We Keep Coding

Creating namedtuple valid for differents parameters

I'm trying to figure it out a way to create a namedtuple with variable fields depending on the data you receive, in my case, I'm using the data from StatCounter and not on all the periods are the same browsers. I tried this way but it is a bit ugly and I'm sure there is a better way to achieve it.
def namedtuple_fixed(name: str, fields: List[str]) -> namedtuple:
"""Check the fields of the namedtuple and changes the invalid ones."""
fields_fixed: List[str] = []
for field in fields:
field = field.replace(" ", "_")
if field[0].isdigit():
field = f"n{field}"
fields_fixed.append(field)
return namedtuple(name, fields_fixed)
Records: namedtuple = namedtuple("empty_namedtuple", "")
def read_file(file: str) -> List["Records"]:
"""
Read the file with info about the percentage of use of various browsers
"""
global Records
with open(file, encoding="UTF-8") as browsers_file:
reader: Iterator[List[str]] = csv.reader(browsers_file)
field_names: List[str] = next(reader)
Records = namedtuple_fixed("Record", field_names)
result: List[Records] = [
Records(
*[
dt.datetime.strptime(n, "%Y-%m").date()
if record.index(n) == 0
else float(n)
for n in record
]
)
for record in reader
]
return result
The "namedtuple_fixed" function is to fix the names that have invalid identifiers.
Basically, I want to create a named tuple that receives a variable number of parameters, depending on the file you want to analyze. And if it's with type checking incorporated (I mean using NamedTuple from the typing module), much better.
Thanks in advance.

This solves my problem, but just partially
class Record(SimpleNamespace):
def __repr__(self):
items = [f"{key}={value!r}" for key, value in self.__dict__.items()]
return f"Record({', '.join(items)})"
Using the types.SimpleSpace documentation
And it can cause problems, like for example if you initiallize a Record like the following:
foo = Record(**{"a": 1, "3a": 2})
print(foo.a) # Ok
print(foo.3a) # Syntax Error

Python search replace with multiple Json objects

I wasn't sure how to search for this but I am trying to make a script that dynamically launches programs. I will have a couple of JSON files and I want to be able to do a search replace sort of thing.
So I'll setup an example:
config.json
{
"global_vars": {
"BASEDIR": "/app",
"CONFIG_DIR": "{BASEDIR}/config",
"LOG_DIR": "{BASEDIR}/log",
"CONFIG_ARCHIVE_DIR": "{CONFIG_DIR}/archive"
}
}
Then process.json
{
"name": "Dummy_Process",
"binary": "java",
"executable": "DummyProcess-0.1.0.jar",
"launch_args": "-Dspring.config.location={CONFIG_DIR}/application.yml -Dlogging.config={CONFIG_DIR}/logback-spring.xml -jar {executable}",
"startup_log": "{LOG_DIR}/startup_{name}.out"
}
Now I want to be able to load both of these JSON objects and be able to use the values there to update. So like "CONFIG_ARCHIVE_DIR": "{CONFIG_DIR}/archive" will become CONFIG_ARCHIVE_DIR": "/app/config/archive"
Does anyone know a good way to do this recursively because I'm running into issues when I'm trying to use something like CONFIG_DIR which requires BASEDIR first.
I have this function that loads all the data:
#Recursive function, loops and loads all values into data
def _load_data(data,obj):
for i in obj.keys():
if isinstance(obj[i],str):
data[i]=obj[i]
if isinstance(obj[i],dict):
data=_load_data(data,obj[i])
return data
Then I have this function:
def _update_data(data,data_str=""):
if not data_str:
data_str=json.dumps(data)
for i in data.keys():
if isinstance(data[i],str):
data_str=data_str.replace("{"+i+"}",data[i])
if isinstance(data[i],dict):
data=_update_data(data,data_str)
return json.loads(data_str)
So this works for one level but I don't know if this is the best way to do it. It stops working when I hit a case like the CONFIG_DIR because it would need to loop over the data multiple times. First it needs to update the BASEDIR then once more to update CONFIG_DIR. suggestion welcome.
The end goal of this script is to create a start/stop/status script to manage all of our binaries. They all use different binaries to start and I want one Processes file for multiple servers. Each process will have a servers array to tell the start/stop script what to run on given server. Maybe there's something like this already out there so if there is, please point me in the direction.
I will be running on Linux and prefer to use Python. I want something smart and easy for someone else to pickup and use/modify.

I made something that works with the example files you provided. Note that I didn't handle multiple keys or non-dictionaries in the data. This function accepts a list of the dictionaries obtained after JSON parsing your input files. It uses the fact that re.sub can accept a function for the replacement value and calls that function with each match. I am sure there are plenty of improvements that could be made to this, but it should get you started at least.
def make_config(configs):
replacements = {}
def find_defs(config):
# Find leaf nodes of the dictionary.
defs = {}
for k, v in config.items():
if isinstance(v, dict):
# Nested dictionary so recurse.
defs.update(find_defs(v))
else:
defs[k] = v
return defs
for config in configs:
replacements.update(find_defs(config))
def make_replacement(m):
# Construct the replacement string.
name = m.group(0).strip('{}')
if name in replacements:
# Replace replacement strings in the replacement string.
new = re.sub('\{[^}]+\}', make_replacement, replacements[name])
# Cache result
replacements[name] = new
return new
raise Exception('Replacement string for {} not found'.format(name))
finalconfig = {}
for name, value in replacements.items():
finalconfig[name] = re.sub('\{[^}]+\}', make_replacement, value)
return finalconfig
With this input:
[
{
"global_vars": {
"BASEDIR": "/app",
"CONFIG_DIR": "{BASEDIR}/config",
"LOG_DIR": "{BASEDIR}/log",
"CONFIG_ARCHIVE_DIR": "{CONFIG_DIR}/archive"
}
},
{
"name": "Dummy_Process",
"binary": "java",
"executable": "DummyProcess-0.1.0.jar",
"launch_args": "-Dspring.config.location={CONFIG_DIR}/application.yml -Dlogging.config={CONFIG_DIR}/logback-spring.xml -jar {executable}",
"startup_log": "{LOG_DIR}/startup_{name}.out"
}
]
It gives this output:
{
'BASEDIR': '/app',
'CONFIG_ARCHIVE_DIR': '/app/config/archive',
'CONFIG_DIR': '/app/config',
'LOG_DIR': '/app/log',
'binary': 'java',
'executable': 'DummyProcess-0.1.0.jar',
'launch_args': '-Dspring.config.location=/app/config/application.yml -Dlogging.config=/app/config/logback-spring.xml -jar DummyProcess-0.1.0.jar',
'name': 'Dummy_Process',
'startup_log': '/app/log/startup_Dummy_Process.out'
}

As an alternative to the answer by #FamousJameous and if you don't mind changing to ini format, you can also use the python built-in configparser which already has support to expand variables.

I implemented a solution with a class (Config) with a couple of functions:
_load: simply convert from JSON to a Python object;
_extract_params: loop over the document (output of _load) and add them to a class object (self.params);
_loop: loop over the object returned from _extract_params and, if the values contains any {param}, call the _transform method;
_transform: replace the {param} in the values with the correct values, if there is any '{' in the value linked to the param that needs to be replaced, call again the function
I hope I was clear enough, here is the code:
import json
import re
config = """{
"global_vars": {
"BASEDIR": "/app",
"CONFIG_DIR": "{BASEDIR}/config",
"LOG_DIR": "{BASEDIR}/log",
"CONFIG_ARCHIVE_DIR": "{CONFIG_DIR}/archive"
}
}"""
process = """{
"name": "Dummy_Process",
"binary": "java",
"executable": "DummyProcess-0.1.0.jar",
"launch_args": "-Dspring.config.location={CONFIG_DIR}/application.yml -Dlogging.config={CONFIG_DIR}/logback-spring.xml -jar {executable}",
"startup_log": "{LOG_DIR}/startup_{name}.out"
}
"""
class Config(object):
def __init__(self, documents):
self.documents = documents
self.params = {}
self.output = {}
# Loads JSON to dictionary
def _load(self, document):
obj = json.loads(document)
return obj
# Extracts the config parameters in a dictionary
def _extract_params(self, document):
for k, v in document.items():
if isinstance(v, dict):
# Recursion for inner dictionaries
self._extract_params(v)
else:
# if not a dict set params[k] as v
self.params[k] = v
return self.params
# Loop on the configs dictionary
def _loop(self, params):
for key, value in params.items():
# if there is any parameter inside the value
if len(re.findall(r'{([^}]*)\}', value)) > 0:
findings = re.findall(r'{([^}]*)\}', value)
# call the transform function
self._transform(params, key, findings)
return self.output
# Replace all the findings with the correct value
def _transform(self, object, key, findings):
# Iterate over the found params
for finding in findings:
# if { -> recursion to set all the needed values right
if '{' in object[finding]:
self._transform(object, finding, re.findall(r'{([^}]*)\}', object[finding]))
# Do de actual replace
object[key] = object[key].replace('{'+finding+'}', object[finding])
self.output = object
return self.output
# Entry point
def process_document(self):
params = {}
# _load the documents and extract the params
for document in self.documents:
params.update(self._extract_params(self._load(document)))
# _loop over the params
return self._loop(params)
# return self.output
if __name__ == '__main__':
config = Config([config, process])
print(config.process_document())
I am sure there are many other better ways to reach your goal, but I still hope this can bu useful to you.

ndb.Key filter for MapReduce input_reader

Playing with new Google App Engine MapReduce library filters for input_reader I would like to know how can I filter by ndb.Key.
I read this post and I've played with datetime, string, int, float, in filters tuples, but How I can filter by ndb.Key?
When I try to filter by a ndb.Key I get this error:
BadReaderParamsError: Expected Key, got u"Key('Clients', 406)"
Or this error:
TypeError: Key('Clients', 406) is not JSON serializable
I tried to pass a ndb.Key object and string representation of the ndb.Key.
Here are my two filters tuples:
Sample 1:
input_reader': {
'input_reader': 'mapreduce.input_readers.DatastoreInputReader',
'entity_kind': 'model.Sales',
'filters': [("client","=", ndb.Key('Clients', 406))]
}
Sample 2:
input_reader': {
'input_reader': 'mapreduce.input_readers.DatastoreInputReader',
'entity_kind': 'model.Sales',
'filters': [("client","=", "%s" % ndb.Key('Clients', 406))]
}

This is a bit tricky.
If you look at the code on Google Code you can see that mapreduce.model defines a JSON_DEFAULTS dict which determines the classes that get special-case handling in JSON serialization/deserialization: by default, just datetime. So, you can monkey-patch the ndb.Key class into there, and provide it with functions to do that serialization/deserialization - something like:
from mapreduce import model
def _JsonEncodeKey(o):
"""Json encode an ndb.Key object."""
return {'key_string': o.urlsafe()}
def _JsonDecodeKey(d):
"""Json decode a ndb.Key object."""
return ndb.Key(urlsafe=d['key_string'])
model.JSON_DEFAULTS[ndb.Key] = (_JsonEncodeKey, _JsonDecodeKey)
model._TYPE_IDS['Key'] = ndb.Key
You may also need to repeat those last two lines to patch mapreduce.lib.pipeline.util as well.
Also note if you do this, you'll need to ensure that this gets run on any instance that runs any part of a mapreduce: the easiest way to do this is to write a wrapper script that imports the above registration code, as well as mapreduce.main.APP, and override the mapreduce URL in your app.yaml to point to your wrapper.

Make your own input reader based on DatastoreInputReader, which knows how to decode key-based filters:
class DatastoreKeyInputReader(input_readers.DatastoreKeyInputReader):
"""Augment the base input reader to accommodate ReferenceProperty filters"""
def __init__(self, *args, **kwargs):
try:
filters = kwargs['filters']
decoded = []
for f in filters:
value = f[2]
if isinstance(value, list):
value = db.Key.from_path(*value)
decoded.append((f[0], f[1], value))
kwargs['filters'] = decoded
except KeyError:
pass
super(DatastoreKeyInputReader, self).__init__(*args, **kwargs)
Run this function on your filters before passing them in as options:
def encode_filters(filters):
if filters is not None:
encoded = []
for f in filters:
value = f[2]
if isinstance(value, db.Model):
value = value.key()
if isinstance(value, db.Key):
value = value.to_path()
entry = (f[0], f[1], value)
encoded.append(entry)
filters = encoded
return filters

Are you aware of the to_old_key() and from_old_key() methods?

I had the same problem and came up with a workaround with computed properties.
You can add to your Sales model a new ndb.ComputedProperty with the Key id. Ids are just strings, so you wont have any JSON problems.
client_id = ndb.ComputedProperty(lambda self: self.client.id())
And then add that condition to your mapreduce query filters
input_reader': {
'input_reader': 'mapreduce.input_readers.DatastoreInputReader',
'entity_kind': 'model.Sales',
'filters': [("client_id","=", '406']
}
The only drawback is that Computed properties are not indexed and stored until you call the put() parameter, so you will have to traverse all the Sales entities and save them:
for sale in Sales.query().fetch():
sale.put()

Convert pango markup string to GtkTextTag properties

I've got a gtk.TextView that I'd like to add markup-like text to. I know this can be achieved through the use of gtk.TextTag which you can create with similar properties as a pango markup string. I noticed there is no easy way to just say set_markup to a gtk.TextBuffer much like you can with multiple other widgets. Instead you have to create a TextTag, give it properties, and then insert it into the TextBuffer's TagTable specifying the iters that the tag applies to.
I'd ideally like to create a function that can convert a pango markup string into a TextTag to get the same effect. But gtk doesn't appear to have that functionality built-in.
I've noticed that you can use pango.parse_markup() on a marked up string and it will create a pango.AttributeList which contains information regarding the properties set on the string and the indices that they occur at. But there are slight differences in each type of attribute that make it difficult to generalize for every case. Is there a better way to go about this? Or is pango markup just not meant to be converted into gtk.TextTag's?

I finally worked out my own solution to this problem. I created a function that parses the markup string (using pango.parse_markup). Through reading the documentation and python introspection, I was able to work out how to take pango.Attribute and turn convert it into properties that a GtkTextTag can use.
Here's the function:
def parse_markup_string(string):
'''
Parses the string and returns a MarkupProps instance
'''
#The 'value' of an attribute...for some reason the same attribute is called several different things...
attr_values = ('value', 'ink_rect', 'logical_rect', 'desc', 'color')
#Get the AttributeList and text
attr_list, text, accel = pango.parse_markup( string )
attr_iter = attr_list.get_iterator()
#Create the converter
props = MarkupProps()
props.text = text
val = True
while val:
attrs = attr_iter.get_attrs()
for attr in attrs:
name = attr.type
start = attr.start_index
end = attr.end_index
name = pango.AttrType(name).value_nick
value = None
#Figure out which 'value' attribute to use...there's only one per pango.Attribute
for attr_value in attr_values:
if hasattr( attr, attr_value ):
value = getattr( attr, attr_value )
break
#There are some irregularities...'font_desc' of the pango.Attribute
#should be mapped to the 'font' property of a GtkTextTag
if name == 'font_desc':
name = 'font'
props.add( name, value, start, end )
val = attr_iter.next()
return props
This function creates a MarkupProps() object that has the ability to generate GtkTextTags along with the index in the text to apply them to.
Here's the object:
class MarkupProps():
'''
Stores properties that contain indices and appropriate values for that property.
Includes an iterator that generates GtkTextTags with the start and end indices to
apply them to
'''
def __init__(self):
'''
properties = ( {
'properties': {'foreground': 'green', 'background': 'red'}
'start': 0,
'end': 3
},
{
'properties': {'font': 'Lucida Sans 10'},
'start': 1,
'end':2,
},
)
'''
self.properties = []#Sequence containing all the properties, and values, organized by like start and end indices
self.text = ""#The raw text without any markup
def add( self, label, value, start, end ):
'''
Add a property to MarkupProps. If the start and end indices are already in
a property dictionary, then add the property:value entry into
that property, otherwise create a new one
'''
for prop in self.properties:
if prop['start'] == start and prop['end'] == end:
prop['properties'].update({label:value})
else:
new_prop = {
'properties': {label:value},
'start': start,
'end':end,
}
self.properties.append( new_prop )
def __iter__(self):
'''
Creates a GtkTextTag for each dict of properties
Yields (TextTag, start, end)
'''
for prop in self.properties:
tag = gtk.TextTag()
tag.set_properties( **prop['properties'] )
yield (tag, prop['start'], prop['end'])
So with this function and the MarkupProps object, I am able to, given a pango markup string, breakdown the string into it's properties, and text form, and then convert that into GtkTextTags.

Haven't followed GTK+ development, maybe they added something lately, but see these bugs: #59390 and #505478. Since they are not closed, likely nothing is done.

Parsing a file with multiple xmls in it

Is there a way to parse a file which contains multiple xmls in it?
eg., if I have a file called stocks.xml and within the stocks.xml i have more than one xml content, is there any way to parse this xml file ?.
-- stocks.xml
<?xml version="1.0" encoding="ASCII"?><PRODUCT><ID>A001</ID>..</PRODUCT><SHOP-1><QUANTITY>nn</QUANITY><SHOP-1><QUANTITY>nn</QUANITY>
<?xml version="1.0" encoding="ASCII"?><PRODUCT><ID>A002</ID>..</PRODUCT><SHOP-1><QUANTITY>nn</QUANITY><SHOP-1><QUANTITY>nn</QUANITY>

If you can assume that each xml document begins with <?xml version="1.0" ..., simply read the file line-by-line looking for a lines that match that pattern (or, read all the data and then do a search through the data).
Once you find a line, keep it, and append subsequent lines until the next xml document is found or you hit EOF. lather, rinse, repeat.
You now have one xml document in a string. You can then parse the string using the normal XML parsing tools, or you write it to a file.
This will work fine in most cases, but of course it could fall down if one of your embedded xml documents contains data that exactly matches the same pattern as the beginning of a document. Most likely you don't have to worry about that, and if you do there are ways to avoid that with a little more cleverness.
The right solution really depends on your needs. If you're creating a general purpose must-work-at-all-times solution this might not be right for you. For real world, special purpose problems it's probably more than Good Enough, and often Good Enough is indeed Good Enough.

You should see this python program by Michiel de Hoon
And if you want to parse multiple files, then a rule to detect that we are in other xml must be developed, for example,at first you read <stocks> .... and at the end you must reead </stocks> when you find that then if there is something else,well, continue reading and do the same parser until reach eof.
# Copyright 2008 by Michiel de Hoon. All rights reserved.
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
"""Parser for XML results returned by NCBI's Entrez Utilities. This
parser is used by the read() function in Bio.Entrez, and is not intended
be used directly.
"""
# The question is how to represent an XML file as Python objects. Some
# XML files returned by NCBI look like lists, others look like dictionaries,
# and others look like a mix of lists and dictionaries.
#
# My approach is to classify each possible element in the XML as a plain
# string, an integer, a list, a dictionary, or a structure. The latter is a
# dictionary where the same key can occur multiple times; in Python, it is
# represented as a dictionary where that key occurs once, pointing to a list
# of values found in the XML file.
#
# The parser then goes through the XML and creates the appropriate Python
# object for each element. The different levels encountered in the XML are
# preserved on the Python side. So a subelement of a subelement of an element
# is a value in a dictionary that is stored in a list which is a value in
# some other dictionary (or a value in a list which itself belongs to a list
# which is a value in a dictionary, and so on). Attributes encountered in
# the XML are stored as a dictionary in a member .attributes of each element,
# and the tag name is saved in a member .tag.
#
# To decide which kind of Python object corresponds to each element in the
# XML, the parser analyzes the DTD referred at the top of (almost) every
# XML file returned by the Entrez Utilities. This is preferred over a hand-
# written solution, since the number of DTDs is rather large and their
# contents may change over time. About half the code in this parser deals
# wih parsing the DTD, and the other half with the XML itself.
import os.path
import urlparse
import urllib
import warnings
from xml.parsers import expat
# The following four classes are used to add a member .attributes to integers,
# strings, lists, and dictionaries, respectively.
class IntegerElement(int):
def __repr__(self):
text = int.__repr__(self)
try:
attributes = self.attributes
except AttributeError:
return text
return "IntegerElement(%s, attributes=%s)" % (text, repr(attributes))
class StringElement(str):
def __repr__(self):
text = str.__repr__(self)
try:
attributes = self.attributes
except AttributeError:
return text
return "StringElement(%s, attributes=%s)" % (text, repr(attributes))
class UnicodeElement(unicode):
def __repr__(self):
text = unicode.__repr__(self)
try:
attributes = self.attributes
except AttributeError:
return text
return "UnicodeElement(%s, attributes=%s)" % (text, repr(attributes))
class ListElement(list):
def __repr__(self):
text = list.__repr__(self)
try:
attributes = self.attributes
except AttributeError:
return text
return "ListElement(%s, attributes=%s)" % (text, repr(attributes))
class DictionaryElement(dict):
def __repr__(self):
text = dict.__repr__(self)
try:
attributes = self.attributes
except AttributeError:
return text
return "DictElement(%s, attributes=%s)" % (text, repr(attributes))
# A StructureElement is like a dictionary, but some of its keys can have
# multiple values associated with it. These values are stored in a list
# under each key.
class StructureElement(dict):
def __init__(self, keys):
dict.__init__(self)
for key in keys:
dict.__setitem__(self, key, [])
self.listkeys = keys
def __setitem__(self, key, value):
if key in self.listkeys:
self[key].append(value)
else:
dict.__setitem__(self, key, value)
def __repr__(self):
text = dict.__repr__(self)
try:
attributes = self.attributes
except AttributeError:
return text
return "DictElement(%s, attributes=%s)" % (text, repr(attributes))
class NotXMLError(ValueError):
def __init__(self, message):
self.msg = message
def __str__(self):
return "Failed to parse the XML data (%s). Please make sure that the input data are in XML format." % self.msg
class CorruptedXMLError(ValueError):
def __init__(self, message):
self.msg = message
def __str__(self):
return "Failed to parse the XML data (%s). Please make sure that the input data are not corrupted." % self.msg
class ValidationError(ValueError):
"""Validating parsers raise this error if the parser finds a tag in the XML that is not defined in the DTD. Non-validating parsers do not raise this error. The Bio.Entrez.read and Bio.Entrez.parse functions use validating parsers by default (see those functions for more information)"""
def __init__(self, name):
self.name = name
def __str__(self):
return "Failed to find tag '%s' in the DTD. To skip all tags that are not represented in the DTD, please call Bio.Entrez.read or Bio.Entrez.parse with validate=False." % self.name
class DataHandler:
home = os.path.expanduser('~')
local_dtd_dir = os.path.join(home, '.biopython', 'Bio', 'Entrez', 'DTDs')
del home
from Bio import Entrez
global_dtd_dir = os.path.join(str(Entrez.__path__[0]), "DTDs")
del Entrez
def __init__(self, validate):
self.stack = []
self.errors = []
self.integers = []
self.strings = []
self.lists = []
self.dictionaries = []
self.structures = {}
self.items = []
self.dtd_urls = []
self.validating = validate
self.parser = expat.ParserCreate(namespace_separator=" ")
self.parser.SetParamEntityParsing(expat.XML_PARAM_ENTITY_PARSING_ALWAYS)
self.parser.XmlDeclHandler = self.xmlDeclHandler
def read(self, handle):
"""Set up the parser and let it parse the XML results"""
try:
self.parser.ParseFile(handle)
except expat.ExpatError, e:
if self.parser.StartElementHandler:
# We saw the initial <!xml declaration, so we can be sure that
# we are parsing XML data. Most likely, the XML file is
# corrupted.
raise CorruptedXMLError(e)
else:
# We have not seen the initial <!xml declaration, so probably
# the input data is not in XML format.
raise NotXMLError(e)
try:
return self.object
except AttributeError:
if self.parser.StartElementHandler:
# We saw the initial <!xml declaration, and expat didn't notice
# any errors, so self.object should be defined. If not, this is
# a bug.
raise RuntimeError("Failed to parse the XML file correctly, possibly due to a bug in Bio.Entrez. Please contact the Biopython developers at biopython-dev#biopython.org for assistance.")
else:
# We did not see the initial <!xml declaration, so probably
# the input data is not in XML format.
raise NotXMLError("XML declaration not found")
def parse(self, handle):
BLOCK = 1024
while True:
#Read in another block of the file...
text = handle.read(BLOCK)
if not text:
# We have reached the end of the XML file
if self.stack:
# No more XML data, but there is still some unfinished
# business
raise CorruptedXMLError
try:
for record in self.object:
yield record
except AttributeError:
if self.parser.StartElementHandler:
# We saw the initial <!xml declaration, and expat
# didn't notice any errors, so self.object should be
# defined. If not, this is a bug.
raise RuntimeError("Failed to parse the XML file correctly, possibly due to a bug in Bio.Entrez. Please contact the Biopython developers at biopython-dev#biopython.org for assistance.")
else:
# We did not see the initial <!xml declaration, so
# probably the input data is not in XML format.
raise NotXMLError("XML declaration not found")
self.parser.Parse("", True)
self.parser = None
return
try:
self.parser.Parse(text, False)
except expat.ExpatError, e:
if self.parser.StartElementHandler:
# We saw the initial <!xml declaration, so we can be sure
# that we are parsing XML data. Most likely, the XML file
# is corrupted.
raise CorruptedXMLError(e)
else:
# We have not seen the initial <!xml declaration, so
# probably the input data is not in XML format.
raise NotXMLError(e)
if not self.stack:
# Haven't read enough from the XML file yet
continue
records = self.stack[0]
if not isinstance(records, list):
raise ValueError("The XML file does not represent a list. Please use Entrez.read instead of Entrez.parse")
while len(records) > 1: # Then the top record is finished
record = records.pop(0)
yield record
def xmlDeclHandler(self, version, encoding, standalone):
# XML declaration found; set the handlers
self.parser.StartElementHandler = self.startElementHandler
self.parser.EndElementHandler = self.endElementHandler
self.parser.CharacterDataHandler = self.characterDataHandler
self.parser.ExternalEntityRefHandler = self.externalEntityRefHandler
self.parser.StartNamespaceDeclHandler = self.startNamespaceDeclHandler
def startNamespaceDeclHandler(self, prefix, un):
raise NotImplementedError("The Bio.Entrez parser cannot handle XML data that make use of XML namespaces")
def startElementHandler(self, name, attrs):
self.content = ""
if name in self.lists:
object = ListElement()
elif name in self.dictionaries:
object = DictionaryElement()
elif name in self.structures:
object = StructureElement(self.structures[name])
elif name in self.items: # Only appears in ESummary
name = str(attrs["Name"]) # convert from Unicode
del attrs["Name"]
itemtype = str(attrs["Type"]) # convert from Unicode
del attrs["Type"]
if itemtype=="Structure":
object = DictionaryElement()
elif name in ("ArticleIds", "History"):
object = StructureElement(["pubmed", "medline"])
elif itemtype=="List":
object = ListElement()
else:
object = StringElement()
object.itemname = name
object.itemtype = itemtype
elif name in self.strings + self.errors + self.integers:
self.attributes = attrs
return
else:
# Element not found in DTD
if self.validating:
raise ValidationError(name)
else:
# this will not be stored in the record
object = ""
if object!="":
object.tag = name
if attrs:
object.attributes = dict(attrs)
if len(self.stack)!=0:
current = self.stack[-1]
try:
current.append(object)
except AttributeError:
current[name] = object
self.stack.append(object)
def endElementHandler(self, name):
value = self.content
if name in self.errors:
if value=="":
return
else:
raise RuntimeError(value)
elif name in self.integers:
value = IntegerElement(value)
elif name in self.strings:
# Convert Unicode strings to plain strings if possible
try:
value = StringElement(value)
except UnicodeEncodeError:
value = UnicodeElement(value)
elif name in self.items:
self.object = self.stack.pop()
if self.object.itemtype in ("List", "Structure"):
return
elif self.object.itemtype=="Integer" and value:
value = IntegerElement(value)
else:
# Convert Unicode strings to plain strings if possible
try:
value = StringElement(value)
except UnicodeEncodeError:
value = UnicodeElement(value)
name = self.object.itemname
else:
self.object = self.stack.pop()
return
value.tag = name
if self.attributes:
value.attributes = dict(self.attributes)
del self.attributes
current = self.stack[-1]
if current!="":
try:
current.append(value)
except AttributeError:
current[name] = value
def characterDataHandler(self, content):
self.content += content
def elementDecl(self, name, model):
"""This callback function is called for each element declaration:
<!ELEMENT name (...)>
encountered in a DTD. The purpose of this function is to determine
whether this element should be regarded as a string, integer, list
dictionary, structure, or error."""
if name.upper()=="ERROR":
self.errors.append(name)
return
if name=='Item' and model==(expat.model.XML_CTYPE_MIXED,
expat.model.XML_CQUANT_REP,
None, ((expat.model.XML_CTYPE_NAME,
expat.model.XML_CQUANT_NONE,
'Item',
()
),
)
):
# Special case. As far as I can tell, this only occurs in the
# eSummary DTD.
self.items.append(name)
return
# First, remove ignorable parentheses around declarations
while (model[0] in (expat.model.XML_CTYPE_SEQ,
expat.model.XML_CTYPE_CHOICE)
and model[1] in (expat.model.XML_CQUANT_NONE,
expat.model.XML_CQUANT_OPT)
and len(model[3])==1):
model = model[3][0]
# PCDATA declarations correspond to strings
if model[0] in (expat.model.XML_CTYPE_MIXED,
expat.model.XML_CTYPE_EMPTY):
self.strings.append(name)
return
# List-type elements
if (model[0] in (expat.model.XML_CTYPE_CHOICE,
expat.model.XML_CTYPE_SEQ) and
model[1] in (expat.model.XML_CQUANT_PLUS,
expat.model.XML_CQUANT_REP)):
self.lists.append(name)
return
# This is the tricky case. Check which keys can occur multiple
# times. If only one key is possible, and it can occur multiple
# times, then this is a list. If more than one key is possible,
# but none of them can occur multiple times, then this is a
# dictionary. Otherwise, this is a structure.
# In 'single' and 'multiple', we keep track which keys can occur
# only once, and which can occur multiple times.
single = []
multiple = []
# The 'count' function is called recursively to make sure all the
# children in this model are counted. Error keys are ignored;
# they raise an exception in Python.
def count(model):
quantifier, name, children = model[1:]
if name==None:
if quantifier in (expat.model.XML_CQUANT_PLUS,
expat.model.XML_CQUANT_REP):
for child in children:
multiple.append(child[2])
else:
for child in children:
count(child)
elif name.upper()!="ERROR":
if quantifier in (expat.model.XML_CQUANT_NONE,
expat.model.XML_CQUANT_OPT):
single.append(name)
elif quantifier in (expat.model.XML_CQUANT_PLUS,
expat.model.XML_CQUANT_REP):
multiple.append(name)
count(model)
if len(single)==0 and len(multiple)==1:
self.lists.append(name)
elif len(multiple)==0:
self.dictionaries.append(name)
else:
self.structures.update({name: multiple})
def open_dtd_file(self, filename):
path = os.path.join(DataHandler.local_dtd_dir, filename)
try:
handle = open(path, "rb")
except IOError:
pass
else:
return handle
path = os.path.join(DataHandler.global_dtd_dir, filename)
try:
handle = open(path, "rb")
except IOError:
pass
else:
return handle
return None
def externalEntityRefHandler(self, context, base, systemId, publicId):
"""The purpose of this function is to load the DTD locally, instead
of downloading it from the URL specified in the XML. Using the local
DTD results in much faster parsing. If the DTD is not found locally,
we try to download it. If new DTDs become available from NCBI,
putting them in Bio/Entrez/DTDs will allow the parser to see them."""
urlinfo = urlparse.urlparse(systemId)
#Following attribute requires Python 2.5+
#if urlinfo.scheme=='http':
if urlinfo[0]=='http':
# Then this is an absolute path to the DTD.
url = systemId
elif urlinfo[0]=='':
# Then this is a relative path to the DTD.
# Look at the parent URL to find the full path.
url = self.dtd_urls[-1]
source = os.path.dirname(url)
url = os.path.join(source, systemId)
self.dtd_urls.append(url)
# First, try to load the local version of the DTD file
location, filename = os.path.split(systemId)
handle = self.open_dtd_file(filename)
if not handle:
# DTD is not available as a local file. Try accessing it through
# the internet instead.
message = """\
Unable to load DTD file %s.
Bio.Entrez uses NCBI's DTD files to parse XML files returned by NCBI Entrez.
Though most of NCBI's DTD files are included in the Biopython distribution,
sometimes you may find that a particular DTD file is missing. While we can
access the DTD file through the internet, the parser is much faster if the
required DTD files are available locally.
For this purpose, please download %s from
%s
and save it either in directory
%s
or in directory
%s
in order for Bio.Entrez to find it.
Alternatively, you can save %s in the directory
Bio/Entrez/DTDs in the Biopython distribution, and reinstall Biopython.
Please also inform the Biopython developers about this missing DTD, by
reporting a bug on http://bugzilla.open-bio.org/ or sign up to our mailing
list and emailing us, so that we can include it with the next release of
Biopython.
Proceeding to access the DTD file through the internet...
""" % (filename, filename, url, self.global_dtd_dir, self.local_dtd_dir, filename)
warnings.warn(message)
try:
handle = urllib.urlopen(url)
except IOError:
raise RuntimeException("Failed to access %s at %s" % (filename, url))
parser = self.parser.ExternalEntityParserCreate(context)
parser.ElementDeclHandler = self.elementDecl
parser.ParseFile(handle)
handle.close()
self.dtd_urls.pop()
return 1

So you have a file containing multiple XML documents one after the other? Here is an example which strips out the <?xml ?> PIs and wraps the data in a root tag to parse the whole thing as a single XML document:
import re
import lxml.etree
re_strip_pi = re.compile('<\?xml [^?>]+\?>', re.M)
data = '<root>' + open('stocks.xml', 'rb').read() + '</root>'
match = re_strip_pi.search(data)
data = re_strip_pi.sub('', data)
tree = lxml.etree.fromstring(match.group() + data)
for prod in tree.xpath('//PRODUCT'):
print prod

You can't have multiple XML documents in one XML file. Split the documents - composed in whatever way - into single XML files and parse them one-by-one.