import numpy as np

DATA_DIR = "../data"
OUT_DIR = "./out"
MODEL_DIR = "./models"

NB_THREADS = 1024
NB_THREADS_2D = (32, 32)
NB_THREADS_3D = (16, 16, 4)
M = int(np.log2(NB_THREADS_2D[1]))

# Save state to avoid recalulation on restart
SAVE_STATE = True
# Redo the state even if it's already saved
FORCE_REDO = False
# Use NJIT to greatly accelerate runtime
COMPILE_WITH_C = False
# Use GPU to greatly accelerate runtime (as priority over NJIT)
GPU_BOOSTED = True
# Number of weak classifiers
# TS = [1]
# TS = [1, 5, 10]
# TS = [1, 5, 10, 25, 50]
# TS = [1, 5, 10, 25, 50, 100, 200]
# TS = [1, 5, 10, 25, 50, 100, 200, 300]
TS = [1, 5, 10, 25, 50, 100, 200, 300, 400, 500, 1000]
# Enable verbose output (for debugging purposes)
__DEBUG = False
# Debugging options
if __DEBUG:
	IDX_INSPECT = 4548
	IDX_INSPECT_OFFSET = 100
	np.seterr(all = 'raise')
	# Debug option (image width * log_10(length) + extra characters)
	np.set_printoptions(linewidth = 19 * 6 + 3)