def show_swath_pycoast(self, start, period=None):
"""
A helper method that displays the orbital swath
starting at datetime start, for a period number of minutes.
If, start is iterable, then the method assumes it is an iterable
of datetimes, plotting a number of swaths at those times.
"""
# test if start is iterable, EAFP style:
try:
for e in start:
pass
except TypeError:
start = [start]
start.sort()
from PIL import Image
from pycoast import ContourWriterAGG
from pydecorate import DecoratorAGG
img = Image.new('RGB', (650, 650))
proj4_string = ""
for x in self.working_projection:
proj4_string += "+%s=%s "%(x,self.working_projection[x])
area_extent = (-6700000.0, -6700000.0, 6700000.0, 6700000.0)
area_def = (proj4_string, area_extent)
cw = ContourWriterAGG()
cw.add_grid(img, area_def, (10.0,10.0),(2.0,2.0), fill='blue',
outline='gray', outline_opacity=130, minor_outline=None, write_text=False)
# Plot granules
for t in start:
# fetch the coordinates
xys_segs = self.swath_working_projection(t, period)
for xys in xys_segs:
lls = self.proj(xys[0],xys[1],inverse=True)
cw.add_polygon(img, area_def, zip(lls[0], lls[1]), outline="blue", fill="blue", fill_opacity=70, width=1)
cw.add_coastlines(img, area_def, resolution='l')
aoi_coords = zip(*self.aoi)
## TODO: Handle single point case properly
if len(aoi_coords) == 1:
x, y = aoi_coords[0]
d = 0.5
line_coords = [(x-d,y),(x+d,y)]
cw.add_line(img, area_def, line_coords,
outline="red", fill="red", fill_opacity=100, width=2)
elif len(aoi_coords) == 2:
cw.add_line(img, area_def, aoi_coords, outline="red", fill="red", fill_opacity=100, width=10)
else:
cw.add_polygon(img, area_def, aoi_coords, outline="red", fill="red", fill_opacity=100, width=2)
# Decorate
dc = DecoratorAGG(img)
text = "Granules from time: %s + %.2f min."%(start[0].strftime('%Y.%m.%d %H:%M:%S'),
(start[-1]-start[0]).total_seconds()/60.0)
dc.align_bottom()
dc.add_text(text,height=0)
img.show()
def decorate_pilimg(self, pilimg):
"""Apply decorations to an image
Parameters
----------
pilimg : PIL.Image
"""
dc = DecoratorAGG(pilimg)
dc.align_bottom()
self.apply_colorbar(dc)
self.apply_label(dc)
def show_image(data, dataname, save_png, colors="rainbow", min_data=None, max_data=None, title=None, add_colorscale=True):
if min_data is None:
min_data=data.min()
if max_data is None:
max_data=data.max()
img = trollimage(data, mode="L", fill_value=[0,0,0])
colormap = get_colormap(colors, min_data, max_data)
img.colorize(colormap)
if title is not None:
title_color=(255,255,255)
from PIL import ImageFont
from PIL import ImageDraw
PIL_image=img.pil_image()
draw = ImageDraw.Draw(PIL_image)
fontsize=18
font = ImageFont.truetype("/usr/openv/java/jre/lib/fonts/LucidaTypewriterBold.ttf", fontsize)
draw.text( (10, 10), title, title_color, font=font)
if add_colorscale:
dc = DecoratorAGG(PIL_image)
colormap_r = colormap.reverse()
dc.align_right()
dc.write_vertically()
dc.add_scale(colormap_r, extend=True, tick_marks=5, minor_tick_marks=1, line_opacity=100) #, tick_marks=tick_marks, minor_tick_marks=minor_tick_marks, unit=units
show_or_save_image(PIL_image, save_png, dataname)
def test_colorbar(tmp_path, orientation_func_name, align_func_name, clims):
fn = tmp_path / "test_colorbar.png"
img = Image.fromarray(np.zeros((200, 100, 3), dtype=np.uint8))
dc = DecoratorAGG(img)
getattr(dc, align_func_name)()
getattr(dc, orientation_func_name)()
cmap = rdbu.set_range(*clims, inplace=False)
dc.add_scale(cmap, extend=True, tick_marks=5.0, line_opacity=100, unit="K")
img.save(fn)
# check results
output_img = Image.open(fn)
arr = np.array(output_img)
_assert_colorbar_orientation_alignment(arr, orientation_func_name,
align_func_name)
def add_decorate(orig, fill_value=None, **decorate):
"""Decorate an image with text and/or logos/images.
This call adds text/logos in order as given in the input to keep the
alignment features available in pydecorate.
An example of the decorate config::
decorate = {
'decorate': [
{'logo': {'logo_path': <path to a logo>, 'height': 143, 'bg': 'white', 'bg_opacity': 255}},
{'text': {'txt': start_time_txt,
'align': {'top_bottom': 'bottom', 'left_right': 'right'},
'font': <path to ttf font>,
'font_size': 22,
'height': 30,
'bg': 'black',
'bg_opacity': 255,
'line': 'white'}}
]
}
Any numbers of text/logo in any order can be added to the decorate list,
but the order of the list is kept as described above.
Note that a feature given in one element, eg. bg (which is the background color)
will also apply on the next elements unless a new value is given.
align is a special keyword telling where in the image to start adding features, top_bottom is either top or bottom
and left_right is either left or right.
"""
LOG.info("Decorate image.")
# Need to create this here to possible keep the alignment
# when adding text and/or logo with pydecorate
if hasattr(orig, 'convert'):
# image must be in RGB space to work with pycoast/pydecorate
orig = orig.convert('RGBA' if orig.mode.endswith('A') else 'RGB')
elif not orig.mode.startswith('RGB'):
raise RuntimeError("'trollimage' 1.6+ required to support adding "
"overlays/decorations to non-RGB data.")
img_orig = orig.pil_image(fill_value=fill_value)
from pydecorate import DecoratorAGG
dc = DecoratorAGG(img_orig)
# decorate need to be a list to maintain the alignment
# as ordered in the list
img = orig
if 'decorate' in decorate:
for dec in decorate['decorate']:
if 'logo' in dec:
img = add_logo(img, dc, img_orig, logo=dec['logo'])
elif 'text' in dec:
img = add_text(img, dc, img_orig, text=dec['text'])
elif 'scale' in dec:
img = add_scale(img, dc, img_orig, scale=dec['scale'])
return img
def add_decorate(orig, fill_value=None, **decorate):
"""Decorate an image with text and/or logos/images.
This call adds text/logos in order as given in the input to keep the
alignment features available in pydecorate.
An example of the decorate config::
decorate = {
'decorate': [
{'logo': {'logo_path': <path to a logo>, 'height': 143, 'bg': 'white', 'bg_opacity': 255}},
{'text': {'txt': start_time_txt,
'align': {'top_bottom': 'bottom', 'left_right': 'right'},
'font': <path to ttf font>,
'font_size': 22,
'height': 30,
'bg': 'black',
'bg_opacity': 255,
'line': 'white'}}
]
}
Any numbers of text/logo in any order can be added to the decorate list,
but the order of the list is kept as described above.
Note that a feature given in one element, eg. bg (which is the background color)
will also apply on the next elements unless a new value is given.
align is a special keyword telling where in the image to start adding features, top_bottom is either top or bottom
and left_right is either left or right.
"""
LOG.info("Decorate image.")
# Need to create this here to possible keep the alignment
# when adding text and/or logo with pydecorate
img_orig = orig.pil_image(fill_value=fill_value)
from pydecorate import DecoratorAGG
dc = DecoratorAGG(img_orig)
# decorate need to be a list to maintain the alignment
# as ordered in the list
if 'decorate' in decorate:
for dec in decorate['decorate']:
if 'logo' in dec:
add_logo(orig, dc, img_orig, logo=dec['logo'])
elif 'text' in dec:
add_text(orig, dc, img_orig, text=dec['text'])
def test_style_retention():
# import aggdraw
from PIL import Image
from trollimage.colormap import rdbu
from pydecorate import DecoratorAGG
# font = aggdraw.Font("navy", DEJAVU_FONT, size=20)
# font_scale = aggdraw.Font("black", DEJAVU_FONT, size=12)
rdbu.colors = rdbu.colors[::-1]
rdbu.set_range(-90, 10)
img = Image.open(os.path.join(REPOS_ROOT, "BMNG_clouds_201109181715_areaT2.png"))
dc = DecoratorAGG(img)
# dc.write_vertically()
# dc.add_logo("logos/pytroll_light_big.png")
# dc.add_logo("logos/NASA_Logo.gif",margins=[10,10],bg='yellow')
# dc.add_logo("logos/pytroll_light_big.png")
# font = aggdraw.Font("blue", DEJAVU_FONT, size=16)
# dc.add_text("Some text",font=font)
# dc.align_right()
dc.add_scale(rdbu, extend=True, tick_marks=5.0, line_opacity=100, unit="K")
# dc.align_bottom()
# dc.add_scale(rdbu, extend=True, tick_marks=2.0, line_opacity=100, width=60)
# dc.align_right()
# dc.write_vertically()
dc.align_bottom()
dc.add_scale(rdbu, extend=True, tick_marks=5.0, line_opacity=100, unit="K")
# dc.align_left()
# dc.add_scale(rdbu, extend=True, font=font_scale, tick_marks=2.0, minor_tick_marks=1.0,
# line_opacity=100, width=60, unit='K')
# img.show()
img.save("style_retention.png")
def savefig(fname, loc=1, decorate=True, **kwargs):
"""save figure and add the MONET logo .
Parameters
----------
fname : str
output file name.
loc : int
the location for the monet logo.
decorate : bool
Description of parameter `decorate`.
**kwargs : dict
kwargs for the matplotlib.pyplot.savefig function.
Returns
-------
type
Description of returned object.
"""
import io
import os
import sys
from PIL import Image
import matplotlib.pyplot as plt
try:
from pydecorate import DecoratorAGG
pydecorate = True
except ImportError:
pydecorate = False
plt.savefig(fname, **kwargs)
if pydecorate and decorate:
img = Image.open(fname)
dc = DecoratorAGG(img)
if loc == 1:
dc.align_bottom()
elif loc == 2:
dc.align_bottom()
dc.align_right()
elif loc == 3:
dc.align_right()
# sys.argv[0])[-5] + 'data/MONET_logo.png'
# print(os.path.basename(__file__))
logo = os.path.abspath(__file__)[:-17] + 'data/MONET-logo.png'
# print(logo)
dc.add_logo(logo)
if fname.split('.')[-1] == 'png':
img.save(fname, "PNG")
elif fname.split('.')[-1] == 'jpg':
img.save(fname, "JPEG")
def scatter_rad_rcz(in_msg):
# get date of the last SEVIRI observation
if in_msg.datetime is None:
in_msg.get_last_SEVIRI_date()
yearS = str(in_msg.datetime.year)
#yearS = yearS[2:]
monthS = "%02d" % in_msg.datetime.month
dayS = "%02d" % in_msg.datetime.day
hourS = "%02d" % in_msg.datetime.hour
minS = "%02d" % in_msg.datetime.minute
dateS = yearS + '-' + monthS + '-' + dayS
timeS = hourS + '-' + minS
if in_msg.sat_nr is None:
in_msg.sat_nr = choose_msg(in_msg.datetime, in_msg.RSS)
# check if PyResample is loaded
try:
# Work around for on demand import of pyresample. pyresample depends
# on scipy.spatial which memory leaks on multiple imports
IS_PYRESAMPLE_LOADED = False
from pyresample import geometry
from mpop.projector import get_area_def
IS_PYRESAMPLE_LOADED = True
except ImportError:
LOGGER.warning(
"pyresample missing. Can only work in satellite projection")
if in_msg.datetime.year > 2012:
if in_msg.sat_nr == 8:
area_loaded = get_area_def("EuropeCanary35")
elif in_msg.sat_nr == 9: # rapid scan service satellite
area_loaded = get_area_def("EuropeCanary95")
elif in_msg.sat_nr == 10: # default satellite
area_loaded = get_area_def(
"met09globeFull"
) # full disk service, like EUMETSATs NWC-SAF products
elif in_msg.sat_nr == 0: # fake satellite for reprojected ccs4 data in netCDF
area_loaded = get_area_def("ccs4") #
#area_loaded = get_area_def("EuropeCanary")
#area_loaded = get_area_def("alps") # new projection of SAM
else:
print("*** Error, unknown satellite number ", in_msg.sat_nr)
area_loaded = get_area_def("hsaf") #
else:
if in_msg.sat_nr == 8:
area_loaded = get_area_def("EuropeCanary95")
elif in_msg.sat_nr == 9: # default satellite
area_loaded = get_area_def("EuropeCanary")
# define contour write for coasts, borders, rivers
cw = ContourWriterAGG(in_msg.mapDir)
if type(in_msg.sat_nr) is int:
sat_nr_str = str(in_msg.sat_nr).zfill(2)
elif type(in_msg.sat_nr) is str:
sat_nr_str = in_msg.sat_nr
else:
print("*** Waring, unknown type of sat_nr", type(in_msg.sat_nr))
sat_nr_str = in_msg.sat_nr
if in_msg.verbose:
print('*** Create plots for ')
print(' Satellite/Sensor: ' + in_msg.sat + ' ' + sat_nr_str)
print(' Date/Time: ' + dateS + ' ' + hourS + ':' + minS +
'UTC')
print(' RGBs: ', in_msg.RGBs)
print(' Area: ', in_msg.areas)
# check if input data is complete
if in_msg.verbose:
print("*** check input data")
RGBs = check_input(in_msg, in_msg.sat + sat_nr_str, in_msg.datetime)
if len(RGBs) != len(in_msg.RGBs):
print("*** Warning, input not complete.")
print("*** Warning, process only: ", RGBs)
# define time and data object
global_data = GeostationaryFactory.create_scene(in_msg.sat, sat_nr_str,
"seviri", in_msg.datetime)
# print "type(global_data) ", type(global_data) # <class 'mpop.scene.SatelliteInstrumentScene'>
# print "dir(global_data)", dir(global_data) [..., '__init__', ... 'area', 'area_def', 'area_id', 'channel_list', 'channels',
# 'channels_to_load', 'check_channels', 'fullname', 'get_area', 'image', 'info', 'instrument_name', 'lat', 'load', 'loaded_channels',
# 'lon', 'number', 'orbit', 'project', 'remove_attribute', 'satname', 'save', 'set_area', 'time_slot', 'unload', 'variant']
global_data_radar = GeostationaryFactory.create_scene(
"swissradar", "", "radar", in_msg.datetime)
global_data_radar.load(['precip'])
if len(RGBs) == 0:
return RGBs
if in_msg.verbose:
print(
"*** load satellite channels for " + in_msg.sat + sat_nr_str + " ",
global_data.fullname)
# initialize processed RGBs
RGBs_done = []
# load all channels / information
for rgb in RGBs:
if in_msg.verbose:
print(" load prerequisites for: ", rgb)
if rgb in products.MSG or rgb in products.MSG_color:
for channel in products.MSG:
if rgb.find(
channel
) != -1: # if a channel name (IR_108) is in the rgb name (IR_108c)
if in_msg.verbose:
print(" load prerequisites by name: ", channel)
if in_msg.reader_level is None:
global_data.load(
[channel], area_extent=area_loaded.area_extent
) # try all reader levels load the corresponding data
else:
global_data.load([channel],
area_extent=area_loaded.area_extent,
reader_level=in_msg.reader_level
) # load the corresponding data
if rgb in products.RGBs_buildin or rgb in products.RGBs_user:
obj_image = get_image(global_data,
rgb) # find corresponding RGB image object
if in_msg.verbose:
print(" load prerequisites by function: ",
obj_image.prerequisites)
global_data.load(
obj_image.prerequisites,
area_extent=area_loaded.area_extent) # load prerequisites
if rgb in products.CMa or rgb in products.CT or rgb in products.CTTH or rgb in products.SPhR:
if rgb in products.CMa:
pge = "CloudMask"
elif rgb in products.CT:
pge = "CloudType"
elif rgb in products.CTTH:
pge = "CTTH"
elif rgb in products.SPhR:
pge = "SPhR"
else:
print("*** Error in scatter_rad_rcz (" +
inspect.getfile(inspect.currentframe()) + ")")
print(" unknown NWC-SAF PGE ", rgb)
quit()
if in_msg.verbose:
print(" load NWC-SAF product: " + pge)
global_data.load(
[pge],
calibrate=in_msg.nwcsaf_calibrate,
reader_level="seviri-level3"
) # False, area_extent=area_loaded.area_extent (difficulties to find correct h5 input file)
#print global_data.loaded_channels()
#loaded_channels = [chn.name for chn in global_data.loaded_channels()]
#if pge not in loaded_channels:
# return []
if area_loaded != global_data[pge].area:
print("*** Warning: NWC-SAF input file on a differnt grid (" +
global_data[pge].area.name +
") than suggested input area (" + area_loaded.name + ")")
print(" use " + global_data[pge].area.name +
" as standard grid")
area_loaded = global_data[pge].area
convert_NWCSAF_to_radiance_format(global_data, area_loaded, rgb,
IS_PYRESAMPLE_LOADED)
if rgb in products.HSAF:
if in_msg.verbose:
print(" load hsaf product by name: ", rgb)
global_data.load(
[rgb]
) # , area_extent=area_loaded.area_extent load the corresponding data
if in_msg.HRV_enhancement:
# load also the HRV channel (there is a check inside in the load function, if the channel is already loaded)
if in_msg.verbose:
print(
" load additionally the HRV channel for HR enhancement")
global_data.load(["HRV"], area_extent=area_loaded.area_extent)
# loaded_channels = [chn.name for chn in global_data.loaded_channels()]
# print loaded_channels
# check if all prerequisites are loaded
#rgb_complete = []
#for rgb in RGBs:
# all_loaded = True
# if rgb in products.RGBs_buildin or rgb in products.RGB_user:
# obj_image = get_image(global_data, rgb)
# for pre in obj_image.prerequisites:
# if pre not in loaded_channels:
# all_loaded = False
# elif rgb in products.MSG_color:
# if rgb.replace("c","") not in loaded_channels:
# all_loaded = False
# else:
# if rgb not in loaded_channels:
# all_loaded = False
# if all_loaded:
# rgb_complete.append(rgb)
#print "rgb_complete", rgb_complete
# preprojecting the data to another area
# --------------------------------------
for area in in_msg.areas:
print("")
obj_area = get_area_def(area)
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)
resolution = 'i'
if in_msg.mapResolution is None:
if area.find("EuropeCanary") != -1:
resolution = 'l'
if area.find("ccs4") != -1:
resolution = 'i'
if area.find("ticino") != -1:
resolution = 'h'
else:
resolution = 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)
# save reprojected data
if area in in_msg.save_reprojected_data: # and area != area_loaded
_sat_nr = int(data.number) - 7 if int(data.number) - 7 > 0 else 0
nc_dir = (
global_data.time_slot.strftime(in_msg.reprojected_data_dir) % {
"area": area,
"msg": "MSG" + str(_sat_nr)
})
nc_file = (global_data.time_slot.strftime(
in_msg.reprojected_data_filename) % {
"area": area,
"msg": "MSG" + str(_sat_nr)
})
ncOutputFile = nc_dir + nc_file
# check if output directory exists, if not create it
path = dirname(ncOutputFile)
if not exists(path):
if in_msg.verbose:
print('... create output directory: ' + path)
makedirs(path)
if in_msg.verbose:
print("... save reprojected data: ncview " + ncOutputFile +
" &")
#data.save(ncOutputFile, to_format="netcdf4", compression=False)
data.save(ncOutputFile, band_axis=0, concatenate_bands=False)
# mask for the cloud depths tests (masked data)
#if area == 'ccs4':
if area == False:
print('... apply convective mask')
mask_depth = data.image.mask_clouddepth()
#print type(mask_depth.max)
#print dir(mask_depth.max)
index = where(
mask_depth <
5) # less than 5 (of 6) tests successfull -> mask out
for rgb in RGBs:
if rgb in products.MSG_color:
rgb2 = rgb.replace("c", "")
data[rgb2].data.mask[index] = True
fill_value = data[rgb2].data.fill_value
#data["IR_108"].data[index] = fill_value
#print "data[IR_108].data.min/max ", data["IR_108"].data.min(), data["IR_108"].data.max()
#if rgb == "IR_108c":
# print type(data["IR_108"].data)
# print dir(data["IR_108"].data)
#print data["IR_108"].data.mask
# 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()
print("y.shape ", global_data_radar['precip'].data.shape)
from numpy import copy
y = copy(global_data_radar['precip'].data)
y = y.ravel()
print("y.shape ", y.shape)
if 1 == 0:
if 'X' in locals():
del X
from numpy import column_stack, append, concatenate
for rgb in RGBs:
# poor mans parallax correction
if rgb in products.MSG_color:
rgb2 = rgb.replace("c", "")
else:
rgb2 = rgb
x1 = data[rgb2].data.ravel()
if 'X' not in locals():
X = x1
X = [X]
else:
concatenate((X, [x1]), axis=0)
print("X.shape ", X.shape)
X = append(X, [[1] * len(x1)], axis=1)
print("y.shape ", y.shape)
#theta = np.linalg.lstsq(X,y)[0]
return
ind_gt_1 = y > 1
x = x[ind_gt_1]
y = y[ind_gt_1]
ind_lt_200 = y < 200
x = x[ind_lt_200]
y = y[ind_lt_200]
#ind_gt_0 = x>0
#x = x[ind_gt_0]
#y = y[ind_gt_0]
#X = np.column_stack(x+[[1]*len(x[0])])
#beta_hat = np.linalg.lstsq(X,y)[0]
#print beta_hat
#X_hat= np.dot(X,theta)
#y_hat = X_hat.reshape((640, 710))
# creating plots/images
if in_msg.make_plots:
ind_cloudy = data['CTH'].data > 0
ind_clear = data['CTH'].data <= 0
ind_cloudy = ind_cloudy.ravel()
for rgb in RGBs:
if rgb in products.MSG_color:
rgb2 = rgb.replace("c", "")
else:
rgb2 = rgb
if rgb == 'ir108':
rgb2 = 'IR_108'
# poor mans parallax correction
if 1 == 0:
print("... poor mans parallax correction")
data[rgb2].data[25:640, :] = data[rgb2].data[0:615, :]
#data[rgb2].data[15:640,:] = data[rgb2].data[0:625,:]
data[rgb2].data[:, 0:700] = data[rgb2].data[:, 10:710]
# create output filename
outputDir = format_name(in_msg.outputDir,
data.time_slot,
area=area,
rgb=rgb,
sat_nr=data.number)
outputFile = outputDir + format_name(in_msg.outputFile,
data.time_slot,
area=area,
rgb=rgb,
sat_nr=data.number)
PIL_image = create_PIL_image(
rgb, data, in_msg
) # !!! in_msg.colorbar[rgb] is initialized inside (give attention to rgbs) !!!
if 1 == 1:
y = copy(global_data_radar['precip'].data)
ind_gt_300 = y > 300 # replace no rain marker with 0mm/h
y[ind_gt_300] = 0
y = y.ravel()
print("y.shape ", y.shape)
x = copy(data[rgb2].data)
x = x.ravel()
## get rid of clear sky
x = x[ind_cloudy]
y = y[ind_cloudy]
#ind_gt_01 = x>0.1
#x = x[ind_gt_01]
#y = y[ind_gt_01]
# get rid of no rain limits for rainfall
ind_gt_01 = y > 0.1
x = x[ind_gt_01]
y = y[ind_gt_01]
ind_lt_300 = y < 300
x = x[ind_lt_300]
y = y[ind_lt_300]
plt.figure()
plt.title('Scatterplot precipitation - radiance')
plt.xlabel(rgb)
plt.ylabel('precipitation in mm/h')
plt.scatter(x, y) #, s=area, c=colors, alpha=0.5
outputDir = format_name(in_msg.outputDir,
data.time_slot,
area=area,
rgb=rgb,
sat_nr=data.number)
outputFileScatter = outputDir + format_name(
'scatterplot_%(area)s_%Y%m%d%H%M_%(rgb)s_precip_pc.png',
data.time_slot,
area=area,
rgb=rgb,
sat_nr=data.number)
#plt.show()
from numpy import arange
x_line = arange(x.min(), x.max(), 1)
print("*** display " + outputFileScatter + " &")
from numpy import ones, linalg, array
print(x.min(), x.max(), y.min(), y.max())
A = array([x, ones(x.size)])
w = linalg.lstsq(A.T, y)[0] # obtaining the parameters
y_line = w[0] * x_line + w[1] # regression line
#---
#from scipy import stats
#slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)
#print "slope, intercept, r_value, p_value, std_err"
#print slope, intercept, r_value, p_value, std_err
#y_line = slope*x_line + intercept
from pylab import plot
plot(x_line, y_line, 'r-')
plt.savefig(outputFileScatter)
y_hat = w[0] * data[rgb2].data + w[1]
print("y_hat.shape: ", y_hat.shape)
# set clear sky to 0
y_hat[ind_clear] = 0
y_hat = ma.asarray(y_hat)
y_hat.mask = (y_hat == 9999.9) | (y_hat <= 0.0001)
from trollimage.colormap import RainRate
colormap = rainbow
min_data = 0.0
#max_data=y_hat.max()
max_data = 8
colormap.set_range(min_data, max_data)
#colormap = RainRate
in_msg.colormap[rgb] = colormap
units = 'mm/h'
img = trollimage(y_hat, mode="L")
img.colorize(in_msg.colormap[rgb])
in_msg.colormap[rgb] = colormap.reverse()
PIL_image = img.pil_image()
outputFile = outputDir + format_name(
'fit_%(area)s_%Y%m%d%H%M_%(rgb)s_precip.png',
data.time_slot,
area=area,
rgb=rgb,
sat_nr=data.number)
#PIL_image.save(outputFile)
## add coasts, borders, and rivers, database is heree
## http://www.soest.hawaii.edu/pwessel/gshhs/index.html
## possible resolutions
## f full resolution: Original (full) data resolution.
## h high resolution: About 80 % reduction in size and quality.
## i intermediate resolution: Another ~80 % reduction.
## l low resolution: Another ~80 % reduction.
## c crude resolution: Another ~80 % reduction.
if in_msg.add_rivers:
if in_msg.verbose:
print(" add rivers to image (resolution=" +
resolution + ")")
cw.add_rivers(PIL_image,
area_tuple,
outline='blue',
resolution=resolution,
outline_opacity=127,
width=0.5,
level=5) #
if in_msg.verbose:
print(" add lakes to image (resolution=" +
resolution + ")")
cw.add_coastlines(PIL_image,
area_tuple,
outline='blue',
resolution=resolution,
outline_opacity=127,
width=0.5,
level=2) #, outline_opacity=0
if in_msg.add_borders:
if in_msg.verbose:
print(" add coastlines to image (resolution=" +
resolution + ")")
cw.add_coastlines(PIL_image,
area_tuple,
outline=(255, 0, 0),
resolution=resolution,
width=1) #, outline_opacity=0
if in_msg.verbose:
print(" add borders to image (resolution=" +
resolution + ")")
cw.add_borders(PIL_image,
area_tuple,
outline=(255, 0, 0),
resolution=resolution,
width=1) #, outline_opacity=0
#if area.find("EuropeCanary") != -1 or area.find("ccs4") != -1:
dc = DecoratorAGG(PIL_image)
# add title to image
if in_msg.add_title:
PIL_image = add_title(PIL_image, rgb, int(data.number),
dateS, hourS, minS, area, dc,
in_msg.font_file, in_msg.verbose)
# 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()
dc.add_logo("../logos/meteoSwiss3.jpg", height=60.0)
dc.add_logo("../logos/pytroll3.jpg", height=60.0)
# add colorscale
if in_msg.add_colorscale and in_msg.colormap[rgb] is not None:
dc.align_right()
dc.write_vertically()
font_scale = aggdraw.Font(
"black",
"/usr/share/fonts/truetype/ttf-dejavu/DejaVuSerif-Bold.ttf",
size=16)
# get tick marks
tick_marks = 20 # default
minor_tick_marks = 5 # default
if rgb in list(in_msg.tick_marks.keys()):
tick_marks = in_msg.tick_marks[rgb]
if rgb in list(in_msg.minor_tick_marks.keys()):
minor_tick_marks = in_msg.minor_tick_marks[rgb]
if rgb.find(
"-"
) != -1: # for channel differences use tickmarks of 1
tick_marks = 1
minor_tick_marks = 1
tick_marks = 2 # default
minor_tick_marks = 1 # default
if in_msg.verbose:
print('... add colorscale')
dc.add_scale(in_msg.colormap[rgb],
extend=True,
tick_marks=tick_marks,
minor_tick_marks=minor_tick_marks,
font=font_scale,
line_opacity=100) #, unit='T / K'
## test to plot a wind barb
#import matplotlib.pyplot as plt
#ax = plt.axes(PIL_image)
#ax.barbs(0, 0, 20, 20, length=8, pivot='middle', barbcolor='red')
#ax.barbs(8, 46, 20, 20, length=8, pivot='middle', barbcolor='red')
# 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 in_msg.verbose:
print('... save final file :' + outputFile)
PIL_image.save(outputFile,
optimize=True) # optimize -> minimize file size
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)
# copy to another place
if in_msg.scpOutput:
if in_msg.verbose:
print("... secure copy " + outputFile + " to " +
in_msg.scpOutputDir)
subprocess.call("scp " + in_msg.scpID + " " + outputFile +
" " + in_msg.scpOutputDir + " 2>&1 &",
shell=True)
if in_msg.compress_to_8bit:
if in_msg.verbose:
print("... secure copy " +
outputFile.replace(".png", "-fs8.png") +
" to " + in_msg.scpOutputDir)
subprocess.call(
"scp " + in_msg.scpID + " " +
outputFile.replace(".png", "-fs8.png") + " " +
in_msg.scpOutputDir + " 2>&1 &",
shell=True)
if rgb not in RGBs_done:
RGBs_done.append(rgb)
## start postprocessing
if area in in_msg.postprocessing_areas:
postprocessing(in_msg, global_data.time_slot, data.number, area)
if in_msg.verbose:
print(" ")
return RGBs_done
def main():
parser = get_parser()
args = parser.parse_args()
levels = [logging.ERROR, logging.WARN, logging.INFO, logging.DEBUG]
logging.basicConfig(level=levels[min(3, args.verbosity)])
if args.output_filename is None:
args.output_filename = [x[:-3] + "png" for x in args.input_tiff]
else:
assert len(args.output_filename) == len(
args.input_tiff
), "Output filenames must be equal to number of input tiffs"
if not (args.add_borders or args.add_coastlines or args.add_grid
or args.add_rivers or args.add_colorbar):
LOG.error(
"Please specify one of the '--add-X' options to modify the image")
return -1
# we may be dealing with large images that look like decompression bombs
# let's turn off the check for the image size in PIL/Pillow
Image.MAX_IMAGE_PIXELS = None
for input_tiff, output_filename in zip(args.input_tiff,
args.output_filename):
LOG.info("Creating {} from {}".format(output_filename, input_tiff))
gtiff = gdal.Open(input_tiff)
proj4_str = osr.SpatialReference(gtiff.GetProjection()).ExportToProj4()
ul_x, res_x, _, ul_y, _, res_y = gtiff.GetGeoTransform()
half_pixel_x = res_x / 2.
half_pixel_y = res_y / 2.
area_extent = (
ul_x - half_pixel_x, # lower-left X
ul_y + res_y * gtiff.RasterYSize - half_pixel_y, # lower-left Y
ul_x + res_x * gtiff.RasterXSize + half_pixel_x, # upper-right X
ul_y + half_pixel_y, # upper-right Y
)
img = Image.open(input_tiff).convert('RGBA' if gtiff.RasterCount in (
2, 4) else 'RGB')
area_def = (proj4_str, area_extent)
cw = ContourWriterAGG(args.shapes_dir)
if args.add_coastlines:
outline = args.coastlines_outline[0] if len(
args.coastlines_outline) == 1 else tuple(
int(x) for x in args.coastlines_outline)
if args.coastlines_fill:
fill = args.coastlines_fill[0] if len(
args.coastlines_fill) == 1 else tuple(
int(x) for x in args.coastlines_fill)
else:
fill = None
cw.add_coastlines(img,
area_def,
resolution=args.coastlines_resolution,
level=args.coastlines_level,
width=args.coastlines_width,
outline=outline,
fill=fill)
if args.add_rivers:
outline = args.rivers_outline[0] if len(
args.rivers_outline) == 1 else tuple(
int(x) for x in args.rivers_outline)
cw.add_rivers(img,
area_def,
resolution=args.rivers_resolution,
level=args.rivers_level,
width=args.rivers_width,
outline=outline)
if args.add_borders:
outline = args.borders_outline[0] if len(
args.borders_outline) == 1 else tuple(
int(x) for x in args.borders_outline)
cw.add_borders(img,
area_def,
resolution=args.borders_resolution,
level=args.borders_level,
outline=outline,
width=args.borders_width)
if args.add_grid:
outline = args.grid_outline[0] if len(
args.grid_outline) == 1 else tuple(
int(x) for x in args.grid_outline)
minor_outline = args.grid_minor_outline[0] if len(
args.grid_minor_outline) == 1 else tuple(
int(x) for x in args.grid_minor_outline)
fill = args.grid_fill[0] if len(args.grid_fill) == 1 else tuple(
int(x) for x in args.grid_fill)
font_path = find_font(args.grid_font, args.grid_text_size)
font = Font(outline, font_path, size=args.grid_text_size)
cw.add_grid(img,
area_def,
args.grid_D,
args.grid_d,
font,
fill=fill,
outline=outline,
minor_outline=minor_outline,
write_text=args.grid_text,
width=args.grid_width,
lon_placement=args.grid_lon_placement,
lat_placement=args.grid_lat_placement)
if args.add_colorbar:
from pydecorate import DecoratorAGG
font_color = args.colorbar_text_color
font_color = font_color[0] if len(font_color) == 1 else tuple(
int(x) for x in font_color)
font_path = find_font(args.colorbar_font, args.colorbar_text_size)
# this actually needs an aggdraw font
font = Font(font_color, font_path, size=args.colorbar_text_size)
band_count = gtiff.RasterCount
if band_count not in [1, 2]:
raise ValueError("Can't add colorbar to RGB/RGBA image")
# figure out what colormap we are dealing with
band = gtiff.GetRasterBand(1)
cmap = get_colormap(band, band_count)
# figure out our limits
vmin = args.colorbar_min
vmax = args.colorbar_max
metadata = gtiff.GetMetadata_Dict()
vmin = vmin or metadata.get('min_in')
vmax = vmax or metadata.get('max_in')
if isinstance(vmin, str):
vmin = float(vmin)
if isinstance(vmax, str):
vmax = float(vmax)
if vmin is None or vmax is None:
data = gtiff.GetRasterBand(1).ReadAsArray()
vmin = vmin or np.iinfo(data.dtype).min
vmax = vmax or np.iinfo(data.dtype).max
cmap.set_range(vmin, vmax)
dc = DecoratorAGG(img)
if args.colorbar_align == 'top':
dc.align_top()
elif args.colorbar_align == 'bottom':
dc.align_bottom()
elif args.colorbar_align == 'left':
dc.align_left()
elif args.colorbar_align == 'right':
dc.align_right()
if args.colorbar_vertical:
dc.write_vertically()
else:
dc.write_horizontally()
if args.colorbar_width is None or args.colorbar_height is None:
LOG.warning("'--colorbar-width' or '--colorbar-height' were "
"not specified. Forcing '--colorbar-extend'.")
args.colorbar_extend = True
kwargs = {}
if args.colorbar_width:
kwargs['width'] = args.colorbar_width
if args.colorbar_height:
kwargs['height'] = args.colorbar_height
dc.add_scale(cmap,
extend=args.colorbar_extend,
font=font,
line=font_color,
tick_marks=args.colorbar_tick_marks,
title=args.colorbar_title,
unit=args.colorbar_units,
**kwargs)
img.save(output_filename)
def plot(data, text, minmax=None, transpose=False,
filename=None, position=(-16.743860721,64.915348712,712.4),
tilt=None, target_position=None, target_name="", vfov=18.5, tick_distance=50.0):
if transpose:
data = data.transpose()
if minmax is None:
rainbow.set_range(data.min(),data.max())
else:
rainbow.set_range(minmax[0],minmax[1])
img = Image(data, mode="L")
img.colorize(rainbow)
img = img.pil_image()
# Decoration
dc = DecoratorAGG(img)
dc.align_bottom()
#dc.add_logo("/home/sat/dev/pydecorate/logos/VI_Logo_Transp.png")
#dc.add_logo("Nicarnica-Aviation-22-oct-300x102.png")
try:
dc.add_logo("/home/master/bin/futurevolc_logo.png", height=47)
dc.add_logo("/home/master/bin/nicarnica_logo.png")
dc.add_logo("/home/master/bin/vi_logo.png")
except IOError:
dc.add_logo("bin/futurevolc_logo.png", height=47)
dc.add_logo("bin/nicarnica_logo.png")
dc.add_logo("bin/vi_logo.png")
dc.add_text(text, font=font)
tick_space = 5.0
#dc.add_scale(rainbow, extend=True, unit='°C', tick_marks=tick_space, minor_tick_marks=tick_space)
# target distance and km/px
ny = data.shape[0]
nx = data.shape[1]
distance = distance_longlat(target_position,position)
# plot grid
lines, texts = prepare_graticule(nx, ny, distance=distance, vfov=vfov,
tilt=tilt, tick_distance=tick_distance,
height=position[2],target_height=target_position[2],
target_name=target_name)
draw_lines(img, lines)
draw_texts(img, texts)
if filename == None:
img.show()
else:
img.save(filename, "JPEG", quality=90)
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
def nostradamus_rain(in_msg):
if in_msg.datetime is None:
in_msg.get_last_SEVIRI_date()
if in_msg.end_date is None:
in_msg.end_date = in_msg.datetime
#in_msg.end_date = in_msg.datetime + timedelta(15)
delta = timedelta(minutes=15)
# automatic choise of the FULL DISK SERVICE Meteosat satellite
if in_msg.datetime < datetime(2008, 5, 13, 0, 0): # before 13.05.2008 only nominal MSG1 (meteosat8), no Rapid Scan Service yet
sat_nr = "08"
elif in_msg.datetime < datetime(2013, 2, 27, 9, 0): # 13.05.2008 ... 27.02.2013
sat_nr = "09" # MSG-2 (meteosat9) became nominal satellite, MSG-1 (meteosat8) started RSS
elif in_msg.datetime < datetime(2018, 3, 9, 0, 0): # 27.02.2013 9:00UTC ... 09.03.2013
sat_nr = "10" # MSG-3 (meteosat10) became nominal satellite, MSG-2 started RSS (MSG1 is backup for MSG2)
else:
sat_nr = "11"
print ("... work with Meteosat"+str(sat_nr))
print ("")
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 (' Start Date/Time: '+ str(in_msg.datetime))
print (' End Date/Time: '+ str(in_msg.datetime))
print (' Areas: ', in_msg.areas)
for area in in_msg.plots.keys():
print (' plots['+area+']: ', in_msg.plots[area])
#print (' parallax_correction: ', in_msg.parallax_correction)
#print (' reader level: ', in_msg.reader_level)
## read in all the constants files
print('=================================')
print('*** load the constant fields (radar mask, viewing geometry, and land/sea mask plus surface elevation)')
global_radar_mask, global_vg, global_ls_ele = load_constant_fields(sat_nr)
###############################################
## load the mlp for the precip detection (pd) #
###############################################
if in_msg.model == 'mlp':
dir_start_pd= './models/precipitation_detection/mlp/2hl_100100hu_10-7alpha_log/'
dir_start_rr= './models/precipitation_rate/mlp/2hl_5050hu_10-2alpha_log/'
if not in_msg.read_from_netCDF:
clf_pd = joblib.load(dir_start_pd+'clf.pkl')
scaler_pd = joblib.load(dir_start_pd+'scaler.pkl')
feature_list_pd = joblib.load(dir_start_pd+'featurelist.pkl')
thres_pd=np.load(dir_start_pd+'opt_orig_posteriorprobab_thres.npy')
#########################################
## load the mlp for the rain rates (rr) #
#########################################
reg_rr = joblib.load(dir_start_rr+'reg.pkl')
scaler_rr = joblib.load(dir_start_rr+'scaler.pkl')
feature_list_rr = joblib.load(dir_start_rr+'featurelist.pkl')
####################################
## load the reference sets for a climatological probab matching (pm) if requested
####################################
if in_msg.probab_match:
# load in the ref data sets created with the script: rr_probab_matching_create_refset.ipynb
ody_rr_ref=np.load(dir_start_rr+'pm_valid_data_ody_rr_ref.npy')
pred_rr_ref=np.load(dir_start_rr+'pm_valid_data_pred_rr_ref.npy')
# initialize processed RGBs
plots_done={}
time_slot = copy.deepcopy(in_msg.datetime)
while time_slot <= in_msg.end_date:
print('... processing for time: ', time_slot)
################################################
## CHOOSE THE SETUP (time_slot, area, model)
################################################
##########################
## LOAD THE NEEDED INPUTS
##########################
if not in_msg.read_from_netCDF:
## read observations at the specific time
print('=================================')
print('*** load the time slot specific fields with in_msg.parallax_gapfilling:', in_msg.parallax_gapfilling)
global_radar, global_sat, global_nwc, global_cth, global_hsaf = load_input(sat_nr, time_slot, in_msg.parallax_gapfilling, read_HSAF=in_msg.read_HSAF)
# def load_input(sat_nr, time_slot, par_fill, read_HSAF=True):
else:
print('read Odyssey radar composite')
from mpop.satellites import GeostationaryFactory
global_radar = GeostationaryFactory.create_scene("odyssey", "", "radar", time_slot)
global_radar.load(['RATE'])
print(global_radar)
print('=========================')
for area in in_msg.areas:
print ("================================")
print ("*** PROCESSING FOR AREA: "+area)
# declare "precipitation detection" and "rainrate dictionary", the applied model (e.g. MLP) is used as key
pd = {}
rr = {}
plots_done[area]=[]
if in_msg.read_from_netCDF:
# reproject Odyssey radar mask to area of interest
#radar_mask = global_radar_mask.project(area, precompute=True)
data_radar = global_radar.project(area, precompute=True)
# radar mask to see where odyssey ground truth exists
mask_r = data_radar['RATE-MASK'].data.data==False
rr['ody'] = copy.deepcopy(data_radar['RATE'].data.data)
# do not trust values below 0.3 & above 130 -> do not consider it as rain and set all values to 0
rr['ody'][np.logical_or(rr['ody'] < 0.3,rr['ody'] >= 130.0)] = 0.0
print (rr['ody'].min(), rr['ody'].max(), rr['ody'].shape, type(rr['ody']))
from netCDF4 import Dataset
# read from file
outdir_netCDF = time_slot.strftime('/data/COALITION2/database/meteosat/nostradamus_RR/%Y/%m/%d/')
file_netCDF = time_slot.strftime('MSG_rr-'+in_msg.model+'-'+area+'_%Y%m%d%H%M.nc')
print ("*** read precip prediction from", outdir_netCDF+"/"+file_netCDF)
ncfile = Dataset(outdir_netCDF+"/"+file_netCDF,'r')
rr_tmp = ncfile.variables['rainfall_rate'][:,:]
### now, we read radar data directly from odyssey file
#rr['ody'] = ncfile.variables['rainfall_rate (odyssey)'][:,:]
#print (rr['ody'].min(), rr['ody'].max(), rr['ody'].shape, type(rr['ody']))
### now, we read radar mask directly from odyssey file
#mask_r = ncfile.variables['odyssey_mask'][:,:]
#print ("... convert mask_r (1, 0) from int to bolean (True, False)")
#mask_r = (mask_r == 1)
# create fake mask_h (where rainfall is larger than 0 mm/h)
mask_h = rr_tmp>0
pd[in_msg.model] = rr_tmp>0
rr_tmp = rr_tmp.flatten()
# remove 0 entries
rr_tmp = rr_tmp [ rr_tmp != 0 ]
if False:
import matplotlib.pyplot as plt
#fig = plt.figure()
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(6,6))
plt.subplot(2, 1, 1)
plt.imshow(mask_h)
#plt.colorbar()
plt.subplot(2, 1, 2)
plt.imshow(mask_r)
#plt.colorbar()
fig.savefig("mask_h_mask_r_netCDF.png")
print("... display mask_h_mask_r_netCDF.png &")
#plt.show()
#quit()
else:
## project all data to the desired projection
radar_mask, vg, ls_ele, data_radar, data_sat, data_nwc, data_cth, data_hsaf = \
project_data(area, global_radar_mask, global_vg, global_ls_ele, global_radar, global_sat, global_nwc, global_cth, global_hsaf, read_HSAF=in_msg.read_HSAF)
###########################################################
## SINGLE TIME SLOT TO CARRY OUT A FULL RAIN RATE RETRIEVAL
###########################################################
# preprocess the data
# mask_h: field indicating where NWCSAF products are available & thus where predictions are carried out: True if NWCSAF products available
# mask_r: field indicating where radar products are available: True if radar product is available
# mask_rnt: field indicating where radar product available but not trustworthy: i.e. in threshold_mask, 0<rr<0.3, rr>130 overlaid: True if radar product is NOT trustworthy
all_data, all_data_names, mask_h, mask_r, mask_rnt, rr['ody'], rr['hsaf'], lon, lat = \
pd_rr_preprocess_data_single_scene( area, time_slot,
radar_mask, vg, ls_ele,
data_radar, data_sat, data_nwc, data_cth, data_hsaf,
in_msg.parallax_gapfilling, 'rr', read_HSAF=in_msg.read_HSAF)
#pd_rr_preprocess_data_single_scene( sat_nr, area, time_slot, 'nearest', 'rr', read_HSAF=False)
if False:
import matplotlib.pyplot as plt
#fig = plt.figure()
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(6,6))
plt.subplot(2, 1, 1)
plt.imshow(mask_h)
#plt.colorbar()
plt.subplot(2, 1, 2)
plt.imshow(mask_r)
#plt.colorbar()
fig.savefig("mask_h_mask_r.png")
print("... display mask_h_mask_r.png &")
#plt.show()
#quit()
del rr['hsaf'] # since not actually needed in this script
# project all data to desired projection
# ...
print('... predictions at ' + str(mask_h.sum())+' out of ' +str(mask_h.flatten().shape[0])+ ' points')
####################################
## precip detection
####################################
# create y_pd, y_hsaf_pd, X_raw_pd
y_pd_vec, y_hsaf_pd_vec, X_raw, feature_list = pd_rr_create_y_yhsaf_Xraw( all_data, all_data_names, 'pd', cut_precip=False )
del y_pd_vec, y_hsaf_pd_vec # (since not actually ever needed in this script)
if in_msg.remove_vg==True:
print('... remove viewing geometry from predictors')
feature_list = np.append(feature_list[:6],feature_list[8:])
X_raw = np.hstack([X_raw[:,:6],X_raw[:,8:]])
print(' new X_raw.shape:', X_raw.shape)
feature_list
# check features
if np.array_equal(feature_list, feature_list_pd):
print('OK, input features correspond to input features required by the loaded model')
else:
print('ATTENTION, input features do not correspond to input features required by the loaded model')
quit()
# create X_pd
X_pd=scaler_pd.transform(X_raw)
# create final precip detection fields: opera + hsaf
pd['ody']=rr['ody']>=0.3
# make precip detection predictions
print ("*** make precip detection predictions")
pd_probab = clf_pd.predict_proba(X_pd)[:,1] # probab precip balanced classes
pd_vec_h = pd_probab>=thres_pd
pd[in_msg.model] = np.zeros(lon.shape,dtype=bool)
pd[in_msg.model][mask_h] = pd_vec_h
####################################
## rain rate on above identified precipitating pixels
####################################
# reduce X_raw to the points where rain was predicted by the mlp
X_raw= X_raw[pd_vec_h,:]
# check, if read features correspond to the trained model
if np.array_equal(feature_list, feature_list_rr):
print('OK, input features correspond to input features required by the loaded model')
else:
print('ATTENTION, input features do not correspond to input features required by the loaded model')
quit()
# create X_rr
X_rr=scaler_rr.transform(X_raw)
# rain rate prediction at places where precip detected by mlp
rr_tmp=reg_rr.predict(X_rr)
# carry out a probability machting if requested
if in_msg.probab_match:
print("... do probability matching for:", in_msg.model)
pm_str = str(in_msg.model)+'_pm'
rr_tmp_pm = probab_match_rr_refprovide(ody_rr_ref,pred_rr_ref,rr_tmp)
#rr[pm_str] = np.zeros_like(lon)
rr[pm_str] = np.zeros_like(rr['ody']) # also casts the type float
rr[pm_str][pd[in_msg.model]]=rr_tmp_pm
print("... probability matching done for:", in_msg.model)
# copy rainrate data to the final place
# replace all prediction lower than precipitation detection threshold with threhold rain rate
rr_tmp[rr_tmp<0.3]=0.3 # correct upward all too low predictions (i.e. the ones below the precip detection threshold)
rr[in_msg.model] = np.zeros_like(rr['ody'])
rr[in_msg.model][pd[in_msg.model]]=rr_tmp
#####################################
## SAVE RESULT AS NETCDF
#####################################
if area in in_msg.save_netCDF and (not in_msg.read_from_netCDF):
outdir_netCDF = time_slot.strftime(in_msg.outdir_netCDF)
file_netCDF = time_slot.strftime(in_msg.file_netCDF)
file_netCDF = file_netCDF.replace("%(area)s", area)
file_netCDF = file_netCDF.replace("%(model)s", in_msg.model)
#save_RR_as_netCDF(outdir_netCDF, file_netCDF, rr[in_msg.model], save_rr_ody=True, rr_ody=rr['ody'], save_ody_mask=True, ody_mask=mask_r, zlib=True)
save_RR_as_netCDF(outdir_netCDF, file_netCDF, rr[in_msg.model])
#####################################
## SINGLE TIME SLOT TO DRAW THE MAPS
#####################################
print ("*** start to create plots")
####################################
## plot precip detection
####################################
if 'pdMlp' in in_msg.plots[area]:
mask_rt = np.logical_and(mask_r, mask_rnt==False) # trusted radar i.e. True where I have a trustworthy radar product available
mod_ss = [in_msg.model] + ['ody']
# ver for verification;
ver={}
for x in mod_ss:
ver[x]=np.zeros_like(lon) # sat: no
ver[x][pd[x]>0] = 1 # sat: yes
ver[x][np.logical_and(ver[x]==0,mask_rnt)] = 2 # sat: no (rad clutter)
ver[x][np.logical_and(ver[x]==1,mask_rnt)] = 3 # sat: yes (rad clutter)
ver[x][np.logical_and(mask_rt,np.logical_and(pd[x]==1,pd['ody']==1))] = 4 # hit
ver[x][np.logical_and(mask_rt,np.logical_and(pd[x]==1,pd['ody']==0))] = 5 # false alarm
ver[x][np.logical_and(mask_rt,np.logical_and(pd[x]==0,pd['ody']==0))] = 6 # correct reject
ver[x][np.logical_and(mask_rt,np.logical_and(pd[x]==0,pd['ody']==1))] = 7 # miss
# define colorkey
v_pd=np.array([-0.5,0.5,1.5,2.5,3.5,4.5,5.5,6.5,7.5])
cmap_pd, norm_pd = from_levels_and_colors(v_pd,
colors =['darkgrey', '#984ea3','lightgrey','plum', '#377eb8', '#e41a1c','ivory','#ff7f00'],
extend='neither')
plot_precipitation_detection=False
if plot_precipitation_detection:
# single prediction plot
#fig,ax= plt.subplots(figsize=(20, 10))
#plt.rcParams.update({'font.size': 16})
fig,ax= plt.subplots(figsize=(10, 5))
plt.rcParams.update({'font.size': 8})
plt.rcParams.update({'mathtext.default':'regular'})
m = map_plot(axis=ax,area=area)
m.ax.set_title('precip detection based on sat vs opera')
# plot sat precip detection product against opera product
tick_label_pd_nr=np.array([0,1,2,3,4,5,6,7])
tick_label_pd=['sat: no','sat: yes','sat: no (rad unr)','sat: yes (rad unr)','hit','false alarm','correct reject','miss']
im=m.pcolormesh( lon, lat, ver['mlp'], cmap=cmap_pd, norm=norm_pd, latlon=True )
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="4%", pad=0.05)
cbar = fig.colorbar(im, cax=cax, ticks=tick_label_pd_nr, spacing='uniform')
a=cbar.ax.set_yticklabels(tick_label_pd)
outfile= 'precip_detection_sat'+in_msg.model+'_vs_opera_%s'
fig.savefig((in_msg.outputDir+outfile %time_slot.strftime('%Y%m%d%H%M')), dpi=300, bbox_inches='tight')
print('... create figure: display ' + in_msg.outputDir+outfile %time_slot.strftime('%Y%m%d%H%M') + '.png')
plots_done[area].append('pdMlp')
####################################
## plot rain rate with matplotlib
####################################
if 'rrMatplotlib' in in_msg.plots[area]:
# create the combi rr field
rr['combi']=copy.deepcopy(rr[in_msg.model+'_pm'])
rr['combi'][mask_r]=rr['ody'][mask_r]
# determine where I have >0.3 mm/h precip on the permanent mask -> overlay end picture with a pink(?) color there
pd_nt=np.logical_and(mask_rnt,pd['ody']>=0.3) #precip detected but not trusted
t = time.time()
#fig, axes = plt.subplots(1, 2,figsize=(19, 6))
#plt.rcParams.update({'font.size': 16})
fig, axes = plt.subplots(1, 2,figsize=(9.5, 3))
plt.rcParams.update({'font.size': 8})
plt.rcParams.update({'mathtext.default':'regular'})
## 1st subplot
m = map_plot(axis=axes[0],area=area)
m.ax.set_title('Rain Rate (opera + MSG ANN), '+str(time_slot))
# plot a white colored background where I have data available
v_pd_nt=np.array([0.5,1.5])
cmap_pd_nt, norm_pd_nt = from_levels_and_colors(v_pd_nt, colors=['white'], extend='neither')
im4=m.pcolormesh(lon,lat,np.ones(lon.shape),cmap=cmap_pd_nt,norm=norm_pd_nt,latlon=True)
# plot mask which contains no rad & not trusted rad values
nr_ntr = copy.deepcopy(ver['ody'])
nr_ntr=np.ma.masked_greater(nr_ntr,2)
nr_ntr=np.ma.masked_equal(nr_ntr,1)
im2=m.pcolormesh(lon,lat,nr_ntr,cmap=cmap_pd,norm=norm_pd,latlon=True)
# plot combined precip opera + sat
v_rr = [0.3,0.6,1.2,2.4,4.8,9.6]
cmap_rr,norm_rr=smart_colormap(v_rr,name='coolwarm',extend='max')
im=m.pcolormesh(lon,lat,rr['combi'],cmap=cmap_rr,norm=norm_rr,latlon=True)
# plot pink pixels everywhere on permanently not trusted radar mask where we observe > 0.3 mm/h precip
v_pd_nt=np.array([0.5,1.5])
cmap_pd_nt, norm_pd_nt = from_levels_and_colors(v_pd_nt, colors=['plum'], extend='neither')
im3=m.pcolormesh(lon,lat,pd_nt,cmap=cmap_pd_nt,norm=norm_pd_nt,latlon=True)
## 2nd subplot
# plot purely satellite based precip product
m = map_plot(axis=axes[1],area=area)
m.ax.set_title('Rain Rate (MSG ANN), '+str(time_slot))
if in_msg.IR_108 and not in_msg.read_from_netCDF:
# plot the IR_108 channel
clevs = np.arange(225,316,10)
cmap_sat,norm_sat=smart_colormap(clevs,name='Greys',extend='both')
im4 = m.pcolormesh(lon,lat,data_sat['IR_108_PC'].data,cmap=cmap_sat,norm=norm_sat,latlon=True)
else:
# plot a white surface to distinguish between the regions where the produ
v_pd_nt=np.array([0.5,1.5])
cmap_pd_nt, norm_pd_nt = from_levels_and_colors(v_pd_nt, colors=['white'], extend='neither')
im4=m.pcolormesh(lon,lat,np.ones(lon.shape),cmap=cmap_pd_nt,norm=norm_pd_nt,latlon=True)
if in_msg.probab_match:
im=m.pcolormesh(lon, lat,rr[in_msg.model+'_pm'], cmap=cmap_rr, norm=norm_rr, latlon=True)
else:
im=m.pcolormesh(lon, lat,rr[in_msg.model], cmap=cmap_rr, norm=norm_rr, latlon=True)
if in_msg.IR_108 and in_msg.probab_match:
outfile= 'rr_combioperasat'+in_msg.model+'pm_satIR108'+in_msg.model+'pm_%s'
elif in_msg.IR_108 and (in_msg.probab_match==False):
outfile= 'rr_combioperasat'+in_msg.model+'_satIR108'+in_msg.model+'_%s'
elif (in_msg.IR_108==False) and in_msg.probab_match:
outfile= 'rr_combioperasat'+in_msg.model+'pm_sat'+in_msg.model+'pm_%s'
elif (in_msg.IR_108==False) and (in_msg.probab_match==False):
outfile= 'rr_combioperasat'+in_msg.model+'_sat'+in_msg.model+'_%s'
fig.subplots_adjust(bottom=0.15)
cbar_ax = fig.add_axes([0.25, 0.05, 0.5, 0.05])
cbar=fig.colorbar(im, cax=cbar_ax, orientation='horizontal')
cbar.set_label('$mm\,h^{-1}$')
fig.savefig((in_msg.outputDir+outfile %time_slot.strftime('%Y%m%d%H%M')), dpi=300, bbox_inches='tight')
print('... create figure: display ' + in_msg.outputDir+outfile %time_slot.strftime('%Y%m%d%H%M') + '.png')
elapsed = time.time() - t
print("... elapsed time for creating the rainrate image in seconds: "+str(elapsed))
plots_done[area].append('rrMatplotlib')
####################################
## plot rain rate with trollimage
####################################
plot_trollimage=True
if plot_trollimage:
from plotting_tools import create_trollimage
from plot_msg import add_title
print ("*** create plot with trollimage")
from copy import deepcopy
from trollimage.colormap import RainRate
colormap = deepcopy(RainRate)
# define contour write for coasts, borders, rivers
from pycoast import ContourWriterAGG
cw = ContourWriterAGG(in_msg.mapDir)
from plot_msg import choose_map_resolution
resolution = choose_map_resolution(area, None)
#resolution='l' # odyssey, europe
#resolution='i' # ccs4
print (" resolution=", resolution)
IR_file=time_slot.strftime(in_msg.outputDir+'MSG_IR-108-'+area+'_%Y%m%d%H%M.png')
if 'IR_108' in in_msg.plots[area] and not in_msg.read_from_netCDF:
# create black white background
#img_IR_108 = data_sat.image.channel_image('IR_108_PC')
img_IR_108 = data_sat.image.ir108()
img_IR_108.save(IR_file)
for rgb in in_msg.plots[area]:
if rgb == 'RATE':
prop = np.ma.masked_equal(rr['ody'], 0)
mask2plot=deepcopy(mask_r)
elif rgb =='rrMlp':
prop = np.ma.masked_equal(rr[in_msg.model], 0)
mask2plot=None
elif rgb == 'rrMlpPm':
prop = np.ma.masked_equal(rr[in_msg.model+'_pm'], 0)
mask2plot=None
elif rgb == 'rrOdyMlp':
rr['combi']=copy.deepcopy(rr[in_msg.model])
rr['combi'][mask_r]=rr['ody'][mask_r]
prop = np.ma.masked_equal(rr['combi'], 0)
mask2plot=deepcopy(mask_r)
elif rgb == 'rrOdyMlpPm':
rr['combi']=copy.deepcopy(rr[in_msg.model+'_pm'])
rr['combi'][mask_r] = rr['ody'][mask_r]
prop = np.ma.masked_equal(rr['combi'], 0)
mask2plot=deepcopy(mask_r)
elif rgb == 'IR_108':
continue
else:
"*** Error, unknown product requested"
quit()
filename = None
if area in in_msg.postprocessing_composite:
composite_file = in_msg.outputDir+"/"+'MSG_'+in_msg.postprocessing_composite[area][0]+"-"+area+'_%Y%m%d%H%M.png'
composite_file = composite_file.replace("%(rgb)s", rgb)
else:
composite_file = None
PIL_image = create_trollimage(rgb, prop, colormap, cw, filename, time_slot, area, composite_file=composite_file,
background=IR_file, mask=mask2plot, resolution=resolution, scpOutput=in_msg.scpOutput)
# add title to image
dc = DecoratorAGG(PIL_image)
if in_msg.add_title:
add_title(PIL_image, in_msg.title, rgb, 'MSG', sat_nr, in_msg.datetime, area, dc, in_msg.font_file, True,
title_color=in_msg.title_color, title_y_line_nr=in_msg.title_y_line_nr ) # !!! needs change
# save image as file
outfile = time_slot.strftime(in_msg.outputDir+"/"+in_msg.outputFile).replace("%(rgb)s", rgb).replace("%(area)s", area).replace("%(model)s", in_msg.model)
PIL_image.save(outfile, optimize=True)
if isfile(outfile):
print ("... create figure: display "+outfile+" &")
chmod(outfile, 0777)
plots_done[area].append(rgb)
else:
print ("*** Error: "+outfile+" could not be generated")
quit()
print('=================================')
##############################################
## potential other map setups
##############################################
##############################################
##############################################
## opera composite vs the prediction... but I think it'd be less confusing to only show the prediction
##############################################
if 'OdyVsRr' in in_msg.plots[area]:
fig, axes = plt.subplots(1, 2,figsize=(23.5, 5))
# will be switched to basemap once have new training set together
plt.rcParams.update({'font.size': 16})
plt.rcParams.update({'mathtext.default':'regular'})
# set up nn subplot
m = map_plot(axis=axes[0],area=area)
m.ax.set_title('precip detection based on mlp vs opera')
v_pd=np.array([-0.5,0.5,1.5,2.5,3.5,4.5,5.5,6.5,7.5])
cmap_pd, norm_pd = from_levels_and_colors(v_pd, colors=['darkgrey', '#984ea3','lightgrey','plum', '#377eb8', '#e41a1c','ivory','#ff7f00'], extend='neither')
tick_label_pd_nr=np.array([0,1,2,3,4,5,6,7])
tick_label_pd=['sat: no','sat: yes','sat: no (rad unr)','sat: yes (rad unr)','hit','false alarm','correct reject','miss']
im=m.pcolormesh(lon,lat,ver['mlp'],cmap=cmap_pd, norm=norm_pd, latlon=True)
divider = make_axes_locatable(m.ax)
cax = divider.append_axes("right", size="4%", pad=0.05)
cbar = fig.colorbar(im,cax=cax, ticks=tick_label_pd_nr, spacing='uniform')
cbar.ax.set_yticklabels(tick_label_pd, fontsize=14)
m = map_plot(axis=axes[1],area=area)
m.ax.set_title('precip detection based on opera vs opera')
v_pd=np.array([-0.5,0.5,2.5,3.5,4.5,6.5])
cmap_pd, norm_pd = from_levels_and_colors(v_pd, colors=['darkgrey','lightgrey','plum', '#377eb8','ivory'], extend='neither')
tick_label_pd_nr=np.array([0,1.5,3,4,5.5])
tick_label_pd=['no rad','rad clutter: no','rad clutter: yes','rad: yes','rad: no']
im=m.pcolormesh(lon,lat,ver['ody'],cmap=cmap_pd,norm=norm_pd,latlon=True)
divider = make_axes_locatable(m.ax)
cax = divider.append_axes("right", size="4%", pad=0.05)
cbar = fig.colorbar(im,cax=cax,ticks=tick_label_pd_nr,spacing='uniform')
a=cbar.ax.set_yticklabels(tick_label_pd,fontsize=14)
outfile= 'test_%s'
fig.savefig((in_msg.outputDir+ outfile %time_slot.strftime('%Y%m%d%H%M')), dpi=300, bbox_inches='tight')
print('... create figure: display ' + in_msg.outputDir+outfile %time_slot.strftime('%Y%m%d%H%M') + '.png')
plots_done[area].append('OdyVsRr')
##############################################
## cth visualisation without parallax corr for a test
##############################################
if 'CTH' in in_msg.plots[area]:
fig, axes = plt.subplots(1, 1,figsize=(5, 3))
plt.rcParams.update({'font.size': 16})
plt.rcParams.update({'mathtext.default':'regular'})
## 1st subplot
m = map_plot(axis=axes,area=area)
m.ax.set_title('CTH (without parallax corr)')
v_rr = np.arange(6000,12001,1000)
cmap_rr,norm_rr=smart_colormap(v_rr,name='coolwarm',extend='neither')
im4 = m.pcolormesh(lon,lat,data_cth['CTTH'].height,cmap=cmap_rr,norm=norm_rr,latlon=True)
fig.colorbar(im4)
data_cth['CTTH'].height
outfile= 'CTH_without_parallax_%s'
fig.savefig((in_msg.outputDir+ outfile %time_slot.strftime('%Y%m%d%H%M')), dpi=300, bbox_inches='tight')
print('... create figure: display ' + in_msg.outputDir+outfile %time_slot.strftime('%Y%m%d%H%M') + '.png')
plots_done[area].append('CTH')
# end of area loop
## start postprocessing
for area in in_msg.postprocessing_areas:
postprocessing(in_msg, time_slot, int(sat_nr), area)
# increase the time by a time delta
time_slot += delta
# end of time loop
return plots_done
from PIL import Image
from pydecorate import DecoratorAGG
import aggdraw
font=aggdraw.Font("navy","/usr/share/fonts/truetype/ttf-dejavu/DejaVuSerif.ttf",size=20)
font_scale=aggdraw.Font("black","/usr/share/fonts/truetype/ttf-dejavu/DejaVuSerif.ttf",size=12)
from trollimage.colormap import rdbu
rdbu.colors = rdbu.colors[::-1]
rdbu.set_range(-90, 10)
print type(rdbu)
img = Image.open('BMNG_clouds_201109181715_areaT2.png')
dc = DecoratorAGG(img)
#dc.write_vertically()
#dc.add_logo("logos/pytroll_light_big.png")
#dc.add_logo("logos/NASA_Logo.gif",margins=[10,10],bg='yellow')
#dc.add_logo("logos/pytroll_light_big.png")
font=aggdraw.Font("blue","/usr/share/fonts/truetype/ttf-dejavu/DejaVuSerif.ttf",size=16)
#dc.add_text("Some text",font=font)
#dc.align_right()
print rdbu.values
print rdbu.colors
dc.add_scale(rdbu, extend=True, tick_marks=5.0, line_opacity=100, unit='K')
def show_swath_pycoast(self, start, period=None):
"""
A helper method that displays the orbital swath
starting at datetime start, for a period number of minutes.
If, start is iterable, then the method assumes it is an iterable
of datetimes, plotting a number of swaths at those times.
"""
# test if start is iterable, EAFP style:
try:
for e in start:
pass
except TypeError:
start = [start]
start.sort()
from PIL import Image
from pycoast import ContourWriterAGG
from pydecorate import DecoratorAGG
img = Image.new('RGB', (650, 650))
proj4_string = ""
for x in self.working_projection:
proj4_string += "+%s=%s " % (x, self.working_projection[x])
area_extent = (-6700000.0, -6700000.0, 6700000.0, 6700000.0)
area_def = (proj4_string, area_extent)
cw = ContourWriterAGG()
cw.add_grid(img,
area_def, (10.0, 10.0), (2.0, 2.0),
fill='blue',
outline='gray',
outline_opacity=130,
minor_outline=None,
write_text=False)
# Plot granules
for t in start:
# fetch the coordinates
xys_segs = self.swath_working_projection(t, period)
for xys in xys_segs:
lls = self.proj(xys[0], xys[1], inverse=True)
cw.add_polygon(img,
area_def,
zip(lls[0], lls[1]),
outline="blue",
fill="blue",
fill_opacity=70,
width=1)
cw.add_coastlines(img, area_def, resolution='l')
aoi_coords = zip(*self.aoi)
## TODO: Handle single point case properly
if len(aoi_coords) == 1:
x, y = aoi_coords[0]
d = 0.5
line_coords = [(x - d, y), (x + d, y)]
cw.add_line(img,
area_def,
line_coords,
outline="red",
fill="red",
fill_opacity=100,
width=2)
elif len(aoi_coords) == 2:
cw.add_line(img,
area_def,
aoi_coords,
outline="red",
fill="red",
fill_opacity=100,
width=10)
else:
cw.add_polygon(img,
area_def,
aoi_coords,
outline="red",
fill="red",
fill_opacity=100,
width=2)
# Decorate
dc = DecoratorAGG(img)
text = "Granules from time: %s + %.2f min." % (
start[0].strftime('%Y.%m.%d %H:%M:%S'),
(start[-1] - start[0]).total_seconds() / 60.0)
dc.align_bottom()
dc.add_text(text, height=0)
img.show()
# colormap.set_range(min_data, max_data) # no return value!
#PIL_image=img.pil_image()
if add_borders:
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)
#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, rgb, int(data.number), dateS, hourS, minS,
area, dc, in_msg.font_file, in_msg.verbose)
# create output filename
outputDir = format_name(in_msg.outputDir,
data.time_slot,
area=area,
rgb=rgb,
sat_nr=data.number)
outputFile = outputDir + format_name(
in_msg.outputFile + 'p' + str(it * delta_t).zfill(2) + '.png',
data.time_slot,
import aggdraw
font = aggdraw.Font("navy",
"/usr/share/fonts/truetype/ttf-dejavu/DejaVuSerif.ttf",
size=20)
font_scale = aggdraw.Font(
"black", "/usr/share/fonts/truetype/ttf-dejavu/DejaVuSerif.ttf", size=12)
from trollimage.colormap import rdbu
rdbu.colors = rdbu.colors[::-1]
rdbu.set_range(-90, 10)
print type(rdbu)
img = Image.open('BMNG_clouds_201109181715_areaT2.png')
dc = DecoratorAGG(img)
#dc.write_vertically()
#dc.add_logo("logos/pytroll_light_big.png")
#dc.add_logo("logos/NASA_Logo.gif",margins=[10,10],bg='yellow')
#dc.add_logo("logos/pytroll_light_big.png")
font = aggdraw.Font("blue",
"/usr/share/fonts/truetype/ttf-dejavu/DejaVuSerif.ttf",
size=16)
#dc.add_text("Some text",font=font)
#dc.align_right()
print rdbu.values
print rdbu.colors
dc.add_scale(rdbu, extend=True, tick_marks=5.0, line_opacity=100, unit='K')
def main():
parser = get_parser()
args = parser.parse_args()
levels = [logging.ERROR, logging.WARN, logging.INFO, logging.DEBUG]
logging.basicConfig(level=levels[min(3, args.verbosity)])
if args.output_filename is None:
args.output_filename = [x[:-3] + "png" for x in args.input_tiff]
else:
assert len(args.output_filename) == len(args.input_tiff), "Output filenames must be equal to number of input tiffs"
if not (args.add_borders or args.add_coastlines or args.add_grid or
args.add_rivers or args.add_colorbar):
LOG.error("Please specify one of the '--add-X' options to modify the image")
return -1
# we may be dealing with large images that look like decompression bombs
# let's turn off the check for the image size in PIL/Pillow
Image.MAX_IMAGE_PIXELS = None
for input_tiff, output_filename in zip(args.input_tiff, args.output_filename):
LOG.info("Creating {} from {}".format(output_filename, input_tiff))
gtiff = gdal.Open(input_tiff)
proj4_str = osr.SpatialReference(gtiff.GetProjection()).ExportToProj4()
ul_x, res_x, _, ul_y, _, res_y = gtiff.GetGeoTransform()
half_pixel_x = res_x / 2.
half_pixel_y = res_y / 2.
area_extent = (
ul_x - half_pixel_x, # lower-left X
ul_y + res_y * gtiff.RasterYSize - half_pixel_y, # lower-left Y
ul_x + res_x * gtiff.RasterXSize + half_pixel_x, # upper-right X
ul_y + half_pixel_y, # upper-right Y
)
img = Image.open(input_tiff).convert('RGB')
area_def = (proj4_str, area_extent)
cw = ContourWriterAGG(args.shapes_dir)
if args.add_coastlines:
outline = args.coastlines_outline[0] if len(args.coastlines_outline) == 1 else tuple(int(x) for x in args.coastlines_outline)
if args.coastlines_fill:
fill = args.coastlines_fill[0] if len(args.coastlines_fill) == 1 else tuple(int(x) for x in args.coastlines_fill)
else:
fill = None
cw.add_coastlines(img, area_def, resolution=args.coastlines_resolution, level=args.coastlines_level,
outline=outline, fill=fill)
if args.add_rivers:
outline = args.rivers_outline[0] if len(args.rivers_outline) == 1 else tuple(int(x) for x in args.rivers_outline)
cw.add_rivers(img, area_def,
resolution=args.rivers_resolution, level=args.rivers_level,
outline=outline)
if args.add_borders:
outline = args.borders_outline[0] if len(args.borders_outline) == 1 else tuple(int(x) for x in args.borders_outline)
cw.add_borders(img, area_def, resolution=args.borders_resolution, level=args.borders_level, outline=outline)
if args.add_grid:
outline = args.grid_outline[0] if len(args.grid_outline) == 1 else tuple(int(x) for x in args.grid_outline)
minor_outline = args.grid_minor_outline[0] if len(args.grid_minor_outline) == 1 else tuple(int(x) for x in args.grid_minor_outline)
fill = args.grid_fill[0] if len(args.grid_fill) == 1 else tuple(int(x) for x in args.grid_fill)
font_path = find_font(args.grid_font, args.grid_text_size)
font = Font(outline, font_path, size=args.grid_text_size)
cw.add_grid(img, area_def, args.grid_D, args.grid_d, font,
fill=fill, outline=outline, minor_outline=minor_outline,
write_text=args.grid_text,
lon_placement=args.grid_lon_placement,
lat_placement=args.grid_lat_placement)
if args.add_colorbar:
from pydecorate import DecoratorAGG
font_color = args.colorbar_text_color
font_color = font_color[0] if len(font_color) == 1 else tuple(int(x) for x in font_color)
font_path = find_font(args.colorbar_font, args.colorbar_text_size)
# this actually needs an aggdraw font
font = Font(font_color, font_path, size=args.colorbar_text_size)
band_count = gtiff.RasterCount
if band_count not in [1, 2]:
raise ValueError("Can't add colorbar to RGB/RGBA image")
# figure out what colormap we are dealing with
band = gtiff.GetRasterBand(1)
cmap = get_colormap(band, band_count)
# figure out our limits
vmin = args.colorbar_min
vmax = args.colorbar_max
metadata = gtiff.GetMetadata_Dict()
vmin = vmin or metadata.get('min_in')
vmax = vmax or metadata.get('max_in')
if isinstance(vmin, str):
vmin = float(vmin)
if isinstance(vmax, str):
vmax = float(vmax)
if vmin is None or vmax is None:
data = gtiff.GetRasterBand(1).ReadAsArray()
vmin = vmin or np.iinfo(data.dtype).min
vmax = vmax or np.iinfo(data.dtype).max
cmap.set_range(vmin, vmax)
dc = DecoratorAGG(img)
if args.colorbar_align == 'top':
dc.align_top()
elif args.colorbar_align == 'bottom':
dc.align_bottom()
elif args.colorbar_align == 'left':
dc.align_left()
elif args.colorbar_align == 'right':
dc.align_right()
if args.colorbar_vertical:
dc.write_vertically()
else:
dc.write_horizontally()
if args.colorbar_width is None or args.colorbar_height is None:
LOG.warning("'--colorbar-width' or '--colorbar-height' were "
"not specified. Forcing '--colorbar-extend'.")
args.colorbar_extend = True
kwargs = {}
if args.colorbar_width:
kwargs['width'] = args.colorbar_width
if args.colorbar_height:
kwargs['height'] = args.colorbar_height
dc.add_scale(cmap, extend=args.colorbar_extend,
font=font,
line=font_color,
tick_marks=args.colorbar_tick_marks,
title=args.colorbar_title,
unit=args.colorbar_units,
**kwargs)
img.save(output_filename)
def savefig(fname, loc=1, decorate=True, **kwargs):
import io
import os
import sys
from PIL import Image
import matplotlib.pyplot as plt
try:
from pydecorate import DecoratorAGG
pydecorate = True
except ImportError:
pydecorate = False
plt.savefig(fname, **kwargs)
if pydecorate and decorate:
img = Image.open(fname)
dc = DecoratorAGG(img)
if loc == 1:
dc.align_bottom()
elif loc == 2:
dc.align_bottom()
dc.align_right()
elif loc == 3:
dc.align_right()
# sys.argv[0])[-5] + 'data/MONET_logo.png'
# print(os.path.basename(__file__))
logo = os.path.abspath(__file__)[:-17] + 'data/MONET-logo.png'
# print(logo)
dc.add_logo(logo)
if fname.split('.')[-1] == 'png':
img.save(fname, "PNG")
elif fname.split('.')[-1] == 'jpg':
img.save(fname, "JPEG")
def main():
parser = get_parser()
args = parser.parse_args()
levels = [logging.ERROR, logging.WARN, logging.INFO, logging.DEBUG]
logging.basicConfig(level=levels[min(3, args.verbosity)])
if args.output_filename is None:
args.output_filename = [x[:-3] + "png" for x in args.input_tiff]
else:
assert len(args.output_filename) == len(
args.input_tiff
), "Output filenames must be equal to number of input tiffs"
if not (args.add_borders or args.add_coastlines or args.add_grid or args.add_rivers or args.add_colorbar):
LOG.error("Please specify one of the '--add-X' options to modify the image")
return -1
if args.cache_dir and not os.path.isdir(args.cache_dir):
LOG.info(f"Creating cache directory: {args.cache_dir}")
os.makedirs(args.cache_dir, exist_ok=True)
# we may be dealing with large images that look like decompression bombs
# let's turn off the check for the image size in PIL/Pillow
Image.MAX_IMAGE_PIXELS = None
# gather all options into a single dictionary that we can pass to pycoast
pycoast_options = _args_to_pycoast_dict(args)
for input_tiff, output_filename in zip(args.input_tiff, args.output_filename):
LOG.info("Creating {} from {}".format(output_filename, input_tiff))
img = Image.open(input_tiff)
img_bands = img.getbands()
num_bands = len(img_bands)
# P = palette which we assume to be an RGBA colormap
img = img.convert("RGBA" if num_bands in (2, 4) or "P" in img_bands else "RGB")
if pycoast_options:
area_id = os.path.splitext(input_tiff[0])[0]
area_def = get_area_def_from_raster(input_tiff, area_id=area_id)
cw = ContourWriterAGG(args.shapes_dir)
cw.add_overlay_from_dict(pycoast_options, area_def, background=img)
if args.add_colorbar:
from pydecorate import DecoratorAGG
font_color = args.colorbar_text_color
font_color = font_color[0] if len(font_color) == 1 else tuple(int(x) for x in font_color)
font_path = find_font(args.colorbar_font, args.colorbar_text_size)
# this actually needs an aggdraw font
font = Font(font_color, font_path, size=args.colorbar_text_size)
if num_bands not in (1, 2):
raise ValueError("Can't add colorbar to RGB/RGBA image")
# figure out what colormap we are dealing with
rio_ds = rasterio.open(input_tiff)
input_dtype = np.dtype(rio_ds.meta["dtype"])
rio_ct = _get_rio_colormap(rio_ds, 1)
cmap = get_colormap(input_dtype, rio_ct, num_bands)
# figure out our limits
vmin = args.colorbar_min
vmax = args.colorbar_max
metadata = rio_ds.tags()
vmin = vmin or metadata.get("min_in")
vmax = vmax or metadata.get("max_in")
if isinstance(vmin, str):
vmin = float(vmin)
if isinstance(vmax, str):
vmax = float(vmax)
if vmin is None or vmax is None:
vmin = vmin or np.iinfo(input_dtype).min
vmax = vmax or np.iinfo(input_dtype).max
cmap.set_range(vmin, vmax)
dc = DecoratorAGG(img)
if args.colorbar_align == "top":
dc.align_top()
elif args.colorbar_align == "bottom":
dc.align_bottom()
elif args.colorbar_align == "left":
dc.align_left()
elif args.colorbar_align == "right":
dc.align_right()
if args.colorbar_vertical:
dc.write_vertically()
else:
dc.write_horizontally()
if args.colorbar_width is None or args.colorbar_height is None:
LOG.warning(
"'--colorbar-width' or '--colorbar-height' were " "not specified. Forcing '--colorbar-extend'."
)
args.colorbar_extend = True
kwargs = {}
if args.colorbar_width:
kwargs["width"] = args.colorbar_width
if args.colorbar_height:
kwargs["height"] = args.colorbar_height
dc.add_scale(
cmap,
extend=args.colorbar_extend,
font=font,
line=font_color,
tick_marks=args.colorbar_tick_marks,
title=args.colorbar_title,
unit=args.colorbar_units,
**kwargs,
)
img.save(output_filename)
colorscale = False
black_vel = True
PIL_image = TRTimage(
global_data.traj_IDs, global_data.TRT,
obj_area) # , fill=False, minRank=8, alpha_max=1.0, plot_vel=True
#PIL_image = TRTimage( global_data.traj_IDs, global_data.TRT, obj_area, minRank=15.01) # minRank=8, alpha_max=1.0, plot_vel=True
#PIL_image = TRTimage( global_data.traj_IDs, global_data.TRT, obj_area, TRTcell_ID="2018080113300129", minRank=3) # minRank=8, alpha_max=1.0, plot_vel=True
#PIL_image = TRTimage( global_data.traj_IDs, global_data.TRT, obj_area, TRTcell_ID="2018080721300099", minRank=3) # minRank=8, alpha_max=1.0, plot_vel=True
#PIL_image = TRTimage( global_data.traj_IDs, global_data.TRT, obj_area, TRTcell_ID="2018080710200036", minRank=-1, plot_nowcast=True, fill=True, Rank_predicted=Rank_predicted) # minRank=3, minRank=8, alpha_max=1.0, plot_vel=True
#PIL_image = TRTimage( global_data.traj_IDs, global_data.TRT, obj_area, fill=False) # minRank=8, alpha_max=1.0, plot_vel=True
#PIL_image = TRTimage( global_data.traj_IDs, global_data.TRT, obj_area, plot_nowcast=True) # minRank=8, alpha_max=1.0, plot_vel=True
# create decorator
dc = DecoratorAGG(PIL_image)
draw = ImageDraw.Draw(PIL_image)
if colorscale:
print('... add colorscale ranging from min_data (', min_data,
') to max_data (', max_data, ')')
dc.align_right()
dc.write_vertically()
font_scale = aggdraw.Font(
"black",
"/usr/share/fonts/truetype/ttf-dejavu/DejaVuSerif-Bold.ttf",
size=16)
colormap_r.set_range(min_data, max_data)
dc.add_scale(colormap_r,
extend=True,
tick_marks=tick_marks,
#print colorscale[1:,4], type(colorscale[0,:])
#print colorscale[1:,1:4]
else:
print("*** ERROR, unknown color mode")
print('... use trollimage to ', method,' plot data (min,max)=',min_data, max_data)
if 'fill_value' in locals():
img = trollimage(prop, mode="L", fill_value=fill_value)
else:
img = trollimage(prop, mode="L")
img.colorize(colormap)
PIL_image=img.pil_image()
dc = DecoratorAGG(PIL_image)
# define contour write for coasts, borders, rivers
cw = ContourWriterAGG('/data/OWARNA/hau/maps_pytroll/')
resolution='l'
if area.find("EuropeCanary") != -1:
resolution='l'
if area.find("ccs4") != -1:
resolution='i'
if area.find("ticino") != -1:
resolution='h'
# define area
print('obj_area ', obj_area)
proj4_string = obj_area.proj4_string
if prop_str == 'ACRR':
min_data = 0
max_data = 250
lower_value = 0.15
if lower_value > -1000:
prop[prop < lower_value] = np.ma.masked
LOG.debug("min_data/max_data: " + str(min_data) + " / " + str(max_data))
colormap.set_range(min_data, max_data)
img = trollimage(prop, mode="L", fill_value=fill_value)
img.colorize(colormap)
PIL_image = img.pil_image()
dc = DecoratorAGG(PIL_image)
resolution = 'l'
if False:
cw = ContourWriterAGG('/data/OWARNA/hau/pytroll/shapes/')
cw.add_coastlines(PIL_image,
area_def,
outline='white',
resolution=resolution,
outline_opacity=127,
width=1,
level=2) #, outline_opacity=0
outline = (255, 0, 0)
outline = 'red'
def plot_msg(in_msg):
# get date of the last SEVIRI observation
if in_msg.datetime == None:
in_msg.get_last_SEVIRI_date()
yearS = str(in_msg.datetime.year)
#yearS = yearS[2:]
monthS = "%02d" % in_msg.datetime.month
dayS = "%02d" % in_msg.datetime.day
hourS = "%02d" % in_msg.datetime.hour
minS = "%02d" % in_msg.datetime.minute
dateS=yearS+'-'+monthS+'-'+dayS
timeS=hourS+'-'+minS
if in_msg.sat_nr==None:
in_msg.sat_nr=choose_msg(in_msg.datetime,in_msg.RSS)
if in_msg.datetime.year > 2012:
if in_msg.sat_nr == 8:
area_loaded = get_area_def("EuropeCanary35")
elif in_msg.sat_nr == 9: # rapid scan service satellite
area_loaded = get_area_def("EuropeCanary95")
elif in_msg.sat_nr == 10: # default satellite
area_loaded = get_area_def("met09globeFull") # full disk service, like EUMETSATs NWC-SAF products
elif in_msg.sat_nr == 0: # fake satellite for reprojected ccs4 data in netCDF
area_loaded = get_area_def("ccs4") #
#area_loaded = get_area_def("EuropeCanary")
#area_loaded = get_area_def("alps") # new projection of SAM
else:
print("*** Error, unknown satellite number ", in_msg.sat_nr)
area_loaded = get_area_def("hsaf") #
else:
if in_msg.sat_nr == 8:
area_loaded = get_area_def("EuropeCanary95")
elif in_msg.sat_nr == 9: # default satellite
area_loaded = get_area_def("EuropeCanary")
# define contour write for coasts, borders, rivers
cw = ContourWriterAGG(in_msg.mapDir)
if type(in_msg.sat_nr) is int:
sat_nr_str = str(in_msg.sat_nr).zfill(2)
elif type(in_msg.sat_nr) is str:
sat_nr_str = in_msg.sat_nr
else:
print("*** Waring, unknown type of sat_nr", type(in_msg.sat_nr))
sat_nr_str = in_msg.sat_nr
if in_msg.verbose:
print('*** Create plots for ')
print(' Satellite/Sensor: '+in_msg.sat + ' ' + sat_nr_str)
print(' Date/Time: '+dateS +' '+hourS+':'+minS+'UTC')
print(' RGBs: ', in_msg.RGBs)
print(' Area: ', in_msg.areas)
# check if input data is complete
if in_msg.verbose:
print("*** check input data")
RGBs = check_input(in_msg, in_msg.sat+sat_nr_str, in_msg.datetime)
if len(RGBs) != len(in_msg.RGBs):
print("*** Warning, input not complete.")
print("*** Warning, process only: ", RGBs)
# define satellite data object
global_data = GeostationaryFactory.create_scene(in_msg.sat, sat_nr_str, "seviri", in_msg.datetime)
# print "type(global_data) ", type(global_data) # <class 'mpop.scene.SatelliteInstrumentScene'>
# print "dir(global_data)", dir(global_data) [..., '__init__', ... 'area', 'area_def', 'area_id', 'channel_list', 'channels',
# 'channels_to_load', 'check_channels', 'fullname', 'get_area', 'image', 'info', 'instrument_name', 'lat', 'load', 'loaded_channels',
# 'lon', 'number', 'orbit', 'project', 'remove_attribute', 'satname', 'save', 'set_area', 'time_slot', 'unload', 'variant']
## define satellite data object one scan before
#if in_msg.RSS:
# scan_time = 5 # min
#else:
# scan_time = 15 # min
scan_time = 15 # min
datetime_m1 = in_msg.datetime - timedelta(minutes=scan_time)
global_data_m1 = GeostationaryFactory.create_scene(in_msg.sat, sat_nr_str, "seviri", datetime_m1)
if len(RGBs) == 0:
return RGBs
if in_msg.verbose:
print("*** load satellite channels for "+in_msg.sat+sat_nr_str+" ", global_data.fullname)
# initialize processed RGBs
RGBs_done=[]
# load reflectivities, brightness temperatures, NWC-SAF products ...
print("*** read ", str(in_msg.datetime))
area_loaded = load_products(global_data, RGBs, in_msg, area_loaded)
#print "*** read ", str(datetime_m1)
#area_loaded = load_products(global_data_m1, RGBs, in_msg, area_loaded)
# check if all prerequisites are loaded
#rgb_complete = []
#for rgb in RGBs:
# all_loaded = True
# if rgb in products.RGBs_buildin or rgb in products.RGB_user:
# obj_image = get_image(global_data, rgb)
# for pre in obj_image.prerequisites:
# if pre not in loaded_channels:
# all_loaded = False
# elif rgb in products.MSG_color:
# if rgb.replace("c","") not in loaded_channels:
# all_loaded = False
# else:
# if rgb not in loaded_channels:
# all_loaded = False
# if all_loaded:
# rgb_complete.append(rgb)
#print "rgb_complete", rgb_complete
# preprojecting the data to another area
# --------------------------------------
for area in in_msg.areas:
print("")
obj_area = get_area_def(area)
# reproject data to new area
if obj_area == area_loaded:
if in_msg.verbose:
print("*** Use data for the area loaded: ", area)
#obj_area = area_loaded
data = global_data
data_m1 = global_data_m1
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)
data_m1 = global_data_m1.project(area, precompute=True)
resolution='i'
loaded_products = [chn.name for chn in data.loaded_channels()]
print(loaded_products)
#loaded_products_m1 = [chn.name for chn in data_m1.loaded_channels()]
#print loaded_products_m1
#for prod in loaded_products:
# print "xxx ", prod
# print data_m1[prod]
# data[prod] = data[prod] - data_m1[prod] #
# 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
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)
for rgb in RGBs:
PIL_image = create_PIL_image(rgb, data, in_msg) # !!! in_msg.colorbar[rgb] is initialized inside (give attention to rgbs) !!!
if in_msg.add_rivers:
if in_msg.verbose:
print(" add rivers to image (resolution="+resolution+")")
cw.add_rivers(PIL_image, area_tuple, outline='blue', resolution=resolution, outline_opacity=127, width=0.5, level=5) #
if in_msg.verbose:
print(" add lakes to image (resolution="+resolution+")")
cw.add_coastlines(PIL_image, area_tuple, outline='blue', resolution=resolution, outline_opacity=127, width=0.5, level=2) #, outline_opacity=0
if in_msg.add_borders:
if in_msg.verbose:
print(" add coastlines to image (resolution="+resolution+")")
cw.add_coastlines(PIL_image, area_tuple, outline=(255, 0, 0), resolution=resolution, width=1) #, outline_opacity=0
if in_msg.verbose:
print(" add borders to image (resolution="+resolution+")")
cw.add_borders(PIL_image, area_tuple, outline=(255, 0, 0), resolution=resolution, width=1) #, outline_opacity=0
#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, rgb, int(data.number), dateS, hourS, minS, area, dc, in_msg.font_file, in_msg.verbose )
# 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()
dc.add_logo("../logos/meteoSwiss3.jpg",height=60.0)
dc.add_logo("../logos/pytroll3.jpg",height=60.0)
# add colorscale
if in_msg.add_colorscale and in_msg.colormap[rgb] != None:
add_colorscale(dc, rgb, in_msg)
# create output filename
outputDir = format_name(in_msg.outputDir, data.time_slot, area=area, rgb=rgb, sat_nr=data.number)
outputFile = outputDir + format_name(in_msg.outputFile, data.time_slot, area=area, rgb=rgb, sat_nr=data.number)
# 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 in_msg.verbose:
print('... save final file :' + outputFile)
PIL_image.save(outputFile, optimize=True) # optimize -> minimize file size
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)
# copy to another place
if in_msg.scpOutput:
if in_msg.verbose:
print("... secure copy "+outputFile+ " to "+in_msg.scpOutputDir)
subprocess.call("scp "+in_msg.scpID+" "+outputFile+" "+in_msg.scpOutputDir+" 2>&1 &", shell=True)
if in_msg.compress_to_8bit:
if in_msg.verbose:
print("... secure copy "+outputFile.replace(".png","-fs8.png")+ " to "+in_msg.scpOutputDir)
subprocess.call("scp "+in_msg.scpID+" "+outputFile.replace(".png","-fs8.png")+" "+in_msg.scpOutputDir+" 2>&1 &", shell=True)
if rgb not in RGBs_done:
RGBs_done.append(rgb)
## start postprocessing
if area in in_msg.postprocessing_areas:
postprocessing(in_msg, global_data.time_slot, data.number, area)
if in_msg.verbose:
print(" ")
return RGBs_done
