file_type = 'raw_'
LT_array = []
depth_array = []
profile_array = []
grid = pd.DataFrame(columns=[
'LT', 'c_depth'
]) # Note that there are now row data inserted.for i in range(0,58,2):
for i in range(0, 50, 2):
c_file = 'C' + ("%07d" % (i, ))
c_data = pd.read_pickle(directory + deployment_name + file_type + c_file)
[LT, Td, Nsqu, Lo, R, x_sorted,
idxs] = TKED.thorpe_scales1(c_data["c_depth"].values * -1,
c_data['c_dens'].values,
full_output=True)
c_data['LT'] = LT
grid = pd.concat([grid, c_data])
# plt.plot(LT,c_data["c_depth"].values)
# plt.xlabel("Thorpe_scales (m)")
# plt.ylabel('Depth (m)')
# plt.savefig(outdir+measurement_type+deployment_name+'profile'+str(i)+".png")
# plt.clf()
#plt.show()
grid['c_depth'] = grid['c_depth'].round(0)
grid = grid.groupby(grid['c_depth']).mean()
grid['LT'] = grid['LT'].interpolate().rolling(5).mean().abs()
# # plt.show()
# plt.plot(pres)
# plt.show()
# # plt.plot(sal)
# # plt.show()
# plt.plot(sal)
# plt.show()
CT = gsw.CT_from_t(sal, temp, pres)
SA = gsw.SA_from_SP(sal, pres, 174, -43)
dens = gsw.sigma0(SA, CT)
depth = gsw.z_from_p(pres, -43)
dens = np.squeeze(dens)
depth = np.squeeze(depth)
[LT, Td, Nsqu, Lo, R, x_sorted,
idxs] = TKED.thorpe_scales(depth, dens, full_output=True)
# plt.plot(sal)
# plt.show()
# Tdd = pd.Series(Td)
# Tdd = Tdd.rolling(100).mean()
# Tdd = Tdd**2
# Tdd = Tdd.rolling(100).mean()
# Tdd = np.sqrt(Tdd)
# plt.plot(Tdd)
#plt.show()
fig2, (ax2, ax3, ax4) = plt.subplots(1, 3, sharey=True)
# Temperature
ax2.plot(dens, depth, c='k')
outdir = '../../plots/ctd/LT_D_R/'
deployment_name = 'deploy2_'
measurement_type = 'ctd_'
file_type = 'raw_'
for i in range(10):
c_file = 'C' + ("%07d" % (i, ))
c_data = pd.read_pickle(directory + deployment_name + file_type + c_file)
CT = gsw.CT_from_t(c_data['c_sal'], c_data['c_temp'], c_data['c_pres'])
SA = gsw.SA_from_SP(c_data['c_sal'], c_data['c_pres'], 174, -43)
pdens = gsw.sigma0(SA, CT)
c_data["pdens"] = pdens
[LT, Td, Nsqu, Lo, R, x_sorted,
idxs] = TKED.thorpe_scales(c_data["c_depth"].values * -1,
c_data['pdens'].values,
full_output=True)
#plt.show()
# Temperature
fig2, (ax2, ax3, ax4) = plt.subplots(1, 3, sharey=True)
plt.plot(c_data['pdens'], c_data["c_depth"].values, c='k')
ax2.set_ylabel('Depth (m)')
#ax2.set_ylim(ax2.get_ylim()[::-1]) #this reverses the yaxis (i.e. deep at the bottom)
ax2.set_xlabel('Density (kg/m3)')
ax2.xaxis.set_label_position('top') # this moves the label to the top
ax2.xaxis.set_ticks_position('top') # this moves the ticks to the top
# Salinity
ax3.plot(LT, c_data["c_depth"].values, c='k')
ax3.set_xlabel('LT (m)')
ax3.xaxis.set_label_position('top') # this moves the label to the top
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from ocean_tools import TKED
directory = '../../Data/deployment_raw/';
outdir = '../../plots/ctd/thorpe/';
deployment_name = 'deploy1_';
measurement_type = 'ctd_';
file_type = 'raw_'
i = 0
c_file = 'C'+("%07d" % (i,))
c_data = pd.read_pickle(directory+deployment_name+file_type+c_file)
print(c_data["c_depth"].values.shape)
[LT,Td,Nsqu,Lo,R,x_sorted,idxs] = TKED.thorpe_scales(c_data["c_depth"].values*-1,c_data['c_dens'].values,full_output=True)
print(len(c_data["c_depth"].values*-1))
eps = 0.64*(LT**2)*(Nsqu)**(3/2)
plt.plot((Nsqu))
plt.ylabel("Depth (m)")
plt.xlabel("Thorpes Scale (m)")
plt.show()