# smc_1D-2D_1979-1989Africa.py

# This Python2.7 code was used on CEDA JASMIN to produce monthly
# animation frames for an animated gif that was then made into an MP4
# for showing the soil moisture
# over Africa from 1979-1989 (we didn’t use the full range up to 2012 to reduce the filesize of the animated gif)
# using (as input) monthly-averaged JULES land-only 1D data.
# This Python2.7 code was adapted from the code given in Emma Robinson’s
# data visualization tutorial for plotting JULES data at http://jules.jchmr.org/content/training .
# The adaptation was done in November-December 2017 by Patrick McGuire and Pier Luigi Vidale
# at the University of Reading (email: p.mcguire@reading.ac.uk )
from netCDF4 import Dataset, num2date, date2num
# Import libraries useful for times
import datetime as dt
import calendar as cal
# widget library
#from ipywidgets.widgets import *
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
# Import locators for fancying up plots
from matplotlib.dates import YearLocator, MonthLocator
from matplotlib.ticker import MultipleLocator
def ReadData(fname, vname, rescale):
    # Open the file
    f = Dataset(fname,’r’)
    # Print file headers
    #print f
    # Print variable information
    #print f.variables[‘fqw_gb’]
    #print f.variables[‘ftl_gb’]
    # Read the evaporation variable
    data = f.variables[vname][:]
    # Get the shape
    nt, ny, nx = data.shape
    # Since we know that the y dimension is degenerate, we select y=0 and collapse
    # the data to 2d (nt*nx)
    data = data[:,0,:]
    # Get the fill value for later use
    fill_value = f.variables[vname]._FillValue
    # Get latitude and longitude variables
    # latitude and longitude are ny*nx, so we select y=0 and collapse to a vector
    lat = f.variables[‘latitude’][0,:]
    lon = f.variables[‘longitude’][0,:]
    # Find information about times
    startyr, startmn, startdy = [int(t) \
    for t in f.variables[‘time’].units.split()[2].split(‘-‘)]
    # Read the time and convert to datetime data structures
    # Since we’re looking at monthly averages, we use the time at the start of the
    # month. So we read the time_bounds array and use the lower value
#    time_bounds = f.variables[‘time_bounds’][:]
    time_bounds = f.variables[‘time_bnds’][:]
    time = [dt.datetime(startyr,startmn,startdy)+dt.timedelta(seconds=int(t)) \
    for t in time_bounds[:,0] ]
    # We want to rescale the precip from kg/m2/s to mm/month, so we need to
    # know how long the month is
    days_in_month = np.array([ [cal.monthrange(t.year,t.month)[1],] for t in time ])
    secs_in_day = 86400
    if(rescale):
    # Rescale precip
      data *= (days_in_month * secs_in_day)
    # Close the file
    f.close()
    return data, lon, lat, nt, nx, fill_value
def VectorToGrid(data,lon,lat,nt,nx,fill_value):
    # Define the grid we want to end up on
    lon_min = -180.0
    lon_max = 180.0
    dlon = 0.5
    lat_min = -90.0
    lat_max = 90.0
    dlat = 0.5
    # Create the grid
    grid_lon, grid_lat = np.meshgrid( np.arange( lon_min+dlon/2., lon_max, dlon ), \
    np.arange( lat_min+dlat/2., lat_max, dlat ) )
    # Grid shape
    ny_grid, nx_grid = grid_lon.shape
    # Map the vector to the grid
    # If it’s not a regular lat/lon grid, then use the np.where function to find
    # the right point in the grid
    # indx = [np.where( np.logical_and( grid_lon == lon[i], grid_lat == lat[i] )) \
    # for i in range(nx) ]
    # But, since this is regular lat/lon, we can save time and calculate where
    # each point will be relative to the minimum values
    # This is quicker than the np.where function call
    indx = zip(* [ [ int((lat[i] – lat_min)/dlat), int((lon[i] – lon_min)/dlon) ] \
    for i in range(nx)] )
    # Create a new masked array with no data in it
    data_grid = np.ma.masked_equal( np.ones([nt,ny_grid,nx_grid])*fill_value, \
    fill_value )
    # Put the vector data into the grid
    data_grid[:,indx[0],indx[1]] = data[:]
    return data_grid, grid_lon, grid_lat, lon_min, lon_max, lat_min, lat_max
#Make sure to pick subset LAT/LON’s to be a half a pixel off of where you want (0.25- or 0.75-degree offsets from a degree)
lon_min_Africa = -30.25
lon_max_Africa = 60.25
lat_min_Africa = -40.25
lat_max_Africa = 40.25
#vname = ‘precip’
#vnamelong = ‘Precipitation’
#vnamedir = ‘precip’
#cmap0 = ‘gist_rainbow’
##units = ‘mm/s’
##vmin0 = 0
##vmax0 = 1e-4
##tickformat = ‘%.0e’
#units = ‘mm/month’
#vmin0 = 0
#vmax0 = 300
#tickformat = ‘%.0f’
#rescale = 1
vname = ‘smc_avail_tot’
vnamelong = ‘Soil_moisture_avail_tot’
vnamedir = ‘smc’
cmap0 = ‘gist_rainbow’
units = ‘mm’
vmin0 = 0
vmax0 = 600
tickformat = ‘%.0f’
rescale = 0
#vname = ‘npp_gb’
#vnamelong = ‘Net_primary_production(NPP)’
#vnamedir = ‘npp’
#cmap0 = ‘PRGn’
#units = ‘kg m-2 s-1’
#vmin0 = -5e-8
#vmax0 = 5e-8
#tickformat = ‘%.0e’
#rescale = 0
for year in range(1979,1990):
  fname = ‘Euro44_bvv_nancil_CTL-BCJ-GL_jules-vn4.9p_u-as052globeE_monmean_’+str(year)+’.nc’
  data, lon, lat, nt, nx, fill_value        = ReadData(fname,vname,rescale)
  data_grid,grid_lon,grid_lat,lon_min,lon_max,lat_min,lat_max = VectorToGrid(data,lon,lat,nt,nx,fill_value)
#print data_grid.shape
  for month in range(0,12):
    if(month<9):
      month2=’0’+str(month+1)
    else:
      month2=str(month+1)
    print ‘Year=’+str(year)+’ Month=’+month2
    fig = plt.figure(figsize=(12.,8.))
# Create a new plot
    fig,ax = plt.subplots(1,1)
# Make a world map
    m = Basemap(projection=’cyl’, resolution=’c’, ax = ax , \
llcrnrlat = lat_min_Africa, \
llcrnrlon = lon_min_Africa, \
urcrnrlat = lat_max_Africa, \
urcrnrlon = lon_max_Africa )
    m.drawcoastlines(zorder=2)
    m.fillcontinents([0.8,0.8,0.8],zorder=0)
# latitude lower and upper index
    latli = np.argmin( np.abs( grid_lat[:,0] – lat_min_Africa ) )
    latui = np.argmin( np.abs( grid_lat[:,0] – lat_max_Africa ) )
#    print ‘Lat index min/max=’+str(latli)+’ ‘+str(latui)
#    print ‘Lat min/max=’+str(grid_lat[latli,0])+’ ‘+str(grid_lat[latui,0])
# longitude lower and upper index
    lonli = np.argmin( np.abs( grid_lon[0,:] – lon_min_Africa) )
    lonui = np.argmin( np.abs( grid_lon[0,:] – lon_max_Africa) )
#    print ‘Lon index min/max=’+str(lonli)+’ ‘+str(lonui)
#    print ‘Lon min/max=’+str(grid_lon[0,lonli])+’ ‘+str(grid_lon[0,lonui])
# Plot array as a colormap
#im = ax.imshow(data_grid[7,:,:], cmap = ‘gist_ncar’, \
    im = ax.imshow(data_grid[month,latli:latui,lonli:lonui], cmap = cmap0, \
interpolation = ‘nearest’, origin = ‘lower’, \
vmin = vmin0, vmax = vmax0, zorder=1, \
extent = [lon_min_Africa, lon_max_Africa, lat_min_Africa, lat_max_Africa])
# Set grid
    ax.xaxis.set_major_locator(MultipleLocator(30))
    ax.yaxis.set_major_locator(MultipleLocator(30))
    ax.grid(True)
# Create a colorbar
    plt.colorbar(im, ax = ax, orientation=’horizontal’, \
label=’Monthly mean ‘+vnamelong+’ (‘+units+’): ‘+str(month2)+’-‘+str(year), format = tickformat )
# Save the figure
    fig.savefig(vnamedir+’_pngs_Africa/’+str(vname)+’_’+str(year)+’-‘+str(month2)+’.png’,dpi=150)
# Show the figures on screen
#    plt.show()
    plt.close(fig)