"""
归一化常量 - point1_v30
从 data/each-point-V30/point1_v30/meta/stats.json 自动生成
"""
import numpy as np

# ====== 归一化统计量（从 point1_v30 数据集提取）======

# State 归一化参数 (observation.state) - 14D
STATE_MEAN = np.array([
    -0.09762196860431108,
    1.2337136024206918,
    -0.9923270292799721,
    0.05592833124572148,
    0.8331612513095673,
    -0.036255398000828845,
    0.05881635663632821,
    0.0281259402168327,
    -0.024299265685261622,
    -0.18725553942362186,
    -0.021586281663374743,
    0.6060570740223622,
    -0.01889460844828188,
    0.02485340166256719
], dtype=np.float32)

STATE_STD = np.array([
    0.30070223176018124,
    0.8581525143270478,
    0.5713716491712959,
    0.0536020528357504,
    0.19857747515400234,
    0.13798296392826717,
    0.03234785564412015,
    0.005913943080806638,
    0.009138683212383862,
    0.057220981857687904,
    0.017463762164651352,
    0.026474679532916674,
    0.04291397095881692,
    0.033163443154059294
], dtype=np.float32)

# Action 反归一化参数 - 14D (left_arm 7D + right_arm 7D)
ACTION_MEAN = np.array([
    -0.09563466728772782,
    1.2188388391054852,
    -1.0205979984170253,
    0.05980351151645611,
    0.7756429177913368,
    -0.03388108740391065,
    0.05847346337197445,
    0.027242103722957448,
    -0.018979807006473278,
    -0.2058026173405072,
    -0.017844390944858398,
    0.5078671119565988,
    -0.01954643361667616,
    0.014368052743909286
], dtype=np.float32)

ACTION_STD = np.array([
    0.3032518940999695,
    0.8411646899886063,
    0.5740990465705452,
    0.05945644994245482,
    0.23283106709062928,
    0.1427498783238014,
    0.03752145626492375,
    0.0048017492790026995,
    0.017038866556135936,
    0.05621833904816755,
    0.021437398125901518,
    0.023814408890144745,
    0.044538221806866174,
    0.04747235290605119
], dtype=np.float32)

# Image 归一化参数（ImageNet 标准）
IMAGE_MEAN = np.array([0.485, 0.456, 0.406], dtype=np.float32)
IMAGE_STD = np.array([0.229, 0.224, 0.225], dtype=np.float32)