diff --git a/config/cabin_robot.xml b/config/cabin_robot.xml
index ec60e00b..d229e483 100644
--- a/config/cabin_robot.xml
+++ b/config/cabin_robot.xml
@@ -83,12 +83,12 @@
-
+
-
+
diff --git a/config/robot_description/hc_description/dual_arm.urdf b/config/robot_description/hc_description/dual_arm.urdf
index c22ca9e1..38221c58 100644
--- a/config/robot_description/hc_description/dual_arm.urdf
+++ b/config/robot_description/hc_description/dual_arm.urdf
@@ -559,7 +559,37 @@
-
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
@@ -573,10 +603,11 @@
-
-
+
+
-
+
+
diff --git a/python/hand_eye_calibration/check_precesion.py b/python/hand_eye_calibration/check_precesion.py
new file mode 100644
index 00000000..b5747f63
--- /dev/null
+++ b/python/hand_eye_calibration/check_precesion.py
@@ -0,0 +1,65 @@
+import numpy as np
+import pandas as pd
+from pathlib import Path
+
+
+def get_T_cp():
+ pass
+
+# ---------- 用户给出的 T_ee->cam ----------
+# R_ec = np.array([[ 0.92095 , 0.33864198, 0.19280227],
+# [-0.2465026 , 0.88946636, -0.38481952],
+# [-0.30180718, 0.30687328, 0.90263017]])
+# t_ec = np.array([[-0.00596568],
+# [-0.61356453],
+# [ 0.02675161]])
+
+R_ec = np.array([[ 1 , 0, 0],
+ [0 , 1, 0],
+ [0, 0, 1]])
+t_ec = np.array([[-0.035],
+ [-0.18],
+ [ 0.08]])
+
+T_ec = np.eye(4)
+T_ec[:3, :3] = R_ec
+T_ec[:3, 3] = t_ec.ravel()
+
+# ---------- 读取 RobotToolPose.csv ----------
+csv_path = Path("RobotToolPose.csv")
+if not csv_path.exists():
+ raise FileNotFoundError("RobotToolPose.csv 未上传到工作目录")
+
+mat = np.loadtxt(csv_path, delimiter=',') # 3 × (4*N)
+if mat.shape[0] != 3 or mat.shape[1] % 4 != 0:
+ raise ValueError("RobotToolPose.csv 格式应为 3×(4*N)")
+
+N = mat.shape[1] // 4
+T_list = []
+for i in range(N):
+ block = mat[:, 4*i:4*i+4] # 3×4
+ T = np.eye(4)
+ T[:3,:4] = block
+ T_list.append(T)
+
+# ---------- 计算 T_base->cam = T_base->ee * T_ee->cam ----------
+T_bc_list = [T_be @ T_ec for T_be in T_list]
+
+# 简单检查：平移范数 & 旋转角度（相对于第一帧）
+def rot_to_axis_angle(R):
+ angle = np.arccos(max(min((np.trace(R)-1)/2,1),-1))
+ return angle
+
+trans_norms = [np.linalg.norm(T[:3,3]) for T in T_bc_list]
+rot_angles = [rot_to_axis_angle(T[:3,:3] @ T_bc_list[0][:3,:3].T) for T in T_bc_list]
+
+df = pd.DataFrame({
+ 'frame': np.arange(N),
+ 'tx': [T[0,3] for T in T_bc_list],
+ 'ty': [T[1,3] for T in T_bc_list],
+ 'tz': [T[2,3] for T in T_bc_list],
+ '||t|| (m)': trans_norms,
+ 'Δθ w.r.t #0 (rad)': rot_angles
+})
+
+print(df)
diff --git a/python/hand_eye_calibration/compute_in_hand.py b/python/hand_eye_calibration/compute_in_hand.py
index 9a3b91ab..fd3c49e6 100644
--- a/python/hand_eye_calibration/compute_in_hand.py
+++ b/python/hand_eye_calibration/compute_in_hand.py
@@ -4,15 +4,17 @@
眼在手上 用采集到的图片信息和机械臂位姿信息计算 相机坐标系相对于机械臂末端坐标系的 旋转矩阵和平移向量
A2^{-1}*A1*X=X*B2*B1^{−1}
"""
-
import os
import logging
+
import yaml
+
import cv2
import numpy as np
from scipy.spatial.transform import Rotation as R
+
from libs.auxiliary import find_latest_data_folder
from libs.log_setting import CommonLog
@@ -46,12 +48,15 @@ def func():
# 获取标定板角点的位置
objp = np.zeros((XX * YY, 3), np.float32)
+
objp[:, :2] = np.mgrid[0:XX, 0:YY].T.reshape(-1, 2) # 将世界坐标系建在标定板上，所有点的Z坐标全部为0，所以只需要赋值x和y
objp = L*objp
+
obj_points = [] # 存储3D点
img_points = [] # 存储2D点
+
images_num = [f for f in os.listdir(images_path) if f.endswith('.jpg')]
for i in range(1, len(images_num) + 1): #标定好的图片在images_path路径下，从0.jpg到x.jpg
@@ -62,6 +67,7 @@ def func():
logger_.info(f'读 {image_file}')
+
img = cv2.imread(image_file)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
@@ -70,6 +76,7 @@ def func():
if ret:
+
obj_points.append(objp)
corners2 = cv2.cornerSubPix(gray, corners, (5, 5), (-1, -1), criteria) # 在原角点的基础上寻找亚像素角点
@@ -130,4 +137,3 @@ if __name__ == '__main__':
logger_.info(f"平移向量是:\n { translation_vector}")
logger_.info(f"四元数是：\n { quaternion}")
-
diff --git a/python/hand_eye_calibration/config/config.yaml b/python/hand_eye_calibration/config/config.yaml
new file mode 100644
index 00000000..709826f2
--- /dev/null
+++ b/python/hand_eye_calibration/config/config.yaml
@@ -0,0 +1,5 @@
+# 棋盘格参数
+checkerboard_args:
+ XX : 11 #标定板的中长度对应的角点的个数
+ YY : 8 #标定板的中宽度对应的角点的个数
+ L : 0.015 #标定板一格的长度 单位为米
\ No newline at end of file
diff --git a/python/hand_eye_calibration/data_collect.py b/python/hand_eye_calibration/data_collect.py
new file mode 100644
index 00000000..1f74cfb2
--- /dev/null
+++ b/python/hand_eye_calibration/data_collect.py
@@ -0,0 +1,151 @@
+import os
+import time
+import pyrealsense2 as rs
+import numpy as np
+import cv2
+
+# =============================
+# 1. 关节点位定义（20组，每组7个）
+# =============================
+# JOINT_POSITIONS = [
+# [-0.1756037771702, 1.1800090074539, 1.8522456884384, 1.5761110782623, -2.7331359386444, 0.2563314139843, -0.0606026798487],
+# [-0.1923076510429, 1.1099290847778, 1.8832376003265, 1.7514026165009, -2.6219944953918, 0.1934144645929, 0.2889180481434],
+# [0.2704087197781, 0.9557962417603, 1.7136577367783, 1.6711657047272, -2.5147924423218, 0.0584972538054, 0.783962905407],
+# [0.383531242609, 0.7108500599861, 1.7350907325745, 1.8691725730896, -2.2797479629517, -0.1232690215111, 1.2245988845825],
+# [0.3922453224659, 0.7737803459167, 1.8068689107895, 1.6336191892624, -2.1118586063385, -0.0338453501463, 1.1426001787186],
+# [0.2443187087774, 0.7818641066551, 1.9731577634811, 1.5233805179596, -1.872123837471, 0.0405574627221, 1.1054704189301],
+# [0.1276659220457, 0.9156659245491, 2.0081288814545, 1.4481414556503, -1.8519257307053, 0.2532368600368, 0.8394842743874],
+# [0.1861536949873, 1.0141937732697, 1.8355123996735, 1.1459348201752, -2.2625010013580, 0.5969776511192, 0.2645072638988],
+# [-0.0192848723382, 0.9415585398674, 1.8732609748840, 1.1809715032578, -2.6499755382538, 0.6354467868805, -0.2003904730082],
+# [-0.1747295111418, 1.0453623533249, 1.8939573764801, 1.4446462392807, -2.6536157131195, 0.5147834420204, -0.1335265636444],
+# [-0.0191576723009, 1.0618592500687, 1.8731262683868, 1.4747705459595, -2.4312825202942, 0.3830471336842, 0.2654716968536],
+# [0.1436645090580, 0.8961057662964, 1.9278227090836, 1.5483351945877, -2.1520030498505, 0.2300240248442, 0.7717508077621],
+# [-0.2458887547255, 1.3182551860809, 1.8035455942154, 1.7302685976028, -2.8433899879456, 0.0447540767491, -0.0295310281217],
+# [-0.1628440171480, 1.1346882581711, 1.7724473476410, 1.8686875104904, -2.8621222972870, 0.0011371960863, 0.3351055085659],
+# [0.2227641940117, 0.9846931695938, 1.5547094345093, 1.9238842725754, -2.8571910858154, -0.1052865162492, 0.8961741328239],
+# [0.4523145258427, 0.7738097310066, 1.5341449975967, 1.9425786733627, -2.5897336006165, -0.1147941574454, 1.2599183320999],
+# [0.6323996782303, 0.7300637960434, 1.3266047239304, 2.0460503101349, -2.7566838264465, -0.1049704179168, 1.4412622451782],
+# [0.6435400247574, 0.7136778831482, 1.3097128868103, 2.0483627319336, -2.7511403560638, -0.1215622797608, 1.5219746828079],
+# [0.0131992585957, 1.1347451210022, 1.4958723783493, 1.7922105789185, -3.1400320529938, -0.2474720925093, 0.4145260155201],
+# [-0.1823624074459, 1.4246475696564, 1.7136197090149, 1.6952623128891, -2.9659118652344, -0.1499162465334, -0.0364842526615],
+# ]
+
+# JOINT_POSITIONS = [
+# [-0.718823, 1.40293, 2.16063, 1.88231, -2.7615, 0.489431, -0.26], # 0.05 -0.3 -0.2
+# [-0.708813, 1.12828, 2.50833, 1.80323, -2.31185, 0.655554, 0.0481596], # 0.05 -0.4 -0.12
+# [ -0.741438, 1.25175, 2.32353, 1.87691, -2.52151, 0.551262, -0.127302], # 0.05 -0.35 -0.16
+# [-0.450394, 1.09129, 2.19947, 1.44129, -2.64341, 0.614236, -0.26], # 0.1 -0.4 -0.21
+# [-0.594676, 1.36527, 1.89605, 1.89043, -2.83642, 0.234229, -0.204878], # 0.1 -0.25 -0.21
+# [ -0.741438, 1.25175, 2.32353, 1.87691, -2.52151, 0.551262, -0.127302], # 0.1 -0.33 -0.15
+# [-0.390978, 1.16001, 2.05936, 1.90062, -2.5732, 0.280691, 0.125318], # 0.15 -0.31 -0.14
+# [-0.373488, 1.2204, 1.93236, 1.6772, -2.73648, 0.297523, -0.140674], # 0.15 -0.3 -0.21
+# [-0.401033, 1.34807, 1.77306, 1.81898, -2.87362, 0.135286, -0.111775], # 0.15 -0.23 -0.21
+# [ -0.175812, 1.17802, 1.85193, 1.57614, -2.73311, 0.256294, -0.0606398], # 0.2 -0.3 -0.2
+# [-0.373488, 1.2204, 1.93236, 1.6772, -2.73648, 0.297523, -0.140674], # 0.2 -0.25 -0.17
+# [-0.0984266, 1.07668, 1.88658, 1.2654, -2.72499, 0.414404, -0.234536], # 0.2 -0.36 -0.23
+# [0.0871982, 1.09615, 1.7789, 1.19035, -2.75303, 0.346485, -0.173718], # 0.25 -0.34 -0.22
+# [ 0.0222405, 1.18277, 1.74292, 1.51783, -2.75674, 0.178517, 0.0381994], # 0.25 -0.28 -0.18
+# [0.047511, 1.19839, 1.72007, 1.29276, -2.81499, 0.234448, -0.163403], # 0.25 -0.29 -0.23
+# ]
+
+JOINT_POSITIONS = [
+ [1.1838487386703, 0.4235305488110, 1.2719281911850, 1.5996315479279, -1.8642479181290, -0.1892226040363, 1.5996874570847], # 0.05 -0.3 -0.2
+ [1.1144028902054, 0.9934917092323, 1.2339128255844, 1.9597420692444, -2.7524161338806, -0.1010338962078, 2.0266277790070], # 0.05 -0.4 -0.12
+ [0.7585971355438, 1.2986438274384, 1.5511839389801, 1.9102549552917, -3.1083328723907,0.3383120596409, 1.7495325803757], # 0.05 -0.35 -0.16
+ [0.7717612981796, -0.0227999277413, 1.2341349124908, 0.8974148035049, -1.1484123468399, 1.0799195766449, 1.3140604496002], # 0.1 -0.4 -0.21
+ [0.3262319564819, 1.4255702495575, 1.0224531888962, 1.6128582954407, -3.1161243915558, -1.0476765632629, 0.5188082456589], # 0.1 -0.25 -0.21
+ [0.6811909079552, -0.0465415082872, 0.6306891441345, 0.7774662375450, -0.9128333926201, 1.0805308818817, 0.7373644113541], # 0.1 -0.33 -0.15
+ [0.5894140601158, 0.5544267892838, 0.1245087385178, 0.6875356435776, -1.0255894660950, 1.0803401470184, -0.3253187835217], # 0.15 -0.31 -0.14
+ [-0.0615737587214, 0.5200612545013, 2.2596797943115, 1.9721800088882, -1.8687928915024, -0.0344338789582, 1.2385385036469], # 0.15 -0.3 -0.21
+ [0.9264683723450, 1.2653641700745, 1.7594407796860, 1.5681478977203, -2.9171006679535, 0.1166879013181, 1.7105128765106], # 0.15 -0.23 -0.21
+ [1.0020719766617, 0.4507283568382, 1.0405201911926, 1.0608046054840, -1.5450072288513, 0.1164857149124, 1.1358253955841], # 0.2 -0.3 -0.2
+ [0.7424476146698, 0.0144095467404, 0.9496489167213, 0.8356217741966, -1.2565077543259, 1.0145307779312, 0.8847945928574], # 0.2 -0.25 -0.17
+ [0.2278160750866, 0.7444448471069, 0.9402200579643, 0.5896779298782, -1.5535788536072, 1.0801521539688, -0.3112177252769], # 0.2 -0.36 -0.23
+ [-0.0866613686085, 1.3698191642761, 1.1545568704605, 1.6992814540863, -2.7753651142120, -0.8176611065865, -0.1862865835428], # 0.25 -0.34 -0.22
+ [0.2748872637749, 1.5703777074814, 1.0753053426743, 1.0457334518433, -2.9064140319824, -0.9773108959198, -0.3075242042542], # 0.25 -0.28 -0.18
+ [0.2410523593426, 0.9616132378578, 1.6866004467010, 1.9817161560059, -2.7480125427246, 0.0709219276905, 1.1297777891159],
+ [0.5065582394600, 0.8918085694313, 2.1731114387512, 1.3742331266403, -2.4524819850922, 1.0801626443863, 1.1498324871063],
+ [0.5651561021805, 0.7603342533112, 1.1436395645142, 1.9550926685333, -1.9175499677658, -1.0519500970840, 0.6535356044769],
+ [-0.3106776177883, 0.8671860694885, 2.9035902023315, 1.7579817771912, -1.5784481763840, 1.0808564424515, 1.4593633413315],
+ [0.0202996153384, -0.2583181858063, 1.7621537446976, 1.8765900135040, -1.8188692331314, -0.0442187041044, 0.7810990214348], # 0.25 -0.29 -0.23
+]
+
+input_i = int(input("set index: "))
+# =============================
+# 2. 初始化RealSense
+# =============================
+TARGET_SERIAL = "243122075614"
+ctx = rs.context()
+devices = ctx.query_devices()
+if len(devices) == 0:
+ raise RuntimeError("未检测到任何 RealSense 相机，请检查连接")
+
+selected_device = None
+for dev in devices:
+ if dev.get_info(rs.camera_info.serial_number) == TARGET_SERIAL:
+ selected_device = dev
+ break
+if selected_device is None:
+ raise RuntimeError(f"未找到序列号为 {TARGET_SERIAL} 的 RealSense 相机")
+
+pipeline = rs.pipeline()
+config = rs.config()
+config.enable_device(TARGET_SERIAL)
+config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
+config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)
+pipeline.start(config)
+
+align = rs.align(rs.stream.color)
+
+# =============================
+# 3. 数据采集流程
+# =============================
+output_joint_file = "data/20250804-1331/joints_record.txt"
+
+with open(output_joint_file, "a") as f:
+ # for idx, q in enumerate(JOINT_POSITIONS, start=1):
+ idx = input_i
+ q = JOINT_POSITIONS[input_i]
+ print(f"\n===== 采集点 {idx} =====")
+ cmd = "moveJ one right " + " ".join(str(v) for v in q)
+ os.system(cmd)
+ print("运动完成，等待1秒...")
+ time.sleep(1)
+
+ # 获取关节角度输出
+ tmp_file = "./data/tmp_joint_output.txt"
+ os.system(f"getJoint > {tmp_file}")
+ with open(tmp_file, "r") as tf:
+ lines = tf.readlines()
+ joint_output = lines[-1].strip() if lines else ""
+ print("getJoint 输出：\n", joint_output)
+
+ # 保存关节数据
+ f.write(f"{idx} {joint_output}\n")
+ f.flush()
+
+ # 捕获对齐后的彩色图像
+ for i in range(10):
+ try:
+ frames = pipeline.wait_for_frames(timeout_ms=5000)
+ if frames:
+ break
+ except RuntimeError:
+ print("⚠️ 读取帧超时，重试中...")
+ time.sleep(1)
+ frames = pipeline.wait_for_frames()
+ aligned_frames = align.process(frames)
+ color_frame = aligned_frames.get_color_frame()
+
+ if not color_frame:
+ print("未获取彩色图像，跳过")
+ exit(-1)
+
+ color_image = np.asanyarray(color_frame.get_data())
+ img_path = os.path.join(os.path.dirname(output_joint_file), f"img_{idx}.png")
+ cv2.imwrite(img_path, color_image)
+ print(f"保存图像 {img_path}")
+
+# 停止RealSense
+pipeline.stop()
+print("\n=== 数据采集完成 ===")
diff --git a/python/math/rpy2rotation.py b/python/math/rpy2rotation.py
new file mode 100644
index 00000000..5039208b
--- /dev/null
+++ b/python/math/rpy2rotation.py
@@ -0,0 +1,79 @@
+import numpy as np
+
+def rpy_to_matrix(roll, pitch, yaw, degrees=False):
+ """
+ URDF: R = Rz(yaw) @ Ry(pitch) @ Rx(roll)
+ roll, pitch, yaw 默认为弧度；degrees=True 时按角度输入。
+ """
+ if degrees:
+ roll, pitch, yaw = np.deg2rad([roll, pitch, yaw])
+
+ cr, sr = np.cos(roll), np.sin(roll)
+ cp, sp = np.cos(pitch), np.sin(pitch)
+ cy, sy = np.cos(yaw), np.sin(yaw)
+
+ Rx = np.array([[1, 0, 0],
+ [0, cr, -sr],
+ [0, sr, cr]], dtype=float)
+ Ry = np.array([[ cp, 0, sp],
+ [ 0, 1, 0],
+ [-sp, 0, cp]], dtype=float)
+ Rz = np.array([[cy, -sy, 0],
+ [sy, cy, 0],
+ [ 0, 0, 1]], dtype=float)
+
+ return Rz @ Ry @ Rx
+
+
+def matrix_to_rpy(R, degrees=False, eps=1e-9):
+ """
+ 从旋转矩阵恢复 URDF 的 (roll, pitch, yaw)，满足 R = Rz(yaw)·Ry(pitch)·Rx(roll)。
+ 返回弧度；degrees=True 时返回角度。
+ 含万向节锁处理。
+ """
+ R = np.asarray(R, dtype=float)
+ assert R.shape == (3, 3)
+
+ # 可选：小幅正交化以抑制数值误差（不想要可注释掉）
+ # 使用极分解的简化：R ≈ U*V^T
+ U, _, Vt = np.linalg.svd(R)
+ R = U @ Vt
+
+ # R[2,0] = -sin(pitch)
+ sp_neg = R[2, 0]
+ sp_neg = np.clip(sp_neg, -1.0, 1.0)
+
+ # 非万向节锁：|sp_neg| < 1
+ if abs(sp_neg) < 1.0 - eps:
+ pitch = np.arcsin(-sp_neg) # pitch
+ roll = np.arctan2(R[2, 1], R[2, 2]) # roll
+ yaw = np.arctan2(R[1, 0], R[0, 0]) # yaw
+ else:
+ # 万向节锁：|sp_neg| ≈ 1，此时 yaw 与 roll 耦合
+ # 令 yaw = 0，通过 R 的其它项恢复 roll
+ pitch = np.pi/2 if sp_neg < 0 else -np.pi/2
+ yaw = 0.0
+ # 当 pitch = ±pi/2 时，R[0,1] 与 R[1,1] 携带 roll 信息
+ # 推导自 R = Rz(yaw)·Ry(±pi/2)·Rx(roll)
+ roll = np.arctan2(-R[0, 1] if sp_neg < 0 else R[0, 1],
+ R[1, 1])
+
+ if degrees:
+ return tuple(np.rad2deg([roll, pitch, yaw]))
+ return (roll, pitch, yaw)
+
+
+# ---------- 简单自检 ----------
+if __name__ == "__main__":
+ # rpy = (0.3, -0.6, 1.2) # roll, pitch, yaw (rad)
+ # R = rpy_to_matrix(*rpy)
+ # rpy_back = matrix_to_rpy(R)
+ # print("R:\n", R)
+ # print("rpy back:", rpy_back)
+ # print("max abs diff:", np.max(np.abs(np.array(rpy) - np.array(rpy_back))))
+ R = np.array([
+ [-1, 0, 0],
+ [0, -1, 0],
+ [0, 0, 1]
+ ])
+ print(matrix_to_rpy(R))
\ No newline at end of file
diff --git a/src/devices/canbus/CMakeLists.txt b/src/devices/canbus/CMakeLists.txt
index adea0db4..f9cf48ba 100644
--- a/src/devices/canbus/CMakeLists.txt
+++ b/src/devices/canbus/CMakeLists.txt
@@ -4,7 +4,7 @@ find_package(protobuf REQUIRED)
add_library(canbus SHARED
${CMAKE_CURRENT_SOURCE_DIR}/can_client/socket/socket_can_client_raw.cc
- ${CMAKE_CURRENT_SOURCE_DIR}/can_client/pcan/pcan_client.cc
+# ${CMAKE_CURRENT_SOURCE_DIR}/can_client/pcan/pcan_client.cc
${CMAKE_CURRENT_SOURCE_DIR}/common/byte.cc)
target_include_directories(canbus PUBLIC ${CMAKE_CURRENT_SOURCE_DIR})
@@ -13,9 +13,11 @@ add_library(cmvr_es::device::canbus ALIAS canbus)
target_link_libraries(canbus
PRIVATE
+ proto-objects
+ gRPC::grpc++
protobuf::libprotobuf
glog::glog
- PUBLIC pcanbasic
+# PUBLIC pcanbasic
)
# --------------------------------------------------------
diff --git a/src/devices/robot/humanoid_robot/joint_positions_1.csv b/src/devices/robot/humanoid_robot/joint_positions_1.csv
index b8cfecb5..5f21dbbb 100644
--- a/src/devices/robot/humanoid_robot/joint_positions_1.csv
+++ b/src/devices/robot/humanoid_robot/joint_positions_1.csv
@@ -1,201 +1,201 @@
-idx,q0, q1, q2, q3, q4, q5, q6,R_SHOULDER_P,R_SHOULDER_R,R_SHOULDER_Y,R_ELBOW_R,R_WRIST_P,R_WRIST_Y,R_WRIST_R,pLx, pLy, pLz, pRx, pRy, pRz
-0,-0.060915,-0.408236,-1.315908,-1.570092,1.967174,0.099621,0.174091,0.076603,1.162435,1.720999,1.810691,-2.709591,0.039061,0.356226,0.250063,0.349893,-0.099981,0.250748,-0.25,-0.1
-1,-0.060358,-0.401354,-1.32106,-1.576596,1.961162,0.102057,0.168042,0.079723,1.162522,1.719628,1.807672,-2.710127,0.039137,0.35604,0.250106,0.349963,-0.096917,0.251478,-0.249999,-0.099996
-2,-0.05968,-0.39443,-1.326281,-1.583948,1.955119,0.105347,0.16177,0.082918,1.162616,1.718223,1.804555,-2.71068,0.039224,0.355827,0.250116,0.349818,-0.093782,0.252227,-0.249999,-0.099996
-3,-0.059082,-0.387617,-1.331445,-1.5915,1.949159,0.108962,0.155437,0.086114,1.162716,1.71682,1.801428,-2.711233,0.039315,0.355606,0.250119,0.349573,-0.09065,0.252977,-0.249999,-0.099996
-4,-0.058629,-0.380953,-1.336515,-1.599206,1.943318,0.112876,0.149029,0.089308,1.16282,1.715421,1.798294,-2.711785,0.039412,0.355376,0.250119,0.349229,-0.087527,0.253727,-0.249999,-0.099996
-5,-0.058345,-0.374454,-1.341473,-1.607054,1.937613,0.117085,0.142542,0.0925,1.162929,1.714026,1.795153,-2.712338,0.039512,0.355139,0.250119,0.348787,-0.084416,0.254477,-0.249999,-0.099996
-6,-0.05825,-0.368135,-1.346304,-1.615036,1.932059,0.121585,0.135974,0.095689,1.163043,1.712635,1.792005,-2.712891,0.039617,0.354894,0.250118,0.348247,-0.08132,0.255227,-0.249999,-0.099996
-7,-0.058365,-0.362008,-1.350992,-1.623146,1.926671,0.126373,0.129323,0.098876,1.163161,1.711247,1.78885,-2.713444,0.039725,0.354641,0.250116,0.347611,-0.078242,0.255977,-0.249999,-0.099996
-8,-0.058709,-0.356088,-1.355519,-1.631377,1.921466,0.131446,0.122585,0.102061,1.163284,1.709863,1.785688,-2.713997,0.039839,0.354381,0.250113,0.346878,-0.075186,0.256727,-0.249999,-0.099996
-9,-0.059306,-0.350389,-1.359868,-1.639721,1.91646,0.136799,0.115756,0.105243,1.163411,1.708483,1.782519,-2.71455,0.039956,0.354113,0.250109,0.34605,-0.072155,0.257477,-0.249999,-0.099996
-10,-0.060176,-0.344926,-1.364019,-1.648171,1.911668,0.142428,0.108834,0.108424,1.163543,1.707107,1.779343,-2.715104,0.040077,0.353837,0.250104,0.345128,-0.06915,0.258227,-0.249999,-0.099996
-11,-0.061344,-0.339714,-1.367952,-1.656721,1.907109,0.148328,0.101812,0.111602,1.16368,1.705734,1.776159,-2.715657,0.040203,0.353553,0.250097,0.344111,-0.066176,0.258977,-0.249998,-0.099996
-12,-0.062969,-0.334882,-1.371537,-1.665188,1.9029,0.154381,0.094696,0.114728,1.163818,1.704388,1.773028,-2.7162,0.040328,0.353274,0.250106,0.343021,-0.063264,0.259714,-0.249998,-0.099994
-13,-0.06483,-0.330233,-1.374954,-1.673896,1.898871,0.160799,0.087449,0.1179,1.163963,1.703023,1.769834,-2.716753,0.040461,0.352976,0.2501,0.341822,-0.060361,0.260464,-0.249998,-0.099994
-14,-0.067019,-0.325859,-1.378112,-1.682702,1.895108,0.167483,0.080102,0.121072,1.164112,1.701662,1.766629,-2.717307,0.040599,0.35267,0.250087,0.340532,-0.057495,0.261214,-0.249998,-0.099994
-15,-0.069586,-0.321792,-1.380972,-1.691582,1.891642,0.174418,0.072639,0.124242,1.164266,1.700303,1.763418,-2.71786,0.040741,0.352355,0.250072,0.339154,-0.054671,0.261964,-0.249998,-0.099993
-16,-0.072563,-0.31805,-1.383506,-1.700525,1.888495,0.181599,0.065052,0.12741,1.164424,1.698949,1.760199,-2.718414,0.040887,0.352033,0.250055,0.337689,-0.051892,0.262714,-0.249998,-0.099993
-17,-0.075974,-0.314651,-1.38569,-1.709523,1.885686,0.189019,0.057333,0.130576,1.164587,1.697598,1.756973,-2.718968,0.041036,0.351703,0.250035,0.336137,-0.04916,0.263463,-0.249998,-0.099993
-18,-0.079841,-0.31161,-1.3875,-1.718567,1.883232,0.196671,0.049476,0.13374,1.164754,1.696251,1.75374,-2.719521,0.04119,0.351366,0.250014,0.334502,-0.046479,0.264213,-0.249998,-0.099993
-19,-0.084184,-0.308942,-1.388914,-1.727648,1.881153,0.204548,0.041475,0.136902,1.164925,1.694908,1.750499,-2.720075,0.041348,0.351021,0.24999,0.332783,-0.04385,0.264963,-0.249997,-0.099993
-20,-0.089019,-0.306661,-1.38991,-1.736757,1.879464,0.212643,0.033326,0.140062,1.1651,1.693569,1.747252,-2.72063,0.04151,0.350668,0.249965,0.330984,-0.041278,0.265713,-0.249997,-0.099993
-21,-0.094363,-0.304781,-1.390468,-1.745886,1.878182,0.22095,0.025024,0.14322,1.16528,1.692233,1.743997,-2.721184,0.041675,0.350308,0.249939,0.329106,-0.038763,0.266463,-0.249997,-0.099993
-22,-0.100423,-0.303377,-1.390478,-1.75511,1.877391,0.229577,0.016412,0.146428,1.165467,1.690878,1.740672,-2.721748,0.041851,0.349924,0.249921,0.327118,-0.036287,0.267226,-0.249998,-0.099995
-23,-0.106906,-0.302383,-1.390049,-1.764226,1.877016,0.238282,0.00775,0.149584,1.165655,1.689548,1.7374,-2.722303,0.042025,0.349548,0.249901,0.325084,-0.033897,0.267976,-0.249998,-0.099995
-24,-0.1139,-0.301816,-1.389145,-1.773337,1.877083,0.247173,-0.001063,0.152737,1.165848,1.688223,1.734123,-2.722858,0.042202,0.349164,0.249878,0.322977,-0.031573,0.268726,-0.249998,-0.099995
-25,-0.121686,-0.301807,-1.387596,-1.782444,1.877728,0.256313,-0.010194,0.155921,1.166046,1.686888,1.730797,-2.723419,0.042388,0.348762,0.249873,0.320774,-0.029305,0.269485,-0.249998,-0.099997
-26,-0.129825,-0.302184,-1.385632,-1.791483,1.878778,0.265551,-0.019371,0.159071,1.166246,1.68557,1.727504,-2.723974,0.042573,0.348362,0.249859,0.318524,-0.027119,0.270235,-0.249998,-0.099997
-27,-0.13871,-0.303127,-1.38302,-1.800472,1.880419,0.274993,-0.028835,0.162239,1.166452,1.684247,1.724178,-2.724533,0.042765,0.347948,0.249863,0.316188,-0.024998,0.27099,-0.249999,-0.099998
-28,-0.147884,-0.304436,-1.380018,-1.809418,1.882456,0.284547,-0.038334,0.165385,1.166661,1.682937,1.72087,-2.725089,0.042957,0.347533,0.249854,0.313804,-0.022959,0.27174,-0.249999,-0.099998
-29,-0.157749,-0.306312,-1.376376,-1.818277,1.885089,0.29427,-0.048092,0.168543,1.166874,1.681624,1.717538,-2.725647,0.043156,0.347106,0.249864,0.311343,-0.020992,0.272494,-0.249999,-0.099999
-30,-0.167836,-0.308537,-1.372367,-1.8271,1.88811,0.304106,-0.057867,0.171685,1.167091,1.680321,1.714215,-2.726202,0.043356,0.346676,0.249859,0.308834,-0.019107,0.273244,-0.249999,-0.099999
-31,-0.178284,-0.311188,-1.3679,-1.835862,1.891596,0.314072,-0.067738,0.174826,1.167311,1.679021,1.710885,-2.726758,0.043559,0.346238,0.249851,0.306268,-0.017303,0.273993,-0.249999,-0.099999
-32,-0.189344,-0.314406,-1.362807,-1.844489,1.895686,0.324168,-0.077835,0.177975,1.167536,1.677722,1.707536,-2.727316,0.043768,0.345789,0.249868,0.303633,-0.015576,0.274746,-0.249999,-0.099999
-33,-0.200505,-0.317941,-1.357395,-1.85307,1.900141,0.334361,-0.087902,0.181112,1.167764,1.676429,1.704191,-2.727873,0.043979,0.345336,0.249867,0.300957,-0.013936,0.275496,-0.249999,-0.099999
-34,-0.211906,-0.321871,-1.351569,-1.861564,1.90504,0.344657,-0.098013,0.184249,1.167996,1.675141,1.700838,-2.728429,0.044193,0.344875,0.249865,0.298231,-0.012381,0.276246,-0.249999,-0.099999
-35,-0.223529,-0.326198,-1.345331,-1.869954,1.910385,0.355043,-0.10816,0.187384,1.168232,1.673855,1.697478,-2.728986,0.044412,0.344406,0.249864,0.295457,-0.010913,0.276996,-0.249999,-0.099999
-36,-0.235332,-0.330907,-1.338698,-1.878231,1.916165,0.365511,-0.118324,0.190517,1.168472,1.672574,1.69411,-2.729543,0.044633,0.34393,0.249864,0.292638,-0.009532,0.277746,-0.249999,-0.099999
-37,-0.247268,-0.335982,-1.33169,-1.886387,1.922366,0.376051,-0.12848,0.193649,1.168715,1.671295,1.690734,-2.7301,0.044858,0.343447,0.249865,0.289776,-0.008241,0.278496,-0.249999,-0.099999
-38,-0.259293,-0.341406,-1.324327,-1.894413,1.928971,0.386654,-0.138607,0.196779,1.168962,1.670021,1.687351,-2.730658,0.045087,0.342955,0.249867,0.286875,-0.00704,0.279246,-0.249999,-0.099999
-39,-0.271362,-0.347161,-1.316629,-1.902302,1.935964,0.39731,-0.148681,0.199908,1.169213,1.66875,1.68396,-2.731215,0.045319,0.342456,0.24987,0.283938,-0.00593,0.279996,-0.249999,-0.099999
-40,-0.283431,-0.353228,-1.308617,-1.910045,1.943329,0.408009,-0.158679,0.203036,1.169468,1.667482,1.680561,-2.731774,0.045555,0.341949,0.249873,0.280966,-0.004914,0.280746,-0.249999,-0.099999
-41,-0.29546,-0.35959,-1.300309,-1.917635,1.951048,0.418742,-0.168577,0.206163,1.169727,1.666218,1.677154,-2.732332,0.045794,0.341435,0.249877,0.277964,-0.003991,0.281496,-0.249999,-0.099999
-42,-0.307408,-0.366228,-1.291725,-1.925064,1.959102,0.429498,-0.178352,0.209288,1.169989,1.664958,1.67374,-2.73289,0.046036,0.340913,0.249882,0.274934,-0.003162,0.282246,-0.249999,-0.099999
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-45,-0.342146,-0.387462,-1.264666,-1.946368,1.984945,0.461783,-0.206575,0.218647,1.170796,1.661201,1.663461,-2.734566,0.046782,0.339305,0.249872,0.265727,-0.001259,0.284493,-0.249999,-0.099998
-46,-0.353431,-0.395026,-1.255151,-1.95307,1.994159,0.472532,-0.215647,0.221767,1.171073,1.659954,1.660015,-2.735126,0.047038,0.338752,0.249878,0.262616,-0.000816,0.285243,-0.249999,-0.099998
-47,-0.364474,-0.40278,-1.245444,-1.959571,2.003617,0.483254,-0.224493,0.224886,1.171353,1.658711,1.656561,-2.735686,0.047298,0.338191,0.249884,0.259492,-0.000472,0.285993,-0.249999,-0.099998
-48,-0.375249,-0.410707,-1.235564,-1.965864,2.0133,0.493938,-0.233093,0.228004,1.171636,1.657471,1.653099,-2.736246,0.04756,0.337623,0.24989,0.256359,-0.000225,0.286743,-0.249999,-0.099998
-49,-0.385729,-0.41879,-1.225527,-1.971942,2.023189,0.504573,-0.24143,0.231121,1.171924,1.656235,1.649628,-2.736807,0.047826,0.337047,0.249897,0.25322,-0.000076,0.287493,-0.249999,-0.099998
-50,-0.395894,-0.427014,-1.215347,-1.977796,2.033265,0.515151,-0.249487,0.234237,1.172214,1.655002,1.64615,-2.737368,0.048095,0.336463,0.249903,0.250077,-0.000027,0.288243,-0.249999,-0.099998
-51,-0.405725,-0.435365,-1.205041,-1.98342,2.04351,0.525659,-0.257248,0.237352,1.172508,1.653773,1.642663,-2.737929,0.048368,0.335872,0.249909,0.246935,-0.000075,0.288993,-0.249999,-0.099998
-52,-0.414974,-0.443688,-1.19478,-1.988854,2.053763,0.53604,-0.264557,0.240452,1.172805,1.652552,1.639189,-2.738489,0.04864,0.33528,0.249893,0.24382,-0.00023,0.289739,-0.249998,-0.099997
-53,-0.424075,-0.452245,-1.184272,-1.994001,2.064283,0.546375,-0.271674,0.243565,1.173106,1.65133,1.635686,-2.739051,0.048919,0.334673,0.249897,0.240688,-0.000476,0.290489,-0.249998,-0.099997
-54,-0.432811,-0.460899,-1.17367,-1.99889,2.074925,0.55661,-0.278462,0.246677,1.17341,1.65011,1.632175,-2.739613,0.049201,0.334059,0.249904,0.237563,-0.00082,0.291239,-0.249998,-0.099997
-55,-0.441151,-0.469628,-1.163001,-2.003522,2.085661,0.566733,-0.284898,0.249788,1.173718,1.648894,1.628656,-2.740176,0.049486,0.333437,0.24991,0.234451,-0.001261,0.291989,-0.249998,-0.099997
-56,-0.449084,-0.478419,-1.15228,-2.007891,2.096472,0.576733,-0.290969,0.252898,1.174029,1.647682,1.625128,-2.740739,0.049774,0.332807,0.249916,0.231354,-0.0018,0.292739,-0.249998,-0.099997
-57,-0.456598,-0.487259,-1.141525,-2.011991,2.107338,0.586598,-0.296662,0.256008,1.174343,1.646473,1.621592,-2.741303,0.050066,0.332169,0.249922,0.228275,-0.002436,0.293489,-0.249998,-0.099997
-58,-0.463685,-0.496139,-1.13075,-2.015816,2.118241,0.596319,-0.301967,0.259117,1.174661,1.645267,1.618048,-2.741867,0.05036,0.331524,0.249927,0.225218,-0.003168,0.294239,-0.249998,-0.099997
-59,-0.470337,-0.505045,-1.11997,-2.019361,2.129159,0.605883,-0.306873,0.262225,1.174982,1.644064,1.614495,-2.742431,0.050658,0.33087,0.249933,0.222185,-0.003995,0.294989,-0.249998,-0.099997
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