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milanofthemilanofthe
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Merge pull request #204 from pathsim/feature/xprt-ir-blocks
Add missing XPRT IR blocks: logic, Atan2, Rescale, Alias, discrete Delay
2 parents c579dd7 + f02e3cb commit c2a653d

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‎src/pathsim/blocks/__init__.py‎

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from .noise import *
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from .table import *
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from .relay import *
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from .logic import *
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from .math import *
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from .ctrl import *
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from .lti import *

‎src/pathsim/blocks/delay.py‎

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#########################################################################################
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##
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## TIME DOMAIN DELAY BLOCK
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## TIME DOMAIN DELAY BLOCK
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## (blocks/delay.py)
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##
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#########################################################################################
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import numpy as np
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from collections import deque
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from ._block import Block
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from ..utils.adaptivebuffer import AdaptiveBuffer
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from ..events.schedule import Schedule
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from ..utils.mutable import mutable
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# BLOCKS ================================================================================
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@mutable
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class Delay(Block):
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"""Delays the input signal by a time constant 'tau' in seconds.
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"""Delays the input signal by a time constant 'tau' in seconds.
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Supports two modes of operation:
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Mathematically this block creates a time delay of the input signal like this:
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**Continuous mode** (default, ``sampling_period=None``):
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Uses an adaptive interpolating buffer for continuous-time delay.
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.. math::
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y(t) =
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y(t) =
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\\begin{cases}
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x(t - \\tau) & , t \\geq \\tau \\\\
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0 & , t < \\tau
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\\end{cases}
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**Discrete mode** (``sampling_period`` provided):
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Uses a ring buffer with scheduled sampling events for N-sample delay,
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where ``N = round(tau / sampling_period)``.
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.. math::
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y[k] = x[k - N]
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Note
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----
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The internal adaptive buffer uses interpolation for the evaluation. This is
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required to be compatible with variable step solvers. It has a drawback however.
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The order of the ode solver used will degrade when this block is used, due to
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the interpolation.
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In continuous mode, the internal adaptive buffer uses interpolation for
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the evaluation. This is required to be compatible with variable step solvers.
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It has a drawback however. The order of the ode solver used will degrade
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when this block is used, due to the interpolation.
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Note
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----
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This block supports vector input, meaning we can have multiple parallel
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This block supports vector input, meaning we can have multiple parallel
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delay paths through this block.
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Example
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-------
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The block is initialized like this:
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Continuous-time delay:
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.. code-block:: python
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#5 time units delay
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D = Delay(tau=5)
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Discrete-time N-sample delay (10 samples):
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.. code-block:: python
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D = Delay(tau=0.01, sampling_period=0.001)
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Parameters
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----------
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tau : float
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delay time constant
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delay time constant in seconds
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sampling_period : float, None
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sampling period for discrete mode, default is continuous mode
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Attributes
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----------
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_buffer : AdaptiveBuffer
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internal interpolatable adaptive rolling buffer
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internal interpolatable adaptive rolling buffer (continuous mode)
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_ring : deque
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internal ring buffer for N-sample delay (discrete mode)
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"""
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def __init__(self, tau=1e-3):
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def __init__(self, tau=1e-3, sampling_period=None):
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super().__init__()
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#time delay in seconds
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#time delay in seconds
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self.tau = tau
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#create adaptive buffer
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self._buffer = AdaptiveBuffer(self.tau)
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#params for sampling
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self.sampling_period = sampling_period
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if sampling_period is None:
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#continuous mode: adaptive buffer with interpolation
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self._buffer = AdaptiveBuffer(self.tau)
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else:
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#discrete mode: ring buffer with N-sample delay
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self._n = max(1, round(self.tau / self.sampling_period))
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self._ring = deque([0.0] * self._n, maxlen=self._n + 1)
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#flag to indicate this is a timestep to sample
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self._sample_next_timestep = False
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#internal scheduled event for periodic sampling
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def _sample(t):
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self._sample_next_timestep = True
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self.events = [
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Schedule(
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t_start=0,
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t_period=sampling_period,
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func_act=_sample
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)
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]
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def __len__(self):
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def reset(self):
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super().reset()
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#clear the buffer
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self._buffer.clear()
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if self.sampling_period is None:
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#clear the adaptive buffer
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self._buffer.clear()
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else:
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#clear the ring buffer
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self._ring.clear()
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self._ring.extend([0.0] * self._n)
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def update(self, t):
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"""Evaluation of the buffer at different times
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via interpolation.
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"""Evaluation of the buffer at different times
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via interpolation (continuous) or ring buffer lookup (discrete).
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Parameters
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----------
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t : float
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evaluation time
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"""
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#retrieve value from buffer
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y = self._buffer.get(t)
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self.outputs.update_from_array(y)
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if self.sampling_period is None:
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#continuous mode: retrieve value from buffer
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y = self._buffer.get(t)
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self.outputs.update_from_array(y)
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else:
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#discrete mode: output the oldest value in the ring buffer
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self.outputs[0] = self._ring[0]
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def sample(self, t, dt):
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"""Sample input values and time of sampling
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"""Sample input values and time of sampling
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and add them to the buffer.
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Parameters
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integration timestep
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"""
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#add new value to buffer
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self._buffer.add(t, self.inputs.to_array())
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if self.sampling_period is None:
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#continuous mode: add new value to buffer
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self._buffer.add(t, self.inputs.to_array())
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else:
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#discrete mode: only sample on scheduled events
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if self._sample_next_timestep:
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self._ring.append(self.inputs[0])
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self._sample_next_timestep = False

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