def storeRasterInArray(rasters):
"""Store list of raster or multi-band raster in a ndarray
Parameters
----------
rasters : list
list of rasters to analyse
Return
----------
ndarray
"""
# get raster size with first raster file
data = fut.readRaster(rasters[0], False)
# get rasters or bands number
if len(rasters) == 1:
nbbands = fut.getRasterNbands(rasters[0])
elif len(rasters) > 1:
nbbands = len(rasters)
# Set a empty ndarrays with same dimension as input rasters
outdata = np.zeros([data[1], data[0], nbbands])
# Populate output ndarrays
if len(rasters) == 1:
for nbband in range(nbbands):
outdata[:, :, nbband] = fut.readRaster(rasters[0], True,
nbband + 1)[0]
elif len(rasters) > 1:
for idx, raster in enumerate(rasters):
outdata[:, :, idx] = fut.readRaster(raster, True)[0]
else:
raise Exception("No input raster provided to store in Numpy array")
return outdata
def zonalstats(path,
rasters,
params,
output,
paramstats,
classes="",
bufferDist=None,
nodata=0,
gdalpath="",
systemcall=True,
gdalcachemax="9000"):
"""Compute zonal statistitics (descriptive and categorical)
on multi-band raster or multi-rasters
based on Point (buffered or not) or Polygon zonal vector
Parameters
----------
path : string
working directory
rasters : list
list of rasters to analyse
params : list
list of fid list and vector file
output : vector file (sqlite, shapefile and geojson)
vector file to store statistitics
paramstats : list
list of statistics to compute (e.g. {1:'stats', 2:'rate'})
- paramstats = {1:"rate", 2:"statsmaj", 3:"statsmaj", 4:"stats", 2:stats_cl}
- stats : mean_b, std_b, max_b, min_b
- statsmaj : meanmaj, stdmaj, maxmaj, minmaj of majority class
- rate : rate of each pixel value (classe names)
- stats_cl : mean_cl, std_cl, max_cl, min_cl of one class
- val : value of corresponding pixel (only for Point geometry and without other stats)
classes : nomenclature file
nomenclature
bufferDist : int
in case of point zonal vector : buffer size
gdalpath : string
path of gdal binaries (for system execution)
systemcall : boolean
if True, wrapped raster are stored in working dir
gdalcachemax : string
gdal cache for wrapping operation (in Mb)
"""
if os.path.exists(output):
return
# Get bands or raster number
if len(rasters) != 1:
nbbands = len(rasters)
else:
nbbands = fut.getRasterNbands(rasters[0])
# Prepare and check validity of statistics methods and input raster
paramstats = checkmethodstats(rasters, paramstats, nbbands)
# Get vector file and FID list
vector, idvals = params
# if no vector subsetting (all features)
if not idvals:
idvals = getFidList(vector)
# vector open and iterate features and/or buffer geom
vectorname = os.path.splitext(os.path.basename(vector))[0]
vectorgeomtype = vf.getGeomType(vector)
vectorbuff = None
# Prepare schema of output geopandas dataframe (geometry type and columns formatting)
schema = setPandasSchema(paramstats, vectorgeomtype, bufferDist)
# Buffer Point vector file
if bufferDist and vectorgeomtype in (1, 4, 1001, 1004):
vectorbuff = os.path.join(path, vectorname + "buff.shp")
_ = bfo.bufferPoly(vector, vectorbuff, bufferDist=bufferDist)
# Store input vector in output geopandas dataframe
vectgpad = gpad.read_file(vector)
# Prepare statistics columns of output geopandas dataframe
stats = definePandasDf(vectgpad, idvals, paramstats, classes)
# Iterate FID list
dataset = vf.openToRead(vector)
lyr = dataset.GetLayer()
for idval in idvals:
if vectorgeomtype in (1, 4, 1001, 1004):
if 'val' in list(paramstats.values()):
lyr.SetAttributeFilter("FID=" + str(idval))
for feat in lyr:
geom = feat.GetGeometryRef()
if geom:
xpt, ypt, _ = geom.GetPoint()
# Switch to buffered vector (Point and bufferDist)
if bufferDist:
if vectorbuff:
vector = vectorbuff
# creation of wrapped rasters
success, bands = extractRasterArray(rasters, paramstats, vector,
vectorgeomtype, idval, gdalpath,
gdalcachemax, systemcall, path)
if success:
if 'val' in list(paramstats.values()):
stats = extractPixelValue(rasters, bands, paramstats, xpt, ypt,
stats, idval)
else:
stats = computeStats(bands, paramstats, stats, idval, nodata)
else:
print(
"gdalwarp problem for feature %s (geometry error, too small area, etc.)"
% (idval))
# Prepare columns name and format of output dataframe
if "rate" in list(paramstats.values()) and classes != "":
stats, schema = formatDataFrame(stats, schema, True, classes)
else:
stats, schema = formatDataFrame(stats, schema)
# exportation
dataframeExport(stats, output, schema)