def load_rgb(satellite, satellite_nr, satellites_name, time_slot, rgb, area,
in_msg, data_CTP):
if rgb != 'CTP':
# read the data we would like to forecast
global_data_RGBforecast = GeostationaryFactory.create_scene(
satellite, satellite_nr, satellites_name, time_slot)
#global_data_RGBforecast = GeostationaryFactory.create_scene(in_msg.sat, str(10), "seviri", time_slot)
# area we would like to read
area_loaded = get_area_def(
"EuropeCanary95") #(in_windshift.areaExtraction)
# load product, global_data is changed in this step!
area_loaded = load_products(global_data_RGBforecast, [rgb], in_msg,
area_loaded)
print '... project data to desired area ', area
fns = global_data_RGBforecast.project(area)
else:
fns = deepcopy(data_CTP["CTP"].data)
return fns[rgb].data
def plot_msg_minus_cosmo(in_msg):
# do statistics for the last full hour (minutes=0, seconds=0)
in_msg.datetime = datetime(in_msg.datetime.year, in_msg.datetime.month,
in_msg.datetime.day, in_msg.datetime.hour, 0, 0)
area_loaded = choose_area_loaded_msg(in_msg.sat, in_msg.sat_nr,
in_msg.datetime)
# define contour write for coasts, borders, rivers
cw = ContourWriterAGG(in_msg.mapDir)
# check if input data is complete
if in_msg.verbose:
print("*** check input data for ", in_msg.sat_str())
RGBs = check_input(in_msg,
in_msg.sat_str(layout="%(sat)s") + in_msg.sat_nr_str(),
in_msg.datetime)
# in_msg.sat_nr might be changed to backup satellite
if in_msg.verbose:
print('*** Create plots for ')
print(' Satellite/Sensor: ' + in_msg.sat_str())
print(' Satellite number: ' + in_msg.sat_nr_str() + ' // ' +
str(in_msg.sat_nr))
print(' Satellite instrument: ' + in_msg.instrument)
print(' Date/Time: ' + str(in_msg.datetime))
print(' RGBs: ', in_msg.RGBs)
print(' Area: ', in_msg.areas)
print(' reader level: ', in_msg.reader_level)
# define satellite data object
#global_data = GeostationaryFactory.create_scene(in_msg.sat, in_msg.sat_nr_str(), "seviri", in_msg.datetime)
global_data = GeostationaryFactory.create_scene(in_msg.sat_str(),
in_msg.sat_nr_str(),
in_msg.instrument,
in_msg.datetime)
# global_data = GeostationaryFactory.create_scene("msg-ot", "", "Overshooting_Tops", in_msg.datetime)
if len(RGBs) == 0 and len(in_msg.postprocessing_areas) == 0:
return RGBs
if in_msg.verbose:
print(
"*** load satellite channels for " + in_msg.sat_str() +
in_msg.sat_nr_str() + " ", global_data.fullname)
# initialize processed RGBs
RGBs_done = []
# -------------------------------------------------------------------
# load reflectivities, brightness temperatures, NWC-SAF products ...
# -------------------------------------------------------------------
area_loaded = load_products(global_data, RGBs, in_msg, area_loaded)
cosmo_input_file = "input_cosmo_cronjob.py"
print("... read COSMO input file: ", cosmo_input_file)
in_cosmo = parse_commandline_and_read_inputfile(
input_file=cosmo_input_file)
# add composite
in_msg.scpOutput = True
in_msg.resize_montage = 70
in_msg.postprocessing_montage = [[
"MSG_IR-108cpc", "COSMO_SYNMSG-BT-CL-IR10.8",
"MSG_IR-108-COSMO-minus-MSGpc"
]]
in_msg.scpProducts = [[
"MSG_IR-108cpc", "COSMO_SYNMSG-BT-CL-IR10.8",
"MSG_IR-108-COSMO-minus-MSGpc"
]]
#in_msg.scpProducts = ["all"]
# define satellite data object
cosmo_data = GeostationaryFactory.create_scene(in_cosmo.sat_str(),
in_cosmo.sat_nr_str(),
in_cosmo.instrument,
in_cosmo.datetime)
area_loaded_cosmo = load_products(cosmo_data, ['SYNMSG_BT_CL_IR10.8'],
in_cosmo, area_loaded)
# preprojecting the data to another area
# --------------------------------------
if len(RGBs) > 0:
for area in in_msg.areas:
print("")
obj_area = get_area_def(area)
if area != 'ccs4':
print("*** WARNING, diff MSG-COSMO only implemented for ccs4")
continue
# reproject data to new area
print(area_loaded)
if obj_area == area_loaded:
if in_msg.verbose:
print("*** Use data for the area loaded: ", area)
#obj_area = area_loaded
data = global_data
resolution = 'l'
else:
if in_msg.verbose:
print("*** Reproject data to area: ", area,
"(org projection: ", area_loaded.name, ")")
obj_area = get_area_def(area)
# PROJECT data to new area
data = global_data.project(area, precompute=True)
resolution = 'i'
if in_msg.parallax_correction:
loaded_products = [chn.name for chn in data.loaded_channels()]
if 'CTH' not in loaded_products:
print("*** Error in plot_msg (" +
inspect.getfile(inspect.currentframe()) + ")")
print(
" Cloud Top Height is needed for parallax correction "
)
print(
" either load CTH or specify the estimation of the CTH in the input file (load 10.8 in this case)"
)
quit()
if in_msg.verbose:
print(
" perform parallax correction for loaded channels: ",
loaded_products)
data = data.parallax_corr(fill=in_msg.parallax_gapfilling,
estimate_cth=in_msg.estimate_cth,
replace=True)
# save reprojected data
if area in in_msg.save_reprojected_data:
save_reprojected_data(data, area, in_msg)
# apply a mask to the data (switched off at the moment)
if False:
mask_data(data, area)
# save average values
if in_msg.save_statistics:
mean_array = zeros(len(RGBs))
#statisticFile = '/data/COALITION2/database/meteosat/ccs4/'+yearS+'/'+monthS+'/'+dayS+'/MSG_'+area+'_'+yearS[2:]+monthS+dayS+'.txt'
statisticFile = './' + yearS + '-' + monthS + '-' + dayS + '/MSG_' + area + '_' + yearS[
2:] + monthS + dayS + '.txt'
if in_msg.verbose:
print("*** write statistics (average values) to " +
statisticFile)
f1 = open(statisticFile, 'a') # mode append
i_rgb = 0
for rgb in RGBs:
if rgb in products.MSG_color:
mean_array[i_rgb] = data[rgb.replace("c",
"")].data.mean()
i_rgb = i_rgb + 1
# create string to write
str2write = dateS + ' ' + hourS + ' : ' + minS + ' UTC '
for mm in mean_array:
str2write = str2write + ' ' + "%7.2f" % mm
str2write = str2write + "\n"
f1.write(str2write)
f1.close()
# creating plots/images
if in_msg.make_plots:
# choose map resolution
in_msg.resolution = choose_map_resolution(
area, in_msg.mapResolution)
# define area
proj4_string = obj_area.proj4_string
# e.g. proj4_string = '+proj=geos +lon_0=0.0 +a=6378169.00 +b=6356583.80 +h=35785831.0'
area_extent = obj_area.area_extent
# e.g. area_extent = (-5570248.4773392612, -5567248.074173444, 5567248.074173444, 5570248.4773392612)
area_tuple = (proj4_string, area_extent)
RGBs = ['IR_108-COSMO-minus-MSG']
print(data['IR_108'].data.shape)
print(cosmo_data['SYNMSG_BT_CL_IR10.8'].data.shape)
diff_MSG_COSMO = cosmo_data['SYNMSG_BT_CL_IR10.8'].data - data[
'IR_108'].data
HRV_enhance_str = ''
# add IR difference as "channel object" to satellite regional "data" object
data.channels.append(
Channel(name=RGBs[0],
wavelength_range=[0., 0., 0.],
resolution=data['IR_108'].resolution,
data=diff_MSG_COSMO))
for rgb in RGBs:
if not check_loaded_channels(rgb, data):
continue
PIL_image = create_PIL_image(rgb,
data,
in_msg,
obj_area=obj_area)
# !!! in_msg.colorbar[rgb] is initialized inside (give attention to rgbs) !!!
add_borders_and_rivers(PIL_image,
cw,
area_tuple,
add_borders=in_msg.add_borders,
border_color=in_msg.border_color,
add_rivers=in_msg.add_rivers,
river_color=in_msg.river_color,
resolution=in_msg.resolution,
verbose=in_msg.verbose)
# indicate mask
if in_msg.indicate_mask:
PIL_image = indicate_mask(rgb, PIL_image, data,
in_msg.verbose)
#if area.find("EuropeCanary") != -1 or area.find("ccs4") != -1:
dc = DecoratorAGG(PIL_image)
# add title to image
if in_msg.add_title:
add_title(PIL_image,
in_msg.title,
HRV_enhance_str + rgb,
in_msg.sat_str(),
data.sat_nr(),
in_msg.datetime,
area,
dc,
in_msg.font_file,
in_msg.verbose,
title_color=in_msg.title_color,
title_y_line_nr=in_msg.title_y_line_nr
) # !!! needs change
# add MeteoSwiss and Pytroll logo
if in_msg.add_logos:
if in_msg.verbose:
print('... add logos')
dc.align_right()
if in_msg.add_colorscale:
dc.write_vertically()
if PIL_image.mode != 'L':
height = 60 # height=60.0 normal resolution
dc.add_logo(in_msg.logos_dir + "/pytroll3.jpg",
height=height) # height=60.0
dc.add_logo(in_msg.logos_dir + "/meteoSwiss3.jpg",
height=height)
dc.add_logo(
in_msg.logos_dir +
"/EUMETSAT_logo2_tiny_white_square.png",
height=height) # height=60.0
# add colorscale
if in_msg.add_colorscale and in_msg.colormap[rgb] != None:
if rgb in products.MSG_color:
unit = data[rgb.replace("c", "")].info['units']
#elif rgb in products.MSG or rgb in products.NWCSAF or rgb in products.HSAF:
# unit = data[rgb].info['units']
else:
unit = None
loaded_channels = [
chn.name for chn in data.loaded_channels()
]
if rgb in loaded_channels:
if hasattr(data[rgb], 'info'):
print(" hasattr(data[rgb], 'info')",
list(data[rgb].info.keys()))
if 'units' in list(data[rgb].info.keys()):
print(
"'units' in data[rgb].info.keys()")
unit = data[rgb].info['units']
print("... units = ", unit)
add_colorscale(dc, rgb, in_msg, unit=unit)
if in_msg.parallax_correction:
parallax_correction_str = 'pc'
else:
parallax_correction_str = ''
rgb += parallax_correction_str
# create output filename
outputDir = format_name(
in_msg.outputDir,
data.time_slot,
area=area,
rgb=rgb,
sat=data.satname,
sat_nr=data.sat_nr()) # !!! needs change
outputFile = outputDir + "/" + format_name(
in_msg.outputFile,
data.time_slot,
area=area,
rgb=rgb,
sat=data.satname,
sat_nr=data.sat_nr()) # !!! needs change
# check if output directory exists, if not create it
path = dirname(outputFile)
if not exists(path):
if in_msg.verbose:
print('... create output directory: ' + path)
makedirs(path)
# save file
if exists(outputFile) and not in_msg.overwrite:
if stat(outputFile).st_size > 0:
print('... outputFile ' + outputFile +
' already exists (keep old file)')
else:
print(
'*** Warning, outputFile' + outputFile +
' already exists, but is empty (overwrite file)'
)
PIL_image.save(outputFile, optimize=True
) # optimize -> minimize file size
chmod(
outputFile, 0o777
) ## FOR PYTHON3: 0o664 # give access read/write access to group members
else:
if in_msg.verbose:
print('... save final file: ' + outputFile)
PIL_image.save(
outputFile,
optimize=True) # optimize -> minimize file size
chmod(
outputFile, 0o777
) ## FOR PYTHON3: 0o664 # give access read/write access to group members
if in_msg.compress_to_8bit:
if in_msg.verbose:
print('... compress to 8 bit image: display ' +
outputFile.replace(".png", "-fs8.png") +
' &')
subprocess.call(
"/usr/bin/pngquant -force 256 " + outputFile +
" 2>&1 &",
shell=True) # 256 == "number of colors"
#if in_msg.verbose:
# print " add coastlines to "+outputFile
## alternative: reopen image and modify it (takes longer due to additional reading and saving)
#cw.add_rivers_to_file(img, area_tuple, level=5, outline='blue', width=0.5, outline_opacity=127)
#cw.add_coastlines_to_file(outputFile, obj_area, resolution=resolution, level=4)
#cw.add_borders_to_file(outputFile, obj_area, outline=outline, resolution=resolution)
# secure copy file to another place
if in_msg.scpOutput:
if (rgb in in_msg.scpProducts) or ('all' in [
x.lower()
for x in in_msg.scpProducts if type(x) == str
]):
scpOutputDir = format_name(in_msg.scpOutputDir,
data.time_slot,
area=area,
rgb=rgb,
sat=data.satname,
sat_nr=data.sat_nr())
if in_msg.compress_to_8bit:
if in_msg.verbose:
print("... secure copy " +
outputFile.replace(
".png", "-fs8.png") + " to " +
scpOutputDir)
subprocess.call(
"scp " + in_msg.scpID + " " +
outputFile.replace(".png", "-fs8.png") +
" " + scpOutputDir + " 2>&1 &",
shell=True)
else:
if in_msg.verbose:
print("... secure copy " + outputFile +
" to " + scpOutputDir)
subprocess.call("scp " + in_msg.scpID + " " +
outputFile + " " +
scpOutputDir + " 2>&1 &",
shell=True)
if in_msg.scpOutput and in_msg.scpID2 != None and in_msg.scpOutputDir2 != None:
if (rgb in in_msg.scpProducts2) or ('all' in [
x.lower()
for x in in_msg.scpProducts2 if type(x) == str
]):
scpOutputDir2 = format_name(in_msg.scpOutputDir2,
data.time_slot,
area=area,
rgb=rgb,
sat=data.satname,
sat_nr=data.sat_nr())
if in_msg.compress_to_8bit:
if in_msg.verbose:
print("... secure copy " +
outputFile.replace(
".png", "-fs8.png") + " to " +
scpOutputDir2)
subprocess.call(
"scp " + in_msg.scpID2 + " " +
outputFile.replace(".png", "-fs8.png") +
" " + scpOutputDir2 + " 2>&1 &",
shell=True)
else:
if in_msg.verbose:
print("... secure copy " + outputFile +
" to " + scpOutputDir2)
subprocess.call("scp " + in_msg.scpID2 + " " +
outputFile + " " +
scpOutputDir2 + " 2>&1 &",
shell=True)
if 'ninjotif' in in_msg.outputFormats:
ninjotif_file = format_name(outputDir + '/' +
in_msg.ninjotifFilename,
data.time_slot,
sat_nr=data.sat_nr(),
RSS=in_msg.RSS,
area=area,
rgb=rgb)
from plot_coalition2 import pilimage2geoimage
GEO_image = pilimage2geoimage(PIL_image, obj_area,
data.time_slot)
GEO_image.save(ninjotif_file,
fformat='mpop.imageo.formats.ninjotiff',
ninjo_product_name=rgb,
chan_id=products.ninjo_chan_id[
rgb.replace("_", "-") + "_" + area],
nbits=8)
chmod(ninjotif_file, 0o777)
print(("... save ninjotif image: display ",
ninjotif_file, " &"))
if rgb not in RGBs_done:
RGBs_done.append(rgb)
## start postprocessing
for area in in_msg.postprocessing_areas:
postprocessing(in_msg, global_data.time_slot, int(data.sat_nr()),
area)
if in_msg.verbose:
print(" ")
return RGBs_done
# define area
proj4_string = obj_area.proj4_string
# e.g. proj4_string = '+proj=geos +lon_0=0.0 +a=6378169.00 +b=6356583.80 +h=35785831.0'
area_extent = obj_area.area_extent
# e.g. area_extent = (-5570248.4773392612, -5567248.074173444, 5567248.074173444, 5570248.4773392612)
area_tuple = (proj4_string, area_extent)
# read CTP to distinguish high, medium and low clouds
global_data_CTP = GeostationaryFactory.create_scene(
in_msg.sat,
str(in_msg.sat_nr).zfill(2), "seviri", time_slot)
#global_data_CTP = GeostationaryFactory.create_scene(in_msg.sat, str(10), "seviri", time_slot)
area_loaded = get_area_def(
"EuropeCanary95") #(in_windshift.areaExtraction)
area_loaded = load_products(global_data_CTP, ['CTP'], in_msg,
area_loaded)
data_CTP = global_data_CTP.project(area)
[nx, ny] = data_CTP['CTP'].data.shape
# read all rgbs
global_data = GeostationaryFactory.create_scene(
in_msg.sat,
str(in_msg.sat_nr).zfill(2), "seviri", time_slot)
#global_data_CTP = GeostationaryFactory.create_scene(in_msg.sat, str(10), "seviri", time_slot)
area_loaded = get_area_def(
"EuropeCanary95") #(in_windshift.areaExtraction)
area_loaded = load_products(global_data, rgbs, in_msg, area_loaded)
data = global_data.project(area)
if downscaling_data == True:
for i in range(5, 65, 5):
leadS = "%02d" % i
#diff["t"+leadS] = {}
diff = []
diff1 = []
yearS, monthS, dayS, hourS, minS = string_date(time_slot0 +
timedelta(minutes=i))
#print ("*** read data for ", in_msg.sat_str(),in_msg.sat_nr_str(), "seviri", time_slot0+timedelta(minutes = i))
global_data = GeostationaryFactory.create_scene(
in_msg.sat_str(), in_msg.sat_nr_str(), "seviri",
time_slot0 + timedelta(minutes=i))
area_loaded = get_area_def(
"EuropeCanary95") #(in_windshift.areaExtraction)
area_loaded = load_products(global_data, ['CTT'], in_msg, area_loaded)
data = global_data.project("ccs4")
img_obs = deepcopy(data['CTT'].data)
img_obs.mask[:, :] = False
if True:
print("pickles/" + year0S + month0S + day0S + "_" + hour0S +
min0S + "_CTT_t" + leadS + "_1layer.p")
tmp = pickle.load(
open(
"pickles/" + year0S + month0S + day0S + "_" + hour0S +
min0S + "_CTT_t" + leadS + "_1layer.p", "rb"))
tmp = (tmp[0] - img_obs)
diff1.append(tmp)
tmp = tmp.flatten()
def properties_cells(t1,
tStop,
current_labels=None,
metadata=None,
labels_dir=None,
outputDir_labels=None,
in_msg=None,
sat_data=None):
rgb_load = [
'WV_062', 'WV_073', 'IR_039', 'IR_087', 'IR_097', 'IR_108', 'IR_120',
'IR_134'
] #,'CTP','CTT']
#rgb_out = 'WV_062minusIR_108'
only_obs_noForecast = False
rapid_scan_mode = True
#if only_obs_noForecast == True:
# in_dir = '/opt/users/'+in_msg.user+'/PyTroll/scripts//Mecikalski_obs/cosmo/Channels/labels/'
#elif rapid_scan_mode == True:
# in_dir = '/opt/users/'+in_msg.user+'/PyTroll/scripts//Mecikalski_RapidScan/cosmo/Channels/labels//'
#else:
# in_dir = '/opt/users/'+in_msg.user+'/PyTroll/scripts//Mecikalski/cosmo/Channels/labels/'
# load a few standard things
if in_msg is None:
print("*** Error, in property_cells (property_cells)")
print(" no input class passed as argument")
quit()
from get_input_msg import get_input_msg
in_msg = get_input_msg('input_template')
in_msg.resolution = 'i'
in_msg.sat_nr = 9
in_msg.add_title = False
in_msg.outputDir = './pics/'
in_msg.outputFile = 'WS_%(rgb)s-%(area)s_%y%m%d%H%M'
in_msg.fill_value = [0, 0, 0] # black
in_msg.reader_level = "seviri-level4"
# satellite for HRW winds
sat_nr = "08" #in_windshift.sat_nr
area = "ccs4" #c2"#"ccs4" #in_windshift.ObjArea
# define area object
obj_area = get_area_def(area) #(in_windshift.ObjArea)
# define area
proj4_string = obj_area.proj4_string
# e.g. proj4_string = '+proj=geos +lon_0=0.0 +a=6378169.00 +b=6356583.80 +h=35785831.0'
area_extent = obj_area.area_extent
# e.g. area_extent = (-5570248.4773392612, -5567248.074173444, 5567248.074173444, 5570248.4773392612)
area_tuple = (proj4_string, area_extent)
mean_108_evolution = []
area34 = []
split34 = []
merge34 = []
t_start34 = 0
t_end34 = 0
lonely_cells = 0
cell_interesting = 77
count_double = 0
#labels_dir = '/data/cinesat/out/labels/'
if labels_dir is None:
labels_dir = '/opt/users/' + in_msg.user + '/PyTroll/scripts/labels/' #compatible to all users
print("... use default directory to save labels: " + labels_dir)
# loop over time
while t1 <= tStop:
print(in_msg.sat, str(in_msg.sat_nr), "seviri", t1)
if sat_data is None:
# now read the data we would like to forecast
global_data = GeostationaryFactory.create_scene(
in_msg.sat, str(in_msg.sat_nr), "seviri", t1)
#global_data_RGBforecast = GeostationaryFactory.create_scene(in_msg.sat, str(10), "seviri", time_slot)
# area we would like to read
area_loaded = get_area_def(
"EuropeCanary95") #(in_windshift.areaExtraction)
# load product, global_data is changed in this step!
area_loaded = load_products(global_data, rgb_load, in_msg,
area_loaded)
print('... project data to desired area ', area)
data = global_data.project(area, precompute=True)
else:
data = sat_data
yearS = str(t1.year)
monthS = "%02d" % t1.month
dayS = "%02d" % t1.day
hourS = "%02d" % t1.hour
minS = "%02d" % t1.minute
nx, ny = data[rgb_load[0]].data.shape
# create array for all channel values
values_rgb = np.zeros((len(rgb_load), nx, ny))
# copy all observations/channels into one large numpy array
for rrgb in range(len(rgb_load)):
values_rgb[rrgb, :, :] = deepcopy(
data[rgb_load[rrgb]].data) #-data_108[rgb_load[1]].data
if current_labels is None:
print("--- reading labels from shelve files")
filename = labels_dir + 'Labels_%s.shelve' % (yearS + monthS +
dayS + hourS + minS)
myShelve = shelve.open(filename)
data1 = deepcopy(myShelve['labels'])
metadata = deepcopy(myShelve['metadata'])
myShelve.close()
else:
print("--- recieving labels from plot_coaltion2")
data1 = deepcopy(current_labels)
data_new = np.zeros(data1.shape)
all_cells = {}
# t0 is 5min before t1
t0 = t1 - timedelta(minutes=5)
year0S = str(t0.year)
month0S = "%02d" % t0.month
day0S = "%02d" % t0.day
hour0S = "%02d" % t0.hour
min0S = "%02d" % t0.minute
file_previous_labels = labels_dir + 'Labels_%s*' % (
year0S + month0S + day0S + hour0S + min0S)
filename1 = glob.glob(file_previous_labels)
print("the previous filename is: ", filename1)
if t0.hour == 0 and t0.minute == 0:
check_date = True
else:
check_date = False
if len(filename1) > 0 or check_date:
first_time_step = False
else:
first_time_step = True
if first_time_step:
# these labels are random numbers assigned in COALITION2 (different number for each cell)
data0 = np.array(data1, 'uint32')
labels0 = np.unique(data0[data0 > 0])
id_data = yearS + monthS + dayS + hourS + minS
#list_id = []
# loop over all cell labels
for i in range(1, len(labels0) + 1):
#create a mask which has 1s only where the current cell is
mask_current_label = np.zeros(data1.shape)
mask_current_label = np.where(data1 == i, 1, 0)
# calculate: coordinates center of mass
center = ndimage.measurements.center_of_mass(
mask_current_label)
center = np.rint(center)
# calculate means of the satellite channels (brightness temperatures)
values1 = []
for rrgb in range(len(rgb_load)):
these = values_rgb[rrgb, :, :]
values_cell = these[np.where(mask_current_label == 1)]
values1.append(values_cell.mean())
# take i as cell id and save cells properties
all_cells["ID" + str(i)] = Cells()
all_cells["ID" + str(i)].t_start = [
t1.year, t1.month, t1.day, t1.hour, t1.minute
] # True
all_cells["ID" + str(
i
)].origin = "t0" # "start_programm", "day_before", "merge", "split", "enters_area", "appear"
all_cells["ID" + str(i)].mean108 = values1
all_cells["ID" + str(i)].area_px = sum(sum(mask_current_label))
data_new = deepcopy(data0)
else:
# read cell labels from previous time step t0
id_data0 = year0S + month0S + day0S + hour0S + min0S
file_previous_labels = labels_dir + 'Labels_%s.shelve' % (
year0S + month0S + day0S + hour0S + min0S)
myShelve = shelve.open(file_previous_labels)
data0 = deepcopy(myShelve['labels'])
myShelve.close()
# extract unique cell labels corresponding to the ID at t0
data0 = np.array(data0, 'uint32')
labels0 = np.unique(
data0[data0 > 0]) # this might be an empty tuple [] !HAU!
print("this should match with output previous step \n", labels0)
connections = []
for con in labels0:
connections.append(["ID" + str(con)])
# total number of cell at t0
if len(labels0) == 0:
new_id_num = 0
else:
new_id_num = labels0.max() + 1 # this does not work for []
#these labels are random numbers assigned in COALITION2 (different number for each cell)
data1 = np.array(data1, 'uint32')
labels1 = np.unique(data1) # this might be an empty [] !HAU!
# new id number for the new cells at t1
if labels0.size == 0:
new_id_num = 1
else:
try:
new_id_num = labels0.max() + 1
except ValueError:
print("labels0: ", labels0)
print(type(labels0))
print("quitting in properties_cells line 397")
quit()
#list to make sure you record every split
list_previous = []
# loop through cells at t1
for i in labels1: #range(1,len(labels1)+1):
if i != 0:
#required to correct the output "data_new" if the ID of a cell changes because a bigger cell takes it!!!
correct_id_already_created = 0
#create a mask which has 1s only where the current cell is
mask_current_label = np.zeros(data1.shape)
mask_current_label = np.where(data1 == i, 1, 0)
#store coordinates center of mass
center = ndimage.measurements.center_of_mass(
mask_current_label)
center = np.rint(center)
values1 = []
for rrgb in range(len(rgb_load)):
these = values_rgb[rrgb, :, :]
values_cell = these[np.where(mask_current_label == 1)]
values1.append(values_cell.mean())
## put calculation of mean value in a function (and also consider more properties later)
#take the values of the 10.8 channel for the current cell
#values1 = values_interest[np.where(mask_current_label == 1)]
# consider the area of the current cell in the previous time step (TEST OVERLAPPING)
previous_t = data0 * mask_current_label
# store the ID number of all the overlapping cells at t0 !!! (change to minimum overlapping to consider them)
labels_previous = np.unique(previous_t[previous_t > 0])
##### new cell with no correspondence in previous time step #####
if len(labels_previous) == 0:
#Store the values for the current cell, with the new ID
all_cells["ID" + str(new_id_num)] = Cells()
all_cells["ID" + str(new_id_num)].t_start = [
t1.year, t1.month, t1.day, t1.hour, t1.minute
] # True
#check if the cell appeared in the middle of the area or came from outside the domain
if check_position(mask_current_label):
all_cells["ID" +
str(new_id_num)].origin = "from_outside"
else:
all_cells["ID" + str(new_id_num)].origin = "appear"
all_cells["ID" +
str(new_id_num)].mean108 = values1 #.mean()
all_cells["ID" + str(new_id_num)].area_px = sum(
sum(mask_current_label))
#store the ID number which will be used to create the data_new (with numbers corresponding to ID cell) for next time step
label_current = new_id_num
new_id_num += 1
##### cell with one correspondence in previous time step #####
elif len(labels_previous) == 1:
#check if a cell exists at current time already with the same ID (derived from same cell at previous time step)
if check_cell_same_ID(
all_cells, "ID" + str(labels_previous[0])
): #if "ID" + str(labels_previous[0]) in all_cells.keys():
id_current, id_samePrevious, correct_id_already_created, label_current, all_cells = define_IDs_cell_same_ID(
all_cells, mask_current_label,
labels_previous[0], new_id_num)
#if correct_id_already_created != 0:
# connections = correct_connections(connections, id_samePrevious, all_cells, id_current)
new_id_num += 1
# If there is no cell with that ID yet, the current cell gets it
else:
id_current = "ID" + str(labels_previous[0])
all_cells[id_current] = Cells()
#store the ID number which will be used to create the data_new (with numbers corresponding to ID cell) for next time step
label_current = labels_previous[0]
#Store the values for the current cell
all_cells[id_current].origin = "from_previous"
all_cells[id_current].id_prev = [
"ID" + str(labels_previous[0])
]
all_cells[id_current].area_px = sum(
sum(mask_current_label))
all_cells[id_current].mean108 = values1 #.mean()
"""
lc=0
for con in range(len(connections)):
if connections[con][0] == "ID" + str(labels_previous[0]):
print "id_current",id_current
lc+=1
connections[con].append(id_current)
if lc == 0:
lonely_cells+=1
"""
#add the label of the previous cell (t0) which will be used at the end to make sure all split are recognized
list_previous.append(labels_previous[0])
##### cell with more then one correspondence in previous time step #####
else:
largest_previous = labels_previous[0]
max_tot_px = 0
#scan through the cells the current comes from and look for the biggest (you'll use that ID num)
for h in range(len(labels_previous)):
current_label = labels_previous[h]
count_px = np.where(data0 == current_label, 1, 0)
tot_px = sum(sum(count_px))
if tot_px > max_tot_px:
largest_previous = current_label
max_tot_px = tot_px
#add the label of the previous cell (t0) which will be used at the end to make sure all split are recognized
list_previous.append(current_label)
"""
lc = 0
for con in range(len(connections)):
if connections[con][0] == "ID" + str(labels_previous[h]):
connections[con].append("ID" + str(current_label))
lc +=1
if lc == 0:
lonely_cells +=1
"""
id_current = "ID" + str(largest_previous)
if check_cell_same_ID(
all_cells, id_current
): #if "ID" + str(labels_previous[0]) in all_cells.keys():
id_current, id_samePrevious, correct_id_already_created, label_current, all_cells = define_IDs_cell_same_ID(
all_cells, mask_current_label,
largest_previous, new_id_num)
#if correct_id_already_created != 0:
# connections = correct_connections(connections, id_samePrevious, all_cells, id_current)
new_id_num = new_id_num + 1
else:
label_current = largest_previous
id_current = "ID" + str(largest_previous)
all_cells[id_current] = Cells()
all_cells[id_current].mean108 = values1 #.mean()
all_cells[id_current].origin = "merge"
all_cells[id_current].area_px = sum(
sum(mask_current_label))
all_cells[id_current].id_prev = [
"ID" + str(labels_previous[lp])
for lp in range(len(labels_previous))
]
print("more correspondence ",
("ID" + str(largest_previous)), "coming from ", [
"ID" + str(labels_previous[lp])
for lp in range(len(labels_previous))
])
if correct_id_already_created != 0:
data_new[data_new ==
label_current] = correct_id_already_created
data_new[mask_current_label == 1] = label_current
all_cells["ID" + str(label_current)].center = center
#identify labels the current cells are created from that are repeated (meaning the cell split)
labels_repeated = np.unique([
"ID" + str(x) for x in list_previous
if list_previous.count(x) > 1
])
#make sure that the cells that come from splitting cells get a split
for items in all_cells:
item = all_cells[items]
if item.split != 1:
for n_prev in range(len(item.id_prev)):
if item.id_prev[n_prev] in labels_repeated:
item.split = 1
labels, numobjects = ndimage.label(data_new)
print("....starting updating cells")
if outputDir_labels is not None:
make_figureLabels(deepcopy(data_new),
all_cells,
obj_area,
outputDir_labels,
colorbar=False,
vmin=False,
vmax=False,
white_background=True,
t=t1)
data_new = data_new.astype(
'uint32'
) #unsigned char int https://docs.python.org/2/library/array.html
filename = labels_dir + 'Labels_%s.shelve' % (yearS + monthS +
dayS + hourS + minS)
myShelve = shelve.open(filename)
myShelve['labels'] = deepcopy(data_new)
myShelve.close()
filenames_for_permission = glob.glob(
labels_dir + 'Labels_%s*' %
(yearS + monthS + dayS + hourS + minS))
for file_per in filenames_for_permission:
print(("modified permission: ", file_per))
os.chmod(file_per, 0o664) ## FOR PYTHON3: 0o664
print(("....updated cells labels", filename))
list_cells = list(all_cells.keys())
for cell_connection in list_cells:
ancestors = all_cells[cell_connection].id_prev
for ancestor in ancestors:
for con in range(len(connections)):
if connections[con][0] == ancestor:
connections[con].append(cell_connection)
filename = labels_dir + 'Labels_%s.shelve' % (
year0S + month0S + day0S + hour0S + min0S)
d = shelve.open(filename)
d['connections'] = deepcopy(connections)
d.close()
print(("....updated cells connections",
labels_dir + 'Labels_%s.shelve' %
(year0S + month0S + day0S + hour0S + min0S)))
filenames_for_permission = glob.glob(
labels_dir + 'Labels_%s*' %
(year0S + month0S + day0S + hour0S + min0S))
for file_per in filenames_for_permission:
os.chmod(file_per, 0o664) ## FOR PYTHON3: 0o664
print("....starting updating cells")
filename = create_dir(labels_dir + 'Labels_%s.shelve' %
(yearS + monthS + dayS + hourS + minS))
myShelve = shelve.open(filename)
dict_cells = {
'cells': all_cells,
'labels': data_new,
'metadata': metadata
}
myShelve.update(dict_cells)
# close the shelve
myShelve.close()
print("....updated all cells")
filenames_for_permission = glob.glob(
labels_dir + 'Labels_%s*' % (yearS + monthS + dayS + hourS + minS))
for file_per in filenames_for_permission:
print(("modified permission: ", file_per))
os.chmod(file_per, 0o664) ## FOR PYTHON3: 0o664
t1 = t1 + timedelta(minutes=5)
return data_new, first_time_step
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)
# read HRW wind vectors
hrw_data = read_HRW("meteosat", sat_nr, "seviri", time_slot, ntimes, \
min_correlation=min_correlation, min_conf_nwp=min_conf_nwp, \
min_conf_no_nwp=min_conf_no_nwp, cloud_type=cloud_type)
# now read the data we would like to forecast
global_data = GeostationaryFactory.create_scene(
in_msg.sat,
str(in_msg.sat_nr).zfill(2), "seviri", time_slot)
# area we would like to read
area_loaded = get_area_def("EuropeCanary95")
# load product
area_loaded = load_products(global_data, [rgb], in_msg, area_loaded)
#pge = get_NWC_pge_name(rgb)
#print '...load satellite channel ', pge
#global_data.load([pge], reader_level="seviri-level3")
#if rgb=='CTT':
# nwcsaf_calibrate=False
#else:
# nwcsaf_calibrate=False
#convert_NWCSAF_to_radiance_format(global_data, area, rgb, nwcsaf_calibrate, True)
#from trollimage.image import Image as trollimage
#min_data = 0.
#max_data = float(len(global_data[rgb].palette)-1)
#if in_msg.verbose:
if len(RGBs) > 0:
# exit loop, if input is found
break
else:
# else wait 20s and try again
import time
time.sleep(25)
print("*** read CTP for ", in_msg.sat_str(), in_msg.sat_nr_str(),
"seviri", str(time_slot))
global_data_CTP = GeostationaryFactory.create_scene(
in_msg.sat_str(), in_msg.sat_nr_str(), "seviri", time_slot)
#global_data_CTP = GeostationaryFactory.create_scene(in_msg.sat, in_msg.sat_nr_str(), "seviri", time_slot)
#global_data_CTP = GeostationaryFactory.create_scene(in_msg.sat, str(10), "seviri", time_slot)
#area_loaded = get_area_def("EuropeCanary95") #(in_windshift.areaExtraction)
area_loaded_CTP = load_products(global_data_CTP, ['CTP'], in_msg,
get_area_def("alps95"))
data_CTP = global_data_CTP.project(area, precompute=True)
[nx, ny] = data_CTP['CTP'].data.shape
# read all rgbs
print("*** read all other channels for ", in_msg.sat_str(),
in_msg.sat_nr_str(), "seviri", str(time_slot))
global_data = GeostationaryFactory.create_scene(
in_msg.sat_str(), in_msg.sat_nr_str(), "seviri", time_slot)
#global_data_CTP = GeostationaryFactory.create_scene(in_msg.sat, str(10), "seviri", time_slot)
area_loaded = get_area_def(
"EuropeCanary95") #(in_windshift.areaExtraction)
area_loaded = load_products(global_data, rgbs, in_msg, area_loaded)
data = global_data.project(area, precompute=True)
