#!/usr/bin/env python
# coding=utf-8
# -------------------------------------------------------------------------------
# ____ _ _____ ____ _
# | _ \ _ _| | |_ _/ ___| _ __ (_) ___ ___
# | |_) | | | | | | | \___ \| '_ \| |/ __/ _ \
# | __/| |_| | |___| | ___) | |_) | | (_| __/
# |_| \__, |_____|_| |____/| .__/|_|\___\___|
# |___/ |_|
#
# Name: raw_classes.py
# Purpose: Implements helper classes that contain
#
# Author: Nuno Brum (nuno.brum@gmail.com)
#
# Created: 19-06-2022
# Licence: refer to the LICENSE file
# -------------------------------------------------------------------------------
"""
Defines base classes for the RAW file data structures.
"""
import numpy as np
from numpy import zeros, complex128, float32, float64
from binascii import b2a_hex
from typing import Union, List
class DataSet(object):
"""
This is the base class for storing all traces of a RAW file. Returned by the get_trace() or by the get_axis()
methods.
Normally the user doesn't have to be aware of this class. It is only used internally to encapsulate the different
implementations of the wave population.
Data can be retrieved directly by using the [] operator.
If numpy is available, the numpy vector can be retrieved by using the get_wave() method.
The parameter whattype defines what is the trace representing in the simulation, Voltage, Current a Time or
Frequency.
"""
def __init__(self, name, whattype, datalen, numerical_type='real'):
"""Base Class for both Axis and Trace Classes.
Defines the common operations between both."""
self.name = name
self.whattype = whattype
self.numerical_type = numerical_type
if numerical_type == 'double':
self.data = zeros(datalen, dtype=float64)
elif numerical_type == 'real':
self.data = zeros(datalen, dtype=float32)
elif numerical_type == 'complex':
self.data = zeros(datalen, dtype=complex128)
else:
raise NotImplementedError
def __str__(self):
if isinstance(self.data[0], float):
# data = ["%e" % value for value in self.data]
return "name:'%s'\ntype:'%s'\nlen:%d\n%s" % (self.name, self.whattype, len(self.data), str(self.data))
elif isinstance(self.data[0], complex):
return "name: {}\ntype: {}\nlen: {:d}\n{}".format(self.name, self.whattype, len(self.data), str(self.data))
else:
data = [b2a_hex(value) for value in self.data]
return "name:'%s'\ntype:'%s'\nlen:%d\n%s" % (self.name, self.whattype, len(self.data), str(data))
def __len__(self):
return len(self.data)
def __iter__(self):
return iter(self.data)
def __getitem__(self, item):
return self.data[item]
def get_wave(self) -> np.array:
"""
:return: Internal data array
:rtype: numpy.array
"""
return self.data
[docs]class Axis(DataSet):
"""This class is used to represent the horizontal axis like on a Transient or DC Sweep Simulation. It derives from
the DataSet and defines additional methods that are specific for X axis.
This class is constructed by the get_time_axis() method or by a get_trace(0) command. In RAW files the trace 0 is
always the X Axis. Ex: time for .TRAN simulations and frequency for the .AC simulations.
To access data inside this class, the get_wave() should be used, which implements the support for the STEPed data.
IF Numpy is available, get_wave() will return a numpy array.
In Transient Analysis and in DC transfer characteristic, LTSpice uses doubles to store the axis values.
"""
def __init__(self, name: str, whattype: str, datalen: int, numerical_type: str = 'double'):
super().__init__(name, whattype, datalen, numerical_type)
self.step_info = None
def _set_steps(self, step_info: List[dict]):
self.step_info = step_info
self.step_offsets = [None for _ in range(len(step_info))]
# Now going to calculate the point offset for each step
self.step_offsets[0] = 0
i = 1
k = 1
while i < len(self.data):
if self.data[i] == self.data[0]:
# print(k, i, self.data[i], self.data[i+1])
self.step_offsets[k] = i
k += 1
i += 1
if k != len(self.step_info):
raise SpiceReadException("The file a different number of steps than expected.\n" +
"Expecting %d got %d" % (len(self.step_offsets), k))
[docs] def step_offset(self, step: int):
"""
In Stepped RAW files, several simulations runs are stored in the same RAW file. This function returns the
offset within the binary stream where each step starts.
:param step: Number of the step within the RAW file
:type step: int
:return: The offset within the RAW file
:rtype: int
"""
if self.step_info is None:
if step > 0:
return len(self.data)
else:
return 0
else:
if step >= len(self.step_offsets):
return len(self.data)
else:
return self.step_offsets[step]
[docs] def get_wave(self, step: int = 0) -> np.array:
"""
Returns a vector containing the wave values. If numpy is installed, data is returned as a numpy array.
If not, the wave is returned as a list of floats.
If stepped data is present in the array, the user should specify which step is to be returned. Failing to do so,
will return all available steps concatenated together.
:param step: Optional step in stepped data raw files.
:type step: int
:return: The trace values
:rtype: numpy.array
"""
if step == 0:
wave = self.data[:self.step_offset(1)]
else:
wave = self.data[self.step_offset(step):self.step_offset(step + 1)]
if self.name == 'time': # This is a bug in LTSpice, where the time axis values are sometimes negative
return np.abs(wave)
else:
return wave
[docs] def get_time_axis(self, step: int = 0):
"""
**Deprecated**. Use get_wave() instead.
Returns the time axis raw data. Please note that the time axis may not have a constant time step. LTSpice will
increase the time-step in simulation phases where there aren't value changes, and decrease time step in
the parts where more time accuracy is needed.
:param step: Optional step number if reading a raw file with stepped data.
:type step: int
:return: time axis
:rtype: numpy.array
"""
assert self.name == 'time', \
"This function is only applicable to transient analysis, where a bug exists on the time signal"
return self.get_wave(step)
[docs] def get_point(self, n, step: int = 0) -> Union[float, complex]:
"""
Get a point from the dataset
:param n: position on the vector
:type n:int
:param step: step index
:type step: int
:returns: Value of the data point
:rtype: float or complex
"""
return self.data[n + self.step_offset(step)]
def __getitem__(self, item) -> Union[float, complex]:
"""This is only here for compatibility with previous code. """
assert self.step_info is None, "Indexing should not be used with stepped data. Use get_point or get_wave"
return self.data.__getitem__(item)
[docs] def get_position(self, t, step: int = 0) -> Union[int, float]:
"""
Returns the position of a point in the axis. If the point doesn't exist, an interpolation is done between the
two closest points.
For example, if the point requested is 1.0001ms and the closest points that exist in the axis are t[100]=1ms and
t[101]=1.001ms, then the return value will be 100 + (1.0001ms-1ms)/(1.001ms-1ms) = 100.1
:param t: point in axis to search for
:type t: float
:param step: step number
:type step: int
:returns: The position of parameter /t/ in the axis
:rtype: int, float
"""
if self.name == 'time':
timex = self.get_time_axis(step)
else:
timex = self.get_wave(step)
for i, x in enumerate(timex):
if x == t:
return i
elif x > t:
# Needs to interpolate the data
if i == 0:
raise IndexError("Time position is lower than t0")
frac = (t - timex[i-1])/(timex[i] - timex[i-1])
return i - 1 + frac
[docs] def get_len(self, step: int = 0) -> int:
"""
Returns the length of the axis.
:param step: Optional parameter the step index.
:type step: int
:return: The number of data points
:rtype: int
"""
return self.step_offset(step + 1) - self.step_offset(step)
def __len__(self):
if self.step_info is None:
return len(self.data)
else:
return self.get_len()
def __iter__(self):
assert self.step_info is None, "Iteration can't be used with stepped data. Use get_wave() method."
return self.data.__iter__()
[docs]class TraceRead(DataSet):
"""This class is used to represent a trace. It derives from DataSet and implements the additional methods to
support STEPed simulations.
This class is constructed by the get_trace() command.
Data can be accessed through the [] and len() operators, or by the get_wave() method.
If numpy is available the get_wave() method will return a numpy array.
"""
def __init__(self, name, whattype, datalen, axis, numerical_type='real'):
super().__init__(name, whattype, datalen, numerical_type)
self.axis = axis
[docs] def get_point(self, n: int, step: int = 0) -> Union[float, complex]:
"""
Implementation of the [] operator.
:param n: item in the array
:type n: int
:param step: Optional step number
:type step: int
:return: float value of the item
:rtype: float
"""
if self.axis is None:
if n != 0:
return self.data[n]
else:
return self.data[step] # This is for the case of stepped operation point simulation.
else:
return self.data[self.axis.step_offset(step) + n]
def __getitem__(self, item) -> Union[float, complex]:
"""This is only here for compatibility with previous code. """
assert self.axis is None or self.axis.step_info is None, \
"Indexing should not be used with stepped data. Use get_point() method"
return self.data.__getitem__(item)
[docs] def get_wave(self, step: int = 0) -> np.array:
"""
Returns the data contained in this object. For stepped simulations an argument must be passed specifying the
step number. If no steps exist, the argument must be left blank.
To know whether stepped data exist, the user can use the get_raw_property('Flags') method.
If numpy is available the get_wave() method will return a numpy array.
:param step: To be used when stepped data exist on the RAW file.
:type step: int
:return: a List or numpy array (if installed) containing the data contained in this object.
:rtype: numpy.array
"""
# print('step size %d' % step)
# print(self.data[self.axis.step_offset(step):self.axis.step_offset(step + 1)])
if self.axis is None:
return super().get_wave()
else:
if step == 0:
return self.data[:self.axis.step_offset(1)]
else:
return self.data[self.axis.step_offset(step):self.axis.step_offset(step + 1)]
[docs] def get_point_at(self, t, step: int = 0) -> Union[float, complex]:
"""
Get a point from the trace at the point specified by the /t/ argument.
If the point doesn't exist on the axis, the data is interpolated using a linear regression between the two
adjacent points.
:param t: point in the axis where to find the point.
:type t: float, float32(numpy) or float64(numpy)
:param step: step index
:type step: int
"""
pos = self.axis.get_position(t, step)
if isinstance(pos, (float, float32, float64)):
offset = self.axis.step_offset(step)
i = int(pos)
last_item = self.get_len(step) - 1
if i < last_item:
f = pos - i
return self.data[offset + i] + f * (self.data[offset + i + 1] - self.data[offset + i])
elif pos == last_item: # This covers the case where a float is given containing the last position
return self.data[offset + i]
else:
raise IndexError(f"The highest index is {last_item}. Received {pos}")
else:
return self.get_point(pos, step)
[docs] def get_len(self, step: int = 0) -> int:
"""
Returns the length of the axis.
:param step: Optional parameter the step index.
:type step: int
:return: The number of data points
:rtype: int
"""
return self.axis.step_offset(step + 1)
def __len__(self):
"""
**Deprecated**
This is only here for compatibility with previous code.
"""
assert self.axis is None or self.axis.step_info is None, \
"len() should not be used with stepped data. Use get_len() method passing the step index"
return len(self.data)
class DummyTrace(object):
"""Dummy Trace for bypassing traces while reading"""
def __init__(self, name, whattype):
"""Base Class for both Axis and Trace Classes.
Defines the common operations between both."""
self.name = name
self.whattype = whattype
class SpiceReadException(Exception):
"""Custom class for exception handling"""
...