def adjusted_zgrad_snow(self,int_length=0.5,ice_cond=2.03,snow_cond=0.33,\
mean_salinity=1.):
import data_series as ds
import numpy as np
import tprof
series_names = [
'surface_temp_' + '{:6.3f}'.format(0. - int_length), 'sftemp_0.000'
]
if set(series_names) & set(self.data.keys()) == set(series_names):
boundary_vars = series_names
else:
boundary_vars = None
tdates = self.temp.dates()
varname = 'zgrad_top_snow_adjusted_' + '{:6.3f}'.format(0. -
int_length)
zgrad_adjust_series = ds.data_series(self.name, '', varname)
for tdate in tdates:
snowpos = self.snowpos_rt(tdate)
if snowpos is not None:
zint = [snowpos[1] - int_length, snowpos[1]]
complete_zprof = tprof.complete_zprof(self,tdate,zint,\
boundary_vars = boundary_vars, adjust_for_snow=True,\
salinity = mean_salinity)
if complete_zprof is not None:
zgrad_adjust = np.polyfit(complete_zprof[0],
complete_zprof[1], 1)[0]
zgrad_adjust_series.data_list[tdate] = zgrad_adjust
self.data[varname] = zgrad_adjust_series
self.data[varname].type = 'regular_temp'
def zgrad_top_snow_adjusted(blist=None,force_new=False,quiet=True,\
int_length = 0.5, mean_salinity = 1):
import buoys
import numpy as np
import data_series as ds
import tprof
buoy_list = buoys.buoylist()
if not blist is None:
buoy_list = [bb for bb in buoy_list if bb in blist]
for buoy_name in buoy_list:
if not quiet:
print 'Calculating top gradient for buoy ' + buoy_name
buoy = buoys.buoy(buoy_name)
buoy.update_series()
buoy.process_temp()
series_names = [
'sftemp_' + '{:6.3f}'.format(0. - int_length), 'sftemp_0.000'
]
if set(series_names) & set(buoy.data.keys()) == set(series_names):
boundary_vars = series_names
else:
boundary_vars = None
tdates = buoy.temp.dates()
varname = 'zgrad_top_snow_adjusted_' + '{:6.3f}'.format(0. -
int_length)
zgrad_adjust_series = ds.data_series(buoy.name, '', varname)
for tdate in tdates:
snowpos = buoy.snowpos_rt(tdate)
if snowpos is not None:
zint = [snowpos[1] - int_length, snowpos[1]]
complete_zprof = tprof.complete_zprof(buoy,tdate,zint,\
boundary_vars = boundary_vars, adjust_for_snow=True,\
salinity = mean_salinity)
if complete_zprof is not None:
zgrad_adjust = np.polyfit(complete_zprof[0],
complete_zprof[1], 1)[0]
zgrad_adjust_series.data_list[tdate] = zgrad_adjust
buoy.data[varname] = zgrad_adjust_series
buoy.data[varname].type = 'regular_temp'
buoy.save_series()
def average_ice_temp(self, layer=1, nlayer=4, restrict_hour=None):
'''Returns data series of the average temperature of given sections of
the ice layer, with times-of-observation equal to those of the
temperature.
e.g. layer=1, nlayer=1 returns the average temp of the whole ice layer
layer=1, nlayer=4 returns the average temp of the top
quarter of the ice layer
layer=4, nlayer=4 returns the average temp of the bottom quarter
of the ice layer'''
import tprof
import data_series as ds
import numpy as np
tdates = self.temp.dates()
varname = 'average_ice_temp.ilayer.' + str(layer) + '.nlayer.' + str(
nlayer)
self.data[varname] = ds.data_series(self.name, '', varname)
if not 'bottom_rt' in list(self.data.keys()) and \
not 'interface_rt' in list(self.data.keys()):
print('Not enough information provided')
return self.data[varname]
if self.data['bottom_rt'].period() is None or \
self.data['interface_rt'].period() is None:
return self.data[varname]
for tdate in tdates:
if restrict_hour is None or tdate.hour == restrict_hour:
if tdate >= max([self.data['bottom_rt'].period()[0],\
self.data['interface_rt'].period()[0]]) and \
tdate <= min([self.data['bottom_rt'].period()[1],\
self.data['interface_rt'].period()[1]]):
zint_f = np.array([self.data['interface_rt'].data_list[tdate],\
self.data['bottom_rt'].data_list[tdate]])
prop_start = float(layer - 1) / float(nlayer)
prop_end = float(layer) / float(nlayer)
zint = [zint_f[0] + prop_start * (zint_f[1] - zint_f[0]),\
zint_f[0] + prop_end * (zint_f[1] - zint_f[0])]
zprof = tprof.complete_zprof(self, tdate, zint)
if zprof is not None:
weights = tprof.zpt_weights(zprof[0])
mean_temp = np.sum(
zprof[1] * weights) / np.sum(weights)
self.data[varname].data_list[tdate] = mean_temp
self.data[varname].type = 'regular'
return self.data[varname]
def temp_statistics(self, layer_calc=[0, 0.4], mode='interface'):
import tprof
import data_series as ds
import numpy as np
if mode == 'base':
icepos_index = 0
prefix = 'base_'
boundary_vars = [
'basal_temp_{:4.3f}'.format(zpos) for zpos in layer_calc
]
elif mode == 'interface':
icepos_index = 1
prefix = 'interface_'
boundary_vars = None
statistics = ['zmean', 'zgrad', 'z_recip_mean', 'z_recip_sq_mean']
layer_label = '{:4.3f}_{:4.3f}'.format(*layer_calc)
varnames = [prefix + layer_label + '_' + stat for stat in statistics]
for varname in varnames:
self.data[varname] = ds.data_series(self.name, '', varname)
self.data[varname].type = 'regular_temp'
tdates = self.temp.dates()
for tdate in tdates:
icepos = self.icepos_rt(tdate)
if not icepos is None:
layer_calc_transform = [
zi + icepos[icepos_index] for zi in layer_calc
]
zprof = tprof.complete_zprof(self,tdate,layer_calc_transform,\
boundary_vars=boundary_vars)
if not zprof is None:
zweights = tprof.zpt_weights(zprof[0])
zgrad = np.polyfit(zprof[0], zprof[1], 1)[0]
zmean = np.sum(zweights * zprof[1])
z_recip_mean = np.sum(zweights * 1. / zprof[1])
z_recip_sq_mean = np.sum(zweights * 1. / (zprof[1])**2.)
data_series = [zmean,zgrad,z_recip_mean,\
z_recip_sq_mean]
for (ii, varname) in enumerate(varnames):
self.data[varname].data_list[tdate] = \
data_series[ii]
return self.data[varname]
def average_snow_temp(self, restrict_hour=None):
'''Returns data series of the average temperature of the snow layer,
with times-of-observation equal to those of the temperature'''
import tprof
import data_series as ds
import numpy as np
tdates = self.temp.dates()
varname = 'average_snow_temp'
self.data[varname] = ds.data_series(self.name, '', varname)
if not 'surface_rt' in list(self.data.keys()) and \
not 'interface_rt' in list(self.data.keys()):
print('Not enough information provided')
return self.data[varname]
if self.data['surface_rt'].period() is None or \
self.data['interface_rt'].period() is None:
return self.data[varname]
for tdate in tdates:
if restrict_hour is None or tdate.hour == restrict_hour:
if tdate >= max([self.data['surface_rt'].period()[0],\
self.data['interface_rt'].period()[0]]) and \
tdate <= min([self.data['surface_rt'].period()[1],\
self.data['interface_rt'].period()[1]]):
zint = [self.data['surface_rt'].data_list[tdate],\
self.data['interface_rt'].data_list[tdate]]
zprof = tprof.complete_zprof(self, tdate, zint)
if zprof is not None:
weights = tprof.zpt_weights(zprof[0])
mean_temp = np.sum(
zprof[1] * weights) / np.sum(weights)
self.data[varname].data_list[tdate] = mean_temp
self.data[varname].type = 'regular'
return self.data[varname]
def lei_statistics(zint=None,quiet=True,blist=None,force_new=False,\
relative_position = 'bottom'):
import buoys
import tprof
import data_series as ds
import numpy as np
if relative_position == 'bottom':
icepos_index = 0
prefix = ''
boundary_vars = ['temp_{:4.3f}'.format(zpos) for zpos in zint]
elif relative_position == 'interface':
icepos_index = 1
prefix = 'int_'
boundary_vars = None
statistics = ['zmean', 'zgrad', 'z_recip_mean', 'z_recip_sq_mean']
if zint is None:
print 'Please specify z-interval'
layer_label = '{:4.3f}_{:4.3f}'.format(*zint)
varnames = [prefix + layer_label + '_' + stat for stat in statistics]
buoy_list = buoys.buoylist()
if not blist is None:
buoy_list = [bb for bb in buoy_list if bb in blist]
for buoy_name in buoy_list:
if not quiet:
print ' '
print 'Calculating Lei statistics of '+\
str(zint[0])+'-'+str(zint[1])+' heat storage series '+\
'for buoy '+buoy_name
print '----------------------------'
buoy = buoys.buoy(buoy_name)
not_there_log = np.array([(not buoy.is_series(varname)) \
for varname in varnames])
if np.sum(not_there_log) > 0 or force_new:
buoy.update_series()
buoy.process_temp()
for varname in varnames:
buoy.data[varname] = ds.data_series(buoy.name, '', varname)
buoy.data[varname].type = 'regular_temp'
tdates = buoy.temp.dates()
for tdate in tdates:
if tdate.day == 1 and tdate.hour == 1:
print tdate
icepos = buoy.icepos_rt(tdate)
if not icepos is None:
zint_transform = [zi + icepos[icepos_index] for zi in zint]
zprof = tprof.complete_zprof(buoy,tdate,zint_transform,\
boundary_vars=boundary_vars)
if not zprof is None:
zweights = tprof.zpt_weights(zprof[0])
zgrad = np.polyfit(zprof[0], zprof[1], 1)[0]
zmean = np.sum(zweights * zprof[1])
z_recip_mean = np.sum(zweights * 1. / zprof[1])
z_recip_sq_mean = np.sum(zweights * 1. /
(zprof[1])**2.)
data_series = [zmean,zgrad,z_recip_mean,\
z_recip_sq_mean]
for (ii, varname) in enumerate(varnames):
buoy.data[varname].data_list[tdate] = \
data_series[ii]
result = 1
buoy.save_series()
else:
result = 2
if not quiet:
print result