#showprogress
#A problem's progress
import sys
import problem
import pylab
import numpy
import copy
sid = sys.argv[1]
DB = "CAT3"
pre = problem.Problems(DB, sid, exp="pre_clean")
post = problem.Problems(DB, sid, exp="post_clean")
mdict = {}
mdict['calc'] = 'o'
mdict['mem'] = 's'
cdict = {}
cdict['calc'] = [0, .2, .8]
cdict['mem'] = [.8, .2, 0]
cdict['calc|mem'] = [.8, .2, .8]
cdict['mem|calc'] = [.8, .2, .8]
pylab.figure(figsize=[14, 8])
plotnum = 1
for kind in ['verified', 'erratic']:
for trained in ['novel', 'trained']:
#clean_data.py
import sys
import problem
import pylab
sid = sys.argv[1]
DB = "CAT3"
pre = problem.Problems(DB, sid)
pre_clean = problem.Problems(DB, sid, "pre_clean", clear=True)
for pid in pre.distinct('id'):
for row in pre.query({'id': pid}):
l = len(row['history'])
print pid, l, row['kind']
if l > 2:
newHist = []
trials = []
for h in row['history']:
if h['trial'] in trials:
pass
else:
newHist.append(h)
trials.append(h['trial'])
row['history'] = newHist
pre.save(row)
for pid in pre.distinct('id'):
#comparor.py
#single subject training effects
import sys
import problem
import pylab
import numpy
import copy
sid = sys.argv[1]
DB = "CAT3"
pre = problem.Problems(DB, sid)
post = problem.Problems(DB, sid, exp="post")
pre.name = "pre"
post.name = "post"
training = ['novel', 'trained']
pylab.figure()
strategies = ['mem', 'calc']
plotnum = 1
for strat in strategies:
RTs = []
solns = []
#clean_data.py
import sys
import problem
import pylab
sid = sys.argv[1]
DB = "CAT3"
post = problem.Problems(DB, sid, exp="post")
post_clean = problem.Problems(DB, sid, "post_clean", clear=True)
for pid in post.distinct('id'):
for row in post.query({'id': pid}):
l = len(row['history'])
ns = [row['n1'], row['n2']]
ns.sort()
prob = problem.Problem(ns)
for h in row['history']:
prob.addResponse(h)
post_clean.append(prob)
print "#####"
for pid in post_clean.distinct('id'):
for row in post_clean.query({'id': pid}):
l = len(row['history'])
print pid, l, row['kind']
#show_conversion.py
import sys
import problem
import pylab
import copy
sid = sys.argv[1]
DB="CAT3"
pre = problem.Problems(DB, sid, exp="pre_clean")
post = problem.Problems(DB, sid, exp="post_clean")
pre_post = problem.Problems(DB, sid, exp="pre_post", clear=True)
for pid in post.distinct('id'):
PRE = pre.query({'kind':'verified', 'id':pid})
POST = post.query({'kind':'verified', 'id':pid})
#find calc-mems
if PRE.count() and POST.count():
pre_strat = PRE[0]['strat']
post_strat = POST[0]['strat']
if pre_strat == post_strat:
conversion = pre_strat
else:
conversion = pre_strat + "|" + post_strat
pre.update({'id':pid}, {'$set': {'conversion': conversion}})
def build(self, strat):
rows = self.problems.query({'strat': strat, 'kind': 'verified'})
rows = rows.sort('solution')
trainingProbs = []
#want to come up with a roughly balanced set of solution sizes (trained vs untrained)
count = 1
for row in rows:
#every other problem should be added to the list
if count % 2:
trainingProbs.append(row)
row['trained'] = 'trained'
else:
row['trained'] = 'novel'
self.problems.posts.save(row)
count += 1
for p in trainingProbs[:self.setSize]:
line = "%s, %s, %s, %s, %s\n" % (problems.sid, p['n1'], p['n2'],
p['strat'], p['solution'])
self.f.write(line)
problems = problem.Problems("CAT3", sys.argv[1])
b = TrainingBuilder(problems)
import problem import pylab import scipy.stats import matplotlib.mlab as mlab import sys, os sys.path.append(os.path.split(os.getcwd())[0]) import mongoTools DB = "CAT3" sid = sys.argv[1] pre_post = problem.Problems(DB, sid=sid, exp="pre_post") strats = ['strat', ['calc', 'mem']] kinds = ['kind', ['verified', 'erratic']] days = ['day', ['pre', 'post']] trained = ['trained', ['novel', 'trained']] #IVs and their levels IVs = [strats, kinds, days, trained] #DVs DVs = ['RT']
