import os
import matplotlib as mpl
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
from erinn.python.utils.io_utils import read_pkl, read_config_file
config_file = os.path.join('..', 'config', 'config.yml')
preprocess_dir = 'noise_10'
train_dir = os.path.join('..', 'data', preprocess_dir, 'train')
valid_dir = os.path.join('..', 'data', preprocess_dir, 'valid')
test_dir = os.path.join('..', 'data', preprocess_dir, 'test')
config = read_config_file(config_file)
iterator_train = os.scandir(train_dir)
iterator_valid = os.scandir(valid_dir)
iterator_test = os.scandir(test_dir)
num = 2
# inspired by https://joseph-long.com/writing/colorbars/
params = {
'image.origin': 'upper',
'image.interpolation': 'nearest',
'image.cmap': 'jet',
'axes.grid': False,
'savefig.dpi': 150, # to adjust notebook inline plot size
'axes.labelsize': 8, # fontsize for x and y labels (was 10)
'axes.titlesize': 8,
'font.size': 8, # was 10
'legend.fontsize': 6, # was 10
from sklearn.externals import joblib
from erinn.python.utils.io_utils import read_pkl, read_config_file
#%% setting
# io
train_dir = '../data/raw_data/train'
test_dir = '../data/raw_data/train'
# random forest
num_train = 1000
num_test = 100
num_cpu = os.cpu_count()
num_tree = 3
random_seed = 42
# plot
config = read_config_file('../config/config.yml')
nx = config['nx']
nz = config['nz']
limit = 5
#%% Read data
X_train = np.array([], dtype='float64')
y_train = np.array([], dtype='float64')
X_test = np.array([], dtype='float64')
y_test = np.array([], dtype='float64')
for i in range(num_train):
filename = os.path.join(train_dir, f'raw_data_{i+1}.pkl')
print(filename)
data = read_pkl(filename)
X_train = np.vstack(