def DownloadData(output_folder, latlim, lonlim):
import pyWAPOR.Functions.Processing_Functions as PF
# Check the latitude and longitude and otherwise set lat or lon on greatest extent
if latlim[0] < -90 or latlim[1] > 90:
print(
'Latitude above 90N or below 90S is not possible. Value set to maximum'
)
latlim[0] = np.max(latlim[0], -90)
latlim[1] = np.min(latlim[1], 90)
if lonlim[0] < -180 or lonlim[1] > 180:
print(
'Longitude must be between 180E and 180W. Now value is set to maximum'
)
lonlim[0] = np.max(lonlim[0], -180)
lonlim[1] = np.min(lonlim[1], 180)
# Check output path and create if not exists
output_folder_end = os.path.join(output_folder, "GlobCover", "Landuse")
if not os.path.exists(output_folder_end):
os.makedirs(output_folder_end)
# Define end output
filename_out_tiff = os.path.join(output_folder_end,
"LC_GLOBCOVER_V2.3.tif")
if not os.path.exists(filename_out_tiff):
# Define url where to download the globcover data
url = r"http://due.esrin.esa.int/files/Globcover2009_V2.3_Global_.zip"
# Create temp folder
output_folder_temp = os.path.join(output_folder, "GlobCover",
"Landuse", "Temp")
if not os.path.exists(output_folder_temp):
os.makedirs(output_folder_temp)
# define output layer
filename_out = os.path.join(output_folder_temp,
"Globcover2009_V2.3_Global_.zip")
# Download the data
urllib.request.urlretrieve(url, filename=filename_out)
# Extract data
PF.Extract_Data(filename_out, output_folder_temp)
# Define extracted tiff file
globcover_filename = os.path.join(
output_folder_temp, "GLOBCOVER_L4_200901_200912_V2.3.tif")
# Open extract file
dest = gdal.Open(globcover_filename)
Array = dest.GetRasterBand(1).ReadAsArray()
# Get information of geotransform and projection
geo = dest.GetGeoTransform()
proj = "WGS84"
# define the spatial ids
Xid = [
np.floor((-geo[0] + lonlim[0]) / geo[1]),
np.ceil((-geo[0] + lonlim[1]) / geo[1])
]
Yid = [
np.floor((geo[3] - latlim[1]) / -geo[5]),
np.ceil((geo[3] - latlim[0]) / -geo[5])
]
# Define the geotransform
Xstart = geo[0] + Xid[0] * geo[1]
Ystart = geo[3] + Yid[0] * geo[5]
geo_out = tuple([Xstart, geo[1], 0, Ystart, 0, geo[5]])
# Clip data out
Array_end = Array[int(Yid[0]):int(Yid[1]), int(Xid[0]):int(Xid[1])]
# Save data as tiff
PF.Save_as_tiff(filename_out_tiff, Array_end, geo_out, proj)
dest = None
# remove temporary folder
shutil.rmtree(output_folder_temp)
def DownloadData(output_folder, latlim, lonlim):
"""
This function downloads DEM data from SRTM
Keyword arguments:
output_folder -- directory of the result
latlim -- [ymin, ymax] (values must be between -60 and 60)
lonlim -- [xmin, xmax] (values must be between -180 and 180)
"""
# WA+ modules
import pyWAPOR.Functions.Processing_Functions as PF
# Check the latitude and longitude and otherwise set lat or lon on greatest extent
if latlim[0] < -60 or latlim[1] > 60:
print(
'Latitude above 60N or below 60S is not possible. Value set to maximum'
)
latlim[0] = np.max(latlim[0], -60)
latlim[1] = np.min(latlim[1], 60)
if lonlim[0] < -180 or lonlim[1] > 180:
print(
'Longitude must be between 180E and 180W. Now value is set to maximum'
)
lonlim[0] = np.max(lonlim[0], -180)
lonlim[1] = np.min(lonlim[1], 180)
# converts the latlim and lonlim into names of the tiles which must be
# downloaded
name, rangeLon, rangeLat = Find_Document_Names(latlim, lonlim)
# Memory for the map x and y shape (starts with zero)
size_X_tot = 0
size_Y_tot = 0
nameResults = []
# Create a temporary folder for processing
output_folder_trash = os.path.join(output_folder, "Temp")
if not os.path.exists(output_folder_trash):
os.makedirs(output_folder_trash)
# Download, extract, and converts all the files to tiff files
for nameFile in name:
try:
# Download the data from
# http://earlywarning.usgs.gov/hydrodata/
output_file, file_name = Download_Data(nameFile,
output_folder_trash)
# extract zip data
PF.Extract_Data(output_file, output_folder_trash)
# The input is the file name and in which directory the data must be stored
file_name_tiff = file_name.replace(".zip", ".tif")
output_tiff = os.path.join(output_folder_trash, file_name_tiff)
# convert data from adf to a tiff file
dest_SRTM = gdal.Open(output_tiff)
geo_out = dest_SRTM.GetGeoTransform()
size_X = dest_SRTM.RasterXSize
size_Y = dest_SRTM.RasterYSize
if (int(size_X) != int(6001) or int(size_Y) != int(6001)):
data = np.ones((6001, 6001)) * -9999
# Create the latitude bound
Vfile = nameFile.split("_")[2][0:2]
Bound2 = 60 - 5 * (int(Vfile) - 1)
# Create the longitude bound
Hfile = nameFile.split("_")[1]
Bound1 = -180 + 5 * (int(Hfile) - 1)
Expected_X_min = Bound1
Expected_Y_max = Bound2
Xid_start = int(
np.round((geo_out[0] - Expected_X_min) / geo_out[1]))
Xid_end = int(
np.round(
((geo_out[0] + size_X * geo_out[1]) - Expected_X_min) /
geo_out[1]))
Yid_start = int(
np.round((Expected_Y_max - geo_out[3]) / (-geo_out[5])))
Yid_end = int(
np.round((Expected_Y_max - (geo_out[3] +
(size_Y * geo_out[5]))) /
(-geo_out[5])))
data_SRTM = dest_SRTM.GetRasterBand(1).ReadAsArray()
data[Yid_start:Yid_end, Xid_start:Xid_end] = data_SRTM
if np.max(data) == 255:
data[data == 255] = -9999
data[data < -9999] = -9999
geo_in = [
Bound1 - 0.5 * 0.00083333333333333, 0.00083333333333333,
0.0, Bound2 + 0.5 * 0.00083333333333333, 0.0,
-0.0008333333333333333333
]
# save chunk as tiff file
PF.Save_as_tiff(name=output_tiff,
data=data,
geo=geo_in,
projection="WGS84")
dest_SRTM = None
except:
# If tile not exist create a replacing zero tile (sea tiles)
file_name_tiff = file_name.replace(".zip", ".tif")
output_tiff = os.path.join(output_folder_trash, file_name_tiff)
file_name = nameFile
data = np.ones((6001, 6001)) * -9999
data = data.astype(np.float32)
# Create the latitude bound
Vfile = nameFile.split("_")[2][0:2]
Bound2 = 60 - 5 * (int(Vfile) - 1)
# Create the longitude bound
Hfile = nameFile.split("_")[1]
Bound1 = -180 + 5 * (int(Hfile) - 1)
# Geospatial data for the tile
geo_in = [
Bound1 - 0.5 * 0.00083333333333333, 0.00083333333333333, 0.0,
Bound2 + 0.5 * 0.00083333333333333, 0.0,
-0.0008333333333333333333
]
# save chunk as tiff file
PF.Save_as_tiff(name=output_tiff,
data=data,
geo=geo_in,
projection="WGS84")
# clip data
Data, Geo_data = PF.clip_data(output_tiff, latlim, lonlim)
size_Y_out = int(np.shape(Data)[0])
size_X_out = int(np.shape(Data)[1])
# Total size of the product so far
size_Y_tot = int(size_Y_tot + size_Y_out)
size_X_tot = int(size_X_tot + size_X_out)
if nameFile is name[0]:
Geo_x_end = Geo_data[0]
Geo_y_end = Geo_data[3]
else:
Geo_x_end = np.min([Geo_x_end, Geo_data[0]])
Geo_y_end = np.max([Geo_y_end, Geo_data[3]])
# create name for chunk
FileNameEnd = "%s_temporary.tif" % (file_name)
nameForEnd = os.path.join(output_folder_trash, FileNameEnd)
nameResults.append(str(nameForEnd))
# save chunk as tiff file
PF.Save_as_tiff(name=nameForEnd,
data=Data,
geo=Geo_data,
projection="WGS84")
size_X_end = int(size_X_tot / len(rangeLat)) + 1 #!
size_Y_end = int(size_Y_tot / len(rangeLon)) + 1 #!
# Define the georeference of the end matrix
geo_out = [Geo_x_end, Geo_data[1], 0, Geo_y_end, 0, Geo_data[5]]
latlim_out = [geo_out[3] + geo_out[5] * size_Y_end, geo_out[3]]
lonlim_out = [geo_out[0], geo_out[0] + geo_out[1] * size_X_end]
# merge chunk together resulting in 1 tiff map
datasetTot = Merge_DEM(latlim_out, lonlim_out, nameResults, size_Y_end,
size_X_end)
datasetTot[datasetTot < -9999] = -9999
# name of the end result
output_DEM_name = "DEM_SRTM_m_3s.tif"
Save_name = os.path.join(output_folder, output_DEM_name)
# Make geotiff file
PF.Save_as_tiff(name=Save_name,
data=datasetTot,
geo=geo_out,
projection="WGS84")
os.chdir(output_folder)
# Delete the temporary folder
try:
shutil.rmtree(output_folder_trash)
except:
pass
return ()