import numpy as np
import scipy.io as spio
import datetime
# Load Related Data
test1 = os.path.join('H:/netData/DynamicModel/jrvicon_data', 'R25T30TestInfo1809131632.pkl')
test2 = os.path.join('H:/netData/DynamicModel/jrvicon_data', 'R25T30TestInfo1809131636.pkl')
test3 = os.path.join('H:/netData/DynamicModel/jrvicon_data', 'R25T30TestInfo1809131642.pkl')
testNo = [test1, test2, test3]
k = 1 # 1 2 3
with open(test1, 'rb') as f:
test1Data = pickle.load(f)
with open(test2, 'rb') as f:
test2Data = pickle.load(f)
with open(test3, 'rb') as f:
test3Data = pickle.load(f)
timestamp1 = myFunc.timestamp(test1)
TestSetting1 = myFunc.testsetting(test1)
testDataProcess = [test1Data, test2Data, test3Data]
##########################################################################
## Create teh interpolation function
# Load the theoretical results for prediction and comparison.
with open('shapeTablePy.pkl', 'rb') as f:
shapeTable = pickle.load(f)
### This part is for referring to the Shape and build the functions
# Interpolate the predication based on the measured data.
pressure = shapeTable.get('pressure')[4:23]/1000
ContractionRatio = shapeTable.get('ContractionRatio')[4:23]
StretchRatio = shapeTable.get('StretchRatio')[4:23]
# Noted: Sep 21 -
import os
import pickle
import numpy as np
from matplotlib import pyplot
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import myFunc
LoadSavedDataFolder = os.path.join('H:/netData/DynamicModel/jrvicon_data',
'R25T30GroupedData1809131632.pkl')
R = 40
timestamp = myFunc.timestamp(LoadSavedDataFolder)
TestSetting = myFunc.testsetting(LoadSavedDataFolder)
with open(LoadSavedDataFolder, 'rb') as f:
MeasuredGroupedData = pickle.load(f)
TestName = [f for f in MeasuredGroupedData.keys()]
# Basic Statistic of Position
marker1 = MeasuredGroupedData.get('marker1')
marker1Info = {'raw': marker1}
marker1Info.update({'mean': marker1.mean(axis=0)})
marker1Info.update({'std': marker1.std(axis=0)})
marker1Info.update({'InitPos': marker1[50:200, :].mean(axis=0)})
# Pressure info
MeasuredSmoothP = MeasuredGroupedData.get('MeasuredSmoothP')
marker1Info.update({'MeasuredSmoothP': MeasuredSmoothP})
test1 = os.path.join('H:/netData/DynamicModel/jrvicon_data',
'R25T30GroupedData1809131632PoseInfo.pkl')
test2 = os.path.join('H:/netData/DynamicModel/jrvicon_data',
'R25T30GroupedData1809131636PoseInfo.pkl')
test3 = os.path.join('H:/netData/DynamicModel/jrvicon_data',
'R25T30GroupedData1809131642PoseInfo.pkl')
testNo = [test1, test2, test3]
k = 3 # 1 2 3
with open(test1, 'rb') as f:
test1Data = pickle.load(f)
with open(test2, 'rb') as f:
test2Data = pickle.load(f)
with open(test3, 'rb') as f:
test3Data = pickle.load(f)
timestamp = myFunc.timestamp(testNo[k - 1])
TestSetting = myFunc.testsetting(testNo[k - 1])
testDataProcess = [test1Data, test2Data, test3Data]
print('test is processing {} {}'.format(TestSetting, timestamp))
### Create teh interpolation function
# Load the theoretical results for prediction and comparison.
with open('shapeTablePy.pkl', 'rb') as f:
shapeTable = pickle.load(f)
# Interpolate the predication based on the measured data.
pressure = shapeTable.get('pressure')[4:23] / 1000
ContractionRatio = shapeTable.get('ContractionRatio')[4:23]
StretchRatio = shapeTable.get('StretchRatio')[4:23]
# Construct the mapping among Pressure and Deforamation
func_P2CR = interp1d(pressure, ContractionRatio, fill_value='extrapolate')
func_P2SR = interp1d(pressure, StretchRatio)
func_CR2P = interp1d(ContractionRatio, pressure, fill_value='extrapolate')