def figure_labels(labels, outputFile, timeObs, dt, area_plot="ccs4", add_name = None, verbose=True):
if verbose:
print("*** produce label figures")
yearS, monthS, dayS, hourS, minS = string_date(timeObs)
data_time = timeObs + timedelta(minutes = dt)
yearSf, monthSf, daySf, hourSf, minSf = string_date(data_time)
labels = np.flipud(labels)
obj_area = get_area_def(area_plot)
fig, ax = prepare_figure(obj_area)
plt.contour(labels,[0.5],colors='y')
#plt.imshow(labels, origin="lower")
PIL_image = fig2img ( fig )
if add_name is not None:
PIL_image.save(create_dir(outputFile)+"Forecast"+yearS+monthS+dayS+"_Obs"+hourS+minS+"_Forc"+hourSf+minSf+add_name+".png")
path = (outputFile)+"Forecast"+yearS+monthS+dayS+"_Obs"+hourS+minS+"_Forc"+hourSf+minSf+add_name+".png"
else:
PIL_image.save(create_dir(outputFile)+"Forecast"+yearS+monthS+dayS+"_Obs"+hourS+minS+"_Forc"+hourSf+minSf+".png")
path = (outputFile)+"Forecast"+yearS+monthS+dayS+"_Obs"+hourS+minS+"_Forc"+hourSf+minSf+".png"
print("... display ",path," &")
plt.close( fig)
PIL_image = img.pil_image()
elif plot_type == 'contours':
import matplotlib.pyplot as plt
from numpy import arange
from mpop.imageo.HRWimage import prepare_figure
from mpop.imageo.TRTimage import fig2img
import matplotlib.cm as cm
(ny, nx) = prop.shape
X, Y = np.meshgrid(list(range(nx)), np.arange(ny - 1, -1, -1))
print(prop.min(), prop.max())
fig, ax = prepare_figure(obj_area)
colormap = cm.autumn_r
vmin = -1.8
vmax = -0.2
# half transparent lowest level
clevels = [-2.5]
CS = plt.contour(X,
Y,
prop,
clevels,
linewidths=1.5,
cmap=colormap,
vmin=vmin,
def make_figureLabels(values,
all_cells,
obj_area,
outputFile,
colorbar=True,
vmin=False,
vmax=False,
white_background=False,
t=None):
import matplotlib as mpl
import pickle
import matplotlib.pyplot as plt
from mpop.projector import get_area_def
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
import numpy as np
from mpop.imageo.TRTimage import fig2img
from mpop.imageo.HRWimage import prepare_figure
yearS = str(t.year)
monthS = "%02d" % t.month
dayS = "%02d" % t.day
hourS = "%02d" % t.hour
minS = "%02d" % t.minute
if vmin == False:
vmin = values.min()
if vmax == False:
vmax = values.max()
if white_background:
#ma.masked_where(values == 0, values)
values = np.flipud(values)
values[values == 0] = np.nan
values = np.ma.array(values, mask=np.isnan(values))
color = 'k'
else:
values = np.flipud(values)
color = 'y'
#obj_area = get_area_def(area)
fig, ax = prepare_figure(obj_area)
mappable = plt.imshow(values, vmin=vmin, vmax=vmax, origin="lower")
for items in all_cells:
item = all_cells[items]
x = item.center[0]
y = item.center[1]
plt.text(int(y),
int(values.shape[0] - x),
items[2:],
color=color,
size=8)
if colorbar:
position = fig.add_axes(
[0.93, 0.2, 0.02, 0.35]
) ## the parameters are the specified position you set: left, bottom, width, height
color_bar = fig.colorbar(mappable, cax=position) ##
plt.setp(plt.getp(color_bar.ax.axes, 'yticklabels'), color='cyan')
if t is not None:
#position=fig.add_axes([0.93,0.2,0.02,0.35])
plt.text(500, 50, t, color=color, size=14)
PIL_image = fig2img(fig)
PIL_image.save(
create_dir(outputFile) + "Labels_" + yearS + monthS + dayS + "_" +
hourS + minS + ".png")
plt.close(fig)
for it in range(1,ps_times.size):
#for it in [140]:
mv_File = ps_times[it].strftime(result_dir+ "%Y-%m-%d_MV_ccs4//PS_MV-ccs4_%y%m%d%H%M.png")
streamplot=False
if streamplot:
# !!! should be checked once more, if 12 (as Vx in pix(1km)/5min, and 12*5min=1h) is correct and if x and y is correct !!!
mv_PIL_image = HRWstreamplot( 12*Vx[it,:,:], -12*Vy[it,:,:], mv_area, '', color_mode='speed', vmax=25, linewidth_max=1.2, colorbar=False) # , colorbar=False, legend=True, legend_loc=3
mv_PIL_image.save(mv_File)
print "display "+mv_File+" &"
quiverplot=False
if quiverplot:
fig, axes = prepare_figure(mv_area)
## Prepare UV field for quiver plot
UV_field = np.moveaxis(np.dstack((Vx[it,:,:],Vy[it,:,:])),2,0)
step = 40; X,Y = np.meshgrid(np.arange(UV_field.shape[2]),np.arange(UV_field.shape[1]))
UV_ = UV_field[:, 0:UV_field.shape[1]:step, 0:UV_field.shape[2]:step]
X_ = X[0:UV_field.shape[1]:step, 0:UV_field.shape[2]:step]; Y_ = Y[0:UV_field.shape[1]:step, 0:UV_field.shape[2]:step]
#axes.quiver(X_, Y_, UV_[0,:,:], -UV_[1,:,:], pivot='tip', color='lightgrey')
axes.quiver(X_, Y_, UV_[0,::-1,:], -UV_[1,:,:], pivot='tip', color='grey')
mv_PIL_image = fig2img(fig)
mv_File = ps_times[it].strftime(result_dir+ "%Y-%m-%d_MV_ccs4//PS_MV-ccs4_%y%m%d%H%M.png")
mv_PIL_image.save(mv_File)
print "display "+mv_File+" &"
create_composite=True
def plot_forecast_area(ttt, model, outputDir, current_labels = None, t_stop=None, BackgroundFile=None, ForeGroundRGBFile=None, labels_dir = '/opt/users/'+getpass.getuser()+'/PyTroll/scripts/labels/', in_msg = None):
verbose = True
if t_stop is None:
t_stop = ttt
ylabel = "area"
while ttt <= t_stop:
yearS, monthS, dayS, hourS, minS = string_date(ttt)
if verbose:
print("******** read cell properties from shelve")
if current_labels is None:
yearS, monthS, dayS, hourS, minS = string_date(ttt)
filename = 'Labels_%s.shelve'%(yearS+monthS+dayS+hourS+minS)
myShelve = shelve.open(filename)
labels_all = deepcopy(myShelve['labels'])
else:
labels_all = deepcopy(current_labels)
if verbose:
print(labels_all)
unique_labels = np.unique(labels_all[labels_all>0])
if verbose:
print(("... cells with unique labels: ", unique_labels))
forecasted_labels = {}
forecasted_areas = []
at_least_one_cell = False
if verbose:
print("*** computing history backward (", labels_dir, ")")
for interesting_cell in unique_labels:
forecasted_labels["ID"+str(interesting_cell)]=[]
# calculate backward history for 1 hour and save it in labels_dir
ind, area, displacement, time, center = history_backward(ttt, interesting_cell, True, in_msg, ttt-timedelta(hours = 1), labels_dir=labels_dir) #-timedelta(minutes = 10))
# current time, cell_id, backward? time_stop
if area is None or len(area)<=1:
if verbose:
print("new cell or cell with COM outside domain")
continue
at_least_one_cell = True
if len(area)<=3:
# if history is too short, use linear extrapolation
t, y = future_properties(time, area, ylabel, "linear")
else:
t, y = future_properties(time, area, ylabel, model)
if False:
ind1, area1, displacement1, time1, center = history_backward(ttt, interesting_cell, False, ttt+timedelta(hours=1), labels_dir=labels_dir)
print("******** computed history forward")
t2 = time1 #[::-1]
y2 = area1 #[::-1]
nx,ny = labels_all.shape
#if verbose:
# print(nx,ny)
label_cell = np.zeros(labels_all.shape)
label_cell[labels_all==interesting_cell] = 1
#pickle.dump(label_cell, open("test_label.p", "wb" ) )
#quit()
dt = 0
if False:
figure_labels(label_cell, outputDir, ttt, dt, area_plot="ccs4", add_name = "_ID"+str(interesting_cell), verbose=verbose)
area_current = sum(sum(label_cell))
forecasted_areas.append(area_current)
indx = np.where(t==ttt)[0] + 1
if verbose:
print("*** compute displacement ")
if displacement.shape[1]==2:
if len(displacement) == 0:
dx = 0
dy = 0
else:
try:
dx = int(round(displacement[:,0].mean()))
dy = int(round(displacement[:,1].mean()))
except ValueError:
print("VALUE ERROR")
print(displacement)
quit()
print(" computed displacement dx, dy = ", dx, dy)
else:
print("wrong displacement")
quit()
labels_in_time={}
index_stop = 12
print(("*** calculate forecasts for cell ID"+str(interesting_cell)))
if verbose:
print("index time area growth")
print("----------------------------")
for i in range(13):
dt += 5
#if verbose:
# print("... for time ", dt ,", index ", indx + i)
if indx+i >= len(y):
index_stop = deepcopy(i)
break
else:
area_new = y[indx+i]
area_prev = y[indx+i-1]
#if verbose:
# print("area px that will be grown ", area_current)
# print("area forecasted ", area_new)
# print("area forecasted prev ", area_prev)
###growth = sqrt(float(area_new)/float(area_current))
if area_new < 0 or len(area_new)==0 or len(area_prev)==0:
if verbose:
print("the cell is predicted to disappear")
index_stop = deepcopy(i)
break
growth = sqrt(float(area_new)/float(area_prev))
#if verbose:
# print("growing by ", growth)
# print("dx ", dx)
# print("dy ", dy)
if verbose:
print((indx + i, dt, area_new, growth))
#figure_labels(label_cell, outputDir, ttt, dt, area_plot="ccs4", add_name = "before")
shifted_label = resize_array(label_cell, dx, dy, nx, ny)
#figure_labels(shifted_label, outputDir, ttt, dt, area_plot="ccs4", add_name = "before_shifted")
#quit()
if verbose:
print((" after shift ", sum(sum(shifted_label))))
if sum(sum(shifted_label))==0: #the cell is outside the domain
break
#center of mass before resizing
center_before = ndimage.measurements.center_of_mass(shifted_label)
center_before = np.rint(center_before)
#if verbose:
# print(" after shift ", sum(sum(shifted_label)))
resized_label = scipy.misc.imresize(shifted_label,float(growth),'nearest')
resized_label[resized_label >0] = 1
temp_label = np.zeros((nx,ny))
#after resizing, the array is larger/smaller than nx,ny --> create new array that contains all the label region
if resized_label.shape[0]<nx:
temp_label[0:resized_label.shape[0],0:resized_label.shape[1]] = deepcopy(resized_label)
else:
x_start = max(min(np.nonzero(resized_label)[0])-1,0)
y_start = max(min(np.nonzero(resized_label)[1])-1,0)
temp_label[0:min(nx,resized_label.shape[0]-x_start),0:min(ny,resized_label.shape[1]-y_start)] = deepcopy(resized_label[x_start:min(x_start+nx,resized_label.shape[0]),y_start:min(y_start+ny,resized_label.shape[1])])
#if verbose:
# print(np.unique(temp_label))
# print(" after resize ", sum(sum(temp_label)))
#figure_labels(resized_label, outputDir, ttt, dt, area_plot="ccs4", add_name = "before_shifted_resized")
#center of mass after resizing
center_after = ndimage.measurements.center_of_mass(temp_label)
center_after = np.rint(center_after)
dx_new,dy_new = center_before - center_after
shifted_label = resize_array(temp_label,dx_new,dy_new, nx, ny)
#if verbose:
# print(" after shift2 ", sum(sum(shifted_label)))
label_cell = np.zeros((nx,ny))
label_cell[0:,0:] = shifted_label[0:nx,0:ny]
if label_cell.shape[0] != nx or label_cell.shape[1] != ny:
print("incorrect size")
quit()
forecasted_labels["ID"+str(interesting_cell)].append(deepcopy(label_cell))
#indx+=1
label_cell = shifted_label #????????????????????????????????????
area_current = sum(sum(label_cell))
if verbose:
print(("end ", area_current))
forecasted_areas.append(area_current)
#add check to make sure the area you produced is more or less correct
t_temp = deepcopy(ttt)
forecasted_time = []
for gg in range(len(forecasted_areas)):
forecasted_time.append(t_temp)
t_temp+=timedelta(minutes = 5)
"""
if verbose:
print("******** produce images")
if False:
t_composite = deepcopy(ttt)
for i in range(min(len(y),index_stop)):
yearSf, monthSf, daySf, hourSf, minSf = string_date(t_composite)
contour_file = outputDir + "Forecast"+yearS+monthS+dayS+"_Obs"+hourS+minS+"_Forc"+hourSf+minSf+"_ID"+str(interesting_cell)+".png"
type_image = "_HRV"
#background_file = "/data/COALITION2/PicturesSatellite//"+yearS+"-"+monthS+"-"+dayS+"/"+yearS+"-"+monthS+"-"+dayS+type_image+"_"+"ccs4"+"/MSG"+type_image+"-"+"ccs4"+"_"+yearS[2:]+monthS+dayS+hourS+minS+".png"
background_file = "/data/COALITION2/PicturesSatellite/LEL_results_wind/"+yearS+"-"+monthS+"-"+dayS+"/RGB-HRV_dam/"+yearS+monthS+dayS+"_"+hourS+minS+"*.png"
out_file1 = create_dir( outputDir+"/Contours/")+"Obs"+hourS+minS+"_Forc"+hourSf+minSf+"_ID"+str(interesting_cell)+".png"
if verbose:
print("... create composite "+contour_file+" "+background_file+" "+out_file1)
#subprocess.call("/usr/bin/composite "+contour_file+" "+background_file+" "+out_file1, shell=True)
if verbose:
print("... saved composite: display ", out_file1, " &")
t_composite+=timedelta(minutes=5)
"""
"""
if False:
fig, ax = plt.subplots()
ax.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))
ax.plot_date(t, y, 'o',label="Fit and extrapolation")
ax.plot_date(forecasted_time, forecasted_areas, '*',label="forecasted")
ax.plot_date(t2, y2, '*', label="Observations")
#ax.set_xlim([t[0]-timedelta(minutes = 5), t2[-1]+timedelta(minutes = 5)])
ax.set_ylabel("area")
ax.legend(loc="best");
fig.savefig(yearS+monthS+dayS+"_"+hourS+minS+"_AreaInTime"+"ID"+str(interesting_cell)+".png")
plt.close( fig)
"""
t_composite = deepcopy(ttt)
# merge coalition2 file with
if ForeGroundRGBFile is None:
currentRGB_im_filename = "/opt/users/"+getpass.getuser()+"/PyTroll/scripts/Mecikalski/cosmo/Channels/indicators_in_time/RGB_dam/"+yearS+monthS+dayS+"_"+hourS+minS+"*ccs4.png"
else:
currentRGB_im_filename = ForeGroundRGBFile
currentRGB_im = glob.glob(currentRGB_im_filename)
if len(currentRGB_im)<1:
print("No file found:", currentRGB_im_filename)
# get background file
if BackgroundFile is None:
background_im_filename = '/data/COALITION2/PicturesSatellite/LEL_results_wind/'+yearS+'-'+monthS+'-'+dayS+'/RGB-HRV_dam/'+yearS+monthS+dayS+'_'+hourS+minS+'*.png'
else:
background_im_filename = BackgroundFile
background_im = glob.glob(background_im_filename)
if len(background_im)>0:
im = plt.imread(background_im[0])
back_exists = True
else:
back_exists = False
#img1 = Image.imread(currentRGB_im[0])
obj_area = get_area_def("ccs4")
fig,ax = prepare_figure(obj_area)
if in_msg.nrt == False:
if back_exists:
plt.imshow(np.flipud(im))
else:
# now read the data we would like to forecast
global_data = GeostationaryFactory.create_scene(in_msg.sat_str(), in_msg.sat_nr_str(), "seviri", ttt)
#global_data_RGBforecast = GeostationaryFactory.create_scene(in_msg.sat, str(10), "seviri", time_slot)
# area we would like to read
area2load = "EuropeCanary95" #"ccs4" #c2"#"ccs4" #in_windshift.ObjArea
area_loaded = get_area_def(area2load )#(in_windshift.areaExtraction)
# load product, global_data is changed in this step!
area_loaded = load_products(global_data, ['IR_108'], in_msg, area_loaded )
data = global_data.project("ccs4")
plt.imshow(np.flipud(data['IR_108'].data),cmap = pylab.gray())
# background file form function argument or default
if BackgroundFile is None:
background_im_filename = '/data/COALITION2/PicturesSatellite/LEL_results_wind/'+yearS+'-'+monthS+'-'+dayS+'/RGB-HRV_dam/'+yearS+monthS+dayS+'_'+hourS+minS+'*.png'
else:
if verbose:
print("... BackgroundFile ", BackgroundFile)
background_im_filename = BackgroundFile
# searching background file (wildcards are possible)
background_im = glob.glob(background_im_filename)
if len(background_im) == 0:
print("*** Error in plot_forecast_area (test_forecast.py)")
print(" no background file found: ", background_im_filename)
quit()
elif len(background_im) > 1:
print("*** Warning in plot_forecast_area (test_forecast.py)")
print(" several background files found: ", background_im)
# read background file
im = plt.imread(background_im[0])
#img1 = Image.imread(currentRGB_im[0])
obj_area = get_area_def("ccs4")
fig,ax = prepare_figure(obj_area)
#plt.imshow(np.flipud(im))
# plot contour lines for all cells
if at_least_one_cell:
time_wanted_minutes = [5,20,40,60]
time_wanted = []
color_wanted = []
vmax = 70
for t_want in time_wanted_minutes:
time_wanted.append((t_want-5)/5)
tmp = (mpl.cm.Blues(float(t_want)/vmax))
tmp1 = [tmp]
color_wanted.append(tmp1)
all_labels_in_time = np.zeros((nx,ny))
for i in range(len(time_wanted)-1,-1,-1):
ind_time = time_wanted [i]
for key, forc_labels in forecasted_labels.items(): #forecasted_labels["ID"+str(interesting_cell)]=[]
if len(forc_labels)>ind_time:
#plt.contour(np.flipud(forc_labels[ind_time]),[0.5],colors = color_wanted_cont[i]) #colors='w') #
all_labels_in_time[forc_labels[ind_time]>0] = time_wanted_minutes[i]
forc_labels_tmp = np.ma.masked_where(all_labels_in_time==0,all_labels_in_time)
plt.contourf(np.flipud(forc_labels_tmp), cmap="Blues", vmin=0, vmax=vmax)
if False:
for i in range(len(time_wanted)):
ind_time = time_wanted [i]
for key, forc_labels in forecasted_labels.items(): #forecasted_labels["ID"+str(interesting_cell)]=[]
if len(forc_labels)>ind_time:
plt.contour(np.flipud(forc_labels[ind_time]),[0.5],colors = color_wanted[i]) #colors='w') #
else:
print("*** Warning, no COALITION2 cell detected ")
print(" produce empty figure ...")
PIL_image = fig2img ( fig )
standardOutputName = in_msg.standardOutputName.replace('%y%m%d%H%M',strftime('%y%m%d%H%M',ttt.timetuple()))
#PIL_image.paste(img1, (0, 0), img1)
if in_msg is None:
PIL_image.save(create_dir(outputDir)+"Forecast"+yearS+monthS+dayS+"_Obs"+hourS+minS+".png")
path = (outputDir)+yearS+monthS+dayS+hourS+minS+"Forecast.png"
else:
# dic_figure={}
# if in_msg.nrt == True:
# dic_figure['rgb']= 'Forecast' #'C2rgbForecastTMP-IR-108'
# else:
# dic_figure['rgb']= 'Forecast-C2rgb'
# dic_figure['area']='ccs4'
# PIL_image.save(create_dir(outputFile)+standardOutputName%dic_figure)
# path = (outputFile)+standardOutputName%dic_figure
# if in_msg.nrt == False:
# dic_figure={}
# dic_figure['rgb']= 'C2rgb-Forecast-HRV' #'C2rgbForecastTMP-IR-108'
# dic_figure['area']='ccs4'
# path_output = (outputFile)+standardOutputName%dic_figure
# print ("creating composite: ",currentRGB_im[0],"+",path)
# subprocess.call("/usr/bin/composite "+currentRGB_im[0]+" "+path+" "+path_output, shell=True)
#print ("... display ",path_output," &")
#dic_figure={}
#dic_figure['rgb']= 'Forecast' #'C2rgbForecastTMP-IR-108'
#dic_figure['area']='ccs4'
outputFile = format_name(create_dir(outputDir)+in_msg.outputFile, ttt, rgb='Forecast', area='ccs4', sat_nr=int(in_msg.sat_nr))
#PIL_image.save(create_dir(outputDir)+in_msg.outputFile%dic_figure)
PIL_image.save(outputFile)
#path = (outputDir)+in_msg.outputFile%dic_figure
path = outputFile
print("... display ",path," &")
plt.close( fig)
if True:
if verbose:
print("path foreground", currentRGB_im[0])
if in_msg is None:
path_composite = (outputFile)+yearS+monthS+dayS+"_Obs"+hourS+minS+"Forecast_composite.png"
else:
# dic_figure={}
# dic_figure['rgb']='C2rgb-Forecast-HRV'
# dic_figure['area']='ccs4'
# path_composite = (outputFile)+standardOutputName%dic_figure
#dic_figure = {}
#dic_figure['rgb'] = "_HRV" #'IR-108'
#dic_figure['area']='ccs4'
#path_IR108 = (outputFile)+standardOutputName%dic_figure
#dic_figure={}
#dic_figure['rgb'] = 'C2rgbForecast-IR-108'
#dic_figure['area'] = 'ccs4'
#path_composite = (outputDir) + in_msg.outputFile%dic_figure
path_composite = format_name( outputDir+in_msg.outputFile, ttt, rgb='C2rgbForecast-IR-108', area='ccs4', sat_nr=int(in_msg.sat_nr))
#dic_figure = {}
#dic_figure['rgb'] = 'IR-108'
#dic_figure['area']='ccs4'
#path_IR108 = (outputDir) + in_msg.outputFile%dic_figure
path_IR108 = format_name( outputDir+in_msg.outputFile, ttt, rgb='IR-108', area='ccs4', sat_nr=int(in_msg.sat_nr))
if in_msg.nrt == True:
if verbose:
print("---starting post processing")
#if area in in_msg.postprocessing_areas:
in_msg.postprocessing_composite = deepcopy(in_msg.postprocessing_composite2)
postprocessing(in_msg, ttt, in_msg.sat_nr, "ccs4")
#print ("... display",path_composite,"&")
if in_msg.scpOutput and in_msg.nrt == True and False: #not necessary because already done within postprocessing
print("... secure copy "+path_composite+ " to "+in_msg.scpOutputDir) #
subprocess.call("scp "+in_msg.scpID+" "+path_composite +" "+in_msg.scpOutputDir+" 2>&1 &", shell=True) #BackgroundFile #
if False:
for i in range(12):
contour_files = glob.glob(outputDir + "Forecast"+yearS+monthS+dayS+"_Obs"+hourS+minS+"_Forc"+hourSf+minSf+"_ID*.png")
if verbose:
print(("Files found: ",contour_files))
if len(contour_files)>0:
background_file = "/data/COALITION2/PicturesSatellite/LEL_results_wind/"+yearS+"-"+monthS+"-"+dayS+"/RGB-HRV_dam/"+yearS+monthS+dayS+"_"+hourS+minS+"*.png"
out_file1 = create_dir( outputDir+"/Contours/")+"Obs"+hourS+minS+"_Forc"+hourSf+minSf+".png"
t_composite+=timedelta(minutes=5)
ttt += timedelta(minutes = 5)