def decomposeDataAndPlot(step, subscript, startCol, fileName):
# For xx component
propertyDecomp = {}
listY = []
for i in range(nProbe):
propertyDecomp['probe' +
str(i)] = probe.propertyData[fileName][:, startCol +
step * i]
listY.append(propertyDecomp['probe' + str(i)])
# listY = np.array(listY)
if caseName == 'ALM_N_H_OneTurb':
listY2 = [listY[3], listY[5], listY[7]]
del listY[7]
del listY[5]
del listY[3]
listY += listY2
nPlot = 2 if caseName == 'ALM_N_H_OneTurb' else 1
nProbePlot = (0, 5, nProbe) if caseName == 'ALM_N_H_OneTurb' else (0,
nProbe)
for i in prange(nPlot):
yLim = (np.min(listY) - np.abs(np.min(listY) * 0.05),
np.max(listY) + np.abs(np.max(listY) * 0.05))
plot = Plot2D(listX[nProbePlot[i]:nProbePlot[i + 1]],
listY[nProbePlot[i]:nProbePlot[i + 1]],
save=saveFig,
name=fileName + '_' + subscript + '_Convergence_' +
str(i),
xLabel=xLabel,
yLabel=yLabel,
figDir=figDir,
xLim=xLim,
yLim=yLim,
figWidth=figWidth,
show=show,
gradientBg=gradientBg)
plot.initializeFigure()
plot.plotFigure(plotsLabel=lineLabels[nProbePlot[i]:nProbePlot[i + 1]])
plot.finalizeFigure()
feat_importance_actual = feat_importance_all.copy()
actual_std = np.zeros_like(all_std)
feat_importance_actual[feat_importance_actual < threshold] = 0.
i0 = 0
for i in range(len(feat_importance_all)):
if feat_importance_actual[i] != 0.:
feat_importance_actual[i] = feat_importance[i0]
actual_std[i] = std[i0]
i0 += 1
myplot = Plot2D(
(np.arange(1, 51), ) * 3,
(feat_importance_all, feat_importance0, feat_importance_actual),
save=True,
show=False,
xlabel='Feature',
ylabel='Importance',
figwitdh='1/3',
figdir=figdir,
name=estimator_name + '_importance_' + rf_selector_threshold,
ylim=(0., 0.5))
myplot.initializeFigure()
myplot.plotFigure(linelabel=('TBRF feature selector', estimator_name,
'TBRF feature selector + ' + estimator_name),
showmarker=True)
myplot.axes.fill_between(np.arange(1, 51),
np.maximum(feat_importance_all - all_std, 0.),
feat_importance_all + all_std,
alpha=0.25,
color=myplot.colors[0],
lw=0.)
# Use magnitude
if name == 'U':
data_sorted = np.sqrt(data_sorted[:, 0]**2 + data_sorted[:, 1]**2 +
data_sorted[:, 2]**2)
data_sorted2 = np.sqrt(data_sorted2[:, 0]**2 +
data_sorted2[:, 1]**2 +
data_sorted2[:, 2]**2)
listx = (data_sorted, data_sorted2)
listy = (points_sorted, points_sorted2)
myplot = Plot2D(listx,
listy,
plot_type='infer',
show=False,
save=True,
name=name,
xlabel=xlabel,
ylabel=r'$\frac{z}{z_i}$ [-]',
figdir=case.avg_folder_path,
figwidth='1/3',
xlim=xlim)
myplot.initializeFigure()
myplot.plotFigure(linelabel=('South', 'West'))
myplot.axes.fill_between(xlim, 27 / 750., 153 / 750., alpha=0.25)
myplot.finalizeFigure()
kappa = .4
uref = 8.
zref = 90.
z0 = .2
if case.slices_orient[slicename] == 'vertical' else \
(r'$x$ [m]', r'$y$ [m]')
# Figure name
if 'kResolved' in properties and 'kSGSmean' in properties:
figname = 'kMean_' + slicenames[i] + slicenames_sub
elif 'epsilonSGSmean' in properties and 'nuSGSmean' in properties:
figname = 'epsilonMean_' + slicenames[i] + slicenames_sub
else:
figname = slicename
if plot_type in ('2D', 'all'):
slicePlot = Plot2D(x2d,
y2d,
vals3d,
name=figname,
xlabel=xlabel,
ylabel=ylabel,
val_label=val_label,
save=save,
show=show,
figdir=case.result_path)
slicePlot.initializeFigure()
slicePlot.plotFigure(contour_lvl=contour_lvl)
slicePlot.finalizeFigure()
if plot_type in ('3D', 'all'):
zlabel = r'$z$ [m]'
# Figure name for 3D plots
if 'kResolved' in properties and 'kSGSmean' in properties:
figname_3d = 'kMean_' + str(slicenames)
elif 'epsilonSGSmean' in properties and 'nuSGSmean' in properties:
figname_3d = 'epsilonMean_' + str(slicenames)
# list_y = [list_y[i] - ymean if i < 3 else ymean1 for i in range(len(list_y))]
for i in range(len(list_y)):
list_y[i] -= ymean if i < 3 else ymean1
# [DEPRECATED]
if figwidth == 'full':
yoffset = 255.
for i in range(3, 6):
list_y[i] += yoffset
for i0 in range(len(fignames)):
list_x = [np.array(g[set_locs[i]][i0]).T for i in range(len(set_locs))]
gplot = Plot2D(list_x=list_x,
list_y=list_y,
name=fignames[i0],
xlabel=xlabel,
ylabel=ylabel,
save=save_fig,
show=show,
figdir=case.result_path,
figwidth=figwidth,
xlim=xlim,
ylim=ylim)
gplot.initializeFigure()
gplot.markercolors = None
# Go through each line location
for i in range(len(list_x)):
# Then for each location, go through each line
for j in range(3):
label = labels[i0][j] if i == 0 else None
# Flip y
gplot.axes.plot(list_x[i][:, j],
else:
list_y = (distance_test.take(sortidx), ) * 2
list_x = (y_test[:, i].take(sortidx),
y_pred_test[:, i].take(sortidx))
ylim = heightlim
xlim = bijlims
turblocs = (90. - 63., 90. + 63.)
xlabel, ylabel = bijlabels[i], 'Range [m]'
bij_plot = Plot2D(list_x=list_x,
list_y=list_y,
name=fignames_bij[i],
xlabel=xlabel,
ylabel=ylabel,
save=save_fig,
show=show,
figwidth='1/3',
figheight_multiplier=figheight_multiplier,
figdir=lineresult_folder,
plot_type='infer',
xlim=xlim,
ylim=ylim)
bij_plot.initializeFigure()
if orient == 'H':
bij_plot.axes.fill_between(turblocs,
bijlims[0],
bijlims[1],
alpha=0.5,
facecolor=bij_plot.colors[2],
zorder=-1)
else:
for i in range(len(E)):
# If plotting horizontal Eii
if i != len(E) - 1:
xList, yList = (Kr, E12ref[:, 0], Kr), (E[i], E12ref[:, 1], E_Kolmo)
plotsLabel = (label, 'Churchfield et al.', 'Kolmogorov model')
# Else if plotting E33
else:
xList, yList = (Kr, E33ref[:, 0], Kr), (E[i], E33ref[:, 1], E_Kolmo)
plotsLabel = (label, 'Churchfield et al.', 'Kolmogorov model')
# Initialize figure
plot = Plot2D(xList,
yList,
xLabel=xLabels,
yLabel=yLabels[i],
name=figNames[i],
save=save,
show=show,
xLim=xLim,
yLim=yLim,
figDir=resultPath)
plot.initializeFigure()
plot.plotFigure(plotsLabel=plotsLabel)
plot.axes[0].fill_between((1. / cellSizes2D[0], xLim[1]),
yLim[0],
yLim[1],
alpha=fillAlpha,
facecolor=plot.gray,
zorder=-1)
plot.finalizeFigure(xyScale=('log', 'log'))
# [DEPRECATED]
xy_bary_pred_v = xy_bary_pred_v[y_idx]
list_ccy_v.append(ccy_v[y_idx])
# Limit predicted barycentric x, y coor
xy_bary_pred_v[xy_bary_pred_v < -0.1] = -0.1
xy_bary_pred_v[xy_bary_pred_v > 1.] = 1.
# Plot Barycentric triangle
list_x = (xy_bary_test_v[:, 0], xy_bary_pred_v[:, 0])
list_y = (xy_bary_test_v[:, 1], xy_bary_pred_v[:, 1])
figname = 'triangle{}'.format(i)
barymap = Plot2D(list_x,
list_y,
name=figname,
xlabel=None,
ylabel=None,
save=save_fig,
show=show,
figwidth='1/3',
figheight_multiplier=1,
figdir=case.result_paths[time],
equalaxis=True)
barymap.initializeFigure()
barymap.axes.add_patch(next(tripatches))
barymap.plotFigure(linelabel=('Truth', 'Prediction'), showmarker=True)
plt.axis('off')
barymap.axes.annotate(r'$\textbf{X}_{2c}$', (0., 0.), (-0.15, 0.))
barymap.axes.annotate(r'$\textbf{X}_{3c}$', (0.5, np.sqrt(3) / 2.))
barymap.axes.annotate(r'$\textbf{X}_{1c}$', (1., 0.))
barymap.finalizeFigure(legloc='upper left')
# Retrieve TKE Production G in Vertical Lines
ymean1 = (max(list_y[-1]) + min(list_y[-1])) / 2.
# Center y around 0 y/D
for i in range(len(list_y)):
list_y[i] -= ymean if i < 3 else ymean1
# if figwidth == 'full':
# yoffset = 255.
# for i in range(3, 6):
# list_y[i] += yoffset
list_x = [g[set_types[i]] for i in range(len(set_types))]
# First plot of G for LES, TBDT, TBRF
gplot = Plot2D(list_x=[None],
list_y=[None],
name=figname,
xlabel=xlabel,
ylabel=ylabel,
save=save_fig,
show=show,
figdir=case.result_path,
figwidth=figwidth,
xlim=xlim,
figheight_multiplier=figheight_multiplier,
ylim=ylim)
gplot.initializeFigure()
gplot.markercolors = None
# Go through each line location
for i in range(len(list_x)):
# Then for each location, go through each line
# for j in range(3):
# label = labels[j] if i == 0 else None
# # Plot each prediction and flip y
# Reference data, 1st column is x, 2nd column is y
refData = readData(refDataName, fileDir=refDataDir, skipRow=0)
# Since refData is sorted from low x to high x, what we want is from low y to high y
# Thus sort the 2nd column
refData = refData[refData[:, 1].argsort()]
# Add both normalized simulation and reference data to lists to plot
xList, yList = [uvwMean / Uhub,
refData[:, 0]], [bl.hLvls / zi, refData[:, 1]]
# X, Y limit to be inline with Churchfield's data
xLim, yLim = (0, 2), (0, 1)
# Initialize figure object
plot = Plot2D(xList,
yList,
xLabel=r'$\langle \overline{U}\rangle /U_{\rm{hub}}$',
yLabel=r'$z/z_i$',
figDir=figDir,
name=profile,
save=save,
show=show,
xLim=xLim,
yLim=yLim)
plot.initializeFigure()
# Fill in the rotor swept area
plot.axes[0].fill_between(xLim, ((zHub + D / 2) / zi, ) * 2,
((zHub - D / 2) / zi, ) * 2,
alpha=0.25,
facecolor=plot.colors[2],
zorder=-1)
plot.axes[0].fill_between(xLim, ((zi + 0.5 * invW) / zi, ) * 2,
((zi - 0.5 * invW) / zi, ) * 2,
alpha=fillAlpha,
facecolor=plot.colors[3],