def test_save_results():
ema_logging.log_to_stderr(ema_logging.DEBUG)
data = util.load_results("./data/1000 flu cases no policy.cPickle", zip=False)
file_name = "test.bz2"
util.save_results(data, file_name)
os.remove(file_name)
ema_logging.debug("removing " + file_name)
def test_load_results():
data = np.random.rand(1000, 1000)
file_name = "test.bz2"
util.save_results(data, file_name)
ema_logging.log_to_stderr(ema_logging.DEBUG)
util.load_results(file_name)
os.remove(file_name)
ema_logging.debug("removing " + file_name)
def test_save_results():
# test for 1d
# test for 2d
# test for 3d
# test for very large
nr_experiments = 10000
experiments = np.recarray((nr_experiments,),
dtype=[('x', float), ('y', float)])
outcome_a = np.random.rand(nr_experiments,1)
results = (experiments, {'a': outcome_a})
save_results(results, r'../data/test.tar.gz')
os.remove('../data/test.tar.gz')
ema_logging.info('1d saved successfully')
nr_experiments = 10000
nr_timesteps = 100
experiments = np.recarray((nr_experiments,),
dtype=[('x', float), ('y', float)])
outcome_a = np.random.rand(nr_experiments,nr_timesteps)
results = (experiments, {'a': outcome_a})
save_results(results, r'../data/test.tar.gz')
os.remove('../data/test.tar.gz')
ema_logging.info('2d saved successfully')
nr_experiments = 10000
nr_timesteps = 100
nr_replications = 10
experiments = np.recarray((nr_experiments,),
dtype=[('x', float), ('y', float)])
outcome_a = np.random.rand(nr_experiments,nr_timesteps,nr_replications)
results = (experiments, {'a': outcome_a})
save_results(results, r'../data/test.tar.gz')
os.remove('../data/test.tar.gz')
ema_logging.info('3d saved successfully')
nr_experiments = 500000
nr_timesteps = 100
experiments = np.recarray((nr_experiments,),
dtype=[('x', float), ('y', float)])
outcome_a = np.random.rand(nr_experiments,nr_timesteps)
results = (experiments, {'a': outcome_a})
save_results(results, r'../data/test.tar.gz')
os.remove('../data/test.tar.gz')
ema_logging.info('extremely long saved successfully')
def test_load_results():
# test for 1d
# test for 2d
# test for 3d
# test for nd
nr_experiments = 10000
experiments = np.recarray((nr_experiments,),
dtype=[('x', float), ('y', float)])
outcome_a = np.random.rand(nr_experiments,1)
results = (experiments, {'a': outcome_a})
save_results(results, r'../data/test.tar.gz')
experiments, outcomes = load_results(r'../data/test.tar.gz')
logical = np.allclose(outcomes['a'],outcome_a)
os.remove('../data/test.tar.gz')
if logical:
ema_logging.info('1d loaded successfully')
nr_experiments = 1000
nr_timesteps = 100
nr_replications = 10
experiments = np.recarray((nr_experiments,),
dtype=[('x', float), ('y', float)])
outcome_a = np.random.rand(nr_experiments,nr_timesteps,nr_replications)
results = (experiments, {'a': outcome_a})
save_results(results, r'../data/test.tar.gz')
experiments, outcomes = load_results(r'../data/test.tar.gz')
logical = np.allclose(outcomes['a'],outcome_a)
os.remove('../data/test.tar.gz')
if logical:
ema_logging.info('3d loaded successfully')
book = open_workbook('PatternSet_Periodic.xls',formatting_info=True)
sheet = book.sheet_by_name('data')
noRuns = sheet.nrows-1
noDataPoints = sheet.ncols-4
print noRuns, noDataPoints
dataSet = np.zeros((noRuns,noDataPoints))
for i in range(noRuns):
output = sheet.row_values(i+1,4)
dataSet[i] = output
results = {'outcome':dataSet}
cases = np.zeros(noRuns, dtype=[('No','i4'),('Label','a30'),('Class ID', 'i4'),('Class Desc','a40')])
for i in range(noRuns):
no = sheet.cell(i+1,0).value
label = sheet.cell(i+1,1).value
classID = sheet.cell(i+1,2).value
classDesc = sheet.cell(i+1,3).value
instance = (no,label,classID, classDesc)
cases [i] = instance
data = (cases,results)
util.save_results(data, 'PatternSet_Periodic.cpickle')
