Several adjustments in analysis scripts to produce plots, maps, tables etc. as on the new website

da/analysis/expand_fluxes.py

@@ -735,18 +735,38 @@ def save_weekly_avg_agg_data(dacycle, region_aggregate='olson'):
             for j in range(19):
                 regionnames.append("%s_%s" % (tc.transnams[i], tc.olsonnams[j],))
         regionnames.extend(tc.oifnams)
+        xform = False
 
         for i, name in enumerate(regionnames):
             lab = 'Aggregate_Region_%03d' % (i + 1,)
             setattr(ncf, lab, name)
 
+    elif region_aggregate == "olson_extended":
+        regionmask = tc.olson_ext_mask
+        dimname = 'olson_ext'
+        dimregs = ncf.add_dim(dimname, regionmask.max())
+        xform = False
+
+        for i, name in enumerate(tc.olsonextnams):
+            lab = 'Aggreate_Region_%03d'%(i+1)
+            setattr(ncf, lab, name)
+
     elif region_aggregate == "transcom":
         regionmask = tc.transcommask
         dimname = 'tc'
         dimregs = ncf.add_region_dim(type='tc')
+        xform = False
+
+    elif region_aggregate == "transcom_extended":
+        regionmask = tc.transcommask
+        dimname = 'tc_ext'
+        dimregs = ncf.add_region_dim(type='tc_ext')
+        xform = True
+
 
     elif region_aggregate == "country":
 
+        xform = False
         countrydict = ct.get_countrydict()
         selected = ['Russia', 'Canada', 'China', 'United States', 'EU27', 'Brazil', 'Australia', 'India'] #,'G8','UNFCCC_annex1','UNFCCC_annex2']
         regionmask = np.zeros((180, 360,), 'float')
@@ -857,10 +877,28 @@ def save_weekly_avg_agg_data(dacycle, region_aggregate='olson'):
                     regiondata.append(aggdata)
 
                 regiondata = np.array(regiondata)
+                try:
+                    regioncov = regiondata.transpose().dot(regiondata) / (data.shape[0] - 1) 
+                except:
+                    regioncov = np.dot(regiondata.transpose(), regiondata) / (data.shape[0] - 1) # Huygens fix 
 
+                if xform:
+                    regiondata = ExtendedTCRegions(regiondata,cov=False)
+                    regioncov = ExtendedTCRegions(regioncov,cov=True)
+                
                 savedict = ncf.standard_var(varname=vname)
+                savedict['name'] = vname.replace('ensemble','covariance') 
+                savedict['units'] = '[mol/region/s]^2'
+                savedict['dims'] = dimdate + dimregs + dimregs
+                savedict['count'] = index
+                savedict['values'] = regioncov
+                ncf.add_data(savedict)
+
+                savedict = ncf.standard_var(varname=vname)
+                savedict['name'] = vname 
                 savedict['units'] = 'mol/region/s'
                 savedict['dims'] = dimdate + dimensemble + dimregs
+
                 
             elif 'flux' in vname:
 
@@ -868,6 +906,9 @@ def save_weekly_avg_agg_data(dacycle, region_aggregate='olson'):
 
                 regiondata = state_to_grid(data * area, regionmask, reverse=True, mapname=region_aggregate)
 
+                if xform:
+                    regiondata = ExtendedTCRegions(regiondata)
+
                 savedict = ncf.standard_var(varname=vname)
                 savedict['dims'] = dimdate + dimregs
                 savedict['units'] = 'mol/region/s'
@@ -876,6 +917,8 @@ def save_weekly_avg_agg_data(dacycle, region_aggregate='olson'):
 
                 data = ncf_in.get_variable(vname)[:]
                 regiondata = state_to_grid(data, regionmask, reverse=True, mapname=region_aggregate)
+                if xform:
+                    regiondata = ExtendedTCRegions(regiondata)
 
                 savedict = ncf.standard_var(varname=vname)
                 savedict['dims'] = dimdate + dimregs
@@ -1040,7 +1083,9 @@ if __name__ == "__main__":
         save_weekly_avg_tc_data(dacycle, statevector)
         save_weekly_avg_ext_tc_data(dacycle)
         save_weekly_avg_agg_data(dacycle, region_aggregate='olson')
+        save_weekly_avg_agg_data(dacycle, region_aggregate='olson_extended')
         save_weekly_avg_agg_data(dacycle, region_aggregate='transcom')
+        save_weekly_avg_agg_data(dacycle, region_aggregate='transcom_extended')
         save_weekly_avg_agg_data(dacycle, region_aggregate='country')
 
         dacycle.advance_cycle_times()

da/analysis/siteseries.py

@@ -44,14 +44,14 @@ ts_y2 = 0.72
 ts_yspan = ts_y2 - ts_y1
 
 markersize = 2
-fontsize = 9
+fontsize = 16 
 
 """ 
     Three routines that provide loops around the number of sites. These also create the rundirs and such and make sure the 
     figures that were created are stamped and saved properly
 """
 
-def site_timeseries(analysisdir):
+def site_timeseries(analysisdir,option='final'):
     """***************************************************************************************
     Call example:
 
@@ -75,8 +75,6 @@ def site_timeseries(analysisdir):
     # Loop over site and sitefiles
     #
     for sitefile in sitelist:
-        
-        logging.debug('Making timeseries figures for %s: ' % sitefile)
         #
         # Create filename and extract site codes for the sites that had day/night data separated
         #
@@ -84,10 +82,11 @@ def site_timeseries(analysisdir):
         saveas = os.path.join(mrdir, sitefile[:-3] + '_timeseries')
 
         if not os.path.exists(saveas+'.pdf'):
+            logging.debug('Making timeseries figures for %s: ' % sitefile)
             #
             # Create a figure instance to hold plot
             #
-            fig = plt.figure(1, figsize=(15, 6,))#,frameon=False)
+            fig = plt.figure(1, figsize=(15, 7,))#,frameon=False)
             #
             # Make plot
             #
@@ -95,27 +94,31 @@ def site_timeseries(analysisdir):
             #
             # Save image
             #
-            fig.savefig(saveas, dpi=100)
+            fig.savefig(saveas+'.pdf', dpi=100)
+            fig.savefig(saveas+'.png', dpi=50)
+            fig.savefig(saveas+'.large.png', dpi=150)
             #
             plt.close(fig)
         #
         # Residuals next
         #
-        logging.debug('Making residuals figures for %s: ' % sitefile)
         saveas = os.path.join(mrdir, sitefile[:-3] + '_residuals')
         if not os.path.exists(saveas+'.pdf'):
+            logging.debug('Making residuals figures for %s: ' % sitefile)
             #
             # Create a figure instance to hold plot
             #
-            fig = plt.figure(1, figsize=(15, 6,))#,frameon=False)
+            fig = plt.figure(1, figsize=(15, 7,))#,frameon=False)
             #
             # Make plot
             #
-            fig = residuals_new(fig, filename)
+            fig = residuals_new(fig, filename, option)
             #
             # Save image
             #
-            fig.savefig(saveas, dpi=100)
+            fig.savefig(saveas+'.pdf', dpi=100)
+            fig.savefig(saveas+'.png', dpi=50)
+            fig.savefig(saveas+'.large.png', dpi=150)
             #
             # next in loop over sites
             #
@@ -123,34 +126,75 @@ def site_timeseries(analysisdir):
         #
         # histograms next
         #
-        logging.debug('Making histograms figures for %s: ' % sitefile)
         saveas = os.path.join(mrdir, sitefile[:-3] + '_histograms')
         if not os.path.exists(saveas+'.pdf'):
+            logging.debug('Making histograms figures for %s: ' % sitefile)
             #
             # Create a figure instance to hold plot
             #
-            fig = plt.figure(1, figsize=(15, 6,))#,frameon=False)
+            fig = plt.figure(1, figsize=(15, 7,))#,frameon=False)
             #
             # Make plot
             #
-            fig = timehistograms_new(fig, filename)
+            fig = timehistograms_new(fig, filename, option)
             #
             # Save image
             #
-            fig.savefig(saveas, dpi=100)
+            fig.savefig(saveas+'.pdf', dpi=100)
+            fig.savefig(saveas+'.png', dpi=50)
+            fig.savefig(saveas+'.large.png', dpi=150)
             #
             # next in loop over sites
             #
             plt.close(fig)
-
-def timehistograms_new(fig, infile):
+        #
+        # html table next
+        #
+        saveas = os.path.join(mrdir, sitefile[:-3] + '.html')
+        if not os.path.exists(saveas):
+            logging.debug('Making html info table for %s' % sitefile)
+            f = io.CT_CDF(filename, 'read')
+            with open(saveas, "wt") as fout:
+                with open("sitetable.html", "rt") as fin:
+                    for lineout in fin:
+                        lineout = lineout.replace('site_name',f.site_name)
+                        lineout = lineout.replace('site_country',f.site_country)
+                        lineout = lineout.replace('site_latitude',nice_lat(f.site_latitude,'html'))
+                        lineout = lineout.replace('site_longitude',nice_lon(f.site_longitude,'html'))
+                        lineout = lineout.replace('site_elevation',str(f.site_elevation))
+                        lineout = lineout.replace('intake_height',str(f.variables['intake_height'][:].max()))
+                        if 'co2_hip' in sitefile:
+                            lineout = lineout.replace('site_map','http://carbontracker.eu/development/data/ct_europe/timeseries_molefractions/co2_hip_map.jpg')
+                        else: lineout = lineout.replace('site_map',str(f.site_map))
+                        lineout = lineout.replace('lab_abbr',f.lab_abbr)
+                        lineout = lineout.replace('lab_name',f.lab_name)
+                        lineout = lineout.replace('lab_country',f.lab_country)
+                        if 'lab_provider' in lineout:
+                            if 'Morgui' in f.provider_1_name:
+                                lineout = lineout.replace('lab_provider','J.A. Morgui / M. Ramonet')
+                            else: lineout = lineout.replace('lab_provider',f.provider_1_name)
+                        if 'lab_url' in lineout:
+                            if f.lab_url <> '':
+                                lineout = lineout.replace('lab_url',f.lab_url)
+                            else: lineout = ''
+                        lineout = lineout.replace('lab_logo',f.lab_logo)
+                        lineout = lineout.replace('dataset_selection',f.dataset_selection_tag)
+                        lineout = lineout.replace('dataset_project',f.dataset_project)
+                        lineout = lineout.replace('dataset_calibration_scale',f.dataset_calibration_scale)
+                        if f.variables['modeldatamismatch'][:].max() > 0.0009:
+                            lineout = lineout.replace('assimilated','No')
+                        else: lineout = lineout.replace('assimilated','Yes')
+                        fout.write(lineout)
+            f.close()           
+
+def timehistograms_new(fig, infile, option='final'):
     """
     This routine makes two side-by-side histograms representing summer and winter PDFs of the residuals. It uses the special 
     x-axis and y-axis definitions from above. Note that currently, the PDFs are based on forecast-observed CO2, and not on
     optimized-observed CO2.
     """
 
-    fontsize = 12
+    fontsize = 17
     #
     # Get data
     #
@@ -169,11 +213,14 @@ def timehistograms_new(fig, infile):
     obs = f.get_variable('value') * molefac
     mdm = f.get_variable('modeldatamismatch') * molefac
     hphtr = f.get_variable('totalmolefractionvariance_forecast') * molefac * molefac
-    simulated = f.get_variable('modelsamplesmean_forecast') * molefac
+    if option == 'final':
+        simulated = f.get_variable('modelsamplesmean') * molefac
+    if option == 'forecast':
+        simulated = f.get_variable('modelsamplesmean_forecast') * molefac
     flags = f.get_variable('flag_forecast')
 
     longsitestring = f.site_name + ', ' + f.site_country
-    location = nice_lat(f.site_latitude) + ', ' + nice_lon(f.site_longitude) + ', ' + nice_alt(f.site_elevation)
+    location = nice_lat(f.site_latitude,'python') + ', ' + nice_lon(f.site_longitude,'python') + ', ' + nice_alt(f.site_elevation)
 
     SDSInfo = {}
     for k in f.ncattrs():
@@ -197,15 +244,18 @@ def timehistograms_new(fig, infile):
     flags = flags.compress(sampled)
     
     residual = simulated - obs
-    chisquared = (residual ** 2) / hphtr
+    if option == 'final':
+        chisquared = (residual ** 2) / mdm 
+    elif option == 'forecast':    
+        chisquared = (residual ** 2) / hphtr
 
     rejected = (flags == 2.0)
     notused = (flags == 99.0)
 
-    if notused.all():
-        return fig
-    else:
-        obslabel = 'Residual'
+    #if notused.all():
+    #    return fig
+    #else:
+    obslabel = 'Residual'
 
     sd = pydates[0]
     ed = pydates[-1]
@@ -236,8 +286,13 @@ def timehistograms_new(fig, infile):
 
         # Calculate residual and chi squared values
 
-        res = sel_fc - sel_obs
-        chi = res / np.sqrt(sel_hqhr)
+        #res = sel_fc - sel_obs
+        if option == 'final':
+            res = sel_fc.compress(sel_flags != 2) - sel_obs.compress(sel_flags != 2)
+            chi = res / np.sqrt(sel_mdm.compress(sel_flags != 2))
+        elif option == 'forecast':    
+            res = sel_fc - sel_obs
+            chi = res / np.sqrt(sel_hqhr)
         
         # Get a scaling factor for the x-axis range. Now we will include 5 standard deviations
 
@@ -265,20 +320,31 @@ def timehistograms_new(fig, infile):
         #
         # Add a legend, not as a legend object but simply as text labels
         #
-        labs = [ 
-            '%.2f $\pm$ %.2f' % (res.mean(), res.std()) , \
-            'N = %d' % sel_obs.shape[0], \
-            'Inn. $\chi^2$= %.2f' % (chi ** 2).mean() 
-            ]
+        if option == 'final':
+            strX = ''
+        elif option == 'forecast':
+            strX = 'Inn. '
+        if chi.mean() != chi.mean() or mdm.mean() < 900:
+            labs = [ 
+                '%.2f $\pm$ %.2f' % (res.mean(), res.std()) , \
+                'N = %d' % sel_obs.shape[0], \
+                '%s$\chi^2$= %.2f'%(strX, (chi**2).mean()) 
+                ]
+        else:
+            labs = [ 
+                '%.2f $\pm$ %.2f' % (res.mean(), res.std()) , \
+                'N = %d' % sel_obs.shape[0]
+                ] 
+                    
         # print the above labels onto the figure. Note that I use relative coordinates for their position by specifying the transform=ax.transAxes
         
         for i, l in enumerate(labs):
-            ax.text(0.75, 0.6 - 0.07 * i, l, transform=ax.transAxes, fontsize=fontsize, horizontalalignment='center', color='blue')
+            ax.text(0.75, 0.9 - 0.07 * i, l, transform=ax.transAxes, fontsize=fontsize, horizontalalignment='center', color='blue')
         #
         # Set Tick Font Size on x and y labels
         #
-        dummy = [lab.set_fontsize(0.9 * fontsize) for lab in ax.get_xticklabels()]
-        dummy = [lab.set_fontsize(0.9 * fontsize) for lab in ax.get_yticklabels()]
+        #dummy = [lab.set_fontsize(20) for lab in ax.get_xticklabels()]
+        #dummy = [lab.set_fontsize(20) for lab in ax.get_yticklabels()]
 
         # set limits on x-axis and get limits on y-axis to determine the position of the x-axis labels (offset keyword to make_yaxis)
 
@@ -295,14 +361,15 @@ def timehistograms_new(fig, infile):
         ax.spines['bottom'].set_linewidth(1.5)
         ax.spines['bottom'].set_position(('outward', 10))
 
+        matplotlib.rcParams.update({'font.size': 18})
         ax.xaxis.set_ticks_position('bottom')
 
-        ax.set_xlabel('[%s]'%units)
+        ax.set_xlabel('[%s]'%units,size=16)
 
     #
     # All custom titles and auxiliary info are placed onto the figure directly (fig.text) in relative coordinates
     #
-    fig.text(0.5, 0.02, 'Simulated - Observed %s [%s]\nData from %s to %s' %(species,units,pydates[0].strftime('%d-%b-%Y'), pydates[-1].strftime('%d-%b-%Y')), horizontalalignment='center', fontsize=fontsize - 1)
+    fig.text(0.5, 0.02, 'Simulated - Observed %s [%s]\nData from %s to %s' %(species,units,pydates[0].strftime('%d-%b-%Y'), pydates[-1].strftime('%d-%b-%Y')), horizontalalignment='center', fontsize=fontsize)
     #fig.text(0.75,0.02,'Simulated - Observed\n CO$_2$ ($\mu$mol/mol)',horizontalalignment='center',fontsize=fontsize)
     fig.text(0.5, 0.35, 'model-data\nmismatch:\n%.2f %s' % (sel_mdm[0], units), horizontalalignment='center', fontsize=fontsize, color='green')
     fig.text(0.12, 0.75, 'NH Summer\n(Jun-Sep)', horizontalalignment='center', fontsize=fontsize)
@@ -311,14 +378,19 @@ def timehistograms_new(fig, infile):
     # Title
     #
 
-    plt.suptitle('%s [%s]\n%s, %s, %s ' % (longsitestring, location , SDSInfo['dataset_project'], SDSInfo['lab_name'], SDSInfo['lab_country'],), fontsize=fontsize + 2)
+    plt.suptitle('%s [%s]\n%s, %s, %s ' % (longsitestring, location , SDSInfo['dataset_project'], SDSInfo['lab_name'], SDSInfo['lab_country'],), fontsize=fontsize + 4)
 
     #
     # Add info to plot
     #
-    now = dt.datetime.today()
-    str1 = 'CTDAS2012\n' + now.strftime('%d/%m/%y')
-    fig.text(0.93, 0.95, str1, fontsize=0.75 * fontsize, color='0.5')
+    font0= FontProperties(size=15,style='italic',weight='bold')
+    txt='CarbonTracker Europe\n $\copyright$ Wageningen University'
+    clr='green'
+    fig.text(0.82,0.01,txt,ha='left',font_properties = font0, color=clr )
+
+    #now = dt.datetime.today()
+    #str1 = 'CTDAS2012\n' + now.strftime('%d/%m/%y')
+    #fig.text(0.93, 0.95, str1, fontsize=0.75 * fontsize, color='0.5')
     #str1 = 'data provided by %s'%SDSInfo['provider_1_name']
     #fig.text(0.12,0.16,str1,fontsize=0.8*fontsize,color='0.75')
 
@@ -346,7 +418,7 @@ def timehistograms_new(fig, infile):
     return fig
 
 def timevssite_new(fig, infile):
-
+    fontsize = 17
     #
     # Get data
     #
@@ -367,7 +439,7 @@ def timevssite_new(fig, infile):
     flags = f.get_variable('flag_forecast')
 
     longsitestring = f.site_name + ', ' + f.site_country
-    location = nice_lat(f.site_latitude) + ', ' + nice_lon(f.site_longitude) + ', ' + nice_alt(f.site_elevation)
+    location = nice_lat(f.site_latitude,'python') + ', ' + nice_lon(f.site_longitude,'python') + ', ' + nice_alt(f.site_elevation)
 
     SDSInfo = {}
     for k in f.ncattrs():
@@ -401,8 +473,8 @@ def timevssite_new(fig, infile):
     sd = pydates[0]
     ed = pydates[-1]
 
-    ax1 = fig.add_axes([0.1, 0.15, 0.7, 0.75])
-    ax2 = fig.add_axes([0.85, 0.15, 0.12, 0.75])
+    ax1 = fig.add_axes([0.1, 0.12, 0.7, 0.75])
+    ax2 = fig.add_axes([0.85, 0.12, 0.12, 0.75])
 
     ax1.spines['right'].set_color('none')
     ax1.spines['top'].set_color('none')
@@ -419,7 +491,7 @@ def timevssite_new(fig, infile):
     ax2.spines['bottom'].set_position(('outward', 10))
 
     markersize = 8
-    fontsize = 12
+    fontsize = 16
 
     p = ax1.plot(pydates, obs, marker='o', markeredgewidth=1, linestyle='None', markerfacecolor='None', \
            markeredgecolor='k', label=obslabel , markersize=markersize)
@@ -438,51 +510,61 @@ def timevssite_new(fig, infile):
     #
     # Set up x axis labels
     #
-    dummy = [lab.set_fontsize(0.9 * fontsize) for lab in ax1.get_xticklabels()]
-    dummy = [lab.set_fontsize(0.9 * fontsize) for lab in ax1.get_yticklabels()]
+    #dummy = [lab.set_fontsize(0.9 * fontsize) for lab in ax1.get_xticklabels()]
+    #dummy = [lab.set_fontsize(0.9 * fontsize) for lab in ax1.get_yticklabels()]
     #
     # Location and format of xticks
     #
-    ax1.xaxis.set_minor_locator(pltdt.MonthLocator([1,7],bymonthday=1))
-    ax1.xaxis.set_minor_formatter(pltdt.DateFormatter('%b'))
-    ax1.xaxis.set_major_locator(pltdt.YearLocator())
-    ax1.xaxis.set_major_formatter(pltdt.DateFormatter('\n\n%Y'))
+    ax1.xaxis.set_major_locator(pltdt.MonthLocator([7],bymonthday=7))
+    ax1.xaxis.set_major_formatter(pltdt.DateFormatter('%Y'))
     #
     # Legend
     #
     leg = ax1.legend(prop=FontProperties(size=(0.75 * fontsize)), borderpad=0.1, loc='upper left')
     #leg.get_frame().set_visible(False)
     leg.set_zorder(20)
-    leg.get_frame().set_color('0.9')
-    dummy = [lab.set_fontsize(12) for lab in leg.get_texts()]
+    leg.get_frame().set_color('1.0')
+    dummy = [lab.set_fontsize(16) for lab in leg.get_texts()]
     #
     # include grid
     #
-    ax1.grid(True, axis='y')
+    ax1.grid(True, ls='-', color='0.75', axis='y')
     ax1.autoscale(enable=True, axis='y', tight=False)
     #ax1.set_ylim(obs.min()-3*residual.std(),obs.max()+5*residual.std())
+    #ax1.set_xlim(pltdt.date2num(dt.datetime(sd.year, 1, 1)), pltdt.date2num(dt.datetime(ed.year + 1, 1, 1)))
     ax1.set_xlim(pltdt.date2num(dt.datetime(sd.year, 1, 1)), pltdt.date2num(dt.datetime(ed.year + 1, 1, 1)))
+    #ax1.set_ylim(360,430) #LUT
+     
+    ym = ax1.get_ylim()
+    ymin=ym[0] ; ymax =ym[1]
+    for yr in range(sd.year,ed.year+1,2):
+        x1=dt.datetime(yr,1,1)
+        x2=dt.datetime(yr+1,1,1)
+        ax1.fill([x1,x2,x2,x1],[ymin,ymin,ymax,ymax],color='0.9',zorder=1)
+    
+    ax1.set_ylim(ymin,ymax)
     #
     #
     # Set Tick Font Size
     #
-    dummy = [lab.set_fontsize(0.9 * fontsize) for lab in ax1.get_xticklabels()]
-    dummy = [lab.set_fontsize(0.9 * fontsize) for lab in ax1.get_yticklabels()]
+    #matplotlib.rcParams.update({'font.size': 30})
+    ax1.xaxis.set_ticks_position('bottom')
+    #dummy = [lab.set_fontsize(0.9 * fontsize) for lab in ax1.get_xticklabels()]
+    #dummy = [lab.set_fontsize(0.9 * fontsize) for lab in ax1.get_yticklabels()]
  
     #xtitle='Time'
     #ax1.set_xlabel(xtitle, fontsize=fontsize)   # label x axis
-    ax1.set_ylabel(r"%s [%s]"% (species,units), fontsize=fontsize)  # label y-axis 
+    ax1.set_ylabel(r"%s [%s]"% (species,units), fontsize=fontsize + 5)  # label y-axis 
 
     #
     # Axes 2
     #
+    residual = residual.compress(flags != 2)
     offset = 0.0
-
     n, bins, patches = ax2.hist(residual, max(residual.shape[0] / 15, 15), normed=1, orientation='horizontal')
     p = plt.setp(patches, 'facecolor', 'tan' , 'edgecolor', 'tan', label='None', alpha=0.25)
 
     # Create normal distributions for the line plots over the interval of the x-axis
-
     sc = residual.std()
     bins = np.arange(-4 * sc, 4 * sc, 0.1)
     n = normpdf(bins, residual.mean(), residual.std())   
@@ -495,7 +577,7 @@ def timevssite_new(fig, infile):
         ]
     # print the above labels onto the figure. Note that I use relative coordinates for their position by specifying the transform=ax.transAxes
     
-    ax2.text(0.6, 0.35 + offset, labs[0], transform=ax2.transAxes, fontsize=1.1 * fontsize, horizontalalignment='center', color='k')
+    ax2.text(0.6, 0.01 + offset, labs[0], transform=ax2.transAxes, fontsize=1.1 * fontsize, horizontalalignment='center', color='k')
     offset += -0.05
 
     ax2.set_ylim(-4 * sc, 4 * sc)
@@ -512,27 +594,34 @@ def timevssite_new(fig, infile):
     ax2.spines['bottom'].set_linewidth(1.5)
     ax2.spines['bottom'].set_position(('outward', 10))
 
+    matplotlib.rcParams.update({'font.size': 18})
     ax2.yaxis.set_ticks_position('left')
     ax2.xaxis.set_ticklabels([])
 
     #ax2.set_ylabel(r"CO$_2$ [ppm]", fontsize=fontsize)  # label y-axis 
     #ax2.set_xlabel("frequency", fontsize=fontsize)  # label x-axis 
-    ax2.grid(True, axis='y')
+    #ax2.grid(True, axis='y')
+    ax2.grid(True, ls='-', color='0.75', axis='y')
 
     #
     # Title
     #
 
-    plt.suptitle('%s [%s]\n%s, %s, %s ' % (longsitestring, location , SDSInfo['dataset_project'], SDSInfo['lab_name'], SDSInfo['lab_country'],), fontsize=fontsize + 2)
+    plt.suptitle('%s [%s]\n%s, %s, %s ' % (longsitestring, location , SDSInfo['dataset_project'], SDSInfo['lab_name'], SDSInfo['lab_country'],), fontsize=fontsize + 5)
 
     #
     # Add info to plot
     #
-    now = dt.datetime.today()
-    str1 = 'CTDAS2012\n' + now.strftime('%d/%m/%y')
-    fig.text(0.93, 0.95, str1, fontsize=0.75 * fontsize, color='0.5')
-    str1 = 'data provided by %s' % SDSInfo['provider_1_name']
-    fig.text(0.12, 0.16, str1, fontsize=0.8 * fontsize, color='0.75')
+    font0= FontProperties(size=15,style='italic',weight='bold')
+    txt='CarbonTracker Europe\n $\copyright$ Wageningen University'
+    clr='green'
+    fig.text(0.82,0.01,txt,ha='left',font_properties = font0, color=clr )
+    
+    #now = dt.datetime.today()
+    #str1 = 'CTDAS2012\n' + now.strftime('%d/%m/%y')
+    #fig.text(0.93, 0.95, str1, fontsize=0.75 * fontsize, color='0.5')
+    #str1 = 'data provided by %s' % SDSInfo['provider_1_name']
+    #fig.text(0.12, 0.16, str1, fontsize=0.8 * fontsize, color='0.75')
 
     try:
         img = urllib2.urlopen(SDSInfo['lab_logo']).read()
@@ -557,8 +646,9 @@ def timevssite_new(fig, infile):
 
     return fig
 
-def residuals_new(fig, infile):
-
+def residuals_new(fig, infile, option):
+    
+    fontsize = 17
     #
     # Get data
     #
@@ -575,12 +665,15 @@ def residuals_new(fig, infile):
     date = f.get_variable('time')
     obs = f.get_variable('value') * molefac
     mdm = f.get_variable('modeldatamismatch') * molefac
-    simulated = f.get_variable('modelsamplesmean_forecast') * molefac
+    if option == 'final':
+        simulated = f.get_variable('modelsamplesmean') * molefac
+    if option == 'forecast':
+        simulated = f.get_variable('modelsamplesmean_forecast') * molefac
     hphtr = f.get_variable('totalmolefractionvariance_forecast') * molefac * molefac
     flags = f.get_variable('flag_forecast')
 
     longsitestring = f.site_name + ', ' + f.site_country
-    location = nice_lat(f.site_latitude) + ', ' + nice_lon(f.site_longitude) + ', ' + nice_alt(f.site_elevation)
+    location = nice_lat(f.site_latitude,'python') + ', ' + nice_lon(f.site_longitude,'python') + ', ' + nice_alt(f.site_elevation)
 
     SDSInfo = {}
     for k in f.ncattrs():
@@ -602,22 +695,20 @@ def residuals_new(fig, infile):
     hphtr = hphtr.compress(sampled)
     flags = flags.compress(sampled)
     
-    residual = simulated - obs
-    chisquared = (residual ** 2) / hphtr
-
     rejected = (flags == 2.0)
     notused = (flags == 99.0)
 
-    if notused.all():
-        return fig
-    else:
-        obslabel = 'Residual'
+    residual = simulated - obs