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
ttm = model.time tm = netCDF4.num2date(ttm[:],ttm.units) tmin = datetime.datetime.strptime(date_ini,'%Y-%m-%dT%H:%M:%SZ') tmax = datetime.datetime.strptime(date_end,'%Y-%m-%dT%H:%M:%SZ') oktimem = np.where(np.logical_and(tm >= tmin, tm <= tmax)) timem = tm[oktimem] #%% # Read and process glider data print('Reading glider data') df = read_glider_data_erddap_server(url_glider,dataset_id,var_glider,\ lat_lim,lon_lim,date_ini,date_end,\ scatter_plot='no') if len(df) != 0: depthg_gridded, varg_gridded, timeg, latg, long = \ grid_glider_data_erddap(df,dataset_id,var_glider,delta_z=0.2,contour_plot='no') # 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