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 to_image(dataset, copy=True, **kwargs):
# Only add keywords if they are present
for key in ["mode", "fill_value", "palette"]:
if key in dataset.info:
kwargs.setdefault(key, dataset.info[key])
if dataset.ndim == 2:
return Image([dataset], copy=copy, **kwargs)
elif dataset.ndim == 3:
return Image([band for band in dataset], copy=copy, **kwargs)
else:
raise ValueError(
"Don't know how to convert array with ndim %d to image" %
dataset.ndim)
def pilimage2trollimage(pimage):
(r, g, b) = _image2array(pimage)
return Image((r, g, b), mode="RGB")
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)
print(arr.shape)
print(np.min(arr))
# Get bufr file
base = os.path.split(fogpy.__file__)
inbufr = os.path.join(base[0], '..', 'etc',
'result_{}.bufr'.format(time.strftime("%Y%m%d")))
# bufr_dir = '/data/tleppelt/skydata/'
# bufr_file = "result_{}".format(time.strftime("%Y%m%d"))
# inbufr = os.path.join(bufr_dir, bufr_file)
area_id = "geos_germ"
name = "geos_germ"
proj_id = "geos"
proj_dict = {
'a': '6378169.00',
'lon_0': '0.00',
'h': '35785831.00',
'b': '6356583.80',
'proj': 'geos',
'lat_0': '0.00'
}
x_size = 298 * resize
y_size = 141 * resize
area_extent = (214528.82635591552, 4370087.2110124603, 1108648.9697693815,
4793144.0573926577)
area_def = geometry.AreaDefinition(area_id, name, proj_id, proj_dict,
x_size, y_size, area_extent)
print(area_def)
img = Image(arr[:, :, :3], mode='RGB')
add_to_image(img, area_def, time, inbufr)
cot_img.enhance(stretch="crude")
#bgimg = germ_scene[10.8].as_image()
bgimg = germ_scene.image.ir108()
fls_img, fogmask = germ_scene.image.fls_day(elevation_ger.image_data,
cot_ger.image_data,
reff_ger.image_data,
cwp_ger.image_data)
snow_rgb = germ_scene.image.snow()
daymicro_rgb = germ_scene.image.day_microphysics()
overview = germ_scene.image.overview()
# Merge masked and colorized fog clouds with backgrund infrared image
bgimg.convert("RGB")
fls_img = Image(fls_img.channels[0], mode='L')
fls_img.colorize(fogcol)
fls_img.merge(bgimg)
ele_img = Image(ele_img.channels[0], mode='L')
#cwp_img = Image(cwp_img.channels[0], mode='L')
#cwp_masked = np.ma.array(cwp_ger.image_data, mask=fogmask)
#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")))
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')
