def getMappedGlobcolour(data_dir, min_lon, min_lat, max_lon, max_lat, start_date, end_date):
'''
Extracts subset of globcolour data based on lat/lon bounds and dates in the
iso format '2012-01-31'
'''
file_count = 0
file_list = np.asarray(os.listdir(data_dir))
file_dates = [re.split('[._]', nc_filename)[1] for filename in file_list]
# Get Lat/Lon from first data file in list
nc_dataset = Dataset(file_list[0],'r')
lons = nc_dataset.variables["lon"][:]
lats = nc_dataset.variables["lat"][:]
# Get average of chlorophyl values from date range
for data_file in file_list[date_idx]:
current_file = os.path.join(data_dir,data_file)
# import data as numpy array, then get columns
nc_dataset = Dataset(data_file,'r')
vals = nc_dataset.variables["CHL1_mean"][:][:]
# create running sum of values
if last_vals:
vals = vals+last_vals
last_vals = vals
file_count += 1
vals = vals/file_count #TODO simple average
# date_list = np.zeros(len(nc_rows), dtype=(np.str_, 19))
date_list[:] = str(datetime.datetime.strptime(file_date, '%Y%m%d'))
# Mesh Lat/Lon the unravel to return lists
#TODO make mesh optional (i.e. return grid or lists)
lon_mesh, lat_mesh = np.meshgrid(lons,lats)
nc_coord_ids = np.vstack([nc_rows,nc_cols])
lons, lats = grid.isin_convert(coord=nc_coord_ids)
# Print Globcolour Information
print '\nChl-a Information'
print '-------------------------------------------'
print 'Start Date: ', start_date
print 'End Date: ', end_date
print 'Max Chl-a: ', np.amax(vals)
print 'Min Chl-a: ', np.amin(vals)
return lons, lats, chl_vals
print datafile
nc_filepath, nc_filename = os.path.split(datafile)
file_date = re.split('[._]', nc_filename)[1]
# import data as numpy array, then get colums
#nc_dataset = Dataset(nc_file,'r')
nc_dataset = Dataset(datafile, 'r')
nc_rows = nc_dataset.variables["row"][:]
nc_cols = nc_dataset.variables["col"][:]
nc_vals = nc_dataset.variables["CHL1_mean"][:]
date_list = np.zeros(len(nc_rows), dtype=(np.str_, 19))
date_list[:] = str(datetime.datetime.strptime(file_date, '%Y%m%d'))
nc_coord_ids = np.vstack([nc_rows, nc_cols])
lons, lats = grid.isin_convert(coord=nc_coord_ids)
#print lon.shape,lat.shape,nc_vals.shape
#print 'lon\n',np.amax(lon), np.amin(lon), lon.shape
#print 'lat\n',np.amax(lat), np.amin(lat), lat.shape
#print 'ncvals\n',np.amax(nc_vals), np.amin(nc_vals), np.size(nc_vals)
#print file_date
#print date_list
#print np.swapaxes(np.vstack([lon,lat,nc_vals]),0,1)
#nc_all = np.vstack([file_date,nc_coord_ids,nc_vals])
#grid_coords = isin.isin_convert(nparray_coords)
#grid_chla =
# Write CSV
#coordFile = open('./test-coords.csv', 'wb')
print datafile
nc_filepath, nc_filename = os.path.split(datafile)
file_date = re.split('[._]', nc_filename)[1]
# import data as numpy array, then get colums
#nc_dataset = Dataset(nc_file,'r')
nc_dataset = Dataset(datafile,'r')
nc_rows = nc_dataset.variables["row"][:]
nc_cols = nc_dataset.variables["col"][:]
nc_vals = nc_dataset.variables["CHL1_mean"][:]
date_list = np.zeros(len(nc_rows), dtype=(np.str_, 19))
date_list[:] = str(datetime.datetime.strptime(file_date, '%Y%m%d'))
nc_coord_ids = np.vstack([nc_rows,nc_cols])
lons, lats = grid.isin_convert(coord=nc_coord_ids)
#print lon.shape,lat.shape,nc_vals.shape
#print 'lon\n',np.amax(lon), np.amin(lon), lon.shape
#print 'lat\n',np.amax(lat), np.amin(lat), lat.shape
#print 'ncvals\n',np.amax(nc_vals), np.amin(nc_vals), np.size(nc_vals)
#print file_date
#print date_list
#print np.swapaxes(np.vstack([lon,lat,nc_vals]),0,1)
#nc_all = np.vstack([file_date,nc_coord_ids,nc_vals])
#grid_coords = isin.isin_convert(nparray_coords)
#grid_chla =
# Write CSV
#coordFile = open('./test-coords.csv', 'wb')
