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import numpy as np
import os
from scipy import stats
from scipy.spatial import cKDTree
from scipy.ndimage import binary_dilation
from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error
from skimage.metrics import structural_similarity as ssim
from tqdm import trange
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import matplotlib.cm as cm
import matplotlib
matplotlib.rcParams['font.sans-serif'] = ['Arial']
matplotlib.rcParams['font.size'] = 16
class DataLoader:
'''
Fucntions:
1. load_predictions: 从 npz 文件加载预测和真实浓度场
2. load_metadata: 从 meta txt 文件加载元信息（风速、风向、稳定度、源编号等）
3. load_conds_data: 从 pkl 文件加载条件预测数据（如果有）
4. log2ppm: 将 log 浓度转换为 ppm 浓度（根据给定的关系）
5. get_sample: 根据索引获取单个样本的预测场、真实场、条件预测和元信息
'''
def __init__(self, pred_npz_path, meta_txt_path, conds_pkl_path):
self.pred_npz_path = pred_npz_path
self.meta_txt_path = meta_txt_path
self.conds_pkl_path = conds_pkl_path
self.load_predictions()
self.meta = self.load_metadata()
self.conds_data = self.load_conds_data()
def load_predictions(self):
data = np.load(self.pred_npz_path)
self.preds = data['preds'].squeeze(1)
self.trues = data['trues'].squeeze(1)
_, non_building_mask = PrintMetrics.get_building_area()
self.preds = self.preds * non_building_mask[None, :, :]
self.trues = self.trues * non_building_mask[None, :, :]
return self.preds, self.trues
def load_metadata(self):
df = pd.read_csv(self.meta_txt_path, sep=',', header=None)
df.columns = ['npz_colname']
pattern = r'v([0-9_]+)_d(\d+)_sc(\d+)_s(\d+)'
df[['wind_speed', 'wind_direction', 'sc', 'source_number']] = (
df['npz_colname'].str.extract(pattern))
df['wind_speed'] = df['wind_speed'].str.replace('_', '.').astype(float)
df[['wind_direction', 'sc', 'source_number']] = df[['wind_direction', 'sc',
'source_number']].astype(int)
return df
def load_conds_data(self):
conds_data = np.load(self.conds_pkl_path, allow_pickle=True)
return conds_data
@staticmethod
def log2ppm(log_conc):
log_conc = np.asarray(log_conc)
log_conc = np.minimum(log_conc, 15.0)
ppm_conc = np.expm1(log_conc) * (0.7449)
return np.maximum(ppm_conc, 0.0)
def get_sample(self, idx, in_ppm=True):
psi_f = self.preds[idx]
psi_t = self.trues[idx]
meta = self.meta.iloc[idx]
conds_preds = self.conds_data[idx]['conds']['preds']
if in_ppm:
psi_f = DataLoader.log2ppm(psi_f)
psi_t = DataLoader.log2ppm(psi_t)
conds_preds = DataLoader.log2ppm(conds_preds)
return psi_f, psi_t, conds_preds, meta
class ObservationModel:
'''
Functions:
1. observation_operator_H: 从浓度场 ψ 中提取点位浓度，使用双线性插值（线性算子）
2. observation_operator_H_ens: 对 ensemble 预测场批量应用观测算子，得到每个成员的点位浓度
'''
@staticmethod # 不依赖实例状态，可以直接通过类调用
def observation_operator_H(psi, obs_xy):
# 观测算子 M：
# 从浓度场 ψ 中提取点位浓度
# 使用双线性插值（线性算子）
Hh, Ww = psi.shape
xs = np.clip(obs_xy[:, 0], 0, Ww - 1 - 1e-6)
ys = np.clip(obs_xy[:, 1], 0, Hh - 1 - 1e-6)
x0 = np.floor(xs).astype(int)
y0 = np.floor(ys).astype(int)
x1 = np.clip(x0 + 1, 0, Ww - 1)
y1 = np.clip(y0 + 1, 0, Hh - 1)
dx = xs - x0
dy = ys - y0
f00 = psi[y0, x0]
f10 = psi[y0, x1]
f01 = psi[y1, x0]
f11 = psi[y1, x1]
return (
f00 * (1 - dx) * (1 - dy) +
f10 * dx * (1 - dy) +
f01 * (1 - dx) * dy +
f11 * dx * dy
)
@staticmethod
def observation_operator_H_ens(psi_ens, obs_xy):
"""
psi_ens: (N_ens, H, W)
obs_xy : (n_obs, 2)
return : HX (N_ens, n_obs)
"""
N_ens, Hh, Ww = psi_ens.shape
xs = np.clip(obs_xy[:, 0], 0, Ww - 1 - 1e-6)
ys = np.clip(obs_xy[:, 1], 0, Hh - 1 - 1e-6)
x0 = np.floor(xs).astype(np.int64)
y0 = np.floor(ys).astype(np.int64)
x1 = np.clip(x0 + 1, 0, Ww - 1)
y1 = np.clip(y0 + 1, 0, Hh - 1)
dx = xs - x0
dy = ys - y0
f00 = psi_ens[:, y0, x0]
f10 = psi_ens[:, y0, x1]
f01 = psi_ens[:, y1, x0]
f11 = psi_ens[:, y1, x1]
HX = (
f00 * (1 - dx) * (1 - dy) +
f10 * dx * (1 - dy) +
f01 * (1 - dx) * dy +
f11 * dx * dy
)
return HX
class SamplingStrategies:
# =========================
# (1) Sampling strategies
# =========================
@staticmethod
def sample_random(field_shape, num_points, seed=42):
rng = np.random.default_rng(seed)
H, W = field_shape
_, non_building_mask = PrintMetrics.get_building_area()
valid_idx = np.where(non_building_mask.ravel())[0]
chosen = rng.choice(valid_idx, size=num_points, replace=False)
yy, xx = np.meshgrid(np.arange(H), np.arange(W), indexing="ij")
coords = np.stack([xx.ravel(), yy.ravel()], axis=1)
return coords[chosen].astype(float)
@staticmethod
def sample_uniform(field_shape, num_points, margin=20):
H, W = field_shape
nx = int(np.ceil(np.sqrt(num_points * W / H)))
ny = int(np.ceil(num_points / nx))
xs = np.linspace(margin, W - 1 - margin, nx)
ys = np.linspace(margin, H - 1 - margin, ny)
xx, yy = np.meshgrid(xs, ys)
grid_xy = np.stack([xx.ravel(), yy.ravel()], axis=1)
_, non_building_mask = PrintMetrics.get_building_area()
xi = np.clip(grid_xy[:, 0].astype(int), 0, W - 1)
yi = np.clip(grid_xy[:, 1].astype(int), 0, H - 1)
valid = non_building_mask[yi, xi] == 1
grid_xy = grid_xy[valid]
if len(grid_xy) > num_points:
idx = np.linspace(0, len(grid_xy) - 1, num_points).astype(int)
grid_xy = grid_xy[idx]
return grid_xy
@staticmethod
def two_stage_sampling(
true_field,
pred_field,
num_points,
ens_preds_ppm=None,
seed=42,
# ====== 全局控制 ======
min_dist=22,
n1_ratio=0.65, # Stage1 比例
# ====== Stage1 可调参数 =====
stage1_grad_power=0.8, # 梯度权重幂次
stage1_value_power=1.2, # 值权重幂次
stage1_center_boost=1.2, # 是否增强高值区域
):
# 内部函数: 基于排斥采样的加权随机选择
def repulse_pick(candidate_idx, weights, k, selected_idx):
if k <= 0 or len(candidate_idx) == 0:
return list(selected_idx)
candidate_idx = np.asarray(candidate_idx, dtype=np.int64)
weights = np.maximum(np.asarray(weights, dtype=float), 0.0)
if weights.sum() <= 0:
weights = np.ones_like(weights)
weights = weights / weights.sum()
overs = min(len(candidate_idx), max(k * 15, 200))
cand = rng.choice(candidate_idx, size=overs, replace=False, p=weights)
selected = list(selected_idx)
for idx in cand:
xy = coords[idx]
if not selected:
selected.append(idx)
continue
sel_xy = coords[np.asarray(selected)]
if cKDTree(sel_xy).query(xy, k=1)[0] >= min_dist:
selected.append(idx)
if len(selected) >= k + len(selected_idx):
break
return selected
rng = np.random.default_rng(seed)
H, W = pred_field.shape
yy, xx = np.meshgrid(np.arange(H), np.arange(W), indexing="ij")
coords = np.stack([xx.ravel(), yy.ravel()], axis=1) # 二维坐标网格 (HW, 2) [x,y]
v = np.maximum(pred_field, 0.0).ravel()
vmax = float(v.max())
_, non_building_mask = PrintMetrics.get_building_area()
non_building_flat = non_building_mask.ravel().astype(bool)
if vmax <= 1e-6:
valid_idx = np.where(non_building_flat)[0]
idx = rng.choice(valid_idx, size=num_points, replace=False)
obs_xy = coords[idx]
obs_val = ObservationModel.observation_operator_H(true_field, obs_xy)
return obs_xy, obs_val
n_center_ratio= 1 - n1_ratio
n_center = max(2, int(num_points * n_center_ratio))
n1 = num_points - n_center
# 1. 结构修正（LOG）
z = np.log1p(np.maximum(pred_field, 0.0))
z_flat = z.ravel()
gx, gy = np.gradient(z)
grad = np.sqrt(gx**2 + gy**2).ravel()
nz = z_flat > 1e-6 # 只在非零区域上算分位数，避免大量0把lo/hi压塌
z_nz = z_flat[nz]
if z_nz.size < 50:
support_mask = (grad.reshape(H, W) > np.quantile(grad, 0.90))
else:
lo = np.quantile(z_nz, 0.70)
hi = np.quantile(z_nz, 0.90)
core_mask = (z >= lo) & (z <= hi)
r_out = 12 # 外扩：把结构带膨胀一圈，让采样不只盯着最强边界
support_mask = binary_dilation(core_mask, iterations=r_out)
support_idx = np.where(support_mask.ravel() & non_building_flat)[0]
if len(support_idx) < num_points * 2:
support_idx = np.where(non_building_flat)[0]
# Stage1：梯度主导 + 适度保留外圈
weights1 = (
(grad[support_idx] ** stage1_grad_power) *
(z_flat[support_idx] ** stage1_value_power + 1e-6)
)
if stage1_center_boost > 1.0:
weights1 *= (1 + stage1_center_boost * (z_flat[support_idx] / z_flat.max()))
selected = repulse_pick(support_idx, weights1, n1, [])
# Stage 2: 中心峰值区，只取2-3个点
peak_idx = np.argmax(z_flat * non_building_flat.astype(float))
peak_xy = coords[peak_idx]
# print(f"峰值位置: {peak_xy}, z值: {z_flat[peak_idx]:.3f}")
selected.append(int(peak_idx)) # 直接把峰值点加进去（1个）
# 再在峰值极近邻选1-2个，min_dist放松到5保证不重叠
if n_center > 1:
peak_radius = 10 # 很小的半径，只捕捉最高点附近
stage2_idx = np.where(
non_building_flat &
(np.sqrt((coords[:, 0] - peak_xy[0])**2 +
(coords[:, 1] - peak_xy[1])**2) <= peak_radius)
)[0]
stage2_idx = np.setdiff1d(stage2_idx, np.array(selected))
if len(stage2_idx) >= 1:
weights2 = z_flat[stage2_idx]
weights2 = weights2 / (weights2.sum() + 1e-12)
overs = min(len(stage2_idx), max((n_center - 1) * 10, 20))
cands = rng.choice(stage2_idx, size=overs, replace=False, p=weights2)
for idx in cands:
xy = coords[idx]
if cKDTree(coords[np.array(selected)]).query(xy, k=1)[0] >= 5:
selected.append(int(idx))
if len(selected) >= n_center + len([]): # 只加到n_center个为止
break
if len(selected) - (num_points - n_center) >= n_center:
break
selected = list(dict.fromkeys(selected))
# 补足剩余点（从Stage1结构带里再补，如果selected不够num_points）
if len(selected) < num_points:
remain = np.setdiff1d(support_idx, np.array(selected))
if len(remain) > 0:
w_remain = (
(grad[remain] ** stage1_grad_power) *
(z_flat[remain] ** stage1_value_power + 1e-6)
)
extra = repulse_pick(remain, w_remain,
num_points - len(selected), selected)
selected = extra
obs_xy = coords[np.array(selected[:num_points])]
obs_val = ObservationModel.observation_operator_H(true_field, obs_xy)
return obs_xy, obs_val
@staticmethod
def two_stage_pro(
true_field,
pred_field,
num_points,
ens_preds_ppm=None,
seed=42,
min_dist=22,
n1_ratio=0.65,
stage1_grad_power=0.8,
stage1_value_power=1.2,
stage1_center_boost=1.2,
):
import numpy as np
from scipy.spatial import cKDTree
from scipy.ndimage import binary_dilation
def repulse_pick(candidate_idx, weights, k, selected_idx, this_min_dist):
if k <= 0 or len(candidate_idx) == 0:
return list(selected_idx)
candidate_idx = np.asarray(candidate_idx, dtype=np.int64)
weights = np.maximum(np.asarray(weights, dtype=float), 0.0)
if weights.sum() <= 0:
weights = np.ones_like(weights, dtype=float)
weights = weights / weights.sum()
overs = min(len(candidate_idx), max(k * 15, 200))
cand = rng.choice(candidate_idx, size=overs, replace=False, p=weights)
selected = list(selected_idx)
for idx in cand:
idx = int(idx)
if idx in selected:
continue
xy = coords[idx]
if not selected:
selected.append(idx)
continue
sel_xy = coords[np.asarray(selected)]
if cKDTree(sel_xy).query(xy, k=1)[0] >= this_min_dist:
selected.append(idx)
if len(selected) >= k + len(selected_idx):
break
return selected
rng = np.random.default_rng(seed)
H, W = pred_field.shape
yy, xx = np.meshgrid(np.arange(H), np.arange(W), indexing="ij")
coords = np.stack([xx.ravel(), yy.ravel()], axis=1) # (HW, 2), [x, y]
v = np.maximum(pred_field, 0.0).ravel()
vmax = float(v.max())
_, non_building_mask = PrintMetrics.get_building_area()
non_building_flat = non_building_mask.ravel().astype(bool)
if vmax <= 1e-6:
valid_idx = np.where(non_building_flat)[0]
idx = rng.choice(valid_idx, size=min(num_points, len(valid_idx)), replace=False)
if len(idx) < num_points:
raise ValueError(f"Not enough valid non-building points: need {num_points}, got {len(idx)}")
obs_xy = coords[idx]
obs_val = ObservationModel.observation_operator_H(true_field, obs_xy)
return obs_xy, obs_val
n_center_ratio = 1 - n1_ratio
n_center = max(2, int(num_points * n_center_ratio))
n1 = num_points - n_center
z = np.log1p(np.maximum(pred_field, 0.0))
z_flat = z.ravel()
gx, gy = np.gradient(z)
grad = np.sqrt(gx**2 + gy**2).ravel()
nz = z_flat > 1e-6
z_nz = z_flat[nz]
if z_nz.size < 50:
support_mask = (grad.reshape(H, W) > np.quantile(grad, 0.90))
else:
lo = np.quantile(z_nz, 0.70)
hi = np.quantile(z_nz, 0.90)
core_mask = (z >= lo) & (z <= hi)
# r_out = int(0.60 * num_points)
r_out = int(np.clip(num_points / 3 + 16 / 3, 12, 24)) # 根据 num_points 动态调整外扩半径，保持在12-24范围内
support_mask = binary_dilation(core_mask, iterations=r_out)
support_idx = np.where(support_mask.ravel() & non_building_flat)[0]
if len(support_idx) < num_points * 2:
support_idx = np.where(non_building_flat)[0]
weights1 = (
(grad[support_idx] ** stage1_grad_power) *
(z_flat[support_idx] ** stage1_value_power + 1e-6)
)
if stage1_center_boost > 1.0:
weights1 *= (1 + stage1_center_boost * (z_flat[support_idx] / (z_flat.max() + 1e-12)))
selected = repulse_pick(support_idx, weights1, n1, [], min_dist)
peak_idx = int(np.argmax(z_flat * non_building_flat.astype(float)))
peak_xy = coords[peak_idx]
selected.append(int(peak_idx))
if n_center > 1:
peak_radius = 10
stage2_idx = np.where(
non_building_flat &
(np.sqrt((coords[:, 0] - peak_xy[0]) ** 2 +
(coords[:, 1] - peak_xy[1]) ** 2) <= peak_radius)
)[0]
stage2_idx = np.setdiff1d(stage2_idx, np.array(selected))
if len(stage2_idx) >= 1:
weights2 = z_flat[stage2_idx]
weights2 = weights2 / (weights2.sum() + 1e-12)
overs = min(len(stage2_idx), max((n_center - 1) * 10, 20))
cands = rng.choice(stage2_idx, size=overs, replace=False, p=weights2)
for idx in cands:
idx = int(idx)
xy = coords[idx]
if cKDTree(coords[np.array(selected)]).query(xy, k=1)[0] >= 5:
selected.append(idx)
if len(selected) >= n_center + len([]):
break
if len(selected) - (num_points - n_center) >= n_center:
break
selected = list(dict.fromkeys(selected))
if len(selected) >= num_points:
selected = selected[:num_points]
obs_xy = coords[np.array(selected)]
obs_val = ObservationModel.observation_operator_H(true_field, obs_xy)
return obs_xy, obs_val
remain = np.setdiff1d(support_idx, np.array(selected))
if len(remain) > 0:
for d_try in [
min_dist,
max(1, int(min_dist * 0.7)),
max(1, int(min_dist * 0.4)),
5,
3
]:
if len(selected) >= num_points:
break
remain = np.setdiff1d(support_idx, np.array(selected))
if len(remain) == 0:
break
w_remain = (
(grad[remain] ** stage1_grad_power) *
(z_flat[remain] ** stage1_value_power + 1e-6)
)
selected = repulse_pick(
remain,
w_remain,
num_points - len(selected),
selected,
d_try
)
selected = list(dict.fromkeys(selected))
if len(selected) < num_points:
remain_support = np.setdiff1d(support_idx, np.array(selected))
if len(remain_support) > 0:
need = num_points - len(selected)
extra = rng.choice(
remain_support,
size=min(need, len(remain_support)),
replace=False
)
selected.extend(extra.tolist())
selected = list(dict.fromkeys(selected))
if len(selected) < num_points:
all_valid = np.where(non_building_flat)[0]
remain_all = np.setdiff1d(all_valid, np.array(selected))
if len(remain_all) > 0:
need = num_points - len(selected)
extra = rng.choice(
remain_all,
size=min(need, len(remain_all)),
replace=False
)
selected.extend(extra.tolist())
selected = list(dict.fromkeys(selected))
selected = selected[:num_points]
assert len(selected) == num_points, f"Expected {num_points} points, got {len(selected)}"
obs_xy = coords[np.array(selected)]
obs_val = ObservationModel.observation_operator_H(true_field, obs_xy)
return obs_xy, obs_val
@staticmethod
def smart_two_pass(
enkf,
psi_f,
conds_preds,
true_field,
n1,
n2,
n_rounds=2,
phase1_method='two_stage',
min_dist_p2=22,
under_correct_alpha=1.5,
use_localization=False,
loc_radius_pixobs=35.0,
loc_radius_obsobs=40.0,
seed=42,
verbose=True,
):
"""
多轮迭代选点 + EnKF 同化。
每轮流程：
Phase 1 — 基于当前先验场选 n1 个点，做 pilot EnKF；
Phase 2 — 基于 pilot 残差找欠校正区，再选 n2 个点，做 final EnKF；
本轮分析场作为下一轮的先验（psi_f）。
参数:
enkf : EnKF 实例
psi_f : 初始先验场 (H, W)
conds_preds : 集合预测场 (N_ens, H, W)，协方差来源，全程不变
true_field : 真值场 (H, W)，仅用于观测值提取
n1 : 每轮 Phase-1 选点数
n2 : 每轮 Phase-2 选点数
n_rounds : 迭代轮数（默认 1，即原始两阶段行为）
phase1_method : Phase-1 采样策略（'two_stage' 或其他 generate 支持的方法）
min_dist_p2 : Phase-2 选点与已有点的最小距离（像素）
under_correct_alpha : Phase-2 欠校正权重幂次
use_localization: 是否使用局地化 EnKF
loc_radius_pixobs / loc_radius_obsobs : 局地化半径
seed : 随机种子
verbose : 是否打印中间日志
返回:
psi_a_final : 最终分析场 (H, W)
all_obs_xy : 所有轮次累计观测坐标 (n_rounds*(n1+n2), 2)
all_obs_val : 所有轮次累计观测值
psi_pilot : 最后一轮的 pilot（Phase-1）分析场
obs_xy_p1_last : 最后一轮 Phase-1 选点坐标
"""
conds_preds = np.asarray(conds_preds)
N_ens, H, W = conds_preds.shape
rng = np.random.default_rng(seed)
_, non_building_mask = PrintMetrics.get_building_area()
non_building_flat = non_building_mask.ravel().astype(bool)
yy, xx = np.meshgrid(np.arange(H), np.arange(W), indexing="ij")
coords = np.stack([xx.ravel(), yy.ravel()], axis=1)
# 预先构建集合（整个函数中只用这一个 X_f，协方差永远基于原始集合）
ens_mean = np.mean(conds_preds, axis=0)
X_f_base = conds_preds - ens_mean[None, :, :] + psi_f[None, :, :]
# 累计所有轮次的观测（跨轮次积累，最终一次性返回）
all_obs_xy_list = []
all_obs_val_list = []
# 当前先验：第 1 轮用 psi_f，后续轮次用上一轮的分析场
psi_current = psi_f
psi_pilot = None
obs_xy_p1_last = None
for round_idx in range(n_rounds):
round_seed = seed + round_idx # 每轮不同种子，避免重复采样
# ── Phase 1：基于当前先验选点 + pilot EnKF ────────────────
if phase1_method == 'two_stage':
obs_xy_p1, obs_val_p1 = SamplingStrategies.two_stage_sampling(
true_field=true_field, pred_field=psi_current,
num_points=n1, seed=round_seed)
else:
obs_xy_p1, obs_val_p1 = SamplingStrategies.generate(
true_field, psi_current, n1, method=phase1_method, seed=round_seed)
# pilot EnKF 使用当前先验重新中心化的集合
ens_mean_cur = np.mean(conds_preds, axis=0)
X_f_cur = conds_preds - ens_mean_cur[None, :, :] + psi_current[None, :, :]
if use_localization:
psi_pilot = enkf._enkf_update_localized(
X_f_cur, obs_xy_p1, obs_val_p1,
loc_radius_pixobs, loc_radius_obsobs, round_seed)
else:
psi_pilot = enkf._enkf_update_standard(X_f_cur, obs_xy_p1, obs_val_p1)
if verbose:
from sklearn.metrics import r2_score as _r2
print(f"[SmartEnKF] Round {round_idx+1}/{n_rounds} Phase1: "
f"{n1} 点, pilot R²={_r2(true_field.ravel(), psi_pilot.ravel()):.4f}")
# ── Phase 2：找欠校正区，补充选点 ──────────────────────────
psi_f_flat = psi_current.ravel()
psi_pilot_flat = psi_pilot.ravel()
correction_map = np.abs(psi_pilot_flat - psi_f_flat)
nz_vals = psi_f_flat[non_building_flat & (psi_f_flat > 1e-4)]
prior_thresh = np.quantile(nz_vals, 0.20) if len(nz_vals) > 20 else 1e-4
plume_support = non_building_flat & (psi_f_flat > prior_thresh)
cand_idx = np.where(plume_support)[0]
if len(cand_idx) < n2 * 3:
cand_idx = np.where(non_building_flat & (psi_f_flat > 1e-6))[0]
prior_cand = psi_f_flat[cand_idx]
corr_cand = correction_map[cand_idx]
prior_norm = prior_cand / (prior_cand.max() + 1e-12)
corr_norm = corr_cand / (corr_cand.max() + 1e-12)
under_score = prior_norm * (1.0 - corr_norm + 0.05)
p1_tree = cKDTree(obs_xy_p1)
dist_p1, _ = p1_tree.query(coords[cand_idx], k=1)
dist_w = np.tanh(dist_p1 / (min_dist_p2 * 2.5))
weights_p2 = (under_score ** under_correct_alpha) * (dist_w + 0.05)
weights_p2 = np.maximum(weights_p2, 1e-12)
weights_p2 /= weights_p2.sum()
rng_round = np.random.default_rng(round_seed)
n_over = min(len(cand_idx), max(n2 * 30, 600))
cands = rng_round.choice(cand_idx, size=n_over, replace=False, p=weights_p2)
selected_p2 = []
for cidx in cands:
xy = coords[cidx]
if p1_tree.query(xy, k=1)[0] < min_dist_p2:
continue
if selected_p2:
if cKDTree(coords[np.array(selected_p2)]).query(xy, k=1)[0] < min_dist_p2:
continue
selected_p2.append(int(cidx))
if len(selected_p2) >= n2:
break
if len(selected_p2) < n2:
remain = np.setdiff1d(cand_idx, np.array(selected_p2, dtype=int))
extra = rng_round.choice(remain,
size=min(n2 - len(selected_p2), len(remain)),
replace=False)
selected_p2.extend(extra.tolist())
obs_xy_p2 = coords[np.array(selected_p2[:n2])]
obs_val_p2 = ObservationModel.observation_operator_H(true_field, obs_xy_p2)
if verbose:
print(f"[SmartEnKF] Round {round_idx+1}/{n_rounds} Phase2: 补充 {n2} 个欠校正区域点")
# ── Final：本轮全部点 + 当前先验做最终 EnKF ────────────────
round_obs_xy = np.vstack([obs_xy_p1, obs_xy_p2])
round_obs_val = np.concatenate([obs_val_p1, obs_val_p2])
if use_localization:
psi_a_round = enkf._enkf_update_localized(
X_f_cur, round_obs_xy, round_obs_val,
loc_radius_pixobs, loc_radius_obsobs, round_seed)
else:
psi_a_round = enkf._enkf_update_standard(X_f_cur, round_obs_xy, round_obs_val)
if verbose:
from sklearn.metrics import r2_score as _r2
print(f"[SmartEnKF] Round {round_idx+1}/{n_rounds} Final: "
f"{n1+n2} 点, R²={_r2(true_field.ravel(), psi_a_round.ravel()):.4f}")
# 累计观测，更新先验进入下一轮
all_obs_xy_list.append(round_obs_xy)
all_obs_val_list.append(round_obs_val)
psi_current = np.maximum(psi_a_round, 0.0)
obs_xy_p1_last = obs_xy_p1
all_obs_xy = np.vstack(all_obs_xy_list)
all_obs_val = np.concatenate(all_obs_val_list)
return (psi_current, all_obs_xy, all_obs_val,
np.maximum(psi_pilot, 0.0), obs_xy_p1_last)
@staticmethod
def generate(true_field, pred_field, num_points, method="uniform", seed=42,
enkf=None, conds_preds=None, **sample_params):
field_shape = true_field.shape
if method == "random":
obs_xy = SamplingStrategies.sample_random(field_shape, num_points, seed)
elif method == "uniform":
obs_xy = SamplingStrategies.sample_uniform(field_shape, num_points)
elif method == "two_stage":
obs_xy, _ = SamplingStrategies.two_stage_sampling(
true_field,
pred_field,
num_points,
seed=seed,
**sample_params
)
elif method == "two_stage_pro":
obs_xy, _ = SamplingStrategies.two_stage_pro(
true_field,
pred_field,
num_points,
seed=seed,
**sample_params
)
elif method == "smart_two_pass":
if enkf is None or conds_preds is None:
raise ValueError(
"method='smart_two_pass' 需要传入 enkf 实例和 conds_preds 集合场。"
)
# 解析 n1 / n2（支持用 n1_ratio 自动计算）
n1_ratio = float(sample_params.pop('n1_ratio', 0.6))
n1_default = int(round(num_points * n1_ratio))
n1 = int(sample_params.pop('n1', n1_default))
if num_points > 1:
n1 = max(1, min(n1, num_points - 1))
else:
n1 = 1
n2 = int(sample_params.pop('n2', num_points - n1))
return SamplingStrategies.smart_two_pass(
enkf=enkf,
psi_f=pred_field,
conds_preds=conds_preds,
true_field=true_field,
n1=n1,
n2=n2,
seed=seed,
**sample_params,
)
else:
raise ValueError(f"Unknown observation sampling method: {method}")
obs_val = ObservationModel.observation_operator_H(true_field, obs_xy)
return obs_xy, obs_val
class EnKF:
def __init__(
self,
obs_std_scale=0.08, # relative observation noise level
damping=1.0,
jitter=1e-5,
):
self.obs_std_scale = obs_std_scale
self.damping = damping
self.jitter = jitter
def standard_enkf(self, psi_f, conds_preds, obs_xy, d_obs):
"""
psi_f: Unet预测的最佳先验场 (H, W)
conds_preds: 通过扰动参数生成的集合场 (N_ens, H, W)
obs_xy: 监测站坐标 (n_obs, 2)
d_obs: 监测站真实浓度 (n_obs,)
"""
conds_preds = np.asarray(conds_preds)
N_ens, H, W = conds_preds.shape
n_obs = obs_xy.shape[0]
ens_mean = np.mean(conds_preds, axis=0) # 计算集合均值
# 重要：将集合成员的波动叠加到 Unet 预测场 psi_f 上 ,
# 确保分析场的统计中心是 Unet 预测的那个场，而不是集合均值（可能有偏差导致更新不好）
X_f = conds_preds - ens_mean[None, :, :] + psi_f[None, :, :]
X_f_flat = X_f.reshape(N_ens, -1) # (N_ens, Pixels)
HX = ObservationModel.observation_operator_H_ens(X_f, obs_xy) # (N_ens, n_obs)
HX_mean = np.mean(HX, axis=0)
X_f_bar = np.mean(X_f_flat, axis=0) # 计算偏差矩阵
A_prime = (X_f_flat - X_f_bar[None, :]).T # A_prime (状态偏差): (Pixels, N_ens)
Y_prime = (HX - HX_mean).T # Y_prime (观测空间偏差): (n_obs, N_ens)
# # 构造观测误差矩阵 R_e
# # 基于观测值大小设定自适应噪声 (8% 相对误差)
obs_std = self.obs_std_scale * np.maximum(np.abs(d_obs), 1.0) # 先定标准差
rng = np.random.default_rng(42) # SVD正交化生成E
Z = rng.standard_normal((N_ens, n_obs))
U, _, Vt = np.linalg.svd(Z, full_matrices=False)
Z = U @ Vt * np.sqrt(N_ens - 1)
E = Z * obs_std[None, :] # (N_ens, n_obs)，E.T即为文献中的E矩阵
# 从E计算Re（按照文献公式 Re = EE^T / N-1）
E_T = E.T # (n_obs, N_ens)，对应文献的E
R_e = (E_T @ E_T.T) / (N_ens - 1) # (n_obs, n_obs)
R_e += self.jitter * np.eye(n_obs) # 数值稳定项
Y_o = d_obs[None, :] + E # (N_ens, n_obs)
# 增益计算与状态更新 (对应公式 3-16, 3-17)
# 计算 Pe*H.T 和 H*Pe*H.T 的统计估计值
Pe_HT = (A_prime @ Y_prime.T) / (N_ens - 1)
H_Pe_HT = (Y_prime @ Y_prime.T) / (N_ens - 1)
# 计算集合卡尔曼增益 K_e = Pe*H.T * inverse(H*Pe*H.T + R_e)
# 使用 solve 提高数值稳定性
K_e = np.linalg.solve((H_Pe_HT + R_e).T, Pe_HT.T).T
# 计算创新值 (Innovation): (n_obs, N_ens)
# 每个成员根据自己的观测扰动和预测值进行修正
innovation = (Y_o - HX).T
# 更新系统状态的集合预测矩阵 X_a
# X_a = X_f + K_e * (Y_o - HX_f)
X_a_flat = X_f_flat + (self.damping * (K_e @ innovation)).T
# 输出最终分析场，取集合均值作为最终结果
psi_a_flat = np.mean(X_a_flat, axis=0)
psi_a = psi_a_flat.reshape(H, W)
# 物理约束：确保浓度不为负数
return np.maximum(psi_a, 0.0)
def enkf_localization(self, psi_f, conds_preds, obs_xy, d_obs,
loc_radius_pixobs=40.0, # Pixel-Obs localization radius (in pixels)
loc_radius_obsobs=60.0, # Obs-Obs localization radius (in pixels)
seed=42,
SAVE_DIAGNOSTICS=False,
):
conds_preds = np.asarray(conds_preds)
N_ens, H, W = conds_preds.shape
n_obs = obs_xy.shape[0]
# ========= 1) prior ensemble centered at psi_f =========
ens_mean = np.mean(conds_preds, axis=0)
X_f = conds_preds - ens_mean[None, :, :] + psi_f[None, :, :]
X_f_flat = X_f.reshape(N_ens, -1)
HX = ObservationModel.observation_operator_H_ens(X_f, obs_xy) # 注意：必须用 X_f
HX_mean = np.mean(HX, axis=0)
X_f_bar = np.mean(X_f_flat, axis=0)
A_prime = (X_f_flat - X_f_bar[None, :]).T # (Pixels, N_ens)
Y_prime = (HX - HX_mean).T # (n_obs, N_ens)
# # ========= 2) perturbed obs (deterministic-ish, fixed seed) =========
obs_std = self.obs_std_scale * np.maximum(np.abs(d_obs), 1.0) # 先定标准差
rng = np.random.default_rng(seed) # SVD正交化生成E
Z = rng.standard_normal((N_ens, n_obs))
U, _, Vt = np.linalg.svd(Z, full_matrices=False)
Z = U @ Vt * np.sqrt(N_ens - 1)
E = Z * obs_std[None, :] # (N_ens, n_obs)，E.T即为文献中的E矩阵
# 从E计算Re（按照文献公式 Re = EE^T / N-1）
E_T = E.T # (n_obs, N_ens)，对应文献的E
R_e = (E_T @ E_T.T) / (N_ens - 1) # (n_obs, n_obs)
R_e += self.jitter * np.eye(n_obs) # 数值稳定项
Y_o = d_obs[None, :] + E
# ========= 3) sample covariances =========
Pe_HT = (A_prime @ Y_prime.T) / (N_ens - 1) # (Pixels, n_obs)
H_Pe_HT = (Y_prime @ Y_prime.T) / (N_ens - 1) # (n_obs, n_obs)
# ========= 4) localization =========
# (a) Pixel-Obs localization: rho_xy (Pixels, n_obs)
yy, xx = np.meshgrid(np.arange(H), np.arange(W), indexing="ij")
grid = np.stack([xx.ravel(), yy.ravel()], axis=1) # (Pixels,2)
dx = grid[:, None, 0] - obs_xy[None, :, 0]
dy = grid[:, None, 1] - obs_xy[None, :, 1]
dist2_xy = dx*dx + dy*dy
rho_xy = np.exp(-0.5 * dist2_xy / (loc_radius_pixobs**2))
# (b) Obs-Obs localization: rho_oo (n_obs, n_obs)
dox = obs_xy[:, None, 0] - obs_xy[None, :, 0]
doy = obs_xy[:, None, 1] - obs_xy[None, :, 1]
dist2_oo = dox*dox + doy*doy
rho_oo = np.exp(-0.5 * dist2_oo / (loc_radius_obsobs**2))
Pe_HT = Pe_HT * rho_xy
H_Pe_HT = H_Pe_HT * rho_oo
# ========= [诊断] P_e 的谱结构 =========
# P_e = A_prime @ A_prime.T / (N_ens-1)，直接分解 A_prime 的奇异值更高效
# A_prime shape: (Pixels, N_ens)，SVD给出 P_e 的特征值 = sigma^2
U_ens, sigma, Vt_ens = np.linalg.svd(A_prime / np.sqrt(N_ens - 1), full_matrices=False)
# sigma shape: (N_ens,)，对应 P_e 的特征值平方根
eigenvalues = sigma ** 2 # P_e 的特征值，降序排列
# --- 指标1：有效秩 r_eff = (Σλ)² / Σλ² 衡量特征值分布均匀程度---
# r_eff→1: 近似秩1（能量集中于单一方向）r_eff→N: 各向同性（能量均匀分布）
r_eff = (eigenvalues.sum() ** 2) / (eigenvalues ** 2).sum()
# --- 指标2：主特征值 λ1 = P_e 在主方向上的方差 ---
# 只受幅度参数(v, Q)影响，随d单调增大
lambda1 = eigenvalues[0]
lambda_min = eigenvalues[-2]
# --- 指标3：方向集中度 λ1/λ2 衡量P_e各向异性程度 ---
# 峰值对应最优d配置（d**），超过后集合引入非物理方向
ratio_1_2 = eigenvalues[0] / eigenvalues[1] if len(eigenvalues) > 1 else np.inf
# --- 指标4：主特征向量峰值位置---
# 峰值位置随d系统性漂移，随v/Q不变，随n随机漂移
u1 = U_ens[:, 0].reshape(H, W) # u1 = P_e 的第一特征向量，代表集合扰动的主方向
u1_peak = np.unravel_index(np.abs(u1).argmax(), u1.shape)
# ========= 5) Kalman gain =========
S = H_Pe_HT + R_e
K_e = np.linalg.solve(S.T, Pe_HT.T).T
innovation = (Y_o - HX).T
X_a_flat = X_f_flat + (self.damping * (K_e @ innovation)).T
psi_a = np.mean(X_a_flat, axis=0).reshape(H, W)
psi_a = np.maximum(psi_a, 0.0)
if SAVE_DIAGNOSTICS:
print("=" * 50)
print(f"[P_e 谱诊断]")
print(f" 指标1 r_eff = {r_eff:.2f} # (Σλ)²/Σλ²，建筑影响下界≈2.1")
print(f" 指标2 λ1 = {lambda1:.2f} {lambda_min:.2f} # 主方向方差，随d单调增大")
print(f" 指标3 λ1/λ2 = {ratio_1_2:.2f} # 各向异性，d=45°时峰值→最优配置")
print(f" 指标4 u1峰值位置 = {u1_peak} # d变化时系统漂移，v/Q不变")
print("=" * 50)
diag = {
'r_eff': r_eff,
'lambda1': lambda1,
'ratio_1_2': ratio_1_2,
'u1_peak_row': u1_peak[0],
'u1_peak_col': u1_peak[1],
}
return psi_a, diag
else:
return psi_a
def _enkf_update_standard(self, X_f, obs_xy, d_obs):
"""
标准 EnKF 更新，直接接受已中心化的集合 X_f (N_ens, H, W)。
返回分析场均值 psi_a (H, W)，>=0。
"""
N_ens, H, W = X_f.shape
n_obs = obs_xy.shape[0]
X_f_flat = X_f.reshape(N_ens, -1)
HX = ObservationModel.observation_operator_H_ens(X_f, obs_xy)
HX_mean = np.mean(HX, axis=0)
X_f_bar = np.mean(X_f_flat, axis=0)
A_prime = (X_f_flat - X_f_bar[None, :]).T # (Pixels, N_ens)
Y_prime = (HX - HX_mean).T # (n_obs, N_ens)
obs_std = self.obs_std_scale * np.maximum(np.abs(d_obs), 1.0)
rng = np.random.default_rng(42)
Z = rng.standard_normal((N_ens, n_obs))
U, _, Vt = np.linalg.svd(Z, full_matrices=False)
Z = U @ Vt * np.sqrt(N_ens - 1)
E = Z * obs_std[None, :]
E_T = E.T
R_e = (E_T @ E_T.T) / (N_ens - 1)
R_e += self.jitter * np.eye(n_obs)
Y_o = d_obs[None, :] + E
Pe_HT = (A_prime @ Y_prime.T) / (N_ens - 1)
H_Pe_HT = (Y_prime @ Y_prime.T) / (N_ens - 1)
K_e = np.linalg.solve((H_Pe_HT + R_e).T, Pe_HT.T).T
innovation = (Y_o - HX).T
X_a_flat = X_f_flat + (self.damping * (K_e @ innovation)).T
psi_a = np.mean(X_a_flat, axis=0).reshape(H, W)
return np.maximum(psi_a, 0.0)
def _enkf_update_localized(self, X_f, obs_xy, d_obs,
loc_radius_pixobs=35.0,
loc_radius_obsobs=40.0,
seed=42):
"""
局地化 EnKF 更新，直接接受已中心化的集合 X_f (N_ens, H, W)。
返回分析场均值 psi_a (H, W)，>=0。
"""
N_ens, H, W = X_f.shape
n_obs = obs_xy.shape[0]
X_f_flat = X_f.reshape(N_ens, -1)
HX = ObservationModel.observation_operator_H_ens(X_f, obs_xy)
HX_mean = np.mean(HX, axis=0)
X_f_bar = np.mean(X_f_flat, axis=0)
A_prime = (X_f_flat - X_f_bar[None, :]).T
Y_prime = (HX - HX_mean).T
obs_std = self.obs_std_scale * np.maximum(np.abs(d_obs), 1.0)
rng = np.random.default_rng(seed)
Z = rng.standard_normal((N_ens, n_obs))
U, _, Vt = np.linalg.svd(Z, full_matrices=False)
Z = U @ Vt * np.sqrt(N_ens - 1)
E = Z * obs_std[None, :]
E_T = E.T
R_e = (E_T @ E_T.T) / (N_ens - 1)
R_e += self.jitter * np.eye(n_obs)
Y_o = d_obs[None, :] + E
Pe_HT = (A_prime @ Y_prime.T) / (N_ens - 1)
H_Pe_HT = (Y_prime @ Y_prime.T) / (N_ens - 1)
yy, xx = np.meshgrid(np.arange(H), np.arange(W), indexing="ij")
grid = np.stack([xx.ravel(), yy.ravel()], axis=1)
dx = grid[:, None, 0] - obs_xy[None, :, 0]
dy = grid[:, None, 1] - obs_xy[None, :, 1]
rho_xy = np.exp(-0.5 * (dx*dx + dy*dy) / (loc_radius_pixobs**2))
dox = obs_xy[:, None, 0] - obs_xy[None, :, 0]
doy = obs_xy[:, None, 1] - obs_xy[None, :, 1]
rho_oo = np.exp(-0.5 * (dox*dox + doy*doy) / (loc_radius_obsobs**2))
Pe_HT = Pe_HT * rho_xy
H_Pe_HT = H_Pe_HT * rho_oo
S = H_Pe_HT + R_e
K_e = np.linalg.solve(S.T, Pe_HT.T).T
innovation = (Y_o - HX).T
X_a_flat = X_f_flat + (self.damping * (K_e @ innovation)).T
psi_a = np.mean(X_a_flat, axis=0).reshape(H, W)
return np.maximum(psi_a, 0.0)
class PrintMetrics:
@staticmethod
def pad_center_crop(arr, center_y, center_x, out_h=256, out_w=256):
# Pad and center-crop 2D or 3D array
if arr.ndim == 3:
C, H, W = arr.shape
out = np.zeros((C, out_h, out_w), dtype=arr.dtype)
else:
H, W = arr.shape
out = np.zeros((out_h, out_w), dtype=arr.dtype)
y0, x0 = center_y - out_h // 2, center_x - out_w // 2
y1, x1 = y0 + out_h, x0 + out_w
sy0, sy1 = max(0, y0), min(H, y1)
sx0, sx1 = max(0, x0), min(W, x1)
dy0, dx0 = sy0 - y0, sx0 - x0
dy1, dx1 = dy0 + (sy1 - sy0), dx0 + (sx1 - sx0)
if arr.ndim == 3:
out[:, dy0:dy1, dx0:dx1] = arr[:, sy0:sy1, sx0:sx1]
else:
out[dy0:dy1, dx0:dx1] = arr[sy0:sy1, sx0:sx1]
return out
@staticmethod
def get_building_area():
# load building data
npz_path = '../Gas_unet/Gas_code/dataset_m/5min_m_Data_special/min5_m_v1_0_d270_sc2_s10_04118.npz'
data = np.load(npz_path)
build_data = data['three_channel_data'][0]
non_building_mask = (build_data == 0).astype(np.uint8)
center_y, center_x = 498, 538
build_data_256 = PrintMetrics.pad_center_crop(build_data, center_y,
center_x, 256, 256)
non_building_mask = PrintMetrics.pad_center_crop(non_building_mask,
center_y, center_x, 256, 256)
return build_data_256, non_building_mask
@staticmethod
def weighted_r2(y_true, y_pred, gamma=1.0, eps=1e-12):
"""
Weighted R2 score emphasizing high-value regions.
"""
y_true = np.asarray(y_true)
y_pred = np.asarray(y_pred)
w = np.maximum(y_true, eps) ** gamma
w = w / np.sum(w)
y_bar = np.sum(w * y_true)
num = np.sum(w * (y_true - y_pred) ** 2)
den = np.sum(w * (y_true - y_bar) ** 2)
if den < eps:
return np.nan
return 1.0 - num / den
@staticmethod
def print_metrics(i, wind_speed, wind_direction, sc, source_number,
true_field, pred_field, analysis, obs_xy,
metrics_save_flag=False, metrics_print_flag=True):
"""
Metrics:
1) Field-wise (all pixels)
2) Plume-aware (true > eps)
3) At observations
"""
def nmse_metrics(y_true, y_pred):
nmse = np.mean((y_true.flatten() - y_pred.flatten())**2) / (np.mean(y_true) * np.mean(y_pred) + 1e-12)
return nmse
def nmae_metrics(y_true, y_pred):
nmae = np.mean(np.abs(y_true.flatten() - y_pred.flatten())) / (np.mean(y_true) + 1e-12)
return nmae
# ========= 保留原始 2D 场 =========
_, non_building_mask = PrintMetrics.get_building_area()
true_field = np.where(true_field > 0, true_field, 0) * non_building_mask
pred_field = np.where(pred_field > 0, pred_field, 0) * non_building_mask
analysis = np.where(analysis > 0, analysis, 0) * non_building_mask
true_flat = true_field.ravel()
pred_flat = pred_field.ravel()
ana_flat = analysis.ravel()
# ===============================
# (1) Field-wise (all pixels)
# ===============================
r2_before = r2_score(true_flat, pred_flat)
r2_after = r2_score(true_flat, ana_flat)
mse_before = mean_squared_error(true_flat, pred_flat)
mse_after = mean_squared_error(true_flat, ana_flat)
mae_before = mean_absolute_error(true_flat, pred_flat)
mae_after = mean_absolute_error(true_flat, ana_flat)
nmse_before = nmse_metrics(true_flat, pred_flat)
nmse_after = nmse_metrics(true_flat, ana_flat)
nmae_before = nmae_metrics(true_flat, pred_flat)
nmae_after = nmae_metrics(true_flat, ana_flat)
# ===============================
# (2) Plume-aware (true > eps)
# ===============================
plume_mask = true_flat > 1e-6
true_p = true_flat[plume_mask]
pred_p = pred_flat[plume_mask]
ana_p = ana_flat[plume_mask]
r2_plume_before = r2_score(true_p, pred_p)
r2_plume_after = r2_score(true_p, ana_p)
mse_plume_before = mean_squared_error(true_p, pred_p)
mse_plume_after = mean_squared_error(true_p, ana_p)
mae_plume_before = mean_absolute_error(true_p, pred_p)
mae_plume_after = mean_absolute_error(true_p, ana_p)
nmse_plume_before = nmse_metrics(true_p, pred_p)
nmse_plume_after = nmse_metrics(true_p, ana_p)
nmae_plume_before = nmae_metrics(true_p, pred_p)
nmae_plume_after = nmae_metrics(true_p, ana_p)
# ---- Weighted R2 (plume-aware) ----
wr2_plume_before = PrintMetrics.weighted_r2(true_p, pred_p, gamma=1.0)
wr2_plume_after = PrintMetrics.weighted_r2(true_p, ana_p, gamma=1.0)
# ===============================
# (3) At observations
# ===============================
true_at_obs = ObservationModel.observation_operator_H(true_field, obs_xy)
pred_at_obs = ObservationModel.observation_operator_H(pred_field, obs_xy)
ana_at_obs = ObservationModel.observation_operator_H(analysis, obs_xy)
r2_obs_before = r2_score(true_at_obs, pred_at_obs)
r2_obs_after = r2_score(true_at_obs, ana_at_obs)
mse_obs_before = mean_squared_error(true_at_obs, pred_at_obs)
mse_obs_after = mean_squared_error(true_at_obs, ana_at_obs)
mae_obs_before = mean_absolute_error(true_at_obs, pred_at_obs)
mae_obs_after = mean_absolute_error(true_at_obs, ana_at_obs)
nmse_obs_before = nmse_metrics(true_at_obs, pred_at_obs)
nmse_obs_after = nmse_metrics(true_at_obs, ana_at_obs)
nmae_obs_before = nmae_metrics(true_at_obs, pred_at_obs)
nmae_obs_after = nmae_metrics(true_at_obs, ana_at_obs)
if metrics_print_flag:
print("=== Assimilation Metrics ===")
print("[Field-wise]")
print(f"R2 : {r2_before:.4f}->{r2_after:.4f}")
print(f"MSE : {mse_before:.4f}->{mse_after:.4f}")
print(f"MAE : {mae_before:.4f}->{mae_after:.4f}")
print("[Plume-aware]")
print(f"R2 : {r2_plume_before:.4f}->{r2_plume_after:.4f}")
print(f"MSE : {mse_plume_before:.4f}->{mse_plume_after:.4f}")
print(f"MAE : {mae_plume_before:.4f}->{mae_plume_after:.4f}")
print(f"W-R2 : {wr2_plume_before:.4f}->{wr2_plume_after:.4f}")
print("[At observations]")
print(f"R2 : {r2_obs_before:.4f}->{r2_obs_after:.4f}")
print(f"MSE : {mse_obs_before:.4f}->{mse_obs_after:.4f}")
print(f"MAE : {mae_obs_before:.4f}->{mae_obs_after:.4f}")
if metrics_save_flag:
return {
'idx': i,
'wind_speed': wind_speed,
'wind_direction': wind_direction,
'stability_class': sc,
'source_number': source_number,
"r2_before": r2_before,
"r2_after": r2_after,
"r2_plume_before": r2_plume_before,
"r2_plume_after": r2_plume_after,
"w_r2_plume_before": wr2_plume_before,
"w_r2_plume_after": wr2_plume_after,
"r2_obs_before": r2_obs_before,
"r2_obs_after": r2_obs_after,
"mse_before": mse_before,
"mse_after": mse_after,
"mse_plume_before": mse_plume_before,
"mse_plume_after": mse_plume_after,
"mse_obs_before": mse_obs_before,
"mse_obs_after": mse_obs_after,
"mae_before": mae_before,
"mae_after": mae_after,
"mae_plume_before": mae_plume_before,
"mae_plume_after": mae_plume_after,
"mae_obs_before": mae_obs_before,
"mae_obs_after": mae_obs_after,
"nmse_before": nmse_before,
"nmse_after": nmse_after,
"nmse_plume_before": nmse_plume_before,
"nmse_plume_after": nmse_plume_after,
"nmae_before": nmae_before,
"nmae_after": nmae_after,
"nmae_plume_before": nmae_plume_before,
"nmae_plume_after": nmae_plume_after,
"nmse_obs_before": nmse_obs_before,
"nmse_obs_after": nmse_obs_after,
"nmae_obs_before": nmae_obs_before,
"nmae_obs_after": nmae_obs_after,
}
class Visualization:
def plot_assimilation_with_building(
true_field,
pred_field,
analysis,
obs_xy,
vmax=10,
title_suffix=""
):
"""
- 建筑 mask
- 非建筑区浓度
- 同化前 / 后对比
"""
# ---------- 物理裁剪 + 建筑 mask ----------
build_data_256, non_building_mask = PrintMetrics.get_building_area()
true_field = np.where(true_field > 0, true_field, 0) * non_building_mask
pred_field = np.where(pred_field > 0, pred_field, 0) * non_building_mask
analysis = np.where(analysis > 0, analysis, 0) * non_building_mask
# ---------- 画图 ----------
fig, axs = plt.subplots(1, 3, figsize=(14, 4), dpi=300)
cmap = "inferno"
levels = np.linspace(0, vmax, 21)
im0 = axs[0].contourf(true_field, levels=levels, cmap=cmap, vmin=0, vmax=vmax,
extend='max')
axs[0].set_title('True Field' + title_suffix)
plt.colorbar(im0, ax=axs[0])
im1 = axs[1].contourf(pred_field, levels=levels, cmap=cmap, vmin=0, vmax=vmax,
extend='max')
axs[1].set_title(r'Prior Prediction Field $\psi^{f}$')
plt.colorbar(im1, ax=axs[1])
im2 = axs[2].contourf(analysis, levels=levels, cmap=cmap, vmin=0, vmax=vmax,
extend='max')
axs[2].set_title(r'Analysis $\psi^{a}$')
plt.colorbar(im2, ax=axs[2])
axs[0].scatter(obs_xy[:, 0], obs_xy[:, 1], c='red', s=15, edgecolors='k')
axs[1].scatter(obs_xy[:, 0], obs_xy[:, 1], c='red', s=15, edgecolors='k')
axs[2].scatter(obs_xy[:, 0], obs_xy[:, 1], c='red', s=15, edgecolors='k')
# ---------- 指标 ----------
axs[1].text(
80, 15,
f"$R^2$={r2_score(true_field.ravel(), pred_field.ravel()):.4f}\n"
f"$MSE$={mean_squared_error(true_field.ravel(), pred_field.ravel()):.4f}\n"
f"$MAE@Obs$={mean_absolute_error(ObservationModel.observation_operator_H(true_field, obs_xy),
ObservationModel.observation_operator_H(pred_field, obs_xy)):.3f}",
color='white'
)
axs[2].text(
80, 15,
f"$R^2$={r2_score(true_field.ravel(), analysis.ravel()):.4f}\n"
f"$MSE$={mean_squared_error(true_field.ravel(), analysis.ravel()):.4f}\n"
f"$MAE@Obs$={mean_absolute_error(ObservationModel.observation_operator_H(true_field, obs_xy),
ObservationModel.observation_operator_H(analysis, obs_xy)):.3f}",
color='white'
)
plt.tight_layout()
plt.show()
def plot_assimilation_4panel(
true_field,
pred_field,
analysis,
obs_xy,
obs_val,
vmin=0,
vmax=10,
title_suffix=""
):
# ---------- 物理裁剪 + 建筑 mask ----------
_, non_building_mask = PrintMetrics.get_building_area()
true_field = np.where(true_field > 0, true_field, 0) * non_building_mask
pred_field = np.where(pred_field > 0, pred_field, 0) * non_building_mask
analysis = np.where(analysis > 0, analysis, 0) * non_building_mask
# ---------- Figure ----------
fig, axs = plt.subplots(1, 4, figsize=(18, 4), dpi=300)
cmap = "inferno"
levels = np.linspace(0, vmax, 21)
# ---------- (a) True field ----------
im0 = axs[0].contourf(true_field, levels=levels, cmap=cmap, vmin=vmin, vmax=vmax,
extend='both')
axs[0].set_title("True Field" + title_suffix)
plt.colorbar(im0, ax=axs[0])
# ---------- (b) Prior prediction ----------
im1 = axs[1].contourf(pred_field, levels=levels, cmap=cmap, vmin=vmin, vmax=vmax,
extend='both')
axs[1].set_title(r"Prior Prediction $\psi^{f}$")
plt.colorbar(im1, ax=axs[1])
# ---------- (c) Observations (points only) ----------
sc = axs[2].scatter(
obs_xy[:, 0],
obs_xy[:, 1],
c=obs_val,
cmap=cmap,
vmin=vmin,
vmax=vmax,
s=30,
edgecolors="k",
linewidths=0.4,
alpha=0.9
)
axs[2].set_title("Observations $d_i$")
axs[2].set_xlim(0, true_field.shape[1])
axs[2].set_ylim(true_field.shape[0], 0)
axs[2].set_aspect("equal")
axs[2].invert_yaxis()
plt.colorbar(sc, ax=axs[2], extend='both')
# ---------- (d) Analysis field ----------
im3 = axs[3].contourf(analysis, levels=levels, cmap=cmap, vmin=vmin, vmax=vmax,
extend='both')
axs[3].set_title(r"Analysis $\psi^{a}$")
plt.colorbar(im3, ax=axs[3])
# ---------- Metrics ----------pred_at_obs
axs[1].text(
0.02, 0.95,
f"$R^2$={r2_score(true_field.ravel(), pred_field.ravel()):.4f}\n"
f"$MSE$={mean_squared_error(true_field.ravel(), pred_field.ravel()):.4f}",
transform=axs[1].transAxes,
va="top",
color="white"
)
axs[3].text(
0.02, 0.95,
f"$R^2$={r2_score(true_field.ravel(), analysis.ravel()):.4f}\n"
f"$MSE$={mean_squared_error(true_field.ravel(), analysis.ravel()):.4f}",
transform=axs[3].transAxes,
va="top",
color="white"
)
plt.tight_layout()
plt.show()
def plot_pe_spectrum(all_diags, save_flag=False):
C_BLUE = "#488ABA"
C_ORANGE = "#e5954e"
ds = [diag['d'] for diag in all_diags]
r_eff_values = [diag['r_eff'] for diag in all_diags]
ratio_12_values = [diag['ratio_1_2'] for diag in all_diags]
fig, ax = plt.subplots(figsize=(6, 3), dpi=300)
l1, = ax.plot(ds, r_eff_values,
marker='o', color=C_BLUE, linewidth=1.5,
alpha=0.4,
markersize=6, label=r'$r_{\rm eff}$')
ax.set_xlabel(r'Wind direction', labelpad=3)
ax.set_ylabel(r'Effective rank $r_{\rm eff}$',
color=C_BLUE, labelpad=4)
ax.tick_params(axis='y', colors=C_BLUE)
ax.spines['left'].set_color(C_BLUE)
ax.set_xticks(ds)
ax.set_xticklabels([f'{d}°' for d in ds])
ax.set_ylim(1.5, 3)
# 右轴：λ1/λ2
ax2 = ax.twinx()
l2, = ax2.plot(ds, ratio_12_values,
marker='s', color=C_ORANGE, linewidth=1.5,
alpha=0.4,
markersize=6, label=r'$\lambda_1/\lambda_2$')
ax2.set_ylabel(r'Anisotropy $\lambda_1/\lambda_2$',
color=C_ORANGE, labelpad=4)
ax2.tick_params(axis='y', colors=C_ORANGE)
ax2.spines['right'].set_color(C_ORANGE)
ax2.set_ylim(1.5, 3.5)
opt_idx = int(np.argmax(ratio_12_values))
ax2.axvline(ds[opt_idx], color='grey', linewidth=0.8, linestyle='--', alpha=0.6)
ax2.text(ds[opt_idx] + 0.8, 1.58, r'$d^{**}$', color='grey')
# 统一图例
fig.legend(handles=[l1, l2],
loc='upper left',
bbox_to_anchor=(0.15, 0.95),
ncol=1, frameon=False)
plt.tight_layout()
if save_flag:
plt.savefig('./figures/test1/reff_ratio.png', dpi=300, bbox_inches='tight',
transparent=True)
# plt.savefig('./figures/test1/reff_ratio.svg', dpi=300, bbox_inches='tight', format='svg')
plt.show()
def assimilation_scatter(psi_t_log, psi_f_log, psi_a_log, obs_xy):
def log10_formatter(x, pos):
return r'$10^{%d}$' % x
obs_true = np.log10(ObservationModel.observation_operator_H(psi_t_log, obs_xy)+1e-3)
obs_prior = np.log10(ObservationModel.observation_operator_H(psi_f_log, obs_xy)+1e-3)
obs_analysis = np.log10(ObservationModel.observation_operator_H(psi_a_log, obs_xy)+1e-3)
fig, ax = plt.subplots(figsize=(5, 4.5), dpi=300)
vmin, vamx = -4, 2
lim = [vmin, vamx]
ax.plot(lim, lim, 'k--', lw=1, label='1:1 line', zorder=1)
for obs_pred, label, color in zip(
[obs_prior, obs_analysis],
['Prior', 'Analysis'],
['steelblue', 'tomato']
):
# 散点
ax.scatter(obs_true, obs_pred, s=25, alpha=0.6, color=color, zorder=3)
slope, intercept, r, _, _ = stats.linregress(obs_true, obs_pred)
rmse = np.sqrt(np.mean((obs_pred - obs_true) ** 2))
x_fit = np.linspace(lim[0], lim[1], 100)
ax.plot(x_fit, slope * x_fit + intercept, '-', color=color, lw=1.5,
label=f'{label}r:{r:.2f}', zorder=2)
ax.set_xlabel('log(True)')
ax.set_ylabel('log(Predicted)')
ax.legend(loc='lower right', frameon=False)
ax.set_xlim(-4, 2)
ax.set_ylim(-4, 2)
ax.xaxis.set_major_formatter(ticker.FuncFormatter(log10_formatter))
ax.yaxis.set_major_formatter(ticker.FuncFormatter(log10_formatter))
plt.tight_layout()
plt.show()
def none_assimilation_scatter(psi_t_log, psi_f_log, psi_a_log, obs_xy):
def sample_independent_points(field, obs_xy, num_points=100, seed=42):
H, W = field.shape
yy, xx = np.meshgrid(np.arange(H), np.arange(W), indexing='ij')
all_xy = np.stack([xx.ravel(), yy.ravel()], axis=1)
obs_set = set(map(tuple, np.round(obs_xy).astype(int)))
obs_mask = np.array([tuple(p) not in obs_set for p in all_xy])
_, non_building_mask = PrintMetrics.get_building_area()
building_mask = non_building_mask[yy.ravel(), xx.ravel()] == 1
num_mask = field.ravel() > 1e-4
candidate_xy = all_xy[obs_mask & building_mask & num_mask]
rng = np.random.default_rng(seed)
idx = rng.choice(len(candidate_xy), num_points, replace=False)
return candidate_xy[idx]
test_xy = sample_independent_points(psi_t_log, obs_xy, 200)
obs_true = np.log10(ObservationModel.observation_operator_H(psi_t_log, test_xy) + 1e-6)
obs_prior = np.log10(ObservationModel.observation_operator_H(psi_f_log, test_xy) + 1e-6)
obs_analysis = np.log10(ObservationModel.observation_operator_H(psi_a_log, test_xy) + 1e-6)
def log10_formatter(x, pos):
return r'$10^{%d}$' % x
fig, ax = plt.subplots(figsize=(5, 4.5), dpi=300)
vmin, vmax = -4, 2
lim = [vmin, vmax]
# 1:1 line
ax.plot(lim, lim, 'k--', lw=1, label='1:1 line', zorder=1)
for obs_pred, label, color in zip(
[obs_prior, obs_analysis],
['Prior', 'Analysis'],
['steelblue', 'tomato']
):
# scatter
ax.scatter(obs_true, obs_pred,
s=30,
alpha=0.65,
color=color,
zorder=3)
slope, intercept, r, _, _ = stats.linregress(obs_true, obs_pred)
rmse = np.sqrt(np.mean((obs_pred - obs_true) ** 2))
x_fit = np.linspace(lim[0], lim[1], 100)
ax.plot(x_fit, slope * x_fit + intercept, '-', color=color, lw=1.5,
label=f'{label}r:{r:.2f}', zorder=2)
ax.set_xlim(vmin, vmax)
ax.set_ylim(vmin, vmax)
ax.set_xlabel('log(True)')
ax.set_ylabel('log(Predicted)')
ax.xaxis.set_major_formatter(ticker.FuncFormatter(log10_formatter))
ax.yaxis.set_major_formatter(ticker.FuncFormatter(log10_formatter))
ax.legend(loc='lower right', frameon=False)
plt.tight_layout()
plt.show()
def methods_comparison(source_idx=25,
num_points = 10,
methods = ['random', 'uniform', 'two_stage']):
for method in methods:
print(f"\n=== 观测点采样方法: {method} ===")
data = np.load(f'./dataset/assim_conds/fields_n{num_points}_{method}_obs1.npz', allow_pickle=True)
all_fields = data['all_fields']
sample_data = all_fields[source_idx]
psi_t_log = sample_data['trues_log']
psi_f_log = sample_data['preds_log']
psi_a_log = sample_data['analysis_log']
psi_t_ppm = sample_data['trues_ppm']
psi_f_ppm = sample_data['preds_ppm']
psi_a_ppm = sample_data['analysis_ppm']
obs_xy = sample_data['obs_xy']
obs_value_log = sample_data['obs_value_log']
obs_value_ppm = sample_data['obs_value_ppm']
Visualization.plot_assimilation_with_building(
true_field=psi_t_log,
pred_field=psi_f_log,
analysis=psi_a_log,
obs_xy=obs_xy,
vmax=10,
title_suffix=f" (idx={source_idx})"
)
Visualization.plot_assimilation_4panel(
true_field=psi_t_log,
pred_field=psi_f_log,
analysis=psi_a_log,
obs_xy=obs_xy,
obs_val=obs_value_log,
vmax=10,
title_suffix=f" (idx={source_idx})"
)
Visualization.plot_assimilation_4panel(
true_field=psi_t_ppm,
pred_field=psi_f_ppm,
analysis=psi_a_ppm,
obs_xy=obs_xy,
obs_val=obs_value_ppm,
vmax=200,
title_suffix=f" in PPM SPACE (idx={source_idx})"
)
def plot_n_hist_comparison(obs_tag=1, space_mode="log",
methods=["random", "uniform", "two_stage"],
n_list=[10, 20, 30, 40, 50],
base_dir="./dataset/assim_conds",
plot_mode="after",
target_method="two_stage"):
scope_labels = ["overall", "plume", "obs"]
metric_keys = {
"r2": ["r2", "r2_plume", "r2_obs"],
"nmse": ["nmse", "nmse_plume", "nmse_obs"],
"nmae": ["nmae", "nmae_plume", "nmae_obs"],
}
def load_method_df(space, method, num_points):
candidate_methods = [method]
if method != "two_stage_pro":
candidate_methods.append("two_stage_pro")
for m in candidate_methods:
fp = os.path.join(
base_dir,
f"assimi_{space}_n{num_points}_{m}_obs{obs_tag}.csv"
)
if os.path.exists(fp):
if m != method:
print(f"[Info] {method} not exsist, back to {fp}")
return pd.read_csv(fp)
print(f"[Warning] file not found: {candidate_methods}")
return None
def get_metric_value(df, metric_name):
before_col = f"{metric_name}_before"
after_col = f"{metric_name}_after"
if before_col not in df.columns or after_col not in df.columns:
return np.nan
before_mean = df[before_col].mean()
after_mean = df[after_col].mean()
if plot_mode == "delta":
return after_mean - before_mean
return after_mean
data = {
"r2": np.full((len(n_list), 3), np.nan),
"nmse": np.full((len(n_list), 3), np.nan),
"nmae": np.full((len(n_list), 3), np.nan),
}
for i_n, n in enumerate(n_list):
df = load_method_df(space_mode, target_method, n)
if df is None:
continue
for metric_type in ["r2", "nmse", "nmae"]:
vals = []
for mk in metric_keys[metric_type]:
vals.append(get_metric_value(df, mk))
data[metric_type][i_n, :] = vals
# print(f"space_mode = {space_mode}, plot_mode = {plot_mode}, method = {target_method}")
# for metric_type in ["r2", "mse", "mae"]:
# print(f"\n{metric_type.upper()}:")
# print(pd.DataFrame(data[metric_type], index=n_list, columns=scope_labels))
fig, ax1 = plt.subplots(figsize=(12, 6), dpi=300)
ax2 = ax1.twinx()
x_base = np.arange(len(n_list))
scope_offsets = {
"overall": -0.24,
"plume": 0.00,
"obs": 0.24,
}
bar_w = 0.20
colors = cm.get_cmap("Blues")
scope_colors = {
"overall": colors(0.45),
"plume": colors(0.65),
"obs": colors(0.85),
"edge": colors(0.85),
}
scope_linestyles = {
"overall": "-",
"plume": "--",
"obs": ":",
}
scope_markers_mse = {
"overall": "o",
"plume": "o",
"obs": "o",
}
scope_markers_mae = {
"overall": "s",
"plume": "s",
"obs": "s",
}
# -------------------------
# 左轴：R2 柱状图
# -------------------------
text_r2 = r"$\mathit{R}^2$"
for j, scope in enumerate(scope_labels):
x = x_base + scope_offsets[scope]
y = data["r2"][:, j]
ax1.bar(
x, y,
width=bar_w,
color=scope_colors[scope],
alpha=0.75,
label=f"{text_r2}-{scope}",
edgecolor=scope_colors["edge"],
zorder=2
)
# 给每根柱子加数值
for xi, yi in zip(x, y):
if np.isfinite(yi):
ax1.text(
xi, yi + 0.001, f"{yi:.2f}",
ha="center", va="bottom"
)
ax1.set_ylabel(r"$\mathit{R}^2$")
ax1.set_xticks(x_base)
ax1.set_xticklabels([f"n={n}" for n in n_list])
ax1.grid(axis="y", linestyle="--", alpha=0.25, zorder=0)
# ax1.set_ylim(0.6, 1.1)
if plot_mode == "delta":
ax1.axhline(0, color="k", linewidth=1)
# 在每个 n 下标出 overall / plume / obs
y1_min, y1_max = ax1.get_ylim()
y_text = y1_min - 0.06 * (y1_max - y1_min)
# for i in range(len(n_list)):
# ax1.text(x_base[i] + scope_offsets["overall"], y_text, "overall",
# ha="center", va="top")
# ax1.text(x_base[i] + scope_offsets["plume"], y_text, "plume",
# ha="center", va="top")
# ax1.text(x_base[i] + scope_offsets["obs"], y_text, "obs",
# ha="center", va="top")
for j, scope in enumerate(scope_labels):
x = x_base + scope_offsets[scope]
ax2.plot(
x,
data["nmse"][:, j],
color=scope_colors[scope],
linestyle="--",
marker="o",
linewidth=1.8,
markersize=5,
label=f"NMSE-{scope}",
markeredgecolor=scope_colors["edge"],
zorder=3
)
ax2.plot(
x,
data["nmae"][:, j],
color=scope_colors[scope],
linestyle=":",
marker="s",
linewidth=1.8,
markersize=5,
markeredgecolor=scope_colors["edge"],
label=f"NMAE-{scope}",
zorder=3
)
ax2.set_ylabel("NMSE / NMAE")
# ax2.set_ylim(0, 0.5)
if plot_mode == "delta":
ax2.axhline(0, color="gray", linewidth=1, alpha=0.6)
h1, l1 = ax1.get_legend_handles_labels()
h2, l2 = ax2.get_legend_handles_labels()
ax1.legend(
h1 + h2,
l1 + l2,
frameon=False,
loc="center left",
bbox_to_anchor=(1.15, 0.5)
)
plt.tight_layout()
plt.show()
def cal_all_test_resluts(config, conds_pkl_path=None,
data_path=None,
use_localization=True,
save_fields_flag=True,
save_metrics_flag=False):
'''
使用说明：
sample_method_lists = ["random", "uniform", "two_stage"]
for method in sample_method_lists:
config_test = {
"num_points": 20,
"sample_method": method,
"obs_std_scale": 0.01,
"damping": 1,
"two_stage_params": {
"min_dist": 28,
"n1_ratio": 0.6,
"stage1_support_frac": 0.2,
"stage1_grad_power": 0.8,
"stage1_value_power": 1.2,
"stage1_center_boost": 1.2,
},
}
cal_all_test_resluts(config_test)
'''
# ==== 加载数据 ====
num_points = config['num_points']
sample_method = config['sample_method'] # "random" or "uniform"
# Kalman 参数
obs_std_scale=config['obs_std_scale']
damping=config['damping']
sample_method = config["sample_method"]
sample_params = {}
params_key = f"{sample_method}_params"
if params_key in config and isinstance(config[params_key], dict):
sample_params = config[params_key]
if conds_pkl_path is not None:
loader = DataLoader(
pred_npz_path='./dataset/pre_data/all_test_pred2.npz',
meta_txt_path='./dataset/pre_data/combined_test_special.txt',
conds_pkl_path=conds_pkl_path
)
else:
loader = DataLoader(
pred_npz_path='./dataset/pre_data/all_test_pred2.npz',
meta_txt_path='./dataset/pre_data/combined_test_special.txt',
conds_pkl_path='./dataset/pre_data/pred_condition/test_results/conditioned_results_v0_5_d45_n40.pkl'
)
trues, preds = loader.trues, loader.preds
# print(f"Total test samples: {len(preds)}")
all_metrics_log = []
all_metrics_ppm = []
all_fields = []
enkf = EnKF(obs_std_scale=obs_std_scale, damping=damping)
for i in trange(len(preds), desc="Running assimilation"):
psi_f_ppm, psi_t_ppm, conds_preds, meta = loader.get_sample(idx=i, in_ppm=True)
psi_f_log = np.log1p(np.maximum(psi_f_ppm, 0))
psi_t_log = np.log1p(np.maximum(psi_t_ppm, 0))
conds_log = np.log1p(np.maximum(conds_preds, 0))
if sample_method == "smart_two_pass":
n1_ratio = float(sample_params.get('n1_ratio', 0.6))
n1_default = int(round(num_points * n1_ratio))
n1 = int(sample_params.get('n1', n1_default))
if num_points > 1:
n1 = max(1, min(n1, num_points - 1))
else:
n1 = 1
n2 = num_points - n1
psi_a_log, obs_xy, all_obs_val_log, _, _ = SamplingStrategies.smart_two_pass(
enkf=enkf,
psi_f=psi_f_log,
conds_preds=conds_log,
true_field=psi_t_log,
n1=n1,
n2=n2,
phase1_method=sample_params.get('phase1_method', 'two_stage'),
min_dist_p2=sample_params.get('min_dist_p2', 22),
under_correct_alpha=sample_params.get('under_correct_alpha', 1.5),
use_localization=sample_params.get('use_localization', use_localization),
loc_radius_pixobs=sample_params.get('loc_radius_pixobs', 35.0),
loc_radius_obsobs=sample_params.get('loc_radius_obsobs', 40.0),
seed=42,
verbose=sample_params.get('verbose', False),
)
obs_value_log = np.asarray(all_obs_val_log)
obs_value_ppm = DataLoader.log2ppm(obs_value_log)
else:
obs_xy, obs_value_ppm = SamplingStrategies.generate(psi_t_ppm, psi_f_ppm, num_points=num_points,
seed=42, method=sample_method,
ens_preds_ppm=conds_preds,
**sample_params
)
d_obs_log = np.log1p(np.maximum(obs_value_ppm, 0)) # avoid log(0)
if use_localization:
psi_a_log = enkf.enkf_localization(psi_f_log, conds_log, obs_xy, d_obs_log,
loc_radius_pixobs=35.0,
loc_radius_obsobs=30.0)
else:
psi_a_log = enkf.standard_enkf(psi_f_log, conds_log, obs_xy, d_obs_log)
# 计算innovation，判断是否需要同化
obs_prior_at_obs = np.log1p(np.maximum(
ObservationModel.observation_operator_H(psi_f_ppm, obs_xy), 0
))
obs_innovation = np.mean(np.abs(obs_prior_at_obs - d_obs_log))
threshold = config.get('innovation_threshold', 0.05)
if obs_innovation < threshold:
psi_a_log = psi_f_log
else:
psi_a_log = enkf.enkf_localization(
psi_f_log, conds_log, obs_xy, d_obs_log,
loc_radius_pixobs=35.0,
loc_radius_obsobs=30.0
)
obs_value_log = np.log1p(np.maximum(obs_value_ppm, 0)) # avoid log(0)
psi_a_ppm = DataLoader.log2ppm(psi_a_log)
# 计算指标
metrics_log = PrintMetrics.print_metrics(
i=i,
wind_speed=meta['wind_speed'],
wind_direction=meta['wind_direction'],
sc=meta['sc'],
source_number=meta['source_number'],
true_field=psi_t_log,
pred_field=psi_f_log,
analysis=psi_a_log,
obs_xy=obs_xy,
metrics_save_flag=True,
metrics_print_flag=False
)
all_metrics_log.append(metrics_log)
metrics_ppm = PrintMetrics.print_metrics(
i=i,
wind_speed=meta['wind_speed'],
wind_direction=meta['wind_direction'],
sc=meta['sc'],
source_number=meta['source_number'],
true_field=psi_t_ppm,
pred_field=psi_f_ppm,
analysis=psi_a_ppm,
obs_xy=obs_xy,
metrics_save_flag=True,
metrics_print_flag=False
)
all_metrics_ppm.append(metrics_ppm)
all_fields.append({
"idx": i,
"trues_log": psi_t_log,
"preds_log": psi_f_log,
"analysis_log": psi_a_log,
"trues_ppm": psi_t_ppm,
"preds_ppm": psi_f_ppm,
"analysis_ppm": psi_a_ppm,
"obs_xy": obs_xy,
"obs_value_log": obs_value_log,
"obs_value_ppm": obs_value_ppm,
})
data_paths = f'./dataset/assim_conds/{data_path}'
if not os.path.exists(data_paths):
os.makedirs(data_paths)
if save_fields_flag:
np.savez_compressed(f'./dataset/assim_conds/{data_path}/fields_n{num_points}_{sample_method}_obs{int(obs_std_scale*100)}_damping{damping}.npz',
all_fields=all_fields)
all_metrics_df_log = pd.DataFrame(all_metrics_log)
all_metrics_df_ppm = pd.DataFrame(all_metrics_ppm)
if save_metrics_flag:
all_metrics_df_ppm.to_csv(f'./dataset/assim_conds/{data_path}/assimi_ppm_n{num_points}_{sample_method}_obs{int(obs_std_scale*100)}_damping{damping}.csv', index=False)
all_metrics_df_log.to_csv(f'./dataset/assim_conds/{data_path}/assimi_log_n{num_points}_{sample_method}_obs{int(obs_std_scale*100)}_damping{damping}.csv', index=False)
print("\n=== 平均指标提升 ===")
metrics_list = ['r2', 'w_r2_plume', 'r2_plume','mse', 'mae']
for metric in metrics_list:
before_mean = all_metrics_df_log[f"{metric}_before"].mean()
after_mean = all_metrics_df_log[f"{metric}_after"].mean()
delta = after_mean - before_mean
print(f'{metric.upper()}: before={before_mean:.4f}, after={after_mean:.4f}, delta={delta:.4f}')
before_mean_ppm = all_metrics_df_ppm[f"{metric}_before"].mean()
after_mean_ppm = all_metrics_df_ppm[f"{metric}_after"].mean()
delta_ppm = after_mean_ppm - before_mean_ppm
print(f'PPM {metric.upper()}: before={before_mean_ppm:.4f}, after={after_mean_ppm:.4f}, delta={delta_ppm:.4f}') |