def make_reproject(path, final_path, file_name, show='off'):
"""
Функция для перепроецирования снимков согласно параметрам указываемым в dom.
После перепроецирования к файлу снимку генерируется и добавляется маска
:param path: Путь до папки в которой лежит файл
:param final_path: Путь до папки в которую нужно положить перепроецированный файл
:param file_name: Имя файла (исходного)
:param show: Флаг для отрисовки [2] канала. По умолчанию 'off', чтобы включить show='on'
:return: Перепроецированный файл с иходным file_name
"""
print path + file_name
nansat_obj = Nansat(path + file_name)
# Для маленького конечного куска
#dom = Domain('+proj=latlong +datum=WGS84 +ellps=WGS84 +no_defs', '-lle -86.20 45.10 -86.10 45.20 -ts 300 300')
# Для всего района
dom = Domain('+proj=latlong +datum=WGS84 +ellps=WGS84 +no_defs', '-lle -86.3 44.6 -85.2 45.3 -ts 300 200')
nansat_obj.reproject(dom)
nansat_obj = create_mask(nansat_obj)
if show == 'on':
plt.imshow(nansat_obj[2])
plt.colorbar()
plt.show()
nansat_obj.export(final_path + file_name + '.reproject.nc')
def update_icemap_mosaic(inp_filename, inp_data, out_filename, out_domain,
out_metadata):
if os.path.exists(out_filename):
mos_array = Nansat(out_filename)[1]
else:
mos_array = np.zeros(out_domain.shape(), np.uint8) + 255
# read classification data and reproject onto mosaic domain
n = Nansat(inp_filename)
if inp_data is None:
n.reproject_gcps()
n.reproject(out_domain)
inp_data = dict(arr=n[1], mask=n[2])
# put data into mosaic array
gpi = (inp_data['mask'] == 1) * (inp_data['arr'] < 255)
mos_array[gpi] = inp_data['arr'][gpi]
# export
n_out = Nansat.from_domain(out_domain)
n_out.add_band(array=mos_array, parameters={'name': 'classification'})
n_out.set_metadata(n.get_metadata())
n_out.set_metadata(out_metadata)
n_out = add_colortable(n_out)
n_out.export(out_filename, driver='GTiff', options=['COMPRESS=LZW'])
return inp_data
def test_reproject_domain_if_tps_is_given(self):
n = Nansat(self.test_file_gcps,
log_level=40,
mapper=self.default_mapper)
d = Domain(4326, "-te 27 70 30 72 -ts 500 500")
n.reproject(d, tps=False)
tmpfilename = os.path.join(self.tmp_data_path,
'nansat_reproject_domain.png')
n.write_figure(tmpfilename, 2, clim='hist')
self.assertEqual(n.shape(), (500, 500))
self.assertEqual(type(n[1]), np.ndarray)
self.assertTrue(n.has_band('swathmask'))
n = Nansat(self.test_file_gcps,
log_level=40,
mapper=self.default_mapper)
d = Domain(4326, "-te 27 70 30 72 -ts 500 500")
n.reproject(d, tps=True)
tmpfilename = os.path.join(self.tmp_data_path,
'nansat_reproject_domain.png')
n.write_figure(tmpfilename, 2, clim='hist')
self.assertEqual(n.shape(), (500, 500))
self.assertEqual(type(n[1]), np.ndarray)
self.assertTrue(n.has_band('swathmask'))
def boreali_processing(obj, final_path):
wavelen = [412, 443, 469, 488, 531, 547, 555, 645, 667, 678]
cpa_limits = [0.01, 2,
0.01, 1,
0.01, 1, 10]
b = Boreali('michigan', wavelen)
n = Nansat(obj)
dom = Domain('+proj=latlong +datum=WGS84 +ellps=WGS84 +no_defs', '-lle -86.3 44.6 -85.2 45.3 -ts 300 200')
n.reproject(dom)
theta = numpy.zeros_like(n[2])
custom_n = Nansat(domain=n)
band_rrs_numbers = list(map(lambda x: n._get_band_number('Rrs_' + str(x)),
wavelen))
for index in range(0, len(wavelen)):
# Преобразуем в Rrsw
rrsw = n[band_rrs_numbers[index]] / (0.52 + 1.7 * n[band_rrs_numbers[index]])
custom_n.add_band(rrsw, parameters={'name': 'Rrsw_' + str(wavelen[index]),
'units': 'sr-1',
'wavelength': wavelen[index]})
custom_n = create_mask(custom_n)
cpa = b.process(custom_n, cpa_limits, mask=custom_n['mask'], theta=theta, threads=4)
custom_n.add_band(array=cpa[0], parameters={'name': 'chl',
'long_name': 'Chlorophyl-a',
'units': 'mg m-3'})
custom_n.add_band(array=cpa[1], parameters={'name': 'tsm',
'long_name': 'Total suspended matter',
'units': 'g m-3'})
custom_n.add_band(array=cpa[2], parameters={'name': 'doc',
'long_name': 'Dissolved organic carbon',
'units': 'gC m-3'})
custom_n.add_band(array=cpa[3], parameters={'name': 'mse',
'long_name': 'Root Mean Square Error',
'units': 'sr-1'})
custom_n.add_band(array=cpa[4], parameters={'name': 'mask',
'long_name': 'L2 Boreali mask',
'units': '1'})
custom_n.export(final_path + obj.split('/')[-1] + 'cpa_deep.nc')
fig_params = {'legend': True,
'LEGEND_HEIGHT': 0.5,
'NAME_LOCATION_Y': 0,
'mask_array': cpa[4],
'mask_lut': {1: [255, 255, 255], 2: [128, 128, 128], 4: [200, 200, 255]}}
custom_n.write_figure(final_path + obj.split('/')[-1] + 'chl_deep.png', 'chl', clim=[0, 1.], **fig_params)
custom_n.write_figure(final_path + obj.split('/')[-1] + 'tsm_deep.png', 'tsm', clim=[0, 1.], **fig_params)
custom_n.write_figure(final_path + obj.split('/')[-1] + 'doc_deep.png', 'doc', clim=[0, .2], **fig_params)
custom_n.write_figure(final_path + obj.split('/')[-1] + 'mse_deep.png', 'mse', clim=[1e-5, 1e-2], logarithm=True, **fig_params)
n.write_figure(final_path + obj.split('/')[-1] + 'rgb_deep.png',
[16, 14, 6],
clim=[[0, 0, 0], [0.006, 0.04, 0.024]],
mask_array=cpa[4],
mask_lut={2: [128, 128, 128]})
def test_reproject_no_addmask(self):
""" Should not add swath mask and return 0 in areas out of swath """
n = Nansat(self.test_file_gcps, log_level=40, mapper=self.default_mapper)
d = Domain(4326, '-te -92.08 26.85 -92.00 26.91 -ts 200 200')
n.reproject(d, addmask=False)
b = n[1]
self.assertTrue(not n.has_band('swathmask'))
self.assertTrue(np.isfinite(b[0, 0]))
self.assertTrue(np.isfinite(b[100, 100]))
def test_reproject_of_complex(self):
''' Should return np.nan in areas out of swath '''
n = Nansat(self.test_file_complex, logLevel=40)
d = Domain(4326, '-te -92.08 26.85 -92.00 26.91 -ts 200 200')
n.reproject(d)
b = n[1]
self.assertTrue(n.has_band('swathmask'))
self.assertTrue(np.isnan(b[0, 0]))
self.assertTrue(np.isfinite(b[100, 100]))
def test_reproject_gcps(self):
n1 = Nansat(self.test_file_stere, log_level=40, mapper=self.default_mapper)
n2 = Nansat(self.test_file_gcps, log_level=40, mapper=self.default_mapper)
n1.reproject(n2)
tmpfilename = os.path.join(self.tmp_data_path,
'nansat_reproject_gcps.png')
n1.write_figure(tmpfilename, 2, clim='hist')
self.assertEqual(n1.shape(), n2.shape())
self.assertEqual(type(n1[1]), np.ndarray)
def test_reproject_gcps(self):
n1 = Nansat(self.test_file_stere, logLevel=40)
n2 = Nansat(self.test_file_gcps, logLevel=40)
n1.reproject(n2)
tmpfilename = os.path.join(ntd.tmp_data_path,
'nansat_reproject_gcps.png')
n1.write_figure(tmpfilename, 2, clim='hist')
self.assertEqual(n1.shape(), n2.shape())
self.assertEqual(type(n1[1]), np.ndarray)
def test_reproject_domain(self):
n = Nansat(self.test_file_gcps, logLevel=40)
d = Domain(4326, "-te 27 70 30 72 -ts 500 500")
n.reproject(d)
tmpfilename = os.path.join(ntd.tmp_data_path,
'nansat_reproject_domain.png')
n.write_figure(tmpfilename, 2, clim='hist')
self.assertEqual(n.shape(), (500, 500))
self.assertEqual(type(n[1]), np.ndarray)
def test_reproject_no_addmask(self):
''' Should not add swath mask and return 0 in areas out of swath '''
n = Nansat(self.test_file_complex, logLevel=40)
d = Domain(4326, '-te -92.08 26.85 -92.00 26.91 -ts 200 200')
n.reproject(d, addmask=False)
b = n[1]
self.assertTrue(not n.has_band('swathmask'))
self.assertTrue(np.isfinite(b[0, 0]))
self.assertTrue(np.isfinite(b[100, 100]))
def test_reproject_domain_if_source_and_destination_domain_span_entire_lons(self, mock_Nansat):
n = Nansat(self.test_file_arctic, log_level=40, mapper=self.default_mapper)
d = Domain(4326, "-te -180 180 60 90 -ts 500 500")
n.reproject(d)
tmpfilename = os.path.join(self.tmp_data_path, 'nansat_reproject_domain.png')
n.write_figure(tmpfilename, 2, clim='hist')
self.assertEqual(n.shape(), (500, 500))
self.assertEqual(type(n[1]), np.ndarray)
self.assertTrue(n.has_band('swathmask'))
def test_reproject_of_complex(self):
""" Should return np.nan in areas out of swath """
n = Nansat(self.test_file_complex, log_level=40, mapper=self.default_mapper)
d = Domain(4326, '-te -92.08 26.85 -92.00 26.91 -ts 200 200')
n.reproject(d)
b = n[1]
self.assertTrue(n.has_band('swathmask'))
self.assertTrue(np.isnan(b[0, 0]))
self.assertTrue(np.isfinite(b[100, 100]))
def test_reproject_domain(self):
n = Nansat(self.test_file_gcps, logLevel=40)
d = Domain(4326, "-te 27 70 30 72 -ts 500 500")
n.reproject(d)
tmpfilename = os.path.join(ntd.tmp_data_path,
'nansat_reproject_domain.png')
n.write_figure(tmpfilename, 2, clim='hist')
self.assertEqual(n.shape(), (500, 500))
self.assertEqual(type(n[1]), np.ndarray)
def test_reproject_no_addmask(self):
''' Should not add swath mask and return 0 in areas out of swath '''
n = Nansat(self.test_file_complex, logLevel=40)
d = Domain(4326, '-te -92.08 26.85 -92.00 26.91 -ts 200 200')
n.reproject(d, addmask=False)
b = n[1]
self.assertTrue(not n.has_band('swathmask'))
self.assertTrue(np.isfinite(b[0, 0]))
self.assertTrue(np.isfinite(b[100, 100]))
def test_reproject_and_export_band(self):
n1 = Nansat(self.test_file_gcps, logLevel=40)
n2 = Nansat(self.test_file_stere, logLevel=40)
n1.reproject(n2)
tmpfilename = os.path.join(ntd.tmp_data_path,
'nansat_reproject_export_band.nc')
n1.export(tmpfilename, bands=[1])
n = Nansat(tmpfilename, mapperName='generic')
self.assertTrue(os.path.exists(tmpfilename))
self.assertEqual(n.vrt.dataset.RasterCount, 1)
def test_reproject_gcps_on_repro_gcps(self):
n1 = Nansat(self.test_file_stere, logLevel=40)
n2 = Nansat(self.test_file_gcps, logLevel=40)
n2.reproject_GCPs()
n1.reproject(n2)
tmpfilename = os.path.join(ntd.tmp_data_path,
'nansat_reproject_gcps_on_repro_gcps.png')
n1.write_figure(tmpfilename, 2, clim='hist')
self.assertEqual(n1.shape(), n2.shape())
self.assertEqual(type(n1[1]), np.ndarray)
def test_reproject_and_export_band(self):
n1 = Nansat(self.test_file_gcps, logLevel=40)
n2 = Nansat(self.test_file_stere, logLevel=40)
n1.reproject(n2)
tmpfilename = os.path.join(ntd.tmp_data_path,
'nansat_reproject_export_band.nc')
n1.export(tmpfilename, bands=[1])
n = Nansat(tmpfilename, mapperName='generic')
self.assertTrue(os.path.exists(tmpfilename))
self.assertEqual(n.vrt.dataset.RasterCount, 1)
def get_deph(h_max=-1):
data = Nansat('/home/artemm/Documents/Work/MichiganLake/TetsData/michigan_lld.grd')
dom = Domain('+proj=latlong +datum=WGS84 +ellps=WGS84 +no_defs', '-lle -86.3 44.6 -85.2 45.3 -ts 300 200')
data.reproject(dom)
h = numpy.copy(data[1])
if h_max == -1:
h[numpy.where(h > numpy.float32(0.0))] = numpy.nan
h[numpy.where(h < numpy.float32(0.0))] *= numpy.float32(-1)
return h
def test_get_mask(self):
'''Mosaic.Layer should get mask from reprojected file '''
n = Nansat(self.test_file_gcps)
n.reproject(self.domain)
swathmask = n['swathmask']
l = Layer(self.test_file_gcps)
l.make_nansat_object(self.domain)
mask = l.get_mask_array()
self.assertEqual(type(mask), np.ndarray)
self.assertEqual(mask.shape, (650, 700))
np.testing.assert_allclose(mask, swathmask*64)
def test_get_mask(self):
'''Mosaic.Layer should get mask from reprojected file '''
n = Nansat(self.test_file_gcps)
n.reproject(self.domain)
swathmask = n['swathmask']
l = Layer(self.test_file_gcps)
l.make_nansat_object(self.domain)
mask = l.get_mask_array()
self.assertEqual(type(mask), np.ndarray)
self.assertEqual(mask.shape, (650, 700))
np.testing.assert_allclose(mask, swathmask * 64)
def test_add_band_and_reproject(self):
""" Should add band and swath mask and return np.nan in areas out of swath """
n = Nansat(self.test_file_gcps, log_level=40, mapper=self.default_mapper)
d = Domain(4326, "-te 27 70 30 72 -ts 500 500")
n.add_band(np.ones(n.shape(), np.uint8))
n.reproject(d)
b4 = n[4] # added, reprojected band
b5 = n[5] # swathmask
self.assertTrue(n.has_band('swathmask')) # the added band
self.assertTrue(n.has_band('swathmask_0000')) # the actual swathmask
self.assertTrue(b4[0, 0]==0)
self.assertTrue(b4[300, 300] == 1)
self.assertTrue(b5[0, 0]==0)
self.assertTrue(b5[300, 300] == 1)
def test_add_band_and_reproject(self):
""" Should add band and swath mask and return 0 in areas out of swath """
n = Nansat(self.test_file_gcps, log_level=40, mapper=self.default_mapper)
d = Domain(4326, "-te 27 70 30 72 -ts 500 500")
n.add_band(np.ones(n.shape()))
n.reproject(d)
b1 = n[1]
b4 = n[4]
self.assertTrue(n.has_band('swathmask')) # the added band
self.assertTrue(n.has_band('swathmask_0000')) # the actual swathmask
self.assertTrue(b1[0, 0] == 0)
self.assertTrue(b1[300, 300] > 0)
self.assertTrue(np.isnan(b4[0, 0]))
self.assertTrue(b4[300, 300] == 1.)
def test_add_band_and_reproject(self):
''' Should add band and swath mask
and return 0 in areas out of swath '''
n = Nansat(self.test_file_gcps, logLevel=40)
d = Domain(4326, "-te 27 70 30 72 -ts 500 500")
n.add_band(np.ones(n.shape()))
n.reproject(d)
b1 = n[1]
b4 = n[4]
self.assertTrue(n.has_band('swathmask'))
self.assertTrue(b1[0, 0] == 0)
self.assertTrue(b1[300, 300] > 0)
self.assertTrue(np.isnan(b4[0, 0]))
self.assertTrue(b4[300, 300] == 1.)
def test_add_band_and_reproject(self):
''' Should add band and swath mask
and return 0 in areas out of swath '''
n = Nansat(self.test_file_gcps, logLevel=40)
d = Domain(4326, "-te 27 70 30 72 -ts 500 500")
n.add_band(np.ones(n.shape()))
n.reproject(d)
b1 = n[1]
b4 = n[4]
self.assertTrue(n.has_band('swathmask'))
self.assertTrue(b1[0, 0] == 0)
self.assertTrue(b1[300, 300] > 0)
self.assertTrue(np.isnan(b4[0, 0]))
self.assertTrue(b4[300, 300] == 1.)
def _get_masked_windspeed(self, landmask=True, icemask=True):
try:
sar_windspeed = self['windspeed']
except:
raise ValueError('SAR wind has not been calculated, ' \
'execute calculate_wind(winddir) first.')
sar_windspeed[sar_windspeed < 0] = 0
palette = jet
if landmask:
try: # Land mask
sar_windspeed = np.ma.masked_where(self.watermask()[1] == 2,
sar_windspeed)
palette.set_bad([.3, .3, .3], 1.0) # Land is masked (bad)
except:
print 'Land mask not available'
if icemask:
try: # Ice mask
try: # first try local file
ice = Nansat('metno_local_hires_seaice_' +
self.SAR_image_time.strftime('%Y%m%d'),
mapperName='metno_local_hires_seaice')
except: # otherwise Thredds
ice = Nansat('metno_hires_seaice:' +
self.SAR_image_time.strftime('%Y%m%d'))
ice.reproject(self)
iceBandNo = ice._get_band_number(
{'standard_name': 'sea_ice_area_fraction'})
sar_windspeed[ice[iceBandNo] > 0] = -1
palette.set_under('w', 1.0) # Ice is 'under' (-1)
except:
print 'Ice mask not available'
return sar_windspeed, palette
def _get_masked_windspeed(self, landmask=True, icemask=True):
try:
sar_windspeed = self['windspeed']
except:
raise ValueError('SAR wind has not been calculated, ' \
'execute calculate_wind(winddir) first.')
sar_windspeed[sar_windspeed<0] = 0
palette = jet
if landmask:
try: # Land mask
sar_windspeed = np.ma.masked_where(
self.watermask()[1]==2, sar_windspeed)
palette.set_bad([.3, .3, .3], 1.0) # Land is masked (bad)
except:
print 'Land mask not available'
if icemask:
try: # Ice mask
try: # first try local file
ice = Nansat('metno_local_hires_seaice_' +
self.SAR_image_time.strftime('%Y%m%d'),
mapperName='metno_local_hires_seaice')
except: # otherwise Thredds
ice = Nansat('metno_hires_seaice:' +
self.SAR_image_time.strftime('%Y%m%d'))
ice.reproject(self)
iceBandNo = ice._get_band_number(
{'standard_name': 'sea_ice_area_fraction'})
sar_windspeed[ice[iceBandNo]>0] = -1
palette.set_under('w', 1.0) # Ice is 'under' (-1)
except:
print 'Ice mask not available'
return sar_windspeed, palette
from nansat import Nansat, Domain
import matplotlib.pyplot as plt
import numpy as np
n_osw = Nansat('/home/artemm/Desktop/A2014301181500.L2_LAC_OC.x.nccpa_OSW.nc')
n_deep = Nansat('/home/artemm/Desktop/A2014301181500.L2_LAC_OC.x.nccpa_deep.nc')
bottom = Nansat('/home/artemm/Desktop/michigan_lld.grd')
dom = Domain('+proj=latlong +datum=WGS84 +ellps=WGS84 +no_defs', '-lle -86.3 44.6 -85.2 45.3 -ts 300 200')
bottom.reproject(dom)
h = np.copy(bottom[1])
h[np.where(h > np.float32(0.0))] = np.nan
h[np.where(h < np.float32(0.0))] *= np.float32(-1)
"""
test = np.copy(n_osw['chl'])
test[np.where(h <= -20)] = np.nan
plt.imshow(test)
plt.show()
pass
"""
osw_h_mean = []
deep_h_mean = []
for h_max in range(1, np.nanmax(h), 1):
osw = np.copy(n_osw['chl'])
deep = np.copy(n_deep['chl'])
osw[np.where(h >= h_max)] = np.nan
deep[np.where(h >= h_max)] = np.nan
osw_h_mean.append(np.nanmean(osw))
deep_h_mean.append(np.nanmean(deep))
def boreali_osw_processing(obj, final_path):
"""
Мой код в данной функции основан на tutorial.py который я нашел в репозитории boreali.
:param obj: путь до изображения
:param final_path: Путь для сохранения файлов
:return:
"""
wavelen = [412, 443, 469, 488, 531, 547, 555, 645, 667, 678]
cpa_limits = [0.01, 2,
0.01, 1,
0.01, 1, 10]
h = get_deph() # Глубина исследуемого района по батиметрии
b = Boreali('michigan', wavelen)
n = Nansat(obj)
dom = Domain('+proj=latlong +datum=WGS84 +ellps=WGS84 +no_defs', '-lle -86.3 44.6 -85.2 45.3 -ts 300 200')
n.reproject(dom)
theta = numpy.zeros_like(n[2])
custom_n = Nansat(domain=n)
band_rrs_numbers = list(map(lambda x: n._get_band_number('Rrs_' + str(x)),
wavelen)) # Получаем список номеров бандов в которых лежат значения Rrs
# для корректной работы складываем в custom_n значения и Rrs и Rrsw
for index in range(0, len(wavelen)):
rrsw = n[band_rrs_numbers[index]] / (0.52 + 1.7 * n[band_rrs_numbers[index]]) # Пересчитываем Rrs в Rrsw
custom_n.add_band(rrsw, parameters={'name': 'Rrsw_' + str(wavelen[index]), # Складываем в новый объект Rrsw
'units': 'sr-1',
'wavelength': wavelen[index]})
# Складываем в новый объект значения Rrs
custom_n.add_band(n[band_rrs_numbers[index]], parameters={'name': 'Rrs_' + str(wavelen[index]),
'units': 'sr-1',
'wavelength': wavelen[index]})
custom_n = create_mask(custom_n)
cpa = b.process(custom_n, cpa_limits, mask=custom_n['mask'], depth=h, theta=theta, threads=4)
custom_n.add_band(array=cpa[0], parameters={'name': 'chl',
'long_name': 'Chlorophyl-a',
'units': 'mg m-3'})
custom_n.add_band(array=cpa[1], parameters={'name': 'tsm',
'long_name': 'Total suspended matter',
'units': 'g m-3'})
custom_n.add_band(array=cpa[2], parameters={'name': 'doc',
'long_name': 'Dissolved organic carbon',
'units': 'gC m-3'})
custom_n.add_band(array=cpa[3], parameters={'name': 'mse',
'long_name': 'Root Mean Square Error',
'units': 'sr-1'})
custom_n.add_band(array=cpa[4], parameters={'name': 'mask',
'long_name': 'L2 Boreali mask',
'units': '1'})
custom_n.export(final_path + obj.split('/')[-1] + 'cpa_OSW.nc')
fig_params = {'legend': True,
'LEGEND_HEIGHT': 0.5,
'NAME_LOCATION_Y': 0,
'mask_array': cpa[4],
'mask_lut': {1: [255, 255, 255],
2: [128, 128, 128],
4: [200, 200, 255]}}
custom_n.write_figure(final_path + obj.split('/')[-1] + 'chl_OSW.png', 'chl', clim=[0, 1.], **fig_params)
custom_n.write_figure(final_path + obj.split('/')[-1] + 'tsm_OSW.png', 'tsm', clim=[0, 1.], **fig_params)
custom_n.write_figure(final_path + obj.split('/')[-1] + 'doc_OSW.png', 'doc', clim=[0, .2], **fig_params)
custom_n.write_figure(final_path + obj.split('/')[-1] + 'mse_OSW.png', 'mse', clim=[1e-5, 1e-2],
logarithm=True, **fig_params)
n.write_figure(final_path + obj.split('/')[-1] + 'rgb_OSW.png',
[16, 14, 6],
clim=[[0, 0, 0], [0.006, 0.04, 0.024]],
mask_array=cpa[4],
mask_lut={2: [128, 128, 128]})
#~ sigma0 = n[3]
sigma0 = raw_counts**2.0 * sin(deg2rad(inc_angle))
sigma0 = 10*log10(sigma0)
n.add_band(bandID=4, array=sigma0)
# 1. Remove speckle noise (using Lee-Wiener filter)
speckle_filter('wiener', 7)
# Reprojected image into Lat/Lon WGS84 (Simple Cylindrical) projection
# 1. Cancel previous reprojection
# 2. Get corners of the image and the pixel resolution
# 3. Create Domain with stereographic projection, corner coordinates and resolution 1000m
# 4. Reproject
# 5. Write image
n.reproject() # 1.
lons, lats = n.get_corners() # 2.
pxlRes = distancelib.getPixelResolution(array(lats), array(lons), n.shape(), units="deg")
srsString = "+proj=latlong +datum=WGS84 +ellps=WGS84 +no_defs"
#~ extentString = '-lle %f %f %f %f -ts 3000 3000' % (min(lons), min(lats), max(lons), max(lats))
extentString = '-lle %f %f %f %f -tr %f %f' % (min(lons), min(lats), \
max(lons), max(lats), pxlRes[1], pxlRes[0])
d = Domain(srs=srsString, ext=extentString) # 3.
n.reproject(d) # 4.
# get array with watermask (landmask) b
# it must be done after reprojection!
# 1. Get Nansat object with watermask
# 2. Get array from Nansat object. 0 - land, 1 - water
#wm = n.watermask(mod44path='/media/magDesk/media/SOLabNFS/store/auxdata/coastline/mod44w/')
wm = n.watermask(mod44path='/media/data/data/auxdata/coastline/mod44w/')
n.write_geotiffimage(oFileName + '05_geotiff.tif', bandID=1)
# create a NetCDF file with all bands
n.export(oFileName + '06a.nc')
n.export(oFileName + '06b.nc', bottomup=True)
# create a GTiff file with one band (default driver is NetCDF)
n.export_band(oFileName + '07.tif', bandID=1, driver='GTiff')
# get array with watermask (landmask)
# -- Get Nansat object with watermask
wm = n.watermask()[1]
# -- Reproject with cubic interpolation
d = Domain(4326, "-te 27 70.3 31 71.5 -ts 300 300")
n.reproject(d, 2)
# -- Write image
n.write_figure(oFileName + '08_pro.png', clim='hist')
# Get transect of the 1st and 2nd bands corresponding to the given points
values, lonlat, pixlinCoord = n.get_transect(
points=((29.287, 71.153),
(29.275, 71.145),
(29.210, 71.154)),
transect=False,
bandList=[1, 2])
# print the results
print '1stBandVal 2ndBandVal pix/lin lon/lat '
for i in range (len(values[0])):
print '%6d %10d %13.2f /%6.2f %7.2f /%6.2f' % (values[0][i],
values[1][i],
# List bands and georeference of the object
print n
# Write picture with map of the file location
n.write_map(oFileName + 'map.png')
# Write indexed picture with data from the first band
n.write_figure(oFileName + '.png', clim='hist')
# Reproject input image onto map of Norwegian Coast
# 1. Create domain describing the desired map
# 2. Transform the original satellite image
# 3. Write the transfromed image into RGB picture
dLatlong = Domain("+proj=latlong +datum=WGS84 +ellps=WGS84 +no_defs",
"-te 27 70.2 31 71.5 -ts 500 500")
n.reproject(dLatlong)
n.write_figure(oFileName + 'pro.png', bands=[1, 2, 3], clim=[0, 100])
# Export projected satelite image into NetCDF format
n.export(oFileName + '.nc')
# Collect values from interactively drawn transect
# 1. draw transect interactively
# 2. plot the values
values, lonlat, pixlinCoord = n.get_transect()
plt.plot(lonlat[0], values[0], '.-'); plt.show()
# run tests of other nansat components
import test_domain
import test_nansat
import test_figure
class SARWind(Nansat, object):
'''
A class for calculating wind speed from SAR images using CMOD
'''
def __init__(self, sar_image, winddir=None, pixelsize=500):
'''
Parameters
-----------
sar_image : string or Nansat object
SAR image filename (original, raw file)
winddir : int, string, Nansat, None
Auxiliary wind field information needed to calculate
SAR wind (must be or have wind direction)
'''
if isinstance(sar_image, str) or isinstance(sar_image, unicode):
super(SARWind, self).__init__(sar_image)
elif isinstance(sar_image, Nansat):
super(SARWind, self).__init__(domain=sar_image)
self.vrt = sar_image.vrt
# Check that this is a SAR image with VV pol NRCS
try:
self.sigma0_bandNo = self._get_band_number({
'standard_name':
'surface_backwards_scattering_coefficient_of_radar_wave',
'polarization': 'VV'
})
except:
raise TypeError(self.fileName +
' does not have SAR NRCS in VV polarization')
self.SAR_image_time = self.get_time(
self.sigma0_bandNo).replace(tzinfo=None)
if pixelsize != 'fullres':
print 'Resizing SAR image to ' + str(pixelsize) + ' m pixel size'
self.resize(pixelsize=pixelsize)
self.winddir = winddir
if winddir is not None:
self.calculate_wind()
def calculate_wind(self, winddir=None, storeModelSpeed=True):
# Calculate wind speed from SAR sigma0 in VV polarization
if winddir:
self.winddir = winddir
if self.winddir is None or self.winddir == 'online':
self.winddir = 'ncep_wind_online' # default source
if isinstance(self.winddir, int):
# Constant wind direction is input
print 'Using constant wind (from) direction: ' + str(self.winddir) + \
' degrees clockwise from North'
winddirArray = np.ones(self.shape()) * self.winddir
winddir_time = None
storeModelSpeed = False # Not relevant if direction given as number
else:
# Nansat readable file
if isinstance(self.winddir, str):
try:
self.winddir = Nansat(self.winddir)
except:
try:
self.winddir = Nansat(self.winddir + datetime.strftime(
self.SAR_image_time, ':%Y%m%d%H%M'))
except:
pass
if not isinstance(self.winddir, Nansat):
raise ValueError('Wind direction not available')
winddir_time = self.winddir.get_time()[0]
# Bi-linear interpolation onto SAR image
self.winddir.reproject(self, eResampleAlg=1)
# Check time difference between SAR image and wind direction object
timediff = self.SAR_image_time - winddir_time
hoursDiff = np.abs(timediff.total_seconds() / 3600.)
print 'Time difference between SAR image and wind direction: ' \
+ '%.2f' % hoursDiff + ' hours'
print 'SAR image time: ' + str(self.SAR_image_time)
print 'Wind dir time: ' + str(winddir_time)
if hoursDiff > 3:
print '#########################################'
print 'WARNING: time difference exceeds 3 hours!'
print '#########################################'
wind_u_bandNo = self.winddir._get_band_number({
'standard_name':
'eastward_wind',
})
wind_v_bandNo = self.winddir._get_band_number({
'standard_name':
'northward_wind',
})
# Get wind direction
u_array = self.winddir[wind_u_bandNo]
v_array = self.winddir[wind_v_bandNo]
winddirArray = np.degrees(np.arctan2(
-u_array, -v_array)) # 0 from North, 90 from East
# Calculate SAR wind with CMOD
# TODO:
# - add other CMOD versions than CMOD5
print 'Calculating SAR wind with CMOD...'
startTime = datetime.now()
windspeed = cmod5n_inverse(
self[self.sigma0_bandNo],
np.mod(winddirArray - self['SAR_look_direction'], 360),
self['incidence_angle'])
print 'Calculation time: ' + str(datetime.now() - startTime)
windspeed[np.where(np.isnan(windspeed))] = np.nan
windspeed[np.where(np.isinf(windspeed))] = np.nan
# Add wind speed and direction as bands
# TODO: make it possible to update existing bands... See
# https://github.com/nansencenter/nansat/issues/58
self.add_band(array=windspeed,
parameters={
'wkv': 'wind_speed',
'name': 'windspeed',
'time': self.get_time(self.sigma0_bandNo),
'winddir_time': winddir_time
})
self.add_band(array=winddirArray,
parameters={
'wkv': 'wind_from_direction',
'name': 'winddirection',
'time': winddir_time
})
if storeModelSpeed:
self.add_band(array=self.winddir['windspeed'],
parameters={
'wkv': 'wind_speed',
'name': 'model_windspeed',
'time': winddir_time,
})
# TODO: Replace U and V bands with pixelfunctions
u = -windspeed * np.sin((180.0 - winddirArray) * np.pi / 180.0)
v = windspeed * np.cos((180.0 - winddirArray) * np.pi / 180.0)
self.add_band(array=u, parameters={
'wkv': 'eastward_wind',
})
self.add_band(array=v, parameters={
'wkv': 'northward_wind',
})
def _get_masked_windspeed(self, landmask=True, icemask=True):
try:
sar_windspeed = self['windspeed']
except:
raise ValueError('SAR wind has not been calculated, ' \
'execute calculate_wind(winddir) first.')
sar_windspeed[sar_windspeed < 0] = 0
palette = jet
if landmask:
try: # Land mask
sar_windspeed = np.ma.masked_where(self.watermask()[1] == 2,
sar_windspeed)
palette.set_bad([.3, .3, .3], 1.0) # Land is masked (bad)
except:
print 'Land mask not available'
if icemask:
try: # Ice mask
try: # first try local file
ice = Nansat('metno_local_hires_seaice_' +
self.SAR_image_time.strftime('%Y%m%d'),
mapperName='metno_local_hires_seaice')
except: # otherwise Thredds
ice = Nansat('metno_hires_seaice:' +
self.SAR_image_time.strftime('%Y%m%d'))
ice.reproject(self)
iceBandNo = ice._get_band_number(
{'standard_name': 'sea_ice_area_fraction'})
sar_windspeed[ice[iceBandNo] > 0] = -1
palette.set_under('w', 1.0) # Ice is 'under' (-1)
except:
print 'Ice mask not available'
return sar_windspeed, palette
def write_geotiff(self, filename, landmask=True, icemask=True):
sar_windspeed, palette = self._get_masked_windspeed(landmask, icemask)
nansat_geotiff = Nansat(array=sar_windspeed,
domain=self,
parameters={
'name': 'masked_windspeed',
'minmax': '0 20'
})
nansat_geotiff.write_geotiffimage(filename)
def plot(self,
filename=None,
numVectorsX=16,
show=True,
landmask=True,
icemask=True,
flip=True,
maskWindAbove=35):
''' Basic plotting function showing CMOD wind speed
overlaid vectors in SAR image projection'''
try:
sar_windspeed, palette = self._get_masked_windspeed(
landmask, icemask)
except:
raise ValueError('SAR wind has not been calculated, ' \
'execute calculate_wind(winddir) before plotting.')
sar_windspeed[sar_windspeed > maskWindAbove] = np.nan
winddirReductionFactor = np.round(self.vrt.dataset.RasterXSize /
numVectorsX)
# model_winddir is direction from which wind is blowing
winddir_relative_up = 360 - self['winddirection'] + \
self.azimuth_up()
indX = range(0, self.vrt.dataset.RasterXSize, winddirReductionFactor)
indY = range(0, self.vrt.dataset.RasterYSize, winddirReductionFactor)
X, Y = np.meshgrid(indX, indY)
try: # scaling of wind vector length, if model wind is available
model_windspeed = self['model_windspeed']
model_windspeed = model_windspeed[Y, X]
except:
model_windspeed = 8 * np.ones(X.shape)
Ux = np.sin(np.radians(winddir_relative_up[Y, X])) * model_windspeed
Vx = np.cos(np.radians(winddir_relative_up[Y, X])) * model_windspeed
# Make sure North is up, and east is right
if flip == True:
lon, lat = self.get_corners()
if lat[0] < lat[1]:
sar_windspeed = np.flipud(sar_windspeed)
Ux = -np.flipud(Ux)
Vx = -np.flipud(Vx)
if lon[0] > lon[2]:
sar_windspeed = np.fliplr(sar_windspeed)
Ux = np.fliplr(Ux)
Vx = np.fliplr(Vx)
# Plotting
figSize = sar_windspeed.shape
legendPixels = 60.0
legendPadPixels = 5.0
legendFraction = legendPixels / figSize[0]
legendPadFraction = legendPadPixels / figSize[0]
dpi = 100.0
fig = plt.figure()
fig.set_size_inches(
(figSize[1] / dpi,
(figSize[0] / dpi) * (1 + legendFraction + legendPadFraction)))
ax = fig.add_axes([0, 0, 1, 1 + legendFraction])
ax.set_axis_off()
plt.imshow(sar_windspeed, cmap=palette, interpolation='nearest')
plt.clim([0, 20])
cbar = plt.colorbar(orientation='horizontal',
shrink=.80,
aspect=40,
fraction=legendFraction,
pad=legendPadFraction)
cbar.ax.set_ylabel('[m/s]', rotation=0)
cbar.ax.yaxis.set_label_position('right')
ax.quiver(X,
Y,
Ux,
Vx,
angles='xy',
width=0.004,
scale=200,
scale_units='width',
color=[.0, .0, .0],
headaxislength=4)
if filename is not None:
fig.savefig(filename, pad_inches=0, dpi=dpi)
if show:
plt.show()
return fig
def main( argv=None ):
year = '2012'
useMask = False
if argv is None:
argv = sys.argv
if argv is None:
print ( "Please specify the path/year to the asar folder! \n")
return
# Parse arguments
try:
opts, args = getopt.getopt(argv,"hi:o:",["year=","oPath=","iPath=","useMask="])
except getopt.GetoptError:
print 'readASAR.py -year <year> ...'
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print 'readASAR.py -year <year> ...'
sys.exit()
elif opt in ("-year", "--year"):
year = arg
elif opt in ("-oPath", "--oPath"):
oPath = arg
elif opt in ("-iPath", "--iPath"):
iPath = arg
elif opt in ("-useMask", "--useMask"):
useMask = arg
oPath = '/media/SOLabNFS2/tmp/roughness/' + year + '/'
iPath = '/media/SOLabNFS2/store/satellite/asar/' + year + '/'
if not os.path.exists(oPath):
os.makedirs(oPath)
dirNames=os.listdir(iPath)
for dirName in dirNames:
fileNames=os.listdir(iPath+dirName)
for fileName in fileNames:
figureName = oPath + fileName[0:27] + '/' + fileName + '_proj.png'
kmlName = oPath + fileName[0:27] + '/' + fileName + '.kml'
if not os.path.exists(oPath + fileName[0:27] + '/'):
os.makedirs(oPath + fileName[0:27] + '/')
if os.path.isfile(kmlName):
print "%s already processed" % (fileName)
continue
else:
print "%s" % (fileName)
# try to create Nansat object
try:
n = Nansat(iPath + dirName + '/' + fileName, mapperName='asar', logLevel=27)
except Exception as e:
print "Failed to create Nansat object:"
print str(e)
os.rmdir(oPath + fileName[0:27] + '/' )
continue
#~ Get the bands
raw_counts = n[1]
inc_angle = n[2]
#~ NICE image (roughness)
pol = n.bands()[3]['polarization']
if pol == 'HH':
ph = (2.20495, -14.3561e-2, 11.28e-4)
sigma0_hh_ref = exp( ( ph[0]+inc_angle*ph[1]+inc_angle**2*ph[2])*log(10) )
roughness = n[3]/sigma0_hh_ref
elif pol == 'VV':
pv = (2.29373, -15.393e-2, 15.1762e-4)
sigma0_vv_ref = exp( ( pv[0]+inc_angle*pv[1]+inc_angle**2*pv[2])*log(10) )
roughness = n[3]/sigma0_vv_ref
#~ Create new band
n.add_band(bandID=4, array=roughness, \
parameters={'name':'roughness', \
'wkv': 'surface_backwards_scattering_coefficient_of_radar_wave', \
'dataType': 6})
# Reproject image into Lat/Lon WGS84 (Simple Cylindrical) projection
# 1. Cancel previous reprojection
# 2. Get corners of the image and the pixel resolution
# 3. Create Domain with stereographic projection, corner coordinates 1000m
# 4. Reproject
# 5. Write image
n.reproject() # 1.
lons, lats = n.get_corners() # 2.
# Pixel resolution
#~ pxlRes = distancelib.getPixelResolution(array(lats), array(lons), n.shape())
#~ pxlRes = array(pxlRes)*360/40000 # great circle distance
pxlRes = array(distancelib.getPixelResolution(array(lats), array(lons), n.shape(), 'deg'))
ipdb.set_trace()
if min(lats) >= 65 and max(lats) >= 75 and max(lats)-min(lats) >= 13:
pxlRes = array([0.00065, 0.00065])*2 # make the resolution 150x150m
#~ pxlRes = pxlRes*7 # make the resolution worser
srsString = "+proj=latlong +datum=WGS84 +ellps=WGS84 +no_defs"
#~ extentString = '-lle %f %f %f %f -ts 3000 3000' % (min(lons), min(lats), max(lons), max(lats))
extentString = '-lle %f %f %f %f -tr %f %f' % (min(lons), min(lats), \
max(lons), max(lats), pxlRes[1], pxlRes[0])
d = Domain(srs=srsString, ext=extentString) # 3.
n.reproject(d) # 4.
if useMask:
# get array with watermask (landmask) b
# it must be done after reprojection!
# 1. Get Nansat object with watermask
# 2. Get array from Nansat object. 0 - land, 1 - water
#wm = n.watermask(mod44path='/media/magDesk/media/SOLabNFS/store/auxdata/coastline/mod44w/')
wm = n.watermask(mod44path='/media/data/data/auxdata/coastline/mod44w/')
wmArray = wm[1]
#~ ОШИБКА numOfColor=255 не маскирует, потому что в figure.apply_mask: availIndeces = range(self.d['numOfColor'], 255 - 1)
#~ n.write_figure(fileName=figureName, bands=[3], \
#~ numOfColor=255, mask_array=wmArray, mask_lut={0: 0},
#~ clim=[0,0.15], cmapName='gray', transparency=0) # 5.
n.write_figure(fileName=figureName, bands=[4], \
mask_array=wmArray, mask_lut={0: [0,0,0]},
clim=[0,2], cmapName='gray', transparency=[0,0,0]) # 5.
else:
n.write_figure(fileName=figureName, bands=[1], \
clim=[0,2], cmapName='gray', transparency=[0,0,0]) # 5.
# open the input image and convert to RGBA for further tiling with slbtiles
input_img = Image.open(figureName)
output_img = input_img.convert("RGBA")
output_img.save(figureName)
# make KML image
n.write_kml_image(kmlFileName=kmlName, kmlFigureName=figureName)
#~ Change the file permissions
os.chmod(oPath, 0777)
os.chmod(oPath + fileName[0:27] + '/', 0777)
os.chmod(kmlName, 0777)
os.chmod(figureName, 0777)
#~ Change the owner and group
#~ os.chown(oPath, 1111, 1111)
#~ os.chown(oPath + fileName[0:27] + '/', 1111, 1111)
#~ os.chown(kmlName, 1111, 1111)
#~ os.chown(figureName, 1111, 1111)
#~ garbage collection
gc.collect()
class SARWind(Nansat, object):
'''
A class for calculating wind speed from SAR images using CMOD
'''
def __init__(self, sar_image, winddir=None, pixelsize=500):
'''
Parameters
-----------
sar_image : string or Nansat object
SAR image filename (original, raw file)
winddir : int, string, Nansat, None
Auxiliary wind field information needed to calculate
SAR wind (must be or have wind direction)
'''
if isinstance(sar_image, str) or isinstance(sar_image, unicode):
super(SARWind, self).__init__(sar_image)
elif isinstance(sar_image, Nansat):
super(SARWind, self).__init__(domain=sar_image)
self.vrt = sar_image.vrt
# Check that this is a SAR image with VV pol NRCS
try:
self.sigma0_bandNo = self._get_band_number(
{'standard_name':
'surface_backwards_scattering_coefficient_of_radar_wave',
'polarization': 'VV'})
except:
raise TypeError(self.fileName +
' does not have SAR NRCS in VV polarization')
self.SAR_image_time = self.get_time(
self.sigma0_bandNo).replace(tzinfo=None)
if pixelsize != 'fullres':
print 'Resizing SAR image to ' + str(pixelsize) + ' m pixel size'
self.resize(pixelsize=pixelsize)
self.winddir = winddir
if winddir is not None:
self.calculate_wind()
def calculate_wind(self, winddir=None, storeModelSpeed=True):
# Calculate wind speed from SAR sigma0 in VV polarization
if winddir:
self.winddir = winddir
if self.winddir is None or self.winddir == 'online':
self.winddir = 'ncep_wind_online' # default source
if isinstance(self.winddir, int):
# Constant wind direction is input
print 'Using constant wind (from) direction: ' + str(self.winddir) + \
' degrees clockwise from North'
winddirArray = np.ones(self.shape())*self.winddir
winddir_time = None
storeModelSpeed = False # Not relevant if direction given as number
else:
# Nansat readable file
if isinstance(self.winddir, str):
try:
self.winddir = Nansat(self.winddir)
except:
try:
self.winddir = Nansat(self.winddir +
datetime.strftime(
self.SAR_image_time, ':%Y%m%d%H%M'))
except:
pass
if not isinstance(self.winddir, Nansat):
raise ValueError('Wind direction not available')
winddir_time = self.winddir.get_time()[0]
# Bi-linear interpolation onto SAR image
self.winddir.reproject(self, eResampleAlg=1)
# Check time difference between SAR image and wind direction object
timediff = self.SAR_image_time - winddir_time
hoursDiff = np.abs(timediff.total_seconds()/3600.)
print 'Time difference between SAR image and wind direction: ' \
+ '%.2f' % hoursDiff + ' hours'
print 'SAR image time: ' + str(self.SAR_image_time)
print 'Wind dir time: ' + str(winddir_time)
if hoursDiff > 3:
print '#########################################'
print 'WARNING: time difference exceeds 3 hours!'
print '#########################################'
wind_u_bandNo = self.winddir._get_band_number({
'standard_name': 'eastward_wind',
})
wind_v_bandNo = self.winddir._get_band_number({
'standard_name': 'northward_wind',
})
# Get wind direction
u_array = self.winddir[wind_u_bandNo]
v_array = self.winddir[wind_v_bandNo]
winddirArray = np.degrees(
np.arctan2(-u_array, -v_array)) # 0 from North, 90 from East
# Calculate SAR wind with CMOD
# TODO:
# - add other CMOD versions than CMOD5
print 'Calculating SAR wind with CMOD...'
startTime = datetime.now()
windspeed = cmod5n_inverse(self[self.sigma0_bandNo],
np.mod(winddirArray - self['SAR_look_direction'], 360),
self['incidence_angle'])
print 'Calculation time: ' + str(datetime.now() - startTime)
windspeed[np.where(np.isnan(windspeed))] = np.nan
windspeed[np.where(np.isinf(windspeed))] = np.nan
# Add wind speed and direction as bands
# TODO: make it possible to update existing bands... See
# https://github.com/nansencenter/nansat/issues/58
self.add_band(array=windspeed, parameters={
'wkv': 'wind_speed',
'name': 'windspeed',
'time': self.get_time(self.sigma0_bandNo),
'winddir_time': winddir_time
})
self.add_band(array=winddirArray, parameters={
'wkv': 'wind_from_direction',
'name': 'winddirection',
'time': winddir_time
})
if storeModelSpeed:
self.add_band(array=self.winddir['windspeed'], parameters={
'wkv': 'wind_speed',
'name': 'model_windspeed',
'time': winddir_time,
})
# TODO: Replace U and V bands with pixelfunctions
u = -windspeed*np.sin((180.0 - winddirArray)*np.pi/180.0)
v = windspeed*np.cos((180.0 - winddirArray)*np.pi/180.0)
self.add_band(array=u, parameters={
'wkv': 'eastward_wind',
})
self.add_band(array=v, parameters={
'wkv': 'northward_wind',
})
def _get_masked_windspeed(self, landmask=True, icemask=True):
try:
sar_windspeed = self['windspeed']
except:
raise ValueError('SAR wind has not been calculated, ' \
'execute calculate_wind(winddir) first.')
sar_windspeed[sar_windspeed<0] = 0
palette = jet
if landmask:
try: # Land mask
sar_windspeed = np.ma.masked_where(
self.watermask()[1]==2, sar_windspeed)
palette.set_bad([.3, .3, .3], 1.0) # Land is masked (bad)
except:
print 'Land mask not available'
if icemask:
try: # Ice mask
try: # first try local file
ice = Nansat('metno_local_hires_seaice_' +
self.SAR_image_time.strftime('%Y%m%d'),
mapperName='metno_local_hires_seaice')
except: # otherwise Thredds
ice = Nansat('metno_hires_seaice:' +
self.SAR_image_time.strftime('%Y%m%d'))
ice.reproject(self)
iceBandNo = ice._get_band_number(
{'standard_name': 'sea_ice_area_fraction'})
sar_windspeed[ice[iceBandNo]>0] = -1
palette.set_under('w', 1.0) # Ice is 'under' (-1)
except:
print 'Ice mask not available'
return sar_windspeed, palette
def write_geotiff(self, filename, landmask=True, icemask=True):
sar_windspeed, palette = self._get_masked_windspeed(landmask, icemask)
nansat_geotiff = Nansat(array=sar_windspeed, domain=self,
parameters = {'name': 'masked_windspeed',
'minmax': '0 20'})
nansat_geotiff.write_geotiffimage(filename)
def plot(self, filename=None, numVectorsX = 16, show=True,
landmask=True, icemask=True, flip=True, maskWindAbove=35):
''' Basic plotting function showing CMOD wind speed
overlaid vectors in SAR image projection'''
try:
sar_windspeed, palette = self._get_masked_windspeed(landmask, icemask)
except:
raise ValueError('SAR wind has not been calculated, ' \
'execute calculate_wind(winddir) before plotting.')
sar_windspeed[sar_windspeed>maskWindAbove] = np.nan
winddirReductionFactor = np.round(
self.vrt.dataset.RasterXSize/numVectorsX)
# model_winddir is direction from which wind is blowing
winddir_relative_up = 360 - self['winddirection'] + \
self.azimuth_up()
indX = range(0, self.vrt.dataset.RasterXSize, winddirReductionFactor)
indY = range(0, self.vrt.dataset.RasterYSize, winddirReductionFactor)
X, Y = np.meshgrid(indX, indY)
try: # scaling of wind vector length, if model wind is available
model_windspeed = self['model_windspeed']
model_windspeed = model_windspeed[Y, X]
except:
model_windspeed = 8*np.ones(X.shape)
Ux = np.sin(np.radians(winddir_relative_up[Y, X]))*model_windspeed
Vx = np.cos(np.radians(winddir_relative_up[Y, X]))*model_windspeed
# Make sure North is up, and east is right
if flip == True:
lon, lat = self.get_corners()
if lat[0] < lat[1]:
sar_windspeed = np.flipud(sar_windspeed)
Ux = -np.flipud(Ux)
Vx = -np.flipud(Vx)
if lon[0] > lon[2]:
sar_windspeed = np.fliplr(sar_windspeed)
Ux = np.fliplr(Ux)
Vx = np.fliplr(Vx)
# Plotting
figSize = sar_windspeed.shape
legendPixels = 60.0
legendPadPixels = 5.0
legendFraction = legendPixels/figSize[0]
legendPadFraction = legendPadPixels/figSize[0]
dpi=100.0
fig = plt.figure()
fig.set_size_inches((figSize[1]/dpi, (figSize[0]/dpi)*
(1+legendFraction+legendPadFraction)))
ax = fig.add_axes([0,0,1,1+legendFraction])
ax.set_axis_off()
plt.imshow(sar_windspeed, cmap=palette, interpolation='nearest')
plt.clim([0, 20])
cbar = plt.colorbar(orientation='horizontal', shrink=.80,
aspect=40,
fraction=legendFraction, pad=legendPadFraction)
cbar.ax.set_ylabel('[m/s]', rotation=0)
cbar.ax.yaxis.set_label_position('right')
ax.quiver(X, Y, Ux, Vx, angles='xy', width=0.004,
scale=200, scale_units='width',
color=[.0, .0, .0], headaxislength=4)
if filename is not None:
fig.savefig(filename, pad_inches=0, dpi=dpi)
if show:
plt.show()
return fig
n.write_kml(kmlFileName=oFileName + '_preview.kml')
# make image with map of the file location
n.write_map(oFileName + '_map.png')
# Make image reprojected onto map of Northern Europe
# 1. Create Domain object. It describes the desired grid of reprojected image:
# projection, resolution, size, etc. In this case it is geographic projection;
# -10 - 30 E, 50 - 70 W; 2000 x 2000 pixels
# 2. Reproject the Nansat object
# 3. Make simple image
dLatlong = Domain("+proj=latlong +datum=WGS84 +ellps=WGS84 +no_defs", "-te 25 70 35 72 -ts 2000 2000")
dLatlong.write_map(oFileName + '_latlong_map.png')
print 'Latlong Domain:', dLatlong
n.reproject(dLatlong)
n.write_figure(oFileName + '_pro_latlon.png')
# Reprojected image into stereographic projection
# 1. Cancel previous reprojection
# 2. Get corners of the image
# 3. Create Domain with stereographic projection, corner coordinates and resolution 1000m
# 4. Reproject with cubic interpolation
# 5. Write image
n.reproject() # 1.
lons, lats = n.get_corners() # 2.
meanLon = sum(lons, 0.0) / 4.
meanLat = sum(lats, 0.0) / 4.
srsString = "+proj=stere +lon_0=%f +lat_0=%f +k=1 +ellps=WGS84 +datum=WGS84 +no_defs" % (meanLon, meanLat)
extentString = '-lle %f %f %f %f -tr 100 100' % (min(lons), min(lats), max(lons), max(lats))
dStereo = Domain(srsString, extentString) # 3.
oPath = '/home/mag/'
fileName = 'MER_FRS_1PNEPA20100401_154249_000001972088_00140_42278_0585.N1.nc'
# create Nansat object
n = Nansat(iPath + fileName)
# list bands and georeference of the object
print n
# Reprojected image into Lat/Lon WGS84 (Simple Cylindrical) projection
# 1. Cancel previous reprojection
# 2. Get corners of the image and the pixel resolution
# 3. Create Domain with stereographic projection, corner coordinates and resolution 1000m
# 4. Reproject
# 5. Write image
n.reproject() # 1.
lons, lats = n.get_corners() # 2.
pxlRes = distancelib.getPixelResolution(array(lats), array(lons), n[1])
pxlRes = array(pxlRes)*360/40000 # great circle distance
srsString = "+proj=latlong +datum=WGS84 +ellps=WGS84 +no_defs"
#~ extentString = '-lle %f %f %f %f -ts 3000 3000' % (min(lons), min(lats), max(lons), max(lats))
extentString = '-lle %f %f %f %f -tr %f %f' % (min(lons), min(lats), \
max(lons), max(lats), pxlRes[1], pxlRes[0])
d = Domain(srs=srsString, ext=extentString) # 3.
print d
n.reproject(d) # 4.
# get array with watermask (landmask) b
# it must be done after reprojection!
# 1. Get Nansat object with watermask
# 2. Get array from Nansat object. 0 - land, 1 - water
def main(argv=None):
year = '2012'
useMask = False
if argv is None:
argv = sys.argv
if argv is None:
print("Please specify the path/year to the asar folder! \n")
return
# Parse arguments
try:
opts, args = getopt.getopt(argv, "hi:o:",
["year=", "oPath=", "iPath=", "useMask="])
except getopt.GetoptError:
print 'readASAR.py -year <year> ...'
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print 'readASAR.py -year <year> ...'
sys.exit()
elif opt in ("-year", "--year"):
year = arg
elif opt in ("-oPath", "--oPath"):
oPath = arg
elif opt in ("-iPath", "--iPath"):
iPath = arg
elif opt in ("-useMask", "--useMask"):
useMask = arg
oPath = '/media/SOLabNFS2/tmp/roughness/' + year + '/'
iPath = '/media/SOLabNFS2/store/satellite/asar/' + year + '/'
if not os.path.exists(oPath):
os.makedirs(oPath)
dirNames = os.listdir(iPath)
for dirName in dirNames:
fileNames = os.listdir(iPath + dirName)
for fileName in fileNames:
figureName = oPath + fileName[0:27] + '/' + fileName + '_proj.png'
kmlName = oPath + fileName[0:27] + '/' + fileName + '.kml'
if not os.path.exists(oPath + fileName[0:27] + '/'):
os.makedirs(oPath + fileName[0:27] + '/')
if os.path.isfile(kmlName):
print "%s already processed" % (fileName)
continue
else:
print "%s" % (fileName)
# try to create Nansat object
try:
n = Nansat(iPath + dirName + '/' + fileName,
mapperName='asar',
logLevel=27)
except Exception as e:
print "Failed to create Nansat object:"
print str(e)
os.rmdir(oPath + fileName[0:27] + '/')
continue
#~ Get the bands
raw_counts = n[1]
inc_angle = n[2]
#~ NICE image (roughness)
pol = n.bands()[3]['polarization']
if pol == 'HH':
ph = (2.20495, -14.3561e-2, 11.28e-4)
sigma0_hh_ref = exp(
(ph[0] + inc_angle * ph[1] + inc_angle**2 * ph[2]) *
log(10))
roughness = n[3] / sigma0_hh_ref
elif pol == 'VV':
pv = (2.29373, -15.393e-2, 15.1762e-4)
sigma0_vv_ref = exp(
(pv[0] + inc_angle * pv[1] + inc_angle**2 * pv[2]) *
log(10))
roughness = n[3] / sigma0_vv_ref
#~ Create new band
n.add_band(bandID=4, array=roughness, \
parameters={'name':'roughness', \
'wkv': 'surface_backwards_scattering_coefficient_of_radar_wave', \
'dataType': 6})
# Reproject image into Lat/Lon WGS84 (Simple Cylindrical) projection
# 1. Cancel previous reprojection
# 2. Get corners of the image and the pixel resolution
# 3. Create Domain with stereographic projection, corner coordinates 1000m
# 4. Reproject
# 5. Write image
n.reproject() # 1.
lons, lats = n.get_corners() # 2.
# Pixel resolution
#~ pxlRes = distancelib.getPixelResolution(array(lats), array(lons), n.shape())
#~ pxlRes = array(pxlRes)*360/40000 # great circle distance
pxlRes = array(
distancelib.getPixelResolution(array(lats), array(lons),
n.shape(), 'deg'))
ipdb.set_trace()
if min(lats) >= 65 and max(
lats) >= 75 and max(lats) - min(lats) >= 13:
pxlRes = array([0.00065, 0.00065
]) * 2 # make the resolution 150x150m
#~ pxlRes = pxlRes*7 # make the resolution worser
srsString = "+proj=latlong +datum=WGS84 +ellps=WGS84 +no_defs"
#~ extentString = '-lle %f %f %f %f -ts 3000 3000' % (min(lons), min(lats), max(lons), max(lats))
extentString = '-lle %f %f %f %f -tr %f %f' % (min(lons), min(lats), \
max(lons), max(lats), pxlRes[1], pxlRes[0])
d = Domain(srs=srsString, ext=extentString) # 3.
n.reproject(d) # 4.
if useMask:
# get array with watermask (landmask) b
# it must be done after reprojection!
# 1. Get Nansat object with watermask
# 2. Get array from Nansat object. 0 - land, 1 - water
#wm = n.watermask(mod44path='/media/magDesk/media/SOLabNFS/store/auxdata/coastline/mod44w/')
wm = n.watermask(
mod44path='/media/data/data/auxdata/coastline/mod44w/')
wmArray = wm[1]
#~ ОШИБКА numOfColor=255 не маскирует, потому что в figure.apply_mask: availIndeces = range(self.d['numOfColor'], 255 - 1)
#~ n.write_figure(fileName=figureName, bands=[3], \
#~ numOfColor=255, mask_array=wmArray, mask_lut={0: 0},
#~ clim=[0,0.15], cmapName='gray', transparency=0) # 5.
n.write_figure(fileName=figureName, bands=[4], \
mask_array=wmArray, mask_lut={0: [0,0,0]},
clim=[0,2], cmapName='gray', transparency=[0,0,0]) # 5.
else:
n.write_figure(fileName=figureName, bands=[1], \
clim=[0,2], cmapName='gray', transparency=[0,0,0]) # 5.
# open the input image and convert to RGBA for further tiling with slbtiles
input_img = Image.open(figureName)
output_img = input_img.convert("RGBA")
output_img.save(figureName)
# make KML image
n.write_kml_image(kmlFileName=kmlName, kmlFigureName=figureName)
#~ Change the file permissions
os.chmod(oPath, 0777)
os.chmod(oPath + fileName[0:27] + '/', 0777)
os.chmod(kmlName, 0777)
os.chmod(figureName, 0777)
#~ Change the owner and group
#~ os.chown(oPath, 1111, 1111)
#~ os.chown(oPath + fileName[0:27] + '/', 1111, 1111)
#~ os.chown(kmlName, 1111, 1111)
#~ os.chown(figureName, 1111, 1111)
#~ garbage collection
gc.collect()
