def PlotSingle(self, ax, fig, dat, Extent=None, Annotate=False):
if Extent==None:
if self.vmin==-999 and self.vmax==-999:
im = ax.imshow(dat,origin='lower',aspect='auto')
else:
im = ax.imshow(dat,origin='lower',aspect='auto',vmin=self.vmin, vmax=self.vmax)
else:
if self.vmin==-999 and self.vmax==-999:
im = ax.imshow(dat,origin='lower',aspect='auto',extent=Extent)
else:
im = ax.imshow(dat,origin='lower',aspect='auto',extent=Extent,vmin=self.vmin, vmax=self.vmax)
if Annotate:
from matplotlib.legend import rcParams
rcParams.update({'font.size':self.fsize})
fig.subplots_adjust(top=0.94, right=0.96,left=0.05,bottom=0.11)
cb=plt.colorbar(im)
cb.set_label(self.cblabel)
plt.xlabel(self.xlabel, fontsize = self.fsize)
plt.ylabel(self.ylabel, fontsize = self.fsize)
plt.title(self.title, fontsize = self.fsize)
if self.HideAxis:
frame1 = plt.gca()
frame1.axes.get_xaxis().set_visible(False)
frame1.axes.get_yaxis().set_visible(False)
return
def PlotRaw(self, Annotate=False, Extent=None, std=False):
import matplotlib.pyplot as plt
# Make pixel size the same in x- and y-directions.
self.Plotysize = self.Plotxsize * self.AspectRatio
fig = plt.figure(figsize=(self.Plotxsize, self.Plotysize))
if std == True:
tmpdat = self.STD
print("Mean STD", np.mean(tmpdat), np.mean(self.STD / self.rad))
else:
tmpdat = self.rad
if Extent == None:
if self.vmin == -999 and self.vmax == -999:
im = plt.imshow(tmpdat, origin='lower', aspect='auto')
else:
im = plt.imshow(tmpdat,
origin='lower',
aspect='auto',
vmin=self.vmin,
vmax=self.vmax)
else:
if self.vmin == -999 and self.vmax == -999:
im = plt.imshow(tmpdat,
origin='lower',
aspect='auto',
extent=Extent)
else:
im = plt.imshow(tmpdat,
origin='lower',
aspect='auto',
extent=Extent,
vmin=self.vmin,
vmax=self.vmax)
if Annotate:
from matplotlib.legend import rcParams
rcParams.update({'font.size': self.fsize})
fig.subplots_adjust(top=0.94, right=0.96, left=0.11, bottom=0.11)
cb = plt.colorbar()
cb.set_label(self.cblabel)
plt.xlabel(self.xlabel, fontsize=self.fsize)
plt.ylabel(self.ylabel, fontsize=self.fsize)
plt.title(self.title, fontsize=self.fsize)
if self.HideAxis:
frame1 = plt.gca()
frame1.axes.get_xaxis().set_visible(False)
frame1.axes.get_yaxis().set_visible(False)
plt.show()
def plot_images_column(**kwargsall):
from mpl_toolkits.axes_grid1 import make_axes_locatable
nplots=len(kwargsall)
# nplots subplots, the axes array is 1-d
fig, axarr = plt.subplots(nplots, sharex=True, figsize=(14,15))
ip=0
for items in sorted(kwargsall.items()):
kwargs = items[1]
img=kwargs['data']
if kwargs['log_img']:
data = np.log10(img)
data = np.where(data>np.log10(kwargs['threshold']), data, np.log10(kwargs['threshold']))
else:
data = img
data = np.where(data>kwargs['threshold'], data, kwargs['threshold'])
yp = np.arange(0, kwargs['data'].shape[1])
if 'vmin' in kwargs:
im=axarr[ip].imshow(data, origin='lower', aspect='auto', extent=kwargs['extent'],
cmap=kwargs['cmap'], vmin=kwargs['vmin'], vmax=kwargs['vmax'])
else:
im=axarr[ip].imshow(data, origin='lower', aspect='auto', extent=kwargs['extent'], cmap=kwargs['cmap'])
if ip==nplots-1:
axarr[ip].set_xlabel(kwargs['xlabel'], fontsize=FONTSIZE)
axarr[ip].set_ylabel(kwargs['ylabel'], fontsize=FONTSIZE)
axarr[ip].set_title(kwargs['title'], fontsize=FONTSIZE)
# Colorbars are a bit of a mess, from:
# https://matplotlib.org/gallery/axes_grid1/demo_axes_divider.html#sphx-glr-gallery-axes-grid1-demo-axes-divider-py
divider = make_axes_locatable(axarr[ip])
ax_cb = divider.new_horizontal(size="2%", pad=0.05)
fig1 = axarr[ip].get_figure()
fig1.add_axes(ax_cb)
plt.colorbar(im, cax=ax_cb)
ax_cb.yaxis.tick_right()
ax_cb.yaxis.set_tick_params(labelright=True)
ip=ip+1
rcParams.update({'font.size':FONTSIZE})
fig.subplots_adjust(top=0.96, right=0.93,left=0.08,bottom=0.05)
def plot_img(img, **kwargs):
if 'figsize' in kwargs:
figsize=kwargs['figsize']
else:
figsize=(18,7.2)
fig = plt.figure(figsize=figsize)
ax = fig.add_subplot(111)
rcParams.update({'font.size':FONTSIZE})
# fig.subplots_adjust(top=0.99, right=0.98,left=0.09,bottom=0.02)
fig.subplots_adjust(top=0.99, right=0.98,left=0.09,bottom=0.02)
if 'extent' in kwargs:
extent=kwargs['extent']
else:
extent=None
if 'flip_x' in kwargs:
if kwargs['flip_x']:
img=img[:,::-1]
if kwargs['log_img']:
data = np.log10(img)
data = np.where(data>np.log10(kwargs['threshold']), data, np.log10(kwargs['threshold']))
else:
data = img
data = np.where(data>kwargs['threshold'], data, kwargs['threshold'])
print("extent", extent)
if 'vmin' in kwargs:
im = ax.imshow(data, origin='lower', cmap=kwargs['cmap'], extent=extent,
vmin=kwargs['vmin'], vmax=kwargs['vmax'])
else:
im = ax.imshow(data, origin='lower', cmap=kwargs['cmap'], extent=extent)
ax.set_title(kwargs['title'], fontsize=FONTSIZE)
ax.set_xlabel(kwargs['xlabel'], fontsize = FONTSIZE)
ax.set_ylabel(kwargs['ylabel'], fontsize = FONTSIZE)
if 'cb_shrink' in kwargs:
shrink=kwargs['cb_shrink']
else:
shrink=0.64
if 'cb_ticks' in kwargs:
plt.colorbar(im, shrink=shrink, pad=0.02, ticks=kwargs['cb_ticks'])
else:
plt.colorbar(im, shrink=shrink, pad=0.02)
def plot_img_statistics_5(Img, **kwargs):
fig = plt.figure(1, figsize=(14,18))
rcParams.update({'font.size':FONTSIZE})
fig.subplots_adjust(top=0.96, right=0.93,left=0.08,bottom=0.05)
ax1 = plt.subplot(511)
ax2 = plt.subplot(512)
ax3 = plt.subplot(513)
ax4 = plt.subplot(514)
ax5 = plt.subplot(515)
yp = np.arange(0, Img.shape[1])
colorline1='b'
colorline2='r'
if kwargs['log_img']:
data = np.log10(Img.img)
data = np.where(data>np.log10(Img.threshold), data, np.log10(Img.threshold))
else:
data = Img.img
data = np.where(data>Img.threshold, data, Img.threshold)
print("data min/max", data.min(), data.max())
# print ("Centerline", Img.Centerline)
ax1.imshow(data, aspect='auto', origin='lower', extent=kwargs['extent'], cmap=kwargs['cmap'])#, cmap=cmaps.gist_ncar_r)#
ax1.set_ylabel('Vertical pixel number', fontsize=FONTSIZE)
ax1.set_title(kwargs['title'], fontsize=FONTSIZE)
box = ax1._position.bounds
height = box[3]
tmpax = fig.add_axes([box[0], box[1] , box[2], height])
tmpax.set_axis_off()
tmpax.plot(yp,Img.Centerline,c=colorline2)
# tmpax.plot(np.sin(np.linspace(0,np.random.randint(20,1000),1000))*0.4)
tmpax.set_ylim(Img.shape[0],0) #Upside down since origin=lower
tmpax.set_xlim(0,len(yp)-1)
# yval=Img.shape[0]-Img.Centerline # Turn upside down
yval=Img.Centerline
# ax2.plot(yp,Img.Centerline,c=colorline1)
ax2.plot(yp, yval, c=colorline1)
# ax2.set_ylim(ax2.get_ylim()[::-1])
# ax2.set_ylim(20,40)
ax2.set_ylabel('Centerline', fontsize=FONTSIZE)
data = Img.Centerline
data = data[~np.isnan(data)]
indx= np.where(np.absolute(data)>1.0)
print('{:20s} {:7.3f} {:7.2f} {:7d} {:7d} {:7.3f}'.format('Centerline', np.nanmin(Img.Centerline), np.nanmax(Img.Centerline), len(indx[0]),\
data.shape[0], 100*len(indx[0])/float(data.shape[0])))
# ax2.set_ylim(Img.shape[0],0) #Upside down since origin=lower
# ax2.set_ylim(0,Img.shape[0])
ax2.set_ylim(-2,2)
ax2.plot([0,len(yp)-1],[0,0],c='k')
ax2.plot([0,len(yp)-1],[1,1],c='k',linestyle=':')
ax2.plot([0,len(yp)-1],[-1,-1],c='k',linestyle=':')
ax2.set_xlim(0,len(yp)-1)
data = Img.Dispersion
data = data[~np.isnan(data)]
indx= np.where(np.absolute(data)>1.0)
print('{:20s} {:7.3f} {:7.2f} {:7d} {:7d} {:7.3f}'.format('Dispersion',np.nanmin(Img.Dispersion), np.nanmax(Img.Dispersion), len(indx[0]),\
data.shape[0], 100*len(indx[0])/float(data.shape[0])))
ax3.plot(yp,Img.Dispersion,c=colorline1)
# ax3.set_ylim(0,20)
ax3.set_ylim(-2,2)
ax3.plot([0,len(yp)-1],[0,0],c='k')
ax3.plot([0,len(yp)-1],[1,1],c='k',linestyle=':')
ax3.plot([0,len(yp)-1],[-1,-1],c='k',linestyle=':')
ax3.set_ylabel('Abs. dispersion', fontsize=FONTSIZE)
ax3.set_xlim(0,len(yp)-1)
data = Img.RelativeDispersion
data = data[~np.isnan(data)]
indx= np.where(np.absolute(data)>1.0)
print('{:20s} {:7.3f} {:7.2f} {:7d} {:7d} {:7.3f}'.format('RelativeDispersion', np.nanmin(Img.RelativeDispersion), np.nanmax(Img.RelativeDispersion), len(indx[0]),\
data.shape[0], 100*len(indx[0])/float(data.shape[0])))
ax4.plot(yp,Img.RelativeDispersion,c=colorline1)
# ax4.set_ylim(0,15)
ax4.set_ylim(-2,2)
ax4.plot([0,len(yp)-1],[0,0],c='k')
ax4.plot([0,len(yp)-1],[1,1],c='k',linestyle=':')
ax4.plot([0,len(yp)-1],[-1,-1],c='k',linestyle=':')
ax4.set_ylabel('Rel. dispersion', fontsize=FONTSIZE)
ax4.set_xlim(0,len(yp)-1)
data = Img.AbsoluteSkewness
data = data[~np.isnan(data)]
indx= np.where(np.absolute(data)>1.0)
print('{:20s} {:7.3f} {:7.2f} {:7d} {:7d} {:7.3f}'.format('AbsoluteSkewness', np.nanmin(Img.AbsoluteSkewness), np.nanmax(Img.AbsoluteSkewness), len(indx[0]),\
data.shape[0], 100*len(indx[0])/float(data.shape[0])))
ax5.plot(yp,Img.AbsoluteSkewness,c=colorline1)
ax5.set_ylim(-4,4)
ax5.plot([0,len(yp)-1],[0,0],c='k')
ax5.plot([0,len(yp)-1],[1,1],c='k',linestyle=':')
ax5.plot([0,len(yp)-1],[-1,-1],c='k',linestyle=':')
ax5.set_xlabel('Horizontal pixel number', fontsize=FONTSIZE)
ax5.set_ylabel('Abs. skewness', fontsize=FONTSIZE)
ax5.set_xlim(0,len(yp)-1)
def plot_img_statistics(Img, **kwargs):
fig = plt.figure(1, figsize=(14,15))
rcParams.update({'font.size':FONTSIZE})
fig.subplots_adjust(top=0.96, right=0.93,left=0.08,bottom=0.1)
fig.subplots_adjust(hspace=0.4)
ax1 = plt.subplot(311)
ax2 = plt.subplot(312)
ax3 = plt.subplot(313)
yp = np.arange(0, Img.shape[1])
includeLES=False
if kwargs['LESdata'] != None:
includeLES=True
LESImg = kwargs['LESdata']
colorline1='b'
colorline2='r'
colorline3='g'
colorline4='k'
if kwargs['log_img']:
data = np.log10(Img.img)
data = np.where(data>np.log10(Img.threshold), data, np.log10(Img.threshold))
else:
data = Img.img
data = np.where(data>Img.threshold, data, Img.threshold)
print("data min/max", data.min(), data.max())
ax1.imshow(data, aspect='auto', origin='lower', extent=kwargs['extent'], cmap=cmaps.Greys)
ax1.set_ylabel('Vertical pixel number', fontsize=FONTSIZE)
ax1.set_xlabel('Horizontal pixel number', fontsize=FONTSIZE)
if kwargs['title']!='':
ax1.set_title(kwargs['title'], fontsize=FONTSIZE)
box = ax1._position.bounds
height = box[3]
tmpax = fig.add_axes([box[0], box[1] , box[2], height])
tmpax.set_axis_off()
linewidth=3
Img.phis = np.arange(Img.meta["phi1"], Img.meta["phi2"], (Img.meta["phi2"]-Img.meta["phi1"])/Img.img.shape[1])-180
PlotAng=True #False #
indx = np.where(Img.Centerline>0.0)
Img.Centerline = Img.Centerline[indx[0]]
if PlotAng:
tmpy = Img.phis[indx[0]]
AngleShift=1.0
tmpax.plot(tmpy, Img.Centerline,c=colorline2, lw=linewidth, linestyle=':')
tmpax.set_xlim(Img.phis[0],Img.phis[len(Img.phis)-1])
else:
tmpyp = yp[indx[0]]
tmpax.plot(tmpyp, Img.Centerline,c=colorline2, lw=linewidth, linestyle=':')
tmpax.set_xlim(0,len(yp)-1)
tmpax.set_ylim(Img.shape[0],0) #Upside down since origin=lower
# tmpax.set_xlim(Img.meta["phi1"]-180, Img.meta["phi2"]-180)
if includeLES:
D = 0.26 # Distance from camera to plume in km. Roughly estimated from Fig. 2 and tuned
xcentre = LESImg.x[int(LESImg.x.shape[0]/2)]
phis = np.rad2deg(np.arctan((LESImg.x-xcentre)/D) )
# print "LESImg.x", LESImg.x-xcentre
# print "phis", phis[0], phis[len(phis)-1]
# print "Img.thetas", Img.thetas.shape, Img.thetas
tmpCL = np.interp(Img.phis, phis, LESImg.Centerline)
# print "tmpCL", tmpCL.shape, tmpCL.min(), tmpCL.max(), Img.Centerline.min(), Img.Centerline.max()
xpixoffset=10
xoffset= 12 #9#*0.115 # 46/400=0.115
yoffset=9 #+88-78
yscale = Img.shape[0]/float(LESImg.shape[0])
# tmpax.plot(Img.phis+xoffset, tmpCL*yscale+yoffset,c=colorline2, linestyle=':', lw=linewidth)
indxdiff1=20 #0
indxdiff2=len(tmpCL)-20
tmpCL=tmpCL*yscale+yoffset
if PlotAng:
tmpax.plot(Img.phis+AngleShift, tmpCL,c=colorline2, linestyle='-', lw=linewidth)
else:
tmpax.plot(yp[indxdiff1-xoffset:indxdiff2-xoffset]+xpixoffset, tmpCL[indxdiff1-xoffset:indxdiff2-xoffset],c=colorline2, linestyle='-', lw=linewidth)
# CL_diff = (Img.Centerline[indxdiff1:indxdiff2]-tmpCL[indxdiff1-xoffset:indxdiff2-xoffset])/tmpCL[indxdiff1:indxdiff2]
# CL_diff = (Img.Centerline[indxdiff1:indxdiff2]-tmpCL[indxdiff1-xoffset:indxdiff2-xoffset])
# fn = 'Stats_CL_V01.txt'
# fp=open(fn,'w')
# for CLimg, CLLES, diff in zip(Img.Centerline[indxdiff1:indxdiff2], tmpCL[indxdiff1-xoffset:indxdiff2-xoffset], CL_diff):
# fp.write('{:f} {:f} {:f}\n'.format(CLimg, CLLES, diff))
# close(fn)
ximgind1 = 20
ximgind2 = len(Img.Centerline)#-ximgind1
ximg = np.arange(ximgind1, ximgind2)
xlesoffset=0
xlesind1 = 11
xlesind2 = len(LESImg.Centerline)-10#-xlesind1
fn = 'Stats_CL_V03.txt'
fp=open(fn,'a')
tmpimg = Img.Centerline[ximgind1:ximgind2]
tmples = LESImg.Centerline[xlesind1:xlesind2]
xles = np.linspace(ximgind1, ximgind2, len(tmples))
print xles.shape, ximg.shape, tmples.shape, LESImg.Centerline.shape, xles.min(), xles.max(), ximg.min(), ximg.max()
tmpCL = yoffset+yscale*np.interp(ximg, xles, tmples)
CL_diff =tmpimg - tmpCL
for CLimg, CLLES, diff in zip(tmpimg, tmpCL, CL_diff):
fp.write('{:f} {:f} {:f}\n'.format(CLimg, CLLES, diff))
close(fn)
# print("Dispersion", np.nanmin(Img.Dispersion), np.nanmax(Img.Dispersion))
# print "Img.Dispersion", Img.Dispersion.shape, Img.Dispersion
indx = np.where(Img.Dispersion>0.0)
Img.Dispersion = Img.Dispersion[indx[0]]
Img.RelativeDispersion = Img.RelativeDispersion[indx[0]]
pls = []
if PlotAng:
xx = np.arange(0,len(Img.phis))
tmpxx = np.arange(0,len(Img.phis[indx[0]]))
AngleOffSet=180
# pl, = ax2.plot(Img.phis+AngleOffSet,Img.Dispersion,c=colorline1)
pl, = ax2.plot(tmpxx, Img.Dispersion,c=colorline1, linestyle=':')
#pls.append(pl)
# pl, = ax2.plot(Img.phis+AngleOffSet,Img.RelativeDispersion,c=colorline2)
pl, = ax2.plot(tmpxx, Img.RelativeDispersion,c=colorline2, linestyle=':')
# pls.append(pl)
# print xx[len(xx)-1], xx
# ax2.set_xlim(0,xx[len(xx)-1])
ax2.set_xlim(8,400)
else:
tmpyp = yp[indx[0]]
pl, = ax2.plot(tmpyp,Img.Dispersion,c=colorline1, linestyle=':')
#pls.append(pl)
pl, = ax2.plot(tmpyp,Img.RelativeDispersion,c=colorline2, linestyle=':')
# pls.append(pl)
ax2.set_xlim(0,len(yp)-1)
# ax2.set_xlim(1,400)
# ax2.set_ylim(0,20)
ax2.set_ylim(0.1,20)
# tmpax.set_xlim(Img.meta["phi1"]-180, Img.meta["phi2"]-180)
ax2.set_xscale('log')
ax2.set_yscale('log')
ax2.set_ylabel('Dispersion', fontsize=FONTSIZE)
ax2.set_xlabel('Log$_{10}$ of horizontal pixel number', fontsize=FONTSIZE)
# ax2.legend((pls[0], pls[1]), ('Absolute dispersion','Relative dispersion',),
# prop={'size':FONTSIZE}, loc='lower right')
if includeLES:
tmpDIS = np.interp(Img.phis, phis, LESImg.Dispersion)
# tmpDIS = np.interp(yp, phis, LESImg.Dispersion)
# yscale = Img.shape[0]/float(LESImg.shape[0])
yoffset=0
#
tmpDIS=tmpDIS*yscale+yoffset
tmpDIS2 = np.interp(Img.phis, phis, LESImg.RelativeDispersion)
tmpDIS2 =tmpDIS2*yscale+yoffset
if PlotAng:
PixelShift=1
pl, = ax2.plot(xx+PixelShift, tmpDIS,c=colorline1, linestyle='-', lw=linewidth)
# ax2.plot(Img.phis+AngleShift+AngleOffSet, tmpDIS,c=colorline1, linestyle=':', lw=linewidth)
pls.append(pl)
pl, = ax2.plot(xx+PixelShift, tmpDIS2,c=colorline2, linestyle='-', lw=linewidth)
pls.append(pl)
# ax2.plot(Img.phis+AngleShift+AngleOffSet, tmpDIS2*yscale+yoffset,c=colorline2, linestyle=':', lw=linewidth)
else:
pl, = ax2.plot(yp+xoffset, tmpDIS,c=colorline1, linestyle='-', lw=linewidth)
pls.append(pl)
pl, = ax2.plot(yp+xoffset, tmpDIS2, c=colorline2, linestyle='-', lw=linewidth)
pls.append(pl)
indx1=10
indx2=30 # 60#
indx = np.where((yp>indx1) & (yp<indx2))
x = yp[indx]
y = Img.RelativeDispersion[indx]
m,b = np.polyfit(x, y, 1)
print "m,b img", m,b
indx = np.where((yp>indx1+xoffset) & (yp<indx2+xoffset))
x = yp[indx]
y = LESImg.RelativeDispersion[indx]
m,b = np.polyfit(x, y, 1)
print "m,b LES", m,b, pls
ax2.legend((pls[0], pls[1]), ('Absolute dispersion','Relative dispersion',),
prop={'size':FONTSIZE}, loc='lower right')
# AD_diff = (Img.Dispersion[indxdiff1:indxdiff2]-tmpDIS[indxdiff1-xoffset:indxdiff2-xoffset])
# fn = 'Stats_AD_V01.txt'
# fp=open(fn,'a')
# for ADimg, ADLES, diff in zip(Img.Dispersion[indxdiff1:indxdiff2], tmpDIS[indxdiff1-xoffset:indxdiff2-xoffset], AD_diff):
# fp.write('{:f} {:f} {:f}\n'.format(ADimg, ADLES, diff))
# close(fn)
# RD_diff = (Img.RelativeDispersion[indxdiff1:indxdiff2]-tmpDIS2[indxdiff1-xoffset:indxdiff2-xoffset])
# fn = 'Stats_RD_V01.txt'
# fp=open(fn,'a')
# for RDimg, RDLES, diff in zip(Img.RelativeDispersion[indxdiff1:indxdiff2], tmpDIS2[indxdiff1-xoffset:indxdiff2-xoffset], RD_diff):
# fp.write('{:f} {:f} {:f}\n'.format(RDimg, RDLES, diff))
# close(fn)
fn = 'Stats_AD_V03.txt'
fp=open(fn,'a')
tmpimg = Img.Dispersion[ximgind1:ximgind2]
tmples = LESImg.Dispersion[xlesind1:xlesind2]
tmpCL = yscale*np.interp(ximg, xles, tmples)
CL_diff =tmpimg - tmpCL
for CLimg, CLLES, diff in zip(tmpimg, tmpCL, CL_diff):
fp.write('{:f} {:f} {:f}\n'.format(CLimg, CLLES, diff))
close(fn)
fn = 'Stats_RD_V03.txt'
fp=open(fn,'a')
tmpimg = Img.RelativeDispersion[ximgind1:ximgind2]
tmples = LESImg.RelativeDispersion[xlesind1:xlesind2]
tmpCL = yscale*np.interp(ximg, xles, tmples)
CL_diff =tmpimg - tmpCL
for CLimg, CLLES, diff in zip(tmpimg, tmpCL, CL_diff):
fp.write('{:f} {:f} {:f}\n'.format(CLimg, CLLES, diff))
close(fn)
print("AbsoluteSkewness", np.nanmin(Img.AbsoluteSkewness), np.nanmax(Img.AbsoluteSkewness))
print "Img.AbsoluteSkewness", Img.Skewness.shape, Img.Skewness
indx = np.where(Img.Skewness!=0.0)
Img.Skewness = Img.Skewness[indx[0]]
print "Img.AbsoluteSkewness", Img.Skewness.shape, Img.Skewness
if PlotAng:
# ax3.plot(Img.phis, Img.AbsoluteSkewness,c=colorline1, lw=linewidth, linestyle=':')
ax3.plot(Img.phis[indx[0]], Img.Skewness,c=colorline1, lw=linewidth, linestyle=':')
ax3.set_xlim(Img.phis[0],Img.phis[len(Img.phis)-1])
else:
# ax3.plot(yp,Img.AbsoluteSkewness,c=colorline1, linestyle=':')
ax3.plot(yp[indx[0]],Img.Skewness,c=colorline1, linestyle=':')
ax3.set_xlim(0,len(yp))
ax3.set_ylim(-2,2)
# ax3.set_xlabel('Horizontal pixel number', fontsize=FONTSIZE)
ax3.set_xlabel('Horizontal viewing angle', fontsize=FONTSIZE)
ax3.set_ylabel('Skewness', fontsize=FONTSIZE)
if includeLES:
tmpDIS = np.interp(Img.phis, phis, LESImg.Skewness)
# tmpDIS = np.interp(Img.phis, phis, LESImg.AbsoluteSkewness)
# tmpDIS = np.interp(yp, phis, LESImg.Dispersion)
yscale = 1 #Img.shape[0]/float(LESImg.shape[0])
yoffset=0
# ax2.plot(Img.phis+xoffset, tmpDIS*yscale+yoffset,c=colorline1, linestyle=':', lw=linewidth)
if PlotAng:
ax3.plot(Img.phis+AngleShift, tmpDIS*yscale+yoffset,c=colorline1, linestyle='-', lw=linewidth)
else:
ax3.plot(yp[indxdiff1-xoffset:indxdiff2-xoffset]+xpixoffset, tmpDIS[indxdiff1-xoffset:indxdiff2-xoffset]*yscale+yoffset,c=colorline1, linestyle='-', lw=linewidth)
# SK_diff = (Img.AbsoluteSkewness[indxdiff1:indxdiff2]-tmpDIS[indxdiff1-xoffset:indxdiff2-xoffset])/tmpDIS[indxdiff1:indxdiff2]
# SK_diff = (Img.AbsoluteSkewness[indxdiff1:indxdiff2]-tmpDIS[indxdiff1-xoffset:indxdiff2-xoffset])
# fn = 'Stats_Skewness_V01.txt'
# fp=open(fn,'a')
# for SKimg, SKLES, diff in zip(Img.AbsoluteSkewness[indxdiff1:indxdiff2], tmpDIS[indxdiff1-xoffset:indxdiff2-xoffset], SK_diff):
# fp.write('{:f} {:f} {:f}\n'.format(SKimg, SKLES, diff))
# close(fn)
fn = 'Stats_Skewness_V03.txt'
fp=open(fn,'a')
# tmpimg = Img.AbsoluteSkewness[ximgind1:ximgind2]
tmpimg = Img.Skewness[ximgind1:ximgind2]
# tmples = LESImg.AbsoluteSkewness[xlesind1:xlesind2]
tmples = LESImg.Skewness[xlesind1:xlesind2]
tmpCL = yscale*np.interp(ximg, xles, tmples)
CL_diff =tmpimg - tmpCL
for CLimg, CLLES, diff in zip(tmpimg, tmpCL, CL_diff):
fp.write('{:f} {:f} {:f}\n'.format(CLimg, CLLES, diff))
close(fn)
xmin = 677
xmax = 697
xmaL = 5
xmiL = 1
txt = '0.3 nm resolution'
my_legend(host, pl_list, txt, 0.02, 0.87, fsize, 1, plt)
txt = '0.3 nm resolution, fluorescence included'
my_legend(host, pl_list, txt, 0.02, 0.92, fsize, 2, plt)
txt = 'Fluorescence signal'
my_legend(host, pl_list, txt, 0.02, 0.8, fsize, 3, plt)
ymajorLocator = MultipleLocator(2)
par.yaxis.set_major_locator(ymajorLocator)
yminorLocator = MultipleLocator(0.2)
par.yaxis.set_minor_locator(yminorLocator)
rcParams.update({'font.size': fsize})
host.set_xlim(xmin, xmax)
host.set_ylim(ymin, ymax)
host.set_xlabel(r"Wavelength [nm]", fontsize=fsize)
host.set_ylabel(ylabel, fontsize=fsize)
xminorLocator = MultipleLocator(xmiL)
xmajorLocator = MultipleLocator(xmaL)
host.xaxis.set_minor_locator(xminorLocator)
host.xaxis.set_major_locator(xmajorLocator)
ymajorLocator = MultipleLocator(ymaL)
host.yaxis.set_major_locator(ymajorLocator)
yminorLocator = MultipleLocator(ymiL)
host.yaxis.set_minor_locator(yminorLocator)