def test_resample_no_invert_proj(method):
"""Nearest and bilinear should produce valid results with
CHECK_WITH_INVERT_PROJ = False
"""
if method in (Resampling.bilinear, Resampling.cubic,
Resampling.cubic_spline, Resampling.lanczos):
pytest.xfail(
reason="Some resampling methods succeed but produce blank images. "
"See https://github.com/mapbox/rasterio/issues/614")
with rasterio.Env(CHECK_WITH_INVERT_PROJ=False):
with rasterio.open('tests/data/world.rgb.tif') as src:
source = src.read(1)
profile = src.profile.copy()
dst_crs = {'init': 'EPSG:32619'}
# Calculate the ideal dimensions and transformation in the new crs
dst_affine, dst_width, dst_height = calculate_default_transform(
src.crs, dst_crs, src.width, src.height, *src.bounds)
profile['height'] = dst_height
profile['width'] = dst_width
out = np.empty(shape=(dst_height, dst_width), dtype=np.uint8)
# see #614, some resampling methods succeed but produce blank images
out = np.empty(src.shape, dtype=np.uint8)
reproject(
source,
out,
src_transform=src.transform,
src_crs=src.crs,
dst_transform=dst_affine,
dst_crs=dst_crs,
resampling=method)
assert out.mean() > 0
def read_write_bigtiff(out_path, pol):
"""
This method is a proper way to read big GeoTIFF raster data.
"""
with rasterio.Env():
with rasterio.open("%s%s.tif" % (out_path, pol[0])) as src0:
kwargs = src0.profile
kwargs.update(
count=len(pol),
bigtiff="YES",
compress="lzw", # Output will be larger than 4GB
)
with rasterio.open("%s%s.tif" % (out_path, "stack"), "w",
**kwargs) as dst:
for b_id, layer in enumerate(pol):
src = rasterio.open("%s%s.tif" % (out_path, layer))
windows = src.block_windows(1)
for _, window in windows:
src_data = src.read(1, window=window)
dst.write(src_data, window=window, indexes=b_id + 1)
dst.set_band_description(b_id + 1, layer)
def test_reproject_nodata(options, expected):
nodata = 215
with rasterio.Env(**options):
params = uninvertable_reproject_params()
source = np.ones((params.width, params.height), dtype=np.uint8)
out = np.zeros((params.dst_width, params.dst_height),
dtype=source.dtype)
out.fill(120) # Fill with arbitrary value
reproject(source,
out,
src_transform=params.src_transform,
src_crs=params.src_crs,
src_nodata=nodata,
dst_transform=params.dst_transform,
dst_crs=params.dst_crs,
dst_nodata=nodata)
assert (out == 1).sum() == expected
assert (out == nodata).sum() == (params.dst_width * params.dst_height -
expected)
def test_issue1982(capfd):
"""See a curl request for overview file"""
# Note: the underlying GDAL issue has been fixed after 3.1.3. The
# rasterio 1.1.6 wheels published to PyPI will include a patched
# 2.4.4 that also fixes the issue. This test will XPASS in the
# rasterio-wheels tests.
with rasterio.Env(CPL_CURL_VERBOSE=True), rasterio.open(
"https://raw.githubusercontent.com/mapbox/rasterio/master/tests/data/green.tif"
) as src:
image = src.read(
indexes=[1, 2, 3],
window=Window(col_off=-32, row_off=-32, width=64, height=64),
resampling=Resampling.cubic,
boundless=True,
out_shape=(3, 10, 10),
fill_value=42,
)
captured = capfd.readouterr()
assert "green.tif" in captured.err
assert "green.tif.ovr" in captured.err
assert (42 == image[:, :3, :]).all()
assert (42 == image[:, :, :3]).all()
def reproject(src,
src_trans,
dst_trans,
dst_shape,
_crs,
src_nodata=0,
dst_nodata=0,
dtype=np.uint8):
dst = np.empty(dst_shape, dtype=dtype)
with rio.Env():
warp.reproject(source=np.ascontiguousarray(src),
destination=dst,
src_crs=_crs,
dst_crs=_crs,
dst_transform=dst_trans,
src_transform=src_trans,
src_nodata=src_nodata,
dst_nodata=src_nodata,
resampling=enums.Resampling.nearest,
num_threads=4)
return dst
def main(AHN_pc_file):
AHN_pc = read_PC_Data("AHN/" + AHN_pc_file + ".las")
interpolation_methods = ["IDW", "NN", "Laplace", "TINlinear"]
file_name = "DSM_" + AHN_pc_file[4:] + "_"
ff = open("MAE_report_" + AHN_pc_file[4:] + ".txt", "w")
for method in interpolation_methods:
raster = method + "/" + file_name + method + ".tif"
raster_info = read_file(raster)
differences_values = calculate_differences(AHN_pc, raster_info[0],
raster_info[1],
raster_info[2],
raster_info[3],
raster_info[4])
transform = (Affine.translation(raster_info[2][0], raster_info[2][3]) *
Affine.scale(raster_info[1][0], raster_info[1][1]))
with rasterio.Env():
with rasterio.open(method + "/" + file_name +
'verticle_differences.tif',
'w',
driver='GTiff',
height=raster_info[4],
width=raster_info[3],
count=1,
dtype=differences_values.dtype,
crs='EPSG:28992',
transform=transform) as dst:
dst.write(differences_values, 1)
abs_sum = 0
for col in range(raster_info[3]):
for row in range(raster_info[4]):
abs_sum += abs(differences_values[row][col])
N = (raster_info[3] *
raster_info[4]) - np.count_nonzero(differences_values == 0)
MAE = abs_sum / N
ff.write("MAE for " + method + "_" + file_name + " = " + str(MAE) +
"\n")
print("MAE for " + method + "_" + file_name + " = " + str(MAE))
def main(infile, outfile, num_workers=4):
"""Process infile block-by-block and write to a new file
The output is the same as the input, but with band order
reversed.
"""
with rasterio.Env():
with rasterio.open(infile) as src:
# Create a destination dataset based on source params. The
# destination will be tiled, and we'll process the tiles
# concurrently.
profile = src.profile
profile.update(blockxsize=128, blockysize=128, tiled=True)
with rasterio.open(outfile, "w", **profile) as dst:
# Materialize a list of destination block windows
# that we will use in several statements below.
windows = [window for ij, window in dst.block_windows()]
# This generator comprehension gives us raster data
# arrays for each window. Later we will zip a mapping
# of it with the windows list to get (window, result)
# pairs.
data_gen = (src.read(window=window) for window in windows)
with concurrent.futures.ThreadPoolExecutor(
max_workers=num_workers) as executor:
# We map the compute() function over the raster
# data generator, zip the resulting iterator with
# the windows list, and as pairs come back we
# write data to the destination dataset.
for window, result in zip(windows,
executor.map(compute, data_gen)):
dst.write(result, window=window)
def raster_to_vector(value_of_interest = 0, raster_file_path = 'raster.asc', output_vector_file = 'buildings.gpkg'):
"""
Converts a .asc file (or any raster format) to a vector geopackage creating polygons for cells with the given value (default value set to 0)
param: value_of_interest:
raster value assigned to cells which are to be converted to polygons
param: raster_file_path:
file path and file name of raster file to convert to a vector layer/file
param: output_vector_file:
file path and file name of vector file to be saved (as a geopackage)
"""
# read in raster file
mask = None
with rasterio.Env():
with rasterio.open(raster_file_path) as src:
image = src.read(1) # first band
results = (
{'properties': {'raster_val': v}, 'geometry': s}
for i, (s, v)
in enumerate(
shapes(image, mask=mask, transform=src.transform)))
# save polygons to a geodataframe
polygons = list(results)
gpd_polygons = gp.GeoDataFrame.from_features(polygons)
# remove any polygons which are not of interest
gpd_polygons = gpd_polygons[gpd_polygons.raster_val == value_of_interest]
if len(gpd_polygons) == 0:
print('Could not export as no cells matched the expected cell value (%s)' %value_of_interest)
exit(2)
# export file
gpd_polygons.to_file(output_vector_file, layer='buildings', driver="GPKG")
return
def tiled(sources, equal_blocks=True):
""" Tests if raster sources have the same tiling
optionally assert that their block sizes are equal (default: True)
"""
blocksize = None
with rasterio.Env():
for source in sources:
with rasterio.open(source) as src:
if not src.is_tiled:
return (False, "Source(s) are not internally tiled")
if equal_blocks:
this_blocksize = (src.profile['blockxsize'],
src.profile['blockysize'])
if blocksize:
if blocksize != this_blocksize:
return (False, "Blocksizes are not equal")
else:
blocksize = this_blocksize
return (True, "Tiling checks out")
def _wmts(
url: str = None,
tile_format: str = "png",
tile_scale: int = 1,
title: str = "Cloud Optimizied GeoTIFF",
**kwargs: Any,
) -> Tuple[str, str, str]:
"""Handle /wmts requests."""
if tile_scale is not None and isinstance(tile_scale, str):
tile_scale = int(tile_scale)
# Remove QGIS arguments
kwargs.pop("SERVICE", None)
kwargs.pop("REQUEST", None)
kwargs.update(dict(url=url))
query_string = urllib.parse.urlencode(list(kwargs.items()))
# & is an invalid character in XML
query_string = query_string.replace("&", "&")
with rasterio.Env(aws_session):
with COGReader(url) as cog:
info = cog.spatial_info()
return (
"OK",
"application/xml",
wmts_template(
app.host,
query_string,
minzoom=info["minzoom"],
maxzoom=info["maxzoom"],
bounds=info["bounds"],
tile_scale=tile_scale,
tile_format=tile_format,
title=title,
),
)
def fix_mukey(mukey_tif):
"""The mukey tiff might come out with some odd values around the edges,
which makes viewing it a bit tricky. This fixes that."""
mukey_tif = os.path.expanduser(mukey_tif)
# For the profile
profile = rasterio.open(mukey_tif).profile
# For the data array
mukey = xr.open_rasterio(mukey_tif, chunks=(1, 5000, 5000))
# Change everything under 0 to the nan value
array = mukey.data
array[array < 0] = profile["nodata"]
with Client():
array = array.compute()
# save
with rasterio.Env():
with rasterio.open(mukey_tif, "w", **profile) as file:
file.write(array[0].astype(rasterio.int32), 1)
def _save_raster(self):
transform = rasterio.transform.from_origin(
west=self._origin[0],
north=self._origin[1],
xsize=self._raster_cell_size,
ysize=self._raster_cell_size)
self._raster = self._raster.astype(np.float32)
with rasterio.Env():
with rasterio.open(self._get_save_name(),
"w",
driver='GTiff',
height=self._raster.shape[0],
width=self._raster.shape[1],
count=1,
dtype=str(self._raster.dtype),
crs='EPSG:28992',
transform=transform,
nodata=NO_DATA) as dest:
dest.write(self._raster, 1)
def smoosh_rasters(inputRasters, outputRaster, gfs, development):
import rasterio
import rasterio.env
import numpy as np
# if isinstance(inputRasters, list):
# pass
# else:
# inputRasters = [inputRasters]
rasInfo = list(gribdoctor.loadRasterInfo(b) for b in inputRasters)
if abs(rasInfo[0]['affine'].c) > 360 and development == True:
gfs = False
kwargs = rasInfo[0]['kwargs']
elif development == True:
gfs = True
snapShape = gribdoctor.getSnapDims(rasInfo)
snapSrc = gribdoctor.getSnapAffine(rasInfo, snapShape)
allBands = list(
gribdoctor.loadBands(b, snapShape, gfs) for b in inputRasters)
allBands = list(b for sub in allBands for b in sub)
if gfs:
zoomFactor = 2
kwargs = gribdoctor.makeKwargs(allBands, snapSrc, snapShape,
zoomFactor)
else:
zoomFactor = 1
kwargs['count'] = len(allBands)
kwargs['driver'] = 'GTiff'
with rasterio.Env():
with rasterio.open(outputRaster, 'w', **kwargs) as dst:
for i, b in enumerate(allBands):
dst.write_band(i + 1, b)
def _process_file_stream(src_stream,
on_file_cbk,
gdal_opts=None,
region_name=None,
timer=None):
session = _session(region_name)
if timer is not None:
def proc(url, userdata):
t0 = timer()
with rasterio.open(url, 'r') as f:
on_file_cbk(f, userdata, t0=t0)
else:
def proc(url, userdata):
with rasterio.open(url, 'r') as f:
on_file_cbk(f, userdata)
with rasterio.Env(session=session, **gdal_opts):
for userdata, url in src_stream:
proc(url, userdata)
def test_issue2202(dx, dy):
import rasterio.merge
from shapely import wkt
from shapely.affinity import translate
aoi = wkt.loads(
r"POLYGON((11.09 47.94, 11.06 48.01, 11.12 48.11, 11.18 48.11, 11.18 47.94, 11.09 47.94))"
)
aoi = translate(aoi, dx, dy)
with rasterio.Env(AWS_NO_SIGN_REQUEST=True,):
ds = [
rasterio.open(i)
for i in [
"/vsis3/copernicus-dem-30m/Copernicus_DSM_COG_10_N47_00_E011_00_DEM/Copernicus_DSM_COG_10_N47_00_E011_00_DEM.tif",
"/vsis3/copernicus-dem-30m/Copernicus_DSM_COG_10_N48_00_E011_00_DEM/Copernicus_DSM_COG_10_N48_00_E011_00_DEM.tif",
]
]
aux_array, aux_transform = rasterio.merge.merge(datasets=ds, bounds=aoi.bounds)
from rasterio.plot import show
show(aux_array)
def make_raster_difference(scn_fn1, scn_fn2, dst_fn, nodata=-9999.0):
ds = rasterio.open(scn_fn1)
ds2 = rasterio.open(scn_fn2)
with open(dst_fn + '.log', 'w') as fp:
fp.write('scn_fn1 = ' + scn_fn1)
fp.write('scn_fn2 = ' + scn_fn2)
_data1 = np.ma.masked_values(ds.read(), nodata)
_data1 = np.ma.masked_values(_data1, 0)
_data1 = np.ma.masked_values(_data1, 1)
_data2 = np.ma.masked_values(ds2.read(), nodata)
_data2 = np.ma.masked_values(_data2, 0)
_data2 = np.ma.masked_values(_data2, 1)
if not _data1.shape == _data2.shape:
transformed_scn_fn2 = scn_fn2[:-4] + '.x.tif'
transform_to_template_ds(scn_fn1, scn_fn2, transformed_scn_fn2)
return make_raster_difference(scn_fn1, transformed_scn_fn2, dst_fn,
nodata)
data = (_data2[0, :, :] - _data1[0, :, :]) / _data1[0, :, :]
if isinstance(data, np.ma.core.MaskedArray):
data.fill_value = nodata
_data = data.filled()
else:
_data = data
with rasterio.Env():
profile = ds.profile
profile.update(dtype=rasterio.float32,
count=1,
nodata=nodata,
compress='lzw')
with rasterio.open(dst_fn, 'w', **profile) as dst:
dst.write(_data.astype(rasterio.float32), 1)
def wrapped_f(*args, **kwargs):
"""Wrapped functions."""
rio_stream = StringIO()
logger = logging.getLogger("rasterio")
logger.setLevel(logging.DEBUG)
handler = logging.StreamHandler(rio_stream)
logger.addHandler(handler)
gdal_config = config or {}
gdal_config.update({"CPL_DEBUG": "ON", "CPL_CURL_VERBOSE": "TRUE"})
with pipes() as (_, curl_stream):
with rasterio.Env(**gdal_config):
with Timer() as t:
retval = func(*args, **kwargs)
logger.removeHandler(handler)
handler.close()
rio_lines = rio_stream.getvalue().splitlines()
curl_lines = curl_stream.read().splitlines()
results = analyse_logs(rio_lines, curl_lines)
results["Timing"] = t.elapsed
if not kernels:
results.pop("WarpKernels")
if raw:
results["curl"] = curl_lines
results["rasterio"] = rio_lines
if not quiet:
log.info(json.dumps(results))
if add_to_return:
return retval, results
return retval
def test_shapes(basic_image):
"""Test creation of shapes from pixel values."""
with rasterio.Env():
results = list(shapes(basic_image))
assert len(results) == 2
shape, value = results[0]
assert shape == {
'coordinates': [[(2, 2), (2, 5), (5, 5), (5, 2), (2, 2)]],
'type': 'Polygon'
}
assert value == 1
shape, value = results[1]
assert shape == {
'coordinates': [[(0, 0), (0, 10), (10, 10), (10, 0), (0, 0)],
[(2, 2), (5, 2), (5, 5), (2, 5), (2, 2)]],
'type':
'Polygon'
}
assert value == 0
def test_reproject_nodata():
params = default_reproject_params()
nodata = 215
with rasterio.Env():
source = np.ones((params.width, params.height), dtype=np.uint8)
out = np.zeros((params.dst_width, params.dst_height),
dtype=source.dtype)
out.fill(120) # Fill with arbitrary value
reproject(source,
out,
src_transform=params.src_transform,
src_crs=params.src_crs,
src_nodata=nodata,
dst_transform=params.dst_transform,
dst_crs=params.dst_crs,
dst_nodata=nodata)
assert (out == 1).sum() == 6215
assert (out == nodata).sum() == (params.dst_width * params.dst_height -
6215)
def pointArea(lat, lon, loc):
mask = None
with rasterio.Env():
with rasterio.open(loc) as src: #input the classified image
image = src.read(1) # first band
results = ({
'properties': {
'raster_val': v
},
'geometry': s
} for i, (s, v) in enumerate(
shapes(image, mask=mask, transform=src.transform)))
geoms = list(results)
#input the coordinates of the point. must be utm coordinates
point = Point(lat, lon)
gpd = gp.GeoDataFrame.from_features(geoms)
gpd.crs = {'init': 'epsg:32643'}
water_pol = gpd.loc[gpd['raster_val'] == 1]
water_pol.crs = {'init': 'epsg:32643'}
water_pol["area"] = water_pol['geometry'].area
#save the geojson to a location.
water_pol.to_file(
"C:/Users/risha/waterbudgeting/water_pol_konambe.geojson",
driver="GeoJSON")
#read geojson
with open("C:/Users/risha/waterbudgeting/water_pol_konambe.geojson") as f:
js = geojson.load(f)
#find polygon which contains the point
for feature in js['features']:
polygon = shape(feature['geometry'])
if polygon.contains(point):
print('Water area:', feature['properties']['area'])
return feature['properties']['area']
def test_reproject_dst_nodata_default(options, expected):
"""If nodata is not provided, destination will be filled with 0."""
with rasterio.Env(**options):
params = uninvertable_reproject_params()
source = np.ones((params.width, params.height), dtype=np.uint8)
out = np.zeros((params.dst_width, params.dst_height),
dtype=source.dtype)
out.fill(120) # Fill with arbitrary value
reproject(
source,
out,
src_transform=params.src_transform,
src_crs=params.src_crs,
dst_transform=params.dst_transform,
dst_crs=params.dst_crs
)
assert (out == 1).sum() == expected
assert (out == 0).sum() == (params.dst_width *
params.dst_height - expected)
def tileserver_optimized_raster(src, dest):
""" This method converts a raster to a tileserver optimized raster.
The method will reproject the raster to align to the xyz system, in resolution and projection
It will also create overviews
And finally it will arragne the raster in a cog way.
You could take the dest file upload it to a web server that supports ranges and user GeoRaster.get_tile
on it,
You are geranteed that you will get as minimal data as possible
"""
src_raster = tl.GeoRaster2.open(src)
bounding_box = src_raster.footprint().get_shape(tl.constants.WGS84_CRS).bounds
tile = mercantile.bounding_tile(*bounding_box)
dest_resolution = mercator_upper_zoom_level(src_raster)
bounds = tl.GeoVector.from_xyz(tile.x, tile.y, tile.z).get_bounds(tl.constants.WEB_MERCATOR_CRS)
create_options = {
"tiled": "YES",
"blocksize": 256,
"compress": "DEFLATE",
"photometric": "MINISBLACK"
}
with TemporaryDirectory() as temp_dir:
temp_file = os.path.join(temp_dir, 'temp.tif')
warp(src, temp_file, dst_crs=tl.constants.WEB_MERCATOR_CRS, resolution=dest_resolution,
dst_bounds=bounds, create_options=create_options)
with rasterio.Env(GDAL_TIFF_INTERNAL_MASK=True, GDAL_TIFF_OVR_BLOCKSIZE=256):
resampling = rasterio.enums.Resampling.gauss
with rasterio.open(temp_file, 'r+') as tmp_raster:
factors = _calc_overviews_factors(tmp_raster)
tmp_raster.build_overviews(factors, resampling=resampling)
tmp_raster.update_tags(ns='rio_overview', resampling=resampling.name)
telluric_tags = _get_telluric_tags(src)
if telluric_tags:
tmp_raster.update_tags(**telluric_tags)
rasterio_sh.copy(temp_file, dest,
COPY_SRC_OVERVIEWS=True, tiled=True,
compress='DEFLATE', photometric='MINISBLACK')
def get_subset(fkey, features):
import numpy as np
# import pandas as pd
import rasterio
from rasterio.mask import mask
from rasterio.session import AWSSession
s3host_ = S3HOST.replace('http://', '')
s3host_ = s3host_.replace('https://', '')
session = connection('session')
with rasterio.Env(AWSSession(session), AWS_S3_ENDPOINT=s3host_,
AWS_HTTPS='NO', AWS_VIRTUAL_HOSTING=False) as env:
with rasterio.open('/vsis3/{}/{}'.format(BUCKET, fkey)) as src:
out_image, out_transform = mask(src, features, crop=True,
pad=False, all_touched=False)
# print('--out_image.shape: ', out_image.shape)
w_5 = np.percentile(out_image, 5.0)
w_95 = np.percentile(out_image, 95.0)
# print('--w_5, w_95: ', w_5, w_95)
chip_set = np.clip(255 * (out_image - w_5) /(w_95 - w_5),
0, 255).astype(np.uint16)
return chip_set, out_transform
def __init__(self, file):
"""Open the file.
Parameters
----------
file: path to the file
"""
if rasterio is None:
raise ImportError('This feature needs rasterio to be insalled')
# brutally efficient
with rasterio.Env():
with rasterio.open(file) as src:
nxny = (src.width, src.height)
ul_corner = (src.bounds.left, src.bounds.top)
proj = pyproj.Proj(src.crs)
dxdy = (src.res[0], -src.res[1])
grid = Grid(x0y0=ul_corner, nxny=nxny, dxdy=dxdy,
pixel_ref='corner', proj=proj)
# done
self.file = file
GeoDataset.__init__(self, grid)
def geotiff_writer(filename,
trans,
dst_crs,
shape,
n_bands,
dtype=np.uint8,
nodata=0):
"""writes the raster as a multiband geotiff
returns an open geotiff writer
"""
with rio.Env():
with rio.open(filename,
'w',
driver='GTiff',
width=shape[1],
height=shape[0],
count=n_bands,
dtype=dtype,
nodata=nodata,
transform=trans,
crs=dst_crs) as f:
yield f
def clip(self, tile: Tile):
""" Clips this raster according to the size of original geometry of the tile provided
:param tile: Tile element corresponding with this raster
:return: None
"""
try:
self.open()
except Exception:
return False
clip_geom = tile.get_unbuffered_geometry()
clip_height = clip_geom.bounds[3] - clip_geom.bounds[1] # maxy - miny
clip_width = clip_geom.bounds[2] - clip_geom.bounds[0] # maxx - minx
try:
print(self._raster_name, clip_geom)
out_image, out_transform = mask(dataset=self._raster,
shapes=[clip_geom],
crop=True)
out_meta = self._raster.meta.copy()
self.close()
out_meta.update({
"driver": "GTiff",
"height": clip_height / self._base_raster_cell_size,
"width": clip_width / self._base_raster_cell_size,
"transform": out_transform,
})
with rasterio.Env():
with rasterio.open(self.filepath, "w", **out_meta) as dest:
dest.write(out_image)
except Exception as e:
print(e)
return False
def fetch_meta(self) -> Tuple[BoundingBox, Dict[str, Any]]:
"""Open file to fetch metadata."""
LOGGER.debug(f"Fetch metadata data for file {self.url} if exists")
try:
with rasterio.Env(**GDAL_ENV), rasterio.open(self.url) as src:
LOGGER.info(f"File {self.url} exists")
return src.bounds, src.profile
except Exception as e:
if _file_does_not_exist(e):
LOGGER.info(f"File does not exist {self.url}")
raise FileNotFoundError(f"File does not exist: {self.url}")
elif isinstance(e, rasterio.RasterioIOError):
LOGGER.warning(
f"RasterioIO Error while opening {self.url}. Will make attempts to retry"
)
raise
else:
LOGGER.exception(f"Cannot open file {self.url}")
raise
def loadRaster(filePath, bands, bounds):
"""
"""
# with rasterio.drivers():
import rasterio
import rasterio.env
with rasterio.Env():
with rasterio.open(filePath, 'r') as src:
oaff = src.transform
upperLeft = src.index(bounds.left, bounds.top)
lowerRight = src.index(bounds.right, bounds.bottom)
filler = np.zeros((lowerRight[0] - upperLeft[0],
lowerRight[1] - upperLeft[1])) - 999
return np.dstack(
list(
src.read(i[1],
boundless=True,
out=filler,
window=((upperLeft[0], lowerRight[0]),
(upperLeft[1], lowerRight[1])))
for i in bands)), oaff
def _profile(data, nx, ny, bands=1, bounds=None):
"""
Profile for writing depth and gradient. Dtype and band count needs
to be set depending on data.
"""
if bounds is not None:
l, b, r, t = bounds
else:
l, b = np.min(data[:, 0]), np.min(data[:, 1])
r, t = np.max(data[:, 0]), np.max(data[:, 1])
with rasterio.Env():
profile = rasterio.profiles.DefaultGTiffProfile()
transform = rasterio.transform.from_bounds(l, b, r, t, nx, ny)
profile.update(crs=CRS,
transform=transform,
width=nx,
height=ny,
count=bands,
dtype=data.dtype)
return profile
def run(multi_band_file, swir_file, out_fn, green_fn, s3_fn, px_res, p_name):
if (multi_band_file is not None) & (swir_file is not None):
green_arr, prf, g_ndv = read_file(multi_band_file[:-4] + "_b3_" + p_name + "_refl.tif")
swir3_arr, _, swir3_ndv = read_file(swir_file[:-4] + "_b3_" + p_name + "_refl.tif")
elif (green_fn is not None) & (s3_fn is not None):
green_arr, prf, g_ndv = read_file(green_fn)
swir3_arr, _, swir3_ndv = read_file(s3_fn)
else:
sys.exit("Check input files, missing proper input")
ndsi_3, ndsi_3_norm = calc_ndsi(green_arr, swir3_arr, g_ndv, swir3_ndv)
# Write NDSI arrays to file
with rio.Env():
prf.update(
dtype=rio.float32,
count=1,
compress='lzw')
with rio.open(out_fn, 'w', **prf) as dst:
dst.write(np.squeeze(ndsi_3).astype(rio.float32), 1)
with rio.open(out_fn[:-4]+"_minmax.tif", 'w', **prf) as dst:
dst.write(np.squeeze(ndsi_3_norm).astype(rio.float32), 1)