def create_stats_dict_normal( self ):
stats_dict = {}
import numpy as np, db_methods as dbm, pickle
for view in ( 'TT_Features', 'TF_Features' ):
dbm.write_db_table( '.', self.db, view )
a = dbm.table2mem( self.db, view )
a1 = np.array( a )
CSParray = a1[ : , -2: ]
print 'CSParray dims :', np.shape( CSParray )
with open( 'CSParray_' + view + '.pickle', 'w') as f:
pickle.dump( CSParray, f )
#print a1
means, stdevs = dbm.array2stats( a1[:, :-2] )
#print means
#print stdevs
alpha, loc, beta = dbm.CSParray2stats( CSParray, self.scaling )
stats_dict[ view ] = { 'drlw' : { 'mean' : means[0], 'stdev' : stdevs[0] },
'drh' : { 'mean' : means[1], 'stdev' : stdevs[1] },
'dlwH' : { 'mean' : means[2], 'stdev' : stdevs[2] },
'dlwN' : { 'mean' : means[3], 'stdev' : stdevs[3] },
'dheight' : { 'mean' : means[4], 'stdev' : stdevs[4] },
'dlwHadj' : { 'mean' : means[5], 'stdev' : stdevs[5] },
'dlwNadj' : { 'mean' : means[6], 'stdev' : stdevs[6] },
'drlwadj' : { 'mean' : means[7], 'stdev' : stdevs[7] },
'CSPs' : { 'alpha' : alpha, 'loc' : loc, 'beta' : beta } }
#print stats_dict
self.stats_dict = stats_dict
def write_db_stats( self ):
import sqlite3 as sq3, db_methods as dbm
conn = sq3.connect( self.db )
c = conn.cursor()
views = [ str(b[0]) for b in c.execute( 'SELECT name from sqlite_master \
where type = \'view\'' ).fetchall() ]
for view in views:
if view in ( 'TT_Features', 'TF_Features' ):
dbm.write_db_table( '.', self.db, view )
