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glider_transect_model_com.py
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glider_transect_model_com.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Feb 7 14:08:27 2019
@author: aristizabal
"""
#%%
def get_glider_transect_from_GOFS(url_model,var_name_model,model_name,varg,timeg,latg,long,depthg,contour_plot='yes'):
"""
Created on April 23 2020
@author: aristizabal
This function finds the corresponding glider transect in
the Global Ocean Forecasting System (GOFS 3.1)
Inputs:
url_model: url address of model output.
example: 'http://tds.hycom.org/thredds/dodsC/GLBv0.08/expt_93.0/ts3z'
var_name_model: name of variable in model output. Example "water_temp"
model_name: short name of model. Just use to title plots
varg: glider profiles matrix for the variable chosen
latg: glider latitude vector
long: glider longitude vector
timeg: glider time vector
depthg: depth matrix for all profiles
Outputs:
var_model
time_model
depth_model
lat_model
lon_model
"""
import xarray as xr
import netCDF4
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from datetime import datetime
import cmocean
print('Retrieving coordinates from model')
model = xr.open_dataset(url_model,decode_times=False)
lat_model = np.asarray(model.lat[:])
lon_model = np.asarray(model.lon[:])
depth_model = np.asarray(model.depth[:])
ttm = model.time
tm = netCDF4.num2date(ttm[:],ttm.units)
tini = mdates.num2date(mdates.date2num(timeg[0]))
tend = mdates.num2date(mdates.date2num(timeg[-1]))
#tini = timeg[0]
#tend = timeg[-1]
oktimem = np.where(np.logical_and(tm >= tini,tm <= tend))
time_model = tm[oktimem]
# Conversion from glider longitude and latitude to GOFS convention
target_lon = np.empty((len(long),))
target_lon[:] = np.nan
for i,ii in enumerate(long):
if ii < 0:
target_lon[i] = 360 + ii
else:
target_lon[i] = ii
target_lat = latg
# Changing times to timestamp
tstamp_glider = [mdates.date2num(timeg[i]) for i in np.arange(len(timeg))]
ttmodel = np.asarray([datetime(time_model[i].year,time_model[i].month,time_model[i].day,\
time_model[i].hour) for i in np.arange(len(time_model))])
tstamp_model = [mdates.date2num(ttmodel[i]) for i in np.arange(len(ttmodel))]
# interpolating glider lon and lat to lat and lon on model time
sublonm=np.interp(tstamp_model,tstamp_glider,target_lon)
sublatm=np.interp(tstamp_model,tstamp_glider,target_lat)
# getting the model grid positions for sublonm and sublatm
oklonm=np.round(np.interp(sublonm,lon_model,np.arange(len(lon_model)))).astype(int)
oklatm=np.round(np.interp(sublatm,lat_model,np.arange(len(lat_model)))).astype(int)
# Getting glider transect from model
print('Getting glider transect from '+ model_name)
var_model = np.empty((len(depth_model),len(oktimem[0])))
var_model[:] = np.nan
for i in range(len(oktimem[0])):
print(len(oktimem[0]),' ',i)
var_model[:,i] = model.variables[var_name_model][oktimem[0][i],:,oklatm[i],oklonm[i]]
# Countour plot
if contour_plot == 'yes':
if var_name_model == 'water_temp':
color_map = cmocean.cm.thermal
clabel = var_name_model[0].upper()+var_name_model[1:] + ' ($^oC$)'
else:
if var_name_model == 'salinity':
color_map = cmocean.cm.haline
clabel = var_name_model[0].upper()+var_name_model[1:]
else:
color_map = 'RdBu_r'
okg = depthg <= np.nanmax(depthg)
okm = depth_model <= np.nanmax(depthg)
min_val = np.int(np.floor(np.min([np.nanmin(varg[okg]),np.nanmin(var_model[okm])])))
max_val = np.int(np.ceil(np.max([np.nanmax(varg[okg]),np.nanmax(var_model[okm])])))
if var_name_model == 'salinity':
kw = dict(levels = np.arange(min_val,max_val+0.25,0.25))
else:
nlevels = max_val - min_val + 1
kw = dict(levels = np.linspace(min_val,max_val,nlevels))
fig, ax = plt.subplots(figsize=(10, 3))
cs = plt.contourf(ttmodel,-depth_model,var_model,cmap=color_map,**kw)
plt.contour(ttmodel,-depth_model,var_model,[26],colors='k')
cs = fig.colorbar(cs, orientation='vertical')
cs.ax.set_ylabel(clabel,fontsize=14,labelpad=15)
ax.set_xlim(timeg[0], timeg[-1])
ax.set_ylim(-np.nanmax(depthg), 0)
ax.set_ylabel('Depth (m)',fontsize=14)
xfmt = mdates.DateFormatter('%H:%Mh\n%d-%b')
ax.xaxis.set_major_formatter(xfmt)
plt.title('Along Track ' + var_name_model[0].upper() + var_name_model[1:] +\
' Profile ' + model_name,fontsize=16)
return var_model, ttmodel, depth_model, lat_model, lon_model
#%%
def get_glider_transect_from_Amseas(url_amseas,var_name_model,model_name,\
varg,timeg,latg,long,depthg,contour_plot='yes'):
from datetime import datetime, timedelta
import numpy as np
import xarray as xr
import netCDF4
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import cmocean
date_ini = str(timeg[0])
date_end = str(timeg[-1])
yini = int(date_ini.split('-')[0])
mini = int(date_ini.split('-')[1])
dini = int(date_ini.split('-')[2].split('T')[0])
#hini = int(date_ini.split('-')[2].split('T')[1][0:2])
yend = int(date_end.split('-')[0])
mend = int(date_end.split('-')[1])
dend = int(date_end.split('-')[2].split('T')[0])
#hend = int(date_end.split('-')[2].split('T')[1][0:2])
tini = datetime(yini,mini,dini,0)
tend = datetime(yend,mend,dend+1,0)
yini = str(tini).split('-')[0]
mini = str(tini).split('-')[1]
dini = str(tini).split('-')[2].split(' ')[0]
hini = str(tini).split('-')[2].split(' ')[1][0:2]
n_files = (tend-tini).days *8
time_vec = [tini + n*timedelta(hours=3) for n in np.arange(n_files)]
file_amseas0 = url_amseas + yini + mini + dini + \
'/ncom_relo_amseas_u_' + yini + mini + dini + '00' + '_t0' + hini + '.nc'
amseas = xr.open_dataset(file_amseas0,decode_times=False)
print('Getting depth, lat and lon from Amseas')
lat_amseas = np.asarray(amseas.lat[:])
lon_amseas = np.asarray(amseas.lon[:])
depth_amseas = np.asarray(amseas.depth[:])
print('Getting time from Amseas')
tm = []
for t,tt in enumerate(time_vec):
print(tt)
y = str(tt).split('-')[0]
m = str(tt).split('-')[1]
d = str(tt).split('-')[2].split(' ')[0]
h = str(tt).split('-')[2].split(' ')[1][0:2]
file_amseas = url_amseas + y + m + d + \
'/ncom_relo_amseas_u_' + y + m + d + '00' + '_t0' + h + '.nc'
amseas = xr.open_dataset(file_amseas,decode_times=False)
ttm = amseas.time
tm.append(datetime.strptime(str(netCDF4.num2date(ttm[:],ttm.units)[0]),'%Y-%m-%d %H:%M:%S'))
time_amseas = np.asarray(tm)
# Conversion from glider longitude and latitude to GOFS convention
target_lon = np.empty((len(long),))
target_lon[:] = np.nan
for i,ii in enumerate(long):
if ii < 0:
target_lon[i] = 360 + ii
else:
target_lon[i] = ii
target_lat = latg
# Changing times to timestamp
tstamp_glider = [mdates.date2num(timeg[i]) for i in np.arange(len(timeg))]
tstamp_model = [mdates.date2num(time_amseas[i]) for i in np.arange(len(time_amseas))]
# interpolating glider lon and lat to lat and lon on model time
sublon_amseas = np.interp(tstamp_model,tstamp_glider,target_lon)
sublat_amseas = np.interp(tstamp_model,tstamp_glider,target_lat)
# getting the model grid positions for sublonm and sublatm
oklon_amseas = np.round(np.interp(sublon_amseas,lon_amseas,np.arange(len(lon_amseas)))).astype(int)
oklat_amseas = np.round(np.interp(sublat_amseas,lat_amseas,np.arange(len(lat_amseas)))).astype(int)
var_amseas = np.empty((len(depth_amseas),len(time_vec)))
var_amseas[:] = np.nan
for t,tt in enumerate(time_vec):
print(tt)
y = str(tt).split('-')[0]
m = str(tt).split('-')[1]
d = str(tt).split('-')[2].split(' ')[0]
h = str(tt).split('-')[2].split(' ')[1][0:2]
file_amseas = url_amseas + y + m + d + \
'/ncom_relo_amseas_u_' + y + m + d + '00' + '_t0' + h + '.nc'
amseas = xr.open_dataset(file_amseas,decode_times=False)
var_amseas[:,t] = amseas.variables[var_name_model][0,:,oklat_amseas[t],oklon_amseas[t]]
# Countour plot
if contour_plot == 'yes':
if var_name_model == 'water_temp':
color_map = cmocean.cm.thermal
clabel = var_name_model[0].upper()+var_name_model[1:] + ' ($^oC$)'
else:
if var_name_model == 'salinity':
color_map = cmocean.cm.haline
clabel = var_name_model[0].upper()+var_name_model[1:]
else:
color_map = 'RdBu_r'
okg = depthg <= np.nanmax(depthg)
okm = depth_amseas <= np.nanmax(depthg)
min_val = np.int(np.floor(np.min([np.nanmin(varg[okg]),np.nanmin(var_amseas[okm])])))
max_val = np.int(np.ceil(np.max([np.nanmax(varg[okg]),np.nanmax(var_amseas[okm])])))
if var_name_model == 'salinity':
kw = dict(levels = np.arange(min_val,max_val+0.25,0.25))
else:
nlevels = max_val - min_val + 1
kw = dict(levels = np.linspace(min_val,max_val,nlevels))
fig, ax = plt.subplots(figsize=(10, 3))
cs = plt.contourf(mdates.date2num(time_amseas),-depth_amseas,var_amseas,cmap=color_map,**kw)
plt.contour(mdates.date2num(time_amseas),-depth_amseas,var_amseas,[26],colors='k')
cs = fig.colorbar(cs, orientation='vertical')
cs.ax.set_ylabel(clabel,fontsize=14,labelpad=15)
ax.set_xlim(timeg[0], timeg[-1])
ax.set_ylim(-np.nanmax(depthg), 0)
ax.set_ylabel('Depth (m)',fontsize=14)
xfmt = mdates.DateFormatter('%H:%Mh\n%d-%b')
ax.xaxis.set_major_formatter(xfmt)
plt.title('Along Track ' + var_name_model[0].upper() + var_name_model[1:] +\
' Profile ' + model_name,fontsize=16)
return var_amseas, time_amseas, depth_amseas, lat_amseas, lon_amseas
#%%
def get_transect_from_GOFS(url_model,var_name_model,model_name,time,lat,lon,contour_plot='yes'):
"""
Created on April 23 2020
@author: aristizabal
This function finds any transect in
the Global Ocean Forecasting System (GOFS 3.1)
given time, latitude and longitude
Inputs:
url_model: url address of model output.
example: 'http://tds.hycom.org/thredds/dodsC/GLBv0.08/expt_93.0/ts3z'
var_name_model: name of variable in model output. Example "water_temp"
model_name: short name of model. Just use to title plots
lat: latitude vector
lon: longitude vector
time: time vector of type datetime
Outputs:
var_model
time_model
depth_model
lat_model
lon_model
"""
import xarray as xr
import netCDF4
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from datetime import datetime
import cmocean
print('Retrieving coordinates from model')
model = xr.open_dataset(url_model,decode_times=False)
lat_model = np.asarray(model.lat[:])
lon_model = np.asarray(model.lon[:])
depth_model = np.asarray(model.depth[:])
ttm = model.time
tm = netCDF4.num2date(ttm[:],ttm.units)
tini = mdates.num2date(mdates.date2num(time[0]))
tend = mdates.num2date(mdates.date2num(time[-1]))
#tini = time[0]
#tend = time[-1]
oktimem = np.where(np.logical_and(tm >= tini,tm <= tend))[0]
time_model = tm[oktimem]
# Conversion from glider longitude and latitude to GOFS convention
target_lon = np.empty((len(lon),))
target_lon[:] = np.nan
for i,ii in enumerate(lon):
if ii < 0:
target_lon[i] = 360 + ii
else:
target_lon[i] = ii
target_lat = lat
# Changing times to timestamp
tstamp_glider = [mdates.date2num(time[i]) for i in np.arange(len(time))]
ttmodel = np.asarray([datetime(time_model[i].year,time_model[i].month,time_model[i].day,\
time_model[i].hour) for i in np.arange(len(time_model))])
tstamp_model = [mdates.date2num(ttmodel[i]) for i in np.arange(len(ttmodel))]
# interpolating glider lon and lat to lat and lon on model time
sublonm = np.interp(tstamp_model,tstamp_glider,target_lon)
sublatm = np.interp(tstamp_model,tstamp_glider,target_lat)
# getting the model grid positions for sublonm and sublatm
oklonm = np.round(np.interp(sublonm,lon_model,np.arange(len(lon_model)))).astype(int)
oklatm = np.round(np.interp(sublatm,lat_model,np.arange(len(lat_model)))).astype(int)
# Getting glider transect from model
print('Getting glider transect from '+ model_name)
var_model = np.empty((len(depth_model),len(oktimem)))
var_model[:] = np.nan
for i in range(len(oktimem)):
print(len(oktimem),' ',i)
var_model[:,i] = model.variables[var_name_model][oktimem[i],:,oklatm[i],oklonm[i]]
# Countour plot
if contour_plot == 'yes':
if var_name_model == 'water_temp':
color_map = cmocean.cm.thermal
clabel = var_name_model[0].upper()+var_name_model[1:] + ' ($^oC$)'
else:
if var_name_model == 'salinity':
color_map = cmocean.cm.haline
clabel = var_name_model[0].upper()+var_name_model[1:]
else:
color_map = 'RdBu_r'
clabel = ' '
min_val = np.int(np.floor(np.nanmin(var_model)))
max_val = np.int(np.ceil(np.nanmax(var_model)))
if var_name_model == 'salinity':
kw = dict(levels = np.arange(min_val,max_val+0.25,0.25))
else:
if var_name_model == 'water_temp':
kw = dict(levels = np.arange(min_val,max_val,0.5))
else:
kw = dict(levels = np.arange(min_val,max_val,0.1))
fig, ax = plt.subplots(figsize=(10, 3))
cs = plt.contourf(ttmodel,-depth_model,var_model,cmap=color_map,**kw)
plt.contour(ttmodel,-depth_model,var_model,[26],colors='k')
cs = fig.colorbar(cs, orientation='vertical')
cs.ax.set_ylabel(clabel,fontsize=14,labelpad=15)
ax.set_xlim(time[0], time[-1])
ax.set_ylabel('Depth (m)',fontsize=14)
xfmt = mdates.DateFormatter('%H:%Mh\n%d-%b')
ax.xaxis.set_major_formatter(xfmt)
plt.title('Along Track ' + var_name_model[0].upper() + var_name_model[1:] +\
' Profile ' + model_name,fontsize=16)
return var_model, ttmodel, depth_model, lat_model, lon_model