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Add seed option with numpy.RandomState. #23

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Add seed option with numpy.RandomState. #23

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25 changes: 14 additions & 11 deletions cmnn_catalog.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,8 +11,8 @@


def make_test_and_train(verbose, runid, test_m5, train_m5, test_mcut, train_mcut, force_idet, force_gridet, \
test_N, train_N, cmnn_minNc, user_catalog):
test_N, train_N, cmnn_minNc, user_catalog, seed=None):

if verbose:
print(' ')
print('Starting cmnn_catalog.make_test_and_train(), ',datetime.datetime.now())
Expand Down Expand Up @@ -46,8 +46,8 @@ def make_test_and_train(verbose, runid, test_m5, train_m5, test_mcut, train_mcut
all_tz = np.loadtxt(user_catalog, dtype='float', usecols=(1))
all_tm = np.loadtxt(user_catalog, dtype='float', usecols=(2,3,4,5,6,7))



### # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
### Ensure needed quantities are in numpy arrays
gamma = np.asarray( [0.037,0.038,0.039,0.039,0.04,0.04], dtype='float' )
Expand All @@ -56,6 +56,9 @@ def make_test_and_train(verbose, runid, test_m5, train_m5, test_mcut, train_mcut
np_test_mcut = np.asarray( test_mcut, dtype='float' )
np_train_mcut = np.asarray( train_mcut, dtype='float' )

### Set up numpy RandomState object for reproducitbility
rand_state = np.random.RandomState(seed=seed)

### Calculate the magnitude errors based on the m5 depths
if verbose: print('Calculating magnitude errors.')
all_test_me = np.sqrt( ( 0.04 - gamma ) * ( np.power( 10.0, 0.4*(all_tm[:]-np_test_m5) ) ) + \
Expand All @@ -72,8 +75,8 @@ def make_test_and_train(verbose, runid, test_m5, train_m5, test_mcut, train_mcut

### Calculate observed apparent magnitudes based on the errors
if verbose: print('Calculating observed apparent magnitudes.')
all_test_m = all_tm + all_test_me * np.random.normal( size = (len(all_tm),6) )
all_train_m = all_tm + all_train_me * np.random.normal( size = (len(all_tm),6) )
all_test_m = all_tm + all_test_me * rand_state.normal( size = (len(all_tm),6) )
all_train_m = all_tm + all_train_me * rand_state.normal( size = (len(all_tm),6) )

### Do not allow tm < 18, the approximate saturation point
for f in range(6):
Expand Down Expand Up @@ -152,7 +155,7 @@ def make_test_and_train(verbose, runid, test_m5, train_m5, test_mcut, train_mcut

### Create test.cat
if verbose: print('Opening and writing to ','output/run_'+runid+'/test.cat')
te_rx = np.random.choice( te_x, size=test_N, replace=False )
te_rx = rand_state.choice( te_x, size=test_N, replace=False )
test_fout = open('output/run_'+runid+'/test.cat','w')
for i in te_rx:
test_fout.write(' %10i %10.8f ' % (all_id[i],all_tz[i]) )
Expand All @@ -166,7 +169,7 @@ def make_test_and_train(verbose, runid, test_m5, train_m5, test_mcut, train_mcut

### Create train.cat
if verbose: print('Opening and writing to ','output/run_'+runid+'/train.cat')
tr_rx = np.random.choice( tr_x, size=train_N, replace=False )
tr_rx = rand_state.choice( tr_x, size=train_N, replace=False )
train_fout = open('output/run_'+runid+'/train.cat','w')
for i in tr_rx:
train_fout.write(' %10i %10.8f ' % (all_id[i],all_tz[i]) )
Expand All @@ -188,7 +191,7 @@ def make_plots(verbose, runid):
if verbose:
print(' ')
print('Starting cmnn_catalog.make_plots(), ',datetime.datetime.now())

if verbose: print('Reading test and train catalogs in output/run_'+runid+'/')

if os.path.isdir('output/run_'+runid+'/plot_cats') == False :
Expand Down Expand Up @@ -270,7 +273,7 @@ def make_plots(verbose, runid):
filt_colors = ['darkviolet','darkgreen','red','darkorange','brown','black']
for f in range(6):
tex = np.where( np.isfinite(test_m[:,f]) )[0]
tx = np.random.choice( tex, size=5000, replace=False )
tx = rand_state.choice( tex, size=5000, replace=False )
plt.plot( test_m[tx,f], test_me[tx,f], 'o',alpha=0.5,mew=0, \
color=filt_colors[f],label=filt_names[f])
del tex,tx
Expand All @@ -288,7 +291,7 @@ def make_plots(verbose, runid):
filt_colors = ['darkviolet','darkgreen','red','darkorange','brown','black']
for f in range(6):
trx = np.where( np.isfinite(train_m[:,f]) )[0]
tx = np.random.choice( trx, size=5000, replace=False )
tx = rand_state.choice( trx, size=5000, replace=False )
plt.plot( train_m[tx,f], train_me[tx,f], 'o',alpha=0.5,mew=0, \
color=filt_colors[f],label=filt_names[f])
del trx,tx
Expand Down