write tool

A/2/position_watcher.py

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+import mpmath as mp
+import json
+import sys
+
+if __name__ != "__main__":
+  sys.exit()
+
+mp.dps = 15  # 设置精度为15位小数
+
+kSegLength1 = mp.mpf('2.86')
+kSegLength2 = mp.mpf('1.65')
+kAlpha = mp.mpf('0.55') / (2 * mp.pi)
+
+def Theta2C(theta):
+  tmp = mp.sqrt(1 + theta**2)
+  return kAlpha * 0.5 * (theta * tmp - mp.log(-theta + tmp))
+
+def Theta2Dot(theta):
+  return (kAlpha * theta * mp.cos(theta), kAlpha * theta * mp.sin(theta))
+
+init_C=Theta2C(2*mp.pi*16)
+def GenerateFirstNodeTheta(time_point):
+  cur_C = init_C - time_point
+  def f(theta):
+    return Theta2C(theta) - cur_C
+  return mp.findroot(f, 2*mp.pi*16, solver='secant')
+
+def GenerateFollowNodeTheta(cur_node_theta, expected_distance):
+  cur_node_dot = Theta2Dot(cur_node_theta)
+  def f(theta):
+    test_node_dot = Theta2Dot(theta)
+    actual_distance = mp.sqrt((cur_node_dot[0]-test_node_dot[0])**2 + (cur_node_dot[1]-test_node_dot[1])**2)
+    return actual_distance - expected_distance
+  return mp.findroot(f, cur_node_theta + 0.1, solver='secant')
+
+def CalcMoveList(time_point):
+  first_node_theta = GenerateFirstNodeTheta(time_point)
+  first_node_dot = Theta2Dot(first_node_theta)
+  first_node_C = Theta2C(first_node_theta)
+
+  node_list = [{"theta": first_node_theta, "node": first_node_dot, "C": first_node_C, "v": mp.mpf('1.0')}]
+
+  for i in range(1, 224):
+    expected_distance = kSegLength1 if i == 1 else kSegLength2
+    cur_node_theta = GenerateFollowNodeTheta(node_list[-1]["theta"], expected_distance)
+    cur_node_dot = Theta2Dot(cur_node_theta)
+    cur_node_C = Theta2C(cur_node_theta)
+    node_list.append({"theta": cur_node_theta, "node": cur_node_dot, "C": cur_node_C})
+  for i in range(223):
+    AA = kSegLength1 if i == 0 else kSegLength2
+    theta_i = node_list[i]["theta"]
+    theta_ip1 = node_list[i+1]["theta"]
+    alpha_i = mp.atan(theta_i)
+    alpha_ip1 = mp.atan(theta_ip1)
+    beta_i = mp.acos(((kAlpha*theta_i)**2 + AA**2 - (kAlpha*theta_ip1)**2) / (2*kAlpha*theta_i*AA))
+    gama_i = mp.acos(((kAlpha*theta_ip1)**2 + AA**2 - (kAlpha*theta_i)**2) / (2*kAlpha*theta_ip1*AA))
+    node_list[i+1]["v"] = node_list[i]["v"] * (-mp.cos(alpha_i + beta_i) / mp.cos(alpha_ip1 - gama_i))
+
+  return node_list
+
+
+time_point= float(input())
+print(f"calculating time_point={time_point}")
+time_point_list = CalcMoveList(time_point)
+
+# 将结果转换为float并保留6位小数
+float_res_list = [
+  {k: round(float(v), 6) if isinstance(v, mp.mpf) else 
+    [round(float(x), 6) for x in v] if isinstance(v, tuple) else v
+  for k, v in node.items()}
+  for node in time_point_list
+]
+# print(float_res_list)
+json.dump(float_res_list, sys.stdout, indent=4)

environment.yml

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+name: cumcm2024
+channels:
+  - pytorch
+  - nvidia
+  - defaults
+dependencies:
+  - _libgcc_mutex=0.1=main
+  - _openmp_mutex=5.1=1_gnu
+  - blas=1.0=mkl
+  - brotli-python=1.0.9=py312h6a678d5_8
+  - bzip2=1.0.8=h5eee18b_6
+  - ca-certificates=2024.7.2=h06a4308_0
+  - certifi=2024.7.4=py312h06a4308_0
+  - charset-normalizer=3.3.2=pyhd3eb1b0_0
+  - cuda-cudart=12.1.105=0
+  - cuda-cupti=12.1.105=0
+  - cuda-libraries=12.1.0=0
+  - cuda-nvrtc=12.1.105=0
+  - cuda-nvtx=12.1.105=0
+  - cuda-opencl=12.6.68=0
+  - cuda-runtime=12.1.0=0
+  - cuda-version=12.6=3
+  - expat=2.6.2=h6a678d5_0
+  - ffmpeg=4.3=hf484d3e_0
+  - filelock=3.13.1=py312h06a4308_0
+  - freetype=2.12.1=h4a9f257_0
+  - gmp=6.2.1=h295c915_3
+  - gnutls=3.6.15=he1e5248_0
+  - idna=3.7=py312h06a4308_0
+  - intel-openmp=2023.1.0=hdb19cb5_46306
+  - jinja2=3.1.4=py312h06a4308_0
+  - jpeg=9e=h5eee18b_3
+  - lame=3.100=h7b6447c_0
+  - lcms2=2.12=h3be6417_0
+  - ld_impl_linux-64=2.38=h1181459_1
+  - lerc=3.0=h295c915_0
+  - libcublas=12.1.0.26=0
+  - libcufft=11.0.2.4=0
+  - libcufile=1.11.1.6=0
+  - libcurand=10.3.7.68=0
+  - libcusolver=11.4.4.55=0
+  - libcusparse=12.0.2.55=0
+  - libdeflate=1.17=h5eee18b_1
+  - libffi=3.4.4=h6a678d5_1
+  - libgcc-ng=11.2.0=h1234567_1
+  - libgomp=11.2.0=h1234567_1
+  - libiconv=1.16=h5eee18b_3
+  - libidn2=2.3.4=h5eee18b_0
+  - libjpeg-turbo=2.0.0=h9bf148f_0
+  - libllvm14=14.0.6=hdb19cb5_3
+  - libnpp=12.0.2.50=0
+  - libnvjitlink=12.1.105=0
+  - libnvjpeg=12.1.1.14=0
+  - libpng=1.6.39=h5eee18b_0
+  - libstdcxx-ng=11.2.0=h1234567_1
+  - libtasn1=4.19.0=h5eee18b_0
+  - libtiff=4.5.1=h6a678d5_0
+  - libunistring=0.9.10=h27cfd23_0
+  - libuuid=1.41.5=h5eee18b_0
+  - libwebp-base=1.3.2=h5eee18b_0
+  - llvm-openmp=14.0.6=h9e868ea_0
+  - llvmlite=0.43.0=py312h6a678d5_0
+  - lz4-c=1.9.4=h6a678d5_1
+  - markupsafe=2.1.3=py312h5eee18b_0
+  - mkl=2023.1.0=h213fc3f_46344
+  - mkl-service=2.4.0=py312h5eee18b_1
+  - mkl_fft=1.3.10=py312h5eee18b_0
+  - mkl_random=1.2.7=py312h526ad5a_0
+  - mpmath=1.3.0=py312h06a4308_0
+  - ncurses=6.4=h6a678d5_0
+  - nettle=3.7.3=hbbd107a_1
+  - networkx=3.2.1=py312h06a4308_0
+  - numba=0.60.0=py312h526ad5a_0
+  - numpy=1.26.4=py312hc5e2394_0
+  - numpy-base=1.26.4=py312h0da6c21_0
+  - openh264=2.1.1=h4ff587b_0
+  - openjpeg=2.5.2=he7f1fd0_0
+  - openssl=3.0.14=h5eee18b_0
+  - pillow=10.4.0=py312h5eee18b_0
+  - pip=24.2=py312h06a4308_0
+  - pysocks=1.7.1=py312h06a4308_0
+  - python=3.12.4=h5148396_1
+  - pytorch=2.4.1=py3.12_cuda12.1_cudnn9.1.0_0
+  - pytorch-cuda=12.1=ha16c6d3_5
+  - pytorch-mutex=1.0=cuda
+  - pyyaml=6.0.1=py312h5eee18b_0
+  - readline=8.2=h5eee18b_0
+  - requests=2.32.3=py312h06a4308_0
+  - setuptools=72.1.0=py312h06a4308_0
+  - sqlite=3.45.3=h5eee18b_0
+  - sympy=1.13.2=py312h06a4308_0
+  - tbb=2021.8.0=hdb19cb5_0
+  - tk=8.6.14=h39e8969_0
+  - torchaudio=2.4.1=py312_cu121
+  - torchtriton=3.0.0=py312
+  - torchvision=0.19.1=py312_cu121
+  - typing_extensions=4.11.0=py312h06a4308_0
+  - tzdata=2024a=h04d1e81_0
+  - urllib3=2.2.2=py312h06a4308_0
+  - wheel=0.43.0=py312h06a4308_0
+  - xz=5.4.6=h5eee18b_1
+  - yaml=0.2.5=h7b6447c_0
+  - zlib=1.2.13=h5eee18b_1
+  - zstd=1.5.5=hc292b87_2
+prefix: /home/bird/miniconda3/envs/cumcm2024