How to use the trollimage.image.Image function in trollimage
To help you get started, we’ve selected a few trollimage examples, based on popular ways it is used in public projects.
Secure your code as it's written. Use Snyk Code to scan source code in minutes - no build needed - and fix issues immediately.
pytroll / mpop / mpop / tools.py View on Github 
height = np.ma.masked_where(height <= 0, height, copy=False) * 1000.
if False:
from trollimage.image import Image as trollimage
from trollimage.colormap import rainbow
from copy import deepcopy
# cloud top height
prop = height
min_data = prop.min()
max_data = prop.max()
print " estimated CTH(meter) (min/max): ", min_data, max_data
min_data = 0
max_data = 12000
colormap = deepcopy(rainbow)
colormap.set_range(min_data, max_data)
img = trollimage(prop, mode="L") #, fill_value=[0,0,0]
img.colorize(colormap)
img.show()
# return cloud top height in meter
return heightif type == 'tif':
result_img = GeoImage(filter_img.channels, area,
self.time, fill_value=None,
mode="RGB")
result_img.save(savedir)
else:
filter_img.save(savedir)
logger.info("{} results are plotted to: {}". format(self.name,
savedir))
else:
filter_img.show()
# Create mask image
if isinstance(self.result, np.ma.masked_array):
mask = self.result.mask.squeeze()
mask = np.ma.masked_where(mask == 1, mask)
mask_img = Image(mask, mode='L', fill_value=None)
mask_img.stretch("crude")
mask_img.invert()
mask_img.colorize(maskcol)
if resize != 0:
if not isinstance(resize, int):
resize = int(resize)
mask_img.resize((self.result.shape[0] * resize,
self.result.shape[1] * resize))
# mask_img.merge(bg_img)
if save:
if type == 'tif':
result_img = GeoImage(mask_img.channels, area,
self.time, fill_value=None,
mode="RGB")
result_img.save(maskdir)
else: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)),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)area = self.area
except DummyException:
Warning("Area object not found. Plotting filter result as"
" image")
type = 'png'
# Create image from data
if array is None:
if np.nanmax(self.result) > 1:
self.plotrange = (np.nanmin(self.result),
np.nanmax(self.result))
if type == 'tif':
result_img = GeoImage(self.result.squeeze(), area,
self.time,
mode="L")
else:
result_img = Image(self.result.squeeze(), mode='L',
fill_value=None)
else:
self.plotrange = (np.nanmin(array), np.nanmax(array))
if type == 'tif':
result_img = GeoImage(array.squeeze(), area,
self.time,
mode="L")
else:
result_img = Image(array.squeeze(), mode='L', fill_value=None)
result_img.stretch("crude")
# Colorize image
ylorrd.set_range(*self.plotrange)
logger.info("Set color range to {}".format(self.plotrange))
if not floating_point:
result_img.colorize(ylorrd)
if array is None: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)from trollimage.image import Image
from trollimage.colormap import Colormap
# Define custom fog colormap
fogcol = Colormap((0., (250 / 255.0, 200 / 255.0, 40 / 255.0)),
(1., (1.0, 1.0, 229 / 255.0)))
maskcol = Colormap((1., (250 / 255.0, 200 / 255.0, 40 / 255.0)))
# Get save directory
attrdir = os.path.join(dir, self.name + '_' + name + '_' +
datetime.strftime(self.time,
'%Y%m%d%H%M') + '.' + type)
logger.info("Plotting filter attribute {} to {}".format(name, attrdir))
# Generate image
attr = getattr(self, name)
if attr.dtype == 'bool':
attr = np.ma.masked_where(attr == 1, attr)
attr_img = Image(attr.squeeze(), mode='L', fill_value=None)
attr_img.colorize(fogcol)
# Get background image
bg_img = Image(self.bg_img.squeeze(), mode='L', fill_value=None)
bg_img.stretch("crude")
bg_img.convert("RGB")
bg_img.invert()
if resize != 0:
if not isinstance(resize, int):
resize = int(resize)
bg_img.resize((self.bg_img.shape[0] * resize,
self.bg_img.shape[1] * resize))
attr_img.resize((self.result.shape[0] * resize,
self.result.shape[1] * resize))
try:
# Merging
attr_img.merge(bg_img)fogcol = Colormap((0., (250 / 255.0, 200 / 255.0, 40 / 255.0)),
(1., (1.0, 1.0, 229 / 255.0)))
maskcol = Colormap((1., (250 / 255.0, 200 / 255.0, 40 / 255.0)))
# Get save directory
attrdir = os.path.join(dir, self.name + '_' + name + '_' +
datetime.strftime(self.time,
'%Y%m%d%H%M') + '.' + type)
logger.info("Plotting filter attribute {} to {}".format(name, attrdir))
# Generate image
attr = getattr(self, name)
if attr.dtype == 'bool':
attr = np.ma.masked_where(attr == 1, attr)
attr_img = Image(attr.squeeze(), mode='L', fill_value=None)
attr_img.colorize(fogcol)
# Get background image
bg_img = Image(self.bg_img.squeeze(), mode='L', fill_value=None)
bg_img.stretch("crude")
bg_img.convert("RGB")
bg_img.invert()
if resize != 0:
if not isinstance(resize, int):
resize = int(resize)
bg_img.resize((self.bg_img.shape[0] * resize,
self.bg_img.shape[1] * resize))
attr_img.resize((self.result.shape[0] * resize,
self.result.shape[1] * resize))
try:
# Merging
attr_img.merge(bg_img)
except DummyException:
logger.warning("No merging for attribute plot possible")
if save:
