def score_message(hug_token, message_id: hug.types.number,
score: hug.types.number):
try:
msg = Message.get(Message.token == Token.get(Token.token == hug_token),
Message.id == message_id, Message.reply_to != None)
Score.create(message=msg, value=score)
return {"status": "ok"}
except Message.DoesNotExist:
raise HTTPError(HTTP_404)
except peewee.IntegrityError:
raise HTTPError(HTTP_409)
def scrape_piece(self, db_piece):
"""Scrape a piece page associated with a database piece entry.
Populates the database with Score objects based on what is parsed
off the page.
"""
# Download and parse the piece page
try:
piece_page = PiecePage(db_piece.url)
scores = piece_page.parse_scores()
metadata = piece_page.parse_metadata()
except (PageRequestFailure, PageParseFailure) as e:
if isinstance(e, PageParseFailure):
self._logger.warning("Failed to parse page at {}".format(
db_piece.url))
else:
self._logger.warning(
"Failed to download piece page at {}:".format(
db_piece.url))
self._logger.warning(e.original_exception)
db_piece.failed_scrape = True
commit_session(self._session)
return
# Create scores associated with this piece.
scores_to_add = []
for score in scores:
for dli in score.get('dl_links', []):
if not self._score_in_database(dli):
db_score = Score(piece=db_piece,
composer=db_piece.composer)
db_score.url = dli
db_score.file_format = dli.split('.')[-1]
db_score.name = score.get('meta', {}).get('CPDL#', '')
scores_to_add.append(db_score)
self._session.add_all(scores_to_add)
# Save scraping data in DB.
metadata['scores'] = scores
db_piece.json_metadata = json.dumps(metadata)
db_piece.html_dump = piece_page.get_raw_html()
db_piece.scraped = True
commit_session(self._session)
self._logger.info(
"Successfully scraped {} scores from piece {}.".format(
len(scores_to_add), db_piece.name))
def current_data_geojson(zone, score_key, date_key):
# Grab the data first
scores = Score.by_tag_type_with_date(zone, score_key, date_key)
out = {}
for key, val in scores.items():
out[int(key)] = val
# Read in the json file
with open(f'static/data/{zone}_bg.geojson') as bgfile:
data = json.load(bgfile)
for feature in data['features']:
feature['properties']['score'] = out[int(feature['properties']['GEOID'])]
return Response(json.dumps(data),
mimetype='application/json',
headers={'Content-Disposition':f'attachment;filename=ted_{zone}_{score_key}_{date_key}.geojson'})
def score(zone, score_key, date_key):
"""
Data API call to retrieve score information.
Format of score_key should be:
measure_destination_function_date_period, where
measure = the metric used (Access, Equity, Etc)
destination = the destination (jobs, snap stores, etc)
function = the measurement function (cumulative 45min, travel time)
date = date key for the measure
period = Morning peak (MP), etc.
"""
date = dt.datetime.strptime(date_key, "%Y-%m-%d")
scores = Score.by_tag_type_with_date(zone, score_key, date_key)
return jsonify(scores)
def data_theme(theme, zone, score_key):
if theme == '1':
data = Score.weighted_average_all_pop_types(zone, score_key)
return jsonify(data)
def primal_download(zone, date_key):
scores = Score.by_tag_type_with_date_primal(zone, date_key)
return send_csv(scores.to_dict(orient='records'), f"ted_{zone}_{date_key}_cumulative.csv", scores.columns)
def all_data_csv(zone, date_key):
scores = Score.by_tag_type_with_date_all(zone, date_key)
return send_csv(scores.to_dict(orient='records'), f"ted_{zone}_{date_key}_all.csv", scores.columns)
def current_data_csv(zone, score_key, date_key):
scores = Score.by_tag_type_with_date(zone, score_key, date_key)
out = []
for key, val in scores.items():
out.append({'block_group': key, 'score': val})
return send_csv(out, f"ted_{zone}_{score_key}_{date_key}.csv", ['block_group', 'score'])