def test_STOFileAdapter(self):
adapter = osim.STOFileAdapter()
table = adapter.read(os.path.join(test_dir,
'subject02_grf_HiFreq.mot'))
assert table.getNumRows() == 439
assert table.getNumColumns() == 18
table = adapter.read(
os.path.join(test_dir, 'std_subject01_walk1_ik.mot'))
assert table.getNumRows() == 73
assert table.getNumColumns() == 23
def to_sto(self, filename, metadata=None):
"""
Write a sto file from a Analogs3dOsim
Parameters
----------
filename : string
path of the file to write
metadata : dict, optional
dict with optional metadata to add in the output file
"""
filename = Path(filename)
# Make sure the directory exists, otherwise create it
if not filename.parents[0].is_dir():
filename.parents[0].mkdir()
table = osim.TimeSeriesTable()
# set metadata
table.setColumnLabels(self.get_labels)
if metadata:
for key, value in metadata.items():
table.addTableMetaDataString(key, str(value))
if 'nColumns' not in metadata:
table.addTableMetaDataString('nColumns', str(self.shape[1]))
else:
table.addTableMetaDataString('nColumns', str(self.shape[1]))
table.addTableMetaDataString('nRows', str(self.shape[-1]))
time_vector = np.arange(start=0,
stop=1 / self.get_rate * self.shape[2],
step=1 / self.get_rate)
for iframe in range(self.shape[-1]):
a = self.get_frame(iframe)
row = osim.RowVector(a.ravel().tolist())
table.appendRow(time_vector[iframe], row)
adapter = osim.STOFileAdapter()
adapter.write(table, str(filename))
def test_C3DFileAdapter(self):
try:
adapter = osim.C3DFileAdapter()
except AttributeError:
# C3D support not available. OpenSim was not compiled with BTK.
return
tables = adapter.read(os.path.join(test_dir, 'walking2.c3d'))
markers = tables['markers']
forces = tables['forces']
assert markers.getNumRows() == 1249
assert markers.getNumColumns() == 44
assert forces.getNumRows() == 9992
assert forces.getNumColumns() == 6
tables = adapter.read(os.path.join(test_dir, 'walking5.c3d'))
# Marker data read from C3D.
markers = tables['markers']
assert markers.getNumRows() == 1103
assert markers.getNumColumns() == 40
assert markers.getTableMetaDataString('DataRate') == '250.000000'
assert markers.getTableMetaDataString('Units') == 'mm'
# Flatten marker data.
markersFlat = markers.flatten()
assert markersFlat.getNumRows() == 1103
assert markersFlat.getNumColumns() == 40 * 3
# Make sure flattenned marker data is writable/readable to/from file.
markersFilename = 'markers.sto'
stoAdapter = osim.STOFileAdapter()
stoAdapter.write(markersFlat, markersFilename)
markersDouble = stoAdapter.read(markersFilename)
assert markersDouble.getNumRows() == 1103
assert markersDouble.getNumColumns() == 40 * 3
# Forces data read from C3d.
forces = tables['forces']
assert forces.getNumRows() == 8824
assert forces.getNumColumns() == 6
assert forces.getTableMetaDataString('DataRate') == '2000.000000'
assert forces.getTableMetaDataVectorUnsigned('Types') == (2, 2)
fpCalMats = forces.getTableMetaDataVectorMatrix("CalibrationMatrices")
assert len(fpCalMats) == 2
assert fpCalMats[0].nrow() == 6
assert fpCalMats[0].ncol() == 6
assert fpCalMats[1].nrow() == 6
assert fpCalMats[1].ncol() == 6
fpCorners = forces.getTableMetaDataVectorMatrix("Corners")
assert len(fpCorners) == 2
assert fpCorners[0].nrow() == 3
assert fpCorners[0].ncol() == 4
assert fpCorners[1].nrow() == 3
assert fpCorners[1].ncol() == 4
fpOrigins = forces.getTableMetaDataVectorMatrix("Origins")
assert len(fpOrigins) == 2
assert fpOrigins[0].nrow() == 3
assert fpOrigins[0].ncol() == 1
assert fpOrigins[1].nrow() == 3
assert fpOrigins[1].ncol() == 1
assert forces.getDependentsMetaDataString('units') == ('N', 'mm',
'Nmm', 'N',
'mm', 'Nmm')
# Flatten forces data.
forcesFlat = forces.flatten()
assert forcesFlat.getNumRows() == 8824
assert forcesFlat.getNumColumns() == 6 * 3
# Make sure flattenned forces data is writable/readable to/from file.
forcesFilename = 'forces.sto'
stoAdapter.write(forcesFlat, forcesFilename)
forcesDouble = stoAdapter.read(forcesFilename)
assert forcesDouble.getNumRows() == 8824
assert forcesDouble.getNumColumns() == 6 * 3
# Clean up.
os.remove(markersFilename)
os.remove(forcesFilename)
inverse.set_kinematics_allow_extra_columns(True)
inverse.set_minimize_sum_squared_activations(True)
# Append additional outputs path for quantities that are calculated
# post-hoc using the inverse problem solution.
inverse.append_output_paths('.*normalized_fiber_length')
inverse.append_output_paths('.*passive_force_multiplier')
# Part 4d: Solve! Write the MocoSolution to file.
inverseSolution = inverse.solve()
solution = inverseSolution.getMocoSolution()
solution.write('inverseSolution.sto')
# Part 4e: Get the outputs we calculated from the inverse solution.
inverseOutputs = inverseSolution.getOutputs()
sto = osim.STOFileAdapter()
sto.write(inverseOutputs, 'muscleOutputs.sto')
## Part 5: Muscle-driven Inverse Problem with Passive Assistance
# Part 5a: Create a new muscle-driven model, now adding a SpringGeneralizedForce
# about the knee coordinate.
device = osim.SpringGeneralizedForce('knee_angle_r')
device.setStiffness(50)
device.setRestLength(0)
device.setViscosity(0)
muscleDrivenModel.addForce(device)
# Part 5b: Create a ModelProcessor similar to the previous one, using the same
# reserve actuator strength so we can compare muscle activity accurately.
modelProcessor = osim.ModelProcessor(muscleDrivenModel)
modelProcessor.append(osim.ModOpAddReserves(2))
# COP = 1, /// the center of pressure
# PWA = 2 /// the point of wrench application (Shimba 1984) https://www.ncbi.nlm.nih.gov/pubmed/6715389
markers = tables['markers']
forces = tables['forces']
# Marker data read from C3D.
markers = tables['markers']
# Flatten marker data.
markersFlat = markers.flatten()
# Make sure flattenned marker data is writable/readable to/from file.
markersFilename = 'markers.sto'
stoAdapter = osim.STOFileAdapter()
stoAdapter.write(markersFlat, markersFilename)
markersDouble = stoAdapter.read(markersFilename)
# Forces data read from C3d.
forces = tables['forces']
fpCalMats = forces.getTableMetaDataVectorMatrix("CalibrationMatrices")
fpCorners = forces.getTableMetaDataVectorMatrix("Corners")
fpOrigins = forces.getTableMetaDataVectorMatrix("Origins")
# Flatten forces data.
forcesFlat = forces.flatten()
# Make sure flattenned forces data is writable/readable to/from file.
forcesFilename = 'forces.sto'
stoAdapter.write(forcesFlat, forcesFilename)
def __init__(
self,
model,
trajectory_filepath,
bilateral=True,
ref_files=None,
colormap=None,
output=None,
):
self.model = model
self.model.initSystem()
self.trajectory_filepath = trajectory_filepath
self.trajectory = osim.MocoTrajectory(self.trajectory_filepath)
self.bilateral = bilateral
self.ref_files = ref_files
self.colormap = colormap
trajectory_fname = ntpath.basename(self.trajectory_filepath)
trajectory_fname = trajectory_fname.replace('.sto', '')
trajectory_fname = trajectory_fname.replace('.mot', '')
self.trajectory_fname = trajectory_fname
if output:
self.output = output
else:
self.output = trajectory_fname + '_report.pdf'
# Get any reference files provided by the user and create a list of NumPy
# arrays to use in plotting.
self.refs = list()
if ref_files != None:
for ref_file in ref_files:
filename, file_ext = os.path.splitext(ref_file)
# If the reference is a file with metadata indicating that
# rotational data is in degrees, convert to radians and write to
# a new file to be used for plotting.
# TODO: don't write the extra file permanently
if file_ext == '.sto' or file_ext == '.mot':
ref = osim.TimeSeriesTable(ref_file)
if (ref.hasTableMetaDataKey('inDegrees')
and ref.getTableMetaDataAsString('inDegrees')
== 'yes'):
simbodyEngine = self.model.getSimbodyEngine()
simbodyEngine.convertDegreesToRadians(ref)
ref_file = ref_file.replace(file_ext,
'_radians' + file_ext)
sto_adapter = osim.STOFileAdapter()
sto_adapter.write(ref, ref_file)
num_header_rows = 1
with open(ref_file) as f:
for line in f:
if not line.startswith('endheader'):
num_header_rows += 1
else:
break
this_ref = np.genfromtxt(ref_file,
names=True,
delimiter='\t',
skip_header=num_header_rows)
self.refs.append(this_ref)
# Load the colormap provided by the user. Use a default colormap ('jet')
# if not provided. Uniformly sample the colormap based on the number of
# reference data sets, plus one for the MocoTrajectory.
import matplotlib.cm as cm
if colormap is None: colormap = 'jet'
self.cmap_samples = np.linspace(0.1, 0.9, len(self.refs) + 1)
self.cmap = cm.get_cmap(colormap)
## Legend handles and labels.
# ===========================
# Create legend handles and labels that can be used to create a figure
# legend that is applicable all figures.
self.legend_handles = list()
self.legend_labels = list()
all_files = list()
if ref_files != None: all_files += ref_files
all_files.append(trajectory_filepath)
lw = 8 / len(self.cmap_samples)
if lw < 0.5: lw = 0.5
if lw > 2: lw = 2
for sample, file in zip(self.cmap_samples, all_files):
color = self.cmap(sample)
import matplotlib.lines as mlines
if bilateral:
r = mlines.Line2D([], [], ls='-', color=color, linewidth=lw)
self.legend_handles.append(r)
self.legend_labels.append(file + ' (right leg)')
l = mlines.Line2D([], [], ls='--', color=color, linewidth=lw)
self.legend_handles.append(l)
self.legend_labels.append(file + ' (left leg)')
else:
h = mlines.Line2D([], [], ls='-', color=color, linewidth=lw)
self.legend_handles.append(h)
self.legend_labels.append(file)
# Time
# -----
# Convert trajectory time vector to a plotting-friendly NumPy array.
self.time = convert(self.trajectory.getTime())
# Create a conservative set of x-tick values based on the time vector.
nexttime = math.ceil(self.time[0] * 10) / 10
nexttolast = math.floor(self.time[-1] * 10) / 10
if (nexttolast - nexttime) < 1.5:
self.timeticks = np.arange(nexttime, nexttolast, 0.2)
else:
self.timeticks = None
self.plots_per_page = 15.0
self.num_cols = 3
# Add an extra row to hold the legend and other infromation.
self.num_rows = (self.plots_per_page / 3) + 1
def read_STO(dirname, filename):
adapter = osim.STOFileAdapter()
return adapter.read(dirname + os.sep + filename + '.sto')
