Example 9: Temporal average
===========================

This example demonstrates how to calculate temporal mean and plot with GINCCO_lib
using :func:`GINCCO_lib.temporal_mean.monthly_mean`.


Code Example
------------

Now we will import the library and the grid

.. code-block:: python

    # =========================
    # IMPORTS
    # =========================
    import numpy as np
    from netCDF4 import Dataset
    from datetime import *
    import GINCCO_lib as gc

    # =========================
    # CONFIGURATION
    # =========================
    tstart = datetime(2010, 1, 1)
    tend   = datetime(2011, 12, 31)

    path = '/work/users/tungnd/GOT271/GOT_REF5/OFFLINE/'

    # =========================
    # LOAD GRID AND DEPTH
    # =========================
    fgrid   = Dataset(path + 'grid.nc', 'r')
    lat_t   = fgrid.variables['latitude_t'][:]
    lon_t   = fgrid.variables['longitude_t'][:]
    depth_t   = fgrid.variables['depth_t'][0,:,:]
    dxy_t   = fgrid.variables['dxdy_t'][:]
    mask_t_var = fgrid.variables['mask_t']
    if mask_t_var.ndim == 3:
        mask_t = mask_t_var[0, :, :]
    elif mask_t_var.ndim == 2:
        mask_t = mask_t_var[:, :]
    depth_t[mask_t==0] = np.nan



Now define the box to average. In this example, we will define 3 boxes

.. code-block:: python

    # Step 1: Define a box
    lon_min_box = [106, 107.1, 105.5]
    lon_max_box = [107, 108.1, 106.7]
    lat_min_box = [20, 20, 18.9]
    lat_max_box = [21, 21, 19.9]
    label_list = ['Box 1', 'Box 2', 'Box 3']

    # Step 2: Plot boxes
    gc.map_draw_box(
        lon_min=105, lon_max=111,
        lat_min=16.5, lat_max=22,
        title="Locations of regions and bathymetry (in m)",
        lon_data=lon_t,
        lat_data=lat_t,
        data_draw=depth_t[:, :],
        lon_min_box = lon_min_box, 
        lon_max_box = lon_max_box, 
        lat_min_box = lat_min_box, 
        lat_max_box = lat_max_box,
        label = label_list,
        path_save="/prod/projects/data/tungnd/figure/",
        name_save="map_box"
    )

.. image:: ../_static/example8.1.png
   :width: 500px
   :align: center



Now import data, calculate the mean and plot it

.. code-block:: python

    # Step 3: Import salinity map
    # data_draw shape = (number of points, number of days)
    sal_surface = gc.import_surface(path, 'sal', tstart, tend, ignore_missing='False')

    # Step 4: Calculate salinity mean
    sal_mean = np.zeros((len(lon_min_box), (tend-tstart).days +1    ))

    for i in range(0, len(lat_min_box)):
        sal_mean[i,:] = gc.spatial_average(sal_surface,
            dxy_t,
            mask_ocean=mask_t,
            lon_t=lon_t,
            lat_t=lat_t,
            lon_min=lon_min_box[i],
            lon_max=lon_max_box[i],
            lat_min=lat_min_box[i],
            lat_max=lat_max_box[i],   
        )

    # Step 5: Monthly mean
    sal_mean_monthly, label = gc.monthly_mean(data = sal_mean, tstart = tstart,tend =  tend, time_axis = 1)

    # Step 6: plot
    gc.plot_point_monthly(
        title="Monthly mean surface salinity in the box",
        time_label = label,             # sequence of str or anything convertible to str, len = n_time
        data_point = sal_mean_monthly,  # numpy array, shape (n_point, n_time) or (n_time,)
        n_xticks_desired=8,             # user suggestion; function will adjust for nice spacing
        path_save="/prod/projects/data/tungnd/figure/",
        name_save="figure9",
        point_labels=label_list   # optional list of names for each point
    )



.. image:: ../_static/example9_43847.png
   :width: 700px
   :align: center