def __init__(self, collection, selector, block_num=None, objects_oi=None, mongo_reload=False):
if mongo_reload:
conn = pymongo.Connection(port = 22334, host = 'localhost')
db = conn.mturk
col = db[collection]
data = list(col.find(selector))
print 'Loaded from mongoDB ...'
else:
local_backup = BKPDATAPATH + collection + '_bkp.pkl'
data = pk.load(open(local_backup, 'r'))
print 'Loaded from local mongo backup...'
if (collection == 'objectome64') | (collection == 'objectome_imglvl'):
ind_subj_flag = 0
semantic_data = 0
elif collection == 'monkobjectome':
ind_subj_flag = 1
semantic_data = 0
elif collection == 'mobj-semantic':
ind_subj_flag = 0
semantic_data = 1
if block_num != None:
models = all_models[block_num]
outfn = out_filenames[ind_subj_flag][block_num]
elif objects_oi != None:
models = objects_oi
outfn = 'tmp'
models_idxs = {}
invert_rosch_labels = {}
for idx, model in enumerate(models):
models_idxs[model] = idx
invert_rosch_labels[rl.return_label(model)] = idx
rosch_labels = invert_rosch_labels.keys()
self.collection = collection
self.models = models
self.models_idxs = models_idxs
self.rosch_labels = rosch_labels
self.invert_rosch_labels = invert_rosch_labels
self.subj_data = data
self.numObjs = len(models)
self.outfn = outfn
self.keep_subj = np.nan
if semantic_data == 1:
self.get_semantic_data()
else:
self.get_data()
if mongo_reload == True:
self.backup_to_disk()
def run_random():
""" DELETION OF RANDOM AXIS FOR FIXED BOUNDARY ONE VS OTHER """
import time
start = time.clock()
params = pk.load(file('/mindhive/dicarlolab/u/esolomon/objectome_32/64obj_20dim_modelparams.pkl', 'rb'))
cents_all = params['centers']
radii_all = np.sqrt(params['radii'][:,0,0]) # this should be Lcovs
cents_all_silenced = nullspc_rand(cents_all, np.random.randint(100))
cents_all_silenced = nullspc_rand(cents_all_silenced, np.random.randint(100))
import objectome as obj
# objs = obj.models_combined16
objs = obj.models_simulation
# objs = obj.models_objectome64
print 'Random silencing simulation with objects: ' + str(objs)
inds = []
for m in objs:
inds.append(obj.models_objectome64.index(m))
import dldata.human_data.roschlib as rl
models = rl.ps64_models
obj_names_all = [rl.return_label(m) for m in models]
cents = cents_all[inds,:]
cents_silence = cents_all_silenced[inds,:]
radii = radii_all[inds]
obj_names = [obj_names_all[ii] for ii in inds]
# dp_o, dp_s, obj_pairs = perf_all_flexible(cents, cents_silence, radii, obj_names)
# # plot_comparisons(dp_o, dp_s)
# res_flex = {'dp_o':dp_o, 'dp_s':dp_s, 'objn':obj_pairs}
dp_o, dp_s, bias_o, bias_s, perf_o, perf_s, obj_pairs = perf_all_fixed(cents, cents_silence, radii, obj_names)
res_fix = {'dp_o':dp_o, 'dp_s':dp_s,
'bias_o':bias_o, 'bias_s':bias_s,
'perf_o':perf_o, 'perf_s':perf_s,
'objn':obj_pairs}
res = {'fix':res_fix}
io.savemat('pspace/multrandDeletion0408_wface.mat', res)
finish = time.clock()
print 'Done in ' + str(finish-start) + ' s'
return res