def tsladjust(nmtree, tslmin, lftargmax):
"""
Adjust all muscles to have a maximum (dimensionless) operating length of lftargmax,
with a minimum tendon slack length of tslmin (in units of length e.g. meters)
"""
for z in nmtree.NeuromechanicFile.Muscles.Muscle:
MTL = z.MusculoTendonLength.x_
LF0 = z.Model.OptimalFiberLength.x_
TSL = z.Model.TendonSlackLength.x_
PEN = z.Model.PennationAngle.x_
mxMTL = MTL
# max(MTL[:,ii]) # Get the maximum muscle length
mnMTL = MTL
# min(MTL[:,ii]) # Get the minimum muscle length
lfmax = math.sqrt((mxMTL - TSL) ** 2 + (LF0 * math.sin(PEN)) ** 2)
lfmin = math.sqrt((mnMTL - TSL) ** 2 + (LF0 * math.sin(PEN)) ** 2)
# Solve for TSL to give lfmax/lf0 = 1.05; Minimum TSL = 5mm
TSL = max(tslmin, mxMTL - math.sqrt(lftargmax ** 2 - math.sin(PEN) ** 2) * LF0)
z.Model.TendonSlackLength.x_ = TSL
# See what lfmax and lfmin will be
lfmax = math.sqrt((mxMTL - TSL) ** 2 + (LF0 * math.sin(PEN)) ** 2)
lfmin = math.sqrt((mnMTL - TSL) ** 2 + (LF0 * math.sin(PEN)) ** 2)
nm.system_log(z.Name.x_ + ": [LFmax/LF0, LFmin/LF0] = [" + str(lfmax / LF0) + " , " + str(lfmin / LF0) + "]")
def print(*arg):
""" Prints whatever arg is to the neuromechanic logfile and log window """
if type(arg) == type(" "):
nm.system_log(arg)
else:
try:
x = " ".join(map(str, arg))
except:
x = str(arg)
nm.system_log(x)
def __getattr__(self, itemc):
item = itemc.lower()
if item == "x_":
outvar = nm.tree_getvalue(self.__nodep)
if type(outvar) == type([]):
outvar = np.array(outvar)
# if this is a list make it a numpy array
return outvar
elif item == "p_":
return self.__nodep
elif item == "c_":
return self.__nodechildren
elif item == "a_":
return self.__nodeattributes
elif item == "tag_":
return self.__tag
elif item == "xs_":
outvar = self.x_
return xmlstr(outvar)
elif item == "print_":
strng = "<" + self.__tag
for x in self.__nodeattributes:
# The node attributes dictionary is keyed with the attribute name and the value of the dictionary is a TreeList of length 1 element which is the Tree of the attribute itself;
strng += " " + x + "=" + self.__nodeattributes[x].xs_
strng += ">"
for x in self.__nodechildren:
z = self.__nodechildren[x].print_
for zz in z:
strng += zz
value = self.x_
if value is not None:
strng += self.xs_
strng += "</" + self.__tag + ">"
return strng
elif item == "expointer_":
return nm.tree_getnodeexternalpointer(self.__nodep)
elif item == "enum1_":
return nm.tree_getnodeenum1(self.__nodep)
elif item == "list_":
nm.system_log("Attributes:")
for x in self.__nodeattributes:
# The node attributes dictionary is keyed with the attribute name and the value of the dictionary is a TreeList of length 1 element which is the Tree of the attribute itself;
nm.system_log(" " + x + "=" + self.__nodeattributes[x].xs_)
nm.system_log("Children:")
for x in self.__nodechildren:
nm.system_log(" " + x + " (" + str(len(self.__nodechildren[x])) + ")")
nm.system_log("Value:")
nm.system_log(" " + self.xs_)
nm.system_log(" ")
# if this is a list make it a numpy array
return None
elif item in self.__nodechildren:
return self.__nodechildren[item]
elif item in self.__nodeattributes:
return self.__nodeattributes[item]
# return nm.tree_getvalue(self.__nodeattributes[item].__nodep);
elif item in self.__nodecomments:
return nm.tree_getvalue(self.__nodecomments[item].__nodep)
else:
return TreeList([])