def main():
etl()
input_file_path = "{}/{}".format(ec2_input_path, INPUT_FILE_NAME)
nc_fid = netCDF4.Dataset(input_file_path, 'r')
nc_attrs, nc_dims, nc_vars = aqueduct3.ncdump(nc_fid)
print(nc_attrs, nc_dims, nc_vars)
y_dimension = nc_fid.variables["lat"].shape[0]
x_dimension = nc_fid.variables["lon"].shape[0]
default_geotransform, default_geoprojection = aqueduct3.get_global_georeference(
np.ones([y_dimension, x_dimension]))
Z = nc_fid.variables[EXPORT_VARIABLE][:, :]
Z = np.flipud(Z)
aqueduct3.write_geotiff(output_file_path,
default_geotransform,
default_geoprojection,
Z,
nodata_value=-9999,
datatype=gdal.GDT_Int32)
get_ipython().system(
'aws s3 cp {ec2_output_path} {s3_output_path} --recursive')
# In[4]:
get_ipython().system('rm -r {ec2_output_path}')
get_ipython().system('mkdir -p {ec2_output_path}')
# In[5]:
"""
This cell loops over the images in a netCDF. There are a couple of PCRGlobWB specific properties so
be careful when using with other netCDFs. PCRGLOBWB specific properties include datatype (float32),
nodata value, time format, minmax value etc.
"""
default_geotransform, default_geoprojection = aqueduct3.get_global_georeference(
np.ones([Y_DIMENSION_5MIN, X_DIMENSION_5MIN]))
for root, dirs, file_names in os.walk(ec2_input_path):
for file_name in file_names:
if file_name.endswith(".nc4") or file_name.endswith(".nc"):
print(file_name)
input_path = os.path.join(root, file_name)
output_path = aqueduct3.netCDF4_to_geotiff(file_name, input_path,
ec2_output_path,
default_geotransform,
default_geoprojection)
# In[ ]:
files = os.listdir(ec2_output_path)
print("Number of files: " + str(len(files)))