def _create_scene(self):
"""Create a scene object from available data.
Returns
-------
satpy.scene.Scene
Inialized scene object
"""
data = self.message.data
filter_parameters = {
"start_time": data["start_time"] - ORBIT_SLACK,
"end_time": data["end_time"] + ORBIT_SLACK,
"platform_name": data["platform_name"],
}
filenames = find_files_and_readers(
base_dir="/viirs/sdr",
reader="viirs_sdr",
filter_parameters=filter_parameters,
)
try:
scene = Scene(filenames=filenames, reader="viirs_sdr")
except ValueError as e:
logger.exception("Loading files didn't go well: %s", filenames)
raise e
return scene
def step_impl(context):
from satpy import Scene, find_files_and_readers
from datetime import datetime
os.chdir("/tmp/")
reader_files = find_files_and_readers(sensor="viirs",
start_time=datetime(2015, 3, 11, 11, 20),
end_time=datetime(2015, 3, 11, 11, 26))
scn = Scene(filenames=reader_files)
context.available_dataset_ids = scn.available_dataset_ids()
def step_impl_user_checks_availability(context):
from satpy import Scene, find_files_and_readers
from datetime import datetime
os.chdir("/tmp/")
reader_files = find_files_and_readers(sensor="viirs",
start_time=datetime(2015, 3, 11, 11, 20),
end_time=datetime(2015, 3, 11, 11, 26))
scn = Scene(filenames=reader_files)
context.available_dataset_ids = scn.available_dataset_ids()
def step_impl(context):
from satpy import Scene, find_files_and_readers
from datetime import datetime
os.chdir("/tmp/")
readers_files = find_files_and_readers(sensor='viirs',
start_time=datetime(2015, 3, 11, 11, 20),
end_time=datetime(2015, 3, 11, 11, 26))
scn = Scene(filenames=readers_files)
scn.load(["M02"])
context.scene = scn
def step_impl_user_loads_no_config(context):
from satpy import Scene, find_files_and_readers
from datetime import datetime
os.chdir("/tmp/")
readers_files = find_files_and_readers(sensor='viirs',
start_time=datetime(2015, 3, 11, 11, 20),
end_time=datetime(2015, 3, 11, 11, 26))
scn = Scene(filenames=readers_files)
scn.load(["M02"])
context.scene = scn
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 return_files(time, hrit_files):
from satpy import find_files_and_readers
from datetime import datetime
first = 0
last = len(time) - 1
yearF, monthF, dayF, hourF, minuteF, secondF = time[first].tt_calendar()
yearL, monthL, dayL, hourL, minuteL, secondL = time[last].tt_calendar()
if len(time) == 1:
time[last].tt = time[last].tt + 1 / 3600
yearL, monthL, dayL, hourL, minuteL, secondL = time[last].tt_calendar()
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')
return files
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 ()
# Exercise 6
from pathlib import Path
input_dir = Path("data")
# 1. Read the Scene that you downloaded from the data directory using SatPy. [2P]
from satpy import Scene, find_files_and_readers, writers, composites
files = find_files_and_readers(
base_dir="C:/Users/Kochti/Desktop/Studium/Python/exercise-6-TLKoch/data/",
reader="seviri_l1b_nc")
scn = Scene(filenames=files)
# 2. Load the composites "natural_color" and "convection" [2P]
scn.load(["natural_color"])
scn.load(["convection"])
# 3. Resample the fulldisk to the Dem. Rep. Kongo and its neighbours [4P]
# by defining your own area in Lambert Azimuthal Equal Area.
# Use the following settings:
# - lat and lon of origin: -3/23
# - width and height of the resulting domain: 500px
# - projection x/y coordinates of lower left: -15E5
# - projection x/y coordinates of upper right: 15E5
from pyresample.geometry import AreaDefinition
area_id = "Dem. Rep. Kongo"
description = "Dem. Rep. Kongo and its adjacent areas in Lambert Azimuthal Equal Area projection"
proj_id = "Kongo"
proj_dict = {"proj": "laea", "lat_0": -3, "lon_0": 23}
time_slot)
#global_data = GeostationaryFactory.create_scene("meteosat", "10", "seviri", time_slot)
# read data that you like to correct for parallax and the Cloud Top Height product of the NWC-SAF
## ----------------------------------------------------------------------------------------------
global_data.load(channels)
if verbose:
loaded_channels = [c.name for c in global_data.loaded_channels()]
print loaded_channels
# read CTH with satpy
from satpy import Scene, find_files_and_readers
files_nwc = find_files_and_readers(
sensor='seviri',
start_time=time_slot,
end_time=time_slot,
#base_dir=time_slot.strftime("/data/COALITION2/database/meteosat/SAFNWC_v2013/%Y/%m/%d/"),
base_dir=time_slot.strftime(
"/data/COALITION2/database/meteosat/SAFNWC_v2016/%Y/%m/%d/CTTH/"),
reader='nc_nwcsaf_msg')
#print files_nwc
#files = dict(files_sat.items() + files_nwc.items())
files = dict(files_nwc.items())
global_nwc = Scene(filenames=files)
#global_nwc.load(nwcsaf.product['cloud_top_height']) # this will load a RGB representation
global_nwc.load(['ctth_alti'])
if verbose:
print type(global_nwc['ctth_alti'].values)
# reproject data to your desired projection
## ----------------------------------------
#area="EuropeCanaryS95"
def photo_of_area(hrit_files,
central_lat,
central_lon,
elevation,
time,
dpi,
photo_path,
load_photo='VIS006',
all_lines=True):
"""Return path of photo
Parameters
----------
parameter_name : parameter_type
Quick description of it.
Returns
-------
photos: array
Array of saved on disc files.
"""
# Loading decompressed previously photos
from satpy import Scene
# from glob import glob
import matplotlib.pyplot as plt
from satpy import find_files_and_readers
from datetime import datetime
import cartopy.crs as ccrs
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("udalosie")
# 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])
new_scn = scn
crs = new_scn[load_photo].attrs['area'].to_cartopy_crs()
div = 30
y, x = polygon_surrounding_sat(central_lat, central_lon, elevation, div)
extent = calculate_extent(x, y)
ax = plt.axes(projection=ccrs.Sinusoidal(central_lon, central_lat))
ax.set_extent(extent)
if (all_lines):
ax.gridlines()
ax.coastlines(resolution='50m', color='red')
ax.coastlines()
# ax.gridlines()
# ax.set_global()
plt.imshow(new_scn[load_photo],
transform=crs,
extent=crs.bounds,
origin='upper',
cmap='gray')
# cbar = plt.colorbar()
# cbar.set_label("Kelvin")
# plt.imsave('imsave.png', im, cmap='gray')
name_img = photo_path + load_photo + "_" + datetime(
yearF, monthF, dayF, hourF, minuteF).__str__() + '.png'
plt.savefig(name_img, dpi=dpi)
# print(name_img)
return name_img
from satpy.utils import debug_on
debug_on()
from satpy import find_files_and_readers, Scene
from datetime import datetime
files_sat = find_files_and_readers(
sensor='seviri',
start_time=datetime(2015, 7, 7, 12, 0),
end_time=datetime(2015, 7, 7, 12, 0),
base_dir=
"/data/COALITION2/database/meteosat/radiance_HRIT/case-studies/2015/07/07/",
reader="seviri_l1b_hrit")
files = dict(files_sat.items())
global_scene = Scene(
filenames=files) # not allowed any more: reader="hrit_msg",
global_scene.load([10.8])
area = "EuropeCanaryS95"
local_scene = global_scene.resample(area)
import numpy as np
from satpy.composites import BWCompositor
from satpy.enhancements import colorize
from satpy.writers import to_image
arr = np.array([[0, 0, 0], [255, 0, 0]])
np.save("/tmp/binary_colormap.npy", arr)
# end_time=datetime(2017, 10, 11, 12, 59),
# base_dir="/home/a001673/data/satellite/Sentinel-3",
# reader='nc_olci_l1b')
#scn = Scene(filenames=files)"
#def find_files_and_readers(start_time=None, end_time=None, base_dir=None,
# reader=None, sensor=None, ppp_config_dir=get_environ_config_dir(),
# filter_parameters=None, reader_kwargs=None):
make_rgb = False
if make_rgb:
files_sat = find_files_and_readers(
sensor='seviri',
start_time=datetime(2015, 7, 7, 12, 0),
end_time=datetime(2015, 7, 7, 12, 0),
base_dir=
"/data/COALITION2/database/meteosat/radiance_HRIT/case-studies/2015/07/07/",
reader='seviri_l1b_hrit')
global_scene = Scene(filenames=files_sat)
# global_scene.readers
# {'seviri_l1b_hrit': <satpy.readers.yaml_reader.GEOSegmentYAMLReader object at 0x7ff6be969df0>}
#global_scene.load([0.6, 0.8, 10.8])
#global_scene.load(['IR_120', 'IR_134'])
global_scene.load(['overview', 0.6, 0.8])
#print(global_scene.keys())
#print(global_scene.datasets)
global_scene.available_dataset_names()
#from satpy.utils import debug_on
#debug_on()
# new version of satpy after 0.8
#################################
from satpy import Scene, find_files_and_readers
from datetime import datetime
#def find_files_and_readers(start_time=None, end_time=None, base_dir=None,
# reader=None, sensor=None, ppp_config_dir=get_environ_config_dir(),
# filter_parameters=None, reader_kwargs=None):
files = find_files_and_readers(
sensor='radar',
start_time=datetime(2018, 4, 27, 12, 0),
end_time=datetime(2018, 4, 27, 12, 15),
base_dir="/data/COALITION2/database/radar/odyssey/2018/04/27",
reader='h5_odyssey')
#print files
global_scene = Scene(filenames=files)
global_scene.load(['rr'])
global_scene.available_dataset_names()
#print(global_scene[0.6]) # works only if you load also the 0.6 channel, but not an RGB that contains the 0.6
#!!# print(global_scene['overview']) ### this one does only work in the develop version
#from satpy import DatasetID
#my_channel_id = DatasetID(name='IR_016', calibration='radiance')
#global_scene.load([my_channel_id])
- 'NUCAPS-sciEDR_{am_pm:2s}_{platform_shortname:3s}_s{start_time:%Y%m%d%H%M%S}_e{end_time:%Y%m%d%H%M%S}_STC_fsr.nc'
"""
#from satpy.utils import debug_on
#debug_on()
from satpy import Scene, find_files_and_readers
import numpy as np
from datetime import datetime
start_time = datetime.strptime("202003020000070", "%Y%m%d%H%M%S%f")
end_time = datetime.strptime("202003020000370", "%Y%m%d%H%M%S%f")
files_sat = find_files_and_readers(
start_time=start_time,
end_time=end_time, # creation_time=creation_time,
base_dir="/data/COALITION2/database/NUCAPS/2020/03/02",
reader='nucaps')
print(files_sat)
global_scene = Scene(reader="nucaps", filenames=files_sat)
var = "H2O_MR"
global_scene.load([var], pressure_levels=True)
#print(global_scene)
#print("============")
print(global_scene[var])
print(type(global_scene[var].data))
var_np = global_scene[var].data.compute()
def show_overall(hrit_files,
central_lat,
central_lon,
elevation,
time,
dpi,
photo_path,
all_lines=False,
pro='PlateCarree',
load_photo=['VIS006'],
save=False):
"""Return path of photo
Parameters
----------
pro : string
PlateCarree
Sinusoidal
Returns
-------
photos: array
Array of saved on disc files.
"""
from satpy.scene import Scene
from satpy import find_files_and_readers
from datetime import datetime
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import numpy as np
# %matplotlib inline
import matplotlib.pyplot as plt
### START - CALCULATES OVERALL EXTENT ###
div = 30
x, y = [], []
for i in range(len(central_lat)):
y_temp, x_temp = polygon_surrounding_sat(central_lat[i],
central_lon[i], elevation[i],
div)
x = np.append(x, x_temp)
y = np.append(y, y_temp)
extent = calculate_extent(x, y)
### END - CALCULATES OVERALL EXTENT ###
first = 0
last = len(time) - 1
yearF, monthF, dayF, hourF, minuteF, secondF = time[first].tt_calendar()
yearL, monthL, dayL, hourL, minuteL, secondL = time[last].tt_calendar()
if len(time) == 1:
time[last].tt = time[last].tt + 1 / 3600
yearL, monthL, dayL, hourL, minuteL, secondL = time[last].tt_calendar()
# 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)
new_scn = scn
crs = new_scn[load_photo[0]].attrs['area'].to_cartopy_crs()
# ax = plt.axes(projection=crs)
central_lon, central_lat = (extent[0] + extent[1]) / 2, (extent[2] +
extent[3]) / 2
if pro == 'PlateCarree':
ax = plt.axes(projection=ccrs.PlateCarree())
pro_name = '_PlateCarree_'
ax.set_global()
if pro == 'Sinusoidal':
ax = plt.axes(projection=ccrs.Sinusoidal(central_lon, central_lat))
ax.set_extent(extent)
pro_name = '_Sinusoidal_'
if all_lines:
ax.coastlines(color='red')
ax.gridlines(color='red')
ax.add_feature(cfeature.BORDERS, color='red')
pro_name = pro_name + 'lines_'
plt.imshow(new_scn[load_photo[0]],
transform=crs,
extent=crs.bounds,
origin='upper',
cmap='gray')
if save:
if isinstance(load_photo[0], float):
photo_type = str(load_photo[0])
else:
photo_type = load_photo[0]
name = photo_path + photo_type + pro_name + '_{date:%Y-%m-%d_%H_%M_%S}.png'.format(
date=scn.start_time)
plt.savefig(name, dpi=dpi)
if not save:
plt.show()
return ()
def set_of_photos(hrit_files,
central_lat,
central_lon,
elevation,
time,
dpi,
photo_path,
all_lines=False,
load_photo=['VIS006']):
"""Return path of photo
Parameters
----------
parameter_name : parameter_type
Quick description of it.
Returns
-------
photos: array
Array of saved on disc files.
"""
# Loading decompressed previously photos
from satpy import Scene
import numpy as np
# from glob import glob
import matplotlib.pyplot as plt
from satpy import find_files_and_readers
from datetime import datetime
import cartopy.crs as ccrs
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("udalosie")
# 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)
load_photo = scn.available_dataset_names()
# HRV does not work properly
load_photo.remove('HRV')
# print(load_photo)
# print(scn.available_dataset_names())
div = 30
name_img = []
for photo_type in load_photo:
# print(photo_type)
scn.load([photo_type])
# print(scn)
for i in range(len(time)):
new_scn = scn
# print(new_scn)
crs = new_scn[photo_type].attrs['area'].to_cartopy_crs()
y, x = polygon_surrounding_sat(central_lat[i], central_lon[i],
elevation[i], div)
extent = calculate_extent(x, y)
ax = plt.axes(
projection=ccrs.Sinusoidal(central_lon[i], central_lat[i]))
ax.set_extent(extent)
if (all_lines):
ax.gridlines()
ax.coastlines(resolution='50m', color='red')
ax.coastlines()
# ax.gridlines()
# ax.set_global()
plt.imshow(new_scn[photo_type],
transform=crs,
extent=crs.bounds,
origin='upper',
cmap='gray')
# cbar = plt.colorbar()
# cbar.set_label("Kelvin")
# plt.imsave('imsave.png', im, cmap='gray')
name = photo_path + photo_type + "_" + time[i].utc_iso() + '.png'
plt.savefig(name, dpi=dpi)
name_img = np.append(name_img, name)
return (name_img)
from datetime import datetime
from satpy import Scene, find_files_and_readers
from pyresample import load_area
from eoana.config import Settings
if __name__ == '__main__':
settings = Settings()
sen3_data_l2 = 'C:/Temp/Satellit/sentinel_data'
# datetime(YEAR, MOMNTH, DAY, HOUR, MINUTE)
filenames = find_files_and_readers(
start_time=datetime(2021, 7, 25, 7, 20),
end_time=datetime(2021, 7, 25, 11, 25),
base_dir=sen3_data_l2,
reader='olci_l2',
sensor='olci',
)
""" Create Scene object """
scn = Scene(filenames=filenames)
""" Load selected datasets
Available OLCI Level 2 datasets are:
'chl_nn.nc','chl_oc4me.nc',
'iop_nn.nc','iwv.nc','par.nc','trsp.nc','tsm_nn.nc','w_aer.nc',
'Oa01_reflectance.nc','Oa02_reflectance.nc','Oa03_reflectance.nc','Oa04_reflectance.nc',
'Oa05_reflectance.nc','Oa06_reflectance.nc','Oa07_reflectance.nc','Oa08_reflectance.nc',
'Oa09_reflectance.nc','Oa10_reflectance.nc','Oa11_reflectance.nc','Oa12_reflectance.nc',
'Oa16_reflectance.nc','Oa17_reflectance.nc','Oa18_reflectance.nc','Oa21_reflectance.nc',
'wqsf.nc'
"""
def read_GOES_satpy(date_str, channel, zoom = True):
# Extract the channel wavelength using the input string
# -----------------------------------------------------
channel = channel_dict[str(channel)]['wavelength']
# Determine the correct GOES files associated with the date
# ---------------------------------------------------------
dt_date_str = datetime.strptime(date_str,"%Y%m%d%H%M")
dt_date_str_end = dt_date_str + timedelta(minutes = 10)
# Use the Satpy find_files_and_readers to grab the files
# ------------------------------------------------------
files = find_files_and_readers(start_time = dt_date_str, \
end_time = dt_date_str_end, base_dir = data_dir, reader = 'abi_l1b')
print(files)
# Extract the goes true-color plot limits
# ----------------------------------------
lat_lims = [29.5, 48.0]
lon_lims = [-122.0, -100.5]
# Use satpy (Scene) to open the file
# ----------------------------------
scn = Scene(reader = 'abi_l1b', filenames = files)
# Load the desired channel data
# -----------------------------
scn.load([channel], calibration = [calib_dict[channel]])
## Set the map projection and center the data
## ------------------------------------------
#my_area = scn[channel].attrs['area'].compute_optimal_bb_area({\
# 'proj':'lcc', 'lon_0': lon_lims[0], 'lat_0': lat_lims[0], \
# 'lat_1': lat_lims[0], 'lat_2': lat_lims[0]})
#new_scn = scn.resample(my_area)
##!## Enhance the image for plotting
##!## ------------------------------
##!#var = get_enhanced_image(scn[channel]).data
##!#var = var.transpose('y','x','bands')
# Zoom the image on the desired area
# ----------------------------------
if(zoom):
scn = scn.crop(ll_bbox = (lon_lims[0] + 0.65, lat_lims[0], \
lon_lims[1] - 0.65, lat_lims[1]))
# Extract the lats, lons, and data
# -----------------------------------------------------
lons, lats = scn[channel].attrs['area'].get_lonlats()
var = scn[channel].data
# Extract the map projection from the data for plotting
# -----------------------------------------------------
crs = scn[channel].attrs['area'].to_cartopy_crs()
# Extract the appropriate units
# -----------------------------
units = label_dict[channel]
#units = scn[channel].units
plabel = calib_dict[channel].title() + ' [' + units + ']'
return var, crs, lons, lats, lat_lims, lon_lims, plabel
# substracting one scan time (as the start time of scan is returned)
t1 -= timedelta(seconds=nmin * 60)
LOG.info(" start time of last scan: " + str(t1))
return t1
sat = "MSG3"
start_time = get_last_SEVIRI_date(True, delay=5)
print(start_time)
files_sat = find_files_and_readers(sensor='seviri',
start_time=start_time,
end_time=start_time,
base_dir="/data/cinesat/in/eumetcast1/",
reader='seviri_l1b_hrit')
files = deepcopy(files_sat['seviri_l1b_hrit'])
for f in files:
if not (sat in f):
files_sat['seviri_l1b_hrit'].remove(f)
continue
global_scene = Scene(reader="seviri_l1b_hrit", filenames=files_sat)
print("")
print("=======================")
print(dir(global_scene))
print("=======================")
show_interactively = False
save_black_white_png = False
print("")
print("")
print("*** Creating LSCL (low stratus confidence level) product")
print("")
# read MSG (full disk service) L2
#################################
print("... read " + sat + " L1.5 data")
print(" search for HRIT files in " + base_dir_sat)
files_sat = find_files_and_readers(sensor='seviri',
start_time=start_time,
end_time=start_time,
base_dir=base_dir_sat,
reader='seviri_l1b_hrit')
files = deepcopy(files_sat['seviri_l1b_hrit'])
#print(" found SEVIRI files: ", files_sat)
for f in files:
if not (sat in f):
files_sat['seviri_l1b_hrit'].remove(f)
continue
if ("HRV" in f) or ("VIS006" in f) or ("VIS008" in f) or (
"IR_016" in f) or ("IR_039" in f):
files_sat['seviri_l1b_hrit'].remove(f)
continue
if ("WV_062" in f) or ("WV_073" in f) or ("IR_097" in f) or (
"IR_108" in f) or ("IR_134" in f):
def colorplot_with_band(self,
band,
HSD_Dir,
imgFile,
*args,
axLatRange=[20, 60],
axLonRange=[90, 140],
cmap=None,
pixels=100,
**kwargs):
"""
colorplot the variables together with radiance data.
Parameters
----------
band: int
band number [1-16]. See band specification in
`../doc/2018_A_Yamashita.md`
HSD_Dir: str
path for hosting the HSD files.
imgFile: str
filename of the exported image
Keywords
--------
axLatRange: list
latitude range of the plot (default: [20, 60]). [degree]
axLonRange: list
longitude range of the plot (default: [90, 140]). [degree]
cmap: str
colormap name.
pixels: int
resampled pixels of the band data (default: 100). Take care of
time consumption when pixels > 1000!
History
-------
2020-02-24 First version.
"""
files = find_files_and_readers(
start_time=(self.mTime - dt.timedelta(seconds=300)),
end_time=(self.mTime + dt.timedelta(seconds=300)),
base_dir=HSD_Dir,
reader='ahi_hsd')
matched_files = []
for file in files['ahi_hsd']:
if fnmatch.fnmatch(
os.path.basename(file),
'HS_H08_*_B{0:02d}_FLDK_*_S0[1234]*DAT*'.format(band)):
matched_files.append(file)
h8_scene = Scene(filenames=matched_files,
reader='ahi_hsd',
sensor='ahi')
band_label = 'B{0:02d}'.format(band)
h8_scene.load([band_label])
roi = create_area_def('roi', {
'proj': 'eqc',
'ellps': 'WGS84'
},
width=pixels,
height=pixels,
area_extent=[
axLonRange[0], axLatRange[0], axLonRange[1],
axLatRange[1]
],
units='degrees')
roi_scene = h8_scene.resample(roi)
# read China boundaries
with open(os.path.join(PROJECTDIR, 'include', 'CN-border-La.dat'),
'r') as fd:
context = fd.read()
blocks = [cnt for cnt in context.split('>') if len(cnt) > 0]
borders = [
np.fromstring(block, dtype=float, sep=' ') for block in blocks
]
LON, LAT = np.meshgrid(self.lon, self.lat)
fig = plt.figure(figsize=[8, 8])
plt.tight_layout(False)
# Set projection and plot the main figure
ax1 = plt.axes([0.1, 0.1, 0.8, 0.8], projection=ccrs.PlateCarree())
# Add ocean, land, rivers and lakes
ax1.add_feature(cfeature.OCEAN.with_scale('50m'))
ax1.add_feature(cfeature.LAND.with_scale('50m'))
ax1.add_feature(cfeature.RIVERS.with_scale('50m'))
ax1.add_feature(cfeature.LAKES.with_scale('50m'))
# Plot border lines
for line in borders:
ax1.plot(line[0::2],
line[1::2],
'-',
lw=1,
color='k',
transform=ccrs.Geodetic())
# loading colormap
if cmap is None:
cmap = chiljet_colormap()
# Plot gridlines
crs = roi.to_cartopy_crs()
pcmesh_band = ax1.imshow(roi_scene[band_label],
transform=crs,
origin='upper',
extent=crs.bounds,
cmap='Greys')
pcmesh = ax1.pcolormesh(LON,
LAT,
self.data,
vmin=kwargs['vmin'],
vmax=kwargs['vmax'],
cmap=cmap,
transform=ccrs.PlateCarree())
ax1.set_xticks(
np.linspace(axLonRange[0], axLonRange[1], 5, endpoint=True))
ax1.set_yticks(
np.linspace(axLatRange[0], axLatRange[1], 5, endpoint=True))
lon_formatter = LongitudeFormatter(number_format='.1f',
degree_symbol='',
dateline_direction_label=True)
lat_formatter = LatitudeFormatter(number_format='.1f',
degree_symbol='')
ax1.xaxis.set_major_formatter(lon_formatter)
ax1.yaxis.set_major_formatter(lat_formatter)
ax1.set_ylim(axLatRange)
ax1.set_xlim(axLonRange)
ax1.set_title('{0} {1}'.format(
self.mTime.strftime('%Y-%m-%d %H:%M (Himawari-8)'),
self.long_name))
if 'cb_ticks' not in kwargs.keys():
kwargs['cb_ticks'] = np.linspace(kwargs['vmin'],
kwargs['vmax'],
5,
endpoint=True)
cbar = fig.colorbar(pcmesh,
fraction=0.03,
ticks=kwargs['cb_ticks'],
orientation='vertical')
cbar.ax.tick_params(direction='out', labelsize=15, pad=5)
cbar.ax.set_title(self.unit, fontsize=10)
if 'cb_ticklabels' in kwargs.keys():
cbar.ax.set_yticklabels(kwargs['cb_ticklabels'])
# Show figure
# plt.show()
plt.savefig(imgFile)
plt.close()
def __init__(self,
filenames=None,
reader=None,
filter_parameters=None,
reader_kwargs=None,
ppp_config_dir=get_environ_config_dir(),
base_dir=None,
sensor=None,
start_time=None,
end_time=None,
area=None):
"""Initialize Scene with Reader and Compositor objects.
To load data `filenames` and preferably `reader` must be specified. If `filenames` is provided without `reader`
then the available readers will be searched for a Reader that can support the provided files. This can take
a considerable amount of time so it is recommended that `reader` always be provided. Note without `filenames`
the Scene is created with no Readers available requiring Datasets to be added manually::
scn = Scene()
scn['my_dataset'] = Dataset(my_data_array, **my_info)
Args:
filenames (iterable or dict): A sequence of files that will be used to load data from. A ``dict`` object
should map reader names to a list of filenames for that reader.
reader (str or list): The name of the reader to use for loading the data or a list of names.
filter_parameters (dict): Specify loaded file filtering parameters.
Shortcut for `reader_kwargs['filter_parameters']`.
reader_kwargs (dict): Keyword arguments to pass to specific reader instances.
ppp_config_dir (str): The directory containing the configuration files for satpy.
base_dir (str): (DEPRECATED) The directory to search for files containing the
data to load. If *filenames* is also provided,
this is ignored.
sensor (list or str): (DEPRECATED: Use `find_files_and_readers` function) Limit used files by provided
sensors.
area (AreaDefinition): (DEPRECATED: Use `filter_parameters`) Limit used files by geographic area.
start_time (datetime): (DEPRECATED: Use `filter_parameters`) Limit used files by starting time.
end_time (datetime): (DEPRECATED: Use `filter_parameters`) Limit used files by ending time.
"""
super(Scene, self).__init__()
# Set the PPP_CONFIG_DIR in the environment in case it's used elsewhere in pytroll
LOG.debug("Setting 'PPP_CONFIG_DIR' to '%s'", ppp_config_dir)
os.environ["PPP_CONFIG_DIR"] = self.ppp_config_dir = ppp_config_dir
if not filenames and (start_time or end_time or base_dir):
import warnings
warnings.warn(
"Deprecated: Use " +
"'from satpy import find_files_and_readers' to find files")
from satpy import find_files_and_readers
filenames = find_files_and_readers(
start_time=start_time,
end_time=end_time,
base_dir=base_dir,
reader=reader,
sensor=sensor,
ppp_config_dir=self.ppp_config_dir,
reader_kwargs=reader_kwargs,
)
elif start_time or end_time or area:
import warnings
warnings.warn(
"Deprecated: Use " +
"'filter_parameters' to filter loaded files by 'start_time', "
+ "'end_time', or 'area'.")
fp = filter_parameters if filter_parameters else {}
fp.update({
'start_time': start_time,
'end_time': end_time,
'area': area,
})
filter_parameters = fp
if filter_parameters:
if reader_kwargs is None:
reader_kwargs = {}
reader_kwargs.setdefault('filter_parameters',
{}).update(filter_parameters)
if filenames and isinstance(filenames, str):
raise ValueError(
"'filenames' must be a list of files: Scene(filenames=[filename])"
)
self.readers = self.create_reader_instances(
filenames=filenames, reader=reader, reader_kwargs=reader_kwargs)
self.attrs.update(self._compute_metadata_from_readers())
self.datasets = DatasetDict()
self.cpl = CompositorLoader(self.ppp_config_dir)
comps, mods = self.cpl.load_compositors(self.attrs['sensor'])
self.wishlist = set()
self.dep_tree = DependencyTree(self.readers, comps, mods)
self.resamplers = {}
# Exercise 6
from pathlib import Path
import satpy
from pyresample.geometry import AreaDefinition
from utils import createResArea
input_dir = Path("data")
input_dir.mkdir(parents=True, exist_ok=True)
plot_dir = Path("plots")
plot_dir.mkdir(parents=True, exist_ok=True)
# 1. Read the Scene that you downloaded from the data directory using SatPy. [2P]
files = satpy.find_files_and_readers(base_dir=input_dir)
scn = satpy.Scene(files)
# 2. Load the composites "natural_color" and "convection" [2P]
scn.load(["natural_color", "convection"])
# 3. Resample the fulldisk to the Dem. Rep. Kongo and its neighbours [4P]
# by defining your own area in Lambert Azimuthal Equal Area.
# Use the following settings:
# - lat and lon of origin: -3/23
# - width and height of the resulting domain: 500px
# - projection x/y coordinates of lower left: -15E5
# - projection x/y coordinates of upper right: 15E5
# resample to defined area (using function from utils)
def_area = createResArea(
area_ID="Kongo",
description=
"The Kongo and neighbouring countries in Lambert Equal Area projection",
proj_id="LambertEqualArea",
del(combined.attrs['wavelength'])
del(combined.attrs['resolution'])
del(combined.attrs['polarization'])
del(combined.attrs['calibration'])
del(combined.attrs['level'])
del(combined.attrs['start_time'])
del(combined.attrs['end_time'])
from satpy import Scene, find_files_and_readers
from datetime import datetime
start_time = datetime.strptime("202002031333190", "%Y%m%d%H%M%S%f")
end_time = datetime.strptime("202002031333190", "%Y%m%d%H%M%S%f")
files_sat = find_files_and_readers(start_time=start_time, end_time=end_time,
base_dir="/data/COALITION2/database/NUCAPS/2020/02/03",
reader='nucaps',prerequisites=[DatasetID('hej')])
global_scene = Scene(reader="nucaps", filenames=files_sat)
global_scene.load(["Temperature"],pressure_levels=True)
del(global_scene[var].attrs['wavelength'])
del(global_scene[var].attrs['resolution'])
del(global_scene[var].attrs['polarization'])
del(global_scene[var].attrs['calibration'])
del(global_scene[var].attrs['level'])
del(global_scene[var].attrs['modifiers'])
global_scene[var].attrs.shape = (120,100)
global_scene[var].attrs['start_time'] = str(global_scene[var].attrs['start_time'])
global_scene[var].attrs['end_time'] = str(global_scene[var].attrs['end_time'])
# Exercise 6
from satpy import Scene, find_files_and_readers, writers, composites
input_dir = "/home/eike/Documents/studium/10_Semester/python_kurs/exercise-6-eikeschott/data/"
output_dir= "/home/eike/Documents/studium/10_Semester/python_kurs/exercise-6-eikeschott/data/output"
# 1. Read the Scene that you downloaded from the data directory using SatPy. [2P]
files = find_files_and_readers(base_dir=input_dir, reader="seviri_l1b_nc")
scn = Scene(filenames=files)
# 2. Load the composites "natural_color" and "convection" [2P]
scn.load(["natural_color"])
scn.load(["convection"])
# 3. Resample the fulldisk to the Dem. Rep. Kongo and its neighbours [4P]
# by defining your own area in Lambert Azimuthal Equal Area.
# Use the following settings:
# - lat and lon of origin: -3/23
# - width and height of the resulting domain: 500px
# - projection x/y coordinates of lower left: -15E5
# - projection x/y coordinates of upper right: 15E5
area_id = "Dem. Rep. Kongo"
description = "Dem. Rep. Kongo und Umgebung in der Lambert Azimuthal Equal Area Projektion"
proj_id = "Dem. Rep. Kongo"
proj_dict = {"proj": "laea", "lat_ts": -3, "lon_0": 23}
width = 500
height = 500
# -*- 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')
# Exercise 6
from pathlib import Path
main_dir = Path("./")
output_dir = main_dir / "results"
import utils as uts
# 1. Read the Scene that you downloaded from the data directory using SatPy. [2P]
import satpy as satpy
files = satpy.find_files_and_readers(base_dir="./data", reader="seviri_l1b_nc")
scn = satpy.Scene(filenames=files) #save it as a satpy scene
scn.available_composite_names() #to check which composites are aviable
# 2. Load the composites "natural_color" and "convection" [2P]
scn.load(["natural_color"]) # can be skipped thanks to function printSatImg
scn.load(["convection"]) # can be skipped thanks to function printSatImg
# 3. Resample the fulldisk to the Dem. Rep. Kongo and its neighbours [4P]
# by defining your own area in Lambert Azimuthal Equal Area.
# Use the following settings:
# - lat and lon of origin: -3/23
# - width and height of the resulting domain: 500px
# - projection x/y coordinates of lower left: -15E5
# - projection x/y coordinates of upper right: 15E5
# see function printSatImg in Task 4
# 4. Save both loaded composites of the resampled Scene as simple png images. [2P]
uts.dirCreate(output_dir)
uts.printSatImg(scn=scn,
scene="natural_color",
proj="laea",
lat_or=-3,
lon_or=23,
width=500,
# import os
# os.environ['PROJ_LIB'] = '/_PYTHON_INSTALLATION_PATH_/Library/share/proj'
import numpy as np
from datetime import datetime
from satpy import Scene, find_files_and_readers
from senplot.plotting.map import PlotSatProd
if __name__ == '__main__':
sen3_data_l2 = 'C:/Temp/Satellit/sentinel_data'
# datetime(YEAR, MOMNTH, DAY, HOUR, MINUTE)
filenames = find_files_and_readers(
start_time=datetime(2020, 6, 1, 7, 10),
end_time=datetime(2020, 6, 1, 12, 50),
base_dir=sen3_data_l2,
reader='slstr_l2',
sensor='slstr_l2',
)
""" Create Scene object """
scn = Scene(filenames=filenames)
""" Load selected datasets
Available SLSTR Level 2 datasets are:
'sea_surface_temperature', 'longitude', 'latitude'
"""
datasets = ['sea_surface_temperature', 'longitude', 'latitude']
scn.load(datasets)
""" Resample data to grid (projecting) """
# area_spec = 'baws' # 1000 m resolution grid over the Baltic Sea incl. Kattegatt and Skagerrak
# scn = scn.resample(area_spec, radius_of_influence=1000)
""" Temperature data are stored as Kelvin degrees. Convert to Celsius values: """
start_time = get_last_SEVIRI_date(False, delay=3)
end_time = start_time
base_dir = "/data/cinesat/in/eumetcast1/"
#file_pattern='S_NWC_' + product + '_' + sat_id + '_*_' + timestamp_NWCSAF + extension
#if glob.glob(file_pattern):
# print ("NWCSAF data product " + product + " exists and is readable")
# # creates list of available products
# product_list_to_process.append(nwcsaf.product.get(product)[:])
#else:
# print ("NWCSAF data product " + product + " is missing or is not readable")
print("... search files in " + base_dir)
files_nwc = find_files_and_readers(sensor='seviri',
start_time=start_time,
end_time=end_time,
base_dir=base_dir,
reader='nwcsaf-geo')
#print (files_nwc)
#files = dict(files_sat.items() + files_nwc.items())
files = dict(list(files_nwc.items()))
global_scene = Scene(filenames=files)
global_scene.available_dataset_names()
#!!# print(global_scene['overview']) ### this one does only work in the develop version
print("")
print("available_composite_names")
print(global_scene.available_composite_names())
if make_images:
# import os
# os.environ['PROJ_LIB'] = '/_PYTHON_INSTALLATION_PATH_/Library/share/proj'
import numpy as np
from datetime import datetime
from satpy import Scene, find_files_and_readers
from senplot.plotting.map import PlotSatProd
if __name__ == '__main__':
sen3_data_l2 = 'C:/Temp/Satellit/sentinel_data'
# datetime(YEAR, MOMNTH, DAY, HOUR, MINUTE)
filenames = find_files_and_readers(
start_time=datetime(2021, 2, 13, 7, 10),
end_time=datetime(2021, 2, 13, 12, 50),
base_dir=sen3_data_l2,
reader='olci_l2',
sensor='olci',
)
""" Create Scene object """
scn = Scene(filenames=filenames)
""" Load selected datasets
Available OLCI Level 2 datasets are:
'chl_nn.nc','chl_oc4me.nc',
'iop_nn.nc','iwv.nc','par.nc','trsp.nc','tsm_nn.nc','w_aer.nc',
'Oa01_reflectance.nc','Oa02_reflectance.nc','Oa03_reflectance.nc','Oa04_reflectance.nc','Oa05_reflectance.nc','Oa06_reflectance.nc','Oa07_reflectance.nc','Oa08_reflectance.nc','Oa09_reflectance.nc','Oa10_reflectance.nc','Oa11_reflectance.nc','Oa12_reflectance.nc','Oa16_reflectance.nc','Oa17_reflectance.nc','Oa18_reflectance.nc','Oa21_reflectance.nc',
'wqsf.nc'
"""
datasets = [
'chl_nn',
# 'chl_oc4me',
