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working_examples_IOOS_servers.py
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working_examples_IOOS_servers.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Feb 5 10:21:51 2019
@author: aristizabal
"""
#%% Cell #1: Read glider data from the IOOS thredds server and plots a
# scatter plot of a glider transect for the entire length of the deployment
from read_glider_data import read_glider_data_thredds_server
#url_thredds = 'https://data.ioos.us/thredds/dodsC/deployments/rutgers/ru33-20180801T1323/ru33-20180801T1323.nc3.nc'
url_thredds = 'http://gliders.ioos.us/thredds/dodsC/deployments/aoml/SG668-20190819T1217/SG668-20190819T1217.nc3.nc'
var_name = 'temperature'
#var = 'salinity'
scatter_plot = 'yes'
varg, timeg, latg, long, depthg, dataset_id = \
read_glider_data_thredds_server(url_thredds,var_name,scatter_plot)
#%% Cell #2: Read glider data from the IOOS thredds server and plots a
# scatter plot of a glider transect for a specific time window
from read_glider_data import read_glider_data_thredds_server
#url_glider = 'https://data.ioos.us/thredds/dodsC/deployments/rutgers/ru33-20180801T1323/ru33-20180801T1323.nc3.nc'
#date_ini = '2018/09/01/00' # year/month/day/hour
#date_end = '2018/09/10/00' # year/month/day/hour
url_thredds = 'http://gliders.ioos.us/thredds/dodsC/deployments/aoml/SG668-20190819T1217/SG668-20190819T1217.nc3.nc'
var_name = 'temperature'
#var = 'salinity'
date_ini = '2019/09/01/00' # year/month/day/hour
date_end = '2019/09/10/00' # year/month/day/hour
scatter_plot = 'yes'
kwargs = dict(date_ini=date_ini,date_end=date_end)
varg, timeg, latg, long, depthg, dataset_id = \
read_glider_data_thredds_server(url_thredds,var_name,scatter_plot,**kwargs)
#%% Cell #3: Same as cell #2, in addition to interpolating the variable
# of interest (temperature, salinity or density) to regular depth levels
# (gridding variables in the vertical)
from read_glider_data import read_glider_data_thredds_server
from process_glider_data import grid_glider_data
#url_thredds = 'https://data.ioos.us/thredds/dodsC/deployments/rutgers/ru33-20180801T1323/ru33-20180801T1323.nc3.nc'
#date_ini = '2018/09/01/00' # year/month/day/hour
#date_end = '2018/09/10/00' # year/month/day/hour
url_thredds = 'http://gliders.ioos.us/thredds/dodsC/deployments/aoml/SG668-20190819T1217/SG668-20190819T1217.nc3.nc'
var_name = 'temperature'
#var = 'salinity'
date_ini = '2019/09/01/00' # year/month/day/hour
date_end = '2019/09/10/00' # year/month/day/hour
scatter_plot = 'no'
kwargs = dict(date_ini=date_ini,date_end=date_end)
tempg, timeg, latg, long, depthg, dataset_id = \
read_glider_data_thredds_server(url_thredds,var_name,scatter_plot,**kwargs)
delta_z = 0.4 # bin size in the vertical when gridding the variable vertical profile
# default value is 0.3
contour_plot = 'yes' # default value is 'yes'
tempg_gridded, timegg, depthg_gridded = \
grid_glider_data(var_name,dataset_id,tempg,timeg,depthg,delta_z,contour_plot)
#%% Cell #4: Search for glider data sets given
# a latitude and longitude box and time window
from read_glider_data import retrieve_dataset_id_erddap_server
# Server location
url_erddap = 'https://data.ioos.us/gliders/erddap'
'''
# MAB
lon_lim = [-75.0,-72.0]
lat_lim = [38.0,40.0]
# date limits
date_ini = '2018/09/01/00'
date_end = '2018/09/10/00'
'''
# Caribbean
lon_lim = [-80,-60.0]
lat_lim = [10.0,30.0]
# date limits
date_ini = '2019/09/01/00'
date_end = '2019/09/10/00'
gliders = retrieve_dataset_id_erddap_server(url_erddap,lat_lim,lon_lim,date_ini,date_end)
print('The gliders found are ')
print(gliders)
#%% Given a glider data set, Search for available
# variables within that data set
from read_glider_data import retrieve_variable_names_erddap_server
# Server location
url_erddap = 'https://data.ioos.us/gliders/erddap'
dataset_id = 'ng231-20190901T0000'
variable_names = retrieve_variable_names_erddap_server(url_erddap,dataset_id)
#%% Read glider data given a dataset_id, a list of variables,
# latitude and logitude limits, a time window (optional).
# It also converts depth and temperatute and salinity (if these variables are
# requested) from vectors to 2 dimensional arrays with dimensions (depth,time)
from read_glider_data import read_glider_variables_erddap_server
# Server location
url_erddap = 'https://data.ioos.us/gliders/erddap'
dataset_id = 'ng231-20190901T0000'
# Caribbean
lon_lim = [-80,-60.0]
lat_lim = [10.0,30.0]
# date limits
date_ini = '2019/09/01/00'
date_end = '2019/09/10/00'
dataset_id = 'ng231-20190901T0000'
kwargs = dict(date_ini=date_ini,date_end=date_end)
variable_names = [
'depth',
'latitude',
'longitude',
'time',
'temperature',
'salinity'
]
df = read_glider_variables_erddap_server(url_erddap,dataset_id,lat_lim,lon_lim,\
variable_names,**kwargs)
#%% Cell #5: Search for glider data sets given a
# latitude and longitude box and time window, choose one those data sets
# (glider_id), plot a scatter plot of the chosen glider transect, grid
# and plot a contour plot of the chosen glider transect
from read_glider_data import retrieve_dataset_id_erddap_server
from read_glider_data import read_glider_data_erddap_server
from process_glider_data import grid_glider_data
# Server location
url_erddap = 'https://data.ioos.us/gliders/erddap'
'''
# MAB
lon_lim = [-75.0,-72.0]
lat_lim = [38.0,40.0]
# date limits
date_ini = '2018/09/01/00'
date_end = '2018/09/10/00'
'''
# Caribbean
lon_lim = [-80,-60.0]
lat_lim = [10.0,30.0]
# date limits
date_ini = '2019/09/01/00'
date_end = '2019/09/10/00'
gliders = retrieve_dataset_id_erddap_server(url_erddap,lat_lim,lon_lim,date_ini,date_end)
dataset_id = gliders[10]
# variable to retrieve
var_name = 'temperature'
#var_name = 'salinity'
kwargs = dict(date_ini=date_ini,date_end=date_end)
scatter_plot = 'yes'
tempg, saltg, timeg, latg, long, depthg = read_glider_data_erddap_server(url_erddap,dataset_id,\
lat_lim,lon_lim,scatter_plot,**kwargs)
#tempg, saltg, timeg, latg, long, depthg = read_glider_data_erddap_server(url_erddap,dataset_id,\
# lat_lim,lon_lim,scatter_plot)
contour_plot = 'yes' # default value is 'yes'
delta_z = 0.4 # default value is 0.3
tempg_gridded, timegg, depthg_gridded = \
grid_glider_data(var_name,dataset_id,tempg,timeg,latg,long,depthg,delta_z,contour_plot)
#%% cell #6: Search for glider data sets given a
# latitude and longitude box and time window, choose one those data sets
# (dataset_id), grid in the vertical the glider transect, get the glider
# transect in the GOFS 3.1 grid, and plot both the transect from the glider
# deployment and GOFS 3.1 output
from read_glider_data import retrieve_dataset_id_erddap_server
from read_glider_data import read_glider_data_erddap_server
from process_glider_data import grid_glider_data
from glider_transect_model_com import get_glider_transect_from_GOFS
# Servers location
url_erddap = 'https://data.ioos.us/gliders/erddap'
url_GOFS = 'http://tds.hycom.org/thredds/dodsC/GLBv0.08/expt_93.0/ts3z'
'''
# MAB
lon_lim = [-75.0,-72.0]
lat_lim = [38.0,40.0]
# date limits
date_ini = '2018/09/01/00'
date_end = '2018/09/10/00'
'''
# Caribbean
lon_lim = [-80,-60.0]
lat_lim = [10.0,30.0]
# date limits
date_ini = '2019/09/01/00'
date_end = '2019/09/10/00'
kwargs = dict(date_ini=date_ini,date_end=date_end)
scatter_plot = 'yes'
contour_plot = 'yes' # default value is 'yes'
delta_z = 0.4 # default value is 0.3
# model variable name
model_name = 'GOFS 3.1'
var_name_model = 'water_temp'
var_name_glider = 'temperature'
gliders = retrieve_dataset_id_erddap_server(url_erddap,lat_lim,lon_lim,date_ini,date_end)
dataset_id = gliders[10]
tempg, saltg, timeg, latg, long, depthg = read_glider_data_erddap_server(url_erddap,dataset_id,\
lat_lim,lon_lim,scatter_plot,**kwargs)
tempg_gridded, timegg, depthg_gridded = \
grid_glider_data(var_name_glider,dataset_id,tempg,timeg,latg,long,depthg,delta_z,contour_plot)
# Get temperature transect from model
temp_GOFS, time_GOFS, depth_GOFS, lat_GOFS, lon_GOFS = \
get_glider_transect_from_GOFS(url_GOFS,var_name_model,model_name,\
tempg,timeg,latg,long,depthg,contour_plot)
#%% cell #7: Search for glider data sets given a
# latitude and longitude box and time window, choose one those data sets
# (dataset_id), grid in the vertical the glider transect, get the glider
# transect in the AmSeas grid, and plot both the transect from the glider
# deployment and the AmSeas output
from read_glider_data import retrieve_dataset_id_erddap_server
from read_glider_data import read_glider_data_erddap_server
from process_glider_data import grid_glider_data
from glider_transect_model_com import get_glider_transect_from_Amseas
# Servers location
url_erddap = 'https://data.ioos.us/gliders/erddap'
url_amseas = 'https://www.ncei.noaa.gov/thredds-coastal/dodsC/amseas/amseas_20130405_to_current/' #'20190901/ncom_relo_amseas_u_2019090100_t003.nc'
# Caribbean
lon_lim = [-80,-60.0]
lat_lim = [10.0,30.0]
# date limits
date_ini = '2019/09/10/00'
date_end = '2019/09/15/00'
kwargs = dict(date_ini=date_ini,date_end=date_end)
scatter_plot = 'yes'
contour_plot = 'yes' # default value is 'yes'
delta_z = 0.4 # default value is 0.3
# model variable name
model_name = 'Amseas'
var_name_model = 'water_temp'
var_name_glider = 'temperature'
gliders = retrieve_dataset_id_erddap_server(url_erddap,lat_lim,lon_lim,date_ini,date_end)
dataset_id = gliders[10]
tempg, saltg, timeg, latg, long, depthg = read_glider_data_erddap_server(url_erddap,dataset_id,\
lat_lim,lon_lim,scatter_plot,**kwargs)
tempg_gridded, timegg, depthg_gridded = \
grid_glider_data(var_name_glider,dataset_id,tempg,timeg,depthg,delta_z,contour_plot)
temp_amseas, time_amseas, depth_amseas, lat_amseas, lon_amseas = \
get_glider_transect_from_Amseas(url_amseas,var_name_model,model_name,\
tempg,timeg,latg,long,depthg,contour_plot='yes')
#%% cell #8: Extract a transect from GOFs 3.1 given
# times, latitudes and longitudes of the transect
from glider_transect_model_com import get_transect_from_GOFS
# Servers location
url_model = 'http://tds.hycom.org/thredds/dodsC/GLBy0.08/expt_93.0/ts3z'
# model variable name
model_name = 'GOFS 3.1'
var_name_model = 'water_temp'
contour_plot = 'yes' # default value is 'yes'
#time = timeg
#lat = latg
#lon = long
from datetime import datetime, timedelta
import numpy as np
ndays = 3
time = np.array([datetime(2020,9,1,0) + timedelta(np.float(i)) for i in np.arange(ndays)])
lat = np.arange(20,20+ndays,1)
lon = np.arange(-45,-45+ndays,1)
var_model, ttmodel, depth_model, lat_model, lon_model = \
get_transect_from_GOFS(url_model,var_name_model,model_name,time,lat,lon,contour_plot='yes')