def execute(self, context):
scene=context.scene
mytool=scene.my_tool
csvFile=bpy.path.abspath(mytool.motionfile)
data = np.genfromtxt(csvFile, dtype=float, delimiter=',', names=True,skip_header=0)
objectNames=readNames(data.dtype.names[1:])
collection=bpy.data.collections['osimModel']
blendosim.model.loadAnimation(collection,data,objectNames)
#bpy.context.scene.update()
return {'FINISHED'}
def loadMarkers(csvFileName):
data = np.genfromtxt(csvFileName, dtype=float, delimiter=',', names=True,skip_header=0)
markerNames=readNames(data.dtype.names[1:])
####Create markers
newCol = bpy.data.collections.new('markers')
bpy.context.scene.collection.children.link(newCol)
for markerName in markerNames:
addMarker(newCol,text=markerName)
#bpy.context.scene.update()
a=bpy.data.collections['markers']
loadAnimation(a,data,markerNames)
def loadModel(osimFile, csvFile, modelRoot='', stlRoot='.', collection=''):
if (collection == ''):
collection = bpy.data.collections.new('osimModel')
bpy.context.scene.collection.children.link(collection)
addModel(osimFile,
modelRoot=modelRoot,
stlRoot=stlRoot,
collection=collection)
data = np.genfromtxt(csvFile,
dtype=float,
delimiter=',',
names=True,
skip_header=0)
objectNames = readNames(data.dtype.names[1:])
loadAnimation(collection, data, objectNames)
def loadMoments(csvFileName):
'''
Load moments by reading a csv file containing the frame index and locrotscale values
'''
data = np.genfromtxt(csvFileName,
dtype=float,
delimiter=',',
names=True,
skip_header=0)
jointNames = readNames(data.dtype.names[1:])
headerNames = data.dtype.names[1:]
N = len(jointNames)
#'''
####Create Arrows for each jointName
newCol = bpy.data.collections.new('moments')
bpy.context.scene.collection.children.link(newCol)
for forceName in jointNames:
addMoment(newCol, bodyName=forceName, text=forceName)
a = bpy.data.collections['moments']
#'''
#d=momentdata2locrot(data)
loadAnimation(a, data, jointNames)
def loadForces(csvFileName):
'''
Load forces by reading a csv file containing the frame index and the p v (and optional m) values.
'''
data = np.genfromtxt(csvFileName,
dtype=float,
delimiter=',',
names=True,
skip_header=0)
fpNames = readNames(data.dtype.names[1:])
headerNames = data.dtype.names[1:]
N = len(fpNames)
#'''
####Create Arrows for each fpname
newCol = bpy.data.collections.new('forces')
bpy.context.scene.collection.children.link(newCol)
for forceName in fpNames:
addForce(newCol, bodyName=forceName, text=forceName)
a = bpy.data.collections['forces']
#'''
d = fpdata2locrot(data)
loadAnimation(a, d, fpNames)
def moment2locrot(data):
''' set the moment locrotscale according to locrotscale data
'''
fpNames = readNames(data.dtype.names[1:])
headerNames = data.dtype.names[1:]
d = data.view(np.float, np.ndarray).reshape(data.shape[0],
len(headerNames) + 1)
N = len(fpNames)
locidx = np.array([4, 5, 6]).astype(int)
for i in range(N):
locidx = np.concatenate([locidx, N * 9 + locidx])
#Use data to determine the locrot of this thing and then make a matrix
#with that info.
baselocidx = np.array([4, 5, 6])
locidx = []
for i in range(0, N):
locidx = np.concatenate((locidx, i * 9 + baselocidx))
idx = locidx.astype(int)
tails = d[:, idx]
arrow = d[:, idx - 3]
rotvals = np.copy(tails)
scalevals = np.copy(tails)
originalarrow = Vector([1, 0, 0])
for i in range(0, arrow.shape[0]):
for j in range(0, N):
#Generate rotation XYZ based on arrow and origin vector
thisarrow = Vector(
[arrow[i, j * 3], arrow[i, j * 3 + 1], arrow[i, j * 3 + 2]])
normarrow = thisarrow.normalized()
quat = originalarrow.rotation_difference(normarrow)
eul = quat.to_euler('XYZ') #maybe use compatibility arg
rotvals[i, 3 * j] = math.degrees(eul[0])
rotvals[i, 3 * j + 1] = math.degrees(eul[1])
rotvals[i, 3 * j + 2] = math.degrees(eul[2])
#Genarate scale in x based on magnitude of arrow
scalevals[i, j * 3] = thisarrow.magnitude / 700
if (thisarrow.magnitude < 20):
scalevals[i, j * 3 + 1] = 0
scalevals[i, j * 3 + 2] = 0
else:
scalevals[i, j * 3 + 1] = 1
scalevals[i, j * 3 + 2] = 1
if (j == 3):
print(fpNames[j])
print(thisarrow)
print(normarrow)
print(eul)
alltbl = np.zeros((tails.shape[0], tails.shape[1] * 3))
locidx = []
rotidx = []
scaleidx = []
baselocidx = np.array([0, 1, 2])
for i in range(0, N):
locidx = np.concatenate((locidx, i * 9 + baselocidx))
rotidx = np.concatenate((rotidx, i * 9 + 3 + baselocidx))
scaleidx = np.concatenate((scaleidx, i * 9 + 6 + baselocidx))
alltbl[:, locidx.astype(int)] = tails
alltbl[:, rotidx.astype(int)] = rotvals
alltbl[:, scaleidx.astype(int)] = scalevals
header = np.expand_dims(data['Header'], 1)
alltbl = np.concatenate([header, alltbl], axis=1)
colnames = ['Header']
for i in range(0, N):
thisfpcols = [
fpNames[i] + '_x', fpNames[i] + '_y', fpNames[i] + '_z',
fpNames[i] + '_rotx', fpNames[i] + '_roty', fpNames[i] + '_rotz',
fpNames[i] + '_scalex', fpNames[i] + '_scaley',
fpNames[i] + '_scalez'
]
colnames = colnames + thisfpcols
out = np.core.records.fromarrays(alltbl.transpose(), names=colnames)
return out