def RetrieveData(Date, args):
"""
This function retrieves MOD13 NDVI data for a given date from the
http://e4ftl01.cr.usgs.gov/ server.
Keyword arguments:
Date -- 'yyyy-mm-dd'
args -- A list of parameters defined in the DownloadData function.
"""
# WAPOR modules
import pyWAPOR.Functions.Processing_Functions as PF
# Argument
[
output_folder, TilesVertical, TilesHorizontal, latlim, lonlim,
username, password, hdf_library
] = args
# Define output file
NDVIfileName = os.path.join(
output_folder, 'NDVI_MYD13Q1_-_16-daily_' + Date.strftime('%Y') + '.' +
Date.strftime('%m') + '.' + Date.strftime('%d') + '.tif')
if not os.path.exists(NDVIfileName):
# Collect the data from the MODIS webpage and returns the data and lat and long in meters of those tiles
try:
Collect_data(TilesHorizontal, TilesVertical, Date, username,
password, output_folder, hdf_library)
# Define the output name of the collect data function
name_collect = os.path.join(output_folder, 'Merged.tif')
# Reproject the MODIS product to epsg_to
epsg_to = '4326'
name_reprojected = PF.reproject_MODIS(name_collect, epsg_to)
# Clip the data to the users extend
data, geo = PF.clip_data(name_reprojected, latlim, lonlim)
# Save results as Gtiff
PF.Save_as_tiff(name=NDVIfileName,
data=data,
geo=geo,
projection='WGS84')
# remove the side products
os.remove(name_collect)
os.remove(name_reprojected)
except:
print("Was not able to download the file")
return True
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 ()
def RetrieveData(Date, args):
"""
This function retrieves MYD11 LST data for a given date from the
https://e4ftl01.cr.usgs.gov/ server.
Keyword arguments:
Date -- 'yyyy-mm-dd'
args -- A list of parameters defined in the DownloadData function.
"""
# WAPOR modules
import pyWAPOR.Functions.Processing_Functions as PF
# Argument
[output_folder, TilesVertical, TilesHorizontal,lonlim, latlim, username, password, hdf_library] = args
# Define output names
LSTfileName = os.path.join(output_folder, 'LST_MYD11A1_K_daily_' + Date.strftime('%Y') + '.' + Date.strftime('%m') + '.' + Date.strftime('%d') + '.tif')
TimefileName = os.path.join(output_folder, 'Time_MYD11A1_hour_daily_' + Date.strftime('%Y') + '.' + Date.strftime('%m') + '.' + Date.strftime('%d') + '.tif')
OnsangfileName = os.path.join(output_folder, 'Angle_MYD11A1_degrees_daily_' + Date.strftime('%Y') + '.' + Date.strftime('%m') + '.' + Date.strftime('%d') + '.tif')
if not (os.path.exists(LSTfileName) and os.path.exists(TimefileName)):
# Collect the data from the MODIS webpage and returns the data and lat and long in meters of those tiles
try:
Collect_data(TilesHorizontal, TilesVertical, Date, username, password, output_folder, hdf_library)
# Define the output name of the collect data function
name_collect = os.path.join(output_folder, 'Merged.tif')
name_collect_time = os.path.join(output_folder, 'Merged_Time.tif')
name_collect_obsang = os.path.join(output_folder, 'Merged_Obsang.tif')
# Reproject the MODIS product to epsg_to
epsg_to ='4326'
name_reprojected = PF.reproject_MODIS(name_collect, epsg_to)
name_reprojected_time = PF.reproject_MODIS(name_collect_time, epsg_to)
name_reprojected_obsang = PF.reproject_MODIS(name_collect_obsang, epsg_to)
# Clip the data to the users extend
data, geo = PF.clip_data(name_reprojected, latlim, lonlim)
data_time, geo = PF.clip_data(name_reprojected_time, latlim, lonlim)
data_obsang, geo = PF.clip_data(name_reprojected_obsang, latlim, lonlim)
# remove wrong values
data[data==0.] = -9999
data_time[data_time==25.5] = -9999
# Save results as Gtiff
PF.Save_as_tiff(name=LSTfileName, data=data, geo=geo, projection='WGS84')
PF.Save_as_tiff(name=TimefileName, data=data_time, geo=geo, projection='WGS84')
PF.Save_as_tiff(name=OnsangfileName, data=data_obsang, geo=geo, projection='WGS84')
# remove the side products
os.remove(name_collect)
os.remove(name_reprojected)
os.remove(name_collect_time)
os.remove(name_reprojected_time)
os.remove(name_collect_obsang)
os.remove(name_reprojected_obsang)
except:
print("Was not able to download the file")
return True