def csv_test():
csv_file = os.path.join(os.path.split(H.__file__)[0],'test','csv_test.csv')
data = H.csv2hyperdatabase(csv_file)
# data.filter_all_null()
for name in data.column_names:
data.add_filter(name, H.filtering.notnull(name= 'notnull_' + name))
H.hyperscatter(data, x=('poolingfactors', H.get_el(0,0)), y=('poolingfactors', H.get_el(1,0)), c = 'bestkendall', title='csv1')
# data.filter('bestkendall', filter_lt(.66))
data.add_filter('poolingfactors', H.filtering.outside(2,8, parse_func=H.get_el(0,0)))
data.add_filter('poolingfactors', H.filtering.between(4,5, parse_func=H.get_el(1,0)))
print data
H.hyperscatter(data, x=('poolingfactors', H.get_el(0,0)), y=('poolingfactors', H.get_el(1,0)), c = 'bestkendall', title='csv2')
import os
import pylab
import hyperplot as H
## Fetching the database on the sql server.
#user = '******'
#host = 'gershwin.iro.umontreal.ca'
#db = 'hamelphi_db'
#table = 'agg_feat2_view'
#data = H.sql2hyperdatabase(user=user,host=host,table=table,db=db)
# Loading from CSV instead (Because of permissions)
csv_file = os.path.join(os.path.split(H.__file__)[0],'demo','pfc_data.csv')
data = H.csv2hyperdatabase(csv_file)
# Applying a couple of filters.
data.add_filter('jobman_status', H.filtering.eq(2))
data.add_filter('pcadim', H.filtering.eq(120))
data.add_filter('overlap', H.filtering.eq(2))
data.add_filter('agglen', H.filtering.between(100,200))
data.add_filter('validaucroctag', H.filtering.gt(0.5))
data.add_filter('time', H.filtering.between(1000,20000))
data.add_filter('dataset',H.filtering.notfind('mfcc'))
# Filtering categories, and changing some category names.
black_list=[]
#box_names={}
black_list = ['skew','kurt','moments','moments_max','moments_max_min']
box_names = {'skew':'3rd moment',
'kurt':'4th moment',