1.1 --- /dev/null Thu Jan 01 00:00:00 1970 +0000
1.2 +++ b/lai/24.histogram+bias_mean.ncl Mon Jan 26 22:08:20 2009 -0500
1.3 @@ -0,0 +1,293 @@
1.4 +;********************************************************
1.5 +; histogram normalized by rain and compute correleration
1.6 +;********************************************************
1.7 +load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/gsn_code.ncl"
1.8 +load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/gsn_csm.ncl"
1.9 +load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/contributed.ncl"
1.10 +
1.11 +procedure pminmax(data:numeric,name:string)
1.12 +begin
1.13 + print ("min/max " + name + " = " + min(data) + "/" + max(data))
1.14 + if(isatt(data,"units")) then
1.15 + print (name + " units = " + data@units)
1.16 + end if
1.17 +end
1.18 +
1.19 +;
1.20 +; Main code.
1.21 +;
1.22 +begin
1.23 +
1.24 +;nclass = 18
1.25 + nclass = 20
1.26 +
1.27 +;************************************************
1.28 +; read in data: observed
1.29 +;************************************************
1.30 + diri1 = "/fis/cgd/cseg/people/jeff/clamp_data/lai/"
1.31 +;fili1 = "land_class_T42.nc"
1.32 + fili1 = "land_class_T42_new.nc"
1.33 + fili2 = "LAI_2000-2005_mean_T42.nc"
1.34 + data_file_ob1 = addfile(diri1+fili1,"r")
1.35 + data_file_ob2 = addfile(diri1+fili2,"r")
1.36 +
1.37 + RAIN1 = tofloat(data_file_ob1->LAND_CLASS)
1.38 + NPP1 = data_file_ob2->LAI
1.39 +;************************************************
1.40 +; read in data: model
1.41 +;************************************************
1.42 + diri2 = "/fis/cgd/cseg/people/jeff/clamp_data/model/"
1.43 +;fili3 = "i01.03cn_1545-1569_ANN_climo.nc"
1.44 + fili3 = "i01.04casa_1605-1629_ANN_climo.nc"
1.45 + data_file_model = addfile(diri2+fili3,"r")
1.46 +
1.47 + NPP2 = data_file_model->TLAI
1.48 +;************************************************
1.49 +; print min/max and unit
1.50 +;************************************************
1.51 + pminmax(RAIN1,"RAIN1")
1.52 + pminmax(NPP1,"NPP1")
1.53 + pminmax(NPP2,"NPP2")
1.54 +
1.55 + RAIN1_1D = ndtooned(RAIN1)
1.56 + NPP1_1D = ndtooned(NPP1)
1.57 + NPP2_1D = ndtooned(NPP2)
1.58 +;
1.59 +; Calculate some "nice" bins for binning the data in equally spaced
1.60 +; ranges.
1.61 +;
1.62 +
1.63 +; nbins = nclass + 1 ; Number of bins to use.
1.64 +; nicevals = nice_mnmxintvl(min(RAIN1_1D),max(RAIN1_1D),nbins,False)
1.65 +; nvals = floattoint((nicevals(1) - nicevals(0))/nicevals(2) + 1)
1.66 +; range = fspan(nicevals(0),nicevals(1),nvals)
1.67 +
1.68 + nclassn = nclass + 1
1.69 + range = fspan(0,nclassn-1,nclassn)
1.70 +
1.71 +; print (nicevals)
1.72 +; print (nvals)
1.73 + print (range)
1.74 +; exit
1.75 +
1.76 +;
1.77 +; Use this range information to grab all the values in a
1.78 +; particular range, and then take an average.
1.79 +;
1.80 + nr = dimsizes(range)
1.81 + nx = nr-1
1.82 + xvalues = new((/2,nx/),typeof(RAIN1_1D))
1.83 + xvalues(0,:) = range(0:nr-2) + (range(1:)-range(0:nr-2))/2.
1.84 + dx = xvalues(0,1) - xvalues(0,0) ; range width
1.85 + dx4 = dx/4 ; 1/4 of the range
1.86 + xvalues(1,:) = xvalues(0,:) - dx/5.
1.87 + yvalues = new((/2,nx/),typeof(RAIN1_1D))
1.88 + mn_yvalues = new((/2,nx/),typeof(RAIN1_1D))
1.89 + mx_yvalues = new((/2,nx/),typeof(RAIN1_1D))
1.90 +
1.91 + do nd=0,1
1.92 +;
1.93 +; See if we are doing model or observational data.
1.94 +;
1.95 + if(nd.eq.0) then
1.96 + data = RAIN1_1D
1.97 + npp_data = NPP1_1D
1.98 + else
1.99 + data = RAIN1_1D
1.100 + npp_data = NPP2_1D
1.101 + end if
1.102 +;
1.103 +; Loop through each range and check for values.
1.104 +;
1.105 + do i=0,nr-2
1.106 + if (i.ne.(nr-2)) then
1.107 + print("")
1.108 + print("In range ["+range(i)+","+range(i+1)+")")
1.109 + idx = ind((range(i).le.data).and.(data.lt.range(i+1)))
1.110 + else
1.111 + print("")
1.112 + print("In range ["+range(i)+",)")
1.113 + idx = ind(range(i).le.data)
1.114 + end if
1.115 +;
1.116 +; Calculate average, and get min and max.
1.117 +;
1.118 + if(.not.any(ismissing(idx))) then
1.119 + yvalues(nd,i) = avg(npp_data(idx))
1.120 + mn_yvalues(nd,i) = min(npp_data(idx))
1.121 + mx_yvalues(nd,i) = max(npp_data(idx))
1.122 + count = dimsizes(idx)
1.123 + else
1.124 + count = 0
1.125 + yvalues(nd,i) = yvalues@_FillValue
1.126 + mn_yvalues(nd,i) = yvalues@_FillValue
1.127 + mx_yvalues(nd,i) = yvalues@_FillValue
1.128 + end if
1.129 +;
1.130 +; Print out information.
1.131 +;
1.132 + print(nd + ": " + count + " points, avg = " + yvalues(nd,i))
1.133 + print("Min/Max: " + mn_yvalues(nd,i) + "/" + mx_yvalues(nd,i))
1.134 +
1.135 +;
1.136 +; Clean up for next time in loop.
1.137 +;
1.138 + delete(idx)
1.139 + end do
1.140 + delete(data)
1.141 + delete(npp_data)
1.142 + end do
1.143 +
1.144 +;
1.145 +; Start the graphics.
1.146 +;
1.147 + wks = gsn_open_wks("ps","xy")
1.148 +
1.149 + res = True
1.150 + res@gsnMaximize = True
1.151 + res@gsnDraw = False
1.152 + res@gsnFrame = False
1.153 + res@xyMarkLineMode = "Markers"
1.154 + res@xyMarkerSizeF = 0.014
1.155 + res@xyMarker = 16
1.156 + res@xyMarkerColors = (/"Brown","Blue"/)
1.157 +; res@trYMinF = min(mn_yvalues) - 10.
1.158 +; res@trYMaxF = max(mx_yvalues) + 10.
1.159 + res@trYMinF = min(mn_yvalues) - 2
1.160 + res@trYMaxF = max(mx_yvalues) + 4
1.161 +
1.162 +; res@tiMainString = "Observed vs i01.03cn"
1.163 + res@tiMainString = "Observed vs i01.04casa"
1.164 +
1.165 + res@tiYAxisString = "Mean LAI (Leaf Area Index)"
1.166 + res@tiXAxisString = "Land Cover Type"
1.167 +;
1.168 +; Add a boxed legend using the more simple method, which won't have
1.169 +; vertical lines going through the markers.
1.170 +;
1.171 + res@pmLegendDisplayMode = "Always"
1.172 +; res@pmLegendWidthF = 0.1
1.173 + res@pmLegendWidthF = 0.08
1.174 + res@pmLegendHeightF = 0.05
1.175 + res@pmLegendOrthogonalPosF = -1.17
1.176 +; res@pmLegendOrthogonalPosF = -1.00 ;(downward)
1.177 +; res@pmLegendParallelPosF = 0.18
1.178 + res@pmLegendParallelPosF = 0.23 ;(rightward)
1.179 +
1.180 +; res@lgPerimOn = False
1.181 + res@lgLabelFontHeightF = 0.015
1.182 +; res@xyExplicitLegendLabels = (/"observed","model_i01.03cn"/)
1.183 + res@xyExplicitLegendLabels = (/"observed","model_i01.04casa"/)
1.184 +
1.185 + xy = gsn_csm_xy(wks,xvalues,yvalues,res)
1.186 +
1.187 + max_bar = new((/2,nx/),graphic)
1.188 + min_bar = new((/2,nx/),graphic)
1.189 + max_cap = new((/2,nx/),graphic)
1.190 + min_cap = new((/2,nx/),graphic)
1.191 +
1.192 + lnres = True
1.193 +
1.194 + line_colors = (/"brown","blue"/)
1.195 + do nd=0,1
1.196 + lnres@gsLineColor = line_colors(nd)
1.197 + do i=0,nx-1
1.198 +
1.199 + if(.not.ismissing(mn_yvalues(nd,i)).and. \
1.200 + .not.ismissing(mx_yvalues(nd,i))) then
1.201 +;
1.202 +; Attach the vertical bar, both above and below the marker.
1.203 +;
1.204 + x1 = xvalues(nd,i)
1.205 + y1 = yvalues(nd,i)
1.206 + y2 = mn_yvalues(nd,i)
1.207 + min_bar(nd,i) = gsn_add_polyline(wks,xy,(/x1,x1/),(/y1,y2/),lnres)
1.208 +
1.209 + y2 = mx_yvalues(nd,i)
1.210 + max_bar(nd,i) = gsn_add_polyline(wks,xy,(/x1,x1/),(/y1,y2/),lnres)
1.211 +;
1.212 +; Attach the horizontal cap line, both above and below the marker.
1.213 +;
1.214 + x1 = xvalues(nd,i) - dx4
1.215 + x2 = xvalues(nd,i) + dx4
1.216 + y1 = mn_yvalues(nd,i)
1.217 + min_cap(nd,i) = gsn_add_polyline(wks,xy,(/x1,x2/),(/y1,y1/),lnres)
1.218 +
1.219 + y1 = mx_yvalues(nd,i)
1.220 + max_cap(nd,i) = gsn_add_polyline(wks,xy,(/x1,x2/),(/y1,y1/),lnres)
1.221 + end if
1.222 + end do
1.223 + end do
1.224 +
1.225 +;
1.226 +; Here's how to do the legend by hand.
1.227 +;
1.228 +; mkres = True ; Marker resources
1.229 +; txres = True ; Text resources
1.230 +; mkres@gsMarkerIndex = 16
1.231 +; mkres@gsMarkerSizeF = 0.02
1.232 +; txres@txFontHeightF = 0.02
1.233 +; txres@txJust = "CenterLeft"
1.234 +;
1.235 +; Change these values if you want to move the marker legend location.
1.236 +; These values are in the same data space as the plot.
1.237 +;
1.238 +; xlg1_cen = 0.2
1.239 +; ylg1_cen = 900.
1.240 +
1.241 +; xlg2_cen = 0.2
1.242 +; ylg2_cen = 760.
1.243 +
1.244 +; mkres@gsMarkerColor = "brown"
1.245 +; lnres@gsLineColor = "brown"
1.246 +
1.247 +; lg_mark_legend1 = gsn_add_polymarker(wks,xy,xlg1_cen,ylg1_cen,mkres)
1.248 +; lg_line_legend1 = gsn_add_polyline(wks,xy,(/xlg1_cen,xlg1_cen/), \
1.249 +; (/ylg1_cen-60,ylg1_cen+60/),lnres)
1.250 +; lg_cap_legend11 = gsn_add_polyline(wks,xy,(/xlg1_cen-0.1,xlg1_cen+0.1/), \
1.251 +; (/ylg1_cen-60,ylg1_cen-60/),lnres)
1.252 +; lg_cap_legend12 = gsn_add_polyline(wks,xy,(/xlg1_cen-0.1,xlg1_cen+0.1/), \
1.253 +; (/ylg1_cen+60,ylg1_cen+60/),lnres)
1.254 +
1.255 +; tx_legend1 = gsn_add_text(wks,xy,"observed",xlg1_cen+0.15,ylg1_cen,txres)
1.256 +
1.257 +; mkres@gsMarkerColor = "blue"
1.258 +; lnres@gsLineColor = "blue"
1.259 +
1.260 +; lg_mark_legend2 = gsn_add_polymarker(wks,xy,xlg2_cen,ylg2_cen,mkres)
1.261 +; lg_line_legend2 = gsn_add_polyline(wks,xy,(/xlg2_cen,xlg2_cen/), \
1.262 +; (/ylg2_cen-60,ylg2_cen+60/),lnres)
1.263 +; lg_cap_legend21 = gsn_add_polyline(wks,xy,(/xlg2_cen-0.1,xlg2_cen+0.1/), \
1.264 +; (/ylg2_cen-60,ylg2_cen-60/),lnres)
1.265 +; lg_cap_legend22 = gsn_add_polyline(wks,xy,(/xlg2_cen-0.1,xlg2_cen+0.1/), \
1.266 +; (/ylg2_cen+60,ylg2_cen+60/),lnres)
1.267 +; tx_legend2 = gsn_add_text(wks,xy,"model_i01.03cn",xlg2_cen+0.15,ylg2_cen,txres)
1.268 +
1.269 + draw(xy)
1.270 + frame(wks)
1.271 + system("convert xy.ps xy.png")
1.272 +
1.273 + u = yvalues(0,:)
1.274 + v = yvalues(1,:)
1.275 +
1.276 + good = ind(.not.ismissing(u) .and. .not.ismissing(v))
1.277 + uu = u(good)
1.278 + vv = v(good)
1.279 + nz = dimsizes(uu)
1.280 + print (nz)
1.281 +
1.282 + ccr = esccr(uu,vv,0)
1.283 + print (ccr)
1.284 +
1.285 +;old eq
1.286 +;bias = sum(((vv-uu)/uu)^2)
1.287 +;M = (1.- sqrt(bias/nz))*5.
1.288 +
1.289 +;new eq
1.290 + bias = sum(abs(vv-uu)/(vv+uu))
1.291 + M = (1.- (bias/nz))*5.
1.292 + print (bias)
1.293 + print (M)
1.294 +
1.295 +end
1.296 +