def create_db(filename, nrows):
class Record(tables.IsDescription):
col1 = tables.Int32Col()
col2 = tables.Int32Col()
col3 = tables.Float64Col()
col4 = tables.Float64Col()
con = open_db(filename, remove=1)
table = con.createTable(con.root, 'table', Record,
filters=filters, expectedrows=nrows)
table.indexFilters = filters
step = 1000*100
scale = 0.1
t1=time()
j = 0
for i in xrange(0, nrows, step):
stop = (j+1)*step
if stop > nrows:
stop = nrows
arr_f8 = numarray.arange(i, stop, type=numarray.Float64)
arr_i4 = numarray.arange(i, stop, type=numarray.Int32)
if userandom:
arr_f8 += random_array.normal(0, stop*scale, shape=[stop-i])
arr_i4 = numarray.array(arr_f8, type=numarray.Int32)
recarr = records.fromarrays([arr_i4, arr_i4, arr_f8, arr_f8])
table.append(recarr)
j += 1
table.flush()
ctime = time()-t1
if verbose:
print "insert time:", round(ctime, 5)
print "Krows/s:", round((nrows/1000.)/ctime, 5)
index_db(table)
close_db(con)
def create_db(filename, nrows):
class Record(tables.IsDescription):
col1 = tables.Int32Col()
col2 = tables.Int32Col()
col3 = tables.Float64Col()
col4 = tables.Float64Col()
con = open_db(filename, remove=1)
table = con.create_table(con.root, 'table', Record,
filters=filters, expectedrows=nrows)
table.indexFilters = filters
step = 1000*100
scale = 0.1
t1=time()
j = 0
for i in xrange(0, nrows, step):
stop = (j+1)*step
if stop > nrows:
stop = nrows
arr_f8 = numarray.arange(i, stop, type=numarray.Float64)
arr_i4 = numarray.arange(i, stop, type=numarray.Int32)
if userandom:
arr_f8 += random_array.normal(0, stop*scale, shape=[stop-i])
arr_i4 = numarray.array(arr_f8, type=numarray.Int32)
recarr = records.fromarrays([arr_i4, arr_i4, arr_f8, arr_f8])
table.append(recarr)
j += 1
table.flush()
ctime = time()-t1
if verbose:
print "insert time:", round(ctime, 5)
print "Krows/s:", round((nrows/1000.)/ctime, 5)
index_db(table)
close_db(con)
def main(argv=None):
if argv is None:
argv = sys.argv
if len(argv)!=6:
print "Usage: %s <method> <frequency> <vmin> <vmax> <datafile>"%argv[0]
return 1
hybrid = "%s_hybrid"%argv[1]
simplex = "%s_simplex"%argv[1]
dfile = argv[5]
hybrid_out = "%s.hybrid_output"%dfile
simplex_out = "%s.simplex_output"%dfile
temp_in = "%s.temp_input"%dfile
frequency = argv[2]
vmin = argv[3]
vmax = argv[4]
os.system("%s %s %s %s %s 200 300 3 %s"%(hybrid,dfile,frequency,vmin,vmax,hybrid_out))
fin = open(hybrid_out,"r")
params = []
paramnames = []
v = []
tin = []
tfit = []
for l in fin:
tokens = l.split()
if tokens[0]=='#':
if tokens[1]!="Attained":
paramnames.append(tokens[1])
params.append(float(tokens[2]))
else:
v.append(float(tokens[0]))
tin.append(float(tokens[1]))
tfit.append(float(tokens[2]))
fin.close()
tres = [tfit[i]-tin[i] for i in xrange(len(tin))]
rms = reduce(lambda x,y: x+y*y, tres,0.0)
rms = math.sqrt(rms/len(tres))
prmstr = ' '.join([str(i) for i in params])
rnd.seed()
meanparams = [0.0]*len(params)
rmsparams = [0.0]*len(params)
niter = 100
for i in xrange(niter):
ret = 1
while(ret!=0):
rmsarr = rnd.normal(0.0,rms,len(tfit))
trand = [tfit[j]+rmsarr[j] for j in xrange(len(tfit))]
fout = open(temp_in,"w")
for j in range(len(v)):
print >>fout,"%f\t%g"%(v[j],trand[j])
fout.close()
ret=os.system("%s %s %s %s %s %s %s"%(simplex,temp_in,frequency,vmin,vmax,prmstr,simplex_out))
simplex_params = []
fin = open(simplex_out,"r")
for l in fin:
tokens = l.split()
if tokens[0]=='#':
if tokens[1]!="Attained":
simplex_params.append(float(tokens[2]))
fin.close()
for j in xrange(len(params)):
meanparams[j]+=simplex_params[j]
rmsparams[j]+=simplex_params[j]*simplex_params[j]
rmsparams=map(lambda x,y: math.sqrt((x-(y**2)/niter)/niter),rmsparams,meanparams)
meanparams=[x/niter for x in meanparams]
print "Residual RMS: ",rms
print "Parameter\tMean\t\tSigma\t\tBest"
for i in range(len(paramnames)):
print "%s\t\t%f\t%f\t%f"%(paramnames[i],meanparams[i],rmsparams[i],params[i])
os.remove(simplex_out)
os.remove(temp_in)
return 0
def gen_cusum(data,reset_value=0.0):
# no obvious way to vectorize this
out = 0.0
for value in data:
out = max(reset_value,out+value)
yield out
########################################################################
if __name__ == "__main__":
import doctest
doctest.testmod()
# batch mode timing tests
import numarray.random_array as narand
import time
nullmodel = GaussianDistribution(mean=550.2,variance=48.2)
altmodel = GaussianDistribution(mean=460.4,variance=39.2)
cdmodel = ChangeDetectionModel(nullmodel,altmodel,threshold=21.0)
for n in range(8):
data = narand.normal(470.0,88.0,shape=10**n)
_t0 = time.time()
out = cdmodel(data)
_t1 = time.time()
secs = _t1 - _t0
print '%s seconds to process %s events (%s events/second) result=%s' \
% (secs,len(data),len(data)/secs,out)
########################################################################