#!/usr/bin/env python
##############################################################################
#
# diffpy.srfit by DANSE Diffraction group
# Simon J. L. Billinge
# (c) 2008 The Trustees of Columbia University
# in the City of New York. All rights reserved.
#
# File coded by: Chris Farrow
#
# See AUTHORS.txt for a list of people who contributed.
# See LICENSE_DANSE.txt for license information.
#
##############################################################################
"""This module contains parsers for PDF data.
PDFParser is suitable for parsing data generated from PDFGetN and
PDFGetX.
See the class documentation for more information.
"""
__all__ = ["PDFParser"]
import re
import numpy
from diffpy.srfit.exceptions import ParseError
from diffpy.srfit.fitbase.profileparser import ProfileParser
[docs]
class PDFParser(ProfileParser):
"""Class for holding a diffraction pattern.
Attributes
Attributes
----------
_format
Name of the data format that this parses (string, default
""). The format string is a unique identifier for the data
format handled by the parser.
_banks
The data from each bank. Each bank contains a
(x, y, dx, dy) tuple:
x
A numpy array containing the independent
variable read from the file.
y
A numpy array containing the profile
from the file.
dx
A numpy array containing the uncertainty in x
read from the file. This is 0 if the
uncertainty cannot be read.
dy
A numpy array containing the uncertainty read
from the file. This is 0 if the uncertainty
cannot be read.
_x
Independent variable from the chosen bank
_y
Profile from the chosen bank
_dx
Uncertainty in independent variable from the chosen bank
_dy
Uncertainty in profile from the chosen bank
_meta
A dictionary containing metadata read from the file.
General Metadata
Attributes
----------
filename
The name of the file from which data was parsed. This key
will not exist if data was not read from file.
nbanks
The number of banks parsed.
bank
The chosen bank number.
Metadata
----------
stype
The scattering type ("X", "N")
qmin
Minimum scattering vector (float)
qmax
Maximum scattering vector (float)
qdamp
Resolution damping factor (float)
qbroad
Resolution broadening factor (float)
spdiameter
Nanoparticle diameter (float)
scale
Data scale (float)
temperature
Temperature (float)
doping
Doping (float)
These may appear in the metadata dictionary.
"""
_format = "PDF"
[docs]
def parseString(self, patstring):
"""Parse a string and set the _x, _y, _dx, _dy and _meta variables.
When _dx or _dy cannot be obtained in the data format it is set to 0.
This wipes out the currently loaded data and selected bank number.
Parameters
----------
patstring
A string containing the pattern
Raises ParseError if the string cannot be parsed
"""
# useful regex patterns:
rx = {"f": r"[-+]?(\d+(\.\d*)?|\d*\.\d+)([eE][-+]?\d+)?"}
# find where does the data start
res = re.search(r"^#+ start data\s*(?:#.*\s+)*", patstring, re.M)
# start_data is position where the first data line starts
if res:
start_data = res.end()
else:
# find line that starts with a floating point number
regexp = r"^\s*%(f)s" % rx
res = re.search(regexp, patstring, re.M)
if res:
start_data = res.start()
else:
start_data = 0
header = patstring[:start_data]
databody = patstring[start_data:].strip()
# find where the metadata starts
metadata = ""
res = re.search(r"^#+\ +metadata\b\n", header, re.M)
if res:
metadata = header[res.end() :]
header = header[: res.start()]
# parse header
meta = self._meta
# stype
if re.search("(x-?ray|PDFgetX)", header, re.I):
meta["stype"] = "X"
elif re.search("(neutron|PDFgetN)", header, re.I):
meta["stype"] = "N"
# qmin
regexp = r"\bqmin *= *(%(f)s)\b" % rx
res = re.search(regexp, header, re.I)
if res:
meta["qmin"] = float(res.groups()[0])
# qmax
regexp = r"\bqmax *= *(%(f)s)\b" % rx
res = re.search(regexp, header, re.I)
if res:
meta["qmax"] = float(res.groups()[0])
# qdamp
regexp = r"\b(?:qdamp|qsig) *= *(%(f)s)\b" % rx
res = re.search(regexp, header, re.I)
if res:
meta["qdamp"] = float(res.groups()[0])
# qbroad
regexp = r"\b(?:qbroad|qalp) *= *(%(f)s)\b" % rx
res = re.search(regexp, header, re.I)
if res:
meta["qbroad"] = float(res.groups()[0])
# spdiameter
regexp = r"\bspdiameter *= *(%(f)s)\b" % rx
res = re.search(regexp, header, re.I)
if res:
meta["spdiameter"] = float(res.groups()[0])
# dscale
regexp = r"\bdscale *= *(%(f)s)\b" % rx
res = re.search(regexp, header, re.I)
if res:
meta["scale"] = float(res.groups()[0])
# temperature
regexp = r"\b(?:temp|temperature|T)\ *=\ *(%(f)s)\b" % rx
res = re.search(regexp, header)
if res:
meta["temperature"] = float(res.groups()[0])
# doping
regexp = r"\b(?:x|doping)\ *=\ *(%(f)s)\b" % rx
res = re.search(regexp, header)
if res:
meta["doping"] = float(res.groups()[0])
# parsing general metadata
if metadata:
regexp = r"\b(\w+)\ *=\ *(%(f)s)\b" % rx
while True:
res = re.search(regexp, metadata, re.M)
if res:
meta[res.groups()[0]] = float(res.groups()[1])
metadata = metadata[res.end() :]
else:
break
# read actual data - robs, Gobs, drobs, dGobs
inf_or_nan = re.compile("(?i)^[+-]?(NaN|Inf)\\b")
has_drobs = True
has_dGobs = True
# raise ParseError if something goes wrong
robs = []
Gobs = []
drobs = []
dGobs = []
try:
for line in databody.split("\n"):
v = line.split()
# there should be at least 2 value in the line
robs.append(float(v[0]))
Gobs.append(float(v[1]))
# drobs is valid if all values are defined and positive
has_drobs = (
has_drobs and len(v) > 2 and not inf_or_nan.match(v[2])
)
if has_drobs:
v2 = float(v[2])
has_drobs = v2 > 0.0
drobs.append(v2)
# dGobs is valid if all values are defined and positive
has_dGobs = (
has_dGobs and len(v) > 3 and not inf_or_nan.match(v[3])
)
if has_dGobs:
v3 = float(v[3])
has_dGobs = v3 > 0.0
dGobs.append(v3)
except (ValueError, IndexError) as err:
raise ParseError(err)
if has_drobs:
drobs = numpy.asarray(drobs)
else:
drobs = None
if has_dGobs:
dGobs = numpy.asarray(dGobs)
else:
dGobs = None
robs = numpy.asarray(robs)
Gobs = numpy.asarray(Gobs)
self._banks.append([robs, Gobs, drobs, dGobs])
return
# End of PDFParser