Icmw pi chamber size spectra plotting

Matplotlib_common/plot_series.py

@@ -26,7 +26,7 @@ def plot_series(var_list, plot_iter, nplotx, nploty, axarr, xscaledict, yscaledi
       series_file.close()
 
       linestyles = ['--', '-.', ':']
-      dashList = [(3,1),(1,1),(4,1,1,1),(4,2)] 
+      dashList = [(None,None),(3,1),(1,1),(4,1,1,1),(4,2)] 
       plot_my_array(axarr, plot_iter, my_times, my_res, nploty, xlabel=xlabel, ylabel=ylabeldict[var], varlabel=file_labels[label_counter], dashes = dashList[label_counter % len(dashList)], xscale=xscaledict[var], yscale=yscaledict[var], xlim=xlimdict[var], ylim=ylimdict[var], linewidth=linewidth)
       label_counter+=1
     plot_iter = plot_iter + 1

Size_spectra/spectra.py

@@ -16,19 +16,27 @@
 #to_lvl = int(argv[5])
 
 if plot_dry == True:
-  size_data = {"rw" : 29, "rd" : 21}
+  size_data = {"rw" : 250, "rd" : 21}
+  #size_data = {"rw" : 29, "rd" : 21}
 else:
-  size_data = {"rw" : 29}
+  size_data = {"rw" : 250}
+  #size_data = {"rw" : 29}
 
 #hardcoded bin edges, need to match UWLCM
-left_edges = {"rw": np.zeros(30), "rd": np.zeros(22)}
-bin_centers = {"rw": np.zeros(29), "rd": np.zeros(21)}
-bin_width = {"rw": np.zeros(29), "rd": np.zeros(21)}
-
-for i in np.arange(30):
-  left_edges["rw"][i] = 10**(-3 + i * .2) * 1e-6 # [m]
-
-for i in np.arange(29):
+#left_edges = {"rw": np.zeros(30), "rd": np.zeros(22)}
+#bin_centers = {"rw": np.zeros(29), "rd": np.zeros(21)}
+#bin_width = {"rw": np.zeros(29), "rd": np.zeros(21)}
+left_edges = {"rw": np.zeros(251), "rd": np.zeros(22)}
+bin_centers = {"rw": np.zeros(250), "rd": np.zeros(21)}
+bin_width = {"rw": np.zeros(250), "rd": np.zeros(21)}
+
+#for i in np.arange(30):
+#  left_edges["rw"][i] = 10**(-3 + i * .2) * 1e-6 # [m]
+for i in np.arange(251):
+  left_edges["rw"][i] = i * 0.2e-6 # [m]
+
+for i in np.arange(250):
+#for i in np.arange(29):
   bin_centers["rw"][i] = 0.5 * (left_edges["rw"][i] + left_edges["rw"][i+1])
   bin_width["rw"][i] = (left_edges["rw"][i+1] - left_edges["rw"][i])
 
@@ -59,8 +67,9 @@
 labels = argv[7:len(argv):2]
 print directories, labels
 
-levels = ["ground", "cloud_base"]
+#levels = ["ground", "cloud_base"]
 #levels = ["all", "pi_chamber_measurement_location"]
+levels = ["all"]#, "icmw_pi_chamber_nowall"]
 
 if plot_dry == True:
   all_data_names = np.append(data_names["rw"], data_names["rd"])
@@ -123,6 +132,8 @@
           total_arr[data][lab] = np.append(total_arr[data][lab], (h5py.File(filename, "r")[data]*rhod)[:,:,:])
         if lvl == "pi_chamber_measurement_location":
           total_arr[data][lab] = np.append(total_arr[data][lab], (h5py.File(filename, "r")[data]*rhod)[32:34,5:6,4:5]) # roughly + assuming dx=0.03125 m
+        if lvl == "icmw_pi_chamber_nowall":
+          total_arr[data][lab] = np.append(total_arr[data][lab], (h5py.File(filename, "r")[data]*rhod)[4:61,4:61,4:29])
 
   #    hists[lab] = np.hist(total_arr, bins=100)
   #    _ = plt.hist(total_arr, bins='auto')
@@ -156,7 +167,9 @@
       r_min = min(r_min, left_edges[rwrd][first_nonzero_idx])
       r_max = max(r_max, left_edges[rwrd][last_nonzero_idx+1])
       print 'r_min = ', r_min, ' r_max = ', r_max
-      plt.step(bin_centers[rwrd] * 1e6 * 2, avg_conc_arr[rwrd][lab] / bin_width[rwrd] / 1e12 / 2, where='mid', label=rwrd + '_' + lab, linewidth=6) # *1e6 to have microns on x, / 1e12 to adjust for width in microns and to have concentration per cm^3; *2 and /2 to get diameters
+      avg_conc_arr[rwrd][lab] =  avg_conc_arr[rwrd][lab] / bin_width[rwrd] / 1e12 / 2 # / 1e12 to adjust for width in microns and to have concentration per cm^3; /2 to get diameters
+      plt.step(bin_centers[rwrd] * 1e6 * 2, avg_conc_arr[rwrd][lab], where='mid', label='N=' + lab, linewidth=6) # *1e6 to have microns on x; *2 to get diameters
+      np.savetxt("LES_N"+lab+"_dsd", avg_conc_arr[rwrd][lab])
 
 #    data = total_arr["rain_rw_mom3"].values()
 

drawbicyc/include/drawbicyc/cases/Dycoms_RF02/plots.hpp

@@ -33,7 +33,8 @@ std::vector<std::string> profs_dycoms({
 //,"rad_flx"
 //, "non_gccn_rw_cl"
 , "gccn_rw_cl_down"
-, "non_gccn_rw_cl_down"
+, "gccn_rw_cl_up"
+//, "non_gccn_rw_cl_down"
 //, "nc_up" 
 //,"sat_RH_up"
 //, "act_conc_up" 

papers/GCCN_LES/cl_nr_series.py

@@ -63,7 +63,7 @@
 
 #axes = plt.gca()
 #axes.tick_params(direction='in')
-labeldict=["{\it ScNc45}", "{\it CuNc75}", "{\it CuNc70NoMix}"]
+labeldict=["{\it Sc45}", "{\it Cu75}", "{\it Cu70NoSGS}"]
 y_arr = np.arange(nploty)
 for y in y_arr:
   #tics inside

papers/GCCN_LES/gccn_rw_profs.py

@@ -16,40 +16,14 @@
 # activate latex text rendering
 rc('text', usetex=True)
 # font size
-plt.rcParams.update({'font.size': 24})
+plt.rcParams.update({'font.size': 10})
 # fig size
-plt.figure(figsize=(20,10))
+plt.figure(figsize=(7.2,4))
 
-# profs = ["gccn_rw_cl_down"]
-# nplotx=1
-# nploty=1
-# 
-# # init the plot
-# fig, axarr = plt.subplots(nplotx, nploty)
-# print 'axarr type: ', type(axarr)
-# print 'axarr shape: ', axarr.shape
-# plot_iter=0
-# 
-# assert len(sys.argv) == 6
-# 
-# 
-# n_profs=3
-# file_names = []
-# file_labels = []
-# file_no = np.arange(1, 2 * n_profs , 2) # 2* because for each line there is data file and label
-# for no in file_no:
-#   print no
-#   print sys.argv[no]
-#   file_names.append(sys.argv[no] + "profiles_7200_18000.dat")
-#   file_labels.append(sys.argv[no+1])
-# 
-# print file_names
-# plot_iter = plot_profiles(profs, plot_iter, nplotx, nploty, axarr, xscaledict, yscaledict, xlimdict_series[cusc_iter], ylimdict_series, xlabel='Time [h]', file_names=file_names, file_labels=file_labels, linewidth=1.5)
-
-linestyles = ['--', '-.', ':']
+linestyles = ['-', '-.', '--']
 dashList = [(3,1),(1,1),(4,1,1,1),(4,2)]
 
-plt.xlabel('mean wet radius of droplets formed on aerosols with $r_\mathrm{dry}>2\mu\mathrm{m}$ [$\mu\mathrm{m}$]')
+plt.xlabel('mean wet radius of droplets formed on aerosols with $r_\mathrm{dry}>2\, \mu\mathrm{m}$ [$\mu\mathrm{m}$]')
 plt.ylabel('height / inversion height')
 
 for no in [2,4,6]:
@@ -60,8 +34,23 @@
 
   profs_file.close()
 
-  plt.plot(my_gccn_rw, my_hgt, label=sys.argv[no+1])
+  plt.plot(my_gccn_rw, my_hgt, label=sys.argv[no+1], ls=linestyles[int(no/2-1)])
 
 plt.legend()
 plt.ylim(0,1.1)
 plt.savefig(sys.argv[1])
+
+plt.clf()
+for no in [2,4,6]:
+  file_name = sys.argv[no] + "profiles_7200_18000.dat"
+  profs_file = open(file_name, "r")
+  my_hgt = read_my_var(profs_file, "position")
+  my_gccn_rw = read_my_var(profs_file, "gccn_rw_cl_up")
+
+  profs_file.close()
+
+  plt.plot(my_gccn_rw, my_hgt, label=sys.argv[no+1])
+
+plt.legend()
+plt.ylim(0,1.1)
+plt.savefig(sys.argv[1]+'updraft.pdf')

papers/GCCN_LES/precip_vs_GCCN.py

@@ -18,7 +18,7 @@
 profs_to_it = int(sys.argv[4])
 qlimit = float(sys.argv[5])
 
-varlabels = ["{\it CuNc35}", "{\it CuNc55}", "{\it CuNc75}"]
+varlabels = ["{\it Sc30}", "{\it Sc40\_salt\_CCN}", "{\it Sc45}", "{\it Sc105}"]
 averaging_period = float(profs_to_it - profs_from_it) / 3600. # period over which series are averaged [h]; NOTE: we assume that series_from(to)_it = profs_from(to)_it / outfreq!
 
 # assumed initial GCCN concentrations
@@ -37,11 +37,11 @@
   series_file_names.append(sys.argv[no] + "series.dat")
   profs_file_names.append(sys.argv[no] + "profiles_"+str(profs_from_it)+"_"+str(profs_to_it)+".dat")
 
-assert(len(series_file_names) == 12)
+assert(len(series_file_names) == 16)
 
 label_counter=0
 
-for it in np.arange(12):
+for it in np.arange(16):
   if(it % 4 == 0):
     mean_surf_precip = []
     tot_acc_surf_precip = []

papers/GCCN_LES/results_vs_CCN.py

@@ -54,7 +54,7 @@
 
 #axes = plt.gca()
 #axes.tick_params(direction='in')
-labeldict=["{\it ScNc30}", "{\it ScNc45}", "{\it ScNc105}"]
+labeldict=["{\it Sc30}", "{\it Sc45}", "{\it Sc105}"]
 y_arr = np.arange(nploty)
 for y in y_arr:
   #tics inside

papers/ICMW2020_CC/compare_profs.py

@@ -13,16 +13,20 @@
 from plot_ranges import * 
 from plot_profs import *
 
-profs = ["cl_nc", "actrw_reff_cl", "cloud_std_dev"]
+profs = ["cl_nc", "actrw_reff_cl", "cloud_std_dev", "rliq", "ratio_mean_volue_r_to_eff_r_cubed"]
 xlabels = {
            "cl_nc": "cloud droplet concentration [1/cc]",
            "actrw_reff_cl": "effective radius of cloud droplets [um]",
-           "cloud_std_dev": "std. dev. of radius of cloud droplets [um]"
+           "cloud_std_dev": "std. dev. of radius of cloud droplets [um]",
+           "rliq": "liquid water [g/kg]",
+           "ratio_mean_volue_r_to_eff_r_cubed": "(mean vol rad) / (eff rad) [1]"
           }
 xlims = {
            "cl_nc": [],
            "actrw_reff_cl": [],
-           "cloud_std_dev": []
+           "cloud_std_dev": [],
+           "rliq": [],
+           "ratio_mean_volue_r_to_eff_r_cubed": []
         }
 
 # activate latex text rendering