def db_setup():
import psiturk.models
from psiturk import db
db.init_db()
db.truncate_tables()
yield
db.truncate_tables()
def launch():
"""Launch the experiment."""
exp = experiment(db.init_db(drop_all=False))
exp.log("Launching experiment...", "-----")
init_db()
exp.recruiter().open_recruitment(n=exp.initial_recruitment_size)
session_psiturk.commit()
session.commit()
return success_response(request_type="launch")
def db_setup(mocker, experiment_dir, tmpdir, request):
import psiturk.db
reload(psiturk.db)
import psiturk.models
psiturk.models.Base.metadata.clear()
reload(psiturk.models)
from psiturk.db import init_db
init_db()
yield
def launch():
"""Launch the experiment."""
exp = experiment(db.init_db(drop_all=False))
exp.log("Launch route hit, initializing tables and opening recruitment.", "-----")
init_db()
exp.recruiter().open_recruitment(n=exp.initial_recruitment_size)
session_psiturk.commit()
session.commit()
exp.log("Experiment successfully launched!", "-----")
data = {"status": "success"}
js = dumps(data)
return Response(js, status=200, mimetype='application/json')
def do_it(participant_attributes={}):
participant_attribute_defaults = {
'workerid': faker.md5(raw_output=False),
'hitid': faker.md5(raw_output=False),
'assignmentid': faker.md5(raw_output=False),
}
participant_attributes = dict(list(
participant_attribute_defaults.items()) + list(participant_attributes.items()))
init_db()
participant = Participant(**participant_attributes)
db_session.add(participant)
db_session.commit()
return participant
import image_slicer import os from psiturk.db import db_session, init_db, Base from custom_models import Pixels init_db() # initialze the data base, creating tables for the custom_models.py if necessary TILEPATH = './static/images/tiles/' TARGETFILE = 'brain.png' NTILES = 16 # first delete all the files in the static/images/tiles folder files = os.listdir(TILEPATH) for myfile in files: if myfile != 'placeholder.txt': print "removing existing file", TILEPATH+myfile os.remove(TILEPATH+myfile) # query existing pixels Pixels.query.delete() # create the tiles using the target image tiles = image_slicer.slice(TARGETFILE, NTILES, save=False) image_slicer.save_tiles(tiles, prefix='tile', directory='static/images/tiles', format='png') # add tiles to database for tile in tiles: pixel_width, pixel_height = tile.image.size print "dimensions:", (pixel_width, pixel_height) , "filename:", tile.filename pixel_attributes = dict(filename = tile.filename, n_completed = 0,
print("created new game of type " + str(new_game.__class__.__name__))
for user in [a, b]:
games[user] = new_game
arg = {
"role": games[user].role_string(user),
"pattern": new_game.__class__.__name__
}
#sio.emit("sendTrainingMessage", "* You've been matched as "+ games[user].role_string(user) + ".", room=user)
sio.emit("instructions", arg, room=user)
#initial task
#new_game.new_task()
db_session.commit()
else:
sio.emit("sendTrainingMessage", "* Waiting for a partner.", room=uid)
#queue = queue
if __name__ == "__main__":
# app = Flask(__name__)
# app.register_blueprint(custom_code)
# app.wsgi_app = socketio.Middleware(sio, app.wsgi_app)
# app.run(host='localhost', port=5000)
import os, sqlalchemy
if not os.path.exists("participants.db"):
init_db()
# import image_slicer
import os
import csv
from psiturk.db import db_session, init_db, Base
from custom_models import Word, Feature, Rating
init_db(
) # initialze the data base, creating tables for the custom_models.py if necessary
# read in the words, definitions, and feature strings
with open('./stims/amt_unique_words.csv', 'rb') as f:
amt_words = list(tuple(rec) for rec in csv.reader(f, delimiter='\t'))
with open('./stims/amt_unique_defs.csv', 'rb') as f:
amt_defs = list(tuple(rec) for rec in csv.reader(f, delimiter='\t'))
with open('./stims/amt_feats.csv', 'rb') as f:
amt_feats = list(tuple(rec) for rec in csv.reader(f, delimiter='\t'))
# populate words table with words and definitions
for idx in range(len(amt_words[0])):
word = Word()
word.word_string = amt_words[0][idx]
word.word_definition = amt_defs[0][idx]
db_session.add(word)
db_session.commit()
# put features in the feature table
for cfeat in amt_feats[0]:
feature = Feature()
feature.feature_string = cfeat