot=debug_plot)
# cubic signed speed
#mod_cspd = mod_spd**3.0
#obs_cspd = obs_spd**3.0
mod_cspd = mod_signed * mod_spd**3.0
obs_cspd = obs_signed * obs_spd**3.0
(mod_cspd_int, obs_cspd_int, step_cspd_int, start_cspd_int) = smooth(mod_cspd, mod_dt, obs_cspd, obs_dt,
debug=debug, debug_plot=debug_plot)
else:
# Time steps
step = mod_time[1] - mod_time[0]
start = mod_time[0]
# Already interpolated, so no need to use smooth...
# speed
(mod_sp_int, obs_sp_int, step_sp_int, start_sp_int) = (mod_spd, obs_spd, step, start)
# direction
(mod_dr_int, obs_dr_int, step_dr_int, start_dr_int) = (mod_dir, obs_dir, step, start)
# u velocity
(mod_u_int, obs_u_int, step_u_int, start_u_int) = (mod_u, obs_u, step, start)
# v velocity
(mod_v_int, obs_v_int, step_v_int, start_v_int) = (mod_v, obs_v, step, start)
# velocity i.e. signed speed
(mod_ve_int, obs_ve_int, step_ve_int, start_ve_int) = (mod_spd, obs_spd, step, start)
# cubic signed speed
#mod_cspd = mod_spd**3.0
#obs_cspd = obs_spd**3.0
mod_cspd = mod_signed * mod_spd**3.0
obs_cspd = obs_signed * obs_spd**3.0
(mod_cspd_int, obs_cspd_int, step_cspd_int, start_cspd_int) = (mod_cspd, obs_cspd, step, start)
if debug: print "...remove directions where velocities are small..."
MIN_VEL = 0.1
indexMin = np.where(obs_sp_int < MIN_VEL)
obs_dr_int[indexMin] = np.nan
obs_u_int[indexMin] = np.nan
obs_v_int[indexMin] = np.nan
obs_ve_int[indexMin] = np.nan
obs_cspd_int[indexMin] = np.nan
indexMin = np.where(mod_sp_int < MIN_VEL)
mod_dr_int[indexMin] = np.nan
mod_u_int[indexMin] = np.nan
mod_v_int[indexMin] = np.nan
mod_ve_int[indexMin] = np.nan
mod_cspd_int[indexMin] = np.nan
if debug: print "...get stats for each tidal variable..."
gear = data['type'] # Type of measurement gear (drifter, adcp,...)
if not gear == 'Drifter':
elev_suite = tidalSuite(gear, mod_el_int, obs_el_int, step_el_int, start_el_int,
[], [], [], [], [], [],
kind='elevation', plot=plot,
save_csv=save_csv, save_path=save_path,
debug=debug, debug_plot=debug_plot)
else:
elev_suite = []
speed_suite = tidalSuite(gear, mod_sp_int, obs_sp_int, step_sp_int, start_sp_int,
[], [], [], [], [], [],
kind='speed', plot=plot,
save_csv=save_csv, save_path=save_path,
debug=debug, debug_plot=debug_plot)
dir_suite = tidalSuite(gear, mod_dr_int, obs_dr_int, step_dr_int, start_dr_int,
mod_u, obs_u, mod_v, obs_v,
mod_dt, obs_dt,
kind='direction', plot=plot,
save_csv=save_csv, save_path=save_path,
debug=debug, debug_plot=debug_plot)
u_suite = tidalSuite(gear, mod_u_int, obs_u_int, step_u_int, start_u_int,
[], [], [], [], [], [],
kind='u velocity', plot=plot, save_csv=save_csv, save_path=save_path,
debug=debug, debug_plot=debug_plot)
v_suite = tidalSuite(gear, mod_v_int, obs_v_int, step_v_int, start_v_int,
[], [], [], [], [], [],
kind='v velocity', plot=plot, save_csv=save_csv, save_path=save_path,
debug=debug, debug_plot=debug_plot)
# TR: requires special treatments from here on
vel_suite = tidalSuite(gear, mod_ve_int, obs_ve_int, step_ve_int, start_ve_int,
mod_u, obs_u, mod_v, obs_v,
mod_dt, obs_dt,
kind='velocity', plot=plot, save_csv=save_csv, save_path=save_path,
debug=debug, debug_plot=debug_plot)
csp_suite = tidalSuite(gear, mod_cspd_int, obs_cspd_int, step_cspd_int, start_cspd_int,
mod_u, obs_u, mod_v, obs_v,
mod_dt, obs_dt,
kind='cubic speed', plot=plot, save_csv=save_csv, save_path=save_path,
debug=debug, debug_plot=debug_plot)
# output statistics in useful format
if debug: print "...CompareUV done."
return (elev_suite, speed_suite, dir_suite, u_suite, v_suite, vel_suite, csp_suite)
def compareUV(data,
threeDim,
depth=5,
plot=False,
save_csv=False,
debug=False,
debug_plot=False):
"""
Does a comprehensive validation process between modeled and observed.
Outputs a list of important statistics for each variable, calculated
using the TidalStats class
Inputs:
- data = dictionary containing all necessary observed and model data
- threeDim = boolean flag, 3D or not
Outputs:
- elev_suite = dictionary of useful statistics for sea elevation
- speed_suite = dictionary of useful statistics for flow speed
- dir_suite = dictionary of useful statistics for flow direction
- u_suite = dictionary of useful statistics for u velocity component
- v_suite = dictionary of useful statistics for v velocity component
- vel_suite = dictionary of useful statistics for signed flow velocity
- csp_suite = dictionary of useful statistics for cubic flow speed
Options:
- depth = interpolation depth from surface, float
- plot = boolean flag for plotting results
- save_csv = boolean flag for saving statistical benchmarks in csv file
"""
if debug: print "CompareUV..."
# take data from input dictionary
mod_time = data['mod_time']
if not data['type'] == 'Drifter':
obs_time = data['obs_time']
mod_el = data['mod_timeseries']['elev']
obs_el = data['obs_timeseries']['elev']
else:
obs_time = data['mod_time']
# Save path & create folder
name = data['name']
save_path = name.split('/')[-1].split('.')[0] + '/'
while exists(save_path):
save_path = save_path[:-1] + '_bis/'
mkdir(save_path)
# Check if 3D simulation
if threeDim:
obs_u_all = data['obs_timeseries']['u']
obs_v_all = data['obs_timeseries']['v']
mod_u_all = data['mod_timeseries']['u']
mod_v_all = data['mod_timeseries']['v']
bins = data['obs_timeseries']['bins']
siglay = data['mod_timeseries']['siglay']
# use depth interpolation to get a single timeseries
mod_depth = mod_el + np.mean(obs_el[~np.isnan(obs_el)])
(mod_u, obs_u) = depthFromSurf(mod_u_all,
mod_depth,
siglay,
obs_u_all,
obs_el,
bins,
depth=depth,
debug=debug,
debug_plot=debug_plot)
(mod_v, obs_v) = depthFromSurf(mod_v_all,
mod_depth,
siglay,
obs_v_all,
obs_el,
bins,
depth=depth,
debug=debug,
debug_plot=debug_plot)
else:
if not data['type'] == 'Drifter':
obs_u = data['obs_timeseries']['ua']
obs_v = data['obs_timeseries']['va']
mod_u = data['mod_timeseries']['ua']
mod_v = data['mod_timeseries']['va']
else:
obs_u = data['obs_timeseries']['u']
obs_v = data['obs_timeseries']['v']
mod_u = data['mod_timeseries']['u']
mod_v = data['mod_timeseries']['v']
if debug: print "...convert times to datetime..."
mod_dt, obs_dt = [], []
for i in mod_time:
mod_dt.append(dn2dt(i))
for j in obs_time:
obs_dt.append(dn2dt(j))
if debug: print "...put data into a useful format..."
mod_spd = np.sqrt(mod_u**2.0 + mod_v**2.0)
obs_spd = np.sqrt(obs_u**2.0 + obs_v**2.0)
mod_dir = np.arctan2(mod_v, mod_u) * 180.0 / np.pi
obs_dir = np.arctan2(obs_v, obs_u) * 180.0 / np.pi
if not data['type'] == 'Drifter':
obs_el = obs_el - np.mean(obs_el[~np.isnan(obs_el)])
# Chose the component with the biggest variance as sign reference
if np.var(mod_v) > np.var(mod_u):
mod_signed = np.sign(mod_v)
obs_signed = np.sign(obs_v)
else:
mod_signed = np.sign(mod_u)
obs_signed = np.sign(obs_u)
if debug:
print "...check if the modeled data lines up with the observed data..."
if (mod_time[-1] < obs_time[0] or obs_time[-1] < mod_time[0]):
raise PyseidonError("---time periods do not match up---")
else:
if debug:
print "...interpolate the data onto a common time step for each data type..."
if not data['type'] == 'Drifter':
# elevation
(mod_el_int, obs_el_int, step_el_int,
start_el_int) = smooth(mod_el,
mod_dt,
obs_el,
obs_dt,
debug=debug,
debug_plot=debug_plot)
# speed
(mod_sp_int, obs_sp_int, step_sp_int,
start_sp_int) = smooth(mod_spd,
mod_dt,
obs_spd,
obs_dt,
debug=debug,
debug_plot=debug_plot)
# direction
(mod_dr_int, obs_dr_int, step_dr_int,
start_dr_int) = smooth(mod_dir,
mod_dt,
obs_dir,
obs_dt,
debug=debug,
debug_plot=debug_plot)
# u velocity
(mod_u_int, obs_u_int, step_u_int,
start_u_int) = smooth(mod_u,
mod_dt,
obs_u,
obs_dt,
debug=debug,
debug_plot=debug_plot)
# v velocity
(mod_v_int, obs_v_int, step_v_int,
start_v_int) = smooth(mod_v,
mod_dt,
obs_v,
obs_dt,
debug=debug,
debug_plot=debug_plot)
# velocity i.e. signed speed
(mod_ve_int, obs_ve_int, step_ve_int,
start_ve_int) = smooth(mod_spd * mod_signed,
mod_dt,
obs_spd * obs_signed,
obs_dt,
debug=debug,
debug_plot=debug_plot)
# cubic signed speed
#mod_cspd = mod_spd**3.0
#obs_cspd = obs_spd**3.0
mod_cspd = mod_signed * mod_spd**3.0
obs_cspd = obs_signed * obs_spd**3.0
(mod_cspd_int, obs_cspd_int, step_cspd_int,
start_cspd_int) = smooth(mod_cspd,
mod_dt,
obs_cspd,
obs_dt,
debug=debug,
debug_plot=debug_plot)
else:
# Time steps
step = mod_time[1] - mod_time[0]
start = mod_time[0]
# Already interpolated, so no need to use smooth...
# speed
(mod_sp_int, obs_sp_int, step_sp_int,
start_sp_int) = (mod_spd, obs_spd, step, start)
# direction
(mod_dr_int, obs_dr_int, step_dr_int,
start_dr_int) = (mod_dir, obs_dir, step, start)
# u velocity
(mod_u_int, obs_u_int, step_u_int, start_u_int) = (mod_u, obs_u,
step, start)
# v velocity
(mod_v_int, obs_v_int, step_v_int, start_v_int) = (mod_v, obs_v,
step, start)
# velocity i.e. signed speed
(mod_ve_int, obs_ve_int, step_ve_int,
start_ve_int) = (mod_spd, obs_spd, step, start)
# cubic signed speed
#mod_cspd = mod_spd**3.0
#obs_cspd = obs_spd**3.0
mod_cspd = mod_signed * mod_spd**3.0
obs_cspd = obs_signed * obs_spd**3.0
(mod_cspd_int, obs_cspd_int, step_cspd_int,
start_cspd_int) = (mod_cspd, obs_cspd, step, start)
if debug: print "...remove directions where velocities are small..."
MIN_VEL = 0.1
indexMin = np.where(obs_sp_int < MIN_VEL)
obs_dr_int[indexMin] = np.nan
obs_u_int[indexMin] = np.nan
obs_v_int[indexMin] = np.nan
obs_ve_int[indexMin] = np.nan
obs_cspd_int[indexMin] = np.nan
indexMin = np.where(mod_sp_int < MIN_VEL)
mod_dr_int[indexMin] = np.nan
mod_u_int[indexMin] = np.nan
mod_v_int[indexMin] = np.nan
mod_ve_int[indexMin] = np.nan
mod_cspd_int[indexMin] = np.nan
if debug: print "...get stats for each tidal variable..."
gear = data['type'] # Type of measurement gear (drifter, adcp,...)
if not gear == 'Drifter':
elev_suite = tidalSuite(gear,
mod_el_int,
obs_el_int,
step_el_int,
start_el_int, [], [], [], [], [], [],
kind='elevation',
plot=plot,
save_csv=save_csv,
save_path=save_path,
debug=debug,
debug_plot=debug_plot)
else:
elev_suite = []
speed_suite = tidalSuite(gear,
mod_sp_int,
obs_sp_int,
step_sp_int,
start_sp_int, [], [], [], [], [], [],
kind='speed',
plot=plot,
save_csv=save_csv,
save_path=save_path,
debug=debug,
debug_plot=debug_plot)
dir_suite = tidalSuite(gear,
mod_dr_int,
obs_dr_int,
step_dr_int,
start_dr_int,
mod_u,
obs_u,
mod_v,
obs_v,
mod_dt,
obs_dt,
kind='direction',
plot=plot,
save_csv=save_csv,
save_path=save_path,
debug=debug,
debug_plot=debug_plot)
u_suite = tidalSuite(gear,
mod_u_int,
obs_u_int,
step_u_int,
start_u_int, [], [], [], [], [], [],
kind='u velocity',
plot=plot,
save_csv=save_csv,
save_path=save_path,
debug=debug,
debug_plot=debug_plot)
v_suite = tidalSuite(gear,
mod_v_int,
obs_v_int,
step_v_int,
start_v_int, [], [], [], [], [], [],
kind='v velocity',
plot=plot,
save_csv=save_csv,
save_path=save_path,
debug=debug,
debug_plot=debug_plot)
# TR: requires special treatments from here on
vel_suite = tidalSuite(gear,
mod_ve_int,
obs_ve_int,
step_ve_int,
start_ve_int,
mod_u,
obs_u,
mod_v,
obs_v,
mod_dt,
obs_dt,
kind='velocity',
plot=plot,
save_csv=save_csv,
save_path=save_path,
debug=debug,
debug_plot=debug_plot)
csp_suite = tidalSuite(gear,
mod_cspd_int,
obs_cspd_int,
step_cspd_int,
start_cspd_int,
mod_u,
obs_u,
mod_v,
obs_v,
mod_dt,
obs_dt,
kind='cubic speed',
plot=plot,
save_csv=save_csv,
save_path=save_path,
debug=debug,
debug_plot=debug_plot)
# output statistics in useful format
if debug: print "...CompareUV done."
return (elev_suite, speed_suite, dir_suite, u_suite, v_suite, vel_suite,
csp_suite)
def compareUV(data):
'''
Does a comprehensive validation process between modeled and observed
data on the following:
Current speed
Current direction
Harmonic constituents (for height and speed)
Outputs a list of important statistics for each variable, calculated
using the TidalStats class
'''
# take data from input dictionary
mod_time = data['mod_time']
obs_time = data['obs_time']
print 'Loading mod timeseries'
mod_u_all = data['mod_timeseries']['u'][:, :, 0]
mod_v_all = data['mod_timeseries']['v'][:, :, 0]
mod_el = data['mod_timeseries']['elev']
print 'Loading obs timeseries'
obs_u_all = data['obs_timeseries']['u']
obs_v_all = data['obs_timeseries']['v']
obs_el = data['obs_timeseries']['elev']
v_obs_harm = data['vel_obs_harmonics']
el_mod_harm = data['elev_mod_harmonics']
el_obs_harm = data['elev_mod_harmonics']
bins = data['obs_timeseries']['bins']
sig = -data['mod_timeseries']['siglay'][:, 0]
# for some reason, the siglayers are repeated within siglay
# this bit of code will pick out only one of those repetitions
siglay = []
for i, v in enumerate(sig):
siglay.append(v)
if (sig[i + 1] < v):
break
siglay = np.asarray(siglay)
print 'siglay: {}'.format(siglay)
print 'mod shape: {}'.format(mod_u_all.shape)
print 'obs shape: {}'.format(obs_u_all.shape)
# use depth interpolation to get a single timeseries
print 'Performing depth interpolation'
mod_depth = mod_el + np.mean(obs_el)
(mod_u, obs_u) = depthFromSurf(mod_u_all, mod_depth, siglay,
obs_u_all, obs_el, bins)
(mod_v, obs_v) = depthFromSurf(mod_v_all, mod_depth, siglay,
obs_v_all, obs_el, bins)
print 'Depth interpolation completed'
# create new coefs based on depth interpolated timeseries
v_mod_harm = ut_solv(mod_time, mod_u, mod_v,
data['lat'], cnstit='auto',
rmin=0.95, notrend=True, method='ols', nodiagn=True,
linci=True, conf_int=True)
# convert times to datetime
mod_dt, obs_dt = [], []
for i in mod_time:
mod_dt.append(dn2dt(i))
for j in obs_time:
obs_dt.append(dn2dt(j))
# put data into a useful format
mod_spd = np.sqrt(mod_u**2 + mod_v**2)
obs_spd = np.sqrt(obs_u**2 + obs_v**2)
mod_dir = np.arctan2(mod_v, mod_u) * 180 / np.pi
obs_dir = np.arctan2(obs_v, obs_u) * 180 / np.pi
obs_el = obs_el - np.mean(obs_el)
# check if the modeled data lines up with the observed data
if (mod_time[-1] < obs_time[0] or obs_time[-1] < mod_time[0]):
pred_uv = ut_reconstr(obs_time, v_mod_harm)
pred_uv = np.asarray(pred_uv)
pred_h = ut_reconstr(obs_time, el_mod_harm)
pred_h = np.asarray(pred_h)
# redo speed and direction and set interpolated variables
mod_sp_int = np.sqrt(pred_uv[0]**2 + pred_uv[1]**2)
mod_ve_int = mod_sp_int * np.sign(pred_uv[1])
mod_dr_int = np.arctan2(pred_uv[1], pred_uv[0]) * 180 / np.pi
mod_el_int = pred_h[0]
mod_u_int = pred_uv[0]
mod_v_int = pred_uv[1]
obs_sp_int = obs_spd
obs_ve_int = obs_spd * np.sign(obs_v)
obs_dr_int = obs_dir
obs_el_int = obs_el
obs_u_int = obs_u
obs_v_int = obs_v
step_int = obs_dt[1] - obs_dt[0]
start_int = obs_dt[0]
else:
# interpolate the data onto a common time step for each data type
# elevation
(mod_el_int, obs_el_int, step_int, start_int) = \
smooth(mod_el, mod_dt, obs_el, obs_dt)
# speed
(mod_sp_int, obs_sp_int, step_int, start_int) = \
smooth(mod_spd, mod_dt, obs_spd, obs_dt)
# direction
(mod_dr_int, obs_dr_int, step_int, start_int) = \
smooth(mod_dir, mod_dt, obs_dir, obs_dt)
# u velocity
(mod_u_int, obs_u_int, step_int, start_int) = \
smooth(mod_u, mod_dt, obs_u, obs_dt)
# v velocity
(mod_v_int, obs_v_int, step_int, start_int) = \
smooth(mod_v, mod_dt, obs_v, obs_dt)
# velocity i.e. signed speed
(mod_ve_int, obs_ve_int, step_int, start_int) = \
smooth(mod_spd * np.sign(mod_v), mod_dt,
obs_spd * np.sign(obs_v), obs_dt)
'''
# separate into ebb and flow
mod_dir_n = get_DirFromN(mod_u_int, mod_v_int)
obs_dir_n = get_DirFromN(obs_u_int, mod_v_int)
mod_signed_s, mod_PA = sign_speed(mod_u_int, mod_v_int, mod_sp_int,
mod_dr_int, 0)
obs_signed_s, obs_PA = sign_speed(obs_u_int, obs_v_int, obs_sp_int,
obs_dr_int, 0)
print mod_signed_s[:20], mod_PA[:20]
print obs_signed_s[:20], obs_PA[:20]
'''
# remove directions where velocities are small
MIN_VEL = 0.5
for i in np.arange(obs_sp_int.size):
if (obs_sp_int[i] < MIN_VEL):
obs_dr_int[i] = np.nan
if (mod_sp_int[i] < MIN_VEL):
mod_dr_int[i] = np.nan
# get stats for each tidal variable
elev_suite = tidalSuite(mod_el_int, obs_el_int, step_int, start_int,
type='elevation', plot=True)
speed_suite = tidalSuite(mod_sp_int, obs_sp_int, step_int, start_int,
type='speed', plot=True)
dir_suite = tidalSuite(mod_dr_int, obs_dr_int, step_int, start_int,
type='direction', plot=False)
u_suite = tidalSuite(mod_u_int, obs_u_int, step_int, start_int,
type='u velocity', plot=False)
v_suite = tidalSuite(mod_v_int, obs_v_int, step_int, start_int,
type='v velocity', plot=False)
vel_suite = tidalSuite(mod_ve_int, obs_ve_int, step_int, start_int,
type='velocity', plot=False)
#ebb_suite = tidalSuite(mod_ebb, obs_ebb, step_int, start_int,
# type='ebb', plot=True)
#flo_suite = tidalSuite(mod_flo, obs_flo, step_int, start_int,
# type='flow', plot=True)
# output statistics in useful format
return (elev_suite, speed_suite, dir_suite, u_suite, v_suite, vel_suite)
def compareUV(data, threeDim, depth=5, plot=False):
'''
Does a comprehensive validation process between modeled and observed
data on the following:
Current speed
Current direction
Harmonic constituents (for height and speed)
Outputs a list of important statistics for each variable, calculated
using the TidalStats class
'''
# take data from input dictionary
mod_time = data['mod_time']
obs_time = data['obs_time']
mod_el = data['mod_timeseries']['elev']
obs_el = data['obs_timeseries']['elev']
v_mod_harm = data['vel_mod_harmonics']
v_obs_harm = data['vel_obs_harmonics']
el_mod_harm = data['elev_mod_harmonics']
el_obs_harm = data['elev_mod_harmonics']
#Check if 3D simulation
if threeDim:
obs_u_all = data['obs_timeseries']['u']
obs_v_all = data['obs_timeseries']['v']
mod_u_all = data['mod_timeseries']['u']
mod_v_all = data['mod_timeseries']['v']
bins = data['obs_timeseries']['bins']
siglay = data['mod_timeseries']['siglay']
# use depth interpolation to get a single timeseries
mod_depth = mod_el + np.mean(obs_el)
(mod_u, obs_u) = depthFromSurf(mod_u_all, mod_depth, siglay,
obs_u_all, obs_el, bins, depth=depth)
(mod_v, obs_v) = depthFromSurf(mod_v_all, mod_depth, siglay,
obs_v_all, obs_el, bins, depth=depth)
else:
obs_u = data['obs_timeseries']['ua']
obs_v = data['obs_timeseries']['va']
mod_u = data['mod_timeseries']['ua']
mod_v = data['mod_timeseries']['va']
# convert times to datetime
mod_dt, obs_dt = [], []
for i in mod_time:
mod_dt.append(dn2dt(i))
for j in obs_time:
obs_dt.append(dn2dt(j))
# put data into a useful format
mod_spd = np.sqrt(mod_u**2 + mod_v**2)
obs_spd = np.sqrt(obs_u**2 + obs_v**2)
mod_dir = np.arctan2(mod_v, mod_u) * 180 / np.pi
obs_dir = np.arctan2(obs_v, obs_u) * 180 / np.pi
obs_el = obs_el - np.mean(obs_el)
# check if the modeled data lines up with the observed data
if (mod_time[-1] < obs_time[0] or obs_time[-1] < mod_time[0]):
pred_uv = ut_reconstr(obs_time, v_mod_harm)
pred_uv = np.asarray(pred_uv)
pred_h = ut_reconstr(obs_time, el_mod_harm)
pred_h = np.asarray(pred_h)
# redo speed and direction and set interpolated variables
mod_sp_int = np.sqrt(pred_uv[0]**2 + pred_uv[1]**2)
mod_ve_int = mod_sp_int * np.sign(pred_uv[1])
mod_dr_int = np.arctan2(pred_uv[1], pred_uv[0]) * 180 / np.pi
mod_el_int = pred_h[0]
mod_u_int = pred_uv[0]
mod_v_int = pred_uv[1]
obs_sp_int = obs_spd
obs_ve_int = obs_spd * np.sign(obs_v)
obs_dr_int = obs_dir
obs_el_int = obs_el
obs_u_int = obs_u
obs_v_int = obs_v
step_int = obs_dt[1] - obs_dt[0]
start_int = obs_dt[0]
else:
# interpolate the data onto a common time step for each data type
# elevation
(mod_el_int, obs_el_int, step_int, start_int) = \
smooth(mod_el, mod_dt, obs_el, obs_dt)
# speed
(mod_sp_int, obs_sp_int, step_int, start_int) = \
smooth(mod_spd, mod_dt, obs_spd, obs_dt)
# direction
(mod_dr_int, obs_dr_int, step_int, start_int) = \
smooth(mod_dir, mod_dt, obs_dir, obs_dt)
# u velocity
(mod_u_int, obs_u_int, step_int, start_int) = \
smooth(mod_u, mod_dt, obs_u, obs_dt)
# v velocity
(mod_v_int, obs_v_int, step_int, start_int) = \
smooth(mod_v, mod_dt, obs_v, obs_dt)
# velocity i.e. signed speed
(mod_ve_int, obs_ve_int, step_int, start_int) = \
smooth(mod_spd * np.sign(mod_v), mod_dt,
obs_spd * np.sign(obs_v), obs_dt)
'''
# separate into ebb and flow
mod_dir_n = get_DirFromN(mod_u_int, mod_v_int)
obs_dir_n = get_DirFromN(obs_u_int, mod_v_int)
mod_signed_s, mod_PA = sign_speed(mod_u_int, mod_v_int, mod_sp_int,
mod_dr_int, 0)
obs_signed_s, obs_PA = sign_speed(obs_u_int, obs_v_int, obs_sp_int,
obs_dr_int, 0)
print mod_signed_s[:20], mod_PA[:20]
print obs_signed_s[:20], obs_PA[:20]
'''
# remove directions where velocities are small
MIN_VEL = 0.5
for i in np.arange(obs_sp_int.size):
if (obs_sp_int[i] < MIN_VEL):
obs_dr_int[i] = np.nan
if (mod_sp_int[i] < MIN_VEL):
mod_dr_int[i] = np.nan
# get stats for each tidal variable
elev_suite = tidalSuite(mod_el_int, obs_el_int, step_int, start_int,
type='elevation', plot=plot)
speed_suite = tidalSuite(mod_sp_int, obs_sp_int, step_int, start_int,
type='speed', plot=plot)
dir_suite = tidalSuite(mod_dr_int, obs_dr_int, step_int, start_int,
type='direction', plot=plot)
u_suite = tidalSuite(mod_u_int, obs_u_int, step_int, start_int,
type='u velocity', plot=plot)
v_suite = tidalSuite(mod_v_int, obs_v_int, step_int, start_int,
type='v velocity', plot=plot)
vel_suite = tidalSuite(mod_ve_int, obs_ve_int, step_int, start_int,
type='velocity', plot=plot)
#ebb_suite = tidalSuite(mod_ebb, obs_ebb, step_int, start_int,
# type='ebb', plot=True)
#flo_suite = tidalSuite(mod_flo, obs_flo, step_int, start_int,
# type='flow', plot=True)
# output statistics in useful format
return (elev_suite, speed_suite, dir_suite, u_suite, v_suite, vel_suite)
def compareUV(data,
threeDim,
depth=5,
plot=False,
save_csv=False,
debug=False,
debug_plot=False):
'''
Does a comprehensive validation process between modeled and observed
data on the following:
Current speed
Current direction
Harmonic constituents (for height and speed)
Outputs a list of important statistics for each variable, calculated
using the TidalStats class
'''
if debug: print "CompareUV..."
# take data from input dictionary
mod_time = data['mod_time']
obs_time = data['obs_time']
mod_el = data['mod_timeseries']['elev']
obs_el = data['obs_timeseries']['elev']
#Check if 3D simulation
if threeDim:
obs_u_all = data['obs_timeseries']['u']
obs_v_all = data['obs_timeseries']['v']
mod_u_all = data['mod_timeseries']['u']
mod_v_all = data['mod_timeseries']['v']
bins = data['obs_timeseries']['bins']
siglay = data['mod_timeseries']['siglay']
# use depth interpolation to get a single timeseries
mod_depth = mod_el + np.mean(obs_el[~np.isnan(obs_el)])
(mod_u, obs_u) = depthFromSurf(mod_u_all,
mod_depth,
siglay,
obs_u_all,
obs_el,
bins,
depth=depth,
debug=debug,
debug_plot=debug_plot)
(mod_v, obs_v) = depthFromSurf(mod_v_all,
mod_depth,
siglay,
obs_v_all,
obs_el,
bins,
depth=depth,
debug=debug,
debug_plot=debug_plot)
else:
obs_u = data['obs_timeseries']['ua']
obs_v = data['obs_timeseries']['va']
mod_u = data['mod_timeseries']['ua']
mod_v = data['mod_timeseries']['va']
if debug: print "...convert times to datetime..."
mod_dt, obs_dt = [], []
for i in mod_time:
mod_dt.append(dn2dt(i))
for j in obs_time:
obs_dt.append(dn2dt(j))
if debug: print "...put data into a useful format..."
mod_spd = np.sqrt(mod_u**2.0 + mod_v**2.0)
obs_spd = np.sqrt(obs_u**2.0 + obs_v**2.0)
mod_dir = np.arctan2(mod_v, mod_u) * 180.0 / np.pi
obs_dir = np.arctan2(obs_v, obs_u) * 180.0 / np.pi
obs_el = obs_el - np.mean(obs_el[~np.isnan(obs_el)])
if debug:
print "...check if the modeled data lines up with the observed data..."
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 the data onto a common time step for each data type..."
# elevation
(mod_el_int, obs_el_int, step_el_int, start_el_int) = \
smooth(mod_el, mod_dt, obs_el, obs_dt,
debug=debug, debug_plot=debug_plot)
# speed
(mod_sp_int, obs_sp_int, step_sp_int, start_sp_int) = \
smooth(mod_spd, mod_dt, obs_spd, obs_dt,
debug=debug, debug_plot=debug_plot)
# direction
(mod_dr_int, obs_dr_int, step_dr_int, start_dr_int) = \
smooth(mod_dir, mod_dt, obs_dir, obs_dt,
debug=debug, debug_plot=debug_plot)
# u velocity
(mod_u_int, obs_u_int, step_u_int, start_u_int) = \
smooth(mod_u, mod_dt, obs_u, obs_dt,
debug=debug, debug_plot=debug_plot)
# v velocity
(mod_v_int, obs_v_int, step_v_int, start_v_int) = \
smooth(mod_v, mod_dt, obs_v, obs_dt,
debug=debug, debug_plot=debug_plot)
# velocity i.e. signed speed
(mod_ve_int, obs_ve_int, step_ve_int, start_ve_int) = \
smooth(mod_spd * np.sign(mod_v), mod_dt,
obs_spd * np.sign(obs_v), obs_dt,
debug=debug, debug_plot=debug_plot)
if debug: print "...separate into ebb and flow..."
## separate into ebb and flow
#mod_dir_n = get_DirFromN(mod_u_int, mod_v_int)
#obs_dir_n = get_DirFromN(obs_u_int, mod_v_int)
#mod_signed_s, mod_PA = sign_speed(mod_u_int, mod_v_int, mod_sp_int,
# mod_dr_int, 0)
#obs_signed_s, obs_PA = sign_speed(obs_u_int, obs_v_int, obs_sp_int,
# obs_dr_int, 0)
#print mod_signed_s[:20], mod_PA[:20]
#print obs_signed_s[:20], obs_PA[:20]
if debug: print "...remove directions where velocities are small..."
MIN_VEL = 0.1
for i in np.arange(obs_sp_int.size):
if (obs_sp_int[i] < MIN_VEL):
obs_dr_int[i] = np.nan
if (mod_sp_int[i] < MIN_VEL):
mod_dr_int[i] = np.nan
if debug: print "...get stats for each tidal variable..."
elev_suite = tidalSuite(mod_el_int,
obs_el_int,
step_el_int,
start_el_int,
type='elevation',
plot=plot,
save_csv=save_csv,
debug=debug,
debug_plot=debug_plot)
speed_suite = tidalSuite(mod_sp_int,
obs_sp_int,
step_sp_int,
start_sp_int,
type='speed',
plot=plot,
save_csv=save_csv,
debug=debug,
debug_plot=debug_plot)
dir_suite = tidalSuite(mod_dr_int,
obs_dr_int,
step_dr_int,
start_dr_int,
type='direction',
plot=plot,
save_csv=save_csv,
debug=debug,
debug_plot=debug_plot)
u_suite = tidalSuite(mod_u_int,
obs_u_int,
step_u_int,
start_u_int,
type='u velocity',
plot=plot,
save_csv=save_csv,
debug=debug,
debug_plot=debug_plot)
v_suite = tidalSuite(mod_v_int,
obs_v_int,
step_v_int,
start_v_int,
type='v velocity',
plot=plot,
save_csv=save_csv,
debug=debug,
debug_plot=debug_plot)
vel_suite = tidalSuite(mod_ve_int,
obs_ve_int,
step_ve_int,
start_ve_int,
type='velocity',
plot=plot,
save_csv=save_csv,
debug=debug,
debug_plot=debug_plot)
#ebb_suite = tidalSuite(mod_ebb, obs_ebb, step_ebb_int, start_ebb_int,
# type='ebb', plot=True, save_csv=save_csv,
# debug=debug, debug_plot=debug_plot)
#flo_suite = tidalSuite(mod_flo, obs_flo, step_flo_int, start_flo_int,
# type='flow', plot=True, save_csv=save_csv,
# debug=debug, debug_plot=debug_plot)
# output statistics in useful format
if debug: print "...CompareUV done."
return (elev_suite, speed_suite, dir_suite, u_suite, v_suite, vel_suite)
