def add_to_array(arr, area, time, bufr, savedir='/tmp', name=None, mode='L',
resize=None, ptsize=None, save=False):
"""Add synoptical reports from stations to provided geolocated image array
"""
# Create array image
arrshp = arr.shape[:2]
print(np.nanmin(arr), np.nanmax(arr))
arr_img = Image(arr, mode=mode)
# arr_img = Image(channels=[arr[:, :, 0], arr[:, :, 1], arr[:, :, 2]],
# mode='RGB')
arr_img.stretch('crude')
arr_img.invert()
arr_img.colorize(maskcol)
arr_img.invert()
# Import bufr
stations = read_synop(bufr, 'visibility')
currentstations = stations[time.strftime("%Y%m%d%H0000")]
lats = [i[2] for i in currentstations]
lons = [i[3] for i in currentstations]
vis = [i[4] for i in currentstations]
# Create array for synop parameter
visarr = np.empty(arrshp)
visarr.fill(np.nan)
x, y = (area.get_xy_from_lonlat(lons, lats))
vis_ma = np.ma.array(vis, mask=x.mask)
if ptsize:
xpt = np.array([])
ypt = np.array([])
for i, j in zip(x, y):
xmesh, ymesh = np.meshgrid(np.linspace(i - ptsize, i + ptsize,
ptsize * 2 + 1),
np.linspace(j - ptsize, j + ptsize,
ptsize * 2 + 1))
xpt = np.append(xpt, xmesh.ravel())
ypt = np.append(ypt, ymesh.ravel())
vispt = np.ma.array([np.full(((ptsize * 2 + 1,
ptsize * 2 + 1)), p) for p in vis_ma])
visarr[ypt.astype(int), xpt.astype(int)] = vispt.ravel()
else:
visarr[y.compressed(), x.compressed()] = vis_ma.compressed()
visarr_ma = np.ma.masked_invalid(visarr)
station_img = Image(visarr_ma, mode='L')
station_img.colorize(vis_colset)
station_img.merge(arr_img)
if resize is not None:
station_img.resize((arrshp[0] * resize, arrshp[1] * resize))
if name is None:
timestr = time.strftime("%Y%m%d%H%M")
name = "fog_filter_example_stations_{}.png".format(timestr)
if save:
savepath = os.path.join(savedir, name)
station_img.save(savepath)
return(station_img)
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 save(self,
filename,
compression=6,
tags=None,
gdal_options=None,
fformat=None,
blocksize=256,
**kwargs):
"""Save the image to the given *filename*. If the extension is "tif",
the image is saved to geotiff_ format, in which case the *compression*
level can be given ([0, 9], 0 meaning off). See also
:meth:`image.Image.save`, :meth:`image.Image.double_save`, and
:meth:`image.Image.secure_save`. The *tags* argument is a dict of tags
to include in the image (as metadata), and the *gdal_options* holds
options for the gdal saving driver. A *blocksize* other than 0 will
result in a tiled image (if possible), with tiles of size equal to
*blocksize*.
If the specified format *fformat* is not know to satpy (and PIL), we
will try to import module *fformat* and call the method `fformat.save`.
.. _geotiff: http://trac.osgeo.org/geotiff/
"""
file_tuple = os.path.splitext(filename)
fformat = fformat or file_tuple[1][1:]
if fformat.lower() in ('tif', 'tiff'):
return self.geotiff_save(filename, compression, tags, gdal_options,
blocksize, **kwargs)
try:
# Let image.pil_save it ?
Image.save(self, filename, compression, fformat=fformat)
except UnknownImageFormat:
# No ... last resort, try to import an external module.
logger.info("Importing image writer module '%s'" % fformat)
try:
saver = __import__(fformat, globals(), locals(), ['save'])
except ImportError:
raise UnknownImageFormat("Unknown image format '%s'" % fformat)
saver.save(self, filename, **kwargs)
def save(self, filename, compression=6,
tags=None, gdal_options=None,
fformat=None, blocksize=256, **kwargs):
"""Save the image to the given *filename*. If the extension is "tif",
the image is saved to geotiff_ format, in which case the *compression*
level can be given ([0, 9], 0 meaning off). See also
:meth:`image.Image.save`, :meth:`image.Image.double_save`, and
:meth:`image.Image.secure_save`. The *tags* argument is a dict of tags
to include in the image (as metadata), and the *gdal_options* holds
options for the gdal saving driver. A *blocksize* other than 0 will
result in a tiled image (if possible), with tiles of size equal to
*blocksize*.
If the specified format *fformat* is not know to MPOP (and PIL), we
will try to import module *fformat* and call the method `fformat.save`.
.. _geotiff: http://trac.osgeo.org/geotiff/
"""
file_tuple = os.path.splitext(filename)
fformat = fformat or file_tuple[1][1:]
if fformat.lower() in ('tif', 'tiff'):
return self.geotiff_save(filename, compression, tags,
gdal_options, blocksize, **kwargs)
try:
# Let image.pil_save it ?
Image.save(self, filename, compression, fformat=fformat)
except UnknownImageFormat:
# No ... last resort, try to import an external module.
logger.info("Importing image writer module '%s'" % fformat)
try:
saver = __import__(fformat, globals(), locals(), ['save'])
except ImportError:
raise UnknownImageFormat(
"Unknown image format '%s'" % fformat)
saver.save(self, filename, **kwargs)
def save(self, filename, compression=6,
tags=None, gdal_options=None,
fformat=None, blocksize=256, writer_options=None, **kwargs):
"""Save the image to the given *filename*. If the extension is "tif",
the image is saved to geotiff_ format, in which case the *compression*
level can be given ([0, 9], 0 meaning off). See also
:meth:`image.Image.save`, :meth:`image.Image.double_save`, and
:meth:`image.Image.secure_save`. The *tags* argument is a dict of tags
to include in the image (as metadata), and the *gdal_options* holds
options for the gdal saving driver. A *blocksize* other than 0 will
result in a tiled image (if possible), with tiles of size equal to
*blocksize*.
If the specified format *fformat* is not know to MPOP (and PIL), we
will try to import module *fformat* and call the method `fformat.save`.
Use *writer_options* to define parameters that should be forwarded to
custom writers. Dictionary keys listed in
mpop.imageo.formats.writer_options will be interpreted by this
function instead of *compression*, *blocksize* and nbits in
*tags* dict.
.. _geotiff: http://trac.osgeo.org/geotiff/
"""
fformat = fformat or os.path.splitext(filename)[1][1:]
# prefer parameters in writer_options dict
# fill dict if parameters are missing
writer_options = writer_options or {}
tags = tags or {}
if writer_options.get(write_opts.WR_OPT_COMPRESSION, None):
compression = writer_options[write_opts.WR_OPT_COMPRESSION]
elif compression is not None:
writer_options[write_opts.WR_OPT_COMPRESSION] = compression
if writer_options.get(write_opts.WR_OPT_BLOCKSIZE, None):
blocksize = writer_options[write_opts.WR_OPT_BLOCKSIZE]
elif blocksize is not None:
writer_options[write_opts.WR_OPT_BLOCKSIZE] = blocksize
if writer_options.get(write_opts.WR_OPT_NBITS, None):
tags['NBITS'] = writer_options[write_opts.WR_OPT_NBITS]
elif tags.get('NBITS') is not None:
writer_options[write_opts.WR_OPT_NBITS] = tags.get('NBITS')
if fformat.lower() in ('tif', 'tiff'):
kwargs = kwargs or {}
kwargs['writer_options'] = writer_options
return self.geotiff_save(filename, compression, tags,
gdal_options, blocksize,
**kwargs)
try:
# Let image.pil_save it ?
Image.save(self, filename, compression, fformat=fformat)
except UnknownImageFormat:
# No ... last resort, try to import an external module.
logger.info("Importing image writer module '%s'" % fformat)
try:
saver = __import__(fformat, globals(), locals(), ['save'])
except ImportError:
raise UnknownImageFormat(
"Unknown image format '%s'" % fformat)
kwargs = kwargs or {}
kwargs['writer_options'] = writer_options
saver.save(self, filename, **kwargs)
img = Timage(imgarr, mode="L")
#black = greys
#black.colors[0] = np.array([0.,0.,0.])
#black.set_range(-40 + 273.15, 30 + 273.15)
greys.set_range(-30 + 273.15, 30 + 273.15)
#spectral.set_range(-90 + 273.15, -40.00001 + 273.15)
#my_cm = spectral + greys
setvak = cmap_from_text("setvak_rev.rgb", norm=True)
setvak_new = setvak.colors[:,:3]
setvak.colors = setvak_new
setvak.set_range(-73 + 273.15, -30.00001 + 273.15)
my_cm = setvak + greys
img.colorize(my_cm)
tmpFile = "/tmp/colir.png"
img.save(tmpFile)
outputFile = "/var/tmp/cll/out/PY_colir-ch_"+yearS+monthS+dayS+hourS+minS+".png"
#background = Image.open(bgFile)
foreground = Image.open(tmpFile)
#background = background.convert("RGBA")
#img_enhancer = ImageEnhance.Brightness(foreground)
#imgN = img_enhancer.enhance(1.5)
#foreground.putalpha(imgN.convert('L'))
foreground = foreground.convert("RGBA")
foreground.save(tmpFile)
#Image.alpha_composite(background, foreground).save(outputFile)
print("convert "+ tmpFile +" \( " +tmpFileB+ " -black-threshold 30% \) -alpha off -comopse copy_opacity -composite " + tmpFile)
def add_to_image(image,
area,
time,
bufr,
savedir='/tmp',
name=None,
bgimg=None,
resize=None,
ptsize=None,
save=False):
"""Add synoptical visibility reports from station data to provided
geolocated image array
"""
arrshp = image.shape[:2]
# Add optional background image
if bgimg is not None:
# Get background image
bg_img = Image(bgimg.squeeze(), mode='L', fill_value=None)
bg_img.stretch("crude")
bg_img.convert("RGB")
# bg_img.invert()
image.merge(bg_img)
# Import bufr
stations = read_synop(bufr, 'visibility')
currentstations = stations[time.strftime("%Y%m%d%H0000")]
lats = [i[2] for i in currentstations]
lons = [i[3] for i in currentstations]
vis = [i[4] for i in currentstations]
# Create array for synop parameter
visarr = np.empty(arrshp)
visarr.fill(np.nan)
# Red - Violet - Blue - Green
vis_colset = Colormap((0, (228 / 255.0, 26 / 255.0, 28 / 255.0)),
(1000, (152 / 255.0, 78 / 255.0, 163 / 255.0)),
(5000, (55 / 255.0, 126 / 255.0, 184 / 255.0)),
(10000, (77 / 255.0, 175 / 255.0, 74 / 255.0)))
x, y = (area.get_xy_from_lonlat(lons, lats))
vis_ma = np.ma.array(vis, mask=x.mask)
if ptsize:
xpt = np.array([])
ypt = np.array([])
for i, j in zip(x, y):
xmesh, ymesh = np.meshgrid(
np.linspace(i - ptsize, i + ptsize, ptsize * 2 + 1),
np.linspace(j - ptsize, j + ptsize, ptsize * 2 + 1))
xpt = np.append(xpt, xmesh.ravel())
ypt = np.append(ypt, ymesh.ravel())
vispt = np.ma.array(
[np.full(((ptsize * 2 + 1, ptsize * 2 + 1)), p) for p in vis_ma])
visarr[ypt.astype(int), xpt.astype(int)] = vispt.ravel()
else:
visarr[y.compressed(), x.compressed()] = vis_ma.compressed()
visarr_ma = np.ma.masked_invalid(visarr)
station_img = Image(visarr_ma, mode='L')
station_img.colorize(vis_colset)
image.convert("RGB")
station_img.merge(image)
if resize is not None:
station_img.resize((arrshp[0] * resize, arrshp[1] * resize))
if name is None:
timestr = time.strftime("%Y%m%d%H%M")
name = "fog_filter_example_stations_{}.png".format(timestr)
if save:
savepath = os.path.join(savedir, name)
station_img.save(savepath)
return (station_img)
if save_black_white_png:
local_scene.save_dataset('lscl', './lscl_' + area + '.png')
print(dir(local_scene.save_dataset))
print('display ./lscl_' + area + '.png &')
# save png file for SATLive
##############################
if area == "cosmo1x150" or area == "cosmo1":
png_file = start_time.strftime('/data/cinesat/out/MSG_lscl-' +
area + '_%y%m%d%H%M.png')
from trollimage.colormap import spectral, greys, ylorrd, rdgy
imgarr = np.array(local_scene['lscl'].data)
from trollimage.image import Image as Timage
img = Timage(imgarr, mode="L")
img.colorize(rdgy.reverse())
img.save(png_file)
# local_scene.save_dataset( 'lscl', png_file )
from pyresample.utils import load_area
swiss = load_area("/opt/users/hau/monti-pytroll/etc/areas.def",
area)
from pycoast import ContourWriterAGG
cw = ContourWriterAGG('/opt/users/common/shapes')
cw.add_borders_to_file(png_file,
swiss,
outline="green",
resolution='i',
level=3,
width=2)
#print(np.histogram(cwp_masked.compressed()))
#ele_img.show()
#cwp_img.show()
#overview.show()
#fls_img.show()
#snow_rgb.show()
#daymicro_rgb.show()
ele_img.save("/tmp/fog_example_msg_ger_elevation_{}.png".format(
time.strftime("%Y%m%d%H%M")))
cot_img.save("/tmp/fog_example_msg_ger_cot_{}.png".format(
time.strftime("%Y%m%d%H%M")))
reff_img.save("/tmp/fog_example_msg_ger_reff_{}.png".format(
time.strftime("%Y%m%d%H%M")))
cwp_img.save("/tmp/fog_example_msg_ger_cwp_{}.png".format(
time.strftime("%Y%m%d%H%M")))
fls_img.save("/tmp/fog_example_msg_ger_fls_{}.png".format(
time.strftime("%Y%m%d%H%M")))
overview.save("/tmp/fog_example_msg_ger_overview_{}.png".format(
time.strftime("%Y%m%d%H%M")))
snow_rgb.save("/tmp/fog_example_msg_ger_snow_rgb_{}.png".format(
time.strftime("%Y%m%d%H%M")))
daymicro_rgb.save("/tmp/fog_example_msg_ger_daymicro_rgb_{}.png".format(
time.strftime("%Y%m%d%H%M")))
#germ_scene["HRV"].show()
# Add synoptical mreports from stations
arrshp = germ_scene[10.8].shape
bufr_dir = '/data/tleppelt/skydata/'
bufr_file = "result_{}".format(time.strftime("%Y%m%d"))
inbufr = os.path.join(bufr_dir, bufr_file)
stations = read_synop(inbufr, 'visibility')
lcd = scn.resample('euro4', radius_of_influence=2000)
sstdata = lcd['sea_surface_temperature'][:]
import numpy as np
arr = np.ma.where(np.less_equal(sstdata, 0), 0, sstdata - 273.15)
# Convert sst to numbers between 0 and 28, corresponding to the lut:
data = np.ma.where(np.less(arr, 0), 28, 28.0 - arr)
data = np.ma.where(np.greater(arr, 23.0), 4, data).round().astype('uint8')
from trollimage.image import Image
from satpy.imageo import palettes
palette = palettes.sstlut_osisaf_metno()
img = Image(data, mode='P', palette=palette)
img.show()
img.save('osisaf_sst_viirs_satpy.png')
from pycoast import ContourWriter
cw_ = ContourWriter('/home/a000680/data/shapes')
pilim = img.pil_image()
area_def = lcd['sea_surface_temperature'].info['area']
cw_.add_coastlines(pilim,
area_def,
resolution='i',
level=1,
outline=(220, 220, 220))
pilim.show()
pilim.save('./osisaf_sst_viirs_satpy_withovl.png')
)
scn.load(['sea_surface_temperature'])
lcd = scn.resample('euro4', radius_of_influence=2000)
sstdata = lcd['sea_surface_temperature'][:]
import numpy as np
arr = np.ma.where(np.less_equal(sstdata, 0), 0, sstdata - 273.15)
# Convert sst to numbers between 0 and 28, corresponding to the lut:
data = np.ma.where(np.less(arr, 0), 28, 28.0 - arr)
data = np.ma.where(np.greater(arr, 23.0), 4, data).round().astype('uint8')
from trollimage.image import Image
from satpy.imageo import palettes
palette = palettes.sstlut_osisaf_metno()
img = Image(data, mode='P', palette=palette)
img.show()
img.save('osisaf_sst_viirs_satpy.png')
from pycoast import ContourWriter
cw_ = ContourWriter('/home/a000680/data/shapes')
pilim = img.pil_image()
area_def = lcd['sea_surface_temperature'].info['area']
cw_.add_coastlines(
pilim, area_def, resolution='i', level=1, outline=(220, 220, 220))
pilim.show()
pilim.save('./osisaf_sst_viirs_satpy_withovl.png')
