get more accurate

A/1/mp_cal.py

@@ -1,7 +1,7 @@
 import mpmath as mp
 import json
 
-mp.dps = 15  # 设置精度为15位小数
+mp.dps = 50  # 设置精度为15位小数
 
 kSegLength1 = mp.mpf('2.86')
 kSegLength2 = mp.mpf('1.65')

A/1/mp_cal_it.py

@@ -1,7 +1,7 @@
 import mpmath as mp
 import json
 
-mp.dps = 15  # 设置精度为15位小数
+mp.dps = 50  # 设置精度为50位小数
 
 kSegLength1 = mp.mpf('2.86')
 kSegLength2 = mp.mpf('1.65')

A/2/collision_cal.py

@@ -9,7 +9,7 @@ import threading
 import numba
 import multiprocessing
 
-mp.dps = 15  # 设置精度为15位小数
+mp.dps = 50  # 设置精度为50位小数
 
 kSegLength1 = mp.mpf('2.86')
 kSegLength2 = mp.mpf('1.65')
@@ -35,7 +35,7 @@ def GenerateFollowNodeTheta(cur_node_theta, expected_distance):
     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',tol=1e-20)
+  return mp.findroot(f, cur_node_theta + 0.1, solver='secant')
 
 kPointsConsidered=50
 def CalcMoveList(time_point):

A/2/position_watcher.py

@@ -8,7 +8,7 @@ import numpy as np
 if __name__ != "__main__":
   sys.exit()
 
-mp.dps = 15  # 设置精度为15位小数
+mp.dps = 50  # 设置精度为15位小数
 
 kSegLength1 = mp.mpf('2.86')
 kSegLength2 = mp.mpf('1.65')

A/3/dragon.py

@@ -11,7 +11,7 @@ import multiprocessing
 import io
 from PIL import Image
 
-mp.dps = 15  # 设置精度为15位小数
+mp.dps = 50  # 设置精度为50位小数
 
 kSegLength1 = mp.mpf('2.86')
 kSegLength2 = mp.mpf('1.65')
@@ -37,7 +37,7 @@ class Dragon:
       test_node_dot = self.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',tol=1e-20)
+    return mp.findroot(f, cur_node_theta + 0.1, solver='secant')
 
 
   def CalcMoveList(self, delta_theta=0):

A/3/full_valid_test.py

@@ -1,15 +1,15 @@
 from dragon import *
-kPitchToTest=0.450338
+kPitchToTest=mp.mpf("0.45033740")
 kDeltaThetaBeg=0
 kDeltaThetaEnd=2*2*3.1415926
-kTotalSteps=100000
+kTotalSteps=10000
 kStepDeltaTheta=(kDeltaThetaEnd-kDeltaThetaBeg)/kTotalSteps
 kParallelNum=24
 tasks_list=[i for i in np.arange(kDeltaThetaBeg, kDeltaThetaEnd, kStepDeltaTheta)]
 task_list_per_process=[tasks_list[i::kParallelNum] for i in range(kParallelNum)]
 print(f"len(task_list_per_thread)={len(task_list_per_process)}",file=sys.stderr)
 def ProcessEntryPoint(arg):
-  dragen = Dragon(mp.mpf(kPitchToTest)/(2*mp.pi))
+  dragen = Dragon(kPitchToTest/(2*mp.pi))
   delta_theta_list, process_id = arg
   logf=open(f"sufficiency_test_{process_id}.log","w")
   print(f"calculating delta_theta_list={delta_theta_list} with process_id={process_id}",file=logf)
@@ -29,7 +29,7 @@ if __name__ == "__main__":
   else:
     print("OK")
     # Now generate an gif for human to check
-    dragen = Dragon(mp.mpf(kPitchToTest)/(2*mp.pi))
+    dragen = Dragon(kPitchToTest/(2*mp.pi))
     kTotalFrames=100
     kStepDeltaTheta=(kDeltaThetaEnd-kDeltaThetaBeg)/kTotalFrames
     frame_list=[]

A/3/sufficiency_test.py

@@ -1,7 +1,7 @@
 from dragon import *
-kBegPitch = 0.4503
-kEndPitch = 0.4504
-kTotalSteps = 100
+kBegPitch = 0.45033
+kEndPitch = 0.45034
+kTotalSteps = 1000
 kStepPitch = (kEndPitch - kBegPitch) / kTotalSteps
 kParallelNum=24
 tasks_list = [kBegPitch + kStepPitch * i for i in range(kTotalSteps)]
@@ -9,7 +9,7 @@ task_list_per_process=[tasks_list[i::kParallelNum] for i in range(kParallelNum)]
 
 kDeltaThetaBeg=0
 kDeltaThetaEnd=5*2*3.1415926
-kStepDeltaTheta=(kDeltaThetaEnd-kDeltaThetaBeg)/1000
+kStepDeltaTheta=(kDeltaThetaEnd-kDeltaThetaBeg)/100
 print(f"len(task_list_per_thread)={len(task_list_per_process)}",file=sys.stderr)
 def ProcessEntryPoint(arg):
   pitch_list, process_id, shared_dict, lock = arg