How to use the dymos.glm.ozone.utils.units.get_rate_units function in dymos
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OpenMDAO / dymos / dymos / glm / ozone / components / implicit_tm_step_comp.py View on Github 
self.dy_dF = dy_dF = {}
self.add_input('h', units=time_units)
for state_name, state in iteritems(self.options['states']):
size = np.prod(state['shape'])
shape = state['shape']
F_name = get_name('F', state_name, i_step=i_step)
y_old_name = get_name('y_old', state_name, i_step=i_step)
y_new_name = get_name('y_new', state_name, i_step=i_step)
self.add_input(
F_name, shape=(num_stages,) + shape,
units=get_rate_units(state['units'], time_units))
self.add_input(
y_old_name, shape=(num_step_vars,) + shape,
units=state['units'])
self.add_output(
y_new_name, shape=(num_step_vars,) + shape,
units=state['units'])
F_arange = np.arange(num_stages * size).reshape(
(num_stages,) + shape)
y_arange = np.arange(num_step_vars * size).reshape(
(num_step_vars,) + shape)
# -----------------F_name = get_name('F', state_name)
y0_name = get_name('y0', state_name)
y_name = get_name('y', state_name)
y0_arange = np.arange(num_step_vars * size).reshape((num_step_vars,) + shape)
y_arange = np.arange(num_times * num_step_vars * size).reshape(
(num_times, num_step_vars,) + shape)
F_arange = np.arange((num_times - 1) * num_stages * size).reshape(
(num_times - 1, num_stages,) + shape)
self.add_input(
F_name,
shape=(num_times - 1, num_stages,) + shape,
units=get_rate_units(state['units'], time_units))
self.add_input(
y0_name,
shape=(num_step_vars,) + shape,
units=state['units'])
self.add_output(
y_name,
shape=(num_times, num_step_vars,) + shape,
units=state['units'])
# -----------------
# (num_times, num_step_vars,) + shape
data1 = np.ones(num_times * num_step_vars * size)
rows1 = np.arange(num_times * num_step_vars * size)self.declare_partials('*', '*', dependent=False)
self.add_input('h', units=time_units)
for state_name, state in iteritems(self.options['states']):
size = np.prod(state['shape'])
y_old_name = get_name('y_old', state_name, i_step=i_step, i_stage=i_stage)
Y_name = get_name('Y', state_name, i_step=i_step, i_stage=i_stage)
for j_stage in range(i_stage):
F_name = get_name('F', state_name, i_step=i_step, i_stage=i_stage, j_stage=j_stage)
self.add_input(
F_name, shape=(1,) + state['shape'],
units=get_rate_units(state['units'], time_units))
self.add_input(
y_old_name, shape=(num_step_vars,) + state['shape'],
units=state['units'])
self.add_output(
Y_name, shape=(1,) + state['shape'],
units=state['units'])
vals = np.zeros((num_step_vars, size))
rows = np.zeros((num_step_vars, size), int)
cols = np.arange(num_step_vars * size)
for ii_step in range(num_step_vars):
vals[ii_step, :] = glm_U[i_stage, ii_step]
rows[ii_step, :] = np.arange(size)
vals = vals.flatten()self.declare_partials('*', '*', dependent=False)
self.add_input('h', units=time_units)
for state_name, state in iteritems(self.options['states']):
size = np.prod(state['shape'])
y_old_name = get_name('y_old', state_name, i_step=i_step)
y_new_name = get_name('y_new', state_name, i_step=i_step)
for j_stage in range(num_stages):
F_name = get_name('F', state_name, i_step=i_step, j_stage=j_stage)
self.add_input(
F_name, shape=(1,) + state['shape'],
units=get_rate_units(state['units'], time_units))
self.add_input(
y_old_name, shape=(num_step_vars,) + state['shape'],
units=state['units'])
self.add_output(
y_new_name, shape=(num_step_vars,) + state['shape'],
units=state['units'])
self.declare_partials(y_new_name, 'h', dependent=True)
y_arange = np.arange(num_step_vars * size).reshape((num_step_vars, size))
# num_step_vars, num_step_vars, size
data = np.einsum('ij,...->ij...', glm_V, np.ones(size)).flatten()
rows = np.einsum('i...,j->ij...', y_arange, np.ones(num_step_vars, int)).flatten()glm_A = self.options['glm_A']
glm_U = self.options['glm_U']
self.add_input('h', units=time_units)
for state_name, state in iteritems(self.options['states']):
size = np.prod(state['shape'])
shape = state['shape']
F_name = get_name('F', state_name, i_step=i_step)
y_old_name = get_name('y_old', state_name, i_step=i_step)
Y_name = get_name('Y', state_name, i_step=i_step)
self.add_input(
F_name, shape=(num_stages,) + shape,
units=get_rate_units(state['units'], time_units))
self.add_input(
y_old_name, shape=(num_step_vars,) + shape,
units=state['units'])
self.add_output(
Y_name, shape=(num_stages,) + shape,
units=state['units'])
Y_arange = np.arange(num_stages * size).reshape(
(num_stages,) + shape)
F_arange = np.arange(num_stages * size).reshape(
(num_stages,) + shape)
y_arange = np.arange(num_step_vars * size).reshape(Y_out_name = get_name('Y_out', state_name)
Y_in_name = get_name('Y_in', state_name)
Y_arange = np.arange((num_times - 1) * num_stages * size).reshape(
(num_times - 1, num_stages,) + shape)
F_arange = np.arange((num_times - 1) * num_stages * size).reshape(
(num_times - 1, num_stages,) + shape)
y_arange = np.arange(num_times * num_step_vars * size).reshape(
(num_times, num_step_vars,) + shape)
self.add_input(
F_name,
shape=(num_times - 1, num_stages,) + shape,
units=get_rate_units(state['units'], time_units))
self.add_input(
y_name,
shape=(num_times, num_step_vars,) + shape,
units=state['units'])
self.add_input(
Y_in_name, val=0.,
shape=(num_times - 1, num_stages,) + shape,
units=state['units'])
self.add_output(
Y_out_name,
shape=(num_times - 1, num_stages,) + shape,
units=state['units'])num_y0 = np.prod(y0_arange.shape)
num_F = np.prod(F_arange.shape)
num_Y = np.prod(Y_arange.shape)
num_y = np.prod(y_arange.shape)
# --------------------------------------------------------------------------------
self.add_input(
y0_name,
shape=(num_step_vars,) + shape,
units=state['units'])
self.add_input(
F_name,
shape=(num_times - 1, num_stages,) + shape,
units=get_rate_units(state['units'], time_units))
self.add_input(
Y_in_name, val=0.,
shape=(num_times - 1, num_stages,) + shape,
units=state['units'])
self.add_output(
Y_out_name,
shape=(num_times - 1, num_stages,) + shape,
units=state['units'])
# -----------------
self.declare_partials(Y_out_name, 'h_vec')
self.declare_partials(Y_out_name, y0_name)
self.declare_partials(Y_out_name, F_name)dymos
Open-Source Optimization of Dynamic Multidisciplinary Systems
Package Health Score
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