print iterable
for direction, pKey, pKey_test1, pKey_test2, pKey_test3, pKey_test4 in iterable:
# ptermfig = hfig.Homfig(title="pterm ", ylabel="$(V-U)_j*du/dx_j$",xlim=[-1,1])
# plotFileNamePterm = pict_path + "test_pterm_nosymm_"+stattype +coordinates[direction]+"_"+StNo+".eps"
#ptermfig.add_plot(pstat_test1["yplus"],pstat_test1[pKey_test1],linestyle='dotted',label='$(V-Uf)_j*du/dx_j$')
#ptermfig.add_plot(pstat_test2["yplus"],pstat_test2[pKey_test2],linestyle='dashed',label='$(V-Uf)_j*dUf/dx_j$')
#ptermfig.hdraw()
#ptermfig.save(plotFileNamePterm)
#print "plot created: " + plotFileNamePterm
#plt.close(ptermfig.fig)
ptermfullvel = hfig.Homfig(title="pterm ",
ylabel="$((V-U)_j*dU/dx_j)^{2}$")
#plotFileNamePterm = pict_path + "VUdUdx_"+stattype +coordinates[direction]+"_"+StNo+".eps"
plotFileNamePterm = pict_path + "dUdx_" + stattype + coordinates[
direction] + "_" + StNo + ".eps"
#ptermfullvel.add_plot(pstat["yplus"],pstat[pKey]/termplus,linestyle='solid',label='excact term, $(V-U)_j*du/dx_j$')
#ptermfullvel.add_plot(pstat_test1["yplus"],pstat_test1[pKey_test1]/termplus,linestyle='dashed',label='$(V-Uf)_j*du/dx_j$')
#ptermfullvel.add_plot(pstat_test2["yplus"],pstat_test2[pKey_test2]/termplus,linestyle='dotted',label='$(V-Uf)_j*dUf/dx_j$')
ptermfullvel.add_plot(pstat_test3["yplus"],
pstat_test3[pKey_test3] / termplus,
linestyle='dotted',
label='$(V-U)_j*dU/dx_j$')
ptermfullvel.add_plot(pstat_test4["yplus"],
pstat_test4[pKey_test4] / termplus,
linestyle='dashed',
label='$(V-U)_j*dUf/dx_j$')
#--------------------------------------------------------------------#
# PLOT DRAW
#
# we use symmetrised data
#--------------------------------------------------------------------#
if __name__ == '__main__':
#--------------------------------------------------------------------#
# time scales
#--------------------------------------------------------------------#
SGStime = hfig.Homfig(title= " SGS relaxation time for fluid particles" ,
ylabel="$\\tau$", xlabel="$y^{+}$")
plotFileName = tc.pict_path + "SGS_aprioriVSmodel.eps"
SGStime.add_plot(relaxation_time['y'],relaxation_time['fluid_SGSles'],label='a priori')
SGStime.add_plot(dissipation_symm['y'],dissipation_symm['tau_modell2'],label='tau_modell2')
SGStime.add_plot(ksgsYoshizawa_symm['y'],ksgsYoshizawa_symm['tau_Yoshizawa'],label='LES,Yoshizawa est.')
SGStime.hdraw()
SGStime.save(plotFileName)
plt.close(SGStime.fig)
#--------------------------------------------------------------------#
# kinetic energy
#--------------------------------------------------------------------#
ksgs = hfig.Homfig(title= " SGS kinetic energy for fluid particles" ,
#return ifilterfalse(lambda x: x=="yplus", set(arg))
arg_sorted = sorted(arg)
return ifilter(lambda x: x <> "yplus", arg_sorted)
# STATISTICS
# pstat = pfields.equationP(StNo,pterm,stattype,"none",*ptermArglist)
# pstat_test1 = pfields.equationP(StNo,pterm,stattype,"none",*ptermArglist_test1)
# pstat_test2 = pfields.equationP(StNo,pterm_test2,stattype,"none",*ptermArglist_test2)
# pstat_test3 = pfields.equationP(StNo,pterm_test2,stattype,"none",*ptermArglist_test3)
# pstat_test4 = pfields.equationP(StNo,pterm_test2,stattype,"none",*ptermArglist_test4)
# FIGURES
pstat_gradient = {}
gradfig = hfig.Homfig(title="gradients of $\overline{U}$",
ylabel="$(d\overline{U}/dx_j)^{+}$",
xscale='log',
xlim=[1, 160])
plotFileNamePterm = pict_path + "gradients_" + stattype + "_" + StNo + ".eps"
for component in ['dx', 'dz']:
pstat_gradient[component] = pfields.equationP(
StNo, lambda x: x, stattype, 'symm', *gradients[component])
iterable1 = keys_no_yplus(pstat_gradient[component].keys())
for pKey in iterable1:
gradfig.add_plot(pstat_gradient[component]["yplus"],
pstat_gradient[component][pKey] / termplus,
linestyle='dotted',
label=labels[pKey[1:]])
pstat_gradient['dy'] = pfields.equationP(StNo, lambda x: x, stattype,
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#
# File name: homfigs_tests.py
# Created by: gemusia
# Creation date: 02-07-2017
# Last modified: 02-07-2017 16:08:37
# Purpose:
#
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
import numpy as np
import matplotlib.pyplot as plt
import homfigs as hf
import unittest
ff = hf.Homfig(title='very important title', xlabel="xxx", ylabel="yyy")
ff.hdraw()
class HomfigTest(unittest.TestCase):
def test_input0(self):
with self.assertRaises(ValueError):
hs.Channel([], [], [], 0, 0, 0)
5:("hek_symm",)}
ylabels = {1:"$<U>_{x}$",2:"$<U>_{y}$",
3:"$<U>_{z}$",4:"$<U_{x},U_{y}>$",
5:"$<U_{y},U_z>$",6:"$<U_{x}U_{z}$",
7:"$e_{k}$"}
ptitles = {"hmean_symm":"mean of U","hstd_symm":"Standard deviation of U","hcor_symm":"Correlation of ","hek_symm":"Kinetic energy"}
#data loading
file_path = expanduser("~") + "/wyniki/apriori/fluid/"
fig_path = file_path
# U streamwise, for channel flow description in PhD
DNS_5200 = np.transpose(np.loadtxt('/home/gemusia/REFERENCE_DATA/DNS_5200_Uplus_LeeMoser15.txt.csv',skiprows=6))
statfig = hfig.Homfig(title="Streamwise velocity", ylabel='$U_{x}^{+}$',xscale='log',xlim=[1,6000])
print DNS_5200
statfig.add_plot(DNS_5200[0],DNS_5200[1],linestyle=LineStyle['DNS'],label='DNS')
statfig.add_plot(DNS_5200[0][:7],DNS_5200[0][:7],linestyle='dashed',label='$y=x$')
statfig.add_plot(DNS_5200[0][6:],2.49*np.log(DNS_5200[0][6:])+5,linestyle='dashdot',label='$y=a\ln(x) + b$')
plt.axvline(x=5,ymin=0,ymax=0.6,linestyle = 'dashed',color='black',linewidth=0.2)
plt.axvline(x=30,ymin=0,ymax=0.6,linestyle = 'dashed',color='black',linewidth=0.2)
plt.axvline(x=700,ymin=0,ymax=0.8,linestyle = 'dashed',color='black',linewidth=0.2)
plt.text(2,10,"I")
plt.text(10,15,"II")
plt.text(80,10,"III")
plt.text(1100,10,"IV")
statfig.hdraw()
fPath=file_path,
fPrefix=["uppf_Ux", "uppf_Uy", "uppf_Uz"])
for skey, sval in statistics.iteritems():
print skey, sval, type(sval[0])
#computation of statistics
stat_DNS = test_set_DNS.statsT(*sval)
stat_LES = test_set_LES.statsT(*sval)
#figures
for a in range(1, len(stat_DNS)): #len(stat_DNS) = len(stat_LES)
#handle for different plot title
if skey in range(2, 5):
statfig = hfig.Homfig(title=ptitles[sval[0]] + sval[1] + " " +
sval[2],
ylabel=ylabels[skey + 1]) #correlations
elif skey == 5:
statfig = hfig.Homfig(title=ptitles[sval[0]],
ylabel=ylabels[6]) #kinetic energy
else:
statfig = hfig.Homfig(title=ptitles[sval[0]] + coordinates[a],
ylabel=ylabels[a])
statfig.add_plot(stat_DNS[0],
stat_DNS[a],
linestyle=LineStyle['DNS'],
label='DNS')
statfig.add_plot(stat_LES[0],
stat_LES[a],
linestyle=LineStyle['LES'],
for stattype in ("pmean", "pstd"):
for key, val2 in terms.iteritems():
statArgList.append([stattype, "symm", ["f" + val2]])
statArgList.append([stattype, "symm", ["p" + val2]])
pstat = pfields.statsP(
StNo, *statArgList) #computation of all required statistic
#to improve efficiency and not opening
#big data files too many times
#figures
for arg in ifilterfalse(lambda x: x == "plus",
set(map(lambda y: y[1:],
pstat.keys()))): #custom , for this case
statfig = hfig.Homfig(ylabel='$(u\\frac{\partial U}{\partial x})^{+}$')
statfig.add_plot(pstat["yplus"],
pstat["f" + arg] / termplus,
linestyle=LineStyle['fterm'],
label=labels['f' + arg[:5]])
statfig.add_plot(pstat["yplus"],
pstat["p" + arg] / termplus,
linestyle=LineStyle['pterm'],
label=labels['p' + arg[:5]])
statfig.hdraw()
plotFileName = pict_path + arg + "_" + StNo + ".eps"
statfig.save(plotFileName)
print "plot created: " + plotFileName
w.writerow(tau.keys())
w.writerows(zip(*tau.values()))
print f_name_tau, " created"
#--------------------------------------------------------------------#
# PLOT DRAW
#
# we use symmetrised data
#--------------------------------------------------------------------#
for simulation, St in product(ptype, Stlist):
corrfig = hfig.Homfig(title=St + ": Correlation coefficient for " +
simulation + " simulation",
ylabel="$rho (t)$",
xlabel="t",
xlim=[0, 800])
plotFileName = pict_path + "rho_" + St + "_" + simulation + ".eps"
for key, arg in zip(datalines.keys(), datalines.values()):
corrfig.add_plot(rho[St + "_" + simulation][int(key)],
linestyle=arg["ls"],
color=arg["c"],
label=arg["lbl"])
corrfig.hdraw()
corrfig.save(plotFileName)
print "plot created: " + plotFileName
plt.close(corrfig.fig)
def h(x):
return np.sin(np.pi / (2 * n)) * np.cos(x / float(n) * np.pi) / (
np.cos(np.pi / (2 * n)) * np.sin(x / float(n) * np.pi))
y = np.arange(2, 31, 1)
z = np.arange(0, 1, 0.1)
print z
print g(z)
file_path = expanduser("~") + "/Documents/Doktorat_current/picts/"
pict_path = file_path
asymmetry_coeff = hfig.Homfig(
title="Difference of right and left side wall-normal filter",
ylabel="$c$",
xlabel="y",
xlim=[1, 31])
# ylabel="$c$", xlabel="y", xlim=[0,1])
plotFileName = pict_path + "filter_skewness.eps"
asymmetry_coeff.add_plot(h(y))
asymmetry_coeff.hdraw()
asymmetry_coeff.save(plotFileName)
print "plot created: " + plotFileName
plt.close(asymmetry_coeff.fig)
tf[1],
ftermx=tf[5],
ftermy=tf[3],
ftermz=tf[4],
ptermx=tf[8],
ptermy=tf[6],
ptermz=tf[7])
for stattype in ("pmean", "pstd"):
for key, val2 in terms.iteritems():
stat_fterm = part.stat_symm(stattype, "symm", "f" + val2)
stat_pterm = part.stat_symm(stattype, "symm", "p" + val2)
#figures
statfig = hfig.Homfig(title=key, ylabel=key)
statfig.add_plot(*stat_fterm,
linestyle=LineStyle['fterm'],
label='fterm')
statfig.add_plot(*stat_pterm,
linestyle=LineStyle['pterm'],
label='pterm')
statfig.hdraw()
statfig.save(fig_path + val2 + "_" + stattype + "_" + val + ".eps")
plt.close(statfig.fig)
'''
tf=np.transpose(np.loadtxt("200_particles"))
# separate plots for each stokes number
for StNo in ptype:
for stattype in ("pmean","pstd"):
# STATISTICS
pstat = pfields.equationP(StNo,(lambda x:x),stattype,"symm",["Ux"],["Ufx"],["Vx"])
sgsStat = pfields.equationP(StNo,(lambda x,y: x-y),stattype,"symm",["Ux","Ufx"],["Uy","Ufy"],["Uz","Ufz"])
# FIGURES
# velocity statistics
statfig = hfig.Homfig(title="Velocities streamwise", ylabel="U")
plotFileName = pict_path +stattype +"_"+StNo+".eps"
for arg in ifilterfalse(lambda x: x=="yplus", set(pstat.keys())): #custom , for this case
statfig.add_plot(pstat["yplus"],pstat[arg],linestyle=LineStyle[arg],label=arg)
statfig.hdraw()
statfig.save(plotFileName)
print "plot created: " + plotFileName
plt.close(statfig.fig)
# SGS statistics
sgsfig = hfig.Homfig(title="SGS velocity", ylabel="$u^*$")
plotFileNameSGS = pict_path + "Usgs_"+stattype +"_"+StNo+".eps"