def ingest_work(config, source_type, output_type, index, sources, geobox):
namemap = get_namemap(config)
measurements = get_measurements(source_type, config)
variable_params = get_variable_params(config)
global_attributes = config['global_attributes']
with datacube.set_options(reproject_threads=1):
fuse_func = {'copy': None}[config.get(FUSER_KEY, 'copy')]
data = Datacube.product_data(sources, geobox, measurements, fuse_func=fuse_func)
nudata = data.rename(namemap)
file_path = get_filename(config, index, sources)
def _make_dataset(labels, sources):
sources_union = union_points(*[source.extent.to_crs(geobox.crs).points for source in sources])
valid_data = intersect_points(geobox.extent.points, sources_union)
dataset = make_dataset(dataset_type=output_type,
sources=sources,
extent=geobox.extent,
center_time=labels['time'],
uri=file_path.absolute().as_uri(),
app_info=get_app_metadata(config, config['filename']),
valid_data=GeoPolygon(valid_data, geobox.crs))
return dataset
datasets = xr_apply(sources, _make_dataset, dtype='O') # Store in Dataarray to associate Time -> Dataset
nudata['dataset'] = datasets_to_doc(datasets)
write_dataset_to_netcdf(nudata, global_attributes, variable_params, file_path)
return datasets
def ingest_work(config, source_type, output_type, tile, tile_index):
_LOG.info('Starting task %s', tile_index)
namemap = get_namemap(config)
measurements = get_measurements(source_type, config)
variable_params = get_variable_params(config)
global_attributes = config['global_attributes']
with datacube.set_options(reproject_threads=1):
fuse_func = {'copy': None}[config.get(FUSER_KEY, 'copy')]
data = Datacube.load_data(tile.sources, tile.geobox, measurements, fuse_func=fuse_func)
nudata = data.rename(namemap)
file_path = get_filename(config, tile_index, tile.sources, version=config['taskfile_version'])
def _make_dataset(labels, sources):
return make_dataset(product=output_type,
sources=sources,
extent=tile.geobox.extent,
center_time=labels['time'],
uri=file_path.absolute().as_uri(),
app_info=get_app_metadata(config, config['filename']),
valid_data=GeoPolygon.from_sources_extents(sources, tile.geobox))
datasets = xr_apply(tile.sources, _make_dataset, dtype='O') # Store in Dataarray to associate Time -> Dataset
nudata['dataset'] = datasets_to_doc(datasets)
write_dataset_to_netcdf(nudata, file_path, global_attributes, variable_params)
_LOG.info('Finished task %s', tile_index)
return datasets
def test_write_dataset_to_netcdf(tmpnetcdf_filename):
affine = Affine.scale(0.1, 0.1) * Affine.translation(20, 30)
geobox = geometry.GeoBox(100, 100, affine, geometry.CRS(GEO_PROJ))
dataset = xarray.Dataset(attrs={'extent': geobox.extent, 'crs': geobox.crs})
for name, coord in geobox.coordinates.items():
dataset[name] = (name, coord.values, {'units': coord.units, 'crs': geobox.crs})
dataset['B10'] = (geobox.dimensions,
np.arange(10000, dtype='int16').reshape(geobox.shape),
{'nodata': 0, 'units': '1', 'crs': geobox.crs})
write_dataset_to_netcdf(dataset, tmpnetcdf_filename, global_attributes={'foo': 'bar'},
variable_params={'B10': {'attrs': {'abc': 'xyz'}}})
with netCDF4.Dataset(tmpnetcdf_filename) as nco:
nco.set_auto_mask(False)
assert 'B10' in nco.variables
var = nco.variables['B10']
assert (var[:] == dataset['B10'].values).all()
assert 'foo' in nco.ncattrs()
assert nco.getncattr('foo') == 'bar'
assert 'abc' in var.ncattrs()
assert var.getncattr('abc') == 'xyz'
def write_your_netcdf(data, dataset_name, filename, crs):
"""
This function turns an xarray dataarray into a dataset so we can write it to netcdf.
It adds on a crs definition from the original array. data = your xarray dataset, dataset_name
is a string describing your variable
Last modified: May 2018
Author: Bex Dunn
"""
#turn array into dataset so we can write the netcdf
if isinstance(data,xr.DataArray):
dataset= data.to_dataset(name=dataset_name)
elif isinstance(data,xr.Dataset):
dataset = data
else:
print('your data might be the wrong type, it is: '+type(data))
#grab our crs attributes to write a spatially-referenced netcdf
dataset.attrs['crs'] = crs
try:
write_dataset_to_netcdf(dataset, filename)
except RuntimeError as err:
print("RuntimeError: {0}".format(err))
def test_useful_error_on_write_empty_dataset(tmpnetcdf_filename):
with pytest.raises(DatacubeException) as excinfo:
ds = xr.Dataset()
write_dataset_to_netcdf(ds, tmpnetcdf_filename)
assert 'empty' in str(excinfo.value)
with pytest.raises(DatacubeException) as excinfo:
ds = xr.Dataset(data_vars={'blue': (('time',), numpy.array([0, 1, 2]))})
write_dataset_to_netcdf(ds, tmpnetcdf_filename)
assert 'CRS' in str(excinfo.value)
def xarray_to_cfnetcdf(data_xarray, output_nc_file, variable_name, crs):
# Data Cube friendly dataset, copy booleans to int8 as bool is not supported
dcf_ds = data_xarray.astype('int8', copy=False).to_dataset(name = variable_name)
# set a valid crs object, DC relies upon the python object so a WKT representation of CRS will fail
dcf_ds.attrs['crs'] = crs
# Set units for year coordinate
dcf_ds.coords['year'].attrs['units'] = 'years since 0'
# Set units for data variable
dcf_ds.data_vars[variable_name].attrs['units'] = 1
# write dataset out using datacube storage method - this is an unfortunate nessicity and we should expose a
# function like this in a nicer way
write_dataset_to_netcdf(dcf_ds, output_nc_file)
def create_files(data_ret, odir, MY_OBS_VAR, dt_list):
for k, data in data_ret.items():
if len(odir) > 0:
global_attributes = {}
global_attributes = dict(
Comment1='Data observed on ' +
','.join([dt.strftime('%Y-%m-%d') for dt in dt_list]))
filename = odir + '/' + 'LATEST_PIXEL_' + ''.join(map(str, k)) \
+ "_CLOUD_FREE_LAST_" + str(period) + "_DAYS"
obs_filename = odir + '/' + 'LATEST_PIXEL_' + ''.join(map(str, k)) \
+ "_CLOUD_FREE_LAST_" + str(period) + "_DAYS_OBS"
try:
ncfl = filename + ".nc"
ncobs = obs_filename + ".nc"
filename = filename + ".tif"
obs_filename = obs_filename + ".tif"
write_dataset_to_netcdf(
data[[MY_OBS_VAR]],
global_attributes=global_attributes,
variable_params={MY_OBS_VAR: {
'zlib': True
}},
filename=Path(ncobs))
write_dataset_to_netcdf(data[['swir1', 'nir', 'green']],
global_attributes=global_attributes,
variable_params={
'swir1': {
'zlib': True
},
'nir': {
'zlib': True
},
'green': {
'zlib': True
}
},
filename=Path(ncfl))
write_geotiff(filename=obs_filename,
dataset=data[[MY_OBS_VAR]])
write_geotiff(filename=filename,
dataset=data[['swir1', 'nir', 'green']],
profile_override={'photometric': 'RGB'})
except RuntimeError as e:
_log.info('File exists ', e)
return
else:
# data['days_since_1970'] = day_arr
my_data[k] = data
print("computing finished and ready as dictionary in my_data ",
str(datetime.now()))
def run(tile, gwf, center_dt):
"""Basic datapreparation recipe 001
Computes mean NDVI for a landsat collection over a given time frame
Args:
tile (tuple): Tuple of (tile indices, Tile object). Tile object can be
loaded as xarray.Dataset using gwf.load()
gwf (GridWorkflow): GridWorkflow object instantiated with the corresponding
product
center_dt (datetime): Date to be used in making the filename
Return:
str: The filename of the netcdf file created
"""
try:
center_dt = center_dt.strftime("%Y-%m-%d")
# TODO: Need a more dynamic way to handle this filename (e.g.: global variable for the path up to datacube_ingest)
nc_filename = os.path.expanduser(
'~/datacube_ingest/recipes/landsat_8_ndvi_mean/ndvi_mean_%d_%d_%s.nc'
% (tile[0][0], tile[0][1], center_dt))
if os.path.isfile(nc_filename):
raise ValueError('%s already exist' % nc_filename)
# Load Landsat sr
sr = gwf.load(
tile[1],
dask_chunks={
'x': 1667,
'y': 1667
},
)
# Compute ndvi
sr['ndvi'] = (sr.nir - sr.red) / (sr.nir + sr.red) * 10000
clear = masking.make_mask(sr.pixel_qa, clear=True)
ndvi = sr.drop(
['pixel_qa', 'blue', 'red', 'green', 'nir', 'swir1', 'swir2'])
ndvi_clear = ndvi.where(clear)
# Run temporal reductions and rename DataArrays
ndvi_mean = ndvi_clear.mean('time', keep_attrs=True)
ndvi_mean['ndvi'].attrs['nodata'] = -9999
ndvi_mean_int = ndvi_mean.apply(to_int)
ndvi_mean_int.attrs['crs'] = sr.attrs['crs']
write_dataset_to_netcdf(ndvi_mean_int,
nc_filename,
netcdfparams={'zlib': True})
return nc_filename
except Exception as e:
print('Tile (%d, %d) not processed. %s' % (tile[0][0], tile[0][1], e))
raise
return None
def test_write_dataset_to_netcdf(tmpnetcdf_filename, odc_style_xr_dataset):
write_dataset_to_netcdf(odc_style_xr_dataset, tmpnetcdf_filename, global_attributes={'foo': 'bar'},
variable_params={'B10': {'attrs': {'abc': 'xyz'}}})
with netCDF4.Dataset(tmpnetcdf_filename) as nco:
nco.set_auto_mask(False)
assert 'B10' in nco.variables
var = nco.variables['B10']
assert (var[:] == odc_style_xr_dataset['B10'].values).all()
assert 'foo' in nco.ncattrs()
assert nco.getncattr('foo') == 'bar'
assert 'abc' in var.ncattrs()
assert var.getncattr('abc') == 'xyz'
def do_ndvi_task(config, task):
global_attributes = config['global_attributes']
variable_params = config['variable_params']
file_path = Path(task['filename'])
output_type = config['ndvi_dataset_type']
measurement = output_type.measurements['ndvi']
output_dtype = np.dtype(measurement['dtype'])
nodata_value = np.dtype(output_dtype).type(measurement['nodata'])
if file_path.exists():
raise OSError(errno.EEXIST, 'Output file already exists',
str(file_path))
measurements = ['red', 'nir']
nbar_tile = task['nbar']
nbar = GridWorkflow.load(nbar_tile, measurements)
ndvi = calculate_ndvi(nbar,
nodata=nodata_value,
dtype=output_dtype,
units=measurement['units'])
def _make_dataset(labels, sources):
assert len(sources)
geobox = nbar.geobox
source_data = union_points(
*[dataset.extent.to_crs(geobox.crs).points for dataset in sources])
valid_data = intersect_points(geobox.extent.points, source_data)
dataset = make_dataset(product=output_type,
sources=sources,
extent=geobox.extent,
center_time=labels['time'],
uri=file_path.absolute().as_uri(),
app_info=get_app_metadata(config),
valid_data=GeoPolygon(valid_data, geobox.crs))
return dataset
datasets = xr_apply(nbar_tile.sources, _make_dataset, dtype='O')
ndvi['dataset'] = datasets_to_doc(datasets)
write_dataset_to_netcdf(
dataset=ndvi,
filename=Path(file_path),
global_attributes=global_attributes,
variable_params=variable_params,
)
return datasets
def write_dataset_to_storage(self,
dataset,
filename,
global_attributes=None,
variable_params=None,
storage_config=None,
**kwargs):
# TODO: Currently ingestor copies chunking info from storage_config to
# variable_params, this logic should probably happen here.
write_dataset_to_netcdf(dataset,
filename,
global_attributes=global_attributes,
variable_params=variable_params,
**kwargs)
return {}
def run(tile, center_dt, path):
"""Basic datapreparation recipe 001
Computes mean NDVI for a landsat collection over a given time frame
Args:
tile (tuple): Tuple of (tile indices, Tile object). Tile object can be
loaded as xarray.Dataset using gwf.load()
center_dt (datetime): Date to be used in making the filename
path (str): Directory where files generated are to be written
Return:
str: The filename of the netcdf file created
"""
try:
center_dt = center_dt.strftime("%Y-%m-%d")
nc_filename = os.path.join(
path,
'ndvi_mean_%d_%d_%s.nc' % (tile[0][0], tile[0][1], center_dt))
if os.path.isfile(nc_filename):
logger.warning(
'%s already exists. Returning filename for database indexing',
nc_filename)
return nc_filename
# Load Landsat sr
sr = GridWorkflow.load(tile[1], dask_chunks={'x': 1667, 'y': 1667})
# Compute ndvi
sr['ndvi'] = (sr.nir - sr.red) / (sr.nir + sr.red) * 10000
clear = masking.make_mask(sr.pixel_qa, clear=True)
ndvi = sr.drop(
['pixel_qa', 'blue', 'red', 'green', 'nir', 'swir1', 'swir2'])
ndvi_clear = ndvi.where(clear)
# Run temporal reductions and rename DataArrays
ndvi_mean = ndvi_clear.mean('time', keep_attrs=True)
ndvi_mean['ndvi'].attrs['nodata'] = -9999
ndvi_mean_int = ndvi_mean.apply(to_int)
ndvi_mean_int.attrs['crs'] = sr.attrs['crs']
write_dataset_to_netcdf(ndvi_mean_int,
nc_filename,
netcdfparams={'zlib': True})
return nc_filename
except Exception as e:
logger.info('Tile (%d, %d) not processed. %s' %
(tile[0][0], tile[0][1], e))
return None
def do_ndvi_task(config, task):
global_attributes = config['global_attributes']
variable_params = config['variable_params']
file_path = Path(task['filename'])
output_type = config['ndvi_dataset_type']
measurement = output_type.measurements['ndvi']
output_dtype = np.dtype(measurement['dtype'])
nodata_value = np.dtype(output_dtype).type(measurement['nodata'])
if file_path.exists():
raise OSError(errno.EEXIST, 'Output file already exists', str(file_path))
measurements = ['red', 'nir']
nbar_tile = task['nbar']
nbar = GridWorkflow.load(nbar_tile, measurements)
ndvi = calculate_ndvi(nbar, nodata=nodata_value, dtype=output_dtype, units=measurement['units'])
def _make_dataset(labels, sources):
assert len(sources)
geobox = nbar.geobox
source_data = union_points(*[dataset.extent.to_crs(geobox.crs).points for dataset in sources])
valid_data = intersect_points(geobox.extent.points, source_data)
dataset = make_dataset(product=output_type,
sources=sources,
extent=geobox.extent,
center_time=labels['time'],
uri=file_path.absolute().as_uri(),
app_info=get_app_metadata(config),
valid_data=GeoPolygon(valid_data, geobox.crs))
return dataset
datasets = xr_apply(nbar_tile.sources, _make_dataset, dtype='O')
ndvi['dataset'] = datasets_to_doc(datasets)
write_dataset_to_netcdf(
dataset=ndvi,
filename=Path(file_path),
global_attributes=global_attributes,
variable_params=variable_params,
)
return datasets
def test_write_dataset_to_netcdf(tmpnetcdf_filename):
affine = Affine.scale(0.1, 0.1)*Affine.translation(20, 30)
geobox = GeoBox(100, 100, affine, CRS(GEO_PROJ))
dataset = xarray.Dataset(attrs={'extent': geobox.extent, 'crs': geobox.crs})
for name, coord in geobox.coordinates.items():
dataset[name] = (name, coord.labels, {'units': coord.units})
dataset['B10'] = (geobox.dimensions, numpy.arange(10000).reshape(geobox.shape), {'nodata': 0, 'units': '1'})
write_dataset_to_netcdf(dataset, {'foo': 'bar'}, {'B10': {'attrs': {'abc': 'xyz'}}}, Path(tmpnetcdf_filename))
with netCDF4.Dataset(tmpnetcdf_filename) as nco:
nco.set_auto_mask(False)
assert 'B10' in nco.variables
var = nco.variables['B10']
assert (var[:] == dataset['B10'].values).all()
assert 'foo' in nco.ncattrs()
assert nco.getncattr('foo') == 'bar'
assert 'abc' in var.ncattrs()
assert var.getncattr('abc') == 'xyz'
def ingest_work(config, source_type, output_type, index, sources, geobox):
namemap = get_namemap(config)
measurements = get_measurements(source_type, config)
variable_params = get_variable_params(config)
global_attributes = config['global_attributes']
with datacube.set_options(reproject_threads=1):
fuse_func = {'copy': None}[config.get(FUSER_KEY, 'copy')]
data = Datacube.product_data(sources,
geobox,
measurements,
fuse_func=fuse_func)
nudata = data.rename(namemap)
file_path = get_filename(config, index, sources)
def _make_dataset(labels, sources):
sources_union = union_points(
*[source.extent.to_crs(geobox.crs).points for source in sources])
valid_data = intersect_points(geobox.extent.points, sources_union)
dataset = make_dataset(dataset_type=output_type,
sources=sources,
extent=geobox.extent,
center_time=labels['time'],
uri=file_path.absolute().as_uri(),
app_info=get_app_metadata(
config, config['filename']),
valid_data=GeoPolygon(valid_data, geobox.crs))
return dataset
datasets = xr_apply(
sources, _make_dataset,
dtype='O') # Store in Dataarray to associate Time -> Dataset
nudata['dataset'] = datasets_to_doc(datasets)
write_dataset_to_netcdf(nudata, global_attributes, variable_params,
file_path)
return datasets
def do_fc_task(config, task):
global_attributes = config['global_attributes']
variable_params = config['variable_params']
file_path = Path(task['filename'])
output_product = config['fc_product']
if file_path.exists():
raise OSError(errno.EEXIST, 'Output file already exists', str(file_path))
nbar_tile: Tile = task['nbar']
nbar = GridWorkflow.load(nbar_tile, ['green', 'red', 'nir', 'swir1', 'swir2'])
output_measurements = config['fc_product'].measurements.values()
fc_dataset = make_fc_tile(nbar, output_measurements, config.get('sensor_regression_coefficients'))
def _make_dataset(labels, sources):
assert sources
dataset = make_dataset(product=output_product,
sources=sources,
extent=nbar.geobox.extent,
center_time=labels['time'],
uri=file_path.absolute().as_uri(),
app_info=get_app_metadata(config),
valid_data=GeoPolygon.from_sources_extents(sources, nbar.geobox))
return dataset
datasets = xr_apply(nbar_tile.sources, _make_dataset, dtype='O')
fc_dataset['dataset'] = datasets_to_doc(datasets)
write_dataset_to_netcdf(
dataset=fc_dataset,
filename=file_path,
global_attributes=global_attributes,
variable_params=variable_params,
)
return datasets
sr_max = sr_dask.max('time', keep_attrs=True, dtype=np.int16, skipna=True)
sr_max.rename(
{
'blue': 'blue_max',
'green': 'green_max',
'red': 'red_max',
'nir': 'nir_max',
'swir1': 'swir1_max',
'swir2': 'swir2_max',
'ndvi': 'ndvi_max'
},
inplace=True)
sr_std = sr_dask.std('time', keep_attrs=True, dtype=np.int16, skipna=True)
sr_std.rename(
{
'blue': 'blue_std',
'green': 'green_std',
'red': 'red_std',
'nir': 'nir_std',
'swir1': 'swir1_std',
'swir2': 'swir2_std',
'ndvi': 'ndvi_std'
},
inplace=True)
# Merge dataarrays
combined = xr.merge([sr_mean, sr_min, sr_max, sr_std])
combined.attrs['crs'] = sr_dask_0.attrs['crs']
print(combined)
# with ProgressBar():
write_dataset_to_netcdf(combined, '/tmp/sr_reduced.nc')
print(lon_range, lat_range)
print(crs)
for platform in platform_list:
product_name = '{}_{}_albers'.format(platform, product_type)
print('Loading product: {}'.format(product_name))
output_file = '/g/data/u46/users/dra547/erf_07_09_2013_' + product_name + '.cdf'
print(output_file)
dataset = dc.load(product=product_name,
x=lon_range,
y=lat_range,
time=(acq_min, acq_max),
group_by='solar_day',
crs=crs,
measurements=measurements_list)
# Load PQ Mask
mask_product = '{}_{}_albers'.format(platform, 'pq')
sensor_pq = dc.load(product=mask_product,
group_by='solar_day',
fuse_func=ga_pq_fuser,
like=dataset)
cloud_free = make_mask(sensor_pq.pixelquality, ga_good_pixel=True)
dataset = dataset.where(cloud_free).fillna(-999).astype('int16')
dataset.attrs[
'crs'] = sensor_pq.crs # Temporarily required until xarray issue #1009 gets into a release
print(dataset)
write_dataset_to_netcdf(dataset, output_file)
def test_netcdf_source(tmpnetcdf_filename):
affine = Affine.scale(0.1, 0.1) * Affine.translation(20, 30)
geobox = geometry.GeoBox(110, 100, affine, geometry.CRS(GEO_PROJ))
dataset = xarray.Dataset(attrs={
'extent': geobox.extent,
'crs': geobox.crs
})
for name, coord in geobox.coordinates.items():
dataset[name] = (name, coord.values, {
'units': coord.units,
'crs': geobox.crs
})
dataset['B10'] = (geobox.dimensions,
numpy.arange(11000,
dtype='int16').reshape(geobox.shape), {
'nodata': 0,
'units': '1',
'crs': geobox.crs
})
write_dataset_to_netcdf(dataset,
tmpnetcdf_filename,
global_attributes={'foo': 'bar'},
variable_params={'B10': {
'attrs': {
'abc': 'xyz'
}
}})
with netCDF4.Dataset(tmpnetcdf_filename) as nco:
nco.set_auto_mask(False)
source = NetCDFDataSource(nco, 'B10')
assert source.crs == geobox.crs
assert source.transform.almost_equals(affine)
assert (source.read() == dataset['B10']).all()
dest = numpy.empty((60, 50))
source.reproject(dest, affine, geobox.crs, 0, Resampling.nearest)
assert (dest == dataset['B10'][:60, :50]).all()
source.reproject(dest, affine * Affine.translation(10, 10), geobox.crs,
0, Resampling.nearest)
assert (dest == dataset['B10'][10:70, 10:60]).all()
source.reproject(dest, affine * Affine.translation(-10, -10),
geobox.crs, 0, Resampling.nearest)
assert (dest[10:, 10:] == dataset['B10'][:50, :40]).all()
dest = numpy.empty((200, 200))
source.reproject(dest, affine, geobox.crs, 0, Resampling.nearest)
assert (dest[:100, :110] == dataset['B10']).all()
source.reproject(dest, affine * Affine.translation(10, 10), geobox.crs,
0, Resampling.nearest)
assert (dest[:90, :100] == dataset['B10'][10:, 10:]).all()
source.reproject(dest, affine * Affine.translation(-10, -10),
geobox.crs, 0, Resampling.nearest)
assert (dest[10:110, 10:120] == dataset['B10']).all()
source.reproject(dest, affine * Affine.scale(2, 2), geobox.crs, 0,
Resampling.nearest)
assert (dest[:50, :55] == dataset['B10'][1::2, 1::2]).all()
source.reproject(
dest,
affine * Affine.scale(2, 2) * Affine.translation(10, 10),
geobox.crs, 0, Resampling.nearest)
assert (dest[:40, :45] == dataset['B10'][21::2, 21::2]).all()
source.reproject(
dest,
affine * Affine.scale(2, 2) * Affine.translation(-10, -10),
geobox.crs, 0, Resampling.nearest)
assert (dest[10:60, 10:65] == dataset['B10'][1::2, 1::2]).all()
import os
from datacube.index.postgres._connections import PostgresDb
from datacube.index._api import Index
from datacube.api import GridWorkflow
from datacube.storage.storage import write_dataset_to_netcdf
from pprint import pprint
import numpy
nc_filename = os.path.expanduser(
'~/datacube_ingest/recipes/ndvi_mean/ndvi_mean_%d_%d_%s.nc' %
(12, -16, '1987'))
db = PostgresDb.from_config()
i = Index(db)
gwf = GridWorkflow(i, product='ls8_espa_mexico')
cells_list = gwf.list_cells(product='ls8_espa_mexico',
x=(-106, -101),
y=(19, 23))
sr = gwf.load(cells_list[(12, -16)], dask_chunks={'x': 1000, 'y': 1000})
sr['ndvi'] = (sr.nir - sr.red) / (sr.nir + sr.red) * 10000
ndvi = sr.drop(['pixel_qa', 'blue', 'red', 'green', 'nir', 'swir1', 'swir2'])
# Run temporal reductions and rename DataArrays
ndvi_mean = ndvi.mean('time', keep_attrs=True)
ndvi_mean = ndvi_mean.astype('int16')
ndvi_mean.attrs['crs'] = sr.attrs['crs']
write_dataset_to_netcdf(ndvi_mean, nc_filename)
print(nc_filename)
def write_dataset_to_storage(self, dataset, *args, **kargs):
"""See :meth:`datacube.drivers.driver.write_dataset_to_storage`
"""
return write_dataset_to_netcdf(dataset, *args, **kargs)
'nir': 'nir_min',
'swir1': 'swir1_min',
'swir2': 'swir2_min',
'ndvi': 'ndvi_min'}, inplace=True)
sr_max = sr.max('time', keep_attrs=True, dtype=np.int16, skipna=True)
sr_max.rename({'blue': 'blue_max',
'green': 'green_max',
'red': 'red_max',
'nir': 'nir_max',
'swir1': 'swir1_max',
'swir2': 'swir2_max',
'ndvi': 'ndvi_max'}, inplace=True)
sr_std = sr.std('time', keep_attrs=True, dtype=np.int16, skipna=True)
sr_std.rename({'blue': 'blue_std',
'green': 'green_std',
'red': 'red_std',
'nir': 'nir_std',
'swir1': 'swir1_std',
'swir2': 'swir2_std',
'ndvi': 'ndvi_std'}, inplace=True)
combined = xr.merge([sr_mean, sr_min, sr_max, sr_std])
combined.attrs['crs'] = sr.attrs['crs']
print(combined)
write_dataset_to_netcdf(combined, sr_out_dc_dask)
time_end = time.time()
timing.append(time_end - time_begin)
# summary
for id, t in enumerate(timing):
print('Test %d completed in %.1f seconds' % (id, t))
inplace=True)
sr_max = sr_clear2.max('time', keep_attrs=True)
sr_max.rename(
{
'blue': 'blue_max',
'green': 'green_max',
'red': 'red_max',
'nir': 'nir_max',
'swir1': 'swir1_max',
'swir2': 'swir2_max',
'ndvi': 'ndvi_max'
},
inplace=True)
sr_std = sr_clear2.std('time', keep_attrs=True)
sr_std.rename(
{
'blue': 'blue_std',
'green': 'green_std',
'red': 'red_std',
'nir': 'nir_std',
'swir1': 'swir1_std',
'swir2': 'swir2_std',
'ndvi': 'ndvi_std'
},
inplace=True)
combined = xr.merge([sr_mean, sr_min, sr_max, sr_std, terrain])
combined.attrs['crs'] = sr.attrs['crs']
print(combined)
with ProgressBar():
write_dataset_to_netcdf(combined, nc_file)
def create_latest_images(data_info, period, odir):
for k, v in data_info.items():
data = data_info[k]
day_arr = np.zeros([data.blue.shape[1], data.blue.shape[2]],
dtype=np.int16)
stored_band = np.zeros((6, 4000, 4000), dtype=np.int16)
dt_list = data_info[k].time.values.astype('M8[D]').astype('O').tolist()
print("looking latest pixels for ",
','.join([dt.strftime('%Y-%m-%d') for dt in dt_list]))
for index, dt in enumerate(
data_info[k].time.values.astype('M8[D]').astype('O').tolist()):
ds = data_info[k].isel(time=index)
days = (dt - MY_REF_DATE).days
day_arr = update_latest_pixel(index, days, ds, day_arr,
stored_band)
data = data.isel(time=0).drop('time')
for count, band in enumerate([
data.blue, data.green, data.red, data.nir, data.swir1,
data.swir2
]):
band.data = stored_band[count]
band.data[band.data == 0] = -999
day_arr[day_arr == 0] = -999
day_arr = xr.DataArray(day_arr, coords=data.coords, dims=['y', 'x'])
data['days_since_1970'] = day_arr
my_data[k] = data
if odir:
global_attributes = {}
global_attributes = dict(
Comment1='Data acquired on ' +
','.join([dt.strftime('%Y-%m-%d') for dt in dt_list]))
FILE_LOC = odir
filename = FILE_LOC + '/' + 'LATEST_PIXEL_' + ''.join(map(
str, k)) + "_" + str(datetime.now().date(
)) + "_CLOUD_FREE_LAST_" + str(period) + "_DAYS.nc"
try:
write_dataset_to_netcdf(data,
global_attributes=global_attributes,
variable_params={
'blue': {
'zlib': True
},
'green': {
'zlib': True
},
'red': {
'zlib': True
},
'nir': {
'zlib': True
},
'swir1': {
'zlib': True
},
'swir2': {
'zlib': True
},
'days_since_1970': {
'zlib': True
}
},
filename=Path(filename))
except RuntimeError as e:
print(e)
return
print("Written onto " + filename)
_log.info('Data written onto %s', filename)
else:
print("computing finished and ready as dictionary in my_data ",
str(datetime.now()))
def create_latest_images(data_info, duration, odir):
for k, v in data_info.iteritems():
data = data_info[k]
day_arr = np.zeros([data.swir1.shape[1], data.swir1.shape[2]],
dtype=np.int16)
stored_band = np.zeros((6, 4000, 4000), dtype=np.int16)
dt_list = list()
dt_tmp_list = data_info[k].time.values.astype('M8[D]').astype(
'O').tolist()
print("looking latest pixels for ",
','.join([dt.strftime('%Y-%m-%d') for dt in dt_tmp_list]))
for index, dt in enumerate(
data_info[k].time.values.astype('M8[D]').astype('O').tolist()):
ds = data_info[k].isel(time=index)
days = (dt - MY_REF_DATE).days
print("count of zero pixel",
str(np.count_nonzero(stored_band[0] == 0)))
if np.count_nonzero(stored_band[0] == 0) > 0:
day_arr = update_latest_pixel(index, days, ds, day_arr,
stored_band)
dt_list.append(dt)
else:
break
print("The dates added are ",
','.join([dt.strftime('%Y-%m-%d') for dt in dt_list]))
print("time delta for cell " + str(k) +
str(len(dt_tmp_list) - len(dt_list)))
data = data.isel(time=0).drop('time')
for count, band in enumerate([data.swir1, data.nir, data.green]):
band.data = stored_band[count]
band.data[band.data == 0] = -999
day_arr[day_arr == 0] = -999
day_arr = xr.DataArray(day_arr, coords=data.coords, dims=['y', 'x'])
my_data[k] = data
if len(odir) > 0:
day_ds = day_arr.to_dataset(name='days_since_1970')
day_ds.attrs = data.attrs
global_attributes = {}
global_attributes = dict(
Comment1='Data observed on ' +
','.join([dt.strftime('%Y-%m-%d') for dt in dt_list]))
filename = odir + '/' + 'LATEST_PIXEL_' + ''.join(map(str, k)) \
+ "_CLOUD_FREE_LAST_" + str(period) + "_DAYS"
obs_filename = odir + '/' + 'LATEST_PIXEL_' + ''.join(map(str, k)) \
+ "_CLOUD_FREE_LAST_" + str(period) + "_DAYS_OBS"
try:
ncfl = filename + ".nc"
ncobs = obs_filename + ".nc"
write_dataset_to_netcdf(data,
global_attributes=global_attributes,
variable_params={
'swir1': {
'zlib': True
},
'nir': {
'zlib': True
},
'green': {
'zlib': True
}
},
filename=Path(ncfl))
write_dataset_to_netcdf(
day_ds,
global_attributes=global_attributes,
variable_params={'days_since_1970': {
'zlib': True
}},
filename=Path(ncobs))
filename = filename + ".tif"
obs_filename = obs_filename + ".tif"
write_geotiff(filename=filename,
dataset=data,
profile_override={'photometric': 'RGB'})
write_geotiff(filename=obs_filename, dataset=day_ds)
except RuntimeError as e:
_log.info('File exists ', e)
return
else:
data['days_since_1970'] = day_arr
my_data[k] = data
print("computing finished and ready as dictionary in my_data ",
str(datetime.now()))
def run(tile, center_dt, path):
"""Basic datapreparation recipe 001
Combines temporal statistics of surface reflectance and ndvi with terrain
metrics
Args:
tile (tuple): Tuple of (tile indices, Tile object). Tile object can be
loaded as xarray.Dataset using gwf.load()
center_dt (datetime): Date to be used in making the filename
path (str): Directory where files generated are to be written
Return:
str: The filename of the netcdf file created
"""
try:
center_dt = center_dt.strftime("%Y-%m-%d")
nc_filename = os.path.join(path, 's2_20m_001_%d_%d_%s.nc' % (tile[0][0], tile[0][1], center_dt))
# Load Landsat sr
if os.path.isfile(nc_filename):
logger.warning('%s already exists. Returning filename for database indexing', nc_filename)
return nc_filename
sr_0 = GridWorkflow.load(tile[1], dask_chunks={'x': 1000, 'y': 1000})
sr_0 = sr_0.apply(func=to_float, keep_attrs=True)
# Load terrain metrics using same spatial parameters than sr
dc = datacube.Datacube(app = 's2_20m_001_%s' % randomword(5))
terrain = dc.load(product='srtm_cgiar_mexico', like=sr_0,
time=(datetime(1970, 1, 1), datetime(2018, 1, 1)),
dask_chunks={'x': 1000, 'y': 1000})
dc.close()
# Keep clear pixels (2: Dark features, 4: Vegetation, 5: Not vegetated,
# 6: Water, 7: Unclassified, 11: Snow/Ice)
sr_1 = sr_0.where(sr_0.pixel_qa.isin([2,4,5,6,7,8,11]))
sr_1 = sr_1.drop('pixel_qa')
# Compute ndvi
sr_1['ndvi'] = ((sr_1.nir - sr_1.red) / (sr_1.nir + sr_1.red)) * 10000
sr_1['ndvi'].attrs['nodata'] = 0
# Compute ndmi
sr_1['ndmi'] = ((sr_1.nir - sr_1.swir1) / (sr_1.nir + sr_1.swir1)) * 10000
sr_1['ndmi'].attrs['nodata'] = 0
# Run temporal reductions and rename DataArrays
sr_mean = sr_1.mean('time', keep_attrs=True, skipna=True)
sr_mean.rename({'blue': 'blue_mean',
'green': 'green_mean',
'red': 'red_mean',
're1': 're1_mean',
're2': 're2_mean',
're3': 're3_mean',
'nir': 'nir_mean',
'swir1': 'swir1_mean',
'swir2': 'swir2_mean',
'ndmi': 'ndmi_mean',
'ndvi': 'ndvi_mean'}, inplace=True)
# Compute min/max/std only for vegetation indices
ndvi_max = sr_1.ndvi.max('time', keep_attrs=True, skipna=True)
ndvi_max = ndvi_max.rename('ndvi_max')
ndvi_max.attrs['nodata'] = 0
ndvi_min = sr_1.ndvi.min('time', keep_attrs=True, skipna=True)
ndvi_min = ndvi_min.rename('ndvi_min')
ndvi_min.attrs['nodata'] = 0
# ndmi
ndmi_max = sr_1.ndmi.max('time', keep_attrs=True, skipna=True)
ndmi_max = ndmi_max.rename('ndmi_max')
ndmi_max.attrs['nodata'] = 0
ndmi_min = sr_1.ndmi.min('time', keep_attrs=True, skipna=True)
ndmi_min = ndmi_min.rename('ndmi_min')
ndmi_min.attrs['nodata'] = 0
# Merge dataarrays
combined = xr.merge([sr_mean.apply(to_int),
to_int(ndvi_max),
to_int(ndvi_min),
to_int(ndmi_max),
to_int(ndmi_min),
terrain])
combined.attrs['crs'] = sr_0.attrs['crs']
combined = combined.compute()
write_dataset_to_netcdf(combined, nc_filename)
return nc_filename
except Exception as e:
logger.warning('Tile (%d, %d) not processed. %s' % (tile[0][0], tile[0][1], e))
return None
def write_product(data, sources, output_prod_info, global_attrs, var_params,
path):
nudata, nudatasets = generate_dataset(data, sources, output_prod_info,
path.absolute().as_uri())
write_dataset_to_netcdf(nudata, global_attrs, var_params, path)
return nudatasets
def write_product(data, sources, output_prod_info, global_attrs, var_params, path):
nudata, nudatasets = generate_dataset(data, sources, output_prod_info, path.absolute().as_uri())
write_dataset_to_netcdf(nudata, global_attrs, var_params, path)
return nudatasets
def run(tile, center_dt, path):
"""Basic datapreparation recipe 001
Combines temporal statistics of surface reflectance and ndvi with terrain
metrics
Args:
tile (tuple): Tuple of (tile indices, Tile object). Tile object can be
loaded as xarray.Dataset using gwf.load()
center_dt (datetime): Date to be used in making the filename
path (str): Directory where files generated are to be written
Return:
str: The filename of the netcdf file created
"""
try:
center_dt = center_dt.strftime("%Y-%m-%d")
nc_filename = os.path.join(
path,
'madmex_001_%d_%d_%s.nc' % (tile[0][0], tile[0][1], center_dt))
# Load Landsat sr
if os.path.isfile(nc_filename):
logger.warning(
'%s already exists. Returning filename for database indexing',
nc_filename)
return nc_filename
sr_0 = GridWorkflow.load(tile[1], dask_chunks={'x': 1667, 'y': 1667})
# Load terrain metrics using same spatial parameters than sr
dc = datacube.Datacube(app='landsat_madmex_001_%s' % randomword(5))
terrain = dc.load(product='srtm_cgiar_mexico',
like=sr_0,
time=(datetime(1970, 1, 1), datetime(2018, 1, 1)),
dask_chunks={
'x': 1667,
'y': 1667
})
dc.close()
# Mask clouds, shadow, water, ice,... and drop qa layer
clear = masking.make_mask(sr_0.pixel_qa,
cloud=False,
cloud_shadow=False,
snow=False)
sr_1 = sr_0.where(clear)
sr_2 = sr_1.drop('pixel_qa')
# Convert Landsat data to float (nodata values are converted to np.Nan)
sr_3 = sr_2.apply(func=to_float, keep_attrs=True)
# Compute ndvi
sr_3['ndvi'] = ((sr_3.nir - sr_3.red) / (sr_3.nir + sr_3.red)) * 10000
sr_3['ndvi'].attrs['nodata'] = -9999
# Run temporal reductions and rename DataArrays
sr_mean = sr_3.mean('time', keep_attrs=True, skipna=True)
sr_mean.rename(
{
'blue': 'blue_mean',
'green': 'green_mean',
'red': 'red_mean',
'nir': 'nir_mean',
'swir1': 'swir1_mean',
'swir2': 'swir2_mean',
'ndvi': 'ndvi_mean'
},
inplace=True)
sr_min = sr_3.min('time', keep_attrs=True, skipna=True)
sr_min.rename(
{
'blue': 'blue_min',
'green': 'green_min',
'red': 'red_min',
'nir': 'nir_min',
'swir1': 'swir1_min',
'swir2': 'swir2_min',
'ndvi': 'ndvi_min'
},
inplace=True)
sr_max = sr_3.max('time', keep_attrs=True, skipna=True)
sr_max.rename(
{
'blue': 'blue_max',
'green': 'green_max',
'red': 'red_max',
'nir': 'nir_max',
'swir1': 'swir1_max',
'swir2': 'swir2_max',
'ndvi': 'ndvi_max'
},
inplace=True)
sr_std = sr_3.std('time', keep_attrs=True, skipna=True)
sr_std.rename(
{
'blue': 'blue_std',
'green': 'green_std',
'red': 'red_std',
'nir': 'nir_std',
'swir1': 'swir1_std',
'swir2': 'swir2_std',
'ndvi': 'ndvi_std'
},
inplace=True)
# Merge dataarrays
combined = xr.merge([
sr_mean.apply(to_int),
sr_min.apply(to_int),
sr_max.apply(to_int),
sr_std.apply(to_int), terrain
])
combined.attrs['crs'] = sr_0.attrs['crs']
write_dataset_to_netcdf(combined, nc_filename)
return nc_filename
except Exception as e:
logger.warning('Tile (%d, %d) not processed. %s' %
(tile[0][0], tile[0][1], e))
return None
