generate_dataset.py

# Packages 
import warnings
warnings.simplefilter('ignore')

import argparse
import sys, os
import gc
import shutil

import multiprocessing
from multiprocessing import Pool

import matplotlib.pyplot as plt
from scipy.stats import qmc
import numpy as np
import glob
import h5py
import fcntl
import time
from time import sleep
from pathlib import Path
import datetime

# Parallize
try:
    from mpi4py import MPI
    comm = MPI.COMM_WORLD
    rank = comm.Get_rank()
    size = comm.Get_size()
except ImportError:
    rank = 0
    size = 1

str_pad_len = 80
str_pad_type = '-'

class Generator():
    def __init__(self, params_ranges, **kwargs):
        """
        Generate dataset by 21cmFAST in parallel.
        Input: params_ranges = {'param1': [min, max], 'param2': [min, max], ...}
        Output: hdf5 storing images and params.
        """
        self.import_py21cmfast()
        self.params_ranges = params_ranges.copy()
        # self.kwargs['num_images'] = num_images
        self.define_kwargs(kwargs)

        # print parameters information
        if rank == 0:
            self.print_kwargs_params()

    def import_py21cmfast(self):
        # py21cmfast will create ~/21cmFAST-cache/wisdoms automatically.
        # To avoid conflicts between processes, it's necessary to do:
        self.default_cache_direc = None
        global p21c
        
        if rank == 0:
            import py21cmfast as p21c
            self.default_cache_direc = os.path.join(Path.home(),"21cmFAST-cache")
            
            # if not os.path.exists(os.path.join(self.default_cache_direc,'wisdoms')):
            #     os.mkdir(os.path.join(self.default_cache_direc,'wisdoms'))
            os.makedirs(os.path.join(self.default_cache_direc,'wisdoms'), exist_ok=True)
            # print(rank, "wisdoms has been made.")

        # print("'comm' in globals():", 'comm' in globals(), rank)
        if 'comm' in globals():
            # print(rank, f"default_cache_direc {self.default_cache_direc} bcast starts.")
            self.default_cache_direc = comm.bcast(self.default_cache_direc, root=0)
            # print(rank, f"default_cache_direc {self.default_cache_direc} has been bcasted.")
            import py21cmfast as p21c

    @property
    def params_ranges(self):
        if not hasattr(self, '_params_ranges'):
            self._params_ranges = "Error."
        return self._params_ranges

    @params_ranges.setter
    def params_ranges(self, value):
        self._params_ranges = value
        for key, value in self._params_ranges.items():
            if type(value) != list:
                self._params_ranges[key] = [value]

    def print_kwargs_params(self):
        if self.kwargs['verbose'] >= 1:
            print(f" Mission: Generate {self.kwargs['num_images']} images by {size}*{self.kwargs['cpus_per_node']} CPUs ".center(str_pad_len, '#'))#, str_pad_type))
            print(f" params: ".center(int(str_pad_len/2),str_pad_type)+f" ranges: ".center(int(str_pad_len/2),str_pad_type))
            for key in self.params_ranges:
                print(f"{key}".center(int(str_pad_len/2))+f"[{self.params_ranges[key][0]}, {self.params_ranges[key][-1]}]".center(int(str_pad_len/2)))
        
        if self.kwargs['verbose'] >= 2:
            print(f" kwargs: ".center(int(str_pad_len/2), str_pad_type)+f" values: ".center(int(str_pad_len/2),str_pad_type))
            
            for key in self.kwargs:
                print(f"{key}".center(int(str_pad_len/2))+f"{self.kwargs[key]}".center(int(str_pad_len/2)))


    def define_kwargs(self, kwargs):
        self.kwargs = dict(
            # local params for Generator.__init__()
            p21c_run = 'lightcone',
            num_images = 9,
            fields = ['brightness_temp',],
            verbose = 2,
            seed = None,
            save_direc_name = "21cmDataset.h5",
            # cache_direc = "_cache",

            # strength param of scipy.stats.qmc.LatinHypercube():
            strength = 1,
            
            # redshift param of py21cmfast.run_coeval():
            redshift = [8,10],
            # max_redshift = 20,
            
            # user_params of py21cmfast.run_coeval():
            BOX_LEN = 150,
            HII_DIM = 60, 
            USE_INTERPOLATION_TABLES = True,
            USE_TS_FLUCT = True,
            
            # cosmo_params of py21cmfast.run_coeval():
            SIGMA_8 = 0.810,
            hlittle = 0.677,
            OMm = 0.310,
            OMb = 0.0490,
            POWER_INDEX = 0.967,

            # write = False,
            cpus_per_node = len(os.sched_getaffinity(0)),
            cache_rmdir = True,
        )

        # update
        self.kwargs = self.kwargs | kwargs

        if type(self.kwargs['redshift']) != list:
            self.kwargs['redshift'] = [self.kwargs['redshift']]

        if type(self.kwargs['fields']) != list:
            self.kwargs['fields'] = [self.kwargs['fields']]
 
        if self.kwargs['num_images'] < size:
            if self.kwargs['verbose'] > 0: print(f"num_images {self.kwargs['num_images']} must be >= the number of nodes {size}.")
            self.kwargs['num_images'] = size
        
        if 'cache_direc' not in self.kwargs:
            self.kwargs['cache_direc'] = os.path.join(
                os.path.dirname(self.kwargs['save_direc_name']),
                '_cache', str(rank),
                )

        if not os.path.exists(self.kwargs['cache_direc']) and self.kwargs['write']:
            os.makedirs(self.kwargs['cache_direc'])
        p21c.config['direc'] = self.kwargs['cache_direc']

        if 'write' not in self.kwargs:
            self.kwargs['write'] = self.kwargs['seed'] != None

    def sample_normalized_params(self):
        """
        sample and scatter to other nodes
        """
        np.random.seed(self.kwargs['seed'])
        if rank == 0:
            sampler = qmc.LatinHypercube(d=len(self.params_ranges), strength=self.kwargs['strength'], seed=np.random.default_rng(self.kwargs['seed']))
            sample = sampler.random(n=self.kwargs['num_images'])
            send_data = np.array_split(sample, size, axis=0)

            if self.kwargs['verbose'] >= 2:
                print(f" Process {rank} scatters data {sample.shape} to {size} nodes ".center(str_pad_len,str_pad_type))
        else:
            send_data = None
        
        if 'comm' in globals():
            recv_data = comm.scatter(send_data, root=0)
        else:
            recv_data = send_data

        return recv_data


    def denormalize(self, normalized_data):
        """
        denormalize data received, and return self.params_node which stores params for each node.
        """
        self.params_node = {}
        for i, kind in enumerate(self.params_ranges):
            x = normalized_data.T[i]
            k = self.params_ranges[kind][-1]-self.params_ranges[kind][0]
            b = self.params_ranges[kind][0]
            self.params_node[kind] = k*x + b


    def return_coeval_or_lightcone(self, kwargs_params_cpu, random_seed):
        if self.kwargs['p21c_run'] == 'coeval':
            coevals_cpu = p21c.run_coeval(
                redshift = kwargs_params_cpu['redshift'],
                user_params = kwargs_params_cpu,
                cosmo_params = p21c.CosmoParams(kwargs_params_cpu),
                astro_params = p21c.AstroParams(kwargs_params_cpu),
                flag_options = p21c.FlagOptions(kwargs_params_cpu),
                random_seed = random_seed,
                write = kwargs_params_cpu['write'],
            )
            dict_cpu = self.coevals2dict(coevals_cpu)
            del coevals_cpu

        elif self.kwargs['p21c_run'] == 'lightcone':
            lightcone_cpu = p21c.run_lightcone(
                redshift = kwargs_params_cpu['redshift'][0],
                #max_redshift = kwargs_params_cpu['redshift'][-1],
                z_heat_max = kwargs_params_cpu['redshift'][-1],
                lightcone_quantities = kwargs_params_cpu['fields'],
                user_params = kwargs_params_cpu,
                cosmo_params = p21c.CosmoParams(kwargs_params_cpu),
                astro_params = p21c.AstroParams(kwargs_params_cpu),
                flag_options = p21c.FlagOptions(kwargs_params_cpu),
                random_seed = random_seed,
                write = kwargs_params_cpu['write'],
            )
            # self.kwargs['node_redshifts'] = lightcone_cpu.node_redshifts
            # print(lightcone_cpu.lightcone_redshifts[-5:])
            dict_cpu = self.lightcone2dict(lightcone_cpu)
            del lightcone_cpu
        
        gc.collect()

        return dict_cpu


    def pool_run(self, params_node_value):
        # All parameters
        pool_run_start = time.perf_counter()

        pid_cpu = multiprocessing.current_process().pid

        random_seed = int(params_node_value[-1])
        params_cpu = {key: params_node_value[i] for (i, key) in enumerate(self.params_node.keys())}
        # self.update_params()

        # concantenate parameters and kwargs
        kwargs_params_cpu = self.kwargs | params_cpu

        # Simulation
        dict_cpu = self.return_coeval_or_lightcone(kwargs_params_cpu,random_seed)

        # Clear cache
        cache_pattern = os.path.join(self.kwargs['cache_direc'], f"*r{random_seed}.h5")
        for filename in glob.glob(cache_pattern):
            os.remove(filename)

        pool_run_end = time.perf_counter()
        
        time_elapsed = time.strftime("%H:%M:%S", time.gmtime(pool_run_end - pool_run_start))

        async_save_time = self.async_save(dict_cpu, np.expand_dims(params_node_value, axis=0))

        if self.kwargs['verbose'] > 2:
            print(f'cpu {pid_cpu}-{rank}, {time_elapsed}, {async_save_time}, params {list(params_cpu.values())}, seed {random_seed}')

        # return dict_cpu


    def lightcone2dict(self, lightcone_cpu):
        images_cpu = {}
        for i, field in enumerate(self.kwargs['fields']):
            images_cpu[field] = np.expand_dims(lightcone_cpu.lightcones[field], axis=0)

        images_cpu["redshifts_distances"] = np.vstack((lightcone_cpu.lightcone_redshifts, lightcone_cpu.lightcone_distances))

        return images_cpu
        

    def coevals2dict(self, coevals_cpu):
        images_cpu = {}
        for i, field in enumerate(self.kwargs['fields']):
            images_cpu[field] = []
            for j, coeval in enumerate(coevals_cpu):
                images_cpu[field].append(coeval.__dict__[field])
        return images_cpu

    def cache_rmdir(self):
        # print(self.kwargs['cache_direc'], "starts")
        if os.path.exists(self.kwargs['cache_direc']) and len(os.listdir(self.kwargs['cache_direc'])) == 0:
            os.rmdir(self.kwargs['cache_direc'])

        if 'comm' in globals():
            # print(rank, "comm to be gathered.")
            recv_data = comm.gather(rank, root=0)
            # print(rank, "comm has been gathered.")

        if rank == 0:
            if os.path.exists(os.path.dirname(self.kwargs['cache_direc'])) and len(os.listdir(os.path.dirname(self.kwargs['cache_direc']))) == 0:
                os.rmdir(os.path.dirname(self.kwargs['cache_direc']))
            if os.path.exists(self.default_cache_direc) and len(os.listdir(self.default_cache_direc)) == 1:
                # print(rank, f"default_cache_direc {self.default_cache_direc} to be removed!!!!!!!")
                shutil.rmtree(self.default_cache_direc)
                # print(rank, f"default_cache_direc {self.default_cache_direc} has been removed!!!!!!")


    def run(self):
        #if rank == 0:
        normalized_params = self.sample_normalized_params()
        self.denormalize(normalized_params)

        pid_node = os.getpid()
        # cpus_per_node = len(os.sched_getaffinity(pid_node))
        cpus_per_node = self.kwargs['cpus_per_node']
        
        if self.kwargs['verbose'] >= 3:
            print(f" node {rank}: {cpus_per_node} CPUs, params.shape {np.array(list(self.params_node.values())).T.shape} ".center(str_pad_len,str_pad_type))

        iterables = np.array(list(self.params_node.values()))
        random_seeds = np.random.randint(1,2**63, size = iterables.shape[-1])
        iterables = np.vstack((iterables, random_seeds)).T

        # run p21c.run_coeval in parallel on multi-CPUs
        loop_num = np.ceil(iterables.shape[0]/cpus_per_node)
        for iterable in np.array_split(iterables, loop_num, axis=0):
            with Pool(cpus_per_node) as p:
                Pool_start = time.perf_counter()
                # dict_node = p.map(self.pool_run, iterable)
                p.map(self.pool_run, iterable)
                # images_node, images_node_MB = self.dict2images(dict_node)
                Pool_end = time.perf_counter()
                time_elapsed = time.strftime("%H:%M:%S", time.gmtime(Pool_end - Pool_start))

                # save images, params as .h5 file
                # async_save_time = self.async_save(images_node, iterable)

                if self.kwargs['verbose'] >= 2 and False:
                    print(f"{time_elapsed}, node {rank}: {images_node_MB} MB images {[np.shape(images_node[field]) for field in self.kwargs['fields']]} ->{async_save_time}-> {os.path.basename(self.kwargs['save_direc_name'])}")
                
        if self.kwargs['cache_rmdir'] == True:
            self.cache_rmdir()

    def dict2images(self, dict_node):
        images_node = {}
        images_node_MB = []
        for field in self.kwargs['fields']:
            images_node[field] = []
            for dict_cpu in dict_node:
                images_node[field].append(dict_cpu[field])
            images_node[field] = np.array(images_node[field])
            images_node_MB.append(round(images_node[field].nbytes / 1024**2))
        
        if 'redshifts_distances' in dict_cpu:
            images_node['redshifts_distances'] = dict_cpu['redshifts_distances']

        return images_node, images_node_MB

    def async_save(self, images_node, params_seeds):
        try_start = time.perf_counter()
        while True:
            try:
                save_start = time.perf_counter()
                try_time = save_start - try_start
                self.save(images_node, params_seeds)
                save_end = time.perf_counter()
                save_time = save_end - save_start
                return f"{try_time:.1f}s/{save_time:.2f}s"
                # break
            except IOError or BlockingIOError:
                if try_time > 30:
                    print(f"cpu {multiprocessing.current_process().pid}-{rank}, try_time = {try_time:.2f} sec")
                    sleep(5)
                else:
                    sleep(0.1)

    # Save as hdf5
    def save(self, images_node, params_seeds):
        #max_num_images = None # self.kwargs['num_images']
        max_num_images = self.kwargs['num_images']
        #print(f"max_num_images = {max_num_images}")
        with h5py.File(self.kwargs['save_direc_name'], 'a') as f:
            if 'kwargs' not in f.keys():
                keys = list(self.kwargs)
                values = [str(value) for value in self.kwargs.values()]
                data = np.transpose(list((keys, values)))
                data = data.tolist()
                f.create_dataset('kwargs', data=data)

            if 'params' not in f.keys():
                grp = f.create_group('params') 
                grp['keys'] = list(self.params_ranges)
                grp.create_dataset(
                    'values',
                    data = params_seeds[:,:-1],
                    maxshape = tuple((max_num_images,) + params_seeds[:,:-1].shape[1:]),
                    )
            else:
                new_size = f['params']['values'].shape[0] + params_seeds.shape[0]
                f['params']['values'].resize(new_size, axis=0)
                f['params']['values'][-params_seeds.shape[0]:] = params_seeds[:,:-1]


            #seeds = np.expand_dims(params_seeds[:,-1], axis=-1)
            seeds = params_seeds[:,-1]            
            if 'seeds' not in f.keys():
                #grp = f.create_group('seeds') 
                #grp['keys'] = list(self.params_ranges) + ['seed']
                f.create_dataset(
                    'seeds',
                    data = seeds.astype(np.int64),
                    #maxshape = tuple((None,) + seeds.shape[1:]),
                    maxshape = (max_num_images,),
                    )
            else:
                new_size = f['seeds'].shape[0] + seeds.shape[0]
                f['seeds'].resize(new_size, axis=0)
                f['seeds'][-seeds.shape[0]:] = seeds.astype(np.int64) 


            if 'redshifts_distances' not in f.keys() and 'redshifts_distances' in images_node:
                f.create_dataset('redshifts_distances', data=images_node['redshifts_distances'])

            for field in self.kwargs['fields']:
                images = images_node[field]
                if field not in f.keys():
                    f.create_dataset(
                        field, 
                        data=images, 
                        maxshape= tuple((max_num_images,) + images.shape[1:])
                    )
                else:
                    new_size = f[field].shape[0] + images.shape[0]
                    f[field].resize(new_size, axis=0)
                    f[field][-images.shape[0]:] = images

if __name__ == '__main__':
    parser = argparse.ArgumentParser(description="generating 21cm dataset")
    parser.add_argument('--num_images', type=int, default=4)
    parser.add_argument('--BOX_LEN', type=int, default=64)
    parser.add_argument('--HII_DIM', type=int, default=128)
    parser.add_argument('--NON_CUBIC_FACTOR', type=int, default=1)
    parser.add_argument('--cpus_per_node', type=int, default=len(os.sched_getaffinity(0)))
    parser.add_argument('--save_direc', type=str, default='.')
    args = parser.parse_args()

    params_ranges = dict(
        ION_Tvir_MIN = 4.8,#5.477,#4.699,#5.6,#4.4, #[4,6],
        HII_EFF_FACTOR = 131.341,#200,#30,#19.037,#131.341, #[10, 250],
        )

    kwargs = dict(
        num_images=args.num_images,#2400,#30000,
        fields = ['brightness_temp', 'density', 'xH_box'],
        BOX_LEN = args.BOX_LEN,#128,
        HII_DIM = args.HII_DIM, 
        verbose = 3, 
        redshift = [7.51, 21.02],#11.93],
        NON_CUBIC_FACTOR = args.NON_CUBIC_FACTOR,
        write = True,
        cpus_per_node = args.cpus_per_node,#10,#112,#20,
        cache_rmdir = False,
        )

    now = datetime.datetime.now().strftime("%m%d-%H%M%S")
    save_name = f"LEN{kwargs['BOX_LEN']}-DIM{kwargs['HII_DIM']}-CUB{kwargs['NON_CUBIC_FACTOR']}-Tvir{params_ranges['ION_Tvir_MIN']}-zeta{params_ranges['HII_EFF_FACTOR']}-{now}.h5"
    kwargs['save_direc_name'] = os.path.join(args.save_direc, save_name)

    generator = Generator(params_ranges, **kwargs)
    generator.run()

    print(f"rank {rank} completed!")
    
    # # testing set, (5*800, 64, 64, 64)
    # params_list = [(4.4,131.341),(5.6,19.037)]#, (4.699,30), (5.477,200), (4.8,131.341)]

    # kwargs = dict(
    #     # p21c_run = 'coeval',
    #     fields = ['brightness_temp', 'density'],
    #     HII_DIM=64, BOX_LEN=60,
    #     verbose=2, redshift=[9,10],
    #     num_images=32,
    #     )
    # for T_vir, zeta in params_list:
    #     params_ranges = dict(
    #     ION_Tvir_MIN = T_vir, # single number is ok,
    #     HII_EFF_FACTOR = [zeta], # list of single number is ok,
    #     save_direc_name=os.path.join(save_direc,"test.h5"),
    #     )
    #     generator = Generator(params_ranges, **kwargs)
    #     generator.run()