{"id":1006,"date":"2018-01-22T19:40:12","date_gmt":"2018-01-22T19:40:12","guid":{"rendered":"https:\/\/research.reading.ac.uk\/landsurfaceprocesses\/?page_id=1006"},"modified":"2018-01-22T19:40:12","modified_gmt":"2018-01-22T19:40:12","slug":"smc_1d-2d_1979-1989africa-py","status":"publish","type":"page","link":"https:\/\/research.reading.ac.uk\/landsurfaceprocesses\/software-examples\/code-samples\/smc_1d-2d_1979-1989africa-py\/","title":{"rendered":"smc_1D-2D_1979-1989Africa.py"},"content":{"rendered":"

#\u00a0smc_1D-2D_1979-1989Africa.py<\/p>\n

# This Python2.7 code was used on CEDA JASMIN to produce monthly<\/div>\n
# animation frames for an animated gif that was then made into an MP4<\/div>\n
# for showing the soil moisture<\/div>\n
# over Africa from 1979-1989 (we didn’t use the full range up to 2012 to reduce the filesize of the animated gif)<\/div>\n
# using (as input) monthly-averaged JULES land-only 1D data.<\/div>\n
# This Python2.7 code was adapted from the code given in Emma Robinson’s<\/div>\n
# data visualization tutorial for plotting JULES data at http:\/\/jules.jchmr.org\/content\/training .<\/div>\n
# The adaptation was done in November-December 2017 by Patrick McGuire and Pier Luigi Vidale<\/div>\n
# at the University of Reading (email: p.mcguire@reading.ac.uk )<\/div>\n
<\/div>\n
from netCDF4 import Dataset, num2date, date2num<\/div>\n
# Import libraries useful for times<\/div>\n
import datetime as dt<\/div>\n
import calendar as cal<\/div>\n
# widget library<\/div>\n
#from ipywidgets.widgets import *<\/div>\n
import numpy as np<\/div>\n
import matplotlib.pyplot as plt<\/div>\n
from mpl_toolkits.basemap import Basemap<\/div>\n
# Import locators for fancying up plots<\/div>\n
from matplotlib.dates import YearLocator, MonthLocator<\/div>\n
from matplotlib.ticker import MultipleLocator<\/div>\n
<\/div>\n
def ReadData(fname, vname, rescale):<\/div>\n
\u00a0 \u00a0 # Open the file<\/div>\n
\u00a0 \u00a0 f = Dataset(fname,’r’)<\/div>\n
<\/div>\n
\u00a0 \u00a0 # Print file headers<\/div>\n
\u00a0 \u00a0 #print f<\/div>\n
<\/div>\n
\u00a0 \u00a0 # Print variable information<\/div>\n
\u00a0 \u00a0 #print f.variables[‘fqw_gb’]<\/div>\n
\u00a0 \u00a0 #print f.variables[‘ftl_gb’]<\/div>\n
<\/div>\n
\u00a0 \u00a0 # Read the evaporation variable<\/div>\n
\u00a0 \u00a0 data = f.variables[vname][:]<\/div>\n
<\/div>\n
\u00a0 \u00a0 # Get the shape<\/div>\n
\u00a0 \u00a0 nt, ny, nx = data.shape<\/div>\n
<\/div>\n
\u00a0 \u00a0 # Since we know that the y dimension is degenerate, we select y=0 and collapse<\/div>\n
\u00a0 \u00a0 # the data to 2d (nt*nx)<\/div>\n
\u00a0 \u00a0 data = data[:,0,:]<\/div>\n
<\/div>\n
\u00a0 \u00a0 # Get the fill value for later use<\/div>\n
\u00a0 \u00a0 fill_value = f.variables[vname]._FillValue<\/div>\n
<\/div>\n
\u00a0 \u00a0 # Get latitude and longitude variables<\/div>\n
\u00a0 \u00a0 # latitude and longitude are ny*nx, so we select y=0 and collapse to a vector<\/div>\n
\u00a0 \u00a0 lat = f.variables[‘latitude’][0,:]<\/div>\n
\u00a0 \u00a0 lon = f.variables[‘longitude’][0,:]<\/div>\n
<\/div>\n
\u00a0 \u00a0 # Find information about times<\/div>\n
\u00a0 \u00a0 startyr, startmn, startdy = [int(t) \\<\/div>\n
\u00a0 \u00a0 for t in f.variables[‘time’].units.split()[2].split(‘-‘)]<\/div>\n
<\/div>\n
\u00a0 \u00a0 # Read the time and convert to datetime data structures<\/div>\n
\u00a0 \u00a0 # Since we’re looking at monthly averages, we use the time at the start of the<\/div>\n
\u00a0 \u00a0 # month. So we read the time_bounds array and use the lower value<\/div>\n
# \u00a0 \u00a0time_bounds = f.variables[‘time_bounds’][:]<\/div>\n
\u00a0 \u00a0 time_bounds = f.variables[‘time_bnds’][:]<\/div>\n
\u00a0 \u00a0 time = [dt.datetime(startyr,startmn,startdy)+dt.timedelta(seconds=int(t)) \\<\/div>\n
\u00a0 \u00a0 for t in time_bounds[:,0] ]<\/div>\n
<\/div>\n
\u00a0 \u00a0 # We want to rescale the precip from kg\/m2\/s to mm\/month, so we need to<\/div>\n
\u00a0 \u00a0 # know how long the month is<\/div>\n
\u00a0 \u00a0 days_in_month = np.array([ [cal.monthrange(t.year,t.month)[1],] for t in time ])<\/div>\n
\u00a0 \u00a0 secs_in_day = 86400<\/div>\n
<\/div>\n
\u00a0 \u00a0 if(rescale):<\/div>\n
\u00a0 \u00a0 # Rescale precip<\/div>\n
\u00a0 \u00a0 \u00a0 data *= (days_in_month * secs_in_day)<\/div>\n
<\/div>\n
\u00a0 \u00a0 # Close the file<\/div>\n
\u00a0 \u00a0 f.close()<\/div>\n
\u00a0 \u00a0 return data, lon, lat, nt, nx, fill_value<\/div>\n
<\/div>\n
def VectorToGrid(data,lon,lat,nt,nx,fill_value):<\/div>\n
\u00a0 \u00a0 # Define the grid we want to end up on<\/div>\n
\u00a0 \u00a0 lon_min = -180.0<\/div>\n
\u00a0 \u00a0 lon_max = 180.0<\/div>\n
\u00a0 \u00a0 dlon = 0.5<\/div>\n
<\/div>\n
\u00a0 \u00a0 lat_min = -90.0<\/div>\n
\u00a0 \u00a0 lat_max = 90.0<\/div>\n
\u00a0 \u00a0 dlat = 0.5<\/div>\n
<\/div>\n
\u00a0 \u00a0 # Create the grid<\/div>\n
\u00a0 \u00a0 grid_lon, grid_lat = np.meshgrid( np.arange( lon_min+dlon\/2., lon_max, dlon ), \\<\/div>\n
\u00a0 \u00a0 np.arange( lat_min+dlat\/2., lat_max, dlat ) )<\/div>\n
<\/div>\n
\u00a0 \u00a0 # Grid shape<\/div>\n
\u00a0 \u00a0 ny_grid, nx_grid = grid_lon.shape<\/div>\n
<\/div>\n
\u00a0 \u00a0 # Map the vector to the grid<\/div>\n
<\/div>\n
\u00a0 \u00a0 # If it’s not a regular lat\/lon grid, then use the np.where function to find<\/div>\n
\u00a0 \u00a0 # the right point in the grid<\/div>\n
\u00a0 \u00a0 # indx = [np.where( np.logical_and( grid_lon == lon[i], grid_lat == lat[i] )) \\<\/div>\n
\u00a0 \u00a0 # for i in range(nx) ]<\/div>\n
<\/div>\n
\u00a0 \u00a0 # But, since this is regular lat\/lon, we can save time and calculate where<\/div>\n
\u00a0 \u00a0 # each point will be relative to the minimum values<\/div>\n
\u00a0 \u00a0 # This is quicker than the np.where function call<\/div>\n
\u00a0 \u00a0 indx = zip(* [ [ int((lat[i] – lat_min)\/dlat), int((lon[i] – lon_min)\/dlon) ] \\<\/div>\n
\u00a0 \u00a0 for i in range(nx)] )<\/div>\n
<\/div>\n
\u00a0 \u00a0 # Create a new masked array with no data in it<\/div>\n
\u00a0 \u00a0 data_grid = np.ma.masked_equal( np.ones([nt,ny_grid,nx_grid])*fill_value, \\<\/div>\n
\u00a0 \u00a0 fill_value )<\/div>\n
<\/div>\n
\u00a0 \u00a0 # Put the vector data into the grid<\/div>\n
\u00a0 \u00a0 data_grid[:,indx[0],indx[1]] = data[:]<\/div>\n
\u00a0 \u00a0 return data_grid, grid_lon, grid_lat, lon_min, lon_max, lat_min, lat_max<\/div>\n
<\/div>\n
#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)<\/div>\n
lon_min_Africa = -30.25<\/div>\n
lon_max_Africa = 60.25<\/div>\n
lat_min_Africa = -40.25<\/div>\n
lat_max_Africa = 40.25<\/div>\n
<\/div>\n
#vname = ‘precip’<\/div>\n
#vnamelong = ‘Precipitation’<\/div>\n
#vnamedir = ‘precip’<\/div>\n
#cmap0 = ‘gist_rainbow’<\/div>\n
##units = ‘mm\/s’<\/div>\n
##vmin0 = 0<\/div>\n
##vmax0 = 1e-4<\/div>\n
##tickformat = ‘%.0e’<\/div>\n
#units = ‘mm\/month’<\/div>\n
#vmin0 = 0<\/div>\n
#vmax0 = 300<\/div>\n
#tickformat = ‘%.0f’<\/div>\n
#rescale = 1<\/div>\n
<\/div>\n
vname = ‘smc_avail_tot’<\/div>\n
vnamelong = ‘Soil_moisture_avail_tot’<\/div>\n
vnamedir = ‘smc’<\/div>\n
cmap0 = ‘gist_rainbow’<\/div>\n
units = ‘mm’<\/div>\n
vmin0 = 0<\/div>\n
vmax0 = 600<\/div>\n
tickformat = ‘%.0f’<\/div>\n
rescale = 0<\/div>\n
<\/div>\n
#vname = ‘npp_gb’<\/div>\n
#vnamelong = ‘Net_primary_production(NPP)’<\/div>\n
#vnamedir = ‘npp’<\/div>\n
#cmap0 = ‘PRGn’<\/div>\n
#units = ‘kg m-2 s-1’<\/div>\n
#vmin0 = -5e-8<\/div>\n
#vmax0 = 5e-8<\/div>\n
#tickformat = ‘%.0e’<\/div>\n
#rescale = 0<\/div>\n
<\/div>\n
for year in range(1979,1990):<\/div>\n
\u00a0 fname = ‘Euro44_bvv_nancil_CTL-BCJ-GL_jules-vn4.9p_u-as052globeE_monmean_’+str(year)+’.nc’<\/div>\n
<\/div>\n
\u00a0 data, lon, lat, nt, nx, fill_value \u00a0 \u00a0 \u00a0 \u00a0= ReadData(fname,vname,rescale)<\/div>\n
\u00a0 data_grid,grid_lon,grid_lat,lon_min,lon_max,lat_min,lat_max = VectorToGrid(data,lon,lat,nt,nx,fill_value)<\/div>\n
<\/div>\n
#print data_grid.shape<\/div>\n
<\/div>\n
\u00a0 for month in range(0,12):<\/div>\n
\u00a0 \u00a0 if(month<9):<\/div>\n
\u00a0 \u00a0 \u00a0 month2=’0’+str(month+1)<\/div>\n
\u00a0 \u00a0 else:<\/div>\n
\u00a0 \u00a0 \u00a0 month2=str(month+1)<\/div>\n
\u00a0 \u00a0 print ‘Year=’+str(year)+’ Month=’+month2<\/div>\n
\u00a0 \u00a0 fig = plt.figure(figsize=(12.,8.))<\/div>\n
<\/div>\n
# Create a new plot<\/div>\n
\u00a0 \u00a0 fig,ax = plt.subplots(1,1)<\/div>\n
<\/div>\n
# Make a world map<\/div>\n
\u00a0 \u00a0 m = Basemap(projection=’cyl’, resolution=’c’, ax = ax , \\<\/div>\n
llcrnrlat = lat_min_Africa, \\<\/div>\n
llcrnrlon = lon_min_Africa, \\<\/div>\n
urcrnrlat = lat_max_Africa, \\<\/div>\n
urcrnrlon = lon_max_Africa )<\/div>\n
\u00a0 \u00a0 m.drawcoastlines(zorder=2)<\/div>\n
\u00a0 \u00a0 m.fillcontinents([0.8,0.8,0.8],zorder=0)<\/div>\n
<\/div>\n
<\/div>\n
# latitude lower and upper index<\/div>\n
\u00a0 \u00a0 latli = np.argmin( np.abs( grid_lat[:,0] – lat_min_Africa ) )<\/div>\n
\u00a0 \u00a0 latui = np.argmin( np.abs( grid_lat[:,0] – lat_max_Africa ) )<\/div>\n
# \u00a0 \u00a0print ‘Lat index min\/max=’+str(latli)+’ ‘+str(latui)<\/div>\n
# \u00a0 \u00a0print ‘Lat min\/max=’+str(grid_lat[latli,0])+’ ‘+str(grid_lat[latui,0])<\/div>\n
<\/div>\n
# longitude lower and upper index<\/div>\n
\u00a0 \u00a0 lonli = np.argmin( np.abs( grid_lon[0,:] – lon_min_Africa) )<\/div>\n
\u00a0 \u00a0 lonui = np.argmin( np.abs( grid_lon[0,:] – lon_max_Africa) )<\/div>\n
# \u00a0 \u00a0print ‘Lon index min\/max=’+str(lonli)+’ ‘+str(lonui)<\/div>\n
# \u00a0 \u00a0print ‘Lon min\/max=’+str(grid_lon[0,lonli])+’ ‘+str(grid_lon[0,lonui])<\/div>\n
<\/div>\n
<\/div>\n
# Plot array as a colormap<\/div>\n
#im = ax.imshow(data_grid[7,:,:], cmap = ‘gist_ncar’, \\<\/div>\n
\u00a0 \u00a0 im = ax.imshow(data_grid[month,latli:latui,lonli:lonui], cmap = cmap0, \\<\/div>\n
interpolation = ‘nearest’, origin = ‘lower’, \\<\/div>\n
vmin = vmin0, vmax = vmax0, zorder=1, \\<\/div>\n
extent = [lon_min_Africa, lon_max_Africa, lat_min_Africa, lat_max_Africa])<\/div>\n
<\/div>\n
# Set grid<\/div>\n
\u00a0 \u00a0 ax.xaxis.set_major_locator(MultipleLocator(30))<\/div>\n
\u00a0 \u00a0 ax.yaxis.set_major_locator(MultipleLocator(30))<\/div>\n
\u00a0 \u00a0 ax.grid(True)<\/div>\n
<\/div>\n
# Create a colorbar<\/div>\n
\u00a0 \u00a0 plt.colorbar(im, ax = ax, orientation=’horizontal’, \\<\/div>\n
label=’Monthly mean ‘+vnamelong+’ (‘+units+’): ‘+str(month2)+’-‘+str(year), format = tickformat )<\/div>\n
<\/div>\n
# Save the figure<\/div>\n
\u00a0 \u00a0 fig.savefig(vnamedir+’_pngs_Africa\/’+str(vname)+’_’+str(year)+’-‘+str(month2)+’.png’,dpi=150)<\/div>\n
<\/div>\n
# Show the figures on screen<\/div>\n
# \u00a0 \u00a0plt.show()<\/div>\n
\u00a0 \u00a0 plt.close(fig)<\/div>\n
<\/div>\n","protected":false},"excerpt":{"rendered":"

#\u00a0smc_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<\/p>\n","protected":false},"author":12,"featured_media":0,"parent":998,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"__cvm_playback_settings":[],"__cvm_video_id":"","footnotes":""},"class_list":["post-1006","page","type-page","status-publish","hentry"],"acf":[],"yoast_head":"\nsmc_1D-2D_1979-1989Africa.py - Land Surface Processes Group<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/research.reading.ac.uk\/landsurfaceprocesses\/software-examples\/code-samples\/smc_1d-2d_1979-1989africa-py\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"smc_1D-2D_1979-1989Africa.py - 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