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pycgmIO.py
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pycgmIO.py
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#pyCGM
# Copyright (c) 2015 Mathew Schwartz <umcadop@gmail.com>
# Core Developers: Seungeun Yeon, Mathew Schwartz
# Contributors Filipe Alves Caixeta, Robert Van-wesep
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
# Input and output of pycgm functions
import sys
from .pyCGM import *
if sys.version_info[0]==2:
import c3d
pyver = 2
print("Using python 2 c3d loader")
else:
from . import c3dpy3 as c3d
pyver = 3
print("Using python 3 c3d loader - c3dpy3")
try:
from ezc3d import c3d as ezc
useEZC3D = True
print("EZC3D Found, using instead of Python c3d")
except:
useEZC3D = False
from math import *
import numpy as np
import xml.etree.ElementTree as ET
import os
import errno
#Used to split the arrays with angles and axis
#Start Joint Angles
SJA=0
#End Joint Angles
EJA=SJA+19*3
#Start Axis
SA=EJA
#End Axis
EA=SA+72*3
def createMotionDataDict(labels,data):
motiondata = []
for frame in data:
mydict={}
for label,xyz in zip(labels,frame):
l=str(label.rstrip())
mydict[l] = xyz
motiondata.append(mydict)
return motiondata
def splitMotionDataDict(motiondata):
if pyver == 2:
labels=motiondata[0].keys()
data=np.zeros((len(motiondata),len(labels),3))
counter=0
for md in motiondata:
data[counter]=np.asarray(md.values())
counter+=1
return labels,data
if pyver == 3:
labels=list(motiondata[0].keys())
data=np.zeros((len(motiondata),len(labels),3))
counter=0
for md in motiondata:
data[counter]=np.asarray(list(md.values()))
counter+=1
return labels,data
def createVskDataDict(labels,data):
vsk={}
for key,data in zip(labels,data):
vsk[key]=data
return vsk
def splitVskDataDict(vsk):
if pyver == 2: return vsk.keys(),np.asarray(vsk.values())
if pyver == 3: return list(vsk.keys()),np.asarray(list(vsk.values()))
def markerKeys():
marker_keys = ['RASI','LASI','RPSI','LPSI','RTHI','LTHI','RKNE','LKNE','RTIB',
'LTIB','RANK','LANK','RTOE','LTOE','LFHD','RFHD','LBHD','RBHD',
'RHEE','LHEE','CLAV','C7','STRN','T10','RSHO','LSHO','RELB','LELB',
'RWRA','RWRB','LWRA','LWRB','RFIN','LFIN']
return marker_keys
def loadEZC3D(filename):
#Relative import mod for python 2 and 3
try: from . import c3dez
except: import c3dez
dataclass = c3dez.C3DData(None, filename)
data = dataAsArray(dataclass.Data['Markers'])
return [data,None,None]
def loadC3D(filename):
if useEZC3D == True:
print("Using EZC3D")
return loadEZC3D(filename)
reader = c3d.Reader(open(filename, 'rb'))
labels = reader.get('POINT:LABELS').string_array
mydict = {}
mydictunlabeled ={}
data = []
dataunlabeled = []
prog_val = 1
counter = 0
data_length = reader.last_frame() - reader.first_frame()
markers=[str(label.rstrip()) for label in labels]
for frame_no, points, analog in reader.read_frames(True,True):
for label, point in zip(markers, points):
#Create a dictionary with format LFHDX: 123
if label[0]=='*':
if point[0]!=np.nan:
mydictunlabeled[label]=point
else:
mydict[label] = point
data.append(mydict)
dataunlabeled.append(mydictunlabeled)
mydict = {}
return [data,dataunlabeled,markers]
def loadCSV(filename):
if filename == '':
self.returnedData.emit(None)
import numpy as np
from numpy.compat import asbytes #probably not needed
fh = open(filename,'r')
fh=iter(fh)
delimiter=','
def rowToDict(row,labels):
dic={}
unlabeleddic={}
if pyver == 2: row=zip(row[0::3],row[1::3],row[2::3])
if pyver == 3: row=list(zip(row[0::3],row[1::3],row[2::3]))
empty=np.asarray([np.nan,np.nan,np.nan],dtype=np.float64)
for coordinates,label in zip(row,labels):
#unlabeled data goes to a different dictionary
if label[0]=="*":
try:
unlabeleddic[label]=np.float64(coordinates)
except:
pass
else:
try:
dic[label]=np.float64(coordinates)
except:
#Missing data from labeled marker is NaN
dic[label]=empty.copy()
return dic,unlabeleddic
def split_line(line):
if pyver == 2: line = asbytes(line).strip(asbytes('\r\n'))
elif pyver == 3: line = line.strip('\r\n')
if line:
return line.split(delimiter)
else:
return []
def parseTrajectories(fh,framesNumber):
delimiter=','
if pyver == 2:
freq=np.float64(split_line(fh.next())[0])
labels=split_line(fh.next())[1::3]
fields=split_line(fh.next())
elif pyver == 3:
freq=np.float64(split_line(next(fh))[0])
labels=split_line(next(fh))[1::3]
fields=split_line(next(fh))
delimiter = asbytes(delimiter)
rows=[]
rowsUnlabeled=[]
if pyver == 2: first_line=fh.next()
elif pyver == 3: first_line=next(fh)
first_elements=split_line(first_line)[1:]
colunsNum=len(first_elements)
first_elements,first_elements_unlabeled=rowToDict(first_elements,labels)
rows.append(first_elements)
rowsUnlabeled.append(first_elements_unlabeled)
for row in fh:
row=split_line(row)[1:]
if len(row)!=colunsNum:
break
elements,unlabeled_elements=rowToDict(row,labels)
rows.append(elements)
rowsUnlabeled.append(unlabeled_elements)
return labels,rows,rowsUnlabeled,freq
###############################################
### Find the trajectories
framesNumber=0
for i in fh:
if i.startswith("TRAJECTORIES"):
#First elements with freq,labels,fields
if pyver == 2: rows=[fh.next(),fh.next(),fh.next()]
if pyver == 3: rows=[next(fh),next(fh),next(fh)]
for j in fh:
if j.startswith("\r\n"):
break
framesNumber=framesNumber+1
rows.append(j)
break
rows=iter(rows)
labels,motionData,unlabeledMotionData,freq=parseTrajectories(rows,framesNumber)
return [motionData,unlabeledMotionData,labels]
def loadData(filename,rawData=True):
print(filename)
if str(filename).endswith('.c3d'):
data = loadC3D(filename)[0]
#add any missing keys
keys = markerKeys()
for frame in data:
for key in keys:
frame.setdefault(key,[np.nan,np.nan,np.nan])
return data
elif str(filename).endswith('.csv'):
return loadCSV(filename)[0]
def dataAsArray(data):
"""
convert a dictionary of markers with xyz data as an array
to an array of dictionaries
Assumes all markers have the same length of data
"""
names = list(data.keys())
dataArray = []
#make the marker arrays a better format
for marker in data:
#Turn multi array into single
xyz = [ np.array(x) for x in zip( data[marker][0],data[marker][1],data[marker][2] ) ]
data[marker] = xyz
#use the first marker to get the length of frames
datalen = len( data[names[0]] )
for i in range(datalen):
frameDict = {}
for marker in data:
frameDict[marker] = data[marker][i]
dataArray.append(frameDict)
return dataArray
def dataAsDict(data,npArray=False):
"""
convert the frame by frame based data to a dictionary of keys
with all motion data as an array per key
takes an option npArray flag, when set to true, will return a numpy array
for each key instead of a list
"""
dataDict = {}
for frame in data:
for key in frame:
dataDict.setdefault(key,[])
dataDict[key].append(frame[key])
if npArray == True:
for key in dataDict:
dataDict[key] = np.array(dataDict[key])
return dataDict
def writeKinetics(CoM_output,kinetics):
"""
temp function to write kinetics data. Just uses numpy.save
"""
np.save(CoM_output,kinetics)
def writeResult(data,filename,**kargs):
"""
Writes the result of the calculation into a csv file
@param data Motion Data as a matrix of frames as rows
@param filename Name to save the csv
@param kargs
delimiter Delimiter for the csv. By default it's using ','
angles True or false to save angles. Or a list of angles to save
axis True of false to save axis. Or a list of axis to save
Examples
#save angles and axis
writeResultNumPy(result,"outputfile0.csv")
#save 'R Hip' angles 'L Foot' and all the axis
writeResultNumPy(result,"outputfile1.csv",angles=['R Hip','L Foot'])
#save only axis "R ANKZ","L ANKO","L ANKX"
writeResultNumPy(result,"outputfile4.csv",angles=False,axis=["R ANKZ","L ANKO","L ANKX"])
#save only angles
writeResultNumPy(result,"outputfile6.csv",axis=False)
"""
labelsAngs =['Pelvis','R Hip','L Hip','R Knee','L Knee','R Ankle',
'L Ankle','R Foot','L Foot',
'Head','Thorax','Neck','Spine','R Shoulder','L Shoulder',
'R Elbow','L Elbow','R Wrist','L Wrist']
labelsAxis =["PELO","PELX","PELY","PELZ","HIPO","HIPX","HIPY","HIPZ","R KNEO","R KNEX","R KNEY","R KNEZ","L KNEO","L KNEX","L KNEY","L KNEZ","R ANKO","R ANKX","R ANKY","R ANKZ","L ANKO","L ANKX","L ANKY","L ANKZ","R FOOO","R FOOX","R FOOY","R FOOZ","L FOOO","L FOOX","L FOOY","L FOOZ","HEAO","HEAX","HEAY","HEAZ","THOO","THOX","THOY","THOZ","R CLAO","R CLAX","R CLAY","R CLAZ","L CLAO","L CLAX","L CLAY","L CLAZ","R HUMO","R HUMX","R HUMY","R HUMZ","L HUMO","L HUMX","L HUMY","L HUMZ","R RADO","R RADX","R RADY","R RADZ","L RADO","L RADX","L RADY","L RADZ","R HANO","R HANX","R HANY","R HANZ","L HANO","L HANX","L HANY","L HANZ"]
outputAngs=True
outputAxis=True
dataFilter=None
delimiter=","
filterData=[]
if 'delimiter' in kargs:
delimiter=kargs['delimiter']
if 'angles' in kargs:
if kargs['angles']==True:
outputAngs=True
elif kargs['angles']==False:
outputAngs=False
labelsAngs=[]
elif isinstance(kargs['angles'], (list, tuple)):
filterData=[i*3 for i in range(len(labelsAngs)) if labelsAngs[i] not in kargs['angles']]
if len(filterData)==0:
outputAngs=False
labelsAngs=[i for i in labelsAngs if i in kargs['angles']]
if 'axis' in kargs:
if kargs['axis']==True:
outputAxis=True
elif kargs['axis']==False:
outputAxis=False
labelsAxis=[]
elif isinstance(kargs['axis'], (list, tuple)):
filteraxis=[i*3+SA for i in range(len(labelsAxis)) if labelsAxis[i] not in kargs['axis']]
filterData=filterData+filteraxis
if len(filteraxis)==0:
outputAxis=False
labelsAxis=[i for i in labelsAxis if i in kargs['axis']]
if len(filterData)>0:
filterData=np.repeat(filterData,3)
filterData[1::3]=filterData[1::3]+1
filterData[2::3]=filterData[2::3]+2
if outputAngs==outputAxis==False:
return
elif outputAngs==False:
print(np.shape(data))
dataFilter=np.transpose(data)
dataFilter=dataFilter[SA:EA]
dataFilter=np.transpose(dataFilter)
print(np.shape(dataFilter))
print(filterData)
filterData=[i-SA for i in filterData]
print(filterData)
elif outputAxis==False:
dataFilter=np.transpose(data)
dataFilter=dataFilter[SJA:EJA]
dataFilter=np.transpose(dataFilter)
if len(filterData)>0:
if type(dataFilter)==type(None):
dataFilter=np.delete(data, filterData, 1)
else:
dataFilter=np.delete(dataFilter, filterData, 1)
if type(dataFilter)==type(None):
dataFilter=data
header=","
headerAngs=["Joint Angle,,,",",,,x = flexion/extension angle",",,,y= abudction/adduction angle",",,,z = external/internal rotation angle",",,,"]
headerAxis=["Joint Coordinate",",,,###O = Origin",",,,###X = X axis orientation",",,,###Y = Y axis orientation",",,,###Z = Z axis orientation"]
for angs,axis in zip(headerAngs,headerAxis):
if outputAngs==True:
header=header+angs+",,,"*(len(labelsAngs)-1)
if outputAxis==True:
header=header+axis+",,,"*(len(labelsAxis)-1)
header=header+"\n"
labels=","
if len(labelsAngs)>0:
labels=labels+",,,".join(labelsAngs)+",,,"
if len(labelsAxis)>0:
labels=labels+",,,".join(labelsAxis)
labels=labels+"\n"
if pyver == 2:
xyz="frame num,"+"X,Y,Z,"*(len(dataFilter[0])/3)
else:
xyz="frame num,"+"X,Y,Z,"*(len(dataFilter[0])//3)
header=header+labels+xyz
#Creates the frame numbers
frames=np.arange(len(dataFilter),dtype=dataFilter[0].dtype)
#Put the frame numbers in the first dimension of the data
dataFilter=np.column_stack((frames,dataFilter))
start = 1500
end = 3600
#dataFilter = dataFilter[start:]
np.savetxt(filename+'.csv', dataFilter, delimiter=delimiter,header=header,fmt="%.15f")
#np.savetxt(filename, dataFilter, delimiter=delimiter,fmt="%.15f")
#np.savez_compressed(filename,dataFilter)
def smKeys():
keys = ['Bodymass', 'Height', 'HeadOffset', 'InterAsisDistance', 'LeftAnkleWidth', 'LeftAsisTrocanterDistance',
'LeftClavicleLength',
'LeftElbowWidth', 'LeftFemurLength', 'LeftFootLength', 'LeftHandLength', 'LeftHandThickness',
'LeftHumerusLength', 'LeftKneeWidth',
'LeftLegLength', 'LeftRadiusLength', 'LeftShoulderOffset', 'LeftTibiaLength', 'LeftWristWidth',
'RightAnkleWidth',
'RightClavicleLength', 'RightElbowWidth', 'RightFemurLength', 'RightFootLength', 'RightHandLength',
'RightHandThickness', 'RightHumerusLength',
'RightKneeWidth', 'RightLegLength', 'RightRadiusLength', 'RightShoulderOffset', 'RightTibiaLength',
'RightWristWidth',
]
return keys
def loadVSK(filename,dict=True):
#Check if the filename is valid
#if not, return None
if filename == '':
return None
# Create Dictionary to store values from VSK file
viconVSK = {}
vskMarkers = []
#Create an XML tree from file
tree = ET.parse(filename)
#Get the root of the file
# <KinematicModel>
root = tree.getroot()
#Store the values of each parameter in a dictionary
# the format is (NAME,VALUE)
vsk_keys=[r.get('NAME') for r in root[0]]
vsk_data = []
for R in root[0]:
val = (R.get('VALUE'))
if val == None:
val = 0
vsk_data.append(float(val))
#vsk_data=np.asarray([float(R.get('VALUE')) for R in root[0]])
#print vsk_keys
if dict==False: return createVskDataDict(vsk_keys,vsk_data)
return [vsk_keys,vsk_data]
def splitDataDict(motionData):
if pyver == 2:
labels = motionData[0].keys()
values = []
for i in range(len(motionData)):
values.append(np.asarray(motionData[i].values()))
return values,labels
if pyver == 3:
labels = list(motionData[0].keys())
values = []
for i in range(len(motionData)):
values.append(np.asarray(list(motionData[i].values())))
return values,labels
def combineDataDict(values,labels):
data = []
tmp_dict = {}
for i in range (len(values)):
for j in range (len(values[i])):
tmp_dict[labels[j]]=values[i][j]
data.append(tmp_dict)
tmp_dict = {}
return data
def make_sure_path_exists(path):
try:
os.makedirs(path)
except OSError as exception:
if exception.errno != errno.EEXIST:
raise