def get_lonlats(self):
"""Obtain GCPs and construct latitude and longitude arrays.
Args:
band (gdal band): Measurement band which comes with GCP's
array_shape (tuple) : The size of the data array
Returns:
coordinates (tuple): A tuple with longitude and latitude arrays
"""
band = self.filehandle
band_x_size = band.RasterXSize
band_y_size = band.RasterYSize
(xpoints, ypoints), (gcp_lons, gcp_lats) = self.get_gcps()
fine_cols = np.arange(band_x_size)
fine_rows = np.arange(band_y_size)
satint = GeoInterpolator((gcp_lons, gcp_lats), (ypoints, xpoints),
(fine_rows, fine_cols), 2, 2)
longitudes, latitudes = satint.interpolate()
# FIXME: check if the array is C-contigious, and make it such if it
# isn't
if longitudes.flags['CONTIGUOUS'] is False:
longitudes = np.ascontiguousarray(longitudes)
if latitudes.flags['CONTIGUOUS'] is False:
latitudes = np.ascontiguousarray(latitudes)
return longitudes, latitudes
def modis1kmto250m(lons1km, lats1km, cores=1):
"""Getting 250m geolocation for modis from 1km tiepoints.
http://www.icare.univ-lille1.fr/tutorials/MODIS_geolocation
"""
if cores > 1:
return _multi(modis1kmto250m, lons1km, lats1km, 10, cores)
cols1km = np.arange(1354)
cols250m = np.arange(1354 * 4) / 4.0
along_track_order = 1
cross_track_order = 3
lines = lons1km.shape[0]
rows1km = np.arange(lines)
rows250m = (np.arange(lines * 4) - 1.5) / 4.0
satint = SatelliteInterpolator((lons1km, lats1km),
(rows1km, cols1km),
(rows250m, cols250m),
along_track_order,
cross_track_order,
chunk_size=40)
satint.fill_borders("y", "x")
lons250m, lats250m = satint.interpolate()
return lons250m, lats250m
def modis1kmto250m(lons1km, lats1km, cores=1):
"""Getting 250m geolocation for modis from 1km tiepoints.
"""
if cores > 1:
return _multi(modis1kmto250m, lons1km, lats1km, 10, cores)
cols1km = np.arange(0, 5416, 4)
cols250m = np.arange(5416)
along_track_order = 1
cross_track_order = 3
lines = lons1km.shape[0] * 4
rows1km = np.arange(1.5, lines, 4)
rows250m = np.arange(lines)
satint = SatelliteInterpolator((lons1km, lats1km),
(rows1km, cols1km),
(rows250m, cols250m),
along_track_order,
cross_track_order,
chunk_size=40)
satint.fill_borders("y", "x")
lons250m, lats250m = satint.interpolate()
return lons250m, lats250m
def modis5kmto1km(lons5km, lats5km):
"""Getting 1km geolocation for modis from 5km tiepoints.
http://www.icare.univ-lille1.fr/tutorials/MODIS_geolocation
"""
from geotiepoints.geointerpolator import \
GeoInterpolator as SatelliteInterpolator
# FIXME: I changed the values here to work with MOD06 - but why?!
cols5km = np.arange(2, 1350, 5) / 5.0
cols1km = np.arange(1350) / 5.0
lines = lons5km.shape[0] * 5
rows5km = np.arange(2, lines, 5) / 5.0
rows1km = np.arange(lines) / 5.0
along_track_order = 1
cross_track_order = 3
satint = SatelliteInterpolator((lons5km, lats5km), (rows5km, cols5km),
(rows1km, cols1km),
along_track_order,
cross_track_order,
chunk_size=10)
satint.fill_borders("y", "x")
lons1km, lats1km = satint.interpolate()
return lons1km, lats1km
def modis5kmto1km(lons5km, lats5km):
"""Getting 1km geolocation for modis from 5km tiepoints.
http://www.icare.univ-lille1.fr/tutorials/MODIS_geolocation
"""
from geotiepoints.geointerpolator import \
GeoInterpolator as SatelliteInterpolator
# FIXME: I changed the values here to work with MOD06 - but why?!
cols5km = np.arange(2, 1350, 5) / 5.0
cols1km = np.arange(1350) / 5.0
lines = lons5km.shape[0] * 5
rows5km = np.arange(2, lines, 5) / 5.0
rows1km = np.arange(lines) / 5.0
along_track_order = 1
cross_track_order = 3
satint = SatelliteInterpolator((lons5km, lats5km),
(rows5km, cols5km),
(rows1km, cols1km),
along_track_order,
cross_track_order,
chunk_size=10)
satint.fill_borders("y", "x")
lons1km, lats1km = satint.interpolate()
return lons1km, lats1km
def modis1kmto250m(lons1km, lats1km, cores=1):
"""Getting 250m geolocation for modis from 1km tiepoints.
http://www.icare.univ-lille1.fr/tutorials/MODIS_geolocation
"""
if cores > 1:
return _multi(modis1kmto250m, lons1km, lats1km, 10, cores)
cols1km = np.arange(1354)
cols250m = np.arange(1354 * 4) / 4.0
along_track_order = 1
cross_track_order = 3
lines = lons1km.shape[0]
rows1km = np.arange(lines)
rows250m = (np.arange(lines * 4) - 1.5) / 4.0
satint = SatelliteInterpolator((lons1km, lats1km), (rows1km, cols1km),
(rows250m, cols250m),
along_track_order,
cross_track_order,
chunk_size=40)
satint.fill_borders("y", "x")
lons250m, lats250m = satint.interpolate()
return lons250m, lats250m
def test_extrapolate_rows(self):
lons = np.arange(10).reshape((2, 5), order="F")
lats = np.arange(10).reshape((2, 5), order="C")
lines = np.array([2, 7])
cols = np.array([2, 7, 12, 17, 22])
hlines = np.arange(10)
hcols = np.arange(24)
satint = GeoInterpolator((lons, lats), (lines, cols), (hlines, hcols))
np.testing.assert_allclose(
satint._extrapolate_rows(satint.tie_data[0], hlines, -0.4, 9.4),
TIES_EXP4)
def test_extrapolate_rows(self):
lons = np.arange(10).reshape((2, 5), order="F")
lats = np.arange(10).reshape((2, 5), order="C")
lines = np.array([2, 7])
cols = np.array([2, 7, 12, 17, 22])
hlines = np.arange(10)
hcols = np.arange(24)
satint = GeoInterpolator((lons, lats), (lines, cols), (hlines, hcols))
np.testing.assert_allclose(satint._extrapolate_rows(satint.tie_data[0],
hlines, -0.4, 9.4),
TIES_EXP4)
def test_extrapolate_cols(self):
lons = np.arange(10).reshape((2, 5), order="F")
lats = np.arange(10).reshape((2, 5), order="C")
lines = np.array([2, 7])
cols = np.array([2, 7, 12, 17, 22])
hlines = np.arange(10)
hcols = np.arange(24)
satint = GeoInterpolator((lons, lats), (lines, cols), (hlines, hcols))
self.assertTrue(np.allclose(satint._extrapolate_cols(satint.tie_data[0]),
TIES_EXP2))
def test_extrapolate_cols(self):
lons = np.arange(10).reshape((2, 5), order="F")
lats = np.arange(10).reshape((2, 5), order="C")
lines = np.array([2, 7])
cols = np.array([2, 7, 12, 17, 22])
hlines = np.arange(10)
hcols = np.arange(24)
satint = GeoInterpolator((lons, lats), (lines, cols), (hlines, hcols))
self.assertTrue(
np.allclose(satint._extrapolate_cols(satint.tie_data[0]),
TIES_EXP2))
def test_fill_col_borders(self):
lons = np.arange(10).reshape((2, 5), order="F")
lats = np.arange(10).reshape((2, 5), order="C")
lines = np.array([2, 7])
cols = np.array([2, 7, 12, 17, 22])
hlines = np.arange(10)
hcols = np.arange(24)
satint = GeoInterpolator((lons, lats), (lines, cols), (hlines, hcols))
satint._fill_col_borders()
np.testing.assert_allclose(satint.tie_data[0], TIES_EXP7)
np.testing.assert_allclose(satint.col_indices,
np.array([0, 2, 7, 12, 17, 22, 23]))
def test_fill_col_borders(self):
lons = np.arange(10).reshape((2, 5), order="F")
lats = np.arange(10).reshape((2, 5), order="C")
lines = np.array([2, 7])
cols = np.array([2, 7, 12, 17, 22])
hlines = np.arange(10)
hcols = np.arange(24)
satint = GeoInterpolator((lons, lats), (lines, cols), (hlines, hcols))
satint._fill_col_borders()
np.testing.assert_allclose(satint.tie_data[0], TIES_EXP7)
np.testing.assert_allclose(satint.col_indices,
np.array([0, 2, 7, 12, 17, 22, 23]))
def metop20kmto1km(lons20km, lats20km):
"""Getting 1km geolocation for metop avhrr from 20km tiepoints.
"""
cols20km = np.array([0] + range(4, 2048, 20) + [2047])
cols1km = np.arange(2048)
lines = lons20km.shape[0]
rows20km = np.arange(lines)
rows1km = np.arange(lines)
along_track_order = 1
cross_track_order = 3
satint = SatelliteInterpolator((lons20km, lats20km), (rows20km, cols20km),
(rows1km, cols1km), along_track_order,
cross_track_order)
return satint.interpolate()
def test_fillborders(self):
lons = np.arange(20).reshape((4, 5), order="F")
lats = np.arange(20).reshape((4, 5), order="C")
lines = np.array([2, 7, 12, 17]) / 5.0
cols = np.array([2, 7, 12, 17, 22])
hlines = np.arange(20) / 5.0
hcols = np.arange(24)
satint = GeoInterpolator(
(lons, lats), (lines, cols), (hlines, hcols), chunk_size=10)
satint.fill_borders('x', 'y')
np.testing.assert_allclose(satint.tie_data[0], TIES_EXP1)
np.testing.assert_allclose(satint.row_indices, np.array(
[0, 2, 7, 9, 10, 12, 17, 19]) / 5.0)
self.assertTrue(
np.allclose(satint.col_indices, np.array([0, 2, 7, 12, 17, 22, 23])))
def test_extrapolate_cols(self):
lons = np.arange(10).reshape((2, 5), order="F")
lats = np.arange(10).reshape((2, 5), order="C")
lines = np.array([2, 7])
cols = np.array([2, 7, 12, 17, 22])
hlines = np.arange(10)
hcols = np.arange(24)
satint = GeoInterpolator((lons, lats), (lines, cols), (hlines, hcols))
self.assertTrue(np.allclose(satint._extrapolate_cols(satint.tie_data[0]),
np.array([[ 6372937.31273379, 6370997. , 6366146.21816553,
6351605.98629588, 6327412.61244969, 6293626.50067273,
6286869.27831734],
[ 6353136.46335726, 6345786.79166939, 6327412.61244969,
6299445.69529922, 6261968.60390423, 6215087.60607344,
6205711.40650728]])))
def test_extrapolate_rows(self):
lons = np.arange(10).reshape((2, 5), order="F")
lats = np.arange(10).reshape((2, 5), order="C")
lines = np.array([2, 7])
cols = np.array([2, 7, 12, 17, 22])
hlines = np.arange(10)
hcols = np.arange(24)
satint = GeoInterpolator((lons, lats), (lines, cols), (hlines, hcols))
self.assertTrue(np.allclose(satint._extrapolate_rows(satint.tie_data[0]),
np.array([[ 6381081.08333225, 6381639.66045187, 6372470.10269454,
6353590.21586788, 6325042.05851245],
[ 6370997. , 6366146.21816553, 6351605.98629588,
6327412.61244969, 6293626.50067273],
[ 6345786.79166939, 6327412.61244969, 6299445.69529922,
6261968.60390423, 6215087.60607344],
[ 6335702.70833714, 6311919.17016336, 6278581.57890056,
6235791.00048604, 6183672.04823372]])))
def test_fillborders(self):
lons = np.arange(20).reshape((4, 5), order="F")
lats = np.arange(20).reshape((4, 5), order="C")
lines = np.array([2, 7, 12, 17]) / 5.0
cols = np.array([2, 7, 12, 17, 22])
hlines = np.arange(20) / 5.0
hcols = np.arange(24)
satint = GeoInterpolator((lons, lats), (lines, cols), (hlines, hcols),
chunk_size=10)
satint.fill_borders('x', 'y')
np.testing.assert_allclose(satint.tie_data[0], TIES_EXP1)
np.testing.assert_allclose(
satint.row_indices,
np.array([0, 2, 7, 9, 10, 12, 17, 19]) / 5.0)
self.assertTrue(
np.allclose(satint.col_indices, np.array([0, 2, 7, 12, 17, 22,
23])))
def metop20kmto1km(lons20km, lats20km):
"""Getting 1km geolocation for metop avhrr from 20km tiepoints.
"""
cols20km = np.array([0] + list(range(4, 2048, 20)) + [2047])
cols1km = np.arange(2048)
lines = lons20km.shape[0]
rows20km = np.arange(lines)
rows1km = np.arange(lines)
along_track_order = 1
cross_track_order = 3
satint = SatelliteInterpolator((lons20km, lats20km),
(rows20km, cols20km),
(rows1km, cols1km),
along_track_order,
cross_track_order)
return satint.interpolate()
def modis5kmto1km(lons5km, lats5km):
"""Getting 1km geolocation for modis from 5km tiepoints.
"""
cols5km = np.arange(2, 1354, 5)
cols1km = np.arange(1354)
lines = lons5km.shape[0] * 5
rows5km = np.arange(2, lines, 5)
rows1km = np.arange(lines)
along_track_order = 1
cross_track_order = 3
satint = SatelliteInterpolator((lons5km, lats5km),
(rows5km, cols5km),
(rows1km, cols1km),
along_track_order,
cross_track_order,
chunk_size=10)
satint.fill_borders("y", "x")
lons1km, lats1km = satint.interpolate()
return lons1km, lats1km
def modis5kmto1km(lons5km, lats5km):
"""Getting 1km geolocation for modis from 5km tiepoints.
http://www.icare.univ-lille1.fr/tutorials/MODIS_geolocation
"""
cols5km = np.arange(2, 1354, 5) / 5.0
cols1km = np.arange(1354) / 5.0
lines = lons5km.shape[0] * 5
rows5km = np.arange(2, lines, 5) / 5.0
rows1km = np.arange(lines) / 5.0
along_track_order = 1
cross_track_order = 3
satint = SatelliteInterpolator((lons5km, lats5km), (rows5km, cols5km),
(rows1km, cols1km),
along_track_order,
cross_track_order,
chunk_size=10)
satint.fill_borders("y", "x")
lons1km, lats1km = satint.interpolate()
return lons1km, lats1km
def modis5kmto1km(lons5km, lats5km):
"""Getting 1km geolocation for modis from 5km tiepoints.
http://www.icare.univ-lille1.fr/tutorials/MODIS_geolocation
"""
cols5km = np.arange(2, 1354, 5) / 5.0
cols1km = np.arange(1354) / 5.0
lines = lons5km.shape[0] * 5
rows5km = np.arange(2, lines, 5) / 5.0
rows1km = np.arange(lines) / 5.0
along_track_order = 1
cross_track_order = 3
satint = SatelliteInterpolator((lons5km, lats5km),
(rows5km, cols5km),
(rows1km, cols1km),
along_track_order,
cross_track_order,
chunk_size=10)
satint.fill_borders("y", "x")
lons1km, lats1km = satint.interpolate()
return lons1km, lats1km
def modis1kmto500m(lons1km, lats1km, cores=1):
"""Getting 500m geolocation for modis from 1km tiepoints.
"""
if cores > 1:
return _multi(modis1kmto500m, lons1km, lats1km, 10, cores)
cols1km = np.arange(0, 2708, 2)
cols500m = np.arange(2708)
lines = lons1km.shape[0] * 2
rows1km = np.arange(0.5, lines, 2)
rows500m = np.arange(lines)
along_track_order = 1
cross_track_order = 3
satint = SatelliteInterpolator((lons1km, lats1km),
(rows1km, cols1km),
(rows500m, cols500m),
along_track_order,
cross_track_order,
chunk_size=20)
satint.fill_borders("y", "x")
lons500m, lats500m = satint.interpolate()
return lons500m, lats500m
def test_fill_row_borders(self):
lons = np.arange(20).reshape((4, 5), order="F")
lats = np.arange(20).reshape((4, 5), order="C")
lines = np.array([2, 7, 12, 17])
cols = np.array([2, 7, 12, 17, 22])
hlines = np.arange(20)
hcols = np.arange(24)
satint = GeoInterpolator((lons, lats), (lines, cols), (hlines, hcols))
satint._fill_row_borders()
self.assertTrue(np.allclose(satint.tie_data[0],
np.array([[ 6381081.08333225, 6371519.34066148, 6328950.00792935,
6253610.69157758, 6145946.19489936],
[ 6370997. , 6354509.6014956 , 6305151.62592155,
6223234.99818839, 6109277.14889072],
[ 6345786.79166939, 6311985.2535809 , 6245655.67090206,
6147295.76471541, 6017604.5338691 ],
[ 6270385.16249031, 6219684.08214232, 6137100.75832981,
6023313.2794414 , 5879194.72399075],
[ 6145476.82098957, 6078546.55734877, 5980676.23854351,
5852716.72120069, 5695705.57359808],
[ 6095513.48438928, 6022091.54743135, 5918106.430629 ,
5784478.09790441, 5622309.91344102]])))
self.assertTrue(np.allclose(satint.row_indices,
np.array([ 0, 2, 7, 12, 17, 19])))
satint = GeoInterpolator((lons, lats), (lines, cols),
(hlines, hcols), chunk_size=10)
satint._fill_row_borders()
self.assertTrue(np.allclose(satint.tie_data[0],
np.array([[ 6381081.08333225, 6371519.34066148, 6328950.00792935,
6253610.69157758, 6145946.19489936],
[ 6370997. , 6354509.6014956 , 6305151.62592155,
6223234.99818839, 6109277.14889072],
[ 6345786.79166939, 6311985.2535809 , 6245655.67090206,
6147295.76471541, 6017604.5338691 ],
[ 6335702.70833714, 6294975.51441502, 6221857.28889426,
6116920.07132621, 5980935.48786045],
[ 6320348.4990906 , 6276139.09205974, 6199670.56624433,
6091551.90273768, 5952590.38414781],
[ 6270385.16249031, 6219684.08214232, 6137100.75832981,
6023313.2794414 , 5879194.72399075],
[ 6145476.82098957, 6078546.55734877, 5980676.23854351,
5852716.72120069, 5695705.57359808],
[ 6095513.48438928, 6022091.54743135, 5918106.430629 ,
5784478.09790441, 5622309.91344102]])))
self.assertTrue(np.allclose(satint.row_indices,
np.array([ 0, 2, 7, 9, 10, 12, 17, 19])))
def test_fillborders(self):
lons = np.arange(20).reshape((4, 5), order="F")
lats = np.arange(20).reshape((4, 5), order="C")
lines = np.array([2, 7, 12, 17])
cols = np.array([2, 7, 12, 17, 22])
hlines = np.arange(20)
hcols = np.arange(24)
satint = GeoInterpolator((lons, lats), (lines, cols), (hlines, hcols), chunk_size=10)
satint.fill_borders('x', 'y')
self.assertTrue(np.allclose(satint.tie_data[0],
np.array([[ 6384905.78040055, 6381081.08333225, 6371519.34066148,
6328950.00792935, 6253610.69157758, 6145946.19489936,
6124413.29556372],
[ 6377591.95940176, 6370997. , 6354509.6014956 ,
6305151.62592155, 6223234.99818839, 6109277.14889072,
6086485.57903118],
[ 6359307.40690478, 6345786.79166939, 6311985.2535809 ,
6245655.67090206, 6147295.76471541, 6017604.5338691 ,
5991666.28769983],
[ 6351993.58590599, 6335702.70833714, 6294975.51441502,
6221857.28889426, 6116920.07132621, 5980935.48786045,
5953738.5711673 ],
[ 6338032.26190294, 6320348.4990906 , 6276139.09205974,
6199670.56624433, 6091551.90273768, 5952590.38414781,
5924798.08042984],
[ 6290665.5946295 , 6270385.16249031, 6219684.08214232,
6137100.75832981, 6023313.2794414 , 5879194.72399075,
5850371.01290062],
[ 6172248.92644589, 6145476.82098957, 6078546.55734877,
5980676.23854351, 5852716.72120069, 5695705.57359808,
5664303.34407756],
[ 6124882.25917245, 6095513.48438928, 6022091.54743135,
5918106.430629 , 5784478.09790441, 5622309.91344102,
5589876.27654834]])))
self.assertTrue(np.allclose(satint.row_indices, np.array([ 0, 2, 7, 9, 10, 12, 17, 19])))
self.assertTrue(np.allclose(satint.col_indices, np.array([ 0, 2, 7, 12, 17, 22, 23])))
# Adam.Dybbroe <*****@*****.**>
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
"""
"""
import numpy as np
from geotiepoints.geointerpolator import GeoInterpolator
lons = np.arange(20).reshape((4, 5), order="F")
lats = np.arange(20).reshape((4, 5), order="C")
lines = np.array([2, 7, 12, 17]) / 5.0
cols = np.array([2, 7, 12, 17, 22])
hlines = np.arange(20) / 5.0
hcols = np.arange(24)
satint = GeoInterpolator(
(lons, lats), (lines, cols), (hlines, hcols), chunk_size=10)
satint.fill_borders('x', 'y')
def main(path, output_folder):
df = pd.DataFrame()
files = glob.glob(path)
for f in files:
print(f'Processing file {f}')
file = SD(f, SDC.READ)
# Seconds since 1993-01-01 at 0:00:00.0 (TAI)
sec93 = file.select('Scan_Start_Time').get()[0,0]
utc = datetime(1993,1,1,0,0) + timedelta(seconds=sec93)
print(f'Acquisition date: {utc.strftime("%Y-%m-%d %H:%M")}')
lat5km = file.select('Latitude').get()
lon5km = file.select('Longitude').get()
mask1km = file.select('Cloud_Mask_1km').get()
height1km = file.select('cloud_top_height_1km').get()
fraction5km = file.select('Cloud_Fraction_Night').get()
tie_rows = np.arange(2,3000,5)[:np.shape(lat5km)[0]]
tie_cols = np.arange(2,1500,5)[:np.shape(lat5km)[1]]
fine_rows = np.arange(0,3000,1)[:np.shape(mask1km)[0]]
fine_cols = np.arange(0,1500,1)[:np.shape(mask1km)[1]]
interpolator = GeoInterpolator(
(lon5km, lat5km),
(tie_rows, tie_cols),
(fine_rows, fine_cols), 2, 2)
lon1km, lat1km = interpolator.interpolate()
lon1km = lon1km.flatten()
lat1km = lat1km.flatten()
lon5km = lon5km.flatten()
lat5km = lat5km.flatten()
cmask_1 = bits_stripping(1, 2, mask1km[:,:,0]).flatten()
cheight = height1km.flatten()
cfraction = fraction5km.flatten()
for location in ['Arkaroola', 'WesternAustralia', 'Mereenie', 'Arkaroola', 'FowlersGap', 'Woomera', 'SidingSpring', 'Koonamoore', 'Riverland']:
if location == 'MtBruce':
lat = -22.608
lon = 118.144
elif location == 'MtMeharry':
lat = -22.980
lon = 118.588
elif location == 'WesternAustralia':
lat = -22.72372
lon = 118.39802
elif location == 'Mereenie':
lat = -23.886
lon = 132.20
elif location == 'Arkaroola':
lat = -30.308
lon = 139.338
elif location == 'FowlersGap':
lat = -31.087
lon = 141.705
elif location == 'Woomera':
lat = -31.099
lon = 136.786
elif location == 'SidingSpring':
lat = -31.2755
lon = 149.067
elif location == 'Koonamoore':
lat = -32.064
lon = 139.383
elif location == 'Moorook':
lat = -34.267
lon = 140.334
elif location == 'Riverland':
lat = -34.4235
lon = 139.897.334
elif location == 'Adelaide':
lat = -34.928889
lon = 138.601111
dlat = 1
dlon = 1
dist1km = np.sqrt( (lon - lon1km) ** 2 + (lat - lat1km) ** 2 )
if np.min(dist1km) < 0.005:
print(f'{location} on map')
grid_x1km, grid_y1km = np.mgrid[lon-dlon:lon+dlon:200j, lat-dlat:lat+dlat:200j]
grid_x5km, grid_y5km = np.mgrid[lon-dlon:lon+dlon:40j, lat-dlat:lat+dlat:40j]
intp_cmask = griddata((lon1km,lat1km),
cmask_1,
(grid_x1km,grid_y1km),
method='nearest')
np.save(f'{output_folder}{location}_cloudmask_{utc.date()}_{utc.time()}_{lon-dlon}_{lon-dlat}_{lat-dlat}_{lat+dlat}.npy', intp_cmask)
intp_cheight = griddata((lon1km,lat1km),
cheight,
(grid_x1km,grid_y1km),
method='nearest')
np.save(f'{output_folder}{location}_cloudheight_{utc.date()}_{utc.time()}_{lon-dlon}_{lon-dlat}_{lat-dlat}_{lat+dlat}.npy', intp_cheight)
intp_cfraction = griddata((lon5km,lat5km),
cfraction,
(grid_x5km,grid_y5km),
method='nearest')
np.save(f'{output_folder}{location}_cloudfraction_{utc.date()}_{utc.time()}_{lon-dlon}_{lon-dlat}_{lat-dlat}_{lat+dlat}.npy', intp_cfraction)
else:
print(f'{location} not on map')
def main(path, output_folder, output_file, location, coords, maps):
df = pd.DataFrame()
files = glob.glob(path)
if location == 'MtBruce':
lat = -22.608
lon = 118.144
elif location == 'MtMeharry':
lat = -22.980
lon = 118.588
elif location == 'Mereenie':
lat = -23.886
lon = 132.20
elif location == 'Arkaroola':
lat = -30.308
lon = 139.338
elif location == 'FowlersGap':
lat = -31.087
lon = 141.705
elif location == 'Woomera':
lat = -31.099
lon = 136.786
elif location == 'SidingSpring':
lat = -31.2755
lon = 149.067
elif location == 'Koonamoore':
lat = -32.064
lon = 139.383
elif location == 'Moorook':
lat = -34.267
lon = 140.334
elif location == 'Adelaide':
lat = -34.928889
lon = 138.601111
else:
#if len(location) > 0:
# raise Exception('This location is not pre-defined. Please specify the coordinates.')
print(len(coords))
if len(coords) != 2:
raise Exception('Please specify a location or coordinates.')
else:
lon = coords[0]
lat = coords[1]
location = f'Lon{lon}_Lat{lat}'
for f in files:
print(f'Processing file {f}')
file = SD(f, SDC.READ)
# Seconds since 1993-01-01 at 0:00:00.0 (TAI)
sec93 = file.select('Scan_Start_Time').get()[0, 0]
utc = datetime(1993, 1, 1, 0, 0) + timedelta(seconds=sec93)
print(f'Acquisition date: {utc.strftime("%Y-%m-%d %H:%M")}')
lat5km = file.select('Latitude').get()
lon5km = file.select('Longitude').get()
mask1km = file.select('Cloud_Mask_1km').get()
height1km = file.select('cloud_top_height_1km').get()
fraction5km = file.select('Cloud_Fraction_Night').get()
tie_rows = np.arange(2, 3000, 5)[:np.shape(lat5km)[0]]
tie_cols = np.arange(2, 1500, 5)[:np.shape(lat5km)[1]]
fine_rows = np.arange(0, 3000, 1)[:np.shape(mask1km)[0]]
fine_cols = np.arange(0, 1500, 1)[:np.shape(mask1km)[1]]
interpolator = GeoInterpolator((lon5km, lat5km), (tie_rows, tie_cols),
(fine_rows, fine_cols), 2, 2)
lon1km, lat1km = interpolator.interpolate()
lon1km = lon1km.flatten()
lat1km = lat1km.flatten()
lon5km = lon5km.flatten()
lat5km = lat5km.flatten()
cmask_0 = bits_stripping(0, 1, mask1km[:, :, 0]).flatten()
cmask_1 = bits_stripping(1, 2, mask1km[:, :, 0]).flatten()
cmask_3 = bits_stripping(3, 1, mask1km[:, :, 0]).flatten()
cmask_4 = bits_stripping(4, 1, mask1km[:, :, 0]).flatten()
cmask_5 = bits_stripping(5, 1, mask1km[:, :, 0]).flatten()
cmask_6 = bits_stripping(6, 2, mask1km[:, :, 0]).flatten()
cmask_8 = bits_stripping(8, 1, mask1km[:, :, 0]).flatten()
cmask_14 = bits_stripping(14, 1, mask1km[:, :, 0]).flatten()
cmask_15 = bits_stripping(15, 1, mask1km[:, :, 0]).flatten()
cmask_17 = bits_stripping(17, 1, mask1km[:, :, 0]).flatten()
cmask_18 = bits_stripping(18, 1, mask1km[:, :, 0]).flatten()
cmask_19 = bits_stripping(19, 1, mask1km[:, :, 0]).flatten()
cheight = height1km.flatten()
cfraction = fraction5km.flatten()
dist1km = np.sqrt((lon - lon1km)**2 + (lat - lat1km)**2)
dist5km = np.sqrt((lon - lon5km)**2 + (lat - lat5km)**2)
if np.min(dist1km) < 0.005:
idx1km = np.argmin(dist1km)
idx5km = np.argmin(dist5km)
df = df.append(
{
'date': utc,
'filename': f,
'cmask_0': cmask_0[idx1km],
'cmask_1': cmask_1[idx1km],
'cmask_3': cmask_3[idx1km],
'cmask_4': cmask_4[idx1km],
'cmask_5': cmask_5[idx1km],
'cmask_6': cmask_6[idx1km],
'cmask_8': cmask_8[idx1km],
'cmask_14': cmask_14[idx1km],
'cmask_15': cmask_15[idx1km],
'cmask_17': cmask_17[idx1km],
'cmask_18': cmask_18[idx1km],
'cmask_19': cmask_19[idx1km],
'cloudfraction': cfraction[idx5km],
'height': cheight[idx1km],
},
ignore_index=True)
if maps:
size_map = 0.25
idx_map = (lon1km > (lon - size_map)) & (lon1km <
(lon + size_map))
idx_map = idx_map & (lat1km >
(lat - size_map)) & (lat1km <
(lat + size_map))
grid_x, grid_y = np.mgrid[
np.min(lon1km[idx_map]):np.max(lon1km[idx_map]):100j,
np.min(lat1km[idx_map]):np.max(lat1km[idx_map]):200j]
intp_cmask = griddata((lon1km[idx_map], lat1km[idx_map]),
cmask_1[idx_map], (grid_x, grid_y),
method='nearest')
intp_cheight = griddata((lon1km[idx_map], lat1km[idx_map]),
cheight[idx_map], (grid_x, grid_y),
method='nearest')
np.save(
f'{output_folder}{location}_cloudmask_{utc.date()}_{utc.time()}_{np.min(lon1km[idx_map])}_{np.max(lon1km[idx_map])}_{np.min(lat1km[idx_map])}_{np.max(lat1km[idx_map])}.npy',
intp_cmask)
np.save(
f'{output_folder}{location}_cloudheight_{utc.date()}_{utc.time()}_{np.min(lon1km[idx_map])}_{np.max(lon1km[idx_map])}_{np.min(lat1km[idx_map])}_{np.max(lat1km[idx_map])}.npy',
intp_cheight)
else:
print('Site not on map')
df = df.sort_values(
by='date', ascending=True
) #reorders dataframe so histogram will be in correct order
if output_file == None:
df.to_csv(
f'{output_folder}{location}_{df.date.min()}_{df.date.max()}.csv',
index=False)
else:
df.to_csv(output_folder + output_file, index=False)
# Adam.Dybbroe <*****@*****.**>
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
"""
"""
import numpy as np
from geotiepoints.geointerpolator import GeoInterpolator
lons = np.arange(20).reshape((4, 5), order="F")
lats = np.arange(20).reshape((4, 5), order="C")
lines = np.array([2, 7, 12, 17]) / 5.0
cols = np.array([2, 7, 12, 17, 22])
hlines = np.arange(20) / 5.0
hcols = np.arange(24)
satint = GeoInterpolator((lons, lats), (lines, cols), (hlines, hcols),
chunk_size=10)
satint.fill_borders('x', 'y')
