ltspice2py.py

#! /usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (c) 2015, 2016, 2017 MURAMATSU Atsushi <[email protected]>
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included
# in all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.

import sys
import os
import io
import math
import locale
import struct
import gzip
import subprocess
from datetime import datetime
from abc import ABCMeta, abstractmethod

import numpy as np

try:
    from typing import (Text, Tuple, List, Dict,
                        IO, Callable, Iterable, Optional, Union, Any)
except ImportError:
    Text = Any = None # type: ignore
    class _DummyCls(object):
        def __getitem__(self, *args): pass # type: ignore
    Tuple = List = Dict = IO = _DummyCls # type: ignore
    Callable = Iterable = Optional = Union = _DummyCls # type: ignore

INDEX_TYPES = ('time', 'frequency')

if sys.version_info[0] >= 3:
    def _to_string_lat1(by): # type: (bytes) -> Text
        return by.decode('latin-1')
    def _to_string_utf16le(by): # type: (bytes) -> Text
        return by.decode('utf-16le')
    def _readline_lat1(fd): # type: (IO[bytes]) -> Text
        return _to_string_lat1(fd.readline())
    def _readline_utf16le(fd): # type: (IO[bytes]) -> Text
        by = fd.readline()
        if len(by) % 2 == 1 and by[-1] == ord("\n"):
            by += fd.read(1)
        return _to_string_utf16le(by)
    def _check_utf16le(by): # type: (bytes) -> bool
        if 0 in by:
            return True
        return False
    _basestring = str
else:
    def _to_string_lat1(by): # type: (bytes) -> Text
        return by
    def _to_string_utf16le(by): # type: (bytes) -> Text
        return unicode(by, coding='utf-16le') # type: ignore
    def _readline_lat1(fd): # type: (IO[bytes]) -> Text
        return _to_string_lat1(fd.readline())
    def _readline_utf16le(fd): # type: (IO[bytes]) -> Text
        by = fd.readline()
        if len(by) % 2 == 1 and by[-1] == "\n":
            by += fd.read(1)
        return _to_string_utf16le(by)
    def _check_utf16le(by): # type: (bytes) -> bool
        if "\0" in by:
            return True
        return False
    _basestring = basestring

LTSPICE_FILES = [
    ("LTspiceXVII", "XVIIx64.exe", 17),
    ("LTspiceXVII", "XVIIx86.exe", 17),
    ("LTspiceIV", "scad3.exe", 4),
]

LTSPICE = None
def get_ltspice_program(forced_version=None):
    # type: (int) -> Optional[List[Text]]
    """Get LTspice command.

    Args:
        forced_version (int, optional): select LTspice version.
            Now supported versions are 4 and 17.
            Default is latest version which is founded.

    Returns:
        None, list[str].
        If LTspice is not found, this function returns None.
    """
    if sys.platform in ('win32', 'win64'):
        progdirs = []
        progdir = os.getenv("PROGRAMFILES")
        if progdir is not None:
            progdirs.append(progdir)
        progdir_x86  = os.getenv("ProgramFiles(x86)")
        if progdir_x86 is not None:
            progdirs.append(progdir_x86)
        for progdir in progdirs:
            for d, f, v in LTSPICE_FILES:
                if (forced_version is not None and v != forced_version):
                    continue
                exefile = os.path.join(progdir, "LTC", d, f)
                if os.path.exists(exefile):
                    return [ exefile ]
    elif sys.platform == 'darwin':
        exefile = "/Applications/LTspice.app/Contents/MacOS/LTspice"
        if os.path.exists(exefile):
            return [ exefile ]
    else: # UN*X with wine
        for progdir in (
                os.path.expanduser("~/.wine/drive_c/Program Files"),
                os.path.expanduser("~/.wine/drive_c/Program Files (x86)")):
            if not os.path.exists(progdir):
                continue
            for d, f, v in LTSPICE_FILES:
                if (forced_version is not None and v != forced_version):
                    continue
                exefile = os.path.join(progdir, "LTC", d, f)
                if os.path.exists(exefile):
                    return [ "wine", exefile ]
    return None
if LTSPICE is None:
    LTSPICE = get_ltspice_program()

def _parse_date(s): # type: (Text) -> datetime
    old_locale = locale.getlocale(locale.LC_TIME)
    try:
        locale.setlocale(locale.LC_TIME, "C")
        return datetime.strptime(s, "%a %b %d %H:%M:%S %Y")
    finally:
        locale.setlocale(locale.LC_TIME, old_locale)

def _parse_any(s): # type: (Text) -> Union[int,float,Text]
    try:
        return int(s)
    except ValueError:
        try:
            return float(s)
        except ValueError:
            return s

def read_ltspice_log(logfile): # type: (Text) -> Optional[Dict[Text,Any]]
    """Read information of LTspice simulation from logfile

    Args:
        logfile (str): filename of logfile.
            Gzipped logfile is also supported.

    Returns:
        dict: informations
    
        Now supported informations are:

        :steps: tuple of (variable, step value)
        :date: Simulated date
        :total elasp time: Total elasp time of simulation
    """
    if not os.path.exists(logfile):
        return None
    
    encoding = 'latin-1'
    if logfile.endswith(".gz"):
        _open = gzip.open # type: Callable[..., IO]
    else:
        _open = io.open
    with _open(logfile, 'rb') as f:
        line = f.readline()
        if _check_utf16le(line):
            encoding = 'utf-16le'
    result = {} # type: Dict[Text,Any]
    step_data = []
    with _open(logfile, 'r', encoding=encoding) as f:
        for line in f:
            if line.startswith(".step "):
                k, v = line[6:].split('=', 1)
                step_data.append((k.strip().lower(), float(v)))
            elif ':' in line:
                k, v = [ s.strip() for s in line.split(':', 1) ]
                k = k.lower()
                if k == 'date':
                    v = _parse_date(v.strip())
                elif k == 'total elapsed time':
                    v = float(v.strip().split()[0])
                result[k] = v
            elif '=' in line:
                k, v = [ s.strip() for s in line.split('=', 1) ]
                result[k.lower()] = _parse_any(v)
    if len(step_data):
        result['steps'] = tuple(step_data)
    return result

class _DataReaderBase(object):
    __metaclass__ = ABCMeta

    @abstractmethod
    def get_bulk_lengths(self):
        # type: () -> Iterable[int]
        pass

    @abstractmethod
    def get_bulk(self, start, end, stride):
        # type: (int, int, int) -> Optional[np.ndarray]
        pass

class _RawDataReader(_DataReaderBase):
    def __init__(self, f, col, row, index, var_using,
                 is_binary, is_complex, is_stepped, readline):
        # type: (IO[bytes], int, int, int, List[int], bool, bool, bool, Callable[[IO[bytes]],Text]) -> None
        
        self.f = f
        self.col = col
        self.row = row
        self.index = index
        self.var_using = np.array(var_using)
        self.readline = readline
        self.is_binary = is_binary
        self.is_complex = is_complex

        if is_binary:
            self.skip_row = self._skip_row_binary
            if is_complex:
                self.get_row = self._get_row_binary_cpx
                self.fmt = "<%dd" % (2 * self.col)
            else:
                self.get_row = self._get_row_binary_real
                fmt = ["<"]
                for c in range(self.col):
                    if c == index:
                        fmt.append("d")
                    else:
                        fmt.append("f")
                self.fmt = "".join(fmt)
            self.rowsize = struct.calcsize(self.fmt)
        else:
            self.get_row = self._get_row_text
            self.skip_row = self._skip_row_text
        
        if is_stepped:
            self.data_bulk = self._check_bulk_info()
        else:
            self.data_bulk = [(self.row, 0)]
        self.next_bulk = 0

    def _get_row_binary_cpx(self, check_n=None): # type: (int) -> np.ndarray
        v = struct.unpack(self.fmt, self.f.read(self.rowsize))
        return np.array([ complex(v[2*i], v[2*i+1]) for i in range(self.col) ])

    def _get_row_binary_real(self, check_n=None): # type: (int) -> np.ndarray
        return np.array(struct.unpack(self.fmt, self.f.read(self.rowsize)))

    def _get_row_text(self, check_n=None): # type: (int) -> np.ndarray
        def _complex(st): # type: (Text) -> complex
            return complex(*[ float(s) for s in st.split(',') ])
        if self.is_complex:
            _parser = _complex # type: Callable[[Text], Union[float,complex]]
        else:
            _parser = float
        result = []
        v, st = self.readline(self.f).strip().split()
        try:
            n = int(v)
        except ValueError:
            raise Exception("broken format")
        if check_n is not None and n != check_n:
            raise Exception("broken format")
        result.append(_parser(st))
        for i in range(1, self.col):
            st = self.readline(self.f).strip()
            result.append(_parser(st))
        return np.array(result)

    def _skip_row_binary(self, num=1, check_n=None): # type: (int, int) -> None
        if num == 0: return
        self.f.seek(num * self.rowsize, 1)

    def _skip_row_text(self, num=1, check_n=None): # type: (int, int) -> None
        for i in range(num):
            v, st = self.readline(self.f).strip().split()
            try:
                n = int(v)
            except ValueError:
                raise Exception("broken format")
            if check_n is not None:
                if n != check_n:
                    raise Exception("broken format")
                check_n += 1
            for j in range(1, self.col):
                st = self.readline(self.f)

    def _check_bulk_info(self): # type: () -> List[Tuple[int, int]]
        data_bulk = [] # type: List[Tuple[int,int]]
        data_pos = self.f.tell()
        first_data = self.get_row()[self.index]
        data_num = 1
        for i in range(1, self.row):
            v = self.get_row()[self.index]
            if v == first_data:
                if data_num == 1:
                    if len(data_bulk) > 0:
                        data_bulk[-1] = (data_bulk[-1][0], data_bulk[-1][1]+1)
                else:
                    data_bulk.append((data_num, 0))
                data_num = 0
            data_num += 1
        if data_num != 1:
            data_bulk.append((data_num, 0))
        self.f.seek(data_pos)
        return data_bulk

    def get_bulk_lengths(self): # type: () -> Iterable[int]
        return (length for length, skip in self.data_bulk)
    
    def get_bulk(self, start, end, stride):
        # type: (int, int, int) -> Optional[np.ndarray]
        
        if end <= start:
            self.skip_row(sum(self.data_bulk[self.next_bulk]), check_n=0)
            self.next_bulk += 1
            return None
        
        length = int(math.ceil((end - start + 0.0) / stride))
        if self.is_complex:
            M = np.empty((length, len(self.var_using)), dtype='complex128')
        else:
            M = np.empty((length, len(self.var_using)), dtype='float64')
        
        data_no = 0
        # skip start
        if start > 0:
            self.skip_row(start, check_n=data_no)
            data_no += start

        if stride == 1: # special but ordinary case
            for i in range(length):
                data = self.get_row(check_n=(data_no + i))
                M[i,:] = data[self.var_using]
            data_no += length
        else:
            for i in range(length):
                data = self.get_row(check_n=data_no)
                data_no += 1
                M[i,:] = data[self.var_using]
                if i != length-1:
                    self.skip_row(stride - 1, check_n=data_no)
                    data_no += stride - 1
        
        # skip end
        row_num = sum(self.data_bulk[self.next_bulk])
        if data_no < row_num:
            self.skip_row(row_num - data_no, check_n=data_no)
        
        self.next_bulk += 1
        return M # type: ignore

class _FastDataReader(_DataReaderBase):
    def __init__(self, f, col, row, index, var_using, is_stepped):
        # type: (IO[bytes], int, int, int, List[int], bool) -> None
        
        self.f = f
        self.data_pos = f.tell()
        self.col = col
        self.row = row
        self.index = index
        self.var_using = var_using
        
        if is_stepped:
            self.data_bulk = self._check_bulk_info()
        else:
            self.data_bulk = [(self.row, 0)]
        self.next_bulk = 0
    
    def _check_bulk_info(self): # type: () -> List[Tuple[int,int]]
        self.f.seek(self.data_pos + 4 * self.row * self.index)
        index = np.array(struct.unpack(
            "<%dd" % self.row, self.f.read(8 * self.row)), dtype=float)
        self.f.seek(self.data_pos)
        
        data_bulk = [] # type: List[Tuple[int,int]]
        first_data = index[0]
        data_num = 1
        for v in index[1:]:
            if v == first_data:
                if data_num == 1:
                    data_bulk[-1] = (data_bulk[-1][0], data_bulk[-1][1]+1)
                else:
                    data_bulk.append((data_num, 0))
                data_num = 0
            data_num += 1
        if data_num != 1:
            data_bulk.append((data_num, 0))
        return data_bulk

    def get_bulk_lengths(self): # type: () -> Iterable[int]
        return (length for length, skip in self.data_bulk)

    def get_bulk(self, start, end, stride):
        # type: (int, int, int) -> Optional[np.ndarray]
        
        if end <= start:
            self.next_bulk += 1
            return None
        self.f.seek(self.data_pos)
        bulk_offset = 0
        for i in range(self.next_bulk):
            bulk_offset += sum(self.data_bulk[i])
        start += bulk_offset
        end += bulk_offset
        length = int(math.ceil((end - start + 0.0) / stride))
        
        M = np.empty((length, len(self.var_using)), dtype='float64')
        
        data_mask = np.zeros(self.row, dtype=bool)
        data_mask[np.array(range(start, end, stride))] = True
        
        for var_index in range(self.col):
            if var_index == self.index:
                fmt = "d"
                size = 8
            else:
                fmt = "f"
                size = 4
            buf = self.f.read(size * self.row)
            if var_index in self.var_using:
                var_pos = self.var_using.index(var_index)
                M[:,var_pos] = np.array(
                    struct.unpack("<%d%s" % (self.row, fmt), buf),
                    dtype=float)[data_mask]
        self.next_bulk += 1
        return M # type: ignore

def read_ltspice_raw(filename, vars=None, header_only=False,
                     start=0, end=None, length=None, stride=1):
    # type: (Text, List[Text], bool, Union[int,float], Union[int,float], int, int) -> Dict[Text,Any]
    """Read simulation data of LTspice.

    Args:
        filename (str): filename of simulation data.
            Transient, AC, Operation result and fastacccess converted
            Transient data are supported.
            Gzipped datafile is also supported.
        vars (list[str], optional): read variables.
            If ``vars`` is None, all variables are read.
        headers_only (bool, optional): header only read mode.
        start (int or float, optional): start of data
        end (int or float, optional): end of data
        length (int, optional): read lenght of data
        stride (int, optional): reading stride of data

    Returns:
        dict: data
    
        Now supported data are:

        :variables: tuple(variables name, vairable type)
        :no. variables: num of variables
        :no. points: num of data points
        :offset: data start offset
        :date: simulation date
        :backannotation:
        :flags:
        :steps: step information (if stepped flag is exist)
        :values: numpy.ndarray (if stepped flag is not exist)
                 tuple of numpy.ndarray (if stepped flag is exist)
    """
    
    is_firstline = True
    is_binary = None
    is_complex = False
    is_forward = False
    is_stepped = False
    is_fastaccess = False
    is_utf16le = False
    col = None
    row = None
    index_col = None
    var_list = [] # type: List[Tuple[Text,Text]]
    var_using = [] # type: List[int]
    result = {} # type: Dict[Text,Any]

    _to_string = None
    _readline = None

    if end is not None and length is not None:
        raise ValueError("cannot set 'end' and 'length' paramters at same time")
    stride = int(stride)
    if stride < 1:
        raise ValueError("'stride' parameter must be 1 or above")

    if filename.endswith(".gz"):
        _open = gzip.open # type: Callable[...,IO]
        basename = filename[:-3]
        logfile = os.path.splitext(basename)[0] + ".log.gz"
        if not os.path.exists(logfile):
            logfile = os.path.splitext(basename)[0] + ".log"
    else:
        _open = open
        logfile = os.path.splitext(filename)[0] + ".log"

    logdata = read_ltspice_log(logfile)

    with _open(filename, 'rb') as f:
        # check encoding is UTF-16LE or not
        xline = f.readline()
        is_utf16le = _check_utf16le(xline)
        if is_utf16le:
            xline += f.read(1) # add upper byte of "\n"
            _to_string = _to_string_utf16le
            _readline = _readline_utf16le
        else:
            _to_string = _to_string_lat1
            _readline = _readline_lat1
        line = _to_string(xline)

        # Read text part
        while line != u"":
            s = line.split(':', 1)
            if len(s) == 1:
                var, value = s[0], u""
            else:
                var, value = s
            var = var.lower()

            if var == "binary":
                is_binary = True
                break
            elif var == "values":
                is_binary = False
                break
            elif var == "variables":
                if col is None:
                    raise Exception("invalid data")
                for i in range(col):
                    s = _readline(f)
                    if s == "":
                        break
                    ss = s.split()
                    if vars is None or ss[1] in vars or ss[2] in INDEX_TYPES:
                        if ss[2] in INDEX_TYPES:
                            index_col = i
                        var_list.append(tuple(ss[1:]))
                        var_using.append(i)
            elif var == "no. variables":
                col = int(value.strip())
                result[var] = col
            elif var == "no. points":
                row = int(value.strip())
                result[var] = row
            elif var == "offset":
                result[var] = float(value.strip())
            elif var == "date":
                result[var] = _parse_date(value.strip())
            elif var == "backannotation":
                if var not in result:
                    result[var] = []
                result[var].extend(value.strip().split())
            elif var == "flags":
                result[var] = tuple(value.strip().split())
                if 'complex' in result[var]:
                    is_complex = True
                if 'forward' in result[var]:
                    is_forward = True
                if 'stepped' in result[var]:
                    is_stepped = True
                if 'fastaccess' in result[var]:
                    is_fastaccess = True
            else:
                result[var] = _parse_any(value.strip())

            line = _readline(f)

        if logdata is not None:
            result["logdata"] = logdata
        result["variables"] = tuple(var_list)
        if header_only:
            result["values"] = None
            if is_stepped and logdata is not None and 'steps' in logdata:
                result["steps"] = logdata['steps']
            return result

        if col is None or row is None or index_col is None or is_binary is None:
            raise Exception("invalid data")
        if is_fastaccess:
            if not is_binary or is_complex:
                raise Exception(
                    "invalid format, fastaccess is only for real binary") 
            reader = _FastDataReader(
                f, col, row, index_col, var_using, is_stepped) # type: _DataReaderBase
        else:
            reader = _RawDataReader(
                f, col, row, index_col, var_using,
                is_binary, is_complex, is_stepped, _readline)

        values = []
        for data_len in reader.get_bulk_lengths():
            xstart = (int(data_len * start)
                      if isinstance(start, float) else start)
            if xstart < 0:
                xstart = max(data_len + xstart, 0)

            max_length = (data_len - xstart) // stride
            if length is not None:
                xlength = (int(data_len * length)
                           if isinstance(length, float) else length)
                xlength = min(xlength, max_length)
                xend = xstart + (xlength - 1)*stride + 1
            elif end is None:
                xlength = max_length
                xend = data_len
            else:
                xend = int(data_len * end) if isinstance(end, float) else end
                if xend < 0:
                    xend = data_len + xend
                xend = max(xend, xstart)
                xlength = (xend - xstart) // stride
            values.append(reader.get_bulk(xstart, xend, stride))

    if is_stepped:
        result["values"] = tuple(values)
        if (logdata is not None and 'steps' in logdata and
            len(logdata['steps']) == len(values)):
            result["steps"] = logdata['steps']
        else:
            result["steps"] = tuple([ ("", i) for i in range(len(values)) ])
    else:
        result["values"] = values[0]

    return result

def make_dataframe(data):
    import pandas as pd
    columns = [name for name, type_ in data['variables']]
    if "steps" in data:
        dfs = []
        for i, step in enumerate(data["steps"]):
            step_name = "STEP:{}=".format(step[0])
            step_val = step[1]
            d = data['values'][i]
            if d is not None:
                df = pd.DataFrame(d, columns=columns)
                df[step_name] = step_val
                dfs.append(df)
        df = pd.concat(dfs)
    else:
        df = pd.DataFrame(data['values'], columns=columns)
    return df

def find_variable(data, key):
    # type: (Dict, Union[Text,Tuple[Text,Text]]) -> Union[None,int,List[int]]
    """Find variable index.

    Args:
        data (dict): data of LTspice output
        key (str or tuple[str,str]): finding variable

    Returns:
        None, int or list[int]: index of variable

        If variable is not found, this function returns None
    """
    
    if isinstance(key, _basestring):
        result = [ i for i, item in enumerate(data['variables'])
                   if item[0] == key ]
    else:
        result = [ i for i, item in enumerate(data['variables'])
                   if item == key ]
    if len(result) == 0:
        return None
    elif len(result) == 1:
        return result[0]
    return result

def run(filename, remove_generated_netlist=True):
    # type: (Text, bool) -> None
    """Run LTspice simulation.

    Args:
        filename (str): filename of ``.asc`` or ``.net``
        remove_generated_netlist (bool, optional):
            Remove generated netlist by this function.
            Default is True.  When argument filename is ``.net`` file,
            this argument is ignored.

    Returns:
        None
    """
    
    filename = os.path.abspath(filename)
    dirname = os.path.split(filename)[0]
    convert_netlist = False
    orig_cwd = os.getcwd()
    if LTSPICE is None:
        raise Exception("LTspice is not found")
    else:
        ltspice = LTSPICE
    
    try:
        os.chdir(dirname)
        if filename.endswith(".asc"):
            convert_netlist = True
            netname = os.path.splitext(filename)[0] + ".net"
            if os.path.exists(netname):
                os.remove(netname)
            subprocess.call(ltspice + ['-netlist', filename ])
            if not os.path.exists(netname):
                raise Exception("Netfile creating error")
        else:
            netname = filename
        subprocess.call(ltspice + [ '-b', netname ])
    finally:
        try:
            if convert_netlist and remove_generated_netlist:
                os.remove(netname)
        except:
            pass
        os.chdir(orig_cwd)

if __name__ == '__main__':
    import sys
    if sys.argv[1].endswith(".raw"):
        data = read_ltspice_raw(sys.argv[1])
        if sys.version_info[0] >= 3:
            print(str(data).encode('latin1'))
        else:
            print(str(data))
            print(data["values"])
    else:
        run(sys.argv[1])

read data from LTSpice's raw file