def main(*argv):
if not argv:
argv = sys.argv
if len(argv) != 3:
print('Usage: write_ndvi <envisat-product> <output-file>')
print(' where envisat-product is the input filename')
print(' and output-file is the output filename.')
print('Example: MER_RR__1P_TEST.N1 my_ndvi.raw')
print
sys.exit(1)
# Open the product
with epr.open(argv[1]) as product:
# The NDVI shall be calculated using bands 6 and 8.
band1_name = 'radiance_6'
band2_name = 'radiance_10'
band1 = product.get_band(band1_name)
band2 = product.get_band(band2_name)
# Allocate memory for the rasters
width = product.get_scene_width()
height = product.get_scene_height()
subsampling_x = 1
subsampling_y = 1
raster1 = band1.create_compatible_raster(width, height,
subsampling_x, subsampling_y)
raster2 = band2.create_compatible_raster(width, height,
subsampling_x, subsampling_y)
# Read the radiance into the raster.
offset_x = 0
offset_y = 0
logging.info('read "%s" data' % band1_name)
band1.read_raster(offset_x, offset_y, raster1)
logging.info('read "%s" data' % band2_name)
band2.read_raster(offset_x, offset_y, raster2)
# Open the output file
logging.info('write ndvi to "%s"' % argv[2])
with open(argv[2], 'wb') as out_stream:
# Loop over all pixel and calculate the NDVI.
#
# @NOTE: looping over data matrices is not the best soluton.
# It is done here just for demostrative purposes
for j in range(height):
for i in range(width):
rad1 = raster1.get_pixel(i, j)
rad2 = raster2.get_pixel(i, j)
if (rad1 + rad2) != 0.0:
ndvi = (rad2 - rad1) / (rad2 + rad1)
else:
ndvi = -1.0
out_stream.write(struct.pack('f', ndvi))
logging.info('ndvi was written success')
def main(*argv):
'''A program for converting producing ENVI raster information from
dataset.
It generates as many raster as there are dataset entrance parameters.
Call::
$ write_bands.py <envisat-product>
<output directory for the raster file>
<dataset name 1>
[<dataset name 2> ... <dataset name N>]
Example::
$ write_bands.py \
MER_RR__1PNPDK20020415_103725_000002702005_00094_00649_1059.N1 \
. latitude
'''
if not argv:
argv = sys.argv
if len(argv) <= 3:
print('Usage: write_bands.py <envisat-product> <output-dir> '
'<dataset-name-1>')
print(' [<dataset-name-2> ... <dataset-name-N>]')
print(' where envisat-product is the input filename')
print(' and output-dir is the output directory')
print(' and dataset-name-1 is the name of the first band to be '
'extracted (mandatory)')
print(' and dataset-name-2 ... dataset-name-N are the names of '
'further bands to be extracted (optional)')
print('Example:')
print(' write_bands MER_RR__2P_TEST.N1 . latitude')
print()
sys.exit(1)
product_file_path = argv[1]
output_dir_path = argv[2]
# Open the product; an argument is a path to product data file
with epr.open(product_file_path) as product:
for band_name in argv[3:]:
write_raw_image(output_dir_path, product, band_name)
def epr2gdal(product, vrt, overwrite_existing=False):
if isinstance(product, str):
filename = product
product = epr.open(filename)
ysize = product.get_scene_height()
xsize = product.get_scene_width()
if os.path.exists(vrt) and not overwrite_existing:
raise ValueError('unable to create "{0}". Already exists'.format(vrt))
driver = gdal.GetDriverByName('VRT')
if driver is None:
raise RuntimeError('unable to get driver "VRT"')
gdal_ds = driver.Create(vrt, xsize, ysize, 0)
if gdal_ds is None:
raise RuntimeError('unable to create "{}" dataset'.format(vrt))
metadata = {
'id_string': product.id_string,
'meris_iodd_version': str(product.meris_iodd_version),
'dataset_names': ','.join(product.get_dataset_names()),
'num_datasets': str(product.get_num_datasets()),
'num_dsds': str(product.get_num_dsds()),
}
gdal_ds.SetMetadata(metadata)
mph = product.get_mph()
metadata = str(mph).replace(' = ', '=').split('\n')
gdal_ds.SetMetadata(metadata, 'MPH')
sph = product.get_sph()
metadata = str(sph).replace(' = ', '=').split('\n')
gdal_ds.SetMetadata(metadata, 'SPH')
for band in product.bands():
epr2gdal_band(band, gdal_ds)
# @TODO: set geographic info
return gdal_ds
def main(*argv):
if not argv:
argv = sys.argv
if len(argv) != 4:
print('Usage: write_bitmask <envisat-product> <bitmask-expression> '
'<output-file>')
print(' where envisat-product is the input filename')
print(' and bitmask-expression is a string containing the bitmask '
'logic')
print(' and output-file is the output filename.')
print('Example:')
print(" MER_RR__2P_TEST.N1 'l2_flags.LAND and not l2_flags.BRIGHT' "
"my_flags.raw")
print
sys.exit(1)
product_file_path = argv[1]
bm_expr = argv[2]
image_file_path = argv[3]
# Open the product; an argument is a path to product data file
with epr.open(product_file_path) as product:
offset_x = 0
offset_y = 0
source_width = product.get_scene_width()
source_height = product.get_scene_height()
source_step_x = 1
source_step_y = 1
bm_raster = epr.create_bitmask_raster(source_width, source_height,
source_step_x, source_step_y)
product.read_bitmask_raster(bm_expr, offset_x, offset_y, bm_raster)
with open(image_file_path, 'wb') as out_stream:
bm_raster.data.tofile(out_stream)
print('Raw image data successfully written to "%s".' % image_file_path)
print('Data type is "byte", size is %d x %d pixels.' % (source_width,
source_height))
def read_n1(self, ingest):
"""
Read N1 file (eg MERIS or AATSR), using pyepr package
Extract the region of interest, and at the same time regrid to a regular grid.
Returns a dictionary containing the parameters that were requested in the metadata file
(plus coordinates) for the specified region.
:param ingest: An IngestImages object instance, which contains metadata and inputdir variables
:return: Dictionary containing the coordinates and data arrays
:return: The value of whichever variable was specified by ingest.metadata['time_variable'].
This will be converted to a proper datetime in the calling method.
"""
import epr
data = {}
image = epr.open(str(ingest.metadata['filename']))
# Get date/time (is a string like '06-APR-2012 09:00:56.832999')
time_temp = image.get_sph().get_field(ingest.metadata['time_variable']).get_elem()
# Regrid coordinates and extract region of interest
longitude = image.get_band('longitude').read_as_array()
latitude = image.get_band('latitude').read_as_array()
new_lon, new_lat = GeoTools.get_new_lat_lon(longitude, latitude, ingest.metadata['region_coords'])
data['longitude'] = new_lon
data['latitude'] = new_lat
# Read in flag array, and extract bit for valid/invalid points to remove missing data points
if 'flag_name' in ingest.metadata.keys():
flag_array = image.get_band(ingest.metadata['flag_name']).read_as_array()
flag_array = flag_array >> ingest.metadata['flag_bit'] & 1
else:
flag_array = None
# Helper function to read a dataset, then extract and regrid to region of interest
# NB we read using the higher level get_band, instead of get_database. This is much simpler to use, and also
# means that values have already had scaling applied and are converted to floats
def get_variable(varname):
array = image.get_band(varname).read_as_array()
if flag_array is not None:
array[flag_array==1] = np.nan
array_roi = GeoTools.extract_region_and_regrid(longitude, latitude, new_lon, new_lat, array)
return array_roi
# Get the variables that were specified in the metadata file
for variable in ingest.metadata['variables']:
data[variable] = get_variable(str(variable)) # str() is needed as json returns unicode
# Get the viewing angles
for angle in ingest.metadata['angle_names']:
data[angle] = get_variable(ingest.metadata['angle_names'][angle])
# If this is MERIS reflectance, apply solar correction
if ingest.metadata['instrument'].lower() == 'meris' and ingest.metadata['vartype'] == 'radiance':
data = self.correct_MERIS(image, longitude, latitude, new_lon, new_lat, ingest.metadata, data)
# If this is AATSR reflectance, apply corrections
if ingest.metadata['instrument'].lower() == 'aatsr' and ingest.metadata['vartype'] == 'reflectance':
data = self.correct_AATSR(image, ingest.metadata, data)
return data, time_temp
for band in product.bands():
epr2gdal_band(band, gdal_ds)
# @TODO: set geographic info
return gdal_ds
if __name__ == '__main__':
filename = 'MER_LRC_2PTGMV20000620_104318_00000104X000_00000_00000_0001.N1'
vrtfilename = os.path.splitext(filename)[0] + '.vrt'
gdal_ds = epr2gdal(filename, vrtfilename)
with epr.open(filename) as product:
band_index = product.get_band_names().index('water_vapour')
band = product.get_band('water_vapour')
eprdata = band.read_as_array()
unit = band.unit
lines_mirrored = band.lines_mirrored
scaling_offset = band.scaling_offset
scaling_factor = band.scaling_factor
gdal_band = gdal_ds.GetRasterBand(band_index + 1)
vrtdata = gdal_band.ReadAsArray()
if lines_mirrored:
vrtdata = vrtdata[:, ::-1]
vrtdata = vrtdata * scaling_factor + scaling_offset
@author: mag
"""
#import numpy
#from pylab import *
#from pylab import imshow
import matplotlib.pyplot as plt
pn = '/home/mag/data/baltic/finngulfWindCases/'
fn = 'ASA_WSM_1PNPDE20110523_084634_000000983102_00395_48254_2349.N1'
fn = 'ASA_WSM_1PNPDE20111127_085336_000002143109_00079_50955_3727.N1'
import epr
product = epr.open(pn + fn)
proc_dataset = product.get_dataset('MAIN_PROCESSING_PARAMS_ADS')
record = proc_dataset.read_record(0)
record.get_field_names()[:20]
field = record.get_field('range_spacing')
product.get_band_names()
# Get proc_data
pd = product.get_band('proc_data')
proc_data = pd.read_as_array(5000, 5000, xoffset=500, yoffset=9000, \
xstep=2, ystep=2)
#!/usr/bin/env python3
from __future__ import print_function
import numpy as np
from matplotlib import pyplot as plt
import epr
FILENAME = 'MER_LRC_2PTGMV20000620_104318_00000104X000_00000_00000_0001.N1'
# load original data
with epr.open(FILENAME) as product:
band = product.get_band('water_vapour')
wv_orig_histogram, orig_bins = np.histogram(band.read_as_array().flat, 50)
# plot water vapour histogram
plt.figure()
plt.bar(orig_bins[:-1], wv_orig_histogram, 0.02, label='original')
plt.grid(True)
plt.title('Water Vapour Histogram')
plt.savefig('water_vapour_histogram_01.png')
# modily scaling facotrs
with epr.open(FILENAME, 'rb+') as product:
dataset = product.get_dataset('Scaling_Factor_GADS')
record = dataset.read_record(0)
field = record.get_field('sf_wvapour')
scaling = field.get_elem()
scaling *= 1.1
