def test_algebra_parser(self):
parser = RasterAlgebraParser()
result = parser.evaluate_raster_algebra(self.data,
'x*(x>11) + 2*y + 3*z*(z==30)',
check_aligned=True)
self.assertEqual(result.bands[0].data().ravel().tolist(),
[10, 10, 14, 15])
def test_algebra_parser_nodata_none(self):
if GDAL_VERSION < (2, 1):
self.skipTest("GDAL >= 2.1 is required for this test.")
parser = RasterAlgebraParser()
self.data['z'].bands[0].nodata_value = None
result = parser.evaluate_raster_algebra({'x': self.data.pop('z')}, 'x')
self.assertEqual(result.bands[0].data().ravel().tolist(), [30, 31, 32, 33])
def get_algebra(self, data, formula):
parser = RasterAlgebraParser()
# Evaluate raster algebra expression, return 400 if not successful
try:
# Evaluate raster algebra expression
result = parser.evaluate_raster_algebra(data, formula)
except:
raise RasterAlgebraException('Failed to evaluate raster algebra.')
# Get array from algebra result
if result.bands[0].nodata_value is None:
result = result.bands[0].data()
else:
result = numpy.ma.masked_values(
result.bands[0].data(),
result.bands[0].nodata_value,
)
# Get colormap.
colormap = self.get_colormap()
# Render tile using the legend data
img, stats = band_data_to_image(result, colormap)
# Return rendered image
return self.write_img_to_response(img, stats)
def test_algebra_parser_nodata_none(self):
if GDAL_VERSION < (2, 1):
self.skipTest("GDAL >= 2.1 is required for this test.")
parser = RasterAlgebraParser()
self.data['z'].bands[0].nodata_value = None
result = parser.evaluate_raster_algebra({'x': self.data.pop('z')}, 'x')
self.assertEqual(result.bands[0].data().ravel().tolist(), [30, 31, 32, 33])
def get(self, request, *args, **kwargs):
parser = RasterAlgebraParser()
# Get layer ids
ids = self.get_ids()
# Get raster data as 1D arrays and store in dict that can be used
# for formula evaluation.
data = {}
for name, layerid in ids.items():
tile = self.get_tile(layerid)
if tile:
data[name] = tile
else:
# Create empty image if any layer misses the required tile
img = Image.new("RGBA", (WEB_MERCATOR_TILESIZE, WEB_MERCATOR_TILESIZE), (0, 0, 0, 0))
return self.write_img_to_response(img, {})
# Get formula from request
formula = request.GET.get('formula')
# Evaluate raster algebra expression, return 400 if not successful
try:
# Evaluate raster algebra expression
result = parser.evaluate_raster_algebra(data, formula)
except:
raise RasterAlgebraException('Failed to evaluate raster algebra.')
# Get array from algebra result
result = numpy.ma.masked_values(
result.bands[0].data(),
result.bands[0].nodata_value,
)
# Render tile
colormap = self.get_colormap()
if colormap:
# Render tile using the legend data
img, stats = band_data_to_image(result, colormap)
else:
# Scale to grayscale rgb (can be colorscheme later on)
result = result.astype('float').ravel()
result = 255 * (result - numpy.min(result)) / (numpy.max(result) - numpy.min(result))
# Create rgba matrix from grayscale array
result = numpy.array((result, result, result, numpy.repeat(255, len(result)))).T
rgba = result.reshape(WEB_MERCATOR_TILESIZE, WEB_MERCATOR_TILESIZE, 4).astype('uint8')
# Create image from array
img = Image.fromarray(rgba)
stats = {}
# Return rendered image
return self.write_img_to_response(img, stats)
def get_algebra(self, data, formula):
parser = RasterAlgebraParser()
# Evaluate raster algebra expression, return 400 if not successful
try:
# Evaluate raster algebra expression
result = parser.evaluate_raster_algebra(data, formula)
except:
raise RasterAlgebraException('Failed to evaluate raster algebra.')
# For pixel value requests, return result as json.
if self.is_pixel_request:
xcoord = float(self.kwargs.get('xcoord'))
ycoord = float(self.kwargs.get('ycoord'))
val = pixel_value_from_point(result, [xcoord, ycoord])
return HttpResponse(
json.dumps({
'x': xcoord,
'y': ycoord,
'value': val
}),
content_type='application/json',
)
# For tif requests, skip colormap and return georeferenced tif file.
if self.kwargs.get('frmt') == 'tif':
vsi_path = os.path.join(VSI_FILESYSTEM_BASE_PATH,
str(uuid.uuid4()))
rast = result.warp({
'name': vsi_path,
'driver': 'tif',
'compress': 'DEFLATE',
})
content_type = IMG_FORMATS['tif'][1]
return HttpResponse(rast.vsi_buffer, content_type)
# Get array from algebra result
if result.bands[0].nodata_value is None:
result = result.bands[0].data()
else:
result = numpy.ma.masked_values(
result.bands[0].data(),
result.bands[0].nodata_value,
)
# Get colormap.
colormap = self.get_colormap()
# Render tile using the legend data
img, stats = band_data_to_image(result, colormap)
# Return rendered image
return self.write_img_to_response(img, stats)
def tiles(self):
"""
Generator that yields an algebra-ready data dictionary for each tile in
the aggregator's tile range.
"""
# Check if any tiles have been matched
if not self.tilerange:
return
algebra_parser = RasterAlgebraParser()
for tilex in range(self.tilerange[0], self.tilerange[2] + 1):
for tiley in range(self.tilerange[1], self.tilerange[3] + 1):
# Prepare a data dictionary with named tiles for algebra evaluation
data = {}
for name, layerid in self.layer_dict.items():
tile = self.get_raster_tile(layerid, self.zoom, tilex,
tiley)
if tile:
data[name] = tile
else:
break
# Ignore this tile if it is missing in any of the input layers
if len(data) < len(self.layer_dict):
continue
# Compute raster algebra
result = algebra_parser.evaluate_raster_algebra(
data, self.formula)
# Convert band data to masked array
result_data = numpy.ma.masked_values(
result.bands[0].data(),
result.bands[0].nodata_value,
)
# Apply rasterized geometry as mask if clip geometry was provided
if self.geom:
result_data = self.mask_by_geom(result, result_data)
yield result_data.compressed()
def get_algebra(self, data, formula):
parser = RasterAlgebraParser()
# Evaluate raster algebra expression, return 400 if not successful
try:
# Evaluate raster algebra expression
result = parser.evaluate_raster_algebra(data, formula)
except:
raise RasterAlgebraException('Failed to evaluate raster algebra.')
# For pixel value requests, return result as json.
if self.is_pixel_request:
xcoord = float(self.kwargs.get('xcoord'))
ycoord = float(self.kwargs.get('ycoord'))
val = pixel_value_from_point(result, [xcoord, ycoord])
return HttpResponse(
json.dumps({
'x': xcoord,
'y': ycoord,
'value': val
}),
content_type='application/json',
)
# Get array from algebra result
if result.bands[0].nodata_value is None:
result = result.bands[0].data()
else:
result = numpy.ma.masked_values(
result.bands[0].data(),
result.bands[0].nodata_value,
)
# Get colormap.
colormap = self.get_colormap()
# Render tile using the legend data
img, stats = band_data_to_image(result, colormap)
# Return rendered image
return self.write_img_to_response(img, stats)
def tiles(self):
"""
Generator that yields an algebra-ready data dictionary for each tile in
the aggregator's tile range.
"""
# Check if any tiles have been matched
if not self.tilerange:
return
algebra_parser = RasterAlgebraParser()
for tilex in range(self.tilerange[0], self.tilerange[2] + 1):
for tiley in range(self.tilerange[1], self.tilerange[3] + 1):
# Prepare a data dictionary with named tiles for algebra evaluation
data = {}
for name, layerid in self.layer_dict.items():
tile = get_raster_tile(layerid, self.zoom, tilex, tiley)
if tile:
data[name] = tile
else:
break
# Ignore this tile if it is missing in any of the input layers
if len(data) < len(self.layer_dict):
continue
# Compute raster algebra
result = algebra_parser.evaluate_raster_algebra(data, self.formula)
# Convert band data to masked array
result_data = numpy.ma.masked_values(
result.bands[0].data(),
result.bands[0].nodata_value,
)
# Apply rasterized geometry as mask if clip geometry was provided
if self.geom:
result_data = self.mask_by_geom(result, result_data)
yield result_data.compressed()
def RasterAlgebra(cr1, cr2, cr3, poids):
""" les noms sont les noms des critères choisis par l'utilisateur"""
# rast1 = RasterData.objects.get(name=name1)
# rast2 = RasterData.objects.get(name=name2)
# rast3 = RasterData.objects.get(name=name3)
rast1 = cr1
rast2 = cr2
rast3 = cr3
# #Valeurs fournies l'utilisateur
# c1, c2, c3 = 3, 5, 7
# # Poids des critères
# poids = RatingCriterion(c1, c2, c3)
# Pondération de cha
parser = RasterAlgebraParser()
d1 = dict(zip(['x'], [rast1.raster50]))
d2 = dict(zip(['x'], [rast2.raster50]))
d3 = dict(zip(['x'], [rast3.raster50]))
f1 = "{}*x".format(poids[0])
f2 = "{}*x".format(poids[1])
f3 = "{}*x".format(poids[2])
rast1_pondere = parser.evaluate_raster_algebra(d1, f1)
rast2_pondere = parser.evaluate_raster_algebra(d2, f2)
rast3_pondere = parser.evaluate_raster_algebra(d3, f3)
data = dict(
zip(['x', 'y', 'z'], [rast1.raster50, rast2.raster50, rast3.raster50]))
formula = "{}*x + {}*y + {}*z".format(poids[0], poids[1], poids[2])
rst = parser.evaluate_raster_algebra(data, formula)
# Enregistrement du resultat
# dbrest = Result(raster = rst)
# dbrest.save()
rasters = [rast1_pondere, rast2_pondere, rast3_pondere, rst]
return rasters
def get_algebra(self, data, formula):
parser = RasterAlgebraParser()
# Evaluate raster algebra expression, return 400 if not successful
try:
# Evaluate raster algebra expression
result = parser.evaluate_raster_algebra(data, formula)
except:
raise RasterAlgebraException('Failed to evaluate raster algebra.')
# Get array from algebra result
result = numpy.ma.masked_values(
result.bands[0].data(),
result.bands[0].nodata_value,
)
# Render tile
colormap = self.get_colormap()
if colormap:
# Render tile using the legend data
img, stats = band_data_to_image(result, colormap)
else:
# Scale to grayscale rgb (can be colorscheme later on)
result = result.astype('float').ravel()
result = 255 * (result - numpy.min(result)) / (numpy.max(result) -
numpy.min(result))
# Create rgba matrix from grayscale array
result = numpy.array(
(result, result, result, numpy.repeat(255, len(result)))).T
rgba = result.reshape(WEB_MERCATOR_TILESIZE, WEB_MERCATOR_TILESIZE,
4).astype('uint8')
# Create image from array
img = Image.fromarray(rgba)
stats = {}
# Return rendered image
return self.write_img_to_response(img, stats)
def get(self, request):
# Initiate algebra parser
parser = RasterAlgebraParser()
# Get formula from request
formula = request.GET.get('formula')
# Get id list from request
ids = self.get_ids()
# Compute tile index range
zoom, xmin, ymin, xmax, ymax = self.get_tile_range()
# Check maximum size of target raster in pixels
max_pixels = getattr(settings, 'RASTER_EXPORT_MAX_PIXELS',
EXPORT_MAX_PIXELS)
if WEB_MERCATOR_TILESIZE * (xmax - xmin) * WEB_MERCATOR_TILESIZE * (
ymax - ymin) > max_pixels:
raise RasterAlgebraException('Export raster too large.')
# Construct an empty raster with the output dimensions
result_raster = self.construct_raster(zoom, xmin, xmax, ymin, ymax)
target = result_raster.bands[0]
# Get raster data as 1D arrays and store in dict that can be used
# for formula evaluation.
for xindex, x in enumerate(range(xmin, xmax + 1)):
for yindex, y in enumerate(range(ymin, ymax + 1)):
data = {}
for name, layerid in ids.items():
tile = get_raster_tile(layerid, zoom, x, y)
if tile:
data[name] = tile
# Ignore this tile if data is not found for all layers
if len(data) != len(ids):
continue
# Evaluate raster algebra expression, return 400 if not successful
try:
# Evaluate raster algebra expression
tile_result = parser.evaluate_raster_algebra(data, formula)
except:
raise RasterAlgebraException(
'Failed to evaluate raster algebra.')
# Update nodata value on target
target.nodata_value = tile_result.bands[0].nodata_value
# Update results raster with algebra
target.data(
data=tile_result.bands[0].data(),
size=(WEB_MERCATOR_TILESIZE, WEB_MERCATOR_TILESIZE),
offset=(xindex * WEB_MERCATOR_TILESIZE,
yindex * WEB_MERCATOR_TILESIZE),
)
# Create filename base with datetime stamp
filename_base = 'algebra_export'
# Add name slug to filename if provided
if request.GET.get('filename', ''):
# Sluggify name
slug = slugify(request.GET.get('filename'))
# Remove all unwanted characters
slug = "".join([c for c in slug if re.match(r'\w|\-', c)])
# Limit length of custom name slug
slug = slug[:MAX_EXPORT_NAME_LENGTH]
# Add name slug to filename base
filename_base += '_' + slug
filename_base += '_{0}'.format(
datetime.now().strftime('%Y_%m_%d_%H_%M'))
# Compress resulting raster file into a zip archive
raster_workdir = getattr(settings, 'RASTER_WORKDIR', None)
dest = NamedTemporaryFile(dir=raster_workdir, suffix='.zip')
dest_zip = zipfile.ZipFile(dest.name, 'w', allowZip64=True)
dest_zip.write(
filename=self.exportfile.name,
arcname=filename_base + '.tif',
compress_type=zipfile.ZIP_DEFLATED,
)
# Write README.txt and COLORMAP.txt files to zip file
self.write_readme(dest_zip)
self.write_colormap(dest_zip)
# Close zip file before returning
dest_zip.close()
# Create file based response containing zip file and return for download
response = FileResponse(open(dest.name, 'rb'),
content_type='application/zip')
response['Content-Disposition'] = 'attachment; filename="{0}"'.format(
filename_base + '.zip')
return response
def test_algebra_parser_nodata_propagation(self):
parser = RasterAlgebraParser()
result = parser.evaluate_raster_algebra(self.data, 'x * (z != NULL) + 99 * (z == NULL)')
self.assertEqual(result.bands[0].data().ravel().tolist(), [10, 99, 12, 13])
def test_algebra_parser_same_raster_twice(self):
parser = RasterAlgebraParser()
result = parser.evaluate_raster_algebra(self.data2, 'x + y')
self.assertEqual(result.bands[0].data().ravel().tolist(), [10, 32, 34, 36])
def test_algebra_parser_same_raster_twice(self):
parser = RasterAlgebraParser()
result = parser.evaluate_raster_algebra(self.data2, 'x + y')
self.assertEqual(result.bands[0].data().ravel().tolist(), [10, 32, 34, 36])
def test_algebra_parser_nodata_propagation(self):
parser = RasterAlgebraParser()
result = parser.evaluate_raster_algebra(self.data, 'x * (z != NULL) + 99 * (z == NULL)')
self.assertEqual(result.bands[0].data().ravel().tolist(), [10, 99, 12, 13])
def test_algebra_parser(self):
parser = RasterAlgebraParser()
result = parser.evaluate_raster_algebra(self.data, 'x*(x>11) + 2*y + 3*z*(z==30)', check_aligned=True)
self.assertEqual(result.bands[0].data().ravel().tolist(), [10, 10, 14, 15])
def get(self, request):
# Initiate algebra parser
parser = RasterAlgebraParser()
# Get formula from request
formula = request.GET.get('formula')
# Get id list from request
ids = self.get_ids()
# Compute tile index range
zoom, xmin, ymin, xmax, ymax = self.get_tile_range()
# Check maximum size of target raster in pixels
if WEB_MERCATOR_TILESIZE * (xmax - xmin) * WEB_MERCATOR_TILESIZE * (ymax - ymin) > EXPORT_MAX_PIXELS:
raise RasterAlgebraException('Export raster too large.')
# Construct an empty raster with the output dimensions
result_raster = self.construct_raster(zoom, xmin, xmax, ymin, ymax)
target = result_raster.bands[0]
# Get raster data as 1D arrays and store in dict that can be used
# for formula evaluation.
for xindex, x in enumerate(range(xmin, xmax + 1)):
for yindex, y in enumerate(range(ymin, ymax + 1)):
data = {}
for name, layerid in ids.items():
tile = get_raster_tile(layerid, zoom, x, y)
if tile:
data[name] = tile
# Ignore this tile if data is not found for all layers
if len(data) != len(ids):
continue
# Evaluate raster algebra expression, return 400 if not successful
try:
# Evaluate raster algebra expression
tile_result = parser.evaluate_raster_algebra(data, formula)
except:
raise RasterAlgebraException('Failed to evaluate raster algebra.')
# Update nodata value on target
target.nodata_value = tile_result.bands[0].nodata_value
# Update results raster with algebra
target.data(
data=tile_result.bands[0].data(),
size=(WEB_MERCATOR_TILESIZE, WEB_MERCATOR_TILESIZE),
offset=(xindex * WEB_MERCATOR_TILESIZE, yindex * WEB_MERCATOR_TILESIZE),
)
# Create filename base with datetime stamp
filename_base = 'algebra_export'
# Add name slug to filename if provided
if request.GET.get('filename', ''):
# Sluggify name
slug = slugify(request.GET.get('filename'))
# Remove all unwanted characters
slug = "".join([c for c in slug if re.match(r'\w|\-', c)])
# Limit length of custom name slug
slug = slug[:MAX_EXPORT_NAME_LENGTH]
# Add name slug to filename base
filename_base += '_' + slug
filename_base += '_{0}'.format(datetime.now().strftime('%Y_%m_%d_%H_%M'))
# Compress resulting raster file into a zip archive
raster_workdir = getattr(settings, 'RASTER_WORKDIR', None)
dest = NamedTemporaryFile(dir=raster_workdir, suffix='.zip')
dest_zip = zipfile.ZipFile(dest.name, 'w', allowZip64=True)
dest_zip.write(
filename=self.exportfile.name,
arcname=filename_base + '.tif',
compress_type=zipfile.ZIP_DEFLATED,
)
# Write README.txt and COLORMAP.txt files to zip file
self.write_readme(dest_zip)
self.write_colormap(dest_zip)
# Close zip file before returning
dest_zip.close()
# Create file based response containing zip file and return for download
response = FileResponse(
open(dest.name, 'rb'),
content_type='application/zip'
)
response['Content-Disposition'] = 'attachment; filename="{0}"'.format(filename_base + '.zip')
return response