def gather_batch_data_run():
datapath_db = data_paths.DataPath()
outpath = datapath_db.fetch("quadratic_batch_data")
print "reading from to: " + outpath
funcname = "correlate.batch_quadratic.call_xspec_run"
caller = batch_handler.MemoizeBatch(funcname, outpath,
verbose=True)
for mode_num in range(0,55,5):
map1_key = "GBT_15hr_map_cleaned_%dmode" % mode_num
map2_key = "GBT_15hr_map_cleaned_%dmode" % mode_num
noise1_key = "GBT_15hr_map_cleaned_%dmode" % mode_num
noise2_key = "GBT_15hr_map_cleaned_%dmode" % mode_num
(pairlist, pairdict) = \
data_paths.cross_maps(map1_key, map2_key,
noise1_key, noise2_key,
map_suffix=";map",
noise_inv_suffix=";noise_inv",
cross_sym="_x_",
pair_former="GBTauto_cross_pairs",
ignore=['param'],
tag1prefix=map1_key + "_",
tag2prefix=map2_key + "_",
verbose=False)
pwr_1d = []
pwr_2d = []
for item in pairdict.keys():
pairrun = pairdict[item]
print pairrun['tag1']
print pairrun['map1']
print pairrun['noise_inv1']
print pairdict[item].keys()
pwr2d_run, pwr1d_run = caller.execute(pairrun['map1'],
pairrun['map2'],
pairrun['noise_inv1'],
pairrun['noise_inv2'])
pwr_2d.append(pwr2d_run)
pwr_1d.append(pwr1d_run)
pe.summarize_1d_agg_pwrspec(pwr_1d,
"pwr_data_%dmodes_1d.dat" % mode_num)
pe.summarize_2d_agg_pwrspec(pwr_2d,
"pwr_data_%dmodes_2d.dat" % mode_num)
def gather_batch_data_run():
datapath_db = data_paths.DataPath()
outpath = datapath_db.fetch("quadratic_batch_data")
print "reading from to: " + outpath
funcname = "correlate.batch_quadratic.call_xspec_run"
caller = batch_handler.MemoizeBatch(funcname, outpath, verbose=True)
for mode_num in range(0, 55, 5):
map1_key = "GBT_15hr_map_cleaned_%dmode" % mode_num
map2_key = "GBT_15hr_map_cleaned_%dmode" % mode_num
noise1_key = "GBT_15hr_map_cleaned_%dmode" % mode_num
noise2_key = "GBT_15hr_map_cleaned_%dmode" % mode_num
(pairlist, pairdict) = \
data_paths.cross_maps(map1_key, map2_key,
noise1_key, noise2_key,
map_suffix=";map",
noise_inv_suffix=";noise_inv",
cross_sym="_x_",
pair_former="GBTauto_cross_pairs",
ignore=['param'],
tag1prefix=map1_key + "_",
tag2prefix=map2_key + "_",
verbose=False)
pwr_1d = []
pwr_2d = []
for item in pairdict.keys():
pairrun = pairdict[item]
print pairrun['tag1']
print pairrun['map1']
print pairrun['noise_inv1']
print pairdict[item].keys()
pwr2d_run, pwr1d_run = caller.execute(pairrun['map1'],
pairrun['map2'],
pairrun['noise_inv1'],
pairrun['noise_inv2'])
pwr_2d.append(pwr2d_run)
pwr_1d.append(pwr1d_run)
pe.summarize_1d_agg_pwrspec(pwr_1d,
"pwr_data_%dmodes_1d.dat" % mode_num)
pe.summarize_2d_agg_pwrspec(pwr_2d,
"pwr_data_%dmodes_2d.dat" % mode_num)
def calculate_1d_transfer_function(pwr_stack1,
pwr_stack2,
tag,
outdir="./plot_data"):
n_runs = len(pwr_stack1)
if n_runs != len(pwr_stack2):
print "These runs are incompatible (different number)."
sys.exit()
(mean1_1d, std1_1d, corrmat1_1d,
covmat_1d) = pe.agg_stat_1d_pwrspec(pwr_stack1)
(mean2_1d, std2_1d, corrmat2_1d,
covmat_1d) = pe.agg_stat_1d_pwrspec(pwr_stack2)
trans_stack = []
for index in range(n_runs):
entry = {}
entry['bin_left'] = pwr_stack1[index]['bin_left']
entry['bin_center'] = pwr_stack1[index]['bin_center']
entry['bin_right'] = pwr_stack1[index]['bin_right']
entry['counts_histo'] = pwr_stack1[index]['counts_histo']
binavg1 = pwr_stack1[index]['binavg']
binavg2 = pwr_stack2[index]['binavg']
entry['binavg'] = binavg1 / binavg2
trans_stack.append(entry)
fileout = outdir + "/" + tag + ".dat"
trans_agg_1d_pwrspec = pe.summarize_1d_agg_pwrspec(trans_stack, fileout)
trans_mean = trans_agg_1d_pwrspec['mean_1d']
# now derive the alternative transfer function by taking the ratio of
# averages
trans_alt = mean1_1d / mean2_1d
bin_left = entry['bin_left']
bin_center = entry['bin_center']
bin_right = entry['bin_right']
fileout = outdir + "/" + tag + "_altavg.dat"
outfile = open(fileout, "w")
for ind in range(len(bin_center)):
outstr = ("%10.15g " * 4 + "\n") % \
(bin_left[ind], bin_center[ind], bin_right[ind], \
trans_alt[ind])
outfile.write(outstr)
outfile.close()
return trans_alt
def calculate_1d_transfer_function(pwr_stack1, pwr_stack2, tag, outdir="./plot_data"):
n_runs = len(pwr_stack1)
if n_runs != len(pwr_stack2):
print "These runs are incompatible (different number)."
sys.exit()
(mean1_1d, std1_1d, corrmat1_1d, covmat_1d) = pe.agg_stat_1d_pwrspec(pwr_stack1)
(mean2_1d, std2_1d, corrmat2_1d, covmat_1d) = pe.agg_stat_1d_pwrspec(pwr_stack2)
trans_stack = []
for index in range(n_runs):
entry = {}
entry['bin_left'] = pwr_stack1[index]['bin_left']
entry['bin_center'] = pwr_stack1[index]['bin_center']
entry['bin_right'] = pwr_stack1[index]['bin_right']
entry['counts_histo'] = pwr_stack1[index]['counts_histo']
binavg1 = pwr_stack1[index]['binavg']
binavg2 = pwr_stack2[index]['binavg']
entry['binavg'] = binavg1/binavg2
trans_stack.append(entry)
fileout = outdir + "/" + tag + ".dat"
trans_agg_1d_pwrspec = pe.summarize_1d_agg_pwrspec(trans_stack, fileout)
trans_mean = trans_agg_1d_pwrspec['mean_1d']
# now derive the alternative transfer function by taking the ratio of
# averages
trans_alt = mean1_1d/mean2_1d
bin_left = entry['bin_left']
bin_center = entry['bin_center']
bin_right = entry['bin_right']
fileout = outdir + "/" + tag + "_altavg.dat"
outfile = open(fileout, "w")
for ind in range(len(bin_center)):
outstr = ("%10.15g " * 4 + "\n") % \
(bin_left[ind], bin_center[ind], bin_right[ind], \
trans_alt[ind])
outfile.write(outstr)
outfile.close()
return trans_alt
