def _make_src_affine(src_data_array):
src_bounds = _get_bounds(src_data_array)
src_left, src_bottom, src_right, src_top = src_bounds
src_resolution_x, src_resolution_y = _get_resolution(src_data_array,
as_tuple=True)
return Affine.translation(src_left, src_top) * Affine.scale(
src_resolution_x, src_resolution_y)
def sources_for_tile(tile):
"""Render a tile's source footprints."""
bounds = Bounds(mercantile.xy_bounds(tile), WEB_MERCATOR_CRS)
shape = Affine.scale(scale) * (256, 256)
resolution = get_resolution_in_meters(bounds, shape)
# convert sources to a list to avoid passing the generator across thread boundaries
return (tile, list(catalog.get_sources(bounds, resolution)))
def areagrid(outraster, c = 0.083333001, r = 6371007.2, minx = -180, miny = -90, w = 360, h = 180):
"""
Generates a global grid in geograpic coordinates
with the area of each gridcell as the value of the cell
Parameters:
outraster = path to output raster file
c = cellsize in decimal degrees
r = radius of earth in desired units (e.g. 6371007.2m for sq. meters)
minx, miny = the south west coordinate of the raster in degrees
w, h = the width and height of the raster in degrees
Returns:
None
"""
c = float(c)
r = float(r)
# make a vector of ones [1,1,1, ... 1] of length equal to the number of cells from west to east.
X = np.ones(round(w/c))
# make a vector counting from 0 to the number of cells from south to north. e.g. [0,1,2,...,179] for 1 deg cells.
Y = np.arange(round(h/c))
# multiply all the numbers in the Y vector by the cell size,
# so it extends from 0 to <180 (if the cell size is different than 1 deg)
# then add the southernmost coordinate (-90deg). This makes a vector of -90 to +90 degrees North
degN = Y*c + miny
# convert degrees vector to radians
radN = degN*np.pi/180.0
# convert the cell size to radians
radc = c * np.pi/180.0
# calculate the area of the cell
# there's some implicit geometry that's been done here already'
# but basically it averages the width of the top of a cell and the bottom of a cell
# and mutiplies it by the height of the cell (which is constant no matter how far or south north you are)
# then by the square of the radius
# since the angles are in radians it works out area correctly
# you end up with a vector of cell area from south to north
A = (np.sin(radN+radc/2)-np.sin(radN-radc/2)) * radc * r**2
# the outer product of any vector and a vector of ones just duplicates the first vector into columns in a matrix
# basically we just copy the latitude vector across from east to west
M = np.outer(A,X)
cols = round(w/c)
rows = round(h/c)
# save the matrix as a raster
with rasterio.open(outraster,'w',
'GTiff',
width=cols,
height=rows,
dtype=M.dtype,
crs={'init': 'EPSG:4326'},
transform=Affine.translation(-cols*c/2, rows*c/2) * Affine.scale(c, -c),
count=1) as dst:
dst.write_band(1,M)
def _get_affine_transform(self, bounding_box):
lng1, lng2 = bounding_box.west, bounding_box.east
lat1, lat2 = bounding_box.south, bounding_box.north
x_scale = self.google_static_maps.image_w / (lng2 - lng1)
y_scale = -self.google_static_maps.image_h / (lat2 - lat1)
affine_translate = Affine.translation(-lng1, -lat2)
affine_scale = Affine.scale(x_scale, y_scale)
# affine_mirror = Affine(1, 0, 0, 0, -1, image_h)
affine_transform = affine_scale * affine_translate
return affine_transform
def example_reproject():
import idfpy
from matplotlib import pyplot as plt
from rasterio import Affine
from rasterio.crs import CRS
from rasterio.warp import reproject, Resampling
import numpy as np
with idfpy.open('bxk1-d-ck.idf') as src:
a = src.read(masked=True)
nr, nc = src.header['nrow'], src.header['ncol']
dx, dy = src.header['dx'], src.header['dy']
src_transform = Affine.from_gdal(*src.geotransform)
# define new grid transform (same extent, 10 times resolution)
dst_transform = Affine.translation(src_transform.c, src_transform.f)
dst_transform *= Affine.scale(dx / 10., -dy / 10.)
# define coordinate system (here RD New)
src_crs = CRS.from_epsg(28992)
# initialize new data array
b = np.empty((10*nr, 10*nc))
# reproject using Rasterio
reproject(
source=a,
destination=b,
src_transform=src_transform,
dst_transform=dst_transform,
src_crs=src_crs,
dst_crs=src_crs,
resampling=Resampling.bilinear,
)
# result as masked array
b = np.ma.masked_equal(b, a.fill_value)
# plot images
fig, axes = plt.subplots(nrows=2, ncols=1)
axes[0].imshow(a.filled(np.nan))
axes[0].set_title('bxk1 original')
axes[1].imshow(b.filled(np.nan))
axes[1].set_title('bxk1 resampled')
plt.show()
def example_reproject():
import idfpy
from matplotlib import pyplot as plt
from rasterio import Affine
from rasterio.crs import CRS
from rasterio.warp import reproject, Resampling
import numpy as np
with idfpy.open('bxk1-d-ck.idf') as src:
a = src.read(masked=True)
nr, nc = src.header['nrow'], src.header['ncol']
dx, dy = src.header['dx'], src.header['dy']
src_transform = Affine.from_gdal(*src.geotransform)
# define new grid transform (same extent, 10 times resolution)
dst_transform = Affine.translation(src_transform.c, src_transform.f)
dst_transform *= Affine.scale(dx / 10., -dy / 10.)
# define coordinate system (here RD New)
src_crs = CRS.from_epsg(28992)
# initialize new data array
b = np.empty((10 * nr, 10 * nc))
# reproject using Rasterio
reproject(
source=a,
destination=b,
src_transform=src_transform,
dst_transform=dst_transform,
src_crs=src_crs,
dst_crs=src_crs,
resampling=Resampling.bilinear,
)
# result as masked array
b = np.ma.masked_equal(b, a.fill_value)
# plot images
fig, axes = plt.subplots(nrows=2, ncols=1)
axes[0].imshow(a.filled(np.nan))
axes[0].set_title('bxk1 original')
axes[1].imshow(b.filled(np.nan))
axes[1].set_title('bxk1 resampled')
plt.show()
def main():
for i in range(2):
dat = nc.Dataset(innc[i])
print dat
#sum monthly values for 2010
dat2010 = dat.variables[varname[i]][600:611,:,:].sum(0)
cols = 720
rows = 360
d = 1/2.0
with rasterio.open(outras[i],'w',
'GTiff',
width=cols,
height=rows,
dtype=dat2010.dtype,
crs={'init': 'EPSG:4326'},
transform=A.translation(-cols*d/2, rows*d/2) * A.scale(d, -d),
count=1) as dst:
dst.write_band(1,dat2010)
def export_array(modelgrid, filename, a, nodata=-9999,
fieldname='value',
**kwargs):
"""
Write a numpy array to Arc Ascii grid or shapefile with the model
reference.
Parameters
----------
filename : str
Path of output file. Export format is determined by
file extention.
'.asc' Arc Ascii grid
'.tif' GeoTIFF (requries rasterio package)
'.shp' Shapefile
a : 2D numpy.ndarray
Array to export
nodata : scalar
Value to assign to np.nan entries (default -9999)
fieldname : str
Attribute field name for array values (shapefile export only).
(default 'values')
kwargs:
keyword arguments to np.savetxt (ascii)
rasterio.open (GeoTIFF)
or flopy.export.shapefile_utils.write_grid_shapefile2
Notes
-----
Rotated grids will be either be unrotated prior to export,
using scipy.ndimage.rotate (Arc Ascii format) or rotation will be
included in their transform property (GeoTiff format). In either case
the pixels will be displayed in the (unrotated) projected geographic
coordinate system, so the pixels will no longer align exactly with the
model grid (as displayed from a shapefile, for example). A key difference
between Arc Ascii and GeoTiff (besides disk usage) is that the
unrotated Arc Ascii will have a different grid size, whereas the GeoTiff
will have the same number of rows and pixels as the original.
"""
if filename.lower().endswith(".asc"):
if len(np.unique(modelgrid.delr)) != len(np.unique(modelgrid.delc)) != 1 \
or modelgrid.delr[0] != modelgrid.delc[0]:
raise ValueError('Arc ascii arrays require a uniform grid.')
xoffset, yoffset = modelgrid.xoffset, modelgrid.yoffset
cellsize = modelgrid.delr[0] # * self.length_multiplier
fmt = kwargs.get('fmt', '%.18e')
a = a.copy()
a[np.isnan(a)] = nodata
if modelgrid.angrot != 0:
try:
from scipy.ndimage import rotate
a = rotate(a, modelgrid.angrot, cval=nodata)
height_rot, width_rot = a.shape
xmin, ymin, xmax, ymax = modelgrid.extent
dx = (xmax - xmin) / width_rot
dy = (ymax - ymin) / height_rot
cellsize = np.max((dx, dy))
xoffset, yoffset = xmin, ymin
except ImportError:
print('scipy package required to export rotated grid.')
filename = '.'.join(
filename.split('.')[:-1]) + '.asc' # enforce .asc ending
nrow, ncol = a.shape
a[np.isnan(a)] = nodata
txt = 'ncols {:d}\n'.format(ncol)
txt += 'nrows {:d}\n'.format(nrow)
txt += 'xllcorner {:f}\n'.format(xoffset)
txt += 'yllcorner {:f}\n'.format(yoffset)
txt += 'cellsize {}\n'.format(cellsize)
# ensure that nodata fmt consistent w values
txt += 'NODATA_value {}\n'.format(fmt) % (nodata)
with open(filename, 'w') as output:
output.write(txt)
with open(filename, 'ab') as output:
np.savetxt(output, a, **kwargs)
print('wrote {}'.format(filename))
elif filename.lower().endswith(".tif"):
if len(np.unique(modelgrid.delr)) != len(np.unique(modelgrid.delc)) != 1 \
or modelgrid.delr[0] != modelgrid.delc[0]:
raise ValueError('GeoTIFF export require a uniform grid.')
try:
import rasterio
from rasterio import Affine
except ImportError:
print('GeoTIFF export requires the rasterio package.')
return
dxdy = modelgrid.delc[0] # * self.length_multiplier
trans = Affine.translation(modelgrid.xoffset, modelgrid.yoffset) * \
Affine.rotation(modelgrid.angrot) * \
Affine.scale(dxdy, -dxdy)
# third dimension is the number of bands
a = a.copy()
if len(a.shape) == 2:
a = np.reshape(a, (1, a.shape[0], a.shape[1]))
if a.dtype.name == 'int64':
a = a.astype('int32')
dtype = rasterio.int32
elif a.dtype.name == 'int32':
dtype = rasterio.int32
elif a.dtype.name == 'float64':
dtype = rasterio.float64
elif a.dtype.name == 'float32':
dtype = rasterio.float32
else:
msg = 'ERROR: invalid dtype "{}"'.format(a.dtype.name)
raise TypeError(msg)
meta = {'count': a.shape[0],
'width': a.shape[2],
'height': a.shape[1],
'nodata': nodata,
'dtype': dtype,
'driver': 'GTiff',
'crs': modelgrid.proj4,
'transform': trans
}
meta.update(kwargs)
with rasterio.open(filename, 'w', **meta) as dst:
dst.write(a)
print('wrote {}'.format(filename))
elif filename.lower().endswith(".shp"):
from ..export.shapefile_utils import write_grid_shapefile2
epsg = kwargs.get('epsg', None)
prj = kwargs.get('prj', None)
if epsg is None and prj is None:
epsg = modelgrid.epsg
write_grid_shapefile2(filename, modelgrid, array_dict={fieldname: a},
nan_val=nodata,
epsg=epsg, prj=prj)
import rasterio
from rasterio import Affine as A
from rasterio.warp import reproject, RESAMPLING
tempdir = '/tmp'
tiffname = os.path.join(tempdir, 'example.tif')
with rasterio.drivers():
# Consider a 512 x 512 raster centered on 0 degrees E and 0 degrees N
# with each pixel covering 15".
rows, cols = src_shape = (512, 512)
dpp = 1.0/240 # decimal degrees per pixel
# The following is equivalent to
# A(dpp, 0, -cols*dpp/2, 0, -dpp, rows*dpp/2).
src_transform = A.translation(-cols*dpp/2, rows*dpp/2) * A.scale(dpp, -dpp)
src_crs = {'init': 'EPSG:4326'}
source = numpy.ones(src_shape, numpy.uint8)*255
# Prepare to reproject this rasters to a 1024 x 1024 dataset in
# Web Mercator (EPSG:3857) with origin at -8928592, 2999585.
dst_shape = (1024, 1024)
dst_transform = A.from_gdal(-237481.5, 425.0, 0.0, 237536.4, 0.0, -425.0)
dst_transform = dst_transform.to_gdal()
dst_crs = {'init': 'EPSG:3857'}
destination = numpy.zeros(dst_shape, numpy.uint8)
reproject(
source,
destination,
src_transform=src_transform,
def _make_src_affine(src_data_array):
src_bounds = _get_bounds(src_data_array)
src_left, src_bottom, src_right, src_top = src_bounds
src_resolution_x, src_resolution_y = _get_resolution(src_data_array, as_tuple=True)
return Affine.translation(src_left, src_top) * Affine.scale(src_resolution_x, src_resolution_y)
def main():
# load and average netcdfs
arr = None
for f in NETCDFS:
ds = nc.Dataset(f,'r')
if arr is None:
print ds.variables.keys()
arr = np.asarray(ds.variables['lwe_thickness']) / len(NETCDFS)
else:
arr += np.asarray(ds.variables['lwe_thickness']) / len(NETCDFS)
# multiply by scale factor
ds = nc.Dataset(SCALER,'r')
print ds.variables.keys()
scaler = np.asarray(ds.variables['SCALE_FACTOR'])
print scaler.shape
arr = arr*scaler
# extract error grids
m_err = np.asarray(ds.variables['MEASUREMENT_ERROR'])
l_err = np.asarray(ds.variables['LEAKAGE_ERROR'])
t_err = np.sqrt(m_err*m_err + l_err*l_err)
# compute slopes, coefficients
print arr.shape
slope_arr = np.zeros(arr.shape[1:])
r2_arr = np.zeros(arr.shape[1:])
p_arr = np.zeros(arr.shape[1:])
print slope_arr.shape
time = np.arange(arr.shape[0])
print time.shape
for i in range(arr.shape[1]):
for j in range(arr.shape[2]):
b1, b0, r2, p, sd = stats.linregress(arr[:,i,j], time)
slope_arr[i,j]=b1
r2_arr[i,j]=r2
p_arr[i,j]=p
# dump to csv
np.savetxt(SLOPE,slope_arr,delimiter=',')
np.savetxt(R2,r2_arr,delimiter=',')
np.savetxt(P,p_arr,delimiter=',')
np.savetxt(ERR,t_err,delimiter=',')
# rescale to WGS84 and dump to tif bands
rows = arr.shape[1]
cols = arr.shape[2]
d = 1
transform = A.translation(-cols*d/2,-rows*d/2) * A.scale(d,d)
print transform
slope_arr = np.roll(slope_arr.astype(rio.float64),180)
r2_arr = np.roll(r2_arr.astype(rio.float64),180)
p_arr = np.roll(p_arr.astype(rio.float64),180)
t_err = np.roll(t_err.astype(rio.float64),180)
with rio.open(OUT, 'w',
'GTiff',
width=cols,
height=rows,
dtype=rio.float64,
crs={'init': 'EPSG:4326'},
transform=transform,
count=4) as out:
out.write_band(1, slope_arr)
out.write_band(2, r2_arr)
out.write_band(3, p_arr)
out.write_band(4, t_err)
def __make_rastertiles_Z__(src_dataset: rio.DatasetReader, world_size: float,
tile_size: int, zoom: int) -> list():
# get bands
src_bands = src_dataset.read()
# structure for store tiles
tiles = []
# get bounds
src_bbox = src_dataset.bounds
src_bbox = [src_bbox.left, src_bbox.top, src_bbox.right, src_bbox.bottom]
# get pixel size
pixel_size = __pixel_size__(world_size, tile_size, zoom)
# get all quadrant
quadrants = __make_quadrants__(src_bbox, zoom, world_size, 1)
for xmin, ymin, xmax, ymax in quadrants:
# get bbox of quadrant
Xmin, Ymin, Xmax, Ymax = list(
__tile_world_bbox__(xmin, ymin, zoom, world_size, tile_size))
# get pixel size
pixel_size = __pixel_size__(world_size, tile_size, zoom)
# make dst shape (3, tsize, tsize), 3 is fix because it's an image RGB
dst_shape = (3, tile_size, tile_size)
# make transform with orig (Xmin, Ymin) and scale (psize, -psize)
dst_transform = A.translation(Xmin, Ymin) * A.scale(
pixel_size, -pixel_size)
dtype = src_dataset.dtypes[0]
if dtype == rio.uint8:
datatype = 1
elif dtype == rio.uint16:
datatype = 2
elif dtype == rio.int16:
datatype = 3
elif dtype == rio.uint32:
datatype = 4
elif dtype == rio.int32:
datatype = 5
elif dtype == rio.float32:
datatype = 6
elif dtype == rio.float64:
datatype = 7
else:
assert False
# init dst bands
dst_bands = np.zeros(dst_shape, dtype=dtype)
count = dst_bands.shape[0]
nodata = 0 if src_dataset.nodata is None else src_dataset.nodata
# make reprojection for each bands
for i in range(count):
try:
reproject(source=src_bands[i],
destination=dst_bands[i],
src_transform=src_dataset.transform,
src_crs=src_dataset.crs,
src_nodata=nodata,
dst_transform=dst_transform,
dst_crs=src_dataset.crs)
except IndexError:
continue
gdal_bands = [{
'data': dst_bands[x],
'nodata_value': nodata
} for x in range(count)]
gdal_raster = GDALRaster({
'srid': WEB_MERCATOR_SRID,
'width': tile_size,
'height': tile_size,
'datatype': datatype,
'nr_of_bands': count,
'origin': [Xmin, Ymin],
'scale': [pixel_size, -pixel_size],
'bands': gdal_bands
})
tiles.append((zoom, xmin, ymin, gdal_raster))
del src_bands
# return structure
return tiles
def reproject_grids(src_array, dst_array, metadata_src, metadata_dst):
"""
Reproject precipitation fields to the domain of another precipitation field.
Parameters
----------
src_array: array-like
Three-dimensional array of shape (t, x, y) containing a time series of
precipitation fields. These precipitation fields will be reprojected.
dst_array: array-like
Array containing a precipitation field or a time series of precipitation
fields. The src_array will be reprojected to the domain of
dst_array.
metadata_src: dict
Metadata dictionary containing the projection, x- and ypixelsize, x1 and
y2 attributes of the src_array as described in the documentation of
:py:mod:`pysteps.io.importers`.
metadata_dst: dict
Metadata dictionary containing the projection, x- and ypixelsize, x1 and
y2 attributes of the dst_array.
Returns
-------
r_rprj: array-like
Three-dimensional array of shape (t, x, y) containing the precipitation
fields of src_array, but reprojected to the domain of dst_array.
metadata: dict
Metadata dictionary containing the projection, x- and ypixelsize, x1 and
y2 attributes of the reprojected src_array.
"""
if not RASTERIO_IMPORTED:
raise MissingOptionalDependency(
"rasterio package is required for the reprojection module, but it is "
"not installed"
)
# Extract the grid info from src_array
src_crs = metadata_src["projection"]
x1_src = metadata_src["x1"]
y2_src = metadata_src["y2"]
xpixelsize_src = metadata_src["xpixelsize"]
ypixelsize_src = metadata_src["ypixelsize"]
src_transform = A.translation(float(x1_src), float(y2_src)) * A.scale(
float(xpixelsize_src), float(-ypixelsize_src)
)
# Extract the grid info from dst_array
dst_crs = metadata_dst["projection"]
x1_dst = metadata_dst["x1"]
y2_dst = metadata_dst["y2"]
xpixelsize_dst = metadata_dst["xpixelsize"]
ypixelsize_dst = metadata_dst["ypixelsize"]
dst_transform = A.translation(float(x1_dst), float(y2_dst)) * A.scale(
float(xpixelsize_dst), float(-ypixelsize_dst)
)
# Initialise the reprojected array
r_rprj = np.zeros((src_array.shape[0], dst_array.shape[-2], dst_array.shape[-1]))
# For every timestep, reproject the precipitation field of src_array to
# the domain of dst_array
if metadata_src["yorigin"] != metadata_dst["yorigin"]:
src_array = src_array[:, ::-1, :]
for i in range(src_array.shape[0]):
reproject(
src_array[i, :, :],
r_rprj[i, :, :],
src_transform=src_transform,
src_crs=src_crs,
dst_transform=dst_transform,
dst_crs=dst_crs,
resampling=Resampling.nearest,
dst_nodata=np.nan,
)
# Update the metadata
metadata = metadata_src.copy()
for key in [
"projection",
"yorigin",
"xpixelsize",
"ypixelsize",
"x1",
"x2",
"y1",
"y2",
"cartesian_unit",
]:
metadata[key] = metadata_dst[key]
return r_rprj, metadata
def tile(sceneid,
tile_x,
tile_y,
tile_z,
rgb=(4, 3, 2),
r_bds=(0, 16000),
g_bds=(0, 16000),
b_bds=(0, 16000),
tilesize=256,
pan=False):
"""Create mercator tile from Landsat-8 data and encodes it in base64.
Attributes
----------
sceneid : str
Landsat sceneid. For scenes after May 2017,
sceneid have to be LANDSAT_PRODUCT_ID.
tile_x : int
Mercator tile X index.
tile_y : int
Mercator tile Y index.
tile_z : int
Mercator tile ZOOM level.
rgb : tuple, int, optional (default: (4, 3, 2))
Bands index for the RGB combination.
r_bds : tuple, int, optional (default: (0, 16000))
First band (red) DN min and max values (DN * 10,000)
used for the linear rescaling.
g_bds : tuple, int, optional (default: (0, 16000))
Second band (green) DN min and max values (DN * 10,000)
used for the linear rescaling.
b_bds : tuple, int, optional (default: (0, 16000))
Third band (blue) DN min and max values (DN * 10,000)
used for the linear rescaling.
tilesize : int, optional (default: 256)
Output image size.
pan : boolean, optional (default: False)
If True, apply pan-sharpening.
Returns
-------
out : numpy ndarray (type: uint8)
"""
scene_params = utils.landsat_parse_scene_id(sceneid)
meta_data = utils.landsat_get_mtl(sceneid).get('L1_METADATA_FILE')
landsat_address = '{}/{}'.format(LANDSAT_BUCKET, scene_params['key'])
wgs_bounds = toa_utils._get_bounds_from_metadata(
meta_data['PRODUCT_METADATA'])
if not utils.tile_exists(wgs_bounds, tile_z, tile_x, tile_y):
raise TileOutsideBounds('Tile {}/{}/{} is outside image bounds'.format(
tile_z, tile_x, tile_y))
mercator_tile = mercantile.Tile(x=tile_x, y=tile_y, z=tile_z)
tile_bounds = mercantile.xy_bounds(mercator_tile)
# define a list of bands Min and Max Values (from input)
histo_cuts = dict(zip(rgb, [r_bds, g_bds, b_bds]))
ms_tile_size = int(tilesize / 2) if pan else tilesize
addresses = ['{}_B{}.TIF'.format(landsat_address, band) for band in rgb]
_tiler = partial(utils.tile_band_worker,
bounds=tile_bounds,
tilesize=ms_tile_size)
with futures.ThreadPoolExecutor(max_workers=3) as executor:
out = np.stack(list(executor.map(_tiler, addresses)))
if pan:
pan_address = '{}_B8.TIF'.format(landsat_address)
matrix_pan = utils.tile_band_worker(pan_address, tile_bounds,
tilesize)
w, s, e, n = tile_bounds
pan_transform = transform.from_bounds(w, s, e, n, tilesize,
tilesize)
vis_transform = pan_transform * Affine.scale(2.)
out = pansharpen(out,
vis_transform,
matrix_pan,
pan_transform,
np.int16,
'EPSG:3857',
'EPSG:3857',
0.2,
method='Brovey',
src_nodata=0)
sun_elev = meta_data['IMAGE_ATTRIBUTES']['SUN_ELEVATION']
for bdx, band in enumerate(rgb):
multi_reflect = meta_data['RADIOMETRIC_RESCALING'].get(
'REFLECTANCE_MULT_BAND_{}'.format(band))
add_reflect = meta_data['RADIOMETRIC_RESCALING'].get(
'REFLECTANCE_ADD_BAND_{}'.format(band))
out[bdx] = 10000 * reflectance.reflectance(
out[bdx], multi_reflect, add_reflect, sun_elev, src_nodata=0)
out[bdx] = np.where(
out[bdx] > 0,
utils.linear_rescale(out[bdx],
in_range=histo_cuts.get(band),
out_range=[1, 255]), 0)
return out.astype(np.uint8)
def tile(sceneid, tile_x, tile_y, tile_z, rgb=(4, 3, 2), tilesize=256, pan=False):
"""Create mercator tile from Landsat-8 data.
Attributes
----------
sceneid : str
Landsat sceneid. For scenes after May 2017,
sceneid have to be LANDSAT_PRODUCT_ID.
tile_x : int
Mercator tile X index.
tile_y : int
Mercator tile Y index.
tile_z : int
Mercator tile ZOOM level.
rgb : tuple, int, optional (default: (4, 3, 2))
Bands index for the RGB combination.
tilesize : int, optional (default: 256)
Output image size.
pan : boolean, optional (default: False)
If True, apply pan-sharpening.
Returns
-------
data : numpy ndarray
mask: numpy array
"""
if not isinstance(rgb, tuple):
rgb = tuple((rgb, ))
scene_params = utils.landsat_parse_scene_id(sceneid)
meta_data = utils.landsat_get_mtl(sceneid).get('L1_METADATA_FILE')
landsat_address = '{}/{}'.format(LANDSAT_BUCKET, scene_params['key'])
wgs_bounds = toa_utils._get_bounds_from_metadata(
meta_data['PRODUCT_METADATA'])
if not utils.tile_exists(wgs_bounds, tile_z, tile_x, tile_y):
raise TileOutsideBounds(
'Tile {}/{}/{} is outside image bounds'.format(
tile_z, tile_x, tile_y))
mercator_tile = mercantile.Tile(x=tile_x, y=tile_y, z=tile_z)
tile_bounds = mercantile.xy_bounds(mercator_tile)
ms_tile_size = int(tilesize / 2) if pan else tilesize
addresses = ['{}_B{}.TIF'.format(landsat_address, band) for band in rgb]
_tiler = partial(utils.tile_band_worker, bounds=tile_bounds, tilesize=ms_tile_size, nodata=0)
with futures.ThreadPoolExecutor(max_workers=3) as executor:
data, masks = zip(*list(executor.map(_tiler, addresses)))
data = np.concatenate(data)
mask = np.all(masks, axis=0).astype(np.uint8) * 255
if pan:
pan_address = '{}_B8.TIF'.format(landsat_address)
matrix_pan, mask = utils.tile_band_worker(pan_address, tile_bounds, tilesize, nodata=0)
w, s, e, n = tile_bounds
pan_transform = transform.from_bounds(w, s, e, n, tilesize, tilesize)
vis_transform = pan_transform * Affine.scale(2.)
data = pansharpen(data, vis_transform, matrix_pan, pan_transform,
np.int16, 'EPSG:3857', 'EPSG:3857', 0.2,
method='Brovey', src_nodata=0)
sun_elev = meta_data['IMAGE_ATTRIBUTES']['SUN_ELEVATION']
for bdx, band in enumerate(rgb):
if int(band) > 9: # TIRS
multi_rad = meta_data['RADIOMETRIC_RESCALING'].get(
'RADIANCE_MULT_BAND_{}'.format(band))
add_rad = meta_data['RADIOMETRIC_RESCALING'].get(
'RADIANCE_ADD_BAND_{}'.format(band))
k1 = meta_data['TIRS_THERMAL_CONSTANTS'].get(
'K1_CONSTANT_BAND_{}'.format(band))
k2 = meta_data['TIRS_THERMAL_CONSTANTS'].get(
'K2_CONSTANT_BAND_{}'.format(band))
data[bdx] = brightness_temp.brightness_temp(
data[bdx], multi_rad, add_rad, k1, k2)
else:
multi_reflect = meta_data['RADIOMETRIC_RESCALING'].get(
'REFLECTANCE_MULT_BAND_{}'.format(band))
add_reflect = meta_data['RADIOMETRIC_RESCALING'].get(
'REFLECTANCE_ADD_BAND_{}'.format(band))
data[bdx] = 10000 * reflectance.reflectance(
data[bdx], multi_reflect, add_reflect, sun_elev)
return data, mask
import rasterio.crs
# order depends on convention
transform = A(geotransform)
with rasterio.Env():
# As source: a 512 x 512 raster centered on 0 degrees E and 0
# degrees N, each pixel covering 15".
rows, cols = src_shape = (512, 512)
d = 1.0/240 # decimal degrees per pixel
# The following is equivalent to
# A(d, 0, -cols*d/2, 0, -d, rows*d/2).
src_transform = A.translation(-cols*d/2, rows*d/2) * A.scale(d, -d)
src_crs = {'init': 'EPSG:4326'}
source = np.ones(src_shape, np.uint8)*255
# Destination: a 1024 x 1024 dataset in Web Mercator (EPSG:3857)
# with origin at 0.0, 0.0.
dst_shape = (1024, 1024)
dst_transform = [-237481.5, 425.0, 0.0, 237536.4, 0.0, -425.0]
dst_crs = {'init': 'EPSG:3857'}
destination = np.zeros(dst_shape, np.uint8)
reproject(
source,
destination,
src_transform=src_transform,
src_crs=src_crs,
def main():
# load and average netcdfs
arr = None
for f in NETCDFS:
ds = nc.Dataset(f, 'r')
if arr is None:
print ds.variables.keys()
arr = np.asarray(ds.variables['lwe_thickness']) / len(NETCDFS)
else:
arr += np.asarray(ds.variables['lwe_thickness']) / len(NETCDFS)
# multiply by scale factor
ds = nc.Dataset(SCALER, 'r')
print ds.variables.keys()
scaler = np.asarray(ds.variables['SCALE_FACTOR'])
print scaler.shape
arr = arr * scaler
# extract error grids
m_err = np.asarray(ds.variables['MEASUREMENT_ERROR'])
l_err = np.asarray(ds.variables['LEAKAGE_ERROR'])
t_err = np.sqrt(m_err * m_err + l_err * l_err)
# compute slopes, coefficients
print arr.shape
slope_arr = np.zeros(arr.shape[1:])
r2_arr = np.zeros(arr.shape[1:])
p_arr = np.zeros(arr.shape[1:])
print slope_arr.shape
time = np.arange(arr.shape[0])
print time.shape
for i in range(arr.shape[1]):
for j in range(arr.shape[2]):
b1, b0, r2, p, sd = stats.linregress(arr[:, i, j], time)
slope_arr[i, j] = b1
r2_arr[i, j] = r2
p_arr[i, j] = p
# dump to csv
np.savetxt(SLOPE, slope_arr, delimiter=',')
np.savetxt(R2, r2_arr, delimiter=',')
np.savetxt(P, p_arr, delimiter=',')
np.savetxt(ERR, t_err, delimiter=',')
# rescale to WGS84 and dump to tif bands
rows = arr.shape[1]
cols = arr.shape[2]
d = 1
transform = A.translation(-cols * d / 2, -rows * d / 2) * A.scale(d, d)
print transform
slope_arr = np.roll(slope_arr.astype(rio.float64), 180)
r2_arr = np.roll(r2_arr.astype(rio.float64), 180)
p_arr = np.roll(p_arr.astype(rio.float64), 180)
t_err = np.roll(t_err.astype(rio.float64), 180)
with rio.open(OUT,
'w',
'GTiff',
width=cols,
height=rows,
dtype=rio.float64,
crs={'init': 'EPSG:4326'},
transform=transform,
count=4) as out:
out.write_band(1, slope_arr)
out.write_band(2, r2_arr)
out.write_band(3, p_arr)
out.write_band(4, t_err)
def export_array(modelgrid, filename, a, nodata=-9999,
fieldname='value',
**kwargs):
"""Write a numpy array to Arc Ascii grid
or shapefile with the model reference.
Parameters
----------
filename : str
Path of output file. Export format is determined by
file extention.
'.asc' Arc Ascii grid
'.tif' GeoTIFF (requries rasterio package)
'.shp' Shapefile
a : 2D numpy.ndarray
Array to export
nodata : scalar
Value to assign to np.nan entries (default -9999)
fieldname : str
Attribute field name for array values (shapefile export only).
(default 'values')
kwargs:
keyword arguments to np.savetxt (ascii)
rasterio.open (GeoTIFF)
or flopy.export.shapefile_utils.write_grid_shapefile2
Notes
-----
Rotated grids will be either be unrotated prior to export,
using scipy.ndimage.rotate (Arc Ascii format) or rotation will be
included in their transform property (GeoTiff format). In either case
the pixels will be displayed in the (unrotated) projected geographic coordinate system,
so the pixels will no longer align exactly with the model grid
(as displayed from a shapefile, for example). A key difference between
Arc Ascii and GeoTiff (besides disk usage) is that the
unrotated Arc Ascii will have a different grid size, whereas the GeoTiff
will have the same number of rows and pixels as the original.
"""
if filename.lower().endswith(".asc"):
if len(np.unique(modelgrid.delr)) != len(np.unique(modelgrid.delc)) != 1 \
or modelgrid.delr[0] != modelgrid.delc[0]:
raise ValueError('Arc ascii arrays require a uniform grid.')
xoffset, yoffset = modelgrid.xoffset, modelgrid.yoffset
cellsize = modelgrid.delr[0] # * self.length_multiplier
fmt = kwargs.get('fmt', '%.18e')
a = a.copy()
a[np.isnan(a)] = nodata
if modelgrid.angrot != 0:
try:
from scipy.ndimage import rotate
a = rotate(a, modelgrid.angrot, cval=nodata)
height_rot, width_rot = a.shape
xmin, ymin, xmax, ymax = modelgrid.extent
dx = (xmax - xmin) / width_rot
dy = (ymax - ymin) / height_rot
cellsize = np.max((dx, dy))
# cellsize = np.cos(np.radians(self.rotation)) * cellsize
xoffset, yoffset = xmin, ymin
except ImportError:
print('scipy package required to export rotated grid.')
pass
filename = '.'.join(
filename.split('.')[:-1]) + '.asc' # enforce .asc ending
nrow, ncol = a.shape
a[np.isnan(a)] = nodata
txt = 'ncols {:d}\n'.format(ncol)
txt += 'nrows {:d}\n'.format(nrow)
txt += 'xllcorner {:f}\n'.format(xoffset)
txt += 'yllcorner {:f}\n'.format(yoffset)
txt += 'cellsize {}\n'.format(cellsize)
# ensure that nodata fmt consistent w values
txt += 'NODATA_value {}\n'.format(fmt) % (nodata)
with open(filename, 'w') as output:
output.write(txt)
with open(filename, 'ab') as output:
np.savetxt(output, a, **kwargs)
print('wrote {}'.format(filename))
elif filename.lower().endswith(".tif"):
if len(np.unique(modelgrid.delr)) != len(np.unique(modelgrid.delc)) != 1 \
or modelgrid.delr[0] != modelgrid.delc[0]:
raise ValueError('GeoTIFF export require a uniform grid.')
try:
import rasterio
from rasterio import Affine
except:
print('GeoTIFF export requires the rasterio package.')
return
dxdy = modelgrid.delc[0] # * self.length_multiplier
trans = Affine.translation(modelgrid.xoffset, modelgrid.yoffset) * \
Affine.rotation(modelgrid.angrot) * \
Affine.scale(dxdy, -dxdy)
# third dimension is the number of bands
a = a.copy()
if len(a.shape) == 2:
a = np.reshape(a, (1, a.shape[0], a.shape[1]))
if a.dtype.name == 'int64':
a = a.astype('int32')
dtype = rasterio.int32
elif a.dtype.name == 'int32':
dtype = rasterio.int32
elif a.dtype.name == 'float64':
dtype = rasterio.float64
elif a.dtype.name == 'float32':
dtype = rasterio.float32
else:
msg = 'ERROR: invalid dtype "{}"'.format(a.dtype.name)
raise TypeError(msg)
meta = {'count': a.shape[0],
'width': a.shape[2],
'height': a.shape[1],
'nodata': nodata,
'dtype': dtype,
'driver': 'GTiff',
'crs': modelgrid.proj4,
'transform': trans
}
meta.update(kwargs)
with rasterio.open(filename, 'w', **meta) as dst:
dst.write(a)
print('wrote {}'.format(filename))
elif filename.lower().endswith(".shp"):
from ..export.shapefile_utils import write_grid_shapefile2
epsg = kwargs.get('epsg', None)
prj = kwargs.get('prj', None)
if epsg is None and prj is None:
epsg = modelgrid.epsg
write_grid_shapefile2(filename, modelgrid, array_dict={fieldname: a},
nan_val=nodata,
epsg=epsg, prj=prj)
import rasterio
from rasterio import Affine as A
from rasterio.warp import reproject, RESAMPLING
tempdir = '/tmp'
tiffname = os.path.join(tempdir, 'example.tif')
with rasterio.drivers():
# Consider a 512 x 512 raster centered on 0 degrees E and 0 degrees N
# with each pixel covering 15".
rows, cols = src_shape = (512, 512)
dpp = 1.0 / 240 # decimal degrees per pixel
# The following is equivalent to
# A(dpp, 0, -cols*dpp/2, 0, -dpp, rows*dpp/2).
src_transform = A.translation(-cols * dpp / 2, rows * dpp / 2) * A.scale(
dpp, -dpp)
src_crs = {'init': 'EPSG:4326'}
source = numpy.ones(src_shape, numpy.uint8) * 255
# Prepare to reproject this rasters to a 1024 x 1024 dataset in
# Web Mercator (EPSG:3857) with origin at -8928592, 2999585.
dst_shape = (1024, 1024)
dst_transform = A.from_gdal(-237481.5, 425.0, 0.0, 237536.4, 0.0, -425.0)
dst_transform = dst_transform.to_gdal()
dst_crs = {'init': 'EPSG:3857'}
destination = numpy.zeros(dst_shape, numpy.uint8)
reproject(source,
destination,
src_transform=src_transform,
src_crs=src_crs,
def __make_imagetiles_Z__(src_dataset: rio.DatasetReader, world_size: float,
tile_size: int, zoom: int) -> list():
# structure for store tiles
tiles = []
# get bounding box
src_bbox = src_dataset.bounds
src_bbox = [src_bbox.left, src_bbox.top, src_bbox.right, src_bbox.bottom]
# get pixel size
pixel_size = __pixel_size__(world_size, tile_size, zoom)
# get all quadrant
quadrants = __make_quadrants__(src_bbox, zoom, world_size, 1)
for xmin, ymin, xmax, ymax in quadrants:
# get bbox of quadrant
Xmin, Ymin, Xmax, Ymax = list(
__tile_world_bbox__(xmin, ymin, zoom, world_size, tile_size))
# get pixel size
pixel_size = __pixel_size__(world_size, tile_size, zoom)
# make dst shape (3, tsize, tsize), 3 is fix because it's an image RGB
dst_shape = (3, tile_size, tile_size)
# make transform with orig (Xmin, Ymin) and scale (psize, -psize)
dst_transform = A.translation(Xmin, Ymin) * A.scale(
pixel_size, -pixel_size)
# init dst bands
dst_bands = np.zeros(dst_shape, dtype=np.uint8)
# make reprojection for each bands
for i in range(3):
reproject(source=src_dataset.read(i + 1),
destination=dst_bands[i],
src_transform=src_dataset.transform,
src_crs=src_dataset.crs,
dst_transform=dst_transform,
dst_crs=src_dataset.crs)
# switch channel fst to channel last
dst_bands = np.rollaxis(dst_bands, 0, 3)
# make alpha band for no data
dst_sum = np.sum(dst_bands, axis=2)
alpha = np.zeros((tile_size, tile_size, 3))
alpha[dst_sum > 0] = np.array([255, 255, 255])
# convert alpha as pilimage
pil_alpha = Image.fromarray(alpha.astype(dtype=np.uint8)).convert('L')
# convert dst_bands as pilimage & put alpha
pil_tile = Image.fromarray(dst_bands)
pil_tile.putalpha(pil_alpha)
# write in a buffer as bytes
buffer = BytesIO()
pil_tile.save(fp=buffer, format="PNG")
# push all in ret structure
tiles.append((zoom, xmin, ymin, buffer))
# return structure
return tiles
def tile(sceneid,
tile_x,
tile_y,
tile_z,
bands=("4", "3", "2"),
tilesize=256,
pan=False):
"""
Create mercator tile from Landsat-8 data.
Attributes
----------
sceneid : str
Landsat sceneid. For scenes after May 2017,
sceneid have to be LANDSAT_PRODUCT_ID.
tile_x : int
Mercator tile X index.
tile_y : int
Mercator tile Y index.
tile_z : int
Mercator tile ZOOM level.
bands : tuple, str, optional (default: ("4", "3", "2"))
Bands index for the RGB combination.
tilesize : int, optional (default: 256)
Output image size.
pan : boolean, optional (default: False)
If True, apply pan-sharpening.
Returns
-------
data : numpy ndarray
mask: numpy array
"""
if not isinstance(bands, tuple):
bands = tuple((bands, ))
for band in bands:
if band not in LANDSAT_BANDS:
raise InvalidBandName(
"{} is not a valid Landsat band name".format(band))
scene_params = _landsat_parse_scene_id(sceneid)
meta_data = _landsat_get_mtl(sceneid).get("L1_METADATA_FILE")
landsat_address = "{}/{}".format(LANDSAT_BUCKET, scene_params["key"])
wgs_bounds = toa_utils._get_bounds_from_metadata(
meta_data["PRODUCT_METADATA"])
if not utils.tile_exists(wgs_bounds, tile_z, tile_x, tile_y):
raise TileOutsideBounds("Tile {}/{}/{} is outside image bounds".format(
tile_z, tile_x, tile_y))
mercator_tile = mercantile.Tile(x=tile_x, y=tile_y, z=tile_z)
tile_bounds = mercantile.xy_bounds(mercator_tile)
ms_tile_size = int(tilesize / 2) if pan else tilesize
addresses = ["{}_B{}.TIF".format(landsat_address, band) for band in bands]
_tiler = partial(utils.tile_read,
bounds=tile_bounds,
tilesize=ms_tile_size,
nodata=0)
with futures.ThreadPoolExecutor(max_workers=MAX_THREADS) as executor:
data, masks = zip(*list(executor.map(_tiler, addresses)))
data = np.concatenate(data)
mask = np.all(masks, axis=0).astype(np.uint8) * 255
if pan:
pan_address = "{}_B8.TIF".format(landsat_address)
matrix_pan, mask = utils.tile_read(pan_address,
tile_bounds,
tilesize,
nodata=0)
w, s, e, n = tile_bounds
pan_transform = transform.from_bounds(w, s, e, n, tilesize,
tilesize)
vis_transform = pan_transform * Affine.scale(2.0)
data = pansharpen(
data,
vis_transform,
matrix_pan,
pan_transform,
np.int16,
"EPSG:3857",
"EPSG:3857",
0.2,
method="Brovey",
src_nodata=0,
)
sun_elev = meta_data["IMAGE_ATTRIBUTES"]["SUN_ELEVATION"]
for bdx, band in enumerate(bands):
if int(band) > 9: # TIRS
multi_rad = meta_data["RADIOMETRIC_RESCALING"].get(
"RADIANCE_MULT_BAND_{}".format(band))
add_rad = meta_data["RADIOMETRIC_RESCALING"].get(
"RADIANCE_ADD_BAND_{}".format(band))
k1 = meta_data["TIRS_THERMAL_CONSTANTS"].get(
"K1_CONSTANT_BAND_{}".format(band))
k2 = meta_data["TIRS_THERMAL_CONSTANTS"].get(
"K2_CONSTANT_BAND_{}".format(band))
data[bdx] = brightness_temp.brightness_temp(
data[bdx], multi_rad, add_rad, k1, k2)
else:
multi_reflect = meta_data["RADIOMETRIC_RESCALING"].get(
"REFLECTANCE_MULT_BAND_{}".format(band))
add_reflect = meta_data["RADIOMETRIC_RESCALING"].get(
"REFLECTANCE_ADD_BAND_{}".format(band))
data[bdx] = 10000 * reflectance.reflectance(
data[bdx], multi_reflect, add_reflect, sun_elev)
return data, mask
