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willurd

# Read the contents of ~/.path into $PATH, if ~/.path exists. ~/.path should be a file
# consisting of one path on each line, such as:
#
#   ~$ cat ~/.path
#   # vim: ft=sh
#   ~/usr/bin
#   /opt/local/bin
#   ... etc ...
#
# Note that comments begin with a hash (#).

shazow

"""SQLAlchemy Metadata and Session object"""
import datetime
import json
import time

from sqlalchemy import MetaData
from sqlalchemy.orm import scoped_session, sessionmaker


__all__ = ['Session', 'metadata', 'Model', 'SchemaEncoder']

pjenvey

class JsonSerializableMixin(object):

    def __json__(self, request):
        """
        Converts all the properties of the object into a dict for use in json.
        You can define the following in your class

        _json_eager_load :
            list of which child classes need to be eagerly loaded. This applies
            to one-to-many relationships defined in SQLAlchemy classes.

gnunicorn

{%- macro form_field_label(field) -%}
    <label for="{{ field.id }}">{{ field.label.text }}
    {%- if field.flags.required -%}
        <abbr title="Diese Feld muss angegeben werden">*</abbr>
    {%- endif %}</label>
{% endmacro %}

{%- macro form_field_description(field) -%}
    {% if field.description %}
        <span class="descr">{{ field.description }}</span>

alienrobotwizard

#!/usr/bin/env ruby

require 'rubygems'
require 'wukong'
require 'wukong/encoding'
require 'configliere' ; Configliere.use(:commandline, :env_var, :define)
require 'set'

Settings.define :id_field,   :type => Integer, :default => 0, :required => true, :description => "What field to use as the document id. (-1) to assign ids"
Settings.define :text_field, :type => Integer, :default => 1, :required => true, :description => "Which field is the text field?"

alexbowe

import nltk

text = """The Buddha, the Godhead, resides quite as comfortably in the circuits of a digital
computer or the gears of a cycle transmission as he does at the top of a mountain
or in the petals of a flower. To think otherwise is to demean the Buddha...which is
to demean oneself."""

# Used when tokenizing words
sentence_re = r'''(?x)      # set flag to allow verbose regexps
      ([A-Z])(\.[A-Z])+\.?  # abbreviations, e.g. U.S.A.

onyxfish

import nltk

with open('sample.txt', 'r') as f:
    sample = f.read()
     
sentences = nltk.sent_tokenize(sample)
tokenized_sentences = [nltk.word_tokenize(sentence) for sentence in sentences]
tagged_sentences = [nltk.pos_tag(sentence) for sentence in tokenized_sentences]
chunked_sentences = nltk.batch_ne_chunk(tagged_sentences, binary=True)