def create_image(path: tp.Optional[str] = None) -> (Scene, tp.Optional[str]):
"""
Create image of the given satellite data of a SentinelSat-2 satellite
:param path: Path to the raw satellite data
:return: (Scene, str), Scene of the satellite image, full path to the .tif create
"""
files = find_files_and_readers(base_dir=path, reader='msi_safe')
_scn = Scene(filenames=files)
_scn.load(['true_color'])
filename = None
if path is not None:
filename = os.path.join(path, 'RGB.tif')
if not os.path.exists(filename):
_scn.save_dataset('true_color',
filename,
writer='simple_image',
fill_value=0)
return _scn, filename
def setUp(self):
"""Create temporary images to test on."""
import tempfile
from datetime import datetime
from pyresample.geometry import AreaDefinition
from satpy.scene import Scene
self.date = datetime(2018, 1, 1)
# Create area definition
pcs_id = 'ETRS89 / LAEA Europe'
proj4_dict = {'init': 'epsg:3035'}
self.x_size = 100
self.y_size = 100
area_extent = (2426378.0132, 1528101.2618, 6293974.6215, 5446513.5222)
self.area_def = AreaDefinition('geotiff_area', pcs_id, pcs_id,
proj4_dict, self.x_size, self.y_size,
area_extent)
# Create datasets for L, LA, RGB and RGBA mode images
r__ = da.random.randint(0, 256, size=(self.y_size, self.x_size),
chunks=(50, 50)).astype(np.uint8)
g__ = da.random.randint(0, 256, size=(self.y_size, self.x_size),
chunks=(50, 50)).astype(np.uint8)
b__ = da.random.randint(0, 256, size=(self.y_size, self.x_size),
chunks=(50, 50)).astype(np.uint8)
a__ = 255 * np.ones((self.y_size, self.x_size), dtype=np.uint8)
a__[:10, :10] = 0
a__ = da.from_array(a__, chunks=(50, 50))
ds_l = xr.DataArray(da.stack([r__]), dims=('bands', 'y', 'x'),
attrs={'name': 'test_l',
'start_time': self.date})
ds_l['bands'] = ['L']
ds_la = xr.DataArray(da.stack([r__, a__]), dims=('bands', 'y', 'x'),
attrs={'name': 'test_la',
'start_time': self.date})
ds_la['bands'] = ['L', 'A']
ds_rgb = xr.DataArray(da.stack([r__, g__, b__]),
dims=('bands', 'y', 'x'),
attrs={'name': 'test_rgb',
'start_time': self.date})
ds_rgb['bands'] = ['R', 'G', 'B']
ds_rgba = xr.DataArray(da.stack([r__, g__, b__, a__]),
dims=('bands', 'y', 'x'),
attrs={'name': 'test_rgba',
'start_time': self.date})
ds_rgba['bands'] = ['R', 'G', 'B', 'A']
# Temp dir for the saved images
self.base_dir = tempfile.mkdtemp()
# Put the datasets to Scene for easy saving
scn = Scene()
scn['l'] = ds_l
scn['l'].attrs['area'] = self.area_def
scn['la'] = ds_la
scn['la'].attrs['area'] = self.area_def
scn['rgb'] = ds_rgb
scn['rgb'].attrs['area'] = self.area_def
scn['rgba'] = ds_rgba
scn['rgba'].attrs['area'] = self.area_def
# Save the images. Two images in PNG and two in GeoTIFF
scn.save_dataset('l', os.path.join(self.base_dir, 'test_l.png'), writer='simple_image')
scn.save_dataset('la', os.path.join(self.base_dir, '20180101_0000_test_la.png'), writer='simple_image')
scn.save_dataset('rgb', os.path.join(self.base_dir, '20180101_0000_test_rgb.tif'), writer='geotiff')
scn.save_dataset('rgba', os.path.join(self.base_dir, 'test_rgba.tif'), writer='geotiff')
self.scn = scn
print(filename)
##!#if(composite):
##!# day_filenames = glob(filename[:50]+'*')
##!# cmpst_add = '_composite'
##!#else:
day_filenames = glob(filename)
cmpst_add = ''
# Extract the modis true-color plot limits
# ----------------------------------------
##lat_lims = plot_limits_dict[dt_date_str.strftime('%Y-%m-%d')][dt_date_str.strftime('%H%M')]['modis_Lat']
##lon_lims = plot_limits_dict[dt_date_str.strftime('%Y-%m-%d')][dt_date_str.strftime('%H%M')]['modis_Lon']
# Use satpy (Scene) to open the file
# ----------------------------------
scn = Scene(reader='modis_l1b', filenames=day_filenames)
# Load true-color data
scn.load(['true_color'])
scn.save_dataset('true_color', 'test_image_true2.png')
scn.show('true_color')
plt.show()
##!#if(save):
##!# outname = 'modis_true_color_' + date_str + zoom_add + cmpst_add + '_satpy.png'
##!# plt.savefig(outname,dpi=300)
##!# print("Saved image",outname)
##!#else:
##!# plt.show()
dayno = dt.timetuple().tm_yday
lat = float(fields[8])
lon = float(fields[9])
# make up the grid to resample into
lats = np.arange(lat - 1, lat + 1,
0.01) # approx 1km resolution, 200km extent
lons = np.arange(lon - 1, lon + 1,
0.01) # approx 1km resolution, 200km extent
lons, lats = np.meshgrid(lons, lats)
grid_def = geometry.GridDefinition(lons=lons, lats=lats)
# copy 11-micron band (C14) to local disk
# See: https://www.goes-r.gov/education/ABI-bands-quick-info.html
gcs_pattern = 'gs://gcp-public-data-goes-16/ABI-L1b-RadF/{0}/{1}/{2}/*C14*_s{0}{1}{2}00*'.format(
dt.year, dayno, dt.hour)
outfile = '{}/tmp_{}{}{}'.format(tmpdir, dt.year, dayno, dt.hour)
os.mkdirs(tmpdir)
copy_command = 'gsutil cp -m {} {}'.format(all_files, outfile)
subprocess.check_call(copy_command.split())
# create image
scene = Scene(filenames=outfile, reader="abi_l1b")
scene.resample(grid_def)
outfile = '{}/ir_{}{}{}.png'.format(outdir, dt.year, dayno, dt.hour)
scene.save_dataset('overview', outfile)
# cleanup
shutil.rmtree(tmpdir)
exit
# -*- coding: utf-8 -*-
"""
Created on Tue May 28 11:09:38 2019
@author: bav
"""
from satpy.scene import Scene
from satpy import find_files_and_readers
from datetime import datetime
files = find_files_and_readers(sensor='olci',
start_time=datetime(2017, 7, 15, 0, 0, 0),
end_time=datetime(2017, 7, 16, 0, 0, 0),
base_dir=".\OLCI scenes",
reader='olci_l1b')
scn = Scene(filenames=files)
scn.load(['true_color'])
scn.save_dataset('true_color', filename='scene' + '.png')
