def _create_global_mosaic(self, fnames, slot, composite):
"""Create and save global mosaic."""
self.logger.info("Building composite %s for slot %s",
composite, str(slot))
scn = Scene()
file_parts = self._get_fname_parts(slot, composite)
fname_out = file_parts["uri"]
img = self._get_existing_image(fname_out)
self.logger.info("Creating composite")
scn['img'] = create_world_composite(fnames,
self.adef,
self.config["lon_limits"],
img=img,
logger=self.logger)
self.logger.info("Saving %s", fname_out)
scn.save_dataset('img', filename=fname_out,
**self.config["save_settings"])
self._send_message(file_parts)
del self.slots[slot][composite]
def simple_export(hrit_files, time, dpi, photo_path, load_photo=['VIS006']):
from satpy import Scene
from satpy import find_files_and_readers
from datetime import datetime
import matplotlib as mpl
mpl.rcParams['figure.dpi'] = dpi
# load_photo = 'VIS006'
first = 0
last = len(time) - 1
# print(len(time),last)
yearF, monthF, dayF, hourF, minuteF, secondF = time[first].tt_calendar()
yearL, monthL, dayL, hourL, minuteL, secondL = time[last].tt_calendar()
# IT IS NOT WORKING FIND SOLUTION - Adding a minute in case there is only one point on map
if len(time) == 1:
time[last].tt = time[last].tt + 1 / 3600
yearL, monthL, dayL, hourL, minuteL, secondL = time[last].tt_calendar()
# print("It works")
# print(yearF, monthF, dayF, hourF, minuteF, secondF )
# print(yearL, monthL, dayL, hourL, minuteL, secondL)
# time[0].tt_calendar()[0]
files = find_files_and_readers(base_dir=hrit_files,
start_time=datetime(yearF, monthF, dayF,
hourF, minuteF),
end_time=datetime(yearL, monthL, dayL,
hourL, minuteL),
reader='seviri_l1b_hrit')
scn = Scene(filenames=files)
scn.load(load_photo)
file = photo_path + 'globe_' + load_photo[
0] + '_{date:%Y-%m-%d_%H_%M_%S}.png'.format(date=scn.start_time)
scn.save_dataset(load_photo[0],
writer='simple_image',
filename=file,
num_threads=8)
return ()
def test_write_and_read_via_scene(test_image_small_mid_atlantic_L, tmp_path):
"""Test that all attributes are written also when writing from scene.
It appears that :func:`Satpy.Scene.save_dataset` does not pass the filename
to the writer. Test that filename is still written to header when saving
this way (the regular way).
"""
import rasterio
sc = Scene()
fn = os.fspath(tmp_path / "test-{name}.tif")
sc["montanha-do-pico"] = test_image_small_mid_atlantic_L.data
sc.save_dataset("montanha-do-pico",
writer="ninjogeotiff",
filename=fn,
fill_value=0,
PhysicUnit="C",
PhysicValue="Temperature",
SatelliteNameID=6400014,
ChannelID=900015,
DataType="GORN")
src = rasterio.open(tmp_path / "test-montanha-do-pico.tif")
tgs = src.tags()
assert tgs["ninjo_FileName"] == os.fspath(tmp_path /
"test-montanha-do-pico.tif")
def prepare_input_data(modis_files):
"""
Prepares validation data for the MODIS CTP retrieval.
Args:
modis_file: List of filenames containing the MODIS input data to use
as input.
Returns:
Dictionary containing the different files required to run the retrieval
on the CALIOP data.
"""
from datetime import datetime
from satpy import Scene
import scipy as sp
from scipy.interpolate import RegularGridInterpolator
from scipy.ndimage import maximum_filter, minimum_filter
from scipy.signal import convolve
import numpy as np
import xarray
from pansat.products.reanalysis.era5 import ERA5Product
from pansat.products.satellite.calipso import clay01km
from pykdtree.kdtree import KDTree
from PIL import Image
#
# Prepare MODIS data.
#
scene = Scene(filenames=modis_files, reader="modis_l1b")
scene.load(["true_color", "31", "32", "latitude", "longitude"], resolution=1000)
scene["true_color_small"] = scene["true_color"][:, ::4, ::4]
scene["bt_11_small"] = scene["31"][::4, ::4]
scene["bt_12_small"] = scene["32"][::4, ::4]
scene["latitude_small"] = scene["latitude"][::4, ::4]
scene["longitude_small"] = scene["longitude"][::4, ::4]
scene.save_dataset("true_color_small", "modis_true_color.png")
image = Image.open("modis_true_color.png")
modis_rgb = np.array(image)
bt_11_rgb = scene["bt_11_small"].compute()
bt_12_rgb = scene["bt_12_small"].compute()
lats_rgb = scene["latitude_small"].compute()
lons_rgb = scene["longitude_small"].compute()
# MODIS data input features.
lats_r = scene["latitude"].compute()
lons_r = scene["longitude"].compute()
bt_11 = scene["31"].compute()
bt_12 = scene["32"].compute()
def mean_filter(img):
k = np.ones((5, 5)) / 25.0
return convolve(img, k, mode="same")
def std_filter(img):
mu = mean_filter(img ** 2)
mu2 = mean_filter(img) ** 2
return np.sqrt(mu - mu2)
bt_11_w = maximum_filter(bt_11, [5, 5])
bt_11_c = minimum_filter(bt_11, [5, 5])
bt_12_w = maximum_filter(bt_12, [5, 5])
bt_12_c = minimum_filter(bt_12, [5, 5])
bt_11_s = std_filter(bt_11)
bt_1112_s = std_filter(bt_11 - bt_12)
#
# Calipso data
#
t_0 = datetime(2016, 10, 12, 17, 00)
t_1 = datetime(2016, 10, 12, 17, 50)
calipso_files = clay01km.download(t_0, t_1)
lat_min = lats_r.data.min()
lat_max = lats_r.data.max()
lon_min = lons_r.data.min()
lon_max = lons_r.data.max()
dataset = clay01km.open(calipso_files[0])
lats_c = dataset["latitude"].data
lons_c = dataset["longitude"].data
ctp_c = dataset["layer_top_pressure"]
cth_c = dataset["layer_top_altitude"]
indices = np.where(
(lats_c > lat_min)
* (lats_c <= lat_max)
* (lons_c > lon_min)
* (lons_c <= lon_max)
)
points = np.hstack([lats_r.data.reshape(-1, 1), lons_r.data.reshape(-1, 1)])
kd_tree = KDTree(points)
points_c = np.hstack([lats_c.reshape(-1, 1), lons_c.reshape(-1, 1)])
d, indices = kd_tree.query(points_c)
valid = d < 0.01
indices = indices[valid]
lats_c = lats_c[valid]
lons_c = lons_c[valid]
ctp_c = ctp_c[valid]
cth_c = cth_c[valid]
bt_11 = bt_11.data.ravel()[indices]
bt_12 = bt_12.data.ravel()[indices]
bt_11_w = bt_11_w.ravel()[indices]
bt_12_w = bt_12_w.ravel()[indices]
bt_11_c = bt_11_c.ravel()[indices]
bt_12_c = bt_12_c.ravel()[indices]
bt_11_s = bt_11_s.ravel()[indices]
bt_1112_s = bt_1112_s.ravel()[indices]
lats_r = lats_r.data.ravel()[indices]
lons_r = lons_r.data.ravel()[indices]
#
# ERA 5 data.
#
t_0 = datetime(2016, 10, 12, 17, 45)
t_1 = datetime(2016, 10, 12, 17, 50)
surface_variables = ["surface_pressure", "2m_temperature", "tcwv"]
domain = [lat_min - 2, lat_max + 2, lon_min - 2, lon_max + 2]
surface_product = ERA5Product("hourly", "surface", surface_variables, domain)
era_surface_files = surface_product.download(t_0, t_1)
pressure_variables = ["temperature"]
pressure_product = ERA5Product("hourly", "pressure", pressure_variables, domain)
era_pressure_files = pressure_product.download(t_0, t_1)
# interpolate pressure data.
era5_data = xarray.open_dataset(era_pressure_files[0])
lats_era = era5_data["latitude"][::-1]
lons_era = era5_data["longitude"]
p_era = era5_data["level"]
p_inds = [np.where(p_era == p)[0] for p in [950, 850, 700, 500, 250]]
pressures = []
for ind in p_inds:
p_interp = RegularGridInterpolator(
[lats_era, lons_era], era5_data["t"].data[0, ind[0], ::-1, :]
)
pressures.append(p_interp((lats_r, lons_r)))
era5_data = xarray.open_dataset(era_surface_files[0])
lats_era = era5_data["latitude"][::-1]
lons_era = era5_data["longitude"]
t_interp = RegularGridInterpolator(
[lats_era, lons_era], era5_data["t2m"].data[0, ::-1, :]
)
t_surf = t_interp((lats_r, lons_r))
p_interp = RegularGridInterpolator(
[lats_era, lons_era], era5_data["sp"].data[0, ::-1, :]
)
p_surf = p_interp((lats_r, lons_r))
tcwv_interp = RegularGridInterpolator(
[lats_era, lons_era], era5_data["tcwv"].data[0, ::-1, :]
)
tcwv = tcwv_interp((lats_r, lons_r))
#
# Assemble input data
#
x = np.zeros((lats_r.size, 16))
x[:, 0] = p_surf
x[:, 1] = t_surf
for i, p in enumerate(pressures):
x[:, 2 + i] = p
x[:, 7] = tcwv
x[:, 8] = bt_12
x[:, 9] = bt_11 - bt_12
x[:, 10] = bt_11_w - bt_12_w
x[:, 11] = bt_11_c - bt_12_c
x[:, 12] = bt_12_w - bt_12
x[:, 13] = bt_12_c - bt_12
x[:, 14] = bt_11_s
x[:, 15] = bt_1112_s
output_data = {
"input_data": x,
"ctp": ctp_c,
"latitude": lats_r,
"longitude": lons_r,
"latitude_rgb": lats_rgb,
"longitude_rgb": lons_rgb,
"modis_rgb": modis_rgb,
"bt_11_rgb": bt_11_rgb,
"bt_12_rgb": bt_12_rgb,
}
return output_data
parser.add_argument('--sat_id', dest='sat_id', action="store", help="Satellite ID", default="8888")
parser.add_argument('--data_cat', dest='data_cat', action="store", help="Category of data (one of GORN, GPRN, P**N)", default="GORN")
parser.add_argument('--area', dest='areadef', action="store", help="Area name, the definition must exist in your areas configuration file", default="nrEURO1km_NPOL_COALeqc")
parser.add_argument('--ph_unit', dest='ph_unit', action="store", help="Physical unit", default="CELSIUS")
parser.add_argument('--data_src', dest='data_src', action="store", help="Data source", default="EUMETCAST")
args = parser.parse_args()
if (args.input_dir != None):
os.chdir(args.input_dir)
cfg = vars(args)
if (args.cfg != None):
with open(args.cfg, 'r') as ymlfile:
cfg = yaml.load(ymlfile)
narea = get_area_def(args.areadef)
global_data = Scene(sensor="images", reader="generic_image", area=narea)
global_data.load(['image'])
global_data['image'].info['area'] = narea
fname = global_data['image'].info['filename']
ofname = fname[:-3] + "tif"
#global_data.save_dataset('image', filename="out.png", writer="simple_image")
global_data.save_dataset('image', filename=ofname, writer="ninjotiff",
sat_id=cfg['sat_id'],
chan_id=cfg['chan_id'],
data_cat=cfg['data_cat'],
data_source=cfg['data_src'],
physic_unit=cfg['ph_unit'])
parser.add_argument('--area', dest='areadef', action="store",
help="Area name, the definition must exist in your areas configuration file",
default="nrEURO1km_NPOL_COALeqc")
parser.add_argument('--ph_unit', dest='ph_unit', action="store", help="Physical unit", default="CELSIUS")
parser.add_argument('--data_src', dest='data_src', action="store", help="Data source", default="EUMETCAST")
args = parser.parse_args()
if (args.input_dir is not None):
os.chdir(args.input_dir)
cfg = vars(args)
if (args.cfg is not None):
with open(args.cfg, 'r') as ymlfile:
cfg = yaml.load(ymlfile, Loader=UnsafeLoader)
narea = get_area_def(args.areadef)
global_data = Scene(reader="generic_image")
global_data.load(['image'])
global_data['image'].info['area'] = narea
fname = global_data['image'].info['filename']
ofname = fname[:-3] + "tif"
# global_data.save_dataset('image', filename="out.png", writer="simple_image")
global_data.save_dataset('image', filename=ofname, writer="ninjotiff",
sat_id=cfg['sat_id'],
chan_id=cfg['chan_id'],
data_cat=cfg['data_cat'],
data_source=cfg['data_src'],
physic_unit=cfg['ph_unit'])
call(["bunzip2","/scratch/hamann/"+bz2file])
testfile=bz2file[:-4]
else:
raise ValueError("Unknown computer"+hostname+": no example file is provided")
filenames=[data_dir+testfile]
print(filenames)
#global_scene = Scene(platform_name="Meteosat-9", sensor="seviri", reader=reader, filenames=filenames)
global_scene = Scene(sensor="seviri", reader=reader, filenames=filenames)
#global_scene.load([0.6, 0.8, 10.8])
global_scene.load(['overview'])
#global_scene.load(["VIS006", "VIS008", "IR_108"])
global_scene.save_dataset('overview', data_dir+'/overview_global.png')
area="ccs4"
#area="SeviriDisk00"
local_scene = global_scene.resample("ccs4")
local_scene.save_dataset('overview', data_dir+'/overview_'+area+'.png')
#print (global_scene)
#print (global_scene[0.6])
#local_scene = global_scene.project("ccs4", precompute=True)
# print (global_scene.available_datasets()) ## does not work
#global_scene.show(0.6)
print("")
print("=======================")
print("resample to "+area)
local_scene = global_scene.resample(area)
print("=======================")
print("")
print("global_scene.available_composite_names()")
print(global_scene.available_composite_names())
print("")
print("=======================")
print("global_scene.save_dataset('overview', pngfile)")
pngfile='./'+sat+'_overview_global.png'
global_scene.save_dataset('overview', pngfile)
print('display '+pngfile+' &')
print("")
print("=======================")
#local_scene.show('overview')
pngfile='./'+sat+'_overview_'+area+'.png'
local_scene.save_dataset('overview', pngfile)
print('display '+pngfile+' &')
new=False
if new==False:
local_scene["ndvi"] = (local_scene[0.8] - local_scene[0.6]) / (local_scene[0.8] + local_scene[0.6])
#from satpy.enhancements import colorize
#colorize(img, **kwargs)
import cartopy.feature as cfeat
import pdb
import pyproj
FILENAMES = glob('/Users/dhueholt/Documents/Data/Sips/20181214/VNP*.nc')
print(len(FILENAMES))
SCN = Scene(reader='viirs_l1b', filenames=FILENAMES)
SCN.load(['DNB'])
SCN.load(['dnb_lon'])
SCN.load(['dnb_lat'])
# MY_AREA = SCN['DNB'].attrs['area'].compute_optimal_bb_area({'proj': 'lcc', 'lon_0': -96.,
# 'lat_0': 39., 'lat_1': 25.,
# 'lat_2': 25.})
# NEW_SCN = SCN.resample(MY_AREA)
SCN.save_dataset(
'DNB',
'/Users/dhueholt/Documents/Hollings_2019/aurora_images/20181214_06480718_08360906_dnb.tif'
)
# CRS = NEW_SCN['DNB'].attrs['area'].to_cartopy_crs()
# lambert_proj = ccrs.LambertConformal()
PlateCarree = ccrs.PlateCarree()
fig = plt.figure(frameon=False)
fig.set_size_inches(16, 10)
AX = plt.axes(projection=PlateCarree)
AX.coastlines()
AX.gridlines()
AX.set_global()
# pdb.set_trace()
data_plot = plt.pcolormesh(SCN['dnb_lon'].data,
SCN['dnb_lat'].data,
SCN['DNB'].data,
## get information about loaded channels
#print("========================")
#print(global_scene.datasets)
## show satellite pictures directly on the screen
#global_scene.show(0.6)
#global_scene.show('VIS006')
#global_scene.show('overview')
## save an RGB as png file
outputfile = time_start.strftime(cwd + '/MSG_' + RGB + '-global' +
'_%Y%m%d%H%M.png')
if False:
print("... save " + outputfile)
global_scene.save_dataset(RGB, outputfile)
print("========================")
## choose area
area = "ccs4"
#area="SeviriDisk00"
#area="cosmo7"
#area="cosmo1"
print("... resample data to another projection")
local_scene = global_scene.resample(area)
outputfile = time_start.strftime(cwd + '/MSG_' + RGB + '-' + area +
'_%Y%m%d%H%M.png')
print("... save " + outputfile)
add_border = True
if add_border:
