--sd_const_multi

plotting/Energy_spectrum/spectrum.py

@@ -3,12 +3,16 @@
 import h5py
 import numpy as np
 from sys import argv
+import matplotlib
+matplotlib.use('Agg')
 import matplotlib.pyplot as plt
 
+
 #velocities = ["u", "v", "w"]
 #velocities = ["u", "v", "w", "cloud_rw_mom3", "rv", "th", "RH", "aerosol_rw_mom3"]
+velocities = ["u", "cloud_rw_mom3", "w"]
 #velocities = ["cloud_rw_mom3"]
-velocities = ["w"]
+#velocities = ["w"]
 
 time_start = int(argv[1])
 time_end = int(argv[2])
@@ -53,23 +57,26 @@
         Exy_avg[vel] += Exy
 
       K = np.fft.rfftfreq(nx - 1)
+      print nx
+      print K
   #    plt.loglog(K, Exy)
-      lmbd = 50. / K # assume dx=50m
+#      lmbd = 50. / K # assume dx=50m
 
-    if (t == time_start and lab==labels[0]):
-      plt.loglog(lmbd, 2e-1* K**(-5./3.) )
-
+#    if (t == time_start and lab==labels[0]):
+#      plt.loglog(lmbd, 2e-1* K**(-5./3.) )
+
+  f1=plt.figure(1)  
   for vel in velocities:
     Exy_avg[vel] /= (time_end - time_start) / outfreq + 1
     Exy_avg[vel] /= to_lvl+1 - from_lvl
   #crudely scale
   #  Exy_avg[vel] /= Exy_avg[vel][len(Exy_avg[vel])-1]
-    plt.loglog(lmbd, Exy_avg[vel] , linewidth=2, label=lab+"_"+vel)
-
-plt.xlim(10**4,10**2)
+  #  plt.loglog(lmbd, Exy_avg[vel] , linewidth=2, label=lab+"_"+vel)
+    plt.loglog(K, Exy_avg[vel] , linewidth=2, label=lab+"_"+vel)
+#plt.xlim(10**4,10**2)
 plt.xlabel("l[m]")
 plt.ylabel("PSD")
 plt.legend()
 plt.grid(True, which='both', linestyle='--')
 plt.title("Mean PSD of w 322m<z<642m @3h")
-plt.show()
+f1.savefig('spectrum.png')

plotting/Lasher_Trapp/Lasher_Trapp_series_comparison.py

@@ -0,0 +1,74 @@
+from matplotlib import rc
+import matplotlib.pyplot as plt
+from matplotlib.ticker import AutoMinorLocator, MultipleLocator
+
+import os
+import sys
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../Matplotlib_common/")
+
+from plot_ranges import xscaledict, yscaledict, xlimdict_series, ylimdict_series
+from plot_series import *
+
+# activate latex text rendering
+rc('text', usetex=True)
+
+Lasher_Trapp_vars = ["lwp", "rwp", "surf_precip", "acc_precip", "cl_nc"]
+
+# init the plot
+nplotx = 2
+nploty= 3
+fig, axarr = plt.subplots(nplotx,nploty)
+
+plot_series( Lasher_Trapp_vars, 0, nplotx, nploty, axarr, xscaledict, yscaledict, xlimdict_series, ylimdict_series, xlabel='Time [h]')
+
+# legend font size
+plt.rcParams.update({'font.size': 8})
+
+# hide axes on empty plots
+if len( Lasher_Trapp_vars) % nploty == 0:
+  nemptyplots = 0
+else:
+  nemptyplots = nploty - len( Lasher_Trapp_vars) % nploty
+emptyplots = np.arange(nploty - nemptyplots, nploty)
+for empty in emptyplots:
+  axarr[nplotx-1, empty].axis('off')
+
+# hide hrzntl tic labels
+x_empty_label = np.arange(0, nplotx-1)
+y_empty_label = np.arange(nploty)
+for x in x_empty_label:
+  for y in y_empty_label:
+    axarr[x,y].set_xticklabels([])
+
+#axes = plt.gca()
+#axes.tick_params(direction='in')
+x_arr = np.arange(nplotx)
+y_arr = np.arange(nploty)
+for x in x_arr:
+  for y in y_arr:
+    #tics inside
+    axarr[x,y].tick_params(direction='in', which='both', top=1, right=1)
+    #minor tics
+    axarr[x,y].xaxis.set_minor_locator(AutoMinorLocator())
+    axarr[x,y].yaxis.set_minor_locator(AutoMinorLocator())
+    #labels and tics font size
+    for item in ([axarr[x,y].xaxis.label, axarr[x,y].yaxis.label] + axarr[x,y].get_xticklabels() + axarr[x,y].get_yticklabels()):
+      item.set_fontsize(8)
+    # subplot numbering
+    if y < nploty - nemptyplots or x < (nplotx - 1):
+      axarr[x,y].text(0.2, 0.9, labeldict[y + x*nploty], fontsize=8, transform=axarr[x,y].transAxes)
+
+#single legend for the whole figure
+handles, labels = axarr[0,0].get_legend_handles_labels()
+
+lgd = fig.legend(handles, labels, handlelength=4, loc='lower center', bbox_to_anchor=(0.45,0))
+
+#figure size
+fig.set_size_inches(7.874, 5. + (len(labels) - 2) * 0.2)# 5.214)#20.75,13.74)
+#distances between subplots and from bottom of the plot
+fig.subplots_adjust(bottom=0.18 + (len(labels) - 2) * 0.03, hspace=0.1, wspace=0.4)
+
+#fig.tight_layout(pad=0, w_pad=0, h_pad=0)
+
+#plt.show()
+fig.savefig(argv[len(sys.argv)-1], bbox_inches='tight', dpi=300)#, bbox_extra_artists=(lgd,))

plotting/Lasher_Trapp/plot_ranges.py

@@ -0,0 +1,105 @@
+xscaledict = {
+  "thl" : "linear",
+  "00rtot" : "linear",
+  "rliq" : "linear",
+  "prflux" : "linear",
+  "cl_nc" : "linear",
+  "clfrac" : "linear",
+  "wvar" : "linear",
+  "w3rd" : "linear",
+  "sat_RH" : "linear",
+  "rad_flx" : "linear",
+  "lwp" : "linear",
+  "rwp" : "linear",
+  "er" : "linear",
+  "wvarmax" : "linear",
+  "surf_precip" : "linear",
+  "acc_precip" : "linear",
+  "cloud_base" : "linear",
+  "gccn_rw_cl" : "linear",
+  "non_gccn_rw_cl" : "linear",
+  "base_prflux_vs_clhght" : "log",
+  "cl_gccn_conc" : "linear"
+}
+
+yscaledict = {
+  "thl" : "linear",
+  "00rtot" : "linear",
+  "rliq" : "linear",
+  "prflux" : "linear",
+  "cl_nc" : "linear",
+  "clfrac" : "linear",
+  "wvar" : "linear",
+  "w3rd" : "linear",
+  "sat_RH" : "linear",
+  "rad_flx" : "linear",
+  "lwp" : "linear",
+  "rwp" : "linear",
+  "er" : "linear",
+  "wvarmax" : "linear",
+  "surf_precip" : "linear",
+  "acc_precip" : "linear",
+  "cloud_base" : "linear",
+  "gccn_rw_cl" : "linear",
+  "non_gccn_rw_cl" : "linear",
+  "base_prflux_vs_clhght" : "linear",
+  "cl_gccn_conc" : "log"
+}
+
+xlimdict_profs = {
+  "thl" : None,
+  "00rtot" : None,
+  "rliq" : None,
+  "prflux" : (0,20),
+  "cl_nc" : (0,90),
+  "clfrac" : None,
+  "wvar" : None,
+  "w3rd" : None,
+  "sat_RH" : None,
+  "rad_flx" : None,
+  "gccn_rw_cl" : (0,90),
+  "non_gccn_rw_cl" : (0,12),
+  "base_prflux_vs_clhght" : (1,10000)
+}
+
+ylimdict_profs = {
+  "thl" : (0,3000),
+  "00rtot" : (0,3000),
+  "rliq" : (0,3000),
+  "prflux" : (0,3000),
+  "cl_nc" : (0,3000),
+  "clfrac" : (0,3000),
+  "wvar" : (0,3000),
+  "w3rd" : (0,3000),
+  "sat_RH" : (0,3000),
+  "rad_flx" : (0,3000),
+  "gccn_rw_cl" : (0,3000),
+  "non_gccn_rw_cl" : (0,3000),
+  "base_prflux_vs_clhght" : (0,2500)
+}
+
+xlimdict_series = {
+  "clfrac" :  None,
+  "cl_nc" :  None,
+  "lwp" :  None,
+  "rwp" :  None,
+  "er" :  None,
+  "wvarmax" :  None,
+  "surf_precip" :  None,
+  "acc_precip" :  None,
+  "cl_gccn_conc" :  None,
+  "cloud_base" :  None
+}
+
+ylimdict_series = {
+  "clfrac" : None,
+  "cl_nc" : None,
+  "lwp" : None,
+  "rwp" : None,
+  "er" : None,
+  "wvarmax" : None,
+  "surf_precip" : None,
+  "acc_precip" : None,#(0,0.07),
+  "cl_gccn_conc" : (1e-6, 1),
+  "cloud_base" : None
+}

plotting/drawbicyc/drawbicyc.cpp

@@ -19,7 +19,7 @@ int main(int argc, char** argv)
     ("qv_qc_2_6_10_min", po::value<bool>()->default_value(false) , "plot comparison of qv and qc fields at 2, 6 and 10 min?")
     ("dir", po::value<std::string>()->required() , "directory containing out_lgrngn")
     ("micro", po::value<std::string>()->required(), "one of: blk_1m, blk_2m, lgrngn")
-    ("type", po::value<std::string>()->required(), "one of: dycoms, moist_thermal, rico")//, base_prflux_vs_clhght")
+    ("type", po::value<std::string>()->required(), "one of: dycoms, moist_thermal, rico, Lasher_Trapp")//, base_prflux_vs_clhght")
   ;
 
   po::variables_map vm;
@@ -33,8 +33,8 @@ int main(int argc, char** argv)
 
   // handling the "type" option
   std::string type = vm["type"].as<std::string>();
-  if(type != "dycoms" && type != "moist_thermal" && type != "rico")// && type != "base_prflux_vs_clhght")
-    throw std::runtime_error("Unrecognized 'type' option, only dycoms, rico, moist_thermal available now");//, base_prflux_vs_clhght available now");
+  if(type != "dycoms" && type != "moist_thermal" && type != "rico" && type != "Lasher_Trapp")// && type != "base_prflux_vs_clhght")
+    throw std::runtime_error("Unrecognized 'type' option, only dycoms, rico, Lasher_Trapp, moist_thermal available now");//, base_prflux_vs_clhght available now");
 
   // should profiles be normalized by inversion height
   const bool normalize_prof = type == "dycoms";
@@ -56,10 +56,10 @@ int main(int argc, char** argv)
   H5::DataSet h5d = h5f.openDataSet("G");
   H5::DataSpace h5s = h5d.getSpace();
   int NDims = h5s.getSimpleExtentNdims();
-  
+
   // detecting if subgrid model was on
   bool sgs = true;
-  try 
+  try
   {
     auto h5g = h5f.openGroup("sgs");
   }