def search(text):
# Split the search text into tokens and escape them properly
tokens = text_to_tokens(text)
tokens = escape_tokens(tokens)
# Create combined search query
query = combined_search_query(MODELS, tokens)
# Perform query and limit output to 20 items
results = query.limit(20).all()
process_result_details(MODELS, results)
results_json = []
for r in results:
result = {
'type': r.model.lower(),
'id': r.id,
'name': r.name,
}
if hasattr(r, 'website') and r.website:
result['website'] = r.website
if hasattr(r, 'icao') and r.icao:
result['icao'] = r.icao
if hasattr(r, 'frequency') and r.frequency:
result['frequency'] = '%.3f' % (float(r.frequency))
results_json.append(result)
return results_json
def search(text):
# Split the search text into tokens and escape them properly
tokens = text_to_tokens(text)
tokens = escape_tokens(tokens)
# Create combined search query
query = combined_search_query(MODELS, tokens)
# Perform query and limit output to 20 items
results = query.limit(20).all()
results = process_results_details(MODELS, results)
results_json = []
for r in results:
result = {
'type': r['model'].lower(),
'id': r['id'],
'name': r['name'],
}
if r.get('website'):
result['website'] = r['website']
if r.get('icao'):
result['icao'] = r['icao']
if r.get('frequency'):
result['frequency'] = '%.3f' % (float(r['frequency']))
results_json.append(result)
return results_json
def search(text):
# Split the search text into tokens and escape them properly
tokens = text_to_tokens(text)
tokens = escape_tokens(tokens)
# Create combined search query
query = combined_search_query(MODELS, tokens)
# Perform query and limit output to 20 items
results = query.limit(20).all()
results = process_results_details(MODELS, results)
results_json = []
for r in results:
result = {
'type': r['model'].lower(),
'id': r['id'],
'name': r['name'],
}
if r.get('website'):
result['website'] = r['website']
if r.get('icao'):
result['icao'] = r['icao']
if r.get('frequency'):
result['frequency'] = '%.3f' % (float(r['frequency']))
results_json.append(result)
return results_json
def search(text):
# Split the search text into tokens and escape them properly
tokens = text_to_tokens(text)
tokens = escape_tokens(tokens)
# Create combined search query
query = combined_search_query(MODELS, tokens)
# Perform query and limit output to 20 items
results = query.limit(20).all()
process_result_details(MODELS, results)
results_json = []
for r in results:
result = {
'type': r.model.lower(),
'id': r.id,
'name': r.name,
}
if hasattr(r, 'website') and r.website:
result['website'] = r.website
if hasattr(r, 'icao') and r.icao:
result['icao'] = r.icao
if hasattr(r, 'frequency') and r.frequency:
result['frequency'] = '%.3f' % (float(r.frequency))
results_json.append(result)
return results_json
def search(text):
# Split the search text into tokens and escape them properly
tokens = text_to_tokens(text)
tokens = escape_tokens(tokens)
# Create combined search query
return combined_search_query(MODELS, tokens)
def search(text):
# Split the search text into tokens and escape them properly
tokens = text_to_tokens(text)
tokens = escape_tokens(tokens)
# Create combined search query
query = combined_search_query(MODELS, tokens)
# Perform query and limit output to 20 items
results = query.limit(20).all()
results = process_results_details(MODELS, results)
results_json = []
for r in results:
result = {"type": r["model"].lower(), "id": r["id"], "name": r["name"]}
if r.get("website"):
result["website"] = r["website"]
if r.get("icao"):
result["icao"] = r["icao"]
if r.get("frequency"):
result["frequency"] = "%.3f" % (float(r["frequency"]))
results_json.append(result)
return results_json
def index(args):
search_text = args['q']
# Split the search text into tokens and escape them properly
tokens = text_to_tokens(search_text)
tokens = escape_tokens(tokens)
# Create combined search query
query = combined_search_query(MODELS, tokens)
# Perform query and limit output to 20 items
results = query.limit(20).all()
results = map(convert, results)
return jsonify(results)
def index(args):
search_text = args['q']
# Split the search text into tokens and escape them properly
tokens = text_to_tokens(search_text)
tokens = escape_tokens(tokens)
# Create combined search query
query = combined_search_query(MODELS, tokens)
# Perform query and limit output to 20 items
results = query.limit(20).all()
results = map(convert, results)
return jsonify(results)
def index():
search_text = request.values.get('text', '').strip()
if not search_text:
return render_template('search/list.jinja')
# Split the search text into tokens and escape them properly
tokens = text_to_tokens(search_text)
tokens = escape_tokens(tokens)
# Create combined search query
query = combined_search_query(MODELS, tokens)
# Perform query and limit output to 20 items
results = query.limit(20).all()
process_result_details(MODELS, results)
return render_template('search/list.jinja',
search_text=search_text,
results=results)
def index():
search_text = request.values.get('text', '').strip()
if not search_text:
return render_template('search/list.jinja')
# Split the search text into tokens and escape them properly
tokens = text_to_tokens(search_text)
tokens = escape_tokens(tokens)
# Create combined search query
query = combined_search_query(MODELS, tokens)
# Perform query and limit output to 20 items
results = query.limit(20).all()
process_result_details(MODELS, results)
return render_template('search/list.jinja',
search_text=search_text,
results=results)
def test_tokenizer():
# Check that this does not throw exceptions
text_to_tokens(u'\\')
text_to_tokens(u'blabla \\')
text_to_tokens(u'"')
text_to_tokens(u'"blabla \\')
# Check that the tokenizer returns expected results
assert text_to_tokens(u'a b c') == [u'a', u'b', u'c']
assert text_to_tokens(u'a \'b c\'') == [u'a', u'b c']
assert text_to_tokens(u'a "b c" d') == [u'a', u'b c', u'd']
assert text_to_tokens(u'old "mac donald" has a FARM') == \
[u'old', u'mac donald', u'has', u'a', u'FARM']
def test_tokenizer():
# Check that this does not throw exceptions
text_to_tokens(u"\\")
text_to_tokens(u"blabla \\")
text_to_tokens(u'"')
text_to_tokens(u'"blabla \\')
# Check that the tokenizer returns expected results
assert text_to_tokens(u"a b c") == [u"a", u"b", u"c"]
assert text_to_tokens(u"a 'b c'") == [u"a", u"b c"]
assert text_to_tokens(u'a "b c" d') == [u"a", u"b c", u"d"]
assert text_to_tokens(u'old "mac donald" has a FARM') == [
u"old",
u"mac donald",
u"has",
u"a",
u"FARM",
]
def test_tokenizer():
# Check that this does not throw exceptions
text_to_tokens('\\')
text_to_tokens('blabla \\')
text_to_tokens('"')
text_to_tokens('"blabla \\')
# Check that the tokenizer returns expected results
eq_(text_to_tokens('a b c'), ['a', 'b', 'c'])
eq_(text_to_tokens('a \'b c\''), ['a', 'b c'])
eq_(text_to_tokens('a "b c" d'), ['a', 'b c', 'd'])
eq_(text_to_tokens('old "mac donald" has a FARM'),
['old', 'mac donald', 'has', 'a', 'FARM'])
def test_tokenizer():
# Check that this does not throw exceptions
text_to_tokens('\\')
text_to_tokens('blabla \\')
text_to_tokens('"')
text_to_tokens('"blabla \\')
# Check that the tokenizer returns expected results
eq_(text_to_tokens('a b c'), ['a', 'b', 'c'])
eq_(text_to_tokens('a \'b c\''), ['a', 'b c'])
eq_(text_to_tokens('a "b c" d'), ['a', 'b c', 'd'])
eq_(text_to_tokens('old "mac donald" has a FARM'),
['old', 'mac donald', 'has', 'a', 'FARM'])
