{"id":1012,"date":"2018-01-22T19:44:21","date_gmt":"2018-01-22T19:44:21","guid":{"rendered":"https:\/\/research.reading.ac.uk\/landsurfaceprocesses\/?page_id=1012"},"modified":"2018-01-22T20:35:34","modified_gmt":"2018-01-22T20:35:34","slug":"smc_namerica_normal1-py","status":"publish","type":"page","link":"https:\/\/research.reading.ac.uk\/landsurfaceprocesses\/software-examples\/code-samples\/smc_namerica_normal1-py\/","title":{"rendered":"smc_1D-2D_1979-2012_NAmerica_Normal1.py"},"content":{"rendered":"

#\u00a0smc_1D-2D_1979-2012_NAmerica_Normal1.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 and the seasonal anomaly of soil moisture<\/div>\n
# as two time series over Normal, Illinois from 1979-2012<\/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
from matplotlib.colors import LinearSegmentedColormap<\/div>\n
from matplotlib.colors import ListedColormap<\/div>\n
<\/div>\n
def ReadData(fname, vname, rescale, compute_timebnds):<\/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 calendar=f.variables[‘time’].calendar<\/div>\n
<\/div>\n
\u00a0 \u00a0 if(compute_timebnds):<\/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 \u00a0 time_bounds = f.variables[‘time_bnds’][:]<\/div>\n
\u00a0 \u00a0 \u00a0 time = [dt.datetime(startyr,startmn,startdy)+dt.timedelta(seconds=int(t)) \\<\/div>\n
\u00a0 \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 \u00a0 days_in_month = np.array([ [cal.monthrange(t.year,t.month)[1],] for t in time ])<\/div>\n
\u00a0 \u00a0 \u00a0 secs_in_day = 86400<\/div>\n
<\/div>\n
\u00a0 \u00a0 \u00a0 if(rescale):<\/div>\n
\u00a0 \u00a0 # Rescale precip<\/div>\n
\u00a0 \u00a0 \u00a0 \u00a0 data *= (days_in_month * secs_in_day)<\/div>\n
\u00a0 \u00a0 else:<\/div>\n
\u00a0 \u00a0 \u00a0 time = 0<\/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, time, calendar, 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
def custom_div_cmap(numcolors=13, name=’custom_div_cmap’,<\/div>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 mincol=’darkred’, midcol=’white’, maxcol=’darkblue’):<\/div>\n
\u00a0 \u00a0 “”” Create a custom diverging colormap with three colors<\/div>\n
<\/div>\n
\u00a0 \u00a0 Default is red to white to blue with 11 colors. \u00a0Colors can be specified<\/div>\n
\u00a0 \u00a0 in any way understandable by matplotlib.colors.ColorConverter.to_rgb()<\/div>\n
\u00a0 \u00a0 “””<\/div>\n
<\/div>\n
<\/div>\n
<\/div>\n
\u00a0 \u00a0 cmap2 = LinearSegmentedColormap.from_list(name=name,<\/div>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0colors =[mincol, midcol, maxcol],<\/div>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0N=numcolors)<\/div>\n
\u00a0 \u00a0 return cmap2<\/div>\n
<\/div>\n
bwr_custom = custom_div_cmap()<\/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
#Africa<\/div>\n
#lon_min_Subset = -30.25<\/div>\n
#lon_max_Subset = 60.25<\/div>\n
#lat_min_Subset = -40.25<\/div>\n
#lat_max_Subset = 40.25<\/div>\n
<\/div>\n
#NAmerica<\/div>\n
lon_min_Subset = -170.25<\/div>\n
lon_max_Subset = -49.75<\/div>\n
lat_min_Subset = 4.75<\/div>\n
lat_max_Subset = 80.25<\/div>\n
<\/div>\n
#Normal, Illinois<\/div>\n
#to nearest quarter degree<\/div>\n
lon_Point = -89.00<\/div>\n
lat_Point = 40.50<\/div>\n
<\/div>\n
#fvarname = [‘EVAP’, ‘GPP’, ‘WAIR’]<\/div>\n
#ovarname= [‘EnsembleLEcor_May12′,’gpp’,’WAI’]<\/div>\n
#mvarname= [‘esoil_mean’,’gpp_mean’,’smc_avail_tot_mean’]<\/div>\n
<\/div>\n
##Set JULES experiments start\/end dates<\/div>\n
#dStart = datetime.datetime(1979,1,1)<\/div>\n
#dEnd = datetime.datetime(1982,12,31)<\/div>\n
#dateIndex = pandas.date_range(start=dStart, end=dEnd, freq=’M’)<\/div>\n
<\/div>\n
##Now set the plot limits<\/div>\n
##t0=datetime.datetime(1997,01,01,00,00) # x axis startpoint<\/div>\n
##t1=datetime.datetime(1999,01,01,00,00) # x axis endpoint<\/div>\n
#t0=datetime.datetime(1979,01,01,00,00) # x axis startpoint<\/div>\n
#t1=datetime.datetime(1982,12,31,00,00) # x axis endpoint<\/div>\n
<\/div>\n
#obs_dir=’\/group_workspaces\/jasmin2\/ncas_generic\/users\/pmcguire\/analysis\/obs\/BGI_validation\/data\/BGI\/’<\/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
#PlotArray = 0<\/div>\n
#PlotSeries = 1<\/div>\n
<\/div>\n
vname = ‘smc_avail_tot’<\/div>\n
vnamelong = ‘Soil_moisture_avail_tot’<\/div>\n
vnamedir = ‘smc_NAmerica_Normal’<\/div>\n
#cmap0 = custom_div_cmap(numcolors=5)<\/div>\n
cmap0=ListedColormap([‘DarkRed’,’Salmon’,’Orange’,’Yellow’,’LightGreen’,’Green’,’Cyan’,’LightBlue’,’Blue’,’DarkBlue’,’Indigo’,’DarkViolet’])<\/div>\n
units = ‘mm’<\/div>\n
vmin0 = 0<\/div>\n
vmax0 = 600<\/div>\n
tickformat = ‘%.0f’<\/div>\n
tickspacing = np.arange(0,650,50.0)<\/div>\n
rescale = 0<\/div>\n
PlotArray = 0<\/div>\n
PlotSeries = 1<\/div>\n
Location = ‘Normal, Illinois’<\/div>\n
YearMin = 1979<\/div>\n
YearMax = 2012<\/div>\n
YEARS = YearMax-YearMin+1<\/div>\n
<\/div>\n
# \u00a0vname = ‘npp_gb’<\/div>\n
# \u00a0vnamelong = ‘Net_primary_production(NPP)’<\/div>\n
# \u00a0vnamedir = ‘npp’<\/div>\n
# \u00a0cmap0 = ‘PRGn’<\/div>\n
# \u00a0units = ‘kg m-2 s-1’<\/div>\n
# \u00a0vmin0 = -5e-8<\/div>\n
# \u00a0vmax0 = 5e-8<\/div>\n
# \u00a0tickformat = ‘%.0e’<\/div>\n
# \u00a0rescale = 0<\/div>\n
# \u00a0PlotArray = 0<\/div>\n
# \u00a0PlotSeries = 1<\/div>\n
<\/div>\n
<\/div>\n
t0=dt.datetime(YearMin,01,01,00,00) # x axis startpoint<\/div>\n
t1=dt.datetime(YearMax,12,31,00,00) # x axis endpoint<\/div>\n
<\/div>\n
fname_avg = ‘Euro44_bvv_nancil_CTL-BCJ-GL_jules-vn4.9p_u-as052globeE_monmean_1979_2012_ymonavg.nc’<\/div>\n
fname_std1 = ‘Euro44_bvv_nancil_CTL-BCJ-GL_jules-vn4.9p_u-as052globeE_monmean_1979_2012_ymonstd1.nc’<\/div>\n
<\/div>\n
data_avg, lon_avg, lat_avg, nt_avg, nx_avg, time_avg, calendar_avg, fill_value_avg \u00a0 \u00a0 \u00a0 \u00a0= ReadData(fname_avg,vname,rescale,compute_timebnds=0)<\/div>\n
data_grid_avg,grid_lon_avg,grid_lat_avg,lon_min_avg,lon_max_avg,lat_min_avg,lat_max_avg = VectorToGrid(data_avg,lon_avg,lat_avg,nt_avg,nx_avg,fill_value_avg)<\/div>\n
<\/div>\n
data_std1, lon_std1, lat_std1, nt_std1, nx_std1, time_std1, calendar_std1, fill_value_std1 \u00a0 \u00a0 \u00a0 \u00a0= ReadData(fname_std1,vname,rescale,compute_timebnds=0)<\/div>\n
data_grid_std1,grid_lon_std1,grid_lat_std1,lon_min_std1,lon_max_std1,lat_min_std1,lat_max_std1 = VectorToGrid(data_std1,lon_std1,lat_std1,nt_std1,nx_std1,fill_value_std1)<\/div>\n
<\/div>\n
<\/div>\n
dates \u00a0 \u00a0 \u00a0 \u00a0 = [dt.date(year,month,1) for year in range(YearMin,YearMax+1) for month in range(1,13)]<\/div>\n
data_series \u00a0 \u00a0 \u00a0 \u00a0 = np.zeros(12*YEARS)<\/div>\n
data_series_avg \u00a0 \u00a0 = np.zeros(12*YEARS)<\/div>\n
data_series_std1 \u00a0 \u00a0= np.zeros(12*YEARS)<\/div>\n
data_series_anom \u00a0 \u00a0= np.zeros(12*YEARS)<\/div>\n
data_series_ones \u00a0 \u00a0= np.ones(12*YEARS)<\/div>\n
data_series_zeros \u00a0 \u00a0= np.zeros(12*YEARS)<\/div>\n
<\/div>\n
for year in range(YearMin,YearMax+1):<\/div>\n
#fname = ‘\/work\/scratch\/pmcguire\/config\/outputs\/wfdei_WRR2_4.9positiverain_16proc45min_U-aq934.monthly.2011.nc’<\/div>\n
# \u00a0 \u00a0print ‘Year=’+str(year)<\/div>\n
<\/div>\n
\u00a0 fname = ‘Euro44_bvv_nancil_CTL-BCJ-GL_jules-vn4.9p_u-as052globeE_monmean_’+str(year)+’.nc’<\/div>\n
\u00a0 fname_anom = ‘Euro44_bvv_nancil_CTL-BCJ-GL_jules-vn4.9p_u-as052globeE_monmean_’+str(year)+’__1979_2012_ymonstd1anom.nc’<\/div>\n
<\/div>\n
<\/div>\n
\u00a0 data, lon, lat, nt, nx, time, calendar0, fill_value \u00a0 \u00a0 \u00a0 \u00a0= ReadData(fname,vname,rescale, compute_timebnds=1)<\/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
\u00a0 data_anom, lon_anom, lat_anom, nt_anom, nx_anom, time_anom, calendar_anom, fill_value_anom \u00a0 \u00a0 \u00a0 \u00a0= ReadData(fname_anom,vname,rescale, compute_timebnds=1)<\/div>\n
\u00a0 data_grid_anom,grid_lon_anom,grid_lat_anom,lon_min_anom,lon_max_anom,lat_min_anom,lat_max_anom = VectorToGrid(data_anom,lon_anom,lat_anom,nt_anom,nx_anom,fill_value_anom)<\/div>\n
#print data_grid.shape<\/div>\n
<\/div>\n
\u00a0 for month in range(0,12):<\/div>\n
\u00a0 \u00a0 dates[month+(year-1979)*12] = time[month]<\/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 \u00a0print ‘Year=’+str(year)+’ Month=’+month2<\/div>\n
# latitude lower and upper index<\/div>\n
\u00a0 \u00a0 latli = np.argmin( np.abs( grid_lat[:,0] – lat_min_Subset ) )<\/div>\n
\u00a0 \u00a0 latui = np.argmin( np.abs( grid_lat[:,0] – lat_max_Subset ) )<\/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
\u00a0 \u00a0 lati = np.argmin( np.abs( grid_lat[:,0] – lat_Point) )<\/div>\n
# \u00a0 \u00a0print ‘Lat index =’+str(lati)<\/div>\n
# \u00a0 \u00a0print ‘Lat =’+str(grid_lat[lati,0])<\/div>\n
<\/div>\n
# longitude lower and upper index<\/div>\n
\u00a0 \u00a0 lonli = np.argmin( np.abs( grid_lon[0,:] – lon_min_Subset) )<\/div>\n
\u00a0 \u00a0 lonui = np.argmin( np.abs( grid_lon[0,:] – lon_max_Subset) )<\/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
\u00a0 \u00a0 loni = np.argmin( np.abs( grid_lon[0,:] – lon_Point) )<\/div>\n
# \u00a0 \u00a0print ‘Lon index =’+str(loni)<\/div>\n
# \u00a0 \u00a0print ‘Lon =’+str(grid_lon[0,loni])<\/div>\n
<\/div>\n
\u00a0 \u00a0 data_series[month+(year-1979)*12] = data_grid[month,lati,loni]<\/div>\n
\u00a0 \u00a0 data_series_avg[month+(year-1979)*12] = data_grid_avg[month,lati,loni]<\/div>\n
\u00a0 \u00a0 data_series_std1[month+(year-1979)*12] = data_grid_std1[month,lati,loni]<\/div>\n
\u00a0 \u00a0 data_series_anom[month+(year-1979)*12] = data_grid_anom[month,lati,loni]<\/div>\n
<\/div>\n
\u00a0 \u00a0 if(PlotArray):<\/div>\n
\u00a0 \u00a0 \u00a0 fig = plt.figure(figsize=(12.,8.))<\/div>\n
<\/div>\n
# Create a new plot<\/div>\n
\u00a0 \u00a0 \u00a0 fig,ax = plt.subplots(1,1)<\/div>\n
<\/div>\n
# Make a world map<\/div>\n
\u00a0 \u00a0 \u00a0 m = Basemap(projection=’cyl’, resolution=’c’, ax = ax , \\<\/div>\n
llcrnrlat = lat_min_Subset, \\<\/div>\n
llcrnrlon = lon_min_Subset, \\<\/div>\n
urcrnrlat = lat_max_Subset, \\<\/div>\n
urcrnrlon = lon_max_Subset )<\/div>\n
\u00a0 \u00a0 \u00a0 m.drawcoastlines(zorder=2)<\/div>\n
\u00a0 \u00a0 \u00a0 m.fillcontinents([0.8,0.8,0.8],zorder=0)<\/div>\n
<\/div>\n
# Plot array as a colormap<\/div>\n
#im = ax.imshow(data_grid[7,:,:], cmap = ‘gist_ncar’, \\<\/div>\n
\u00a0 \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_Subset, lon_max_Subset, lat_min_Subset, lat_max_Subset])<\/div>\n
<\/div>\n
# Set grid<\/div>\n
\u00a0 \u00a0 \u00a0 ax.xaxis.set_major_locator(MultipleLocator(30))<\/div>\n
\u00a0 \u00a0 \u00a0 ax.yaxis.set_major_locator(MultipleLocator(30))<\/div>\n
\u00a0 \u00a0 \u00a0 ax.grid(True)<\/div>\n
<\/div>\n
# Create a colorbar<\/div>\n
\u00a0 \u00a0 \u00a0 cb=plt.colorbar(im, ax = ax, orientation=’horizontal’, \\<\/div>\n
label=’Monthly ‘+vnamelong+’ (‘+units+’): ‘+str(month2)+’-‘+str(year), format = tickformat, ticks = tickspacing, extend=’both’ )<\/div>\n
<\/div>\n
# Save the figure<\/div>\n
#fig.savefig(‘python_sensible_map.png’,dpi=600)<\/div>\n
\u00a0 \u00a0 \u00a0 fig.savefig(vnamedir+’_pngs\/’+str(vname)+’_’+str(year)+’-‘+str(month2)+’.png’,dpi=150)<\/div>\n
<\/div>\n
# Show the figures on screen<\/div>\n
#plt.show()<\/div>\n
\u00a0 \u00a0 \u00a0 plt.close(fig)<\/div>\n
<\/div>\n
if(PlotSeries):<\/div>\n
\u00a0 \u00a0fig = plt.figure(figsize=(12.,8.))<\/div>\n
# \u00a0 \u00a0 \u00a0dates2 = dates<\/div>\n
# \u00a0 \u00a0 \u00a0dates2 = num2date(dates[:],units=’days since 1582-11-15 00:00:00′,calendar=calendar0)<\/div>\n
# Create a new plot<\/div>\n
\u00a0 \u00a0fig,ax = plt.subplots(2,1)<\/div>\n
\u00a0 \u00a0fig.suptitle(‘JULES Soil Moisture in ‘+Location, fontsize=14)<\/div>\n
<\/div>\n
# \u00a0 \u00a0 \u00a0years = dates.YearLocator() \u00a0 # every year<\/div>\n
# \u00a0 \u00a0 \u00a0ax.xaxis.set_major_locator(years)<\/div>\n
\u00a0 \u00a0plt.subplot(2,1,1)<\/div>\n
\u00a0 \u00a0plt.xlim(t0,t1)<\/div>\n
\u00a0 \u00a0print data_series<\/div>\n
\u00a0 \u00a0p0, = plt.plot(dates, data_series, ‘k.-‘,color=’red’, label=’JULES model’)<\/div>\n
\u00a0 \u00a0p1, = plt.plot(dates, data_series_avg, ‘k’,color=’black’, label=’1979-2012 JULES model avg.’)<\/div>\n
\u00a0 \u00a0plt.fill_between(dates, data_series_avg+data_series_std1, data_series_avg-data_series_std1, facecolor=’lightgrey’, edgecolor=’white’)<\/div>\n
\u00a0 \u00a0plt.ylabel(‘Soil Moisture (mm)’)<\/div>\n
\u00a0 \u00a0plt.xlabel(‘Time (yrs)’)<\/div>\n
\u00a0 \u00a0plt.legend(bbox_to_anchor=(0., 1.02, 1., .102),loc=3,ncol=2, mode=”expand”,borderaxespad=0.,fontsize=10)<\/div>\n
<\/div>\n
\u00a0 \u00a0plt.subplot(2,1,2)<\/div>\n
## \u00a0 \u00a0 \u00a0years = mdates.YearLocator() \u00a0 # every year<\/div>\n
## \u00a0 \u00a0 \u00a0ax.xaxis.set_major_locator(years)<\/div>\n
\u00a0 \u00a0plt.xlim(t0,t1)<\/div>\n
\u00a0 \u00a0plt.ylabel(‘Anomaly in Soil Moisture (std. dev.)’)<\/div>\n
\u00a0 \u00a0plt.xlabel(‘Time (yrs)’)<\/div>\n
<\/div>\n
\u00a0 \u00a0p0, = plt.plot(dates, data_series_anom, ‘k.-‘,color=’red’, label=’anomaly of JULES model’)<\/div>\n
\u00a0 \u00a0p1, = plt.plot(dates, data_series_zeros, ‘k’,color=’black’, label=’1979-2012 JULES model avg.’)<\/div>\n
\u00a0 \u00a0plt.fill_between(dates, data_series_ones, -data_series_ones, facecolor=’lightgrey’, edgecolor=’white’)<\/div>\n
<\/div>\n
# Save the figure<\/div>\n
#fig.savefig(‘python_sensible_map.png’,dpi=600)<\/div>\n
\u00a0 \u00a0fig.savefig(vnamedir+’_pngs\/’+str(vname)+’.png’,dpi=150)<\/div>\n
<\/div>\n
\u00a0 \u00a0plt.close(fig)<\/div>\n
<\/div>\n
#air = f.variables[‘esoil_gb’]<\/div>\n
#print air<\/div>\n
#lat = f.variables[‘latitude’]<\/div>\n
#lon = f.variables[‘longitude’]<\/div>\n
#print lat<\/div>\n
#print lon<\/div>\n
#m = Basemap(projection=’npstere’,boundinglat=60,lon_0=0,resolution=’l’)<\/div>\n
#nlon, nlat = np.meshgrid(lon[0,:], lat[0,:])<\/div>\n
#print nlon,nlat<\/div>\n
#x, y = m(lon, lat)<\/div>\n
#print x,y<\/div>\n
#m.fillcontinents(color=’gray’,lake_color=’gray’)<\/div>\n
#m.drawparallels(np.arange(-80.,81.,20.))<\/div>\n
#m.drawmeridians(np.arange(-180.,181.,20.))<\/div>\n
#m.drawmapboundary(fill_color=’white’)<\/div>\n
#cs = m.contourf(x,y,air[0,0,:])<\/div>\n
##imshow(vv[time,:,:])<\/div>\n
#m.colorbar()<\/div>\n
<\/div>\n
#fig.savefig(figname2)<\/div>\n
#plt.close<\/div>\n
#plt.show()<\/div>\n
<\/div>\n","protected":false},"excerpt":{"rendered":"

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