#BSD 3-Clause License # #Copyright (c) 2022, FourCastNet authors #All rights reserved. # #Redistribution and use in source and binary forms, with or without #modification, are permitted provided that the following conditions are met: # #1. Redistributions of source code must retain the above copyright notice, this # list of conditions and the following disclaimer. # #2. Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # #3. Neither the name of the copyright holder nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # #THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" #AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE #IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE #DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE #FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL #DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR #SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER #CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, #OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE #OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # #The code was authored by the following people: # #Jaideep Pathak - NVIDIA Corporation #Shashank Subramanian - NERSC, Lawrence Berkeley National Laboratory #Peter Harrington - NERSC, Lawrence Berkeley National Laboratory #Sanjeev Raja - NERSC, Lawrence Berkeley National Laboratory #Ashesh Chattopadhyay - Rice University #Morteza Mardani - NVIDIA Corporation #Thorsten Kurth - NVIDIA Corporation #David Hall - NVIDIA Corporation #Zongyi Li - California Institute of Technology, NVIDIA Corporation #Kamyar Azizzadenesheli - Purdue University #Pedram Hassanzadeh - Rice University #Karthik Kashinath - NVIDIA Corporation #Animashree Anandkumar - California Institute of Technology, NVIDIA Corporation import torch import numpy as np import h5py years = [1979, 1989, 1999, 2004, 2010] global_means = np.zeros((1,21,1,1)) global_stds = np.zeros((1,21,1,1)) time_means = np.zeros((1,21,721, 1440)) for ii, year in enumerate(years): with h5py.File('/pscratch/sd/s/shas1693/data/era5/train/'+ str(year) + '.h5', 'r') as f: rnd_idx = np.random.randint(0, 1460-500) global_means += np.mean(f['fields'][rnd_idx:rnd_idx+500], keepdims=True, axis = (0,2,3)) global_stds += np.var(f['fields'][rnd_idx:rnd_idx+500], keepdims=True, axis = (0,2,3)) global_means = global_means/len(years) global_stds = np.sqrt(global_stds/len(years)) time_means = time_means/len(years) np.save('/pscratch/sd/s/shas1693/data/era5/global_means.npy', global_means) np.save('/pscratch/sd/s/shas1693/data/era5/global_stds.npy', global_stds) np.save('/pscratch/sd/s/shas1693/data/era5/time_means.npy', time_means) print("means: ", global_means) print("stds: ", global_stds)