import platform

import pwd

import subprocess

import time

import numpy as np

import glob

import os

import cPickle as pickle

maxfloat = np.finfo(np.float32).max

def auto_make_dir(path):

    if not os.path.exists(path):

        os.makedirs(path)

        print 'Created dir', path

def find_model_metadata(metadata_dir, config_name):

    metadata_paths = glob.glob(metadata_dir + '/%s-*' % config_name)

    if not metadata_paths:

        raise ValueError('No metadata files for config %s' % config_name)

    elif len(metadata_paths) > 1:

        raise ValueError('Multiple metadata files for config %s' % config_name)

    print 'Loaded model from', metadata_paths[0]

    return metadata_paths[0]

def get_train_valid_split(train_data_path):

    filename = 'valid_split.pkl'

    # if not os.path.isfile(filename):

    #     print 'Making validation split'

    #     create_validation_split.save_train_validation_ids(filename, train_data_path)

    return load_pkl(filename)

def check_data_paths(data_path):

    if not os.path.isdir(data_path):

        raise ValueError('wrong path to DICOM data')

def get_dir_path(dir_name, root_dir, no_name=True):

    if no_name:

        username = ''

    else:

        username = pwd.getpwuid(os.getuid())[0]

    dir_path = root_dir + '/' + dir_name + '/%s' % username

    if not os.path.isdir(dir_path):

        os.makedirs(dir_path)

    return dir_path

def hms(seconds):

    seconds = np.floor(seconds)

    minutes, seconds = divmod(seconds, 60)

    hours, minutes = divmod(minutes, 60)

    return "%02d:%02d:%02d" % (hours, minutes, seconds)

def timestamp():

    return time.strftime("%Y%m%d-%H%M%S", time.localtime())

def hostname():

    return platform.node()

def generate_expid(arch_name):

    return "%s-%s" % (arch_name, timestamp())

def get_git_revision_hash():

    try:

        return subprocess.check_output(['git', 'rev-parse', 'HEAD']).strip()

    except:

        return 0

def save_pkl(obj, path):

    with open(path, 'wb') as f:

        pickle.dump(obj, f)

def load_pkl(path):

    with open(path, 'rb') as f:

        obj = pickle.load(f)

    return obj

def save_np(obj, path):

    np.save(file=path, arr=obj, fix_imports=True)

def load_np(path):

    return np.load(path)

def copy(from_folder, to_folder):

    command = "cp -r %s %s/." % (from_folder, to_folder)

    print command

    os.system(command)

def current_learning_rate(schedule, idx):

    s = schedule.keys()

    s.sort()

    current_lr = schedule[0]

    for i in s:

        if idx >= i:

            current_lr = schedule[i]

    return current_lr

def get_script_name(file_path):

    return os.path.basename(file_path).replace('.py', '')