# Copyright (c) 2018, Matthias Frey, Paul Scherrer Institut, Villigen PSI, Switzerland
# All rights reserved
#
# Implemented as part of the PhD thesis
# "Precise Simulations of Multibunches in High Intensity Cyclotrons"
#
# This file is part of pyOPALTools.
#
# pyOPALTools is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# You should have received a copy of the GNU General Public License
# along with pyOPALTools. If not, see <https://www.gnu.org/licenses/>.
import numpy as np
import re,os,sys
import pandas as pd
from collections import OrderedDict
from .BaseParser import BaseParser
[docs]class SDDSParser(BaseParser):
[docs] def parse(self, filename):
self.clear()
# check file version
version = self._checkVersion(filename)
# parse header
if version >= 10900:
self._parseHeader1_9(filename)
self._separator = '\s+'
else:
self._parseHeader1_6(filename)
# prevents a bug(?) pd.read_csv
# \s+\t should be equivalent but force the python engine
# it seems the 'c' engine has problems with trailing whitespace in the header strings
# and returns an empty dataset
self._separator = '\s+\t'
# read data
self._dataset = pd.read_csv(filename, skiprows=self._nRows,
sep=self._separator,
names=list(self._units.keys()), index_col=False)
[docs] def clear(self):
"""Clear data.
"""
self._nParameters = 0
self._nRows = 0
self._units = OrderedDict()
self._desc = {}
self._dtypes = {}
self._dataset = None
[docs] def _checkVersion(self, filename):
pattern = 'OPAL (.*) git'
v = 0
with open(filename) as f:
for line in f:
if 'OPAL' and 'git rev.' in line:
line = line.replace('#', '')
obj = re.match(pattern, line)
v = self._version(obj.group(1))
break;
return v
[docs] def _version(self, v):
digits = v.split('.')
i1 = int(digits[0]) * 10000
i2 = int(digits[1]) * 100
return i1 + i2
# returns a column
[docs] def getDataOfVariable(self, varname):
if not self._hasVariable(varname):
raise ValueError("Variable '" + varname + "' not in dataset.")
return self._dataset[varname]
[docs] def getUnitOfVariable(self, varname):
if not self._hasVariable(varname):
raise ValueError("Variable '" + varname + "' not in dataset.")
return self._units[varname]
[docs] def getVariables(self):
return list(self._dataset.columns)
[docs] def getDescriptionOfVariable(self, varname):
if not self._hasVariable(varname):
raise ValueError("Variable '" + varname + "' not in dataset.")
return self._desc[varname]
[docs] def _description(self, f):
for line in f:
self._nRows += 1
if 'text' in line:
pass
elif 'contents' in line:
pass
elif '&end' in line:
break
[docs] def _parameter(self, f):
for line in f:
self._nRows += 1
if 'name' in line:
pass
elif 'type' in line:
pass
elif 'description' in line:
pass
elif '&end' in line:
break
[docs] def _column(self, f):
variable = ''
unit = ''
desc = ''
dtype = ''
for line in f:
self._nRows += 1
if 'name=' in line:
variable = line[line.find('=')+1:-2]
elif 'type' in line:
dtype = line[line.find('=')+1:-2]
elif 'units' in line:
unit = line[line.find('=')+1:-2]
elif 'description' in line:
desc = self.__removeNumber(line[line.find('=')+2:-2])
elif '&end' in line:
break
self._units[variable] = unit
self._desc[variable] = desc
self._dtypes[variable] = self._get_type(dtype)
[docs] def _get_type(self, dtype):
if dtype == 'string':
return str
elif dtype == 'double':
return np.float64
elif dtype == 'float':
return float
elif dtype == 'int':
return int
elif dtype == 'long':
return np.int64
else:
return str
[docs] def _data(self, f):
for line in f:
self._nRows += 1
if 'mode' in line:
pass
elif 'no_row_counts' in line:
pass
elif '&end' in line:
break
def __removeNumber(self, s):
# 25. March 2019
# https://stackoverflow.com/questions/12851791/removing-numbers-from-string
return ''.join([i for i in s if not i.isdigit()])
[docs] def collectStatFileData(self, baseFN, root, yNames):
'''
FIXME: This function shouldn't be part of the parser.
Assumes runOPAL structure: optLinac_40nC_IBF=485.9269768907996_IM ...
where baseFN == optLinac_40nC in the example above.
This function finds all stat files that are one level
below root. An exclude list can be specified.
Two vectors are returned: x with the design variables names and values
(IBF=485.9269768907996 ... from above) and the last value(s) of stat
file data spcified via yNames.
Example:
x = []
y = []
baseFN = 'optLinac_40nC'
exclList = ['tmpl','extrData.py']
root = "."
yNames = ['s','energy']
p = SDDSParser()
(x,y) = p.makeData(baseFN, exclList, root, yNames)
'''
x = []
y = []
fns = [] # full qualified file names
for item in filter(os.path.isdir, os.listdir(root)): # os.listdir(root):
s = item.replace(baseFN+'_', '')
s = s.replace('_', ' ')
x.append(s)
fn=item+'/'+baseFN+'.stat'
if (os.path.isfile(fn)):
self.parse(fn)
yy = []
for name in yNames:
yy.append(self.getDataOfVariable(name))
y.append(yy)
fns.append(fn)
else:
print ('file '+fn+' does not exists')
return (x,y,fns)
@property
def size(self):
return self._dataset.shape[0]
[docs] def _hasVariable(self, varname):
return (varname in self._dataset.columns)
@property
def dataframe(self):
return self._dataset