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fixed Duplex connection path estimation
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@milanofthe
milanofthe committed Dec 13, 2024
1 parent 824f480 commit 3ff9d86
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156 changes: 78 additions & 78 deletions README.md
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Expand Up @@ -96,24 +96,24 @@ Sc.plot()
time, data = Sc.read();
```

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Expand Down Expand Up @@ -181,24 +181,24 @@ Sim.run(3*mu)
Sc.plot(".-");
```

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Expand Down Expand Up @@ -264,24 +264,24 @@ Sim.run(4*tau)
Sco.plot()
```

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Expand All @@ -290,8 +290,8 @@ Sco.plot()



Now the recorded data is of type `Value` and we can evaluate the automatically computed partial derivatives at each timestep. For example
$\partial x(t) / \partial a$ the response with respect to the linear feedback parameter.
Now the recorded data is of type `Value` and we can evaluate the automatically computed partial derivatives at each timestep. For example
the response with respect to the linear feedback parameter ($\partial x(t) / \partial a$ <-> `der(data, a)`).


```python
Expand All @@ -303,9 +303,9 @@ time, [step, data] = Sco.read()
fig, ax = plt.subplots(nrows=1, tight_layout=True, figsize=(8, 4), dpi=120)

#evaluate and plot partial derivatives
ax.plot(time, der(data, a), label="$dx/da$")
ax.plot(time, der(data, x0), label="$dx/dx_0$")
ax.plot(time, der(data, b), label="$dx/db$")
ax.plot(time, der(data, a), label=r"$\partial x / \partial a$")
ax.plot(time, der(data, x0), label=r"$\partial x / \partial x_0$")
ax.plot(time, der(data, b), label=r"$\partial x / \partial b$")

ax.set_xlabel("time [s]")
ax.grid(True)
Expand Down Expand Up @@ -393,24 +393,24 @@ Sim.run(20)
Sc.plot();
```

2024-12-12 10:43:17,751 - INFO - LOGGING enabled
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Expand Down Expand Up @@ -456,7 +456,7 @@ Some of the possible directions for future features are:
- better `__repr__` for the blocks maybe in json format OR just add a `json` method to the blocks and to the connections that builds a netlist representation to save to and load from an interpretable file (compatibility with other system description languages)
- explore block level parallelization (fork-join) with Python 3.13 free-threading, batching based on execution cost
- linearization of blocks and subsystems with the AD framework, linear surrogate models, system wide linearization
- more robust steady state solver and algebraic loop solver
- improved / more robust steady state solver and algebraic loop solver
- methods for periodic steady state analysis
- more extensive testing and validation (as always)

Expand Down
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2 changes: 1 addition & 1 deletion pathsim/__init__.py
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@@ -1,3 +1,3 @@
from .simulation import Simulation
from .connection import Connection
from .connection import Connection, Duplex
from .subsystem import Subsystem, Interface
5 changes: 3 additions & 2 deletions pathsim/blocks/lti.py
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Expand Up @@ -95,8 +95,9 @@ def update(self, t):
#compute implicit balancing update
prev_outputs = self.outputs.copy()
u = dict_to_array(self.inputs)
y = np.dot(self.C, self.engine.get()) + np.dot(self.D, u)
self.outputs = array_to_dict(y)
y_D = np.dot(self.D, u) if np.any(self.D) else 0.0
y_C = np.dot(self.C, self.engine.get())
self.outputs = array_to_dict(y_C + y_D)
return max_error_dicts(prev_outputs, self.outputs)


Expand Down
20 changes: 12 additions & 8 deletions pathsim/connection.py
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Expand Up @@ -143,16 +143,19 @@ class Duplex(Connection):
into an IO-pair.
"""

def __init__(self, *targets):

if len(targets) != 2:
raise ValueError("Duplex needs exactly two targets for bidirectional connection!")

self.targets = [trg if isinstance(trg, (list, tuple)) else (trg, 0) for trg in targets]
def __init__(self, source, target):

self.source = source if isinstance(source, (list, tuple)) else (source, 0)
self.target = target if isinstance(target, (list, tuple)) else (target, 0)

#for path length estimation
self.targets = [self.target]

#flag to set connection active
self._active = True

def __str__(self):
return f"Duplex " + " <-> ".join([f"({trg}, {prt})" for trg, prt in self.targets])
return f"Duplex {self.source} <-> {self.target}"


def update(self):
Expand All @@ -162,7 +165,8 @@ def update(self):
"""

#unpack the two targets
(trg1, prt1), (trg2, prt2) = self.targets
(trg1, prt1) = self.target
(trg2, prt2) = self.source

#bidirectional data transfer
trg1.set(prt1, trg2.get(prt2))
Expand Down
2 changes: 1 addition & 1 deletion pathsim/utils/gilbert.py
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Expand Up @@ -117,6 +117,6 @@ def gilbert_realization(Poles=[], Residues=[], Const=0.0, tolerance=1e-9):
#build block diagonal
A = np.kron(np.eye(n, dtype=float), a)
B = np.kron(np.eye(n, dtype=float), b).T
D = Const
D = Const * np.ones((m, n)) if np.isscalar(Const) else Const

return A, B, C, D

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