openrefine-client/google/refine.py

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#!/usr/bin/env python
"""
Client library to communicate with a Refine server.
"""
import csv
import json
import gzip
import os
import re
import StringIO
import urllib
import urllib2_file
import urllib2
import urlparse
REFINE_HOST = os.environ.get('GOOGLE_REFINE_HOST', '127.0.0.1')
REFINE_PORT = os.environ.get('GOOGLE_REFINE_PORT', '3333')
def to_camel(attr):
"""convert this_attr_name to thisAttrName."""
# Do lower case first letter
return (attr[0].lower() +
re.sub(r'_(.)', lambda x: x.group(1).upper(), attr[1:]))
def from_camel(attr):
"""convert thisAttrName to this_attr_name."""
# Don't add an underscore for capitalized first letter
return re.sub(r'(?<=.)([A-Z])', lambda x: '_' + x.group(1), attr).lower()
class Facet(object):
def __init__(self, column, type, expression='value',
**options):
self.type = type
self.column_name = column
self.name = column # XXX not sure what the difference is yet
self.expression = expression
for k, v in options.items():
setattr(self, k, v)
def as_dict(self):
return dict([(to_camel(k), v) for k, v in self.__dict__.items()
if v is not None])
class TextFacet(Facet):
def __init__(self, column, selection=None, omit_blank=False, omit_error=False, select_blank=False, select_error=False, invert=False, **options):
super(TextFacet, self).__init__(
column,
type='list',
omit_blank=omit_blank,
omit_error=omit_error,
select_blank=select_blank,
select_error=select_error,
invert=invert,
**options)
self.selection = []
if selection is None:
selection = []
elif not isinstance(selection, list):
selection = [selection]
for value in selection:
self.include(value)
def include(self, value):
for s in self.selection:
if s['v']['v'] == value:
return
self.selection.append({'v': {'v': value, 'l': value}})
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return self
def exclude(self, value):
self.selection = [s for s in self.selection
if s['v']['v'] != value]
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return self
def reset(self):
self.selection = []
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return self
class StarredFacet(TextFacet):
def __init__(self, selection=None):
if selection is not None and not isinstance(selection, bool):
raise ValueError('selection must be True or False.')
super(StarredFacet, self).__init__('',
expression='row.starred', selection=selection)
class FlaggedFacet(TextFacet):
def __init__(self, selection=None):
if selection is not None and not isinstance(selection, bool):
raise ValueError('selection must be True or False.')
super(FlaggedFacet, self).__init__('',
expression='row.flagged', selection=selection)
# Capitalize 'From' to get around python's reserved word.
class NumericFacet(Facet):
def __init__(self, column, From=None, to=None, select_blank=True, select_error=True, select_non_numeric=True, select_numeric=True, **options):
super(NumericFacet, self).__init__(
column,
type='range',
select_blank=select_blank,
select_error=select_error,
select_non_numeric=select_non_numeric,
select_numeric=select_numeric,
From=From,
to=to,
**options)
class FacetResponse(object):
def __init__(self, facet):
for k, v in facet.items():
if isinstance(k, bool) or isinstance(k, basestring):
setattr(self, from_camel(k), v)
self.choices = {}
class FacetChoice(object):
def __init__(self, c):
self.count = c['c']
self.selected = c['s']
if 'choices' in facet:
for choice in facet['choices']:
self.choices[choice['v']['v']] = FacetChoice(choice)
if 'blankChoice' in facet:
self.blank_choice = FacetChoice(facet['blankChoice'])
else:
self.blank_choice = None
if 'bins' in facet:
self.bins = facet['bins']
self.base_bins = facet['baseBins']
class FacetsResponse(object):
def __init__(self, facets):
self.facets = [FacetResponse(f) for f in facets['facets']]
self.mode = facets['mode']
class Engine(object):
def __init__(self, facets=None, mode='row-based'):
if facets is None:
facets = []
elif not isinstance(facets, list):
facets = [facets]
self.facets = facets
self.mode = mode
def as_dict(self):
return {
'facets': [f.as_dict() for f in self.facets], # XXX how with json?
'mode': self.mode,
}
def __len__(self):
return len(self.facets)
def as_json(self):
return json.dumps(self.as_dict())
def add_facet(self, facet):
self.facets.append(facet)
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def remove_all(self):
self.facets = []
def reset_all(self):
for facet in self.facets:
facet.reset()
class Sorting(object):
"""Class representing the current sorting order for a project.
Used in RefineProject.get_rows()"""
def __init__(self, criteria=None):
self.criteria = []
if criteria is None:
criteria = []
if not isinstance(criteria, list):
criteria = [criteria]
for criterion in criteria:
if isinstance(criterion, basestring):
criterion = {
'column': criterion,
'valueType': 'string',
'caseSensitive': False,
}
criterion.setdefault('reverse', False)
criterion.setdefault('errorPosition', 1)
criterion.setdefault('blankPosition', 2)
self.criteria.append(criterion)
def as_json(self):
return json.dumps({'criteria': self.criteria})
def __len__(self):
return len(self.criteria)
class RefineServer(object):
"""Communicate with a Refine server."""
def __init__(self, server='http://%s:%s' % (REFINE_HOST, REFINE_PORT)):
self.server = server[:-1] if server.endswith('/') else server
def urlopen(self, command, data=None, project_id=None):
"""Open a Refine URL and optionally POST data."""
url = self.server + '/command/core/' + command
if data is None:
data = {}
if project_id:
# XXX haven't figured out pattern on qs v body
if 'delete' in command:
data['project'] = project_id
else:
url += '?project=' + project_id
req = urllib2.Request(url)
if data:
req.add_data(data) # data = urllib.urlencode(data)
#req.add_header('Accept-Encoding', 'gzip')
response = urllib2.urlopen(req)
if response.info().get('Content-Encoding', None) == 'gzip':
# Need a seekable filestream for gzip
gzip_fp = gzip.GzipFile(fileobj=StringIO.StringIO(response.read()))
# XXX Monkey patch response's filehandle. Better way?
urllib.addbase.__init__(response, gzip_fp)
return response
def urlopen_json(self, *args, **kwargs):
"""Open a Refine URL, optionally POST data, and return parsed JSON."""
response = json.loads(self.urlopen(*args, **kwargs).read())
if 'code' in response and response['code'] != 'ok':
raise Exception(
response['code'] + ': ' +
response.get('message', response.get('stack', response)))
return response
class Refine:
"""Class representing a connection to a Refine server."""
def __init__(self, server, **kwargs):
if isinstance(server, RefineServer):
self.server = server
else:
self.server = RefineServer(server)
def get_version(self):
"""Return version data.
{"revision":"r1836","full_version":"2.0 [r1836]",
"full_name":"Google Refine 2.0 [r1836]","version":"2.0"}"""
return self.server.urlopen_json('get-version')
def list_projects(self):
"""Return a dict of projects indexed by id & name.
{u'1877818633188': {
'id': u'1877818633188', u'name': u'akg',
u'modified': u'2011-04-07T12:30:07Z',
u'created': u'2011-04-07T12:30:07Z'
},
{u'akg': { ... } } ...}"""
projects = self.server.urlopen_json('get-all-project-metadata')['projects']
# Provide a way for projects to be indexed by name too
for project_id, metadata in projects.items():
metadata['id'] = project_id
projects[metadata['name']] = metadata
return projects
def get_project_id_name(self, project):
"""Returns (project_id, project_name) given either."""
projects = self.list_projects()
# Is the project param an integer? If so treat as an id, else a name.
if re.match(r'^\d+$', project):
return project, projects[project]['name']
else:
return projects[project]['id'], project
def open_project(self, project):
"""Open a Refine project referred to by id or name."""
project_id, project_name = self.get_project_id_name(project)
return RefineProject(self.server, project_id, project_name)
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def new_project(self, project_file=None, project_url=None,
project_name=None,
split_into_columns=True,
separator='',
ignore_initial_non_blank_lines=0,
header_lines=1, # use 0 if your data has no header
skip_initial_data_rows=0,
limit=None, # no more than this number of rows
guess_value_type=True, # numbers, dates, etc.
ignore_quotes=False):
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if ((project_file and project_url) or
(not project_file and not project_url)):
raise ValueError('One (only) of project_file and project_url must be set')
def s(opt):
if isinstance(opt, bool):
return 'on' if opt else ''
if opt is None:
return ''
return str(opt)
options = {
'split-into-columns': s(split_into_columns),
'separator': s(separator),
'ignore': s(ignore_initial_non_blank_lines),
'header-lines': s(header_lines),
'skip': s(skip_initial_data_rows), 'limit': s(limit),
'guess-value-type': s(guess_value_type),
'ignore-quotes': s(ignore_quotes),
}
if project_url is not None:
options['url'] = project_url
elif project_file is not None:
options['project-file'] = {
'fd': open(project_file),
'filename': project_file,
}
if project_name is None:
# strip extension and directories
project_name = (project_file or 'New project').rsplit('.', 1)[0]
project_name = os.path.basename(project_name)
options['project-name'] = project_name
response = self.server.urlopen('create-project-from-upload', options)
# expecting a redirect to the new project containing the id in the url
url_params = urlparse.parse_qs(urlparse.urlparse(response.geturl()).query)
if 'project' in url_params:
project_id = url_params['project'][0]
return RefineProject(self.server, project_id, project_name)
else:
raise Exception('Project not created')
def RowsResponseFactory(column_index):
"""Factory for the parsing the output from get_rows().
Uses the project's model's row cell index so that a row can be used
as a dict by column name."""
class RowsResponse(object):
class RefineRows(object):
class RefineRow(object):
def __init__(self, row_response):
self.flagged = row_response['flagged']
self.starred = row_response['starred']
self.index = row_response['i']
self.row = [c['v'] if c else None
for c in row_response['cells']]
def __getitem__(self, column):
# Trailing nulls seem to be stripped from row data
try:
return self.row[column_index[column]]
except IndexError:
return None
def __init__(self, rows_response):
self.rows_response = rows_response
def __iter__(self):
for row_response in self.rows_response:
yield self.RefineRow(row_response)
def __len__(self):
return len(self.rows_response)
def __init__(self, response):
self.mode = response['mode']
self.filtered = response['filtered']
self.start = response['start']
self.limit = response['limit']
self.total = response['total']
# 'pool': {"reconCandidates": {},"recons": {}}
self.pool = response['pool']
self.rows = self.RefineRows(response['rows'])
return RowsResponse
class RefineProject:
"""A Google Refine project."""
def __init__(self, server, project_id=None, project_name=None):
if not isinstance(server, RefineServer):
url = urlparse.urlparse(server)
if url.query:
# Parse out the project ID and create a base server URL
project_id = url.query[8:] # skip project=
server = urlparse.urlunparse((
url.scheme, url.netloc, '', '', '', ''))
server = RefineServer(server)
self.server = server
if not project_id and not project_name:
raise Exception('Missing Refine project ID and name; need at least one of those')
if not project_name or not project_id:
project_id, project_name = Refine(server).get_project_id_name(
project_name or project_id)
self.project_id = project_id
self.project_name = project_name
self.engine = Engine()
self.sorting = Sorting()
# following filled in by get_models()
self.has_records = False
self.column_order = {} # order of column in UI
self.rows_response_factory = None # for parsing get_rows()
self.get_models()
def do_raw(self, command, data):
"""Issue a command to the server & return a response object."""
return self.server.urlopen(command, self.project_id, data)
def do_json(self, command, data=None, include_engine=True):
"""Issue a command to the server, parse & return decoded JSON."""
if include_engine:
if data is None:
data = {}
data['engine'] = self.engine.as_json()
return self.server.urlopen_json(command, project_id=self.project_id,
data=data)
def get_models(self):
"""Fill out column metadata.
column structure is sent in a list of columns in their order.
The cellIndex is used to find that column's data when returned from
get_rows()."""
response = self.do_json('get-models', include_engine=False)
column_model = response['columnModel']
column_index = {}
for i, column in enumerate(column_model['columns']):
name = column['name']
self.column_order[name] = i
column_index[name] = column['cellIndex']
self.key_column = column_model['keyColumnName']
self.has_records = response['recordModel'].get('hasRecords', False)
self.rows_response_factory = RowsResponseFactory(column_index)
# TODO: implement rest
return response
def wait_until_idle(self, polling_delay=0.5):
while True:
response = self.do('get-processes')
if 'processes' in response and len(response['processes']) > 0:
time.sleep(polling_delay)
else:
return
def apply_operations(self, file_path, wait=True):
json = open(file_path).read()
response_json = self.do('apply-operations', {'operations': json})
if response_json['code'] == 'pending' and wait:
self.wait_until_idle()
return 'ok'
return response_json['code'] # can be 'ok' or 'pending'
def export(self, export_format='tsv'):
"""Return a fileobject of a project's data."""
url = ('export-rows/' + urllib.quote(self.project_name) + '.' +
export_format)
return self.do_raw(url, {'format': export_format})
def export_rows(self, **kwargs):
"""Return an iterable of parsed rows of a project's data."""
return csv.reader(self.export(**kwargs), dialect='excel-tab')
def delete(self):
response_json = self.do_json('delete-project', include_engine=False)
return 'code' in response_json and response_json['code'] == 'ok'
def compute_facets(self, facets=None):
if facets:
self.engine = Engine(facets)
response = self.do_json('compute-facets')
return FacetsResponse(response)
def get_rows(self, facets=None, sort_by=None, start=0, limit=10):
if facets:
self.engine = Engine(facets)
if sort_by is not None:
self.sorting = Sorting(sort_by)
response = self.do_json('get-rows', {'sorting': self.sorting.as_json(),
'start': start, 'limit': limit})
return self.rows_response_factory(response)
def reorder_rows(self, sort_by=None):
if sort_by is not None:
self.sorting = Sorting(sort_by)
response = self.do_json('reorder-rows',
{'sorting': self.sorting.as_json()})
# clear sorting
self.sorting = Sorting()
return response
def remove_rows(self, facets=None):
if facets:
self.engine = Engine(facets)
return self.do_json('remove-rows')
def text_transform(self, column, expression, on_error='set-to-blank',
repeat=False, repeat_count=10):
response = self.do_json('text-transform', {
'columnName': column, 'expression': expression,
'onError': on_error, 'repeat': repeat,
'repeatCount': repeat_count})
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return response
def edit(self, column, edit_from, edit_to):
edits = [{'from': [edit_from], 'to': edit_to}]
return self.mass_edit(column, edits)
def mass_edit(self, column, edits, expression='value'):
"""edits is [{'from': ['foo'], 'to': 'bar'}, {...}]"""
edits = json.dumps(edits)
response = self.do_json('mass-edit', {
'columnName': column, 'expression': expression, 'edits': edits})
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return response
clusterer_defaults = {
'binning': {
'type': 'binning',
'function': 'fingerprint',
'params': {},
},
'knn': {
'type': 'knn',
'function': 'levenshtein',
'params': {
'radius': 1,
'blocking-ngram-size': 6,
},
},
}
def compute_clusters(self, column, clusterer_type='binning',
function=None, params=None):
"""Returns a list of clusters of {'value': ..., 'count': ...}."""
clusterer = self.clusterer_defaults[clusterer_type]
if params is not None:
clusterer['params'] = params
if function is not None:
clusterer['function'] = function
clusterer['column'] = column
response = self.do_json('compute-clusters', {
'clusterer': json.dumps(clusterer)})
return [[{'value': x['v'], 'count': x['c']} for x in cluster]
for cluster in response]
def annotate_one_row(self, row, annotation, state=True):
if annotation not in ('starred', 'flagged'):
raise ValueError('annotation must be one of starred or flagged')
state = 'true' if state == True else 'false'
return self.do_json('annotate-one-row', {'row': row.index,
annotation: state})
def flag_row(self, row, flagged=True):
return self.annotate_one_row(row, 'flagged', flagged)
def star_row(self, row, starred=True):
return self.annotate_one_row(row, 'starred', starred)
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def add_column(self, column, new_column, expression='value',
column_insert_index=None, on_error='set-to-blank'):
if column_insert_index is None:
column_insert_index = self.column_order[column] + 1
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response = self.do_json('add-column', {'baseColumnName': column,
'newColumnName': new_column, 'expression': expression,
'columnInsertIndex': column_insert_index, 'onError': on_error})
self.get_models()
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return response
def split_column(self, column, separator=',', mode='separator',
regex=False, guess_cell_type=True,
remove_original_column=True):
response = self.do_json('split-column', {'columnName': column,
'separator': separator, 'mode': mode, 'regex': regex,
'guessCellType': guess_cell_type,
'removeOriginalColumn': remove_original_column})
self.get_models()
return response
def rename_column(self, column, new_column):
response = self.do_json('rename-column', {'oldColumnName': column,
'newColumnName': new_column})
self.get_models()
return response
def reorder_columns(self, new_column_order):
"""Takes an array of column names in the new order."""
response = self.do_json('reorder-columns', {
'columnNames': new_column_order})
self.get_models()
return response
def move_column(self, column, index):
"""Move column to a new position."""
response = self.do_json('move-column', {'columnName': column,
'index': index})
self.get_models()
return response
def blank_down(self, column):
response = self.do_json('blank-down', {'columnName': column})
self.get_models()
return response
def fill_down(self, column):
response = self.do_json('fill-down', {'columnName': column})
self.get_models()
return response
def transpose_columns_into_rows(self, start_column, column_count,
combined_column_name, separator=':', prepend_column_name=True,
ignore_blank_cells=True):
response = self.do_json('transpose-columns-into-rows', {
'startColumnName': start_column, 'columnCount': column_count,
'combinedColumnName': combined_column_name,
'prependColumnName': prepend_column_name,
'separator': separator, 'ignoreBlankCells': ignore_blank_cells})
self.get_models()
return response
def transpose_rows_into_columns(self, column, row_count):
response = self.do_json('transpose-rows-into-columns', {
'columnName': column, 'rowCount': row_count})
self.get_models()
return response