def reconstr(self, time):
if self.coef['aux']['opt']['twodim']:
self.U, self.V = ut_reconstr(time, self.coef)
self.QC.append('ut_reconstr done for velocity')
else:
self.ts_recon, _ = ut_reconstr(time, self.coef)
self.QC.append('ut_reconstr done for elevation')
def Harmonic_reconstruction(self,
harmo,
elevation=True,
velocity=False,
time_ind=slice(None),
debug=False,
**kwarg):
'''
Description:
----------
This function reconstructs the velocity components or the surface elevation
from harmonic coefficients.
Harmonic_reconstruction calls ut_reconstr. This function assumes harmonics
(ut_solv) has already been executed.
Inputs:
------
- Harmo = harmonic coefficient from harmo_analysis
- elevation =True means that ut_reconstr will be done for elevation.
- velocity =True means that ut_reconst will be done for velocity.
- time_ind = time indices to process, list of integers
Output:
------
- Reconstruct = reconstructed signal, dictionary
Options:
-------
Options are the same as for ut_reconstr, which are shown below with
their default values:
cnstit = [], minsnr = 2, minpe = 0
Notes:
-----
For more detailed information about ut_reconstr, please see
https://github.com/wesleybowman/UTide
'''
debug = (debug or self._debug)
time = self._var.matlabTime[time_ind]
#TR_comments: Add debug flag in Utide: debug=self._debug
Reconstruct = {}
if velocity:
U_recon, V_recon = ut_reconstr(time, harmo)
Reconstruct['U'] = U_recon
Reconstruct['V'] = V_recon
if elevation:
elev_recon, _ = ut_reconstr(time, harmo)
Reconstruct['el'] = elev_recon
return Reconstruct
def Harmonic_reconstruction(
self, harmo, elevation=True, velocity=False, time_ind=slice(None), debug=False, **kwarg
):
"""
Description:
----------
This function reconstructs the velocity components or the surface elevation
from harmonic coefficients.
Harmonic_reconstruction calls ut_reconstr. This function assumes harmonics
(ut_solv) has already been executed.
Inputs:
------
- Harmo = harmonic coefficient from harmo_analysis
- elevation =True means that ut_reconstr will be done for elevation.
- velocity =True means that ut_reconst will be done for velocity.
- time_ind = time indices to process, list of integers
Output:
------
- Reconstruct = reconstructed signal, dictionary
Options:
-------
Options are the same as for ut_reconstr, which are shown below with
their default values:
cnstit = [], minsnr = 2, minpe = 0
Notes:
-----
For more detailed information about ut_reconstr, please see
https://github.com/wesleybowman/UTide
"""
debug = debug or self._debug
time = self._var.matlabTime[time_ind]
# TR_comments: Add debug flag in Utide: debug=self._debug
Reconstruct = {}
if velocity:
U_recon, V_recon = ut_reconstr(time, harmo)
Reconstruct["U"] = U_recon
Reconstruct["V"] = V_recon
if elevation:
elev_recon, _ = ut_reconstr(time, harmo)
Reconstruct["el"] = elev_recon
return Reconstruct
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 reconstr(self, harmo, time_ind=slice(None), **kwarg):
'''
Description:
----------
This function reconstructs the velocity components or the surface elevation
from harmonic coefficients.
Harmonic_reconstruction calls ut_reconstr. This function assumes harmonics
(ut_solv) has already been executed.
Inputs:
------
- Harmo = harmonic coefficient from harmo_analysis
Output:
------
- Reconstruct = reconstructed signal, dictionary
Keywords:
------
- time_ind = time indices to process, list of integers
Options:
-------
Options are the same as for ut_reconstr, which are shown below with
their default values:
cnstit = [], minsnr = 2, minpe = 0
Notes:
-----
For more detailed information about ut_reconstr, please see
https://github.com/wesleybowman/UTide
'''
time = self._var.matlabTime[time_ind]
ts_recon, _ = ut_reconstr(time, harmo, **kwarg)
return ts_recon
def reconstr(self, harmo, time_ind=slice(None), **kwarg):
'''
Description:
----------
This function reconstructs the velocity components or the surface elevation
from harmonic coefficients.
Harmonic_reconstruction calls ut_reconstr. This function assumes harmonics
(ut_solv) has already been executed.
Inputs:
------
- Harmo = harmonic coefficient from harmo_analysis
Output:
------
- Reconstruct = reconstructed signal, dictionary
Keywords:
------
- time_ind = time indices to process, list of integers
Options:
-------
Options are the same as for ut_reconstr, which are shown below with
their default values:
cnstit = [], minsnr = 2, minpe = 0
Notes:
-----
For more detailed information about ut_reconstr, please see
https://github.com/wesleybowman/UTide
'''
time = self._var.matlabTime[time_ind]
ts_recon, _ = ut_reconstr(time, harmo, **kwarg)
return ts_recon
def reconstr(self, time):
self.ts_recon, _ = ut_reconstr(time, self.coef)
import numpy as np
def datetime2matlabdn(dt):
ord = dt.toordinal()
mdn = dt + timedelta(days = 366)
frac = (dt-datetime(dt.year,dt.month,dt.day,0,0,0)).seconds / (24.0 * 60.0 * 60.0)
return mdn.toordinal() + frac
# load in struct harmonics
filename = '/EcoII/EcoEII_server_data_tree/code/wesCode/project/june_2013_3D_station.p'
file = open(filename, 'rb')
struct = pickle.load(file)
site = struct['june_2013_3D_station'][0]
harmonics = site['vel_mod_harmonics']
# create datenums and reconstruct
start = datetime(2013, 3, 1, 0, 0, 0)
step = timedelta(minutes=10)
steps = 6 * 24 * 90
dt = start + np.arange(steps) * step
dn = np.zeros(dt.size)
for i, v in enumerate(dt):
dn[i] = datetime2matlabdn(v)
print
pred_uv = ut_reconstr(dn, harmonics)
speed = np.sqrt(pred_uv[0]**2 + pred_uv[1]**2)
# plot
plt.plot(dt, speed)
plt.show()
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']
mod_u = data['mod_timeseries']['ua']
mod_v = data['mod_timeseries']['va']
mod_el = data['mod_timeseries']['elev']
obs_u = data['obs_timeseries']['ua']
obs_v = data['obs_timeseries']['va']
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']
# 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=False)
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=True)
#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 getData():
'''
Extracts data and stores it in a pickle file. I'll write a better
comment later.
'''
# filename = '/home/wesley/github/aidan-projects/grid/dngrid_0001.nc'
# filename = '/home/abalzer/scratch/standard_run_directory/0.0015/output/dngrid_0001.nc'
# filename = '/home/wesley/ncfiles/smallcape_force_0001.nc'
# filename = '/home/abalzer/standard_run_directory/0.0015/output/dngrid_0001.nc'
filename = '/array/data1/rkarsten/dncoarse_bctest/output/dn_coarse_0001.nc'
data = nc.Dataset(filename, 'r')
x = data.variables['x'][:]
y = data.variables['y'][:]
lon = data.variables['lon'][:]
lat = data.variables['lat'][:]
ua = data.variables['ua']
va = data.variables['va']
time = data.variables['time'][:]
trinodes = data.variables['nv'][:]
#h = data.variables['zeta'][:]
(nodexy, uvnodexy, dt, deltat,
hour, thour, TP, rho, g, period,
nodell, uvnodell, trinodes) = ncdatasort(x, y, time*24*3600,
trinodes, lon, lat)
time = mjd2num(time)
Rayleigh = np.array([1])
# adcpFilename = '/home/wesley/github/karsten/adcp/dngrid_adcp_2012.txt'
# adcpFilename = '/home/wesley/github/karsten/adcp/testADCP.txt'
# adcpFilename = '/home/wesleyb/github/karsten/adcp/dngrid_adcp_2012.txt'
adcpFilename = '/array/home/116822s/github/karsten/acadia_dngrid_adcp_2012.txt'
adcp = pd.read_csv(adcpFilename)
lonlat = np.array([adcp['Longitude'], adcp['Latitude']]).T
index = closest_point(lonlat, lon, lat)
# set up lists for output data, grab shape values
adcp_out_u, adcp_out_v = [], []
fvc_out_u, fvc_out_v = [], []
for i, ii in enumerate(index):
tmp_path = adcp.iloc[i, -1]
if tmp_path != 'None':
tmp_adcp = pd.read_csv(tmp_path, index_col=0)
adcp_size = tmp_adcp['u'].values.size
adcp_out_u.append(np.zeros(adcp_size))
adcp_out_v.append(np.zeros(adcp_size))
else:
adcp_out_u.append('Nothing!')
adcp_out_v.append('Nothing!')
fvc_out_u, fvc_out_v = np.zeros([index.size, ua[:, 0].size]), \
np.zeros([index.size, ua[:, 0].size])
adcp_start, adcp_end, adcp_step = [], [], []
# main loop, loads in data
for i, ii in enumerate(index):
path = adcp.iloc[i, -1]
if path != 'None':
ADCP = pd.read_csv(path, index_col=0)
ADCP.index = pd.to_datetime(ADCP.index)
adcp_out_u[i] = ADCP['u'].values
adcp_out_v[i] = ADCP['v'].values
adcp_start.append(ADCP.index[0].to_datetime())
adcp_end.append(ADCP.index[-1].to_datetime())
adcp_step.append(ADCP.index[1].to_datetime() - adcp_start[i])
fvc_out_u[i] = ua[:, ii]
fvc_out_v[i] = va[:, ii]
else:
adcp_start.append('Nothing!')
adcp_end.append('Nothing!')
# remove all those 'Nothing's created from empty data
adcp_out_u = [i for i in adcp_out_u if i != 'Nothing!']
adcp_out_v = [i for i in adcp_out_v if i != 'Nothing!']
adcp_start = [i for i in adcp_start if i != 'Nothing!']
adcp_end = [i for i in adcp_end if i != 'Nothing!']
# set up times
f_start = time[0]
f_end = time[-1]
f_start = datetime.fromordinal(int(f_start)) + \
timedelta(days=(f_start%1)) - timedelta(days=366)
f_end = datetime.fromordinal(int(f_end)) + timedelta(days=(f_end%1)) - \
timedelta(days=366)
f_step = datetime.fromordinal(int(time[1])) + \
timedelta(days=(time[1]%1)) - timedelta(days=366) - f_start
# put together dictionaries, ready for interpolation/smoothing
adcp_dicts = []
fvc_dicts = []
for i in np.arange(len(adcp_start)):
adcp = {}
adcp['start'] = adcp_start[i]
adcp['end'] = adcp_end[i]
adcp['step'] = adcp_step[i]
adcp['pts'] = np.sqrt(adcp_out_u[i]**2 + adcp_out_v[i]**2)
adcp_dicts.append(adcp)
fvc = {}
fvc['start'] = f_start
fvc['end'] = f_end
fvc['step'] = f_step
fvc['pts'] = np.sqrt(fvc_out_u[i]**2 + fvc_out_v[i]**2)
fvc_dicts.append(fvc)
# load data into file using pickle
filename_1 = '/array/home/116822s/tidal_data/stats_test/ADCP_data1.pkl'
out_adcp = open(filename_1, 'wb')
pickle.dump(adcp_dicts, out_adcp)
filename_2 = '/array/home/116822s/tidal_data/stats_test/FVCOM_data1.pkl'
out_fvc = open(filename_2, 'wb')
pickle.dump(fvc_dicts, out_fvc)
out_adcp.close()
out_fvc.close()
# start getting the harmonic data
for i in np.arange(len(fvc_dicts)):
order = ['M2','S2','N2','K2','K1','O1','P1','Q1']
print 'Getting harmonic data for new site'
coef = ut_solv(time, ua[:, ii], va[:, ii], uvnodell[ii, 1],
cnstit=order, rmin=Rayleigh[0], notrend=True, method='ols',
nodiagn=True, linci=True, conf_int=True,
ordercnstit='frq')
# create time array for output time series
start = adcp_dicts[i]['start']
step = adcp_dicts[i]['step']
num_steps = adcp_dicts[i]['pts'].size
series = start + np.arange(num_steps) * step
for v, vv in enumerate(series):
series[v] = datetime2matlabdn(vv)
t = series.astype(float)
# reconstruct the time series using adcp times
time_series = np.asarray(ut_reconstr(t, coef))
time_series = np.sqrt(time_series[0]**2 + time_series[1]**2)
# ASK WESLEY WHAT THIS RETURNS, my idea might not be correct
print np.where((time_series[1] - time_series[0]) != 0)
fvc_dicts[i]['start'] = start
fvc_dicts[i]['end'] = adcp_dicts[i]['end']
fvc_dicts[i]['step'] = step
fvc_dicts[i]['pts'] = time_series
# save harmonic data
filename_3 = '/array/home/116822s/tidal_data/stats_test/hindcast_1.pkl'
out_hind = open(filename_3, 'wb')
pickle.dump(fvc_dicts, out_hind)
out_hind.close()
print 'Done!'
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)