def _test4():
from ANN import fnet_cccec
sac = vutils.RegularTimeSeries('inp1','31jan1990 2400', '1mon', [10000.0, 11000.0, 12000.0, 15000.0])
sac = sac*5.40394e-06+0.178546
sjr = vutils.RegularTimeSeries('inp1','31jan1990 2400', '1mon', [1000.0, 1100.0, 1200.0, 1500.0])
sjr = sjr*1.34396e-05+0.199247
exp = vutils.RegularTimeSeries('inp1','31jan1990 2400', '1mon', [5000.0, 6000.0, 7000.0, 8000.0])
exp = exp*-4.86697e-05+0.178537
dxc = vutils.RegularTimeSeries('inp1','31jan1990 2400', '1mon', [0.0, 0.0, 0.0, 0.0])
dxc = (1.-dxc/31)*0.6+0.2
inps = [dxc,exp,sac,sjr]
inps = map(lambda x: vutils.interpolate(x,'1day'), inps)
inputs = []
for i in range(len(inps)):
print 'Building inputs for ',inps[i]
inputs=inputs+buildinput(inps[i],7,10,7) # weekly averages
print 'Built inputs'
#outputs = []
#ccc_ref = vutils.findpath(g2,'//ccc/ec///%s/'%fpart)[0]
#outputs.append(vutils.interpolate(DataReference.create(ccc_ref,tw).getData(),'1day'))
mi = MultiIterator(inputs)
import jarray
ninps = len(inputs)
input = jarray.zeros(ninps,'f')
output = jarray.zeros(1,'f')
ann = fnet_cccec()
ndata = len(inputs[0])
for input_no in range(31+28+31+1):
mi.advance()
outdata = jarray.zeros(ndata,'f')
fh = open('junk.out','w',15000)
while input_no < ndata:
el = mi.getElement()
i=0
while i < ninps:
input[i] = el.getY(i)
i=i+1
ann.engine(input,output,0)
fh.write('Input #: %d\n'%input_no)
i=0
while i < ninps:
fh.write('%13.6f'%input[i])
if i%5 == 4: fh.write('\n')
i = i+1
fh.write('\nOutput #: %d\n'%input_no)
fh.write('%13.6f\n'%output[0])
outdata[input_no] = output[0]
mi.advance()
input_no=input_no+1
fh.close()
def _test3():
from ANN import fnet_cccec
fpart = 'dxc-op'
g1 = vutils.opendss('anninputs.dss')
g2 = vutils.opendss('annoutputs.dss')
g1.filterBy(fpart)
g2.filterBy(fpart)
refs = g1[:]
tw = vutils.timewindow('01oct1975 0000 - 01sep1991 0000')
#tw = vutils.timewindow('01oct1975 0000 - 01oct1976 0000')
inps = []
for i in range(len(refs)):
inps.append(vutils.interpolate(DataReference.create(refs[i],tw).getData(),'1day'))
inputs = []
for i in range(len(inps)):
print 'Building inputs for ',inps[i]
inputs=inputs+buildinput(inps[i],7,10,7) # weekly averages
print 'Built inputs'
outputs = []
ccc_ref = vutils.findpath(g2,'//ccc/ec///%s/'%fpart)[0]
outputs.append(vutils.interpolate(DataReference.create(ccc_ref,tw).getData(),'1day'))
mi = MultiIterator(inputs)
import jarray
ninps = len(inputs)
input = jarray.zeros(ninps,'f')
output = jarray.zeros(1,'f')
ann = fnet_cccec()
ndata = len(inputs[0])
for input_no in range(365):
mi.advance()
outdata = jarray.zeros(ndata,'f')
while input_no < ndata:
el = mi.getElement()
i=0
while i < ninps:
input[i] = el.getY(i)
i=i+1
ann.engine(input,output,0)
outdata[input_no] = output[0]
mi.advance()
input_no=input_no+1
#
stime = inputs[0].getStartTime()
ti = inputs[0].getTimeInterval()
rtsout = vutils.RegularTimeSeries('/ann/ccc_out/ec///annutils/',str(stime),\
str(ti),outdata)
vutils.plot(rtsout,outputs[0])
rtsout = (rtsout-0.140516)/0.000396563
vutils.writedss('annout.dss','/ann/ccc_out/ec///annutils/',rtsout)
def dump2text(outfile,rtsarray,filter=Constants.DEFAULT_FLAG_FILTER):
"""
"""
xfilter = CompositeFilter([filter,NaNFilter()])
mi = MultiIterator(rtsarray)
fh = open(outfile,'w')
for i in range(len(rtsarray)):
fh.write('%s\n'%rtsarray[i].getName())
while not mi.atEnd():
#
el = mi.getElement()
fh.write(element2text(el,xfilter))
#
mi.advance()
fh.close()
def dump2text(outfile, rtsarray, filter=Constants.DEFAULT_FLAG_FILTER):
"""
"""
xfilter = CompositeFilter([filter, NaNFilter()])
mi = MultiIterator(rtsarray)
fh = open(outfile, 'w')
for i in range(len(rtsarray)):
fh.write('%s\n' % rtsarray[i].getName())
while not mi.atEnd():
#
el = mi.getElement()
fh.write(element2text(el, xfilter))
#
mi.advance()
fh.close()
def dump_patterns(inputs,outputs,outfile,calibration_percentage=0.75,skip = 365,append=0,
fast_write=0):
'''
dump_patterns(inputs,outputs,outfile,calibration_percentage=0.75,append=0):
dumps the patterns to the given outfile.
outfile.cal which contains calibration% of the points
outfile.ver which contains validation% = 100 - calibration% of the points
append=1 appends to the previous .cal and .ver files if they exist
'''
bufsize=15000
#fast_write=0
prev_no_patterns = [0,0]
if append:
cfh = open(outfile+'.cal','r+',bufsize)
vfh = open(outfile+'.ver','r+',bufsize)
id = 0
for fh in [cfh,vfh]:
while 1:
line = string.strip(fh.readline())
if string.find(line,'No. of patterns') >=0:
prev_no_patterns[id] = int(line[string.find(line,':')+1:])
if string.find(line,'Input pattern')>=0: break
fh.seek(0)
id = id + 1
else:
if fast_write:
cfh = PrintStream(BufferedOutputStream(FileOutputStream(outfile+'.cal')))
vfh = PrintStream(BufferedOutputStream(FileOutputStream(outfile+'.ver')))
else:
cfh = open(outfile+'.cal','w',bufsize)
vfh = open(outfile+'.ver','w',bufsize)
#
#if append:
# print 'Prev # of patterns = %s'%prev_no_patterns
npatterns = len(inputs[0]) - skip # skip beginning
#print 'Time Window of inputs[0] %s'%str(inputs[0].getTimeWindow())
#print npatterns,len(inputs[0]),skip
ncalibs = calibration_percentage*npatterns
ninputs = len(inputs)
noutputs = len(outputs)
#print 'Ncalibs: %d & Nverse: %d'%(ncalibs,npatterns-ncalibs)
if append or not fast_write:
cfh.write('''SNNS pattern definition file V3.2
generated at date : %s
No. of patterns : %d
No. of input units : %d
No. of output units : %d
'''%(time.ctime(time.time()),ncalibs+prev_no_patterns[0],ninputs,noutputs))
vfh.write('''SNNS pattern definition file V3.2
generated at date : %s
No. of patterns : %d
No. of input units : %d
No. of output units : %d
'''%(time.ctime(time.time()),npatterns-ncalibs+prev_no_patterns[1],ninputs,noutputs))
else:
cfh.print('''SNNS pattern definition file V3.2
generated at date : %s
No. of patterns : %d
No. of input units : %d
No. of output units : %d
'''%(time.ctime(time.time()),ncalibs+prev_no_patterns[0],ninputs,noutputs))
vfh.print('''SNNS pattern definition file V3.2
generated at date : %s
No. of patterns : %d
No. of input units : %d
No. of output units : %d
'''%(time.ctime(time.time()),npatterns-ncalibs+prev_no_patterns[1],ninputs,noutputs))
#
#goto end of file
if append:
for fh in [cfh,vfh]:
if fast_write:
fh.close()
else:
fh.seek(0,2)
if fast_write:
cfh = PrintStream(BufferedOutputStream(FileOutputStream(outfile+'.cal',1)))
vfh = PrintStream(BufferedOutputStream(FileOutputStream(outfile+'.ver',1)))
#
i=0
all = inputs + outputs
iterator = MultiIterator(all)
npattern = 1
nskip = 0
nprint_intvl = npatterns/10
nprints = 0
while not iterator.atEnd():
if npattern%nprint_intvl == 0:
nprints = nprints + 1
print 'Done: %d%%'%(nprints*10)
if nskip < skip:
nskip=nskip+1
iterator.advance()
continue
if npattern > npatterns: break
el = iterator.getElement()
j=0
#print el
if npattern <= ncalibs:
fh = cfh
pattern_no = npattern
prev_pattern_no = prev_no_patterns[0]
else:
fh = vfh
pattern_no = npattern - ncalibs
prev_pattern_no = prev_no_patterns[1]
if fast_write:
fh.println()
fh.println('# Input pattern %d:'%(pattern_no+prev_pattern_no))
else:
fh.write('\n# Input pattern %d:\n'%(pattern_no+prev_pattern_no))
while j < ninputs:
if fast_write:
fh.print('%10.6f'%el.getY(j))
else:
fh.write('%10.6f'%el.getY(j))
j=j+1
if fast_write:
fh.println()
fh.println('# Output pattern %d:'%(pattern_no+prev_pattern_no))
else:
fh.write('\n# Output pattern %d:\n'%(pattern_no+prev_pattern_no))
while j < ninputs+noutputs:
if fast_write:
fh.print('%10.6f'%el.getY(j))
else:
fh.write('%10.6f'%el.getY(j))
j=j+1
if fast_write:
fh.println()
else:
fh.write('\n')
npattern = npattern+1
iterator.advance()
#
cfh.close()
vfh.close()
nprints=nprints+1
print 'Done: %d%%'%(nprints*10)
el = mi.getElement()
if nan_filter.isAcceptable(el):
i=0
#fh.write('%d\n'%input_no)
while i < ninps:
input[i] = el.getY(i)
#fh.write('%10.5f'%input[i])
i=i+1
ann_obj.engine(input,output,0)
#fh.write('\n%10.5f\n'%output[0])
outarray[input_no] = output[0]
else:
outarray[input_no] = Constants.MISSING_VALUE
mi.advance()
input_no=input_no+1
stime = inputs[0].getStartTime().toString()
#fh.close()
ti = inputs[0].getTimeInterval().toString()
rts = vutils.RegularTimeSeries('/ANN/%s/OUT///ANNUTILS-GEN/'%ann_func,stime,ti,outarray)
return rts
#
def gmodel(ds,so,sb,beta,alpha,gprev=4000):
"""
gmodel(ds,so,sb,beta,alpha,gprev=4000):
This function takes a time series (probably NDO) and a set of parameters
and returns a time series of the EC values.
All the parameters are scaled and look at the source code listing for