def plotIntegralLength(Lux, dz):
# Setting up the y-Axis
sp = Lux.shape
nz = sp[0]
z = np.linspace(0, (nz-1)*dz, nz)
# Setting up data to be plotted
x = [Lux]
y = [z]
# Setting up the plot
xlabel = "Lux(z)"
ylabel = "z"
title = "Integral length scale"
legend = ["Lux(z)"]
# Change to current file location
os.chdir(os.path.dirname(sys.argv[0]))
dir_fileName = "Integral_Length_Luu"
style_dict = {"savefig.format": "svg"}
# Plotting
plt.plot2D(x, y, xlabel=xlabel, ylabel=ylabel, title=title, legend=legend,
dir_fileName=dir_fileName, style_dict=style_dict,
colorScheme='UniS', variation='color',
savePlt=True, showPlt=True)
def plotCorrelationCoeff(Ruu, dx, nz):
# Setting up the x-Axis
sp = Ruu.shape
nx = sp[0]
x = np.linspace(0, (nx-1)*dx, nx)
# Setting up data to be plotted
x = [x]
y = [Ruu[:, nz]]
# Setting up the plot
xlabel = "Delta x"
ylabel = "Ruu (Delta x)"
title = "Correlation Coefficient"
legend = ["Ruu at nz = " + str(nz)]
# Change to current file location
os.chdir(os.path.dirname(sys.argv[0]))
dir_fileName = "Correlation_Coefficient_Ruu"
style_dict = {"savefig.format": "svg"}
# Plotting
plt.plot2D(x, y, xlabel=xlabel, ylabel=ylabel, title=title, legend=legend,
dir_fileName=dir_fileName, style_dict=style_dict,
colorScheme='UniS', variation='color',
savePlt=True, showPlt=True)
def plotTurbulenceIntensity(Iuu, dz):
# Setting up the y-Axis
sp = Iuu.shape
nz = sp[0]
z = np.linspace(0, (nz-1)*dz, nz)
# Setting up data to be plotted
x = [Iuu*100]
y = [z]
# Setting up the plot
xlabel = "Iuu(z)"
ylabel = "z"
title = "Turbulence intensity"
legend = ["Iuu(z)"]
# Change to current file location
os.chdir(os.path.dirname(sys.argv[0]))
dir_fileName = "Turbulence_Intensity_Iuu"
style_dict = {"savefig.format": "svg"}
# Plotting
plt.plot2D(x, y, xlabel=xlabel, ylabel=ylabel, title=title, legend=legend,
dir_fileName=dir_fileName, style_dict=style_dict,
colorScheme='UniS', variation='color',
savePlt=True, showPlt=True)
def main():
# Specify the file name
# fname = str(input("Specify File Name: "))
fname1 = "Timeseries_Baseshear_Fx_1_conv_ref0"
fname2 = "Timeseries_Baseshear_Fx_1_conv_ref1"
fname3 = "Timeseries_Baseshear_Fx_1_conv_ref2"
# Change to current
# Change to current file location
os.chdir(os.path.dirname(sys.argv[0]))
# Load the ax-object
ax1 = pkl.load(open(fname1 + '.pickle', 'rb'))
ax2 = pkl.load(open(fname2 + '.pickle', 'rb'))
ax3 = pkl.load(open(fname3 + '.pickle', 'rb'))
# Get data from the ax-object
x1, y1 = getData(ax1)
x2, y2 = getData(ax2)
x3, y3 = getData(ax3)
# Clean up
plt.close('all')
# Plotting
x = [x1 + 2, x2 + 1, x3]
y = [y1, y2, y3]
plot2D.plot2D(x, y, showPlt=True)
def plotTimeseries(Ti, Fi):
# --- Calculation --#
meanFi, stdFi, _rmsFi, _maxFi, _minFi, _nAvMax, _maxAvFi, _minAvFi = calcStatistics(
Fi)
hLines = [meanFi, meanFi + stdFi, meanFi - stdFi]
hTexts = ["\$F_{x,mean}\$", "\$F_{x,mean+std}\$", "\$F_{x,mean-std}\$"]
# ---- Plotting ----#
xlabel = "$Time$ $[s]$"
ylabel = "\$F_{x} [MN]\$"
title = "Time Series of Base Shear F_{x}"
# Change to current file location
os.chdir(os.path.dirname(sys.argv[0]))
dir_fileName = "Timeseries_Baseshear_Fx"
xlim = []
ylim = []
style_dict = {"lines.linewidth": "0.75", "savefig.format": "svg"}
plt.plot2D(Ti, Fi, xlabel=xlabel, ylabel=ylabel, title=title,
dir_fileName=dir_fileName, hLines=hLines, hTexts=hTexts, xlim=xlim, ylim=ylim,
style_dict=style_dict, colorScheme='UniS', variation='color',
showPlt=True, savePkl=True, savePlt=True)
def plotSpectra(f, Sa): #comp
# Function fit
# # Set up logspace
# fx_fit = np.logspace(np.log10(fx[1]), np.log10(fx[-1]), num=1000)
# # Interpolate to grid
# Sa_fit = griddata(fx, Sa, fx_fit, method='cubic')
# Fit a function to the values
# Sa_fit = savgol_filter(Sa, 51, 3) # window size 51, polynomial order 3
# Setting up the plot
x = [f]
y = [Sa]
#maximum peak frequency
max_fq = f[np.argmax(Sa)]
# ---- Plotting ----#
xlabel = "f"
ylabel = "PSD " #(" + comp + ")
title = "Power Spectral Density"
vLines = [max_fq]
vTexts = [r"\$f_{peak}" + '{:03.4f}'.format(max_fq) + r"\$"]
legend = ["measured"]
# Change to current file location
os.chdir(os.path.dirname(sys.argv[0]))
dir_fileName = "PowerSpectralDensity__Baseshear_Fx"
xlim = []
ylim = [10**-3, 10**1.5]
style_dict = {"savefig.format": "svg"}
plt.plot2D(
x,
y,
xlabel=xlabel,
ylabel=ylabel,
title=title,
legend=legend,
vLines=vLines,
vTexts=vTexts,
dir_fileName=dir_fileName,
xlim=xlim,
ylim=ylim,
xscale='log',
yscale='log',
style_dict=style_dict,
colorScheme='UniS',
variation='color',
savePlt=True,
showPlt=True,
savePkl=True,
)
def replotMerged(x, y, legend, hLines, hTexts, xlabel, ylabel):
plot2D.plot2D(x,
y,
title="Comparison Fx_Mean",
legend=legend,
ylim=[4.5, 6.5],
hLines=hLines,
hTexts=hTexts,
showPlt=True,
xlabel=xlabel,
ylabel=ylabel)
def plot(PSD_u_prime_measured, fx_red):
# --- To compare with exact spectra --- #
# Exact Kaimal Spectra, see "Kaimal, Wyngaard, Izumi, Cote - Spectral
# characteristics of surface layer turbulence", Quarterly Journal of the Royal
# Meteorological Society 98 (1972) or "Cheynet, Jakobsen, Obhrai - Spectral characteristics
# of surface-layer turbulence in the North Sea" (2017)
def PSD_u_prime_exact(n): return (a * n ** gamma) / \
(c + b * n ** alpha) ** beta
# Kaimal Spectra:
a = 1 * 52.5 # Factor 1 or 2 for one- or two-sided specta
b = 33
alpha = 1
beta = 5/3
gamma = 1
c = 1
# --- Fit function to measured spectra --- #
# Fit a function to the values
popt, pcov = curve_fit(fit_func, fx_red, PSD_u_prime_measured)
PSD_u_prime_fit = fit_func(fx_red, *popt)
# Setting up data to be plotted
x = [fx_red]
y = [PSD_u_prime_exact(fx_red), PSD_u_prime_measured, PSD_u_prime_fit]
# Setting up the plot
xlabel = "fx,red"
ylabel = "f x PSD / (u_tau ** 2)"
title = "Turbulent kinetic energy spectrum"
legend = ["exact", "measured", "fit"]
# Change to current file location
os.chdir(os.path.dirname(sys.argv[0]))
dir_fileName = "Turbulenc_kinetic_energy_spectrum_Su"
style_dict = {"savefig.format": "svg"}
# Plotting
plt.plot2D(x, y, xlabel=xlabel, ylabel=ylabel, title=title, legend=legend,
xscale='log', yscale='log',
dir_fileName=dir_fileName, style_dict=style_dict,
colorScheme='UniS', variation='color',
savePlt=True, showPlt=True)
def samplePlots(*, showPlt=False):
# Dataset x-Axis
x = np.linspace(0, 2 * np.pi, 50)
# Change to current file location
os.chdir(os.path.dirname(sys.argv[0]))
# FIRST PLOT
# ----------
y = np.sin(x)
title = "First Plot"
dir_fileName = "plot_as_pickle_01"
# plot2D w/ only specified options, save as .pickle
plot2D.plot2D(x,
y,
title=title,
dir_fileName=dir_fileName,
savePkl=True,
showPlt=showPlt)
# SECOND PLOT
# -----------
y = np.cos(x)
title = "Second Plot"
dir_fileName = "plot_as_pickle_02"
# plot2D w/ only specified options, save as .pickle
plot2D.plot2D(x,
y,
title=title,
dir_fileName=dir_fileName,
savePkl=True,
showPlt=showPlt)
# THIRD PLOT
# ----------
y = np.cos(2 * x)
title = "Third Plot"
dir_fileName = "plot_as_pickle_03"
# plot2D w/ only specified options, save as .pickle
plot2D.plot2D(x,
y,
title=title,
dir_fileName=dir_fileName,
savePkl=True,
showPlt=showPlt)
def main():
# Get name of input file
# fname = sys.argv [1]
fname = "/media/dani/linuxHDD/visualStudio/cfdPostProcessing/WindTunnelPostprocessing/T115_6/T115_6_000.mat"
# Clean up results folder
# delFilesInFolder('T115_6/results')
# Full scale building properties
uH_f = 38 # m/s // Wind speed at z = H (50yr)
H_f = 160 # m // Building height
B = 32 # m // Building width
dns = ['D', 'L'] # // Directions (Drag/Lifts)
# @DL: Folgende Variablen ignorieren,
# Diese wären nur wichtig für Strukturanalyse
nF = 32 # // Number of floors
nM = 4 # // Number of modules
b = 16 # m // Core wall thickness
D = 0.02 # % // Damping
I = 477.924 # m4 // Starting value
E = 28900 * 10 ** 3 # kN/m2 // E-Modulus
mue = 30473 / H_f # t/m // Mass distribution
# Iterate over both directions (D/L)
for dn in dns:
# Load wind tunnel model properties, TPU Database files
wtModelProp = modelProp.wtModelProp(fname)
wtModelProp.loadTPUModelProp()
# Initialize building model properties
buildProp = modelProp.buildProp(H_f, B, nF, nM, dn, E, I, D, uH_f)
# Load aerodynamic forces in model scale
wtModelAeroForces = aeroForces.wtModelAeroForces()
wtModelAeroForces.loadTPUModelForces(wtModelProp, buildProp)
# Calculate scaling factors
scalingFactors = scaling.scalingFactors(wtModelProp, buildProp)
# Scale wind tunnel model
buildProp.scaleBuildProp(wtModelProp, scalingFactors)
# Scale forces
buildAeroForces = aeroForces.buildAeroForces(scalingFactors, wtModelAeroForces)
# All forces are in kN
print("Direction: " + dn)
print("Mean Base Force: " + '{:02.3f}'.format(np.mean(buildAeroForces.BF_p)))
print("Min Base Force: " + '{:02.3f}'.format(np.min(buildAeroForces.BF_p)))
print("Max Base Force: " + '{:02.3f}'.format(np.max(buildAeroForces.BF_p)))
print("Std Base Force: " + '{:02.3f}'.format(np.std(buildAeroForces.BF_p)))
meanBF, stdBF, rmsBF, maxBF, minBF = calcStatistics(buildAeroForces.BF_p)
hLines = [meanBF, meanBF + stdBF, meanBF - stdBF]
hTexts = ["\$F_{x,mean}\$", "\$F_{x,mean+std}\$", "\$F_{x,mean-std}\$"]
# Time series of base forces
t = np.linspace(0, buildProp.nT*buildProp.dT, buildProp.nT)
F = buildAeroForces.BF_p
style_dict = {"lines.linewidth": "0.5", "savefig.format": "svg"}
plt.plot2D(t, F, hLines=hLines, style_dict=style_dict, xlabel="Time [s]", ylabel= "Base Force " + dn + " [kN]", title="Time series of base forces", showPlt=True)
def main(dObject):
# Get ouput directory
outDir = '/media/dani/linuxHDD/openfoam/simpleFoam/testing/'
fname0 = '/media/dani/linuxHDD/openfoam/simpleFoam/testing/1_conv_ref0/postProcessing/forces/0/force.dat'
interpForces = readForces.importForces(fname0)
# Get data to plot
sT = min(interpForces[0])
eT = max(interpForces[0])
dT = interpForces[0][1] - interpForces[0][0]
nT = len(interpForces[0] * dT)
T = np.linspace(sT, eT, nT)
BF = interpForces[2] / (10**6)
# Specify Cut-Off time
print("Specify time range to plot")
sT = int(input("Start Time: "))
eT = int(input("End Time: "))
print("Specify time to start evaluation")
sT_S = int(input("Start Time: "))
for comp in BF:
# Cut the array to desired range
x = T[int(sT_S / dT):int(eT / dT)]
y = BF[comp][int(sT_S / dT):int(eT / dT)]
# Filter anomalies if desired
# flagFilter = input("Shall data for " + comp + " be filtered? (Y/N): ")
# if flagFilter == "Y":
# y = filterDataWithStd(y, comp, dT, sT)
# Calculate statistic properties
meanBF, rmsBF, stdBF = statisticsOverTimeSeries(y)
x = [x]
# Plot the mean / time
y = [meanBF]
xlabel = r"$Time [s]$"
if "F" in comp:
ylabel = r"$Mean F_{} [MN]$".format(comp[1])
legend = [r"$Mean F_{}$".format(comp[1])]
title = "Mean Base Shear"
app = comp + "_" + str(int(round(np.min(x)))) + "_" + str(
int(round(np.max(x))))
dir_fileName = outDir + "Mean_BaseShear_" + app
elif "M" in comp:
ylabel = r"$Mean M_{} [MNm]$".format(comp[1])
legend = [r"$Mean M_{}$".format(comp[1])]
title = "Mean Base Moment"
app = comp + "_" + str(int(round(np.min(x)))) + "_" + str(
int(round(np.max(x))))
dir_fileName = outDir + "Mean_BaseMoment_" + app
xlim = []
ylim = []
style_dict = {"lines.linewidth": 1.0, "figure.figsize": "11.0, 4.8"}
plt.plot2D(x,
y,
xlabel,
ylabel,
title,
legend,
dir_fileName,
xlim=xlim,
ylim=ylim,
xscale='linear',
yscale='linear',
style_dict=style_dict,
colorScheme='TUM',
variation='color')
# Plot the root-mean-square / time
y = [rmsBF]
xlabel = r"$Time [s]$"
if "F" in comp:
ylabel = r"$RMS F_{} [MN]$".format(comp[1])
legend = [r"$RMS F_{}$".format(comp[1])]
title = "Root-Mean-Square Base Shear"
app = comp + "_" + str(int(round(np.min(x)))) + "_" + str(
int(round(np.max(x))))
dir_fileName = outDir + "RMS_BaseShear_" + app
elif "M" in comp:
ylabel = r"$RMS M_{} [MNm]$".format(comp[1])
legend = [r"$RMS M_{}$".format(comp[1])]
title = "Root-Mean-Square Base Moment"
app = comp + "_" + str(int(round(np.min(x)))) + "_" + str(
int(round(np.max(x))))
dir_fileName = outDir + "RMS_BaseMoment_" + app
xlim = []
ylim = []
style_dict = {"lines.linewidth": 1.0, "figure.figsize": "11.0, 4.8"}
plt.plot2D(x,
y,
xlabel,
ylabel,
title,
legend,
dir_fileName,
xlim=xlim,
ylim=ylim,
xscale='linear',
yscale='linear',
style_dict=style_dict,
colorScheme='TUM',
variation='color')
# Plot the standart deviation / time
y = [stdBF]
xlabel = r"$Time [s]$"
if "F" in comp:
ylabel = r"$STD F_{} [MN]$".format(comp[1])
legend = [r"$STD F_{}$".format(comp[1])]
title = "Standard Deviation Base Shear"
app = comp + "_" + str(int(round(np.min(x)))) + "_" + str(
int(round(np.max(x))))
dir_fileName = outDir + "STD_BaseShear_" + app
elif "M" in comp:
ylabel = r"$STD M_{} [MNm]$".format(comp[1])
legend = [r"$STD M_{}$".format(comp[1])]
title = "Standard Deviation Base Moment"
app = comp + "_" + str(int(round(np.min(x)))) + "_" + str(
int(round(np.max(x))))
dir_fileName = outDir + "STD_BaseMoment_" + app
xlim = []
ylim = []
style_dict = {"lines.linewidth": 1.0, "figure.figsize": "11.0, 4.8"}
plt.plot2D(x,
y,
xlabel,
ylabel,
title,
legend,
dir_fileName,
xlim=xlim,
ylim=ylim,
xscale='linear',
yscale='linear',
style_dict=style_dict,
colorScheme='TUM',
variation='color')
print("\n")
def plotConvergence(self, outDir, sT, eT, stat):
dir_fileName = outDir + "Baseshear"
x = self.delta_x_k_m
y = self.S_k_m
if self.convrgFlag == "Convergent":
hLines = [self.S_C, 1.728] #1.551058 4.970328
hTexts = ['estimate', 'Windtunnel']
print(self.S_C)
else:
hLines = None
hTexts = None
# # 1) For Mean
# if "Mean" in designation:
# if "F" in comp:
# ylabel = r"\$\overbar{F_{" + r"{}".format(comp[1]) + r"}}~[MN]\$"
# title = r"Convergence Mean F \textsubscript{" + r"{}".format(comp)[1] + r"}"
# elif "M" in comp:
# ylabel = r"\$\overbar{M_{" + r"{}".format(comp[1]) + r"}}~[MNm]\$"
# title = r"Convergence Mean M \textsubscript{" + r"{}".format(comp)[1] + r"}"
# # 2) For RMS
# elif "RMS" in designation:
# if "F" in comp:
# ylabel = r"\$F_{" + r"{}".format(comp[1]) + r",rms}~[MN]\$"
# title = r"Convergence RMS F \textsubscript{" + r"{}".format(comp)[1] + r"}"
# elif "M" in comp:
# ylabel = r"\$M_{" + r"{}".format(comp[1]) + r",rms}~[MNm]\$"
# title = r"Convergence RMS M \textsubscript{" + r"{}".format(comp)[1] + r"}"
# # 3) For Std
# elif "Std" in designation:
# if "F" in comp:
# ylabel = r"\$F_{" + r"{}".format(comp[1]) + r",}~[MN]\$"
# title = r"Convergence Std F \textsubscript{" + r"{}".format(comp)[1] + r"}"
# elif "M" in comp:
# ylabel = r"\$M_{" + r"{}".format(comp[1]) + r",std}~[MNm]\$"
# title = r"Convergence Std M \textsubscript{" + r"{}".format(comp)[1] + r"}"
# # 4) For Largest20Values
# elif "Largest20" in designation:
# if "F" in comp:
# ylabel = r"\$F_{" + r"{}".format(comp[1]) + r",max}~[MN]\$"
# title = r"Convergence Max F \textsubscript{" + r"{}".format(comp)[1] + r"}"
# elif "M" in comp:
# ylabel = r"\$M_{" + r"{}".format(comp[1]) + r",max}~[MNm]\$"
# title = r"Convergence Max M \textsubscript{" + r"{}".format(comp)[1] + r"}"
# # 5) For Smallest20Values
# elif "Smallest20" in designation:
# if "F" in comp:
# ylabel = r"\$F_{" + r"{}".format(comp[1]) + r",min}~[MN]\$"
# title = r"Convergence Min F \textsubscript{" + r"{}".format(comp)[1] + r"}"
# elif "M" in comp:
# ylabel = r"\$M_{" + r"{}".format(comp[1]) + r",min}~[MNm]\$"
# title = r"Convergence Min M \textsubscript{" + r"{}".format(comp)[1] + r"}"
xlim = [0, max(x) + max(x) * 0.2]
ylim = [0, max(y) + max(y) * 0.5]
# Change to current file location
os.chdir(os.path.dirname(sys.argv[0]))
style_dict = {
"lines.linewidth": 0,
"lines.markersize": 8,
"savefig.format": "pdf",
"lines.marker": "x"
}
xlabel = 'Zellanzahl Refinement Zone [10⁶]'
ylabel = str(stat[3])
title = str(stat[4])
# Punkte mit Text
annotate = [['coarse ' + str('{:03.2f}'.format(y[2])), (x[2], y[2])],
['fine ' + str('{:03.2f}'.format(y[1])), (x[1], y[1])],
['finest ' + str('{:03.2f}'.format(y[0])), (x[0], y[0])]]
plt.plot2D(x,
y,
xlabel=xlabel,
ylabel=ylabel,
title=title,
dir_fileName=dir_fileName,
xlim=xlim,
ylim=ylim,
hLines=hLines,
hTexts=hTexts,
style_dict=style_dict,
variation='color',
savePlt=True,
showPlt=True,
annotate=annotate)