def plot_ts(ax, ax2, fig2, xsub, ysub, h5timeseries):
ax2.cla()
print "\n-------------------------------------------------------------------------------"
disp_min = 0
disp_max = 0
############################# Plot Time Series ##############################
global ref_xsub, ref_ysub
##### 1.1 Plot Reference time series
try:
ref_xsub
ref_ysub
ref_xsub, ref_ysub = check_yx(ref_xsub, ref_ysub, radius, ax, rectColor)
print "----------------------------------------------------"
print "Reference Point:"
print "ref_x=" + str(ref_xsub[0]) + ":" + str(ref_xsub[1])
print "ref_y=" + str(ref_ysub[0]) + ":" + str(ref_ysub[1])
print "-----------------------------"
print "Time series with all dates:"
dis1, dis1_mean, dis1_std, dis1_vel = read_dis(ref_xsub, ref_ysub, dateList1, h5timeseries, unit)
(_, caps, _) = ax2.errorbar(
dates1,
dis1_mean,
yerr=dis1_std,
fmt="-ks",
ms=markerSize2,
lw=0,
alpha=1,
mfc=markerColor_ref,
mew=edgeWidth,
elinewidth=edgeWidth,
ecolor="black",
capsize=markerSize * 0.5,
)
for cap in caps:
cap.set_markeredgewidth(edgeWidth)
disp_min, disp_max = update_lim(disp_min, disp_max, dis1_mean, dis1_std)
if not len(dateList) == len(dateList1):
print "-----------------------------"
print "Time series with dates of interest:"
dis12, dis12_mean, dis12_std, dis12_vel = read_dis(ref_xsub, ref_ysub, dateList, h5timeseries, unit)
(_, caps, _) = ax2.errorbar(
dates,
dis12_mean,
yerr=dis12_std,
fmt="-ks",
ms=markerSize2,
lw=0,
alpha=1,
mfc=markerColor_ref2,
mew=edgeWidth,
elinewidth=edgeWidth,
ecolor="black",
capsize=markerSize * 0.5,
)
for cap in caps:
cap.set_markeredgewidth(edgeWidth)
disp_min, disp_max = update_lim(disp_min, disp_max, dis12_mean, dis12_std)
except:
pass
##### 1.2.0 Read y/x
print "\n----------------------------------------------------"
print "Point of Interest:"
xsub, ysub = check_yx(xsub, ysub, radius, ax, rectColor)
print "x=" + str(xsub[0]) + ":" + str(xsub[1])
print "y=" + str(ysub[0]) + ":" + str(ysub[1])
##### 1.2.1 Plot 2nd time series
try:
timeSeriesFile_2
print "-----------------------------"
print "2nd Time Series:"
dis2, dis2_mean, dis2_std, dis2_vel = read_dis(xsub, ysub, dateList_2, h5timeseries_2, unit)
(_, caps, _) = ax2.errorbar(
dates_2,
dis2_mean,
yerr=dis2_std,
fmt="-ko",
ms=markerSize2,
lw=0,
alpha=1,
mfc=markerColor2,
elinewidth=0,
ecolor="black",
capsize=0,
)
for cap in caps:
cap.set_markeredgewidth(edgeWidth)
disp_min, disp_max = update_lim(disp_min, disp_max, dis2_mean, dis2_std)
except:
pass
##### 1.2.2 Plot 1st time series
print "-----------------------------"
print "Time Series:"
dis, dis_mean, dis_std, dis_vel = read_dis(xsub, ysub, dateList, h5timeseries, unit)
(_, caps, _) = ax2.errorbar(
dates,
dis_mean,
yerr=dis_std,
fmt="-ko",
ms=markerSize,
lw=lineWidth,
alpha=1,
mfc=markerColor,
elinewidth=edgeWidth,
ecolor="black",
capsize=markerSize * 0.5,
)
for cap in caps:
cap.set_markeredgewidth(edgeWidth)
disp_min, disp_max = update_lim(disp_min, disp_max, dis_mean, dis_std)
####################### Figure Format #######################
## x axis format
try:
ax2 = ptime.adjust_xaxis_date(ax2, dateVecMinMax, fontSize)
except:
ax2 = ptime.adjust_xaxis_date(ax2, datevector_all, fontSize)
## y axis format
ax2.set_ylabel("Displacement [" + unit + "]", fontsize=fontSize)
try:
lbound
hbound
ax2.set_ylim(lbound, hbound)
except:
disp_buf = 0.2 * (disp_max - disp_min)
ax2.set_ylim(disp_min - disp_buf, disp_max + disp_buf)
for tick in ax2.yaxis.get_major_ticks():
tick.label.set_fontsize(fontSize)
## title
figTitle = "x=" + str(xsub[0]) + ":" + str(xsub[1]) + ", y=" + str(ysub[0]) + ":" + str(ysub[1])
try:
lonc = ullon + (xsub[0] + xsub[1]) / 2.0 * lon_step
latc = ullat + (ysub[0] + ysub[1]) / 2.0 * lat_step
figTitle += ", lalo=" + "%.4f,%.4f" % (latc, lonc)
except:
pass
ax2.set_title(figTitle)
################## Save and Output #####################
if saveFig == "yes":
print "-----------------------------"
Delay = {}
Delay["displacement"] = dis
Delay["unit"] = unit
Delay["time"] = datevector
Delay["velocity"] = dis_vel[0]
Delay["velocity_unit"] = unit + "/yr"
Delay["velocity_std"] = dis_vel[4]
figBase = "x" + str(xsub[0]) + "_" + str(xsub[1] - 1) + "y" + str(ysub[0]) + "_" + str(ysub[1] - 1)
sio.savemat(figBase + "_ts.mat", {"displacement": Delay})
print "saved " + figBase + "_ts.mat"
fig2.savefig(figBase + "_ts.pdf", bbox_inches="tight", transparent=True, dpi=fig_dpi)
print "saved " + figBase + "_ts.pdf"
if dispFig == "no":
fig.savefig(figBase + "_vel.png", bbox_inches="tight", transparent=True, dpi=fig_dpi)
print "saved " + figBase + "_vel.png"
def main(argv):
##### Default
fontSize = 12
lineWidth = 2
markerColor = 'crimson'
markerSize = 16
disp_fig = 'no'
save_fig = 'yes'
save_list = 'yes'
ref_file = 'reference_date.txt'
drop_file = 'drop_date.txt'
##### Check Inputs
if len(sys.argv)>3:
try:
opts, args = getopt.getopt(argv,'h:f:m:o:x:y:',['help','circle='])
except getopt.GetoptError:
print 'Error in reading input options!'; Usage() ; sys.exit(1)
for opt,arg in opts:
if opt in ("-h","--help"): Usage() ; sys.exit()
elif opt == '-f': File = arg
elif opt == '-m': maskFile = arg
elif opt == '-x': xsub = [int(i) for i in arg.split(':')]; xsub.sort()
elif opt == '-y': ysub = [int(i) for i in arg.split(':')]; ysub.sort()
elif opt == '--circle' : cir_par = [i for i in arg.split(';')]
#elif opt == '-o': outName = arg
else:
try: File = argv[0]
except: Usage(); sys.exit(1)
try: maskFile = argv[1]
except: pass
try: atr = readfile.read_attributes(File)
except: Usage(); sys.exit(1)
ext = os.path.splitext(File)[1].lower()
FileBase = os.path.basename(File).split(ext)[0]
outNameBase = 'spatialMean_'+FileBase
print '\n*************** Spatial Average ******************'
##### Input File Info
k = atr['FILE_TYPE']
print 'Input file is '+k
width = int(atr['WIDTH'])
length = int(atr['FILE_LENGTH'])
h5file = h5py.File(File)
epochList = h5file[k].keys();
epochList = sorted(epochList)
epochNum = len(epochList)
print 'number of epoch: '+str(epochNum)
dates,datevector = ptime.date_list2vector(epochList)
##### Mask Info
try:
Mask_orig,Matr = readfile.read(maskFile)
print 'mask file: '+maskFile
Masking = 'yes'
except:
print 'No mask. Use the whole area for ramp estimation.'
Masking = 'no'
Mask_orig=np.ones((length,width))
Mask = np.zeros((length,width))
Mask[:] = Mask_orig[:]
## Bounding Subset
try:
xsub
ysub
ysub,xsub = subset.check_subset_range(ysub,xsub,atr)
Mask[ysub[0]:ysub[1],xsub[0]:xsub[1]] = Mask_orig[ysub[0]:ysub[1],xsub[0]:xsub[1]]*2
#Mask[0:ysub[0],:] = 0
#Mask[ysub[1]:length,:] = 0
#Mask[:,0:xsub[0]] = 0
#Mask[:,xsub[1]:width] = 0
except:
Mask = Mask_orig*2
print 'No subset input.'
## Circle Inputs
try:
cir_par
for i in range(len(cir_par)):
cir_idx = circle_index(atr,cir_par[i])
Mask[cir_idx] = Mask_orig[cir_idx]
print 'Circle '+str(i)+': '+cir_par[i]
except: print 'No circle of interest input.'
## Mask output
idx = Mask == 2
idxNum = float(sum(sum(idx)))
fig = plt.figure()
plt.imshow(Mask,cmap='gray')
plt.savefig(outNameBase+'_mask.png',bbox_inches='tight')
print 'save mask to '+outNameBase+'_mask.png'
#fig.clf()
##### Calculation
meanList = np.zeros(epochNum)
pixPercent = np.zeros(epochNum)
pixT = 0.7
print 'calculating ...'
print ' Date Mean Percentage'
for i in range(epochNum):
epoch = epochList[i]
d = h5file[k].get(epoch)[:]
#d[Mask==0] = np.nan
meanList[i] = np.nanmean(d[idx])
pixPercent[i] = np.sum(d[idx] >= pixT)/idxNum
print epoch+' : %.2f %.1f%%'%(meanList[i],pixPercent[i]*100)
del d
h5file.close()
##### Reference date - Max Value
top3 = sorted(zip(meanList,epochList), reverse=True)[:3]
print '------------ Top 3 Mean ------------------'
print top3
## Write to txt file
fref = open(ref_file,'w')
fref.write(str(top3[0][1])+'\n')
fref.close()
print 'write optimal reference date to '+ref_file
idxMean = meanList == np.nanmax(meanList)
##### Drop dates - mean threshold
#meanT = 0.7
#idxMean = meanList < meanT
#print '------------ Mean Value < '+str(meanT)+' --------'
#print np.array(epochList)[idxMean]
#print meanList[idxMean]
##### Drop dates - good pixel percentage
pixNumT = 0.7
print '------------ Good Pixel Percentage < %.0f%% -------'%(pixNumT*100)
idxPix = pixPercent < pixNumT
dropEpochList = np.array(epochList)[idxPix]
print dropEpochList
print pixPercent[idxPix]
## Write to txt file
fdrop = open(drop_file,'w')
for i in range(len(dropEpochList)):
fdrop.write(str(dropEpochList[i])+'\n')
fdrop.close()
print 'write drop dates to '+drop_file
print '-------------------------------------------'
##### Display
fig = plt.figure(figsize=(12,12))
ax = fig.add_subplot(211)
ax.plot(dates, meanList, '-ko', ms=markerSize, lw=lineWidth, alpha=0.7, mfc=markerColor)
#ax.plot([dates[0],dates[-1]],[meanT,meanT], '--b', lw=lineWidth)
#sc = ax.scatter(dates, np.tile(0.5,epochNum), c=meanList, s=22**2, alpha=0.3, vmin=0.0, vmax=1.0)
#ax.scatter(np.array(dates)[idxMean], 0.5, c=meanList[idxMean], s=22**2, alpha=1.0, vmin=0.0, vmax=1.0)
ax = ptime.adjust_xaxis_date(ax,datevector)
ax.set_ylim(0,1)
ax.set_title('Spatial Average Value', fontsize=fontSize)
ax.set_xlabel('Time [years]', fontsize=fontSize)
#cbar = plt.colorbar(sc)
#cbar.set_label('Spatial Mean of Normalized Sum Epochs')
ax = fig.add_subplot(212)
ax.plot(dates, pixPercent, '-ko', ms=markerSize, lw=lineWidth, alpha=0.7, mfc=markerColor)
ax.plot([dates[0],dates[-1]],[pixNumT,pixNumT], '--b', lw=lineWidth)
ax = ptime.adjust_xaxis_date(ax,datevector)
ax.set_ylim(0,1)
ax.set_title('Percenrage of Pixels with Value > '+str(pixNumT), fontsize=fontSize)
ax.set_xlabel('Time [years]', fontsize=fontSize)
vals = ax.get_yticks()
ax.set_yticklabels(['{:3.0f}%'.format(i*100) for i in vals])
if save_fig == 'yes':
plt.savefig(outNameBase+'.png',bbox_inches='tight')
print 'save figure to '+outNameBase+'.png'
if disp_fig == 'yes':
plt.show()
##### Output
if save_list == 'yes':
epochList6 = ptime.yymmdd(epochList)
fl = open(outNameBase+'.txt','w')
for i in range(epochNum):
str_line = epochList6[i]+' %.2f %.2f\n'%(meanList[i],pixPercent[i])
fl.write(str_line)
fl.close()
print 'write data to '+outNameBase+'.txt\n'