def setUp(self):
self.u = np.sin
self.input = ph.Input(self.u) # control input
nodes, self.ini_funcs = cure_interval(LagrangeFirstOrder, (0, 1), node_count=3)
register_base("ini_funcs", self.ini_funcs, overwrite=True)
self.phi = ph.TestFunction("ini_funcs") # eigenfunction or something else
self.dphi = ph.TestFunction("ini_funcs", order=1) # eigenfunction or something else
self.dphi_at1 = ph.TestFunction("ini_funcs", order=1, location=1) # eigenfunction or something else
self.field_var = ph.FieldVariable("ini_funcs")
self.field_var_at1 = ph.FieldVariable("ini_funcs", location=1)
def setUp(self):
self.input = ph.Input(np.sin)
self.phi = np.array([cr.Function(lambda x: 2*x)])
register_base("phi", self.phi, overwrite=True)
self.test_func = ph.TestFunction("phi")
nodes, self.ini_funcs = cure_interval(LagrangeFirstOrder, (0, 1), node_count=2)
register_base("ini_funcs", self.ini_funcs, overwrite=True)
self.xdt = ph.TemporalDerivedFieldVariable("ini_funcs", order=1)
self.xdz_at1 = ph.SpatialDerivedFieldVariable("ini_funcs", order=1, location=1)
self.prod = ph.Product(self.input, self.xdt)
def setUp(self):
self.input = ph.Input(np.sin)
self.phi = np.array([cr.Function(lambda x: 2 * x)])
register_base("phi", self.phi, overwrite=True)
self.test_func = ph.TestFunction("phi")
nodes, self.ini_funcs = cure_interval(LagrangeFirstOrder, (0, 1),
node_count=2)
register_base("ini_funcs", self.ini_funcs, overwrite=True)
self.xdt = ph.TemporalDerivedFieldVariable("ini_funcs", order=1)
self.xdz_at1 = ph.SpatialDerivedFieldVariable("ini_funcs",
order=1,
location=1)
self.prod = ph.Product(self.input, self.xdt)
def setUp(self):
self.u = np.sin
self.input = ph.Input(self.u) # control input
nodes, self.ini_funcs = cure_interval(LagrangeFirstOrder, (0, 1),
node_count=3)
register_base("ini_funcs", self.ini_funcs, overwrite=True)
self.phi = ph.TestFunction(
"ini_funcs") # eigenfunction or something else
self.dphi = ph.TestFunction("ini_funcs",
order=1) # eigenfunction or something else
self.dphi_at1 = ph.TestFunction(
"ini_funcs", order=1,
location=1) # eigenfunction or something else
self.field_var = ph.FieldVariable("ini_funcs")
self.field_var_at1 = ph.FieldVariable("ini_funcs", location=1)
def setUp(self):
self.input = ph.Input(np.sin)
phi = cr.Function(np.sin)
psi = cr.Function(np.cos)
self.t_funcs = np.array([phi, psi])
register_base("funcs", self.t_funcs, overwrite=True)
self.test_funcs = ph.TestFunction("funcs")
self.s_funcs = np.array([cr.Function(self.scale)])[[0, 0]]
register_base("scale_funcs", self.s_funcs, overwrite=True)
self.scale_funcs = ph.ScalarFunction("scale_funcs")
nodes, self.ini_funcs = cure_interval(LagrangeFirstOrder, (0, 1), node_count=2)
register_base("prod_ini_funcs", self.ini_funcs, overwrite=True)
self.field_var = ph.FieldVariable("prod_ini_funcs")
self.field_var_dz = ph.SpatialDerivedFieldVariable("prod_ini_funcs", 1)
def setUp(self):
self.input = ph.Input(np.sin)
phi = cr.Function(np.sin)
psi = cr.Function(np.cos)
self.t_funcs = np.array([phi, psi])
register_base("funcs", self.t_funcs, overwrite=True)
self.test_funcs = ph.TestFunction("funcs")
self.s_funcs = np.array([cr.Function(self.scale)])[[0, 0]]
register_base("scale_funcs", self.s_funcs, overwrite=True)
self.scale_funcs = ph.ScalarFunction("scale_funcs")
nodes, self.ini_funcs = cure_interval(LagrangeFirstOrder, (0, 1),
node_count=2)
register_base("prod_ini_funcs", self.ini_funcs, overwrite=True)
self.field_var = ph.FieldVariable("prod_ini_funcs")
self.field_var_dz = ph.SpatialDerivedFieldVariable("prod_ini_funcs", 1)
def shape_generator(self, cls, der_order):
"""
verify the correct connection with visual feedback
"""
dz = pi.Domain((0, 1), step=.001)
dt = pi.Domain((0, 0), num=1)
nodes, funcs = pi.cure_interval(cls, dz.bounds, node_count=11)
pi.register_base("test", funcs, overwrite=True)
# approx_func = pi.Function(np.cos, domain=dz.bounds,
# derivative_handles=[lambda z: -np.sin(z), lambda z: -np.cos(z)])
approx_func = pi.Function(lambda z: np.sin(3*z), domain=dz.bounds,
derivative_handles=[lambda z: 3*np.cos(3*z), lambda z: -9*np.sin(3*z)])
weights = approx_func(nodes)
hull = pi.evaluate_approximation("test", np.atleast_2d(weights),
temp_domain=dt, spat_domain=dz, spat_order=der_order)
if show_plots:
# plot shapefunctions
c_map = pi.visualization.create_colormap(len(funcs))
pw = pg.plot(title="{}-Test".format(cls.__name__))
pw.addLegend()
pw.showGrid(x=True, y=True, alpha=0.5)
[pw.addItem(pg.PlotDataItem(np.array(dz),
weights[idx]*func.derive(der_order)(dz),
pen=pg.mkPen(color=c_map[idx]),
name="{}.{}".format(cls.__name__, idx)))
for idx, func in enumerate(funcs)]
# plot hull curve
pw.addItem(pg.PlotDataItem(np.array(hull.input_data[1]), hull.output_data[0, :],
pen=pg.mkPen(width=2), name="hull-curve"))
# plot original function
pw.addItem(pg.PlotDataItem(np.array(dz), approx_func.derive(der_order)(dz),
pen=pg.mkPen(color="m", width=2, style=pg.QtCore.Qt.DashLine), name="original"))
pg.QtCore.QCoreApplication.instance().exec_()
return np.sum(np.abs(hull.output_data[0, :] - approx_func.derive(der_order)(dz)))
def setUp(self):
nodes, ini_funcs = cure_interval(LagrangeFirstOrder, (0, 1),
node_count=2)
register_base("test_funcs", ini_funcs, overwrite=True)
def shape_generator(self, cls, der_order):
"""
verify the correct connection with visual feedback
"""
dz = pi.Domain((0, 1), step=.001)
dt = pi.Domain((0, 0), num=1)
nodes, funcs = pi.cure_interval(cls, dz.bounds, node_count=11)
pi.register_base("test", funcs, overwrite=True)
# approx_func = pi.Function(np.cos, domain=dz.bounds,
# derivative_handles=[lambda z: -np.sin(z), lambda z: -np.cos(z)])
approx_func = pi.Function(lambda z: np.sin(3 * z),
domain=dz.bounds,
derivative_handles=[
lambda z: 3 * np.cos(3 * z),
lambda z: -9 * np.sin(3 * z)
])
weights = approx_func(nodes)
hull = pi.evaluate_approximation("test",
np.atleast_2d(weights),
temp_domain=dt,
spat_domain=dz,
spat_order=der_order)
if show_plots:
# plot shapefunctions
c_map = pi.visualization.create_colormap(len(funcs))
pw = pg.plot(title="{}-Test".format(cls.__name__))
pw.addLegend()
pw.showGrid(x=True, y=True, alpha=0.5)
[
pw.addItem(
pg.PlotDataItem(np.array(dz),
weights[idx] * func.derive(der_order)(dz),
pen=pg.mkPen(color=c_map[idx]),
name="{}.{}".format(cls.__name__, idx)))
for idx, func in enumerate(funcs)
]
# plot hull curve
pw.addItem(
pg.PlotDataItem(np.array(hull.input_data[1]),
hull.output_data[0, :],
pen=pg.mkPen(width=2),
name="hull-curve"))
# plot original function
pw.addItem(
pg.PlotDataItem(np.array(dz),
approx_func.derive(der_order)(dz),
pen=pg.mkPen(color="m",
width=2,
style=pg.QtCore.Qt.DashLine),
name="original"))
pg.QtCore.QCoreApplication.instance().exec_()
return np.sum(
np.abs(hull.output_data[0, :] - approx_func.derive(der_order)(dz)))
def setUp(self):
nodes, ini_funcs = cure_interval(LagrangeFirstOrder, (0, 1), node_count=2)
register_base("test_funcs", ini_funcs, overwrite=True)
