diff --git a/analyse.sh b/analyse.sh
new file mode 100644
index 0000000..7435b6f
--- /dev/null
+++ b/analyse.sh
@@ -0,0 +1,5 @@
+#!/usr/bin/env bash
+
+# This script is meant to encapsulate all functions in proper order to produce
+# the analyses. Parameters that this script should control: Δt, σ type, input
+# dataset(lightcurve directory), more?
\ No newline at end of file
diff --git a/data/STORM_III/raw/aggregate.csv b/data/STORM_III/raw/aggregate.csv
new file mode 100644
index 0000000..061fdb2
--- /dev/null
+++ b/data/STORM_III/raw/aggregate.csv
@@ -0,0 +1,3963 @@
+filter,Lambda,HJD,flux,eflux,mag,emag,telescopeID
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+HST1158,1157,56693.32371,3.9100e-14,9.8000e-16,15.7941,0.0272,-1
+HST1158,1157,56695.27006,4.1100e-14,9.9000e-16,15.7399,0.0262,-1
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diff --git a/data/STORM_III/raw/aggregate.tab b/data/STORM_III/raw/aggregate.tab
new file mode 100644
index 0000000..0f7e1d2
--- /dev/null
+++ b/data/STORM_III/raw/aggregate.tab
@@ -0,0 +1,3963 @@
+filter   Lambda  HJD          flux        eflux       mag      emag    telescopeID
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+HST1158  1157    56691.54161  3.4800e-14  9.2000e-16  15.9206  0.0287  -1
+HST1158  1157    56692.39404  3.7500e-14  9.5000e-16  15.8395  0.0275  -1
+HST1158  1157    56693.32371  3.9100e-14  9.8000e-16  15.7941  0.0272  -1
+HST1158  1157    56695.27006  4.1100e-14  9.9000e-16  15.7399  0.0262  -1
+HST1158  1157    56696.24593  4.5900e-14  1.0500e-15  15.6200  0.0248  -1
+HST1158  1157    56697.30802  4.8700e-14  1.0800e-15  15.5557  0.0241  -1
+HST1158  1157    56698.30411  5.2500e-14  1.1300e-15  15.4741  0.0234  -1
+HST1158  1157    56699.23377  4.9300e-14  1.1000e-15  15.5424  0.0242  -1
+HST1158  1157    56700.22987  4.8300e-14  1.0800e-15  15.5647  0.0243  -1
+HST1158  1157    56701.35878  4.9000e-14  1.0800e-15  15.5490  0.0239  -1
+HST1158  1157    56702.15568  5.1100e-14  1.1100e-15  15.5035  0.0236  -1
+HST1158  1157    56703.15179  5.2800e-14  1.1300e-15  15.4680  0.0232  -1
+HST1158  1157    56705.34318  4.9000e-14  1.0900e-15  15.5490  0.0242  -1
+HST1158  1157    56706.20637  4.6800e-14  1.0600e-15  15.5989  0.0246  -1
+HST1158  1157    56707.20241  4.9200e-14  1.0800e-15  15.5446  0.0238  -1
+HST1158  1157    56708.26483  4.3400e-14  1.0200e-15  15.6808  0.0255  -1
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+HST1158  1157    56764.24029  4.0000e-14  9.9000e-16  15.7694  0.0269  -1
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diff --git a/data/STORM_III/raw/separated/1158Å.tab b/data/STORM_III/raw/separated/1158Å.tab
new file mode 100644
index 0000000..abbef5c
--- /dev/null
+++ b/data/STORM_III/raw/separated/1158Å.tab
@@ -0,0 +1,171 @@
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+HST1158  1157    56861.81035  3.8400e-14  9.6000e-16  15.8137  0.0271  -1
+HST1158  1157    56862.87203  3.7000e-14  9.5000e-16  15.8540  0.0279  -1
+HST1158  1157    56863.86737  4.1800e-14  1.0100e-15  15.7216  0.0262  -1
+HST1158  1157    56864.86270  4.3200e-14  1.0200e-15  15.6858  0.0256  -1
+HST1158  1157    56865.92441  5.0600e-14  1.1000e-15  15.5142  0.0236  -1
\ No newline at end of file
diff --git a/data/STORM_III/raw/separated/1367Å.tab b/data/STORM_III/raw/separated/1367Å.tab
new file mode 100644
index 0000000..2e7524f
--- /dev/null
+++ b/data/STORM_III/raw/separated/1367Å.tab
@@ -0,0 +1,171 @@
+HST1367  1367    56690.61198  3.4270e-14  6.4000e-16  15.5760  0.0203  -1
+HST1367  1367    56691.54161  3.5450e-14  6.5000e-16  15.5393  0.0199  -1
+HST1367  1367    56692.39404  3.7710e-14  6.7000e-16  15.4722  0.0193  -1
+HST1367  1367    56693.32371  3.8140e-14  6.8000e-16  15.4599  0.0194  -1
+HST1367  1367    56695.27006  4.0940e-14  7.1000e-16  15.3829  0.0188  -1
+HST1367  1367    56696.24593  4.4250e-14  7.5000e-16  15.2985  0.0184  -1
+HST1367  1367    56697.30802  4.5300e-14  7.5000e-16  15.2731  0.0180  -1
+HST1367  1367    56698.30411  4.8270e-14  7.9000e-16  15.2041  0.0178  -1
+HST1367  1367    56699.23377  4.5800e-14  7.6000e-16  15.2611  0.0180  -1
+HST1367  1367    56700.22987  4.6000e-14  7.6000e-16  15.2564  0.0179  -1
+HST1367  1367    56701.35878  4.7460e-14  7.8000e-16  15.2225  0.0178  -1
+HST1367  1367    56702.15568  4.7740e-14  7.8000e-16  15.2161  0.0177  -1
+HST1367  1367    56703.15179  4.7560e-14  7.8000e-16  15.2202  0.0178  -1
+HST1367  1367    56705.34318  4.5770e-14  7.6000e-16  15.2618  0.0180  -1
+HST1367  1367    56706.20637  4.4700e-14  7.5000e-16  15.2875  0.0182  -1
+HST1367  1367    56707.20241  4.6650e-14  7.7000e-16  15.2412  0.0179  -1
+HST1367  1367    56708.26483  4.3240e-14  7.4000e-16  15.3236  0.0186  -1
+HST1367  1367    56709.26083  4.1430e-14  7.1000e-16  15.3700  0.0186  -1
+HST1367  1367    56710.25682  3.7690e-14  6.8000e-16  15.4727  0.0196  -1
+HST1367  1367    56711.11999  3.5220e-14  6.5000e-16  15.5463  0.0200  -1
+HST1367  1367    56712.18231  3.4760e-14  6.4000e-16  15.5606  0.0200  -1
+HST1367  1367    56713.17827  3.5180e-14  6.5000e-16  15.5476  0.0201  -1
+HST1367  1367    56714.24057  3.4450e-14  6.4000e-16  15.5703  0.0202  -1
+HST1367  1367    56715.04390  3.1460e-14  6.1000e-16  15.6689  0.0211  -1
+HST1367  1367    56715.89818  3.0580e-14  6.0000e-16  15.6997  0.0213  -1
+HST1367  1367    56716.83698  2.9640e-14  5.9000e-16  15.7336  0.0216  -1
+HST1367  1367    56718.82771  2.6750e-14  5.6000e-16  15.8450  0.0227  -1
+HST1367  1367    56720.14935  2.6010e-14  5.5000e-16  15.8755  0.0230  -1
+HST1367  1367    56721.01937  2.3760e-14  5.3000e-16  15.9737  0.0242  -1
+HST1367  1367    56722.08622  2.2250e-14  5.1000e-16  16.0450  0.0249  -1
+HST1367  1367    56723.13713  2.1870e-14  5.1000e-16  16.0637  0.0253  -1
+HST1367  1367    56726.06000  2.2960e-14  5.2000e-16  16.0109  0.0246  -1
+HST1367  1367    56727.05588  2.2720e-14  5.1000e-16  16.0223  0.0244  -1
+HST1367  1367    56727.78841  2.3070e-14  5.2000e-16  16.0057  0.0245  -1
+HST1367  1367    56728.91577  2.3910e-14  5.3000e-16  15.9669  0.0241  -1
+HST1367  1367    56729.77981  2.4540e-14  5.4000e-16  15.9386  0.0239  -1
+HST1367  1367    56730.84658  2.4220e-14  5.3000e-16  15.9529  0.0238  -1
+HST1367  1367    56731.97153  2.4690e-14  5.4000e-16  15.9320  0.0237  -1
+HST1367  1367    56732.90863  2.4390e-14  5.3000e-16  15.9453  0.0236  -1
+HST1367  1367    56733.84524  2.4940e-14  5.4000e-16  15.9211  0.0235  -1
+HST1367  1367    56734.84061  2.7630e-14  5.7000e-16  15.8098  0.0224  -1
+HST1367  1367    56735.84670  3.2430e-14  6.2000e-16  15.6359  0.0208  -1
+HST1367  1367    56736.43542  3.5780e-14  6.5000e-16  15.5292  0.0197  -1
+HST1367  1367    56737.68784  3.7880e-14  6.8000e-16  15.4673  0.0195  -1
+HST1367  1367    56738.50324  3.9910e-14  7.0000e-16  15.4106  0.0190  -1
+HST1367  1367    56739.67934  4.2460e-14  7.3000e-16  15.3434  0.0187  -1
+HST1367  1367    56740.36218  4.5440e-14  7.6000e-16  15.2697  0.0182  -1
+HST1367  1367    56741.34840  4.5230e-14  7.6000e-16  15.2747  0.0182  -1
+HST1367  1367    56744.40227  5.4350e-14  8.5000e-16  15.0753  0.0170  -1
+HST1367  1367    56745.25418  5.4790e-14  8.6000e-16  15.0665  0.0170  -1
+HST1367  1367    56746.24994  5.2540e-14  8.4000e-16  15.1121  0.0174  -1
+HST1367  1367    56747.17931  5.6590e-14  8.8000e-16  15.0315  0.0169  -1
+HST1367  1367    56748.24130  5.4970e-14  8.6000e-16  15.0630  0.0170  -1
+HST1367  1367    56749.17062  5.2830e-14  8.4000e-16  15.1061  0.0173  -1
+HST1367  1367    56750.30085  5.1550e-14  8.2000e-16  15.1327  0.0173  -1
+HST1367  1367    56751.16201  4.9370e-14  8.0000e-16  15.1796  0.0176  -1
+HST1367  1367    56752.02495  4.6720e-14  7.7000e-16  15.2395  0.0179  -1
+HST1367  1367    56753.02067  4.0890e-14  7.1000e-16  15.3843  0.0189  -1
+HST1367  1367    56753.97074  3.6190e-14  6.6000e-16  15.5168  0.0198  -1
+HST1367  1367    56755.21128  3.4460e-14  6.4000e-16  15.5700  0.0202  -1
+HST1367  1367    56756.14070  3.5460e-14  6.5000e-16  15.5390  0.0199  -1
+HST1367  1367    56757.13674  3.7260e-14  6.7000e-16  15.4852  0.0195  -1
+HST1367  1367    56759.26156  3.5860e-14  6.6000e-16  15.5268  0.0200  -1
+HST1367  1367    56760.12469  3.5680e-14  6.5000e-16  15.5322  0.0198  -1
+HST1367  1367    56761.05413  3.8880e-14  6.9000e-16  15.4390  0.0193  -1
+HST1367  1367    56762.11625  3.9110e-14  6.9000e-16  15.4326  0.0192  -1
+HST1367  1367    56763.04552  3.9320e-14  6.9000e-16  15.4268  0.0191  -1
+HST1367  1367    56764.24029  4.0430e-14  7.1000e-16  15.3965  0.0191  -1
+HST1367  1367    56765.16953  3.8300e-14  6.8000e-16  15.4553  0.0193  -1
+HST1367  1367    56765.96600  3.9490e-14  6.9000e-16  15.4221  0.0190  -1
+HST1367  1367    56767.02796  4.0830e-14  7.1000e-16  15.3859  0.0189  -1
+HST1367  1367    56768.08988  4.1420e-14  7.2000e-16  15.3703  0.0189  -1
+HST1367  1367    56769.15179  4.5280e-14  7.6000e-16  15.2735  0.0182  -1
+HST1367  1367    56770.08097  4.8510e-14  7.9000e-16  15.1987  0.0177  -1
+HST1367  1367    56771.07649  4.9790e-14  8.1000e-16  15.1704  0.0177  -1
+HST1367  1367    56772.00564  4.9650e-14  8.0000e-16  15.1735  0.0175  -1
+HST1367  1367    56773.06756  5.0550e-14  8.2000e-16  15.1540  0.0176  -1
+HST1367  1367    56773.99674  5.1020e-14  8.2000e-16  15.1440  0.0175  -1
+HST1367  1367    56774.59616  5.0040e-14  8.1000e-16  15.1650  0.0176  -1
+HST1367  1367    56775.59066  5.0330e-14  8.1000e-16  15.1587  0.0175  -1
+HST1367  1367    56776.72386  4.5960e-14  7.7000e-16  15.2574  0.0182  -1
+HST1367  1367    56777.44796  4.6320e-14  7.7000e-16  15.2489  0.0180  -1
+HST1367  1367    56778.45577  4.5450e-14  7.6000e-16  15.2695  0.0182  -1
+HST1367  1367    56779.43943  4.4310e-14  7.5000e-16  15.2970  0.0184  -1
+HST1367  1367    56780.45704  4.5560e-14  7.6000e-16  15.2668  0.0181  -1
+HST1367  1367    56781.36483  4.3530e-14  7.4000e-16  15.3163  0.0185  -1
+HST1367  1367    56782.37260  4.1350e-14  7.2000e-16  15.3721  0.0189  -1
+HST1367  1367    56783.35715  4.5280e-14  7.6000e-16  15.2735  0.0182  -1
+HST1367  1367    56784.75799  5.1180e-14  8.2000e-16  15.1406  0.0174  -1
+HST1367  1367    56785.75234  5.3550e-14  8.5000e-16  15.0914  0.0172  -1
+HST1367  1367    56786.69054  5.0370e-14  8.1000e-16  15.1579  0.0175  -1
+HST1367  1367    56787.28705  4.9370e-14  8.0000e-16  15.1796  0.0176  -1
+HST1367  1367    56788.28281  4.8820e-14  8.0000e-16  15.1918  0.0178  -1
+HST1367  1367    56789.27911  4.6090e-14  7.6000e-16  15.2543  0.0179  -1
+HST1367  1367    56790.27474  4.3350e-14  7.4000e-16  15.3208  0.0185  -1
+HST1367  1367    56791.27099  4.2890e-14  7.3000e-16  15.3324  0.0185  -1
+HST1367  1367    56792.26651  4.5530e-14  7.6000e-16  15.2676  0.0181  -1
+HST1367  1367    56793.19570  4.7410e-14  7.8000e-16  15.2236  0.0179  -1
+HST1367  1367    56794.19111  4.6430e-14  7.7000e-16  15.2463  0.0180  -1
+HST1367  1367    56795.25369  4.3640e-14  7.4000e-16  15.3136  0.0184  -1
+HST1367  1367    56796.10640  4.4230e-14  7.5000e-16  15.2990  0.0184  -1
+HST1367  1367    56797.07521  4.9360e-14  8.0000e-16  15.1799  0.0176  -1
+HST1367  1367    56798.10705  4.9480e-14  8.0000e-16  15.1772  0.0176  -1
+HST1367  1367    56799.15985  4.6920e-14  7.8000e-16  15.2349  0.0180  -1
+HST1367  1367    56800.02187  4.6220e-14  7.7000e-16  15.2512  0.0181  -1
+HST1367  1367    56801.02666  4.8090e-14  7.9000e-16  15.2082  0.0178  -1
+HST1367  1367    56802.02235  4.6330e-14  7.7000e-16  15.2486  0.0180  -1
+HST1367  1367    56803.00813  4.7940e-14  7.9000e-16  15.2116  0.0179  -1
+HST1367  1367    56804.99896  4.4730e-14  7.5000e-16  15.2868  0.0182  -1
+HST1367  1367    56805.99439  4.3460e-14  7.4000e-16  15.3181  0.0185  -1
+HST1367  1367    56807.12253  4.3450e-14  7.4000e-16  15.3183  0.0185  -1
+HST1367  1367    56808.05161  4.4780e-14  7.5000e-16  15.2856  0.0182  -1
+HST1367  1367    56809.11341  4.7070e-14  7.8000e-16  15.2314  0.0180  -1
+HST1367  1367    56809.90978  4.5730e-14  7.6000e-16  15.2628  0.0180  -1
+HST1367  1367    56810.83896  4.4650e-14  7.5000e-16  15.2887  0.0182  -1
+HST1367  1367    56812.03350  4.7190e-14  7.8000e-16  15.2287  0.0179  -1
+HST1367  1367    56812.96262  4.8090e-14  7.9000e-16  15.2082  0.0178  -1
+HST1367  1367    56814.09081  5.0040e-14  8.1000e-16  15.1650  0.0176  -1
+HST1367  1367    56814.88718  5.3240e-14  8.4000e-16  15.0977  0.0171  -1
+HST1367  1367    56816.08171  5.4860e-14  8.6000e-16  15.0652  0.0170  -1
+HST1367  1367    56816.94442  5.5760e-14  8.7000e-16  15.0475  0.0169  -1
+HST1367  1367    56817.93400  6.1860e-14  9.4000e-16  14.9348  0.0165  -1
+HST1367  1367    56818.93309  6.4740e-14  9.7000e-16  14.8854  0.0163  -1
+HST1367  1367    56819.73592  6.4630e-14  9.7000e-16  14.8872  0.0163  -1
+HST1367  1367    56820.99241  6.2060e-14  9.4000e-16  14.9313  0.0164  -1
+HST1367  1367    56821.85505  5.8880e-14  9.1000e-16  14.9884  0.0168  -1
+HST1367  1367    56822.79150  5.7360e-14  8.9000e-16  15.0168  0.0168  -1
+HST1367  1367    56823.84572  5.5530e-14  8.7000e-16  15.0520  0.0170  -1
+HST1367  1367    56824.64194  5.4250e-14  8.5000e-16  15.0773  0.0170  -1
+HST1367  1367    56825.64643  5.1330e-14  8.3000e-16  15.1374  0.0176  -1
+HST1367  1367    56826.89799  4.5880e-14  7.7000e-16  15.2592  0.0182  -1
+HST1367  1367    56827.76806  4.3710e-14  7.4000e-16  15.3119  0.0184  -1
+HST1367  1367    56828.49052  4.3940e-14  7.5000e-16  15.3062  0.0185  -1
+HST1367  1367    56829.61860  4.2040e-14  7.3000e-16  15.3541  0.0189  -1
+HST1367  1367    56830.48353  4.1100e-14  7.1000e-16  15.3787  0.0188  -1
+HST1367  1367    56831.67871  4.1710e-14  7.2000e-16  15.3627  0.0187  -1
+HST1367  1367    56832.27301  4.3800e-14  7.4000e-16  15.3096  0.0183  -1
+HST1367  1367    56833.79952  4.7650e-14  7.9000e-16  15.2181  0.0180  -1
+HST1367  1367    56834.79517  4.7380e-14  7.8000e-16  15.2243  0.0179  -1
+HST1367  1367    56835.79091  4.4550e-14  7.5000e-16  15.2912  0.0183  -1
+HST1367  1367    56836.21394  4.4340e-14  7.5000e-16  15.2963  0.0184  -1
+HST1367  1367    56837.65062  4.4310e-14  7.5000e-16  15.2970  0.0184  -1
+HST1367  1367    56838.18112  4.4490e-14  7.5000e-16  15.2926  0.0183  -1
+HST1367  1367    56839.20259  4.4500e-14  7.5000e-16  15.2924  0.0183  -1
+HST1367  1367    56840.13236  4.5200e-14  7.6000e-16  15.2755  0.0183  -1
+HST1367  1367    56841.19482  4.4420e-14  7.5000e-16  15.2944  0.0183  -1
+HST1367  1367    56842.19077  4.4690e-14  7.5000e-16  15.2878  0.0182  -1
+HST1367  1367    56843.12021  4.2310e-14  7.3000e-16  15.3472  0.0187  -1
+HST1367  1367    56844.18187  4.0820e-14  7.1000e-16  15.3861  0.0189  -1
+HST1367  1367    56845.02099  4.1050e-14  7.1000e-16  15.3800  0.0188  -1
+HST1367  1367    56846.10613  4.0330e-14  7.1000e-16  15.3992  0.0191  -1
+HST1367  1367    56847.03525  3.8870e-14  6.9000e-16  15.4393  0.0193  -1
+HST1367  1367    56848.03074  3.7960e-14  6.8000e-16  15.4650  0.0194  -1
+HST1367  1367    56849.09252  3.7100e-14  6.7000e-16  15.4899  0.0196  -1
+HST1367  1367    56849.99930  3.5540e-14  6.6000e-16  15.5365  0.0202  -1
+HST1367  1367    56850.93055  3.4260e-14  6.4000e-16  15.5763  0.0203  -1
+HST1367  1367    56851.92380  3.3770e-14  6.4000e-16  15.5920  0.0206  -1
+HST1367  1367    56852.91905  3.4190e-14  6.4000e-16  15.5786  0.0203  -1
+HST1367  1367    56854.05049  3.3240e-14  6.3000e-16  15.6091  0.0206  -1
+HST1367  1367    56855.04556  3.4570e-14  6.5000e-16  15.5666  0.0204  -1
+HST1367  1367    56856.17252  3.8000e-14  6.8000e-16  15.4638  0.0194  -1
+HST1367  1367    56857.16738  4.3300e-14  7.4000e-16  15.3221  0.0186  -1
+HST1367  1367    56858.09334  4.5130e-14  7.6000e-16  15.2771  0.0183  -1
+HST1367  1367    56858.89082  4.3890e-14  7.5000e-16  15.3074  0.0186  -1
+HST1367  1367    56860.08301  4.0790e-14  7.1000e-16  15.3869  0.0189  -1
+HST1367  1367    56860.94776  4.0430e-14  7.1000e-16  15.3965  0.0191  -1
+HST1367  1367    56861.81035  3.9860e-14  7.0000e-16  15.4120  0.0191  -1
+HST1367  1367    56862.87203  3.7410e-14  6.8000e-16  15.4808  0.0197  -1
+HST1367  1367    56863.86737  4.1040e-14  7.2000e-16  15.3803  0.0190  -1
+HST1367  1367    56864.86270  4.3310e-14  7.4000e-16  15.3218  0.0186  -1
+HST1367  1367    56865.92441  4.7080e-14  7.8000e-16  15.2312  0.0180  -1
\ No newline at end of file
diff --git a/data/STORM_III/raw/separated/1479Å.tab b/data/STORM_III/raw/separated/1479Å.tab
new file mode 100644
index 0000000..c44cd27
--- /dev/null
+++ b/data/STORM_III/raw/separated/1479Å.tab
@@ -0,0 +1,171 @@
+HST1479  1478    56690.64790  2.9700e-14  4.8000e-16  15.5611  0.0175  -1
+HST1479  1478    56691.57605  3.1600e-14  5.1000e-16  15.4938  0.0175  -1
+HST1479  1478    56692.40835  3.3000e-14  5.2000e-16  15.4468  0.0171  -1
+HST1479  1478    56693.33803  3.3000e-14  5.2000e-16  15.4468  0.0171  -1
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+HST1479  1478    56713.19258  3.0700e-14  4.9000e-16  15.5252  0.0173  -1
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+HST1479  1478    56722.11563  1.9400e-14  3.4000e-16  16.0235  0.0190  -1
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+HST1479  1478    56726.07431  1.9600e-14  3.4000e-16  16.0124  0.0188  -1
+HST1479  1478    56727.07020  1.9100e-14  3.3000e-16  16.0405  0.0188  -1
+HST1479  1478    56727.85087  2.0000e-14  3.5000e-16  15.9905  0.0190  -1
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+HST1479  1478    56729.84390  2.1500e-14  3.6000e-16  15.9119  0.0182  -1
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+HST1479  1478    56731.98584  2.1600e-14  3.7000e-16  15.9069  0.0186  -1
+HST1479  1478    56732.96682  2.1200e-14  3.6000e-16  15.9272  0.0184  -1
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+HST1479  1478    56734.89365  2.4600e-14  4.1000e-16  15.7657  0.0181  -1
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+HST1479  1478    56736.47158  3.1800e-14  5.1000e-16  15.4870  0.0174  -1
+HST1479  1478    56737.72593  3.4100e-14  5.4000e-16  15.4112  0.0172  -1
+HST1479  1478    56738.56381  3.5900e-14  5.6000e-16  15.3553  0.0169  -1
+HST1479  1478    56739.71743  3.8800e-14  6.0000e-16  15.2710  0.0168  -1
+HST1479  1478    56740.41551  4.0800e-14  6.3000e-16  15.2164  0.0168  -1
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+HST1479  1478    56747.19362  5.1000e-14  7.7000e-16  14.9741  0.0164  -1
+HST1479  1478    56748.25561  5.1100e-14  7.7000e-16  14.9720  0.0164  -1
+HST1479  1478    56749.18493  4.8000e-14  7.3000e-16  15.0399  0.0165  -1
+HST1479  1478    56750.31516  4.7100e-14  7.2000e-16  15.0605  0.0166  -1
+HST1479  1478    56751.17632  4.4500e-14  6.8000e-16  15.1221  0.0166  -1
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+HST1479  1478    56754.01499  3.1900e-14  5.1000e-16  15.4836  0.0174  -1
+HST1479  1478    56755.22559  2.9800e-14  4.8000e-16  15.5575  0.0175  -1
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+HST1479  1478    56760.13900  3.2200e-14  5.1000e-16  15.4734  0.0172  -1
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+HST1479  1478    56767.04227  3.6200e-14  5.7000e-16  15.3463  0.0171  -1
+HST1479  1478    56768.10419  3.7500e-14  5.8000e-16  15.3080  0.0168  -1
+HST1479  1478    56769.16610  4.0900e-14  6.3000e-16  15.2137  0.0167  -1
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+HST1479  1478    56771.09080  4.4200e-14  6.8000e-16  15.1295  0.0167  -1
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+HST1479  1478    56774.65909  4.5100e-14  6.9000e-16  15.1076  0.0166  -1
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+HST1479  1478    56785.81330  4.8300e-14  7.4000e-16  15.0332  0.0166  -1
+HST1479  1478    56786.74321  4.5800e-14  7.0000e-16  15.0909  0.0166  -1
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+HST1479  1478    56788.31941  4.4400e-14  6.8000e-16  15.1246  0.0166  -1
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+HST1479  1478    56790.31076  4.0200e-14  6.2000e-16  15.2325  0.0167  -1
+HST1479  1478    56791.30657  4.0700e-14  6.3000e-16  15.2191  0.0168  -1
+HST1479  1478    56792.30214  4.3400e-14  6.7000e-16  15.1493  0.0168  -1
+HST1479  1478    56793.22907  4.5100e-14  6.9000e-16  15.1076  0.0166  -1
+HST1479  1478    56794.22449  4.3100e-14  6.6000e-16  15.1568  0.0166  -1
+HST1479  1478    56795.28855  4.0600e-14  6.3000e-16  15.2217  0.0168  -1
+HST1479  1478    56796.12069  4.1000e-14  6.3000e-16  15.2111  0.0167  -1
+HST1479  1478    56797.10545  4.4300e-14  6.8000e-16  15.1270  0.0167  -1
+HST1479  1478    56798.13794  4.4600e-14  6.8000e-16  15.1197  0.0166  -1
+HST1479  1478    56799.17406  4.3100e-14  6.6000e-16  15.1568  0.0166  -1
+HST1479  1478    56800.03617  4.3000e-14  6.6000e-16  15.1594  0.0167  -1
+HST1479  1478    56801.05698  4.3700e-14  6.7000e-16  15.1418  0.0166  -1
+HST1479  1478    56802.05259  4.3300e-14  6.7000e-16  15.1518  0.0168  -1
+HST1479  1478    56803.02233  4.2600e-14  6.6000e-16  15.1695  0.0168  -1
+HST1479  1478    56805.01317  4.0800e-14  6.3000e-16  15.2164  0.0168  -1
+HST1479  1478    56806.00868  4.0000e-14  6.2000e-16  15.2379  0.0168  -1
+HST1479  1478    56807.13673  3.9600e-14  6.1000e-16  15.2488  0.0167  -1
+HST1479  1478    56808.06590  4.0900e-14  6.3000e-16  15.2137  0.0167  -1
+HST1479  1478    56809.12761  4.2200e-14  6.5000e-16  15.1798  0.0167  -1
+HST1479  1478    56809.92407  4.1200e-14  6.4000e-16  15.2058  0.0169  -1
+HST1479  1478    56810.85316  4.0800e-14  6.3000e-16  15.2164  0.0168  -1
+HST1479  1478    56812.04780  4.2300e-14  6.5000e-16  15.1772  0.0167  -1
+HST1479  1478    56812.97682  4.3000e-14  6.6000e-16  15.1594  0.0167  -1
+HST1479  1478    56814.10510  4.4000e-14  6.7000e-16  15.1344  0.0165  -1
+HST1479  1478    56814.90138  4.6800e-14  7.1000e-16  15.0674  0.0165  -1
+HST1479  1478    56816.09600  4.8000e-14  7.3000e-16  15.0399  0.0165  -1
+HST1479  1478    56816.95862  5.0200e-14  7.6000e-16  14.9913  0.0164  -1
+HST1479  1478    56817.94829  5.6100e-14  8.4000e-16  14.8706  0.0163  -1
+HST1479  1478    56818.94729  5.8300e-14  8.8000e-16  14.8289  0.0164  -1
+HST1479  1478    56819.76880  5.8800e-14  8.8000e-16  14.8196  0.0162  -1
+HST1479  1478    56821.00661  5.5000e-14  8.3000e-16  14.8921  0.0164  -1
+HST1479  1478    56821.86934  5.3800e-14  8.1000e-16  14.9161  0.0163  -1
+HST1479  1478    56822.82277  5.1500e-14  7.8000e-16  14.9635  0.0164  -1
+HST1479  1478    56823.86001  5.1200e-14  7.8000e-16  14.9699  0.0165  -1
+HST1479  1478    56824.67694  5.0200e-14  7.6000e-16  14.9913  0.0164  -1
+HST1479  1478    56825.70221  4.7900e-14  7.3000e-16  15.0422  0.0165  -1
+HST1479  1478    56826.91219  4.2300e-14  6.5000e-16  15.1772  0.0167  -1
+HST1479  1478    56827.79938  3.9600e-14  6.2000e-16  15.2488  0.0170  -1
+HST1479  1478    56828.55547  3.9400e-14  6.1000e-16  15.2543  0.0168  -1
+HST1479  1478    56829.68355  3.7400e-14  5.8000e-16  15.3109  0.0168  -1
+HST1479  1478    56830.54623  3.6300e-14  5.7000e-16  15.3433  0.0170  -1
+HST1479  1478    56831.71228  3.8200e-14  6.0000e-16  15.2879  0.0171  -1
+HST1479  1478    56832.28721  3.8700e-14  6.0000e-16  15.2738  0.0168  -1
+HST1479  1478    56833.81381  4.2300e-14  6.5000e-16  15.1772  0.0167  -1
+HST1479  1478    56834.80937  4.2400e-14  6.5000e-16  15.1746  0.0166  -1
+HST1479  1478    56835.80520  3.9200e-14  6.1000e-16  15.2598  0.0169  -1
+HST1479  1478    56836.25955  3.8700e-14  6.0000e-16  15.2738  0.0168  -1
+HST1479  1478    56837.68533  3.9500e-14  6.1000e-16  15.2515  0.0168  -1
+HST1479  1478    56838.19532  3.8600e-14  6.0000e-16  15.2766  0.0169  -1
+HST1479  1478    56839.26255  4.0000e-14  6.2000e-16  15.2379  0.0168  -1
+HST1479  1478    56840.18170  3.9800e-14  6.2000e-16  15.2433  0.0169  -1
+HST1479  1478    56841.25291  3.9700e-14  6.1000e-16  15.2461  0.0167  -1
+HST1479  1478    56842.24804  3.9600e-14  6.2000e-16  15.2488  0.0170  -1
+HST1479  1478    56843.17307  3.7700e-14  5.9000e-16  15.3022  0.0170  -1
+HST1479  1478    56844.23874  3.6500e-14  5.7000e-16  15.3373  0.0170  -1
+HST1479  1478    56845.03528  3.5300e-14  5.5000e-16  15.3736  0.0169  -1
+HST1479  1478    56846.15860  3.5600e-14  5.6000e-16  15.3644  0.0171  -1
+HST1479  1478    56847.08350  3.4800e-14  5.5000e-16  15.3891  0.0172  -1
+HST1479  1478    56848.07879  3.4000e-14  5.4000e-16  15.4143  0.0172  -1
+HST1479  1478    56849.14464  3.2300e-14  5.1000e-16  15.4700  0.0171  -1
+HST1479  1478    56850.01350  3.1700e-14  5.1000e-16  15.4904  0.0175  -1
+HST1479  1478    56850.94484  3.0500e-14  4.9000e-16  15.5323  0.0174  -1
+HST1479  1478    56851.93800  2.9600e-14  4.8000e-16  15.5648  0.0176  -1
+HST1479  1478    56852.93334  3.0800e-14  4.9000e-16  15.5217  0.0173  -1
+HST1479  1478    56854.06469  2.9200e-14  4.7000e-16  15.5796  0.0175  -1
+HST1479  1478    56855.05985  3.0100e-14  4.9000e-16  15.5466  0.0177  -1
+HST1479  1478    56856.18672  3.4000e-14  5.4000e-16  15.4143  0.0172  -1
+HST1479  1478    56857.18167  3.8500e-14  6.0000e-16  15.2794  0.0169  -1
+HST1479  1478    56858.10754  4.0500e-14  6.3000e-16  15.2244  0.0169  -1
+HST1479  1478    56858.90512  3.9500e-14  6.1000e-16  15.2515  0.0168  -1
+HST1479  1478    56860.09721  3.7000e-14  5.8000e-16  15.3225  0.0170  -1
+HST1479  1478    56860.96205  3.7300e-14  5.8000e-16  15.3138  0.0169  -1
+HST1479  1478    56861.82455  3.5200e-14  5.6000e-16  15.3767  0.0173  -1
+HST1479  1478    56862.88632  3.3200e-14  5.3000e-16  15.4402  0.0173  -1
+HST1479  1478    56863.88157  3.6700e-14  5.8000e-16  15.3314  0.0172  -1
+HST1479  1478    56864.87699  3.8200e-14  6.0000e-16  15.2879  0.0171  -1
+HST1479  1478    56865.93861  4.2400e-14  6.5000e-16  15.1746  0.0166  -1
\ No newline at end of file
diff --git a/data/STORM_III/raw/separated/1746Å.tab b/data/STORM_III/raw/separated/1746Å.tab
new file mode 100644
index 0000000..6491247
--- /dev/null
+++ b/data/STORM_III/raw/separated/1746Å.tab
@@ -0,0 +1,171 @@
+HST1746  1745    56690.64790  2.6700e-14  6.3000e-16  15.3156  0.0256  -1
+HST1746  1745    56691.57605  2.7900e-14  6.4000e-16  15.2679  0.0249  -1
+HST1746  1745    56692.40835  3.0400e-14  6.8000e-16  15.1747  0.0243  -1
+HST1746  1745    56693.33803  3.0400e-14  6.7000e-16  15.1747  0.0239  -1
+HST1746  1745    56695.31449  3.1900e-14  7.1000e-16  15.1224  0.0242  -1
+HST1746  1745    56696.26024  3.4200e-14  7.4000e-16  15.0469  0.0235  -1
+HST1746  1745    56697.32233  3.5000e-14  7.4000e-16  15.0218  0.0230  -1
+HST1746  1745    56698.31842  3.6000e-14  7.5000e-16  14.9912  0.0226  -1
+HST1746  1745    56699.24809  3.5100e-14  7.4000e-16  15.0187  0.0229  -1
+HST1746  1745    56700.24419  3.6500e-14  7.7000e-16  14.9762  0.0229  -1
+HST1746  1745    56701.37310  3.6000e-14  7.6000e-16  14.9912  0.0229  -1
+HST1746  1745    56702.16999  3.6000e-14  7.5000e-16  14.9912  0.0226  -1
+HST1746  1745    56703.16610  3.6300e-14  7.6000e-16  14.9822  0.0227  -1
+HST1746  1745    56705.35750  3.5900e-14  7.6000e-16  14.9942  0.0230  -1
+HST1746  1745    56706.22068  3.4900e-14  7.4000e-16  15.0249  0.0230  -1
+HST1746  1745    56707.21673  3.6000e-14  7.5000e-16  14.9912  0.0226  -1
+HST1746  1745    56708.27914  3.3300e-14  7.3000e-16  15.0758  0.0238  -1
+HST1746  1745    56709.27515  3.3200e-14  7.2000e-16  15.0791  0.0235  -1
+HST1746  1745    56710.27113  2.8900e-14  6.6000e-16  15.2297  0.0248  -1
+HST1746  1745    56711.13430  2.7000e-14  6.3000e-16  15.3035  0.0253  -1
+HST1746  1745    56712.19662  2.8100e-14  6.6000e-16  15.2602  0.0255  -1
+HST1746  1745    56713.19258  2.8100e-14  6.5000e-16  15.2602  0.0251  -1
+HST1746  1745    56714.25488  2.7000e-14  6.3000e-16  15.3035  0.0253  -1
+HST1746  1745    56715.07572  2.6100e-14  6.3000e-16  15.3403  0.0262  -1
+HST1746  1745    56715.93441  2.5200e-14  6.2000e-16  15.3784  0.0267  -1
+HST1746  1745    56716.89488  2.3300e-14  5.9000e-16  15.4635  0.0275  -1
+HST1746  1745    56718.88661  2.1600e-14  5.7000e-16  15.5458  0.0287  -1
+HST1746  1745    56720.16366  2.0900e-14  5.6000e-16  15.5816  0.0291  -1
+HST1746  1745    56721.05105  1.9000e-14  5.3000e-16  15.6850  0.0303  -1
+HST1746  1745    56722.11563  1.8500e-14  5.2000e-16  15.7140  0.0305  -1
+HST1746  1745    56723.15144  1.7400e-14  5.1000e-16  15.7806  0.0318  -1
+HST1746  1745    56726.07431  1.9100e-14  5.4000e-16  15.6793  0.0307  -1
+HST1746  1745    56727.07020  1.8100e-14  5.2000e-16  15.7377  0.0312  -1
+HST1746  1745    56727.85087  1.8600e-14  5.2000e-16  15.7081  0.0304  -1
+HST1746  1745    56728.95060  1.9400e-14  5.3000e-16  15.6624  0.0297  -1
+HST1746  1745    56729.84390  1.9700e-14  5.4000e-16  15.6458  0.0298  -1
+HST1746  1745    56730.90699  2.0100e-14  5.4000e-16  15.6239  0.0292  -1
+HST1746  1745    56731.98584  1.9900e-14  5.4000e-16  15.6348  0.0295  -1
+HST1746  1745    56732.96682  1.9900e-14  5.5000e-16  15.6348  0.0300  -1
+HST1746  1745    56733.89709  2.0000e-14  5.4000e-16  15.6293  0.0293  -1
+HST1746  1745    56734.89365  2.2700e-14  5.8000e-16  15.4919  0.0277  -1
+HST1746  1745    56735.89013  2.5600e-14  6.3000e-16  15.3613  0.0267  -1
+HST1746  1745    56736.47158  2.8500e-14  6.5000e-16  15.2448  0.0248  -1
+HST1746  1745    56737.72593  2.9800e-14  6.7000e-16  15.1964  0.0244  -1
+HST1746  1745    56738.56381  3.2300e-14  7.1000e-16  15.1089  0.0239  -1
+HST1746  1745    56739.71743  3.3600e-14  7.3000e-16  15.0661  0.0236  -1
+HST1746  1745    56740.41551  3.5900e-14  7.5000e-16  14.9942  0.0227  -1
+HST1746  1745    56741.38215  3.5700e-14  7.5000e-16  15.0003  0.0228  -1
+HST1746  1745    56744.43700  4.1600e-14  8.3000e-16  14.8342  0.0217  -1
+HST1746  1745    56745.26849  4.1500e-14  8.3000e-16  14.8368  0.0217  -1
+HST1746  1745    56746.26425  4.2300e-14  8.4000e-16  14.8161  0.0216  -1
+HST1746  1745    56747.19362  4.3700e-14  8.7000e-16  14.7807  0.0216  -1
+HST1746  1745    56748.25561  4.2400e-14  8.4000e-16  14.8135  0.0215  -1
+HST1746  1745    56749.18493  4.0900e-14  8.2000e-16  14.8526  0.0218  -1
+HST1746  1745    56750.31516  4.0800e-14  8.1000e-16  14.8553  0.0216  -1
+HST1746  1745    56751.17632  3.8400e-14  7.9000e-16  14.9211  0.0223  -1
+HST1746  1745    56752.03926  3.7400e-14  7.7000e-16  14.9497  0.0224  -1
+HST1746  1745    56753.03498  3.4200e-14  7.4000e-16  15.0469  0.0235  -1
+HST1746  1745    56754.01499  2.9600e-14  6.7000e-16  15.2037  0.0246  -1
+HST1746  1745    56755.22559  2.6900e-14  6.4000e-16  15.3075  0.0258  -1
+HST1746  1745    56756.15501  2.8500e-14  6.6000e-16  15.2448  0.0251  -1
+HST1746  1745    56757.15105  2.9800e-14  6.8000e-16  15.1964  0.0248  -1
+HST1746  1745    56759.27587  2.9600e-14  6.7000e-16  15.2037  0.0246  -1
+HST1746  1745    56760.13900  2.8400e-14  6.6000e-16  15.2486  0.0252  -1
+HST1746  1745    56761.06844  3.1100e-14  7.0000e-16  15.1500  0.0244  -1
+HST1746  1745    56762.13056  3.0900e-14  6.9000e-16  15.1570  0.0242  -1
+HST1746  1745    56763.05983  3.2000e-14  7.1000e-16  15.1190  0.0241  -1
+HST1746  1745    56764.25460  3.1900e-14  7.1000e-16  15.1224  0.0242  -1
+HST1746  1745    56765.18385  3.1100e-14  6.9000e-16  15.1500  0.0241  -1
+HST1746  1745    56765.98031  3.1900e-14  7.0000e-16  15.1224  0.0238  -1
+HST1746  1745    56767.04227  3.3700e-14  7.4000e-16  15.0628  0.0238  -1
+HST1746  1745    56768.10419  3.2600e-14  7.1000e-16  15.0989  0.0236  -1
+HST1746  1745    56769.16610  3.5200e-14  7.5000e-16  15.0156  0.0231  -1
+HST1746  1745    56770.09528  3.6800e-14  7.7000e-16  14.9673  0.0227  -1
+HST1746  1745    56771.09080  3.8000e-14  7.9000e-16  14.9325  0.0226  -1
+HST1746  1745    56772.01995  3.9500e-14  8.0000e-16  14.8904  0.0220  -1
+HST1746  1745    56773.08187  3.8800e-14  8.0000e-16  14.9098  0.0224  -1
+HST1746  1745    56774.01105  3.9400e-14  8.1000e-16  14.8932  0.0223  -1
+HST1746  1745    56774.65909  3.9200e-14  8.0000e-16  14.8987  0.0222  -1
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+HST1746  1745    56776.78283  3.7300e-14  7.8000e-16  14.9527  0.0227  -1
+HST1746  1745    56777.46227  3.7400e-14  7.8000e-16  14.9497  0.0226  -1
+HST1746  1745    56778.48484  3.6800e-14  7.8000e-16  14.9673  0.0230  -1
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+HST1746  1745    56780.50969  3.5600e-14  7.5000e-16  15.0033  0.0229  -1
+HST1746  1745    56781.37914  3.5700e-14  7.5000e-16  15.0003  0.0228  -1
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+HST1746  1745    56784.81696  3.9200e-14  8.1000e-16  14.8987  0.0224  -1
+HST1746  1745    56785.81330  4.1600e-14  8.3000e-16  14.8342  0.0217  -1
+HST1746  1745    56786.74321  4.0400e-14  8.2000e-16  14.8660  0.0220  -1
+HST1746  1745    56787.32382  3.9100e-14  8.1000e-16  14.9015  0.0225  -1
+HST1746  1745    56788.31941  3.8400e-14  8.0000e-16  14.9211  0.0226  -1
+HST1746  1745    56789.31518  3.6900e-14  7.7000e-16  14.9644  0.0227  -1
+HST1746  1745    56790.31076  3.4900e-14  7.4000e-16  15.0249  0.0230  -1
+HST1746  1745    56791.30657  3.4500e-14  7.4000e-16  15.0374  0.0233  -1
+HST1746  1745    56792.30214  3.5200e-14  7.5000e-16  15.0156  0.0231  -1
+HST1746  1745    56793.22907  3.7600e-14  7.9000e-16  14.9440  0.0228  -1
+HST1746  1745    56794.22449  3.6600e-14  7.7000e-16  14.9732  0.0228  -1
+HST1746  1745    56795.28855  3.4200e-14  7.4000e-16  15.0469  0.0235  -1
+HST1746  1745    56796.12069  3.4800e-14  7.5000e-16  15.0280  0.0234  -1
+HST1746  1745    56797.10545  3.8400e-14  7.9000e-16  14.9211  0.0223  -1
+HST1746  1745    56798.13794  3.7600e-14  7.8000e-16  14.9440  0.0225  -1
+HST1746  1745    56799.17406  3.6600e-14  7.8000e-16  14.9732  0.0231  -1
+HST1746  1745    56800.03617  3.6300e-14  7.8000e-16  14.9822  0.0233  -1
+HST1746  1745    56801.05698  3.6900e-14  7.7000e-16  14.9644  0.0227  -1
+HST1746  1745    56802.05259  3.6500e-14  7.7000e-16  14.9762  0.0229  -1
+HST1746  1745    56803.02233  3.6700e-14  7.8000e-16  14.9703  0.0231  -1
+HST1746  1745    56805.01317  3.5100e-14  7.5000e-16  15.0187  0.0232  -1
+HST1746  1745    56806.00868  3.5500e-14  7.6000e-16  15.0064  0.0232  -1
+HST1746  1745    56807.13673  3.5700e-14  7.6000e-16  15.0003  0.0231  -1
+HST1746  1745    56808.06590  3.6200e-14  7.6000e-16  14.9852  0.0228  -1
+HST1746  1745    56809.12761  3.7600e-14  7.8000e-16  14.9440  0.0225  -1
+HST1746  1745    56809.92407  3.7100e-14  7.7000e-16  14.9585  0.0225  -1
+HST1746  1745    56810.85316  3.5900e-14  7.7000e-16  14.9942  0.0233  -1
+HST1746  1745    56812.04780  3.7500e-14  7.9000e-16  14.9468  0.0229  -1
+HST1746  1745    56812.97682  3.7900e-14  7.9000e-16  14.9353  0.0226  -1
+HST1746  1745    56814.10510  3.9700e-14  8.2000e-16  14.8849  0.0224  -1
+HST1746  1745    56814.90138  4.0900e-14  8.3000e-16  14.8526  0.0220  -1
+HST1746  1745    56816.09600  4.3000e-14  8.5000e-16  14.7983  0.0215  -1
+HST1746  1745    56816.95862  4.4600e-14  8.8000e-16  14.7586  0.0214  -1
+HST1746  1745    56817.94829  4.8100e-14  9.3000e-16  14.6766  0.0210  -1
+HST1746  1745    56818.94729  4.9600e-14  9.4000e-16  14.6432  0.0206  -1
+HST1746  1745    56819.76880  5.0800e-14  9.6000e-16  14.6173  0.0205  -1
+HST1746  1745    56821.00661  4.7900e-14  9.3000e-16  14.6811  0.0211  -1
+HST1746  1745    56821.86934  4.6900e-14  9.1000e-16  14.7040  0.0211  -1
+HST1746  1745    56822.82277  4.4800e-14  8.7000e-16  14.7537  0.0211  -1
+HST1746  1745    56823.86001  4.4000e-14  8.8000e-16  14.7733  0.0217  -1
+HST1746  1745    56824.67694  4.4200e-14  8.7000e-16  14.7684  0.0214  -1
+HST1746  1745    56825.70221  4.2400e-14  8.5000e-16  14.8135  0.0218  -1
+HST1746  1745    56826.91219  3.8300e-14  7.9000e-16  14.9239  0.0224  -1
+HST1746  1745    56827.79938  3.6400e-14  7.7000e-16  14.9792  0.0230  -1
+HST1746  1745    56828.55547  3.5200e-14  7.5000e-16  15.0156  0.0231  -1
+HST1746  1745    56829.68355  3.3600e-14  7.4000e-16  15.0661  0.0239  -1
+HST1746  1745    56830.54623  3.3000e-14  7.3000e-16  15.0856  0.0240  -1
+HST1746  1745    56831.71228  3.4800e-14  7.5000e-16  15.0280  0.0234  -1
+HST1746  1745    56832.28721  3.4600e-14  7.4000e-16  15.0342  0.0232  -1
+HST1746  1745    56833.81381  3.7500e-14  7.9000e-16  14.9468  0.0229  -1
+HST1746  1745    56834.80937  3.7800e-14  7.9000e-16  14.9382  0.0227  -1
+HST1746  1745    56835.80520  3.6200e-14  7.7000e-16  14.9852  0.0231  -1
+HST1746  1745    56836.25955  3.5900e-14  7.6000e-16  14.9942  0.0230  -1
+HST1746  1745    56837.68533  3.6200e-14  7.6000e-16  14.9852  0.0228  -1
+HST1746  1745    56838.19532  3.5200e-14  7.6000e-16  15.0156  0.0234  -1
+HST1746  1745    56839.26255  3.5500e-14  7.7000e-16  15.0064  0.0235  -1
+HST1746  1745    56840.18170  3.6500e-14  7.7000e-16  14.9762  0.0229  -1
+HST1746  1745    56841.25291  3.6400e-14  7.8000e-16  14.9792  0.0233  -1
+HST1746  1745    56842.24804  3.6300e-14  7.7000e-16  14.9822  0.0230  -1
+HST1746  1745    56843.17307  3.4600e-14  7.6000e-16  15.0342  0.0238  -1
+HST1746  1745    56844.23874  3.3600e-14  7.3000e-16  15.0661  0.0236  -1
+HST1746  1745    56845.03528  3.2500e-14  7.3000e-16  15.1022  0.0244  -1
+HST1746  1745    56846.15860  3.2400e-14  7.2000e-16  15.1056  0.0241  -1
+HST1746  1745    56847.08350  3.1800e-14  7.2000e-16  15.1259  0.0246  -1
+HST1746  1745    56848.07879  3.1600e-14  7.1000e-16  15.1327  0.0244  -1
+HST1746  1745    56849.14464  3.1200e-14  7.0000e-16  15.1465  0.0244  -1
+HST1746  1745    56850.01350  2.9200e-14  6.8000e-16  15.2185  0.0253  -1
+HST1746  1745    56850.94484  2.8400e-14  6.6000e-16  15.2486  0.0252  -1
+HST1746  1745    56851.93800  2.8000e-14  6.6000e-16  15.2640  0.0256  -1
+HST1746  1745    56852.93334  2.7500e-14  6.6000e-16  15.2836  0.0261  -1
+HST1746  1745    56854.06469  2.7400e-14  6.5000e-16  15.2875  0.0258  -1
+HST1746  1745    56855.05985  2.8100e-14  6.6000e-16  15.2602  0.0255  -1
+HST1746  1745    56856.18672  3.0100e-14  6.8000e-16  15.1855  0.0245  -1
+HST1746  1745    56857.18167  3.2700e-14  7.3000e-16  15.0956  0.0242  -1
+HST1746  1745    56858.10754  3.5000e-14  7.5000e-16  15.0218  0.0233  -1
+HST1746  1745    56858.90512  3.5000e-14  7.6000e-16  15.0218  0.0236  -1
+HST1746  1745    56860.09721  3.3200e-14  7.2000e-16  15.0791  0.0235  -1
+HST1746  1745    56860.96205  3.3500e-14  7.4000e-16  15.0693  0.0240  -1
+HST1746  1745    56861.82455  3.1200e-14  7.0000e-16  15.1465  0.0244  -1
+HST1746  1745    56862.88632  3.0200e-14  6.9000e-16  15.1819  0.0248  -1
+HST1746  1745    56863.88157  3.2900e-14  7.3000e-16  15.0889  0.0241  -1
+HST1746  1745    56864.87699  3.4200e-14  7.5000e-16  15.0469  0.0238  -1
+HST1746  1745    56865.93861  3.7600e-14  7.8000e-16  14.9440  0.0225  -1
\ No newline at end of file
diff --git a/data/STORM_III/raw/separated/1928Å.tab b/data/STORM_III/raw/separated/1928Å.tab
new file mode 100644
index 0000000..14f12b1
--- /dev/null
+++ b/data/STORM_III/raw/separated/1928Å.tab
@@ -0,0 +1,284 @@
+UVW2     1928    56690.43005  2.7100e-14  6.0200e-16  15.0842  0.0241  -1
+UVW2     1928    56698.36533  3.2800e-14  6.9800e-16  14.8769  0.0231  -1
+UVW2     1928    56699.56636  3.1900e-14  7.1500e-16  14.9071  0.0243  -1
+UVW2     1928    56706.49053  3.2700e-14  7.1000e-16  14.8802  0.0236  -1
+UVW2     1928    56708.23256  3.1100e-14  6.7800e-16  14.9347  0.0237  -1
+UVW2     1928    56709.56658  3.0800e-14  7.6800e-16  14.9452  0.0271  -1
+UVW2     1928    56710.10859  2.9700e-14  6.9200e-16  14.9847  0.0253  -1
+UVW2     1928    56710.17059  2.9700e-14  6.9900e-16  14.9847  0.0256  -1
+UVW2     1928    56710.63959  2.7900e-14  6.4800e-16  15.0526  0.0252  -1
+UVW2     1928    56711.02660  2.9000e-14  6.8800e-16  15.0106  0.0258  -1
+UVW2     1928    56711.36060  2.7400e-14  6.7400e-16  15.0722  0.0267  -1
+UVW2     1928    56712.09561  2.7900e-14  6.4500e-16  15.0526  0.0251  -1
+UVW2     1928    56713.23162  2.7100e-14  6.4800e-16  15.0842  0.0260  -1
+UVW2     1928    56713.51363  2.7600e-14  6.4800e-16  15.0643  0.0255  -1
+UVW2     1928    56713.96163  2.7300e-14  6.5500e-16  15.0762  0.0260  -1
+UVW2     1928    56714.22863  2.7800e-14  6.5400e-16  15.0565  0.0255  -1
+UVW2     1928    56714.51263  2.7300e-14  6.5300e-16  15.0762  0.0260  -1
+UVW2     1928    56715.04464  2.7100e-14  6.4600e-16  15.0842  0.0259  -1
+UVW2     1928    56715.64364  2.4300e-14  1.3400e-15  15.2026  0.0599  -1
+UVW2     1928    56717.04165  2.4400e-14  5.8600e-16  15.1981  0.0261  -1
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+UVW2     1928    56802.88980  3.0300e-14  7.2600e-16  14.9630  0.0260  -1
+UVW2     1928    56804.23170  3.1000e-14  7.0500e-16  14.9382  0.0247  -1
+UVW2     1928    56805.09063  3.2600e-14  7.5600e-16  14.8836  0.0252  -1
+UVW2     1928    56805.97456  3.2500e-14  7.3100e-16  14.8869  0.0244  -1
+UVW2     1928    56806.37053  3.2200e-14  7.2500e-16  14.8970  0.0244  -1
+UVW2     1928    56806.89149  3.2600e-14  7.3900e-16  14.8836  0.0246  -1
+UVW2     1928    56807.22446  3.2000e-14  7.2100e-16  14.9037  0.0245  -1
+UVW2     1928    56807.88941  3.2400e-14  7.3600e-16  14.8902  0.0247  -1
+UVW2     1928    56808.55636  3.2600e-14  7.4000e-16  14.8836  0.0246  -1
+UVW2     1928    56808.88933  3.4300e-14  7.7800e-16  14.8284  0.0246  -1
+UVW2     1928    56809.22131  3.1900e-14  7.3700e-16  14.9071  0.0251  -1
+UVW2     1928    56809.88925  3.3500e-14  7.6300e-16  14.8540  0.0247  -1
+UVW2     1928    56810.22223  3.2800e-14  7.5400e-16  14.8769  0.0250  -1
+UVW2     1928    56810.55520  3.2400e-14  7.3800e-16  14.8902  0.0247  -1
+UVW2     1928    56810.88917  3.3000e-14  7.5200e-16  14.8703  0.0247  -1
+UVW2     1928    56811.22115  3.3300e-14  7.9400e-16  14.8605  0.0259  -1
+UVW2     1928    56811.75611  3.2500e-14  7.4900e-16  14.8869  0.0250  -1
+UVW2     1928    56812.02309  3.3800e-14  7.8000e-16  14.8443  0.0251  -1
+UVW2     1928    56812.37106  3.4900e-14  8.0200e-16  14.8095  0.0250  -1
+UVW2     1928    56812.55504  3.4200e-14  7.8400e-16  14.8315  0.0249  -1
+UVW2     1928    56813.22699  3.5700e-14  8.2900e-16  14.7849  0.0252  -1
+UVW2     1928    56813.97393  3.4100e-14  7.6400e-16  14.8347  0.0243  -1
+UVW2     1928    56814.37090  3.1600e-14  7.1500e-16  14.9174  0.0246  -1
+UVW2     1928    56814.55489  3.5200e-14  8.3000e-16  14.8003  0.0256  -1
+UVW2     1928    56814.62488  3.5500e-14  7.8600e-16  14.7910  0.0240  -1
+UVW2     1928    56815.69680  3.5400e-14  8.1100e-16  14.7941  0.0249  -1
+UVW2     1928    56815.95578  3.6700e-14  8.5000e-16  14.7549  0.0251  -1
+UVW2     1928    56816.22876  3.7500e-14  8.5500e-16  14.7315  0.0248  -1
+UVW2     1928    56817.62365  4.0000e-14  8.8100e-16  14.6615  0.0239  -1
+UVW2     1928    56818.09061  4.0000e-14  8.4800e-16  14.6615  0.0230  -1
+UVW2     1928    56818.23960  4.0400e-14  9.1600e-16  14.6507  0.0246  -1
+UVW2     1928    56818.70456  4.1600e-14  9.5100e-16  14.6189  0.0248  -1
+UVW2     1928    56818.95854  4.1700e-14  9.2900e-16  14.6163  0.0242  -1
+UVW2     1928    56819.69548  4.0300e-14  9.1700e-16  14.6533  0.0247  -1
+UVW2     1928    56820.22644  4.1900e-14  9.4200e-16  14.6111  0.0244  -1
+UVW2     1928    56820.57341  3.9500e-14  9.0100e-16  14.6751  0.0248  -1
+UVW2     1928    56820.63641  4.0700e-14  9.2700e-16  14.6426  0.0247  -1
+UVW2     1928    56820.68841  4.1100e-14  9.3000e-16  14.6320  0.0246  -1
+UVW2     1928    56821.90331  3.9500e-14  8.8400e-16  14.6751  0.0243  -1
+UVW2     1928    56822.36428  3.8300e-14  8.6900e-16  14.7086  0.0246  -1
+UVW2     1928    56823.15521  3.8600e-14  8.8900e-16  14.7001  0.0250  -1
+UVW2     1928    56823.29420  3.5800e-14  8.0800e-16  14.7819  0.0245  -1
+UVW2     1928    56824.61910  3.9100e-14  9.0000e-16  14.6862  0.0250  -1
+UVW2     1928    56825.36204  3.6900e-14  8.3900e-16  14.7490  0.0247  -1
+UVW2     1928    56825.55103  3.7400e-14  8.3900e-16  14.7344  0.0244  -1
+UVW2     1928    56825.95700  3.6700e-14  7.6300e-16  14.7549  0.0226  -1
+UVW2     1928    56826.42996  3.7000e-14  8.5000e-16  14.7461  0.0249  -1
+UVW2     1928    56826.76194  3.4300e-14  7.8800e-16  14.8284  0.0249  -1
+UVW2     1928    56827.03791  3.3800e-14  7.6700e-16  14.8443  0.0246  -1
+UVW2     1928    56827.42889  3.3500e-14  7.7000e-16  14.8540  0.0250  -1
+UVW2     1928    56827.88285  3.2600e-14  7.4400e-16  14.8836  0.0248  -1
+UVW2     1928    56828.15183  3.2800e-14  7.5500e-16  14.8769  0.0250  -1
+UVW2     1928    56828.42681  3.3300e-14  7.6800e-16  14.8605  0.0250  -1
+UVW2     1928    56828.55380  3.3500e-14  7.0800e-16  14.8540  0.0229  -1
+UVW2     1928    56829.01676  3.2500e-14  7.4900e-16  14.8869  0.0250  -1
+UVW2     1928    56829.81470  3.1600e-14  7.4400e-16  14.9174  0.0256  -1
+UVW2     1928    56830.15068  3.2600e-14  7.4300e-16  14.8836  0.0247  -1
+UVW2     1928    56830.42066  3.2500e-14  7.4700e-16  14.8869  0.0250  -1
+UVW2     1928    56832.01954  3.2200e-14  6.4900e-16  14.8970  0.0219  -1
+UVW2     1928    56834.08538  3.1800e-14  6.3900e-16  14.9105  0.0218  -1
+UVW2     1928    56837.21816  3.3300e-14  6.7000e-16  14.8605  0.0218  -1
+UVW2     1928    56839.61198  3.3500e-14  6.8900e-16  14.8540  0.0223  -1
+UVW2     1928    56840.27994  3.3200e-14  6.8900e-16  14.8638  0.0225  -1
+UVW2     1928    56841.95782  3.2700e-14  6.5800e-16  14.8802  0.0218  -1
+UVW2     1928    56844.27966  2.7600e-14  5.5700e-16  15.0643  0.0219  -1
+UVW2     1928    56846.27853  3.1200e-14  6.2800e-16  14.9312  0.0219  -1
+UVW2     1928    56847.74143  2.9800e-14  6.0800e-16  14.9811  0.0222  -1
+UVW2     1928    56849.61031  2.7900e-14  5.6500e-16  15.0526  0.0220  -1
+UVW2     1928    56852.07515  2.7600e-14  5.5700e-16  15.0643  0.0219  -1
+UVW2     1928    56854.28602  2.6500e-14  5.4000e-16  15.1085  0.0221  -1
+UVW2     1928    56854.35701  2.6000e-14  5.9400e-16  15.1292  0.0248  -1
+UVW2     1928    56855.94892  2.6900e-14  5.4300e-16  15.0922  0.0219  -1
+UVW2     1928    56858.08079  3.1650e-14  6.3730e-16  14.9157  0.0219  -1
+UVW2     1928    56859.74670  3.0730e-14  6.1910e-16  14.9477  0.0219  -1
+UVW2     1928    56862.07957  2.9540e-14  5.9240e-16  14.9906  0.0218  -1
+UVW2     1928    56863.60449  2.9990e-14  6.0350e-16  14.9742  0.0218  -1
+UVW2     1928    56865.60539  3.2370e-14  6.4980e-16  14.8913  0.0218  -1
\ No newline at end of file
diff --git a/data/STORM_III/raw/separated/2246Å.tab b/data/STORM_III/raw/separated/2246Å.tab
new file mode 100644
index 0000000..00f9908
--- /dev/null
+++ b/data/STORM_III/raw/separated/2246Å.tab
@@ -0,0 +1,256 @@
+UVM2     2246    56690.43405  2.5600e-14  6.4800e-16  14.8145  0.0275  -1
+UVM2     2246    56698.36933  3.0600e-14  7.4500e-16  14.6208  0.0264  -1
+UVM2     2246    56706.49453  2.9900e-14  7.3500e-16  14.6459  0.0267  -1
+UVM2     2246    56708.23656  3.0500e-14  7.4200e-16  14.6243  0.0264  -1
+UVM2     2246    56710.11059  2.8200e-14  7.6400e-16  14.7095  0.0294  -1
+UVM2     2246    56710.17259  2.8000e-14  7.7600e-16  14.7172  0.0301  -1
+UVM2     2246    56710.64259  2.5700e-14  7.0700e-16  14.8103  0.0299  -1
+UVM2     2246    56711.02860  2.6800e-14  7.4300e-16  14.7648  0.0301  -1
+UVM2     2246    56711.36260  2.6300e-14  7.7400e-16  14.7852  0.0320  -1
+UVM2     2246    56712.09861  2.6200e-14  7.0700e-16  14.7893  0.0293  -1
+UVM2     2246    56713.23362  2.5900e-14  7.1800e-16  14.8018  0.0301  -1
+UVM2     2246    56713.51663  2.5900e-14  7.1500e-16  14.8018  0.0300  -1
+UVM2     2246    56713.96363  2.5700e-14  7.3900e-16  14.8103  0.0312  -1
+UVM2     2246    56714.23063  2.6900e-14  7.3700e-16  14.7607  0.0297  -1
+UVM2     2246    56714.51563  2.5900e-14  7.0900e-16  14.8018  0.0297  -1
+UVM2     2246    56715.04764  2.5500e-14  7.0000e-16  14.8187  0.0298  -1
+UVM2     2246    56717.04365  2.3500e-14  6.5400e-16  14.9074  0.0302  -1
+UVM2     2246    56718.37366  2.2800e-14  6.0900e-16  14.9403  0.0290  -1
+UVM2     2246    56718.64266  2.2500e-14  6.3600e-16  14.9546  0.0307  -1
+UVM2     2246    56719.71666  2.2800e-14  6.2200e-16  14.9403  0.0296  -1
+UVM2     2246    56720.10466  2.2500e-14  6.5600e-16  14.9546  0.0317  -1
+UVM2     2246    56720.38266  2.2100e-14  5.9000e-16  14.9741  0.0290  -1
+UVM2     2246    56720.64266  2.1800e-14  6.1900e-16  14.9890  0.0308  -1
+UVM2     2246    56720.97366  2.0600e-14  5.6500e-16  15.0504  0.0298  -1
+UVM2     2246    56721.38666  2.1200e-14  5.7500e-16  15.0193  0.0294  -1
+UVM2     2246    56721.57466  2.0600e-14  5.9600e-16  15.0504  0.0314  -1
+UVM2     2246    56722.37666  2.0100e-14  5.6400e-16  15.0771  0.0305  -1
+UVM2     2246    56722.84766  1.9900e-14  5.7400e-16  15.0880  0.0313  -1
+UVM2     2246    56723.11765  1.9700e-14  5.5700e-16  15.0989  0.0307  -1
+UVM2     2246    56723.44865  2.0200e-14  5.5600e-16  15.0717  0.0299  -1
+UVM2     2246    56723.89965  1.9800e-14  5.6800e-16  15.0934  0.0311  -1
+UVM2     2246    56724.45365  1.9500e-14  5.5200e-16  15.1100  0.0307  -1
+UVM2     2246    56724.57365  1.9900e-14  5.8900e-16  15.0880  0.0321  -1
+UVM2     2246    56724.83365  1.9100e-14  5.6700e-16  15.1325  0.0322  -1
+UVM2     2246    56725.17264  1.9500e-14  5.5700e-16  15.1100  0.0310  -1
+UVM2     2246    56725.30064  1.9100e-14  5.5800e-16  15.1325  0.0317  -1
+UVM2     2246    56725.64364  1.9700e-14  5.7700e-16  15.0989  0.0318  -1
+UVM2     2246    56726.03264  2.0500e-14  6.3800e-16  15.0557  0.0338  -1
+UVM2     2246    56726.31764  1.9900e-14  5.3900e-16  15.0880  0.0294  -1
+UVM2     2246    56727.03463  1.9994e-14  4.2021e-16  15.0828  0.0228  -1
+UVM2     2246    56727.31663  2.0900e-14  5.5200e-16  15.0347  0.0287  -1
+UVM2     2246    56727.51563  2.0100e-14  5.5700e-16  15.0771  0.0301  -1
+UVM2     2246    56727.85062  2.0000e-14  5.7200e-16  15.0825  0.0311  -1
+UVM2     2246    56728.58562  2.0200e-14  6.0300e-16  15.0717  0.0324  -1
+UVM2     2246    56728.85061  2.0500e-14  6.1600e-16  15.0557  0.0326  -1
+UVM2     2246    56729.64760  2.0700e-14  5.8200e-16  15.0452  0.0305  -1
+UVM2     2246    56730.04160  2.0300e-14  5.7800e-16  15.0664  0.0309  -1
+UVM2     2246    56730.37959  2.2100e-14  5.9800e-16  14.9741  0.0294  -1
+UVM2     2246    56730.63959  2.1600e-14  5.8100e-16  14.9990  0.0292  -1
+UVM2     2246    56730.96959  2.0600e-14  5.6000e-16  15.0504  0.0295  -1
+UVM2     2246    56731.63658  2.1000e-14  5.7100e-16  15.0295  0.0295  -1
+UVM2     2246    56732.05157  2.1100e-14  5.8300e-16  15.0244  0.0300  -1
+UVM2     2246    56732.37756  2.1200e-14  5.6400e-16  15.0193  0.0289  -1
+UVM2     2246    56732.72156  2.1200e-14  5.8200e-16  15.0193  0.0298  -1
+UVM2     2246    56732.98555  2.0800e-14  5.8000e-16  15.0399  0.0303  -1
+UVM2     2246    56733.23755  2.1100e-14  5.5800e-16  15.0244  0.0287  -1
+UVM2     2246    56733.58054  2.1200e-14  5.7600e-16  15.0193  0.0295  -1
+UVM2     2246    56733.97754  2.1400e-14  5.7300e-16  15.0091  0.0291  -1
+UVM2     2246    56734.24453  2.1500e-14  5.5600e-16  15.0040  0.0281  -1
+UVM2     2246    56734.90252  2.2300e-14  6.1600e-16  14.9643  0.0300  -1
+UVM2     2246    56735.30451  2.2400e-14  5.6500e-16  14.9595  0.0274  -1
+UVM2     2246    56735.64350  2.2800e-14  6.1500e-16  14.9403  0.0293  -1
+UVM2     2246    56735.97750  2.2300e-14  5.8600e-16  14.9643  0.0285  -1
+UVM2     2246    56736.30449  2.4900e-14  6.5000e-16  14.8446  0.0283  -1
+UVM2     2246    56736.56949  2.5800e-14  6.9900e-16  14.8060  0.0294  -1
+UVM2     2246    56736.90248  2.6100e-14  7.0100e-16  14.7935  0.0292  -1
+UVM2     2246    56737.51646  2.6800e-14  7.0500e-16  14.7648  0.0286  -1
+UVM2     2246    56737.90246  2.6700e-14  7.2200e-16  14.7688  0.0294  -1
+UVM2     2246    56738.23745  2.7100e-14  7.1500e-16  14.7527  0.0286  -1
+UVM2     2246    56738.51844  2.7900e-14  7.9900e-16  14.7211  0.0311  -1
+UVM2     2246    56738.90243  2.8900e-14  7.8800e-16  14.6829  0.0296  -1
+UVM2     2246    56739.23742  2.8900e-14  7.6000e-16  14.6829  0.0286  -1
+UVM2     2246    56739.58941  2.9500e-14  8.1300e-16  14.6605  0.0299  -1
+UVM2     2246    56739.98740  3.0100e-14  7.8100e-16  14.6387  0.0282  -1
+UVM2     2246    56740.32340  2.9600e-14  7.6600e-16  14.6569  0.0281  -1
+UVM2     2246    56740.52339  3.0700e-14  8.0400e-16  14.6172  0.0284  -1
+UVM2     2246    56740.91838  3.0900e-14  8.3100e-16  14.6102  0.0292  -1
+UVM2     2246    56741.32337  3.0500e-14  8.0300e-16  14.6243  0.0286  -1
+UVM2     2246    56741.51636  3.0700e-14  8.1800e-16  14.6172  0.0289  -1
+UVM2     2246    56741.90235  3.1200e-14  8.1400e-16  14.5997  0.0283  -1
+UVM2     2246    56742.23734  3.0500e-14  7.8300e-16  14.6243  0.0279  -1
+UVM2     2246    56742.72033  3.2900e-14  8.8700e-16  14.5421  0.0293  -1
+UVM2     2246    56742.96932  3.2600e-14  8.4400e-16  14.5521  0.0281  -1
+UVM2     2246    56743.37731  3.4000e-14  8.4800e-16  14.5064  0.0271  -1
+UVM2     2246    56743.51730  3.2500e-14  8.3200e-16  14.5554  0.0278  -1
+UVM2     2246    56744.44428  3.4026e-14  6.0424e-16  14.5056  0.0193  -1
+UVM2     2246    56744.51727  3.3000e-14  8.1800e-16  14.5388  0.0269  -1
+UVM2     2246    56745.38225  3.4900e-14  9.5400e-16  14.4780  0.0297  -1
+UVM2     2246    56745.58624  3.5000e-14  9.1500e-16  14.4749  0.0284  -1
+UVM2     2246    56745.90723  3.5200e-14  9.2000e-16  14.4687  0.0284  -1
+UVM2     2246    56746.24522  3.4500e-14  8.8700e-16  14.4905  0.0279  -1
+UVM2     2246    56746.57821  3.3800e-14  8.8100e-16  14.5128  0.0283  -1
+UVM2     2246    56746.90319  3.4100e-14  8.8000e-16  14.5032  0.0280  -1
+UVM2     2246    56747.70217  3.6500e-14  9.3300e-16  14.4294  0.0278  -1
+UVM2     2246    56748.03616  3.4600e-14  8.9200e-16  14.4874  0.0280  -1
+UVM2     2246    56748.51514  3.5700e-14  9.3700e-16  14.4534  0.0285  -1
+UVM2     2246    56749.10312  3.3600e-14  8.6500e-16  14.5192  0.0280  -1
+UVM2     2246    56749.38011  3.3700e-14  8.5500e-16  14.5160  0.0275  -1
+UVM2     2246    56749.51610  3.4000e-14  8.6700e-16  14.5064  0.0277  -1
+UVM2     2246    56750.45706  3.5000e-14  9.2200e-16  14.4749  0.0286  -1
+UVM2     2246    56750.58206  3.5100e-14  9.2600e-16  14.4718  0.0286  -1
+UVM2     2246    56751.57002  3.2600e-14  8.6300e-16  14.5521  0.0287  -1
+UVM2     2246    56752.04100  3.2000e-14  9.0700e-16  14.5722  0.0308  -1
+UVM2     2246    56752.63698  3.1300e-14  9.2500e-16  14.5962  0.0321  -1
+UVM2     2246    56753.04096  3.0700e-14  8.1400e-16  14.6172  0.0288  -1
+UVM2     2246    56753.36995  3.0200e-14  8.0200e-16  14.6351  0.0288  -1
+UVM2     2246    56753.70593  2.9500e-14  8.7900e-16  14.6605  0.0324  -1
+UVM2     2246    56753.91692  2.8500e-14  7.5500e-16  14.6980  0.0288  -1
+UVM2     2246    56754.17291  2.8000e-14  7.5100e-16  14.7172  0.0291  -1
+UVM2     2246    56754.37190  2.8500e-14  7.2300e-16  14.6980  0.0275  -1
+UVM2     2246    56755.04987  2.7200e-14  7.4100e-16  14.7487  0.0296  -1
+UVM2     2246    56755.37186  2.7700e-14  7.3600e-16  14.7289  0.0288  -1
+UVM2     2246    56755.52385  2.6600e-14  7.1200e-16  14.7729  0.0291  -1
+UVM2     2246    56756.10383  2.7100e-14  7.3000e-16  14.7527  0.0292  -1
+UVM2     2246    56756.30882  2.7000e-14  7.2500e-16  14.7567  0.0292  -1
+UVM2     2246    56756.77480  2.8600e-14  7.5800e-16  14.6942  0.0288  -1
+UVM2     2246    56757.30477  2.8300e-14  7.4400e-16  14.7056  0.0285  -1
+UVM2     2246    56757.52176  2.8300e-14  7.2800e-16  14.7056  0.0279  -1
+UVM2     2246    56757.98474  2.8300e-14  7.2100e-16  14.7056  0.0277  -1
+UVM2     2246    56758.31673  2.7900e-14  7.2400e-16  14.7211  0.0282  -1
+UVM2     2246    56758.52172  2.8000e-14  7.4400e-16  14.7172  0.0288  -1
+UVM2     2246    56758.97169  2.7600e-14  7.1100e-16  14.7328  0.0280  -1
+UVM2     2246    56759.23868  2.7500e-14  7.1500e-16  14.7368  0.0282  -1
+UVM2     2246    56759.83965  2.7100e-14  6.9900e-16  14.7527  0.0280  -1
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+UVM2     2246    56760.30563  2.7600e-14  7.1000e-16  14.7328  0.0279  -1
+UVM2     2246    56761.04559  2.7600e-14  7.2300e-16  14.7328  0.0284  -1
+UVM2     2246    56761.83955  2.9000e-14  7.3200e-16  14.6791  0.0274  -1
+UVM2     2246    56762.04654  2.9000e-14  7.6100e-16  14.6791  0.0285  -1
+UVM2     2246    56762.30853  2.9400e-14  7.8600e-16  14.6642  0.0290  -1
+UVM2     2246    56762.83850  2.7700e-14  8.1800e-16  14.7289  0.0321  -1
+UVM2     2246    56763.05049  2.9000e-14  7.5900e-16  14.6791  0.0284  -1
+UVM2     2246    56763.38447  2.6400e-14  6.9300e-16  14.7811  0.0285  -1
+UVM2     2246    56763.52046  2.6200e-14  7.1800e-16  14.7893  0.0298  -1
+UVM2     2246    56764.04044  2.9200e-14  7.4600e-16  14.6716  0.0277  -1
+UVM2     2246    56764.38942  2.9000e-14  7.4800e-16  14.6791  0.0280  -1
+UVM2     2246    56764.77240  2.8700e-14  7.6100e-16  14.6904  0.0288  -1
+UVM2     2246    56765.12138  2.7500e-14  7.1800e-16  14.7368  0.0283  -1
+UVM2     2246    56765.70435  2.7900e-14  7.7000e-16  14.7211  0.0300  -1
+UVM2     2246    56766.10532  2.8700e-14  7.6800e-16  14.6904  0.0291  -1
+UVM2     2246    56766.38831  2.9600e-14  8.3000e-16  14.6569  0.0304  -1
+UVM2     2246    56766.72629  2.8600e-14  8.1100e-16  14.6942  0.0308  -1
+UVM2     2246    56767.38725  2.8100e-14  7.8000e-16  14.7133  0.0301  -1
+UVM2     2246    56769.38814  3.0000e-14  7.7900e-16  14.6423  0.0282  -1
+UVM2     2246    56770.51607  2.9800e-14  8.5900e-16  14.6496  0.0313  -1
+UVM2     2246    56770.85005  3.1400e-14  8.2000e-16  14.5928  0.0284  -1
+UVM2     2246    56771.18303  3.1400e-14  7.9000e-16  14.5928  0.0273  -1
+UVM2     2246    56774.72581  3.2100e-14  8.8600e-16  14.5688  0.0300  -1
+UVM2     2246    56775.58376  3.1600e-14  8.0600e-16  14.5859  0.0277  -1
+UVM2     2246    56776.04273  3.2500e-14  9.4900e-16  14.5554  0.0317  -1
+UVM2     2246    56776.24472  3.1600e-14  7.8800e-16  14.5859  0.0271  -1
+UVM2     2246    56777.52464  2.9500e-14  7.8700e-16  14.6605  0.0290  -1
+UVM2     2246    56778.58957  3.0800e-14  8.2200e-16  14.6137  0.0290  -1
+UVM2     2246    56779.38351  3.0800e-14  8.2800e-16  14.6137  0.0292  -1
+UVM2     2246    56779.85548  3.1200e-14  8.1700e-16  14.5997  0.0284  -1
+UVM2     2246    56780.25545  2.9100e-14  7.7700e-16  14.6754  0.0290  -1
+UVM2     2246    56780.52344  3.0900e-14  8.3600e-16  14.6102  0.0294  -1
+UVM2     2246    56780.59043  3.1500e-14  8.4400e-16  14.5893  0.0291  -1
+UVM2     2246    56782.10133  2.9900e-14  8.0100e-16  14.6459  0.0291  -1
+UVM2     2246    56782.31332  2.9300e-14  7.4800e-16  14.6679  0.0277  -1
+UVM2     2246    56782.70929  2.9700e-14  7.8800e-16  14.6532  0.0288  -1
+UVM2     2246    56782.98127  3.0700e-14  8.1500e-16  14.6172  0.0288  -1
+UVM2     2246    56783.37024  3.1000e-14  7.9000e-16  14.6067  0.0277  -1
+UVM2     2246    56783.56823  3.0100e-14  8.2200e-16  14.6387  0.0297  -1
+UVM2     2246    56783.96820  3.1400e-14  8.1300e-16  14.5928  0.0281  -1
+UVM2     2246    56785.04313  3.2900e-14  1.0200e-15  14.5421  0.0337  -1
+UVM2     2246    56785.56909  3.3200e-14  8.6900e-16  14.5323  0.0284  -1
+UVM2     2246    56786.37003  3.2800e-14  8.3400e-16  14.5454  0.0276  -1
+UVM2     2246    56786.97699  3.4100e-14  1.1100e-15  14.5032  0.0353  -1
+UVM2     2246    56787.31097  3.3300e-14  8.4300e-16  14.5290  0.0275  -1
+UVM2     2246    56787.71594  3.0800e-14  8.1100e-16  14.6137  0.0286  -1
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+UVM2     2246    56788.30789  3.1200e-14  8.5300e-16  14.5997  0.0297  -1
+UVM2     2246    56789.04884  3.1200e-14  8.0300e-16  14.5997  0.0279  -1
+UVM2     2246    56789.31082  2.9500e-14  7.5400e-16  14.6605  0.0278  -1
+UVM2     2246    56789.65080  3.0500e-14  8.2700e-16  14.6243  0.0294  -1
+UVM2     2246    56789.96977  3.0700e-14  9.0900e-16  14.6172  0.0321  -1
+UVM2     2246    56790.63773  2.9200e-14  1.0800e-15  14.6716  0.0402  -1
+UVM2     2246    56791.63765  3.2400e-14  1.8200e-15  14.5587  0.0610  -1
+UVM2     2246    56791.96963  2.9400e-14  7.5700e-16  14.6642  0.0280  -1
+UVM2     2246    56792.37060  3.0700e-14  8.4800e-16  14.6172  0.0300  -1
+UVM2     2246    56792.51259  2.9900e-14  7.6200e-16  14.6459  0.0277  -1
+UVM2     2246    56792.84856  3.2000e-14  8.1600e-16  14.5722  0.0277  -1
+UVM2     2246    56793.84849  3.1500e-14  8.0100e-16  14.5893  0.0276  -1
+UVM2     2246    56794.25046  3.1000e-14  8.4200e-16  14.6067  0.0295  -1
+UVM2     2246    56794.69743  2.9800e-14  9.1600e-16  14.6496  0.0334  -1
+UVM2     2246    56795.36438  3.0400e-14  9.2800e-16  14.6279  0.0331  -1
+UVM2     2246    56796.89626  3.1700e-14  8.2600e-16  14.5824  0.0283  -1
+UVM2     2246    56798.51214  3.1100e-14  7.9900e-16  14.6032  0.0279  -1
+UVM2     2246    56798.89511  3.0400e-14  7.9300e-16  14.6279  0.0283  -1
+UVM2     2246    56799.23108  3.0400e-14  7.7500e-16  14.6279  0.0277  -1
+UVM2     2246    56799.56706  3.2100e-14  8.1100e-16  14.5688  0.0274  -1
+UVM2     2246    56800.03102  2.9500e-14  7.4200e-16  14.6605  0.0273  -1
+UVM2     2246    56800.36400  3.0500e-14  8.1100e-16  14.6243  0.0289  -1
+UVM2     2246    56800.83096  3.0900e-14  7.8200e-16  14.6102  0.0275  -1
+UVM2     2246    56801.10094  3.1300e-14  7.9300e-16  14.5962  0.0275  -1
+UVM2     2246    56802.10086  3.1800e-14  7.8000e-16  14.5790  0.0266  -1
+UVM2     2246    56802.63282  3.1300e-14  7.7000e-16  14.5962  0.0267  -1
+UVM2     2246    56804.23470  2.9600e-14  7.6300e-16  14.6569  0.0280  -1
+UVM2     2246    56805.09363  3.0800e-14  8.1400e-16  14.6137  0.0287  -1
+UVM2     2246    56805.97756  3.1700e-14  8.2000e-16  14.5824  0.0281  -1
+UVM2     2246    56806.37353  3.1000e-14  8.0500e-16  14.6067  0.0282  -1
+UVM2     2246    56806.89349  3.1200e-14  8.8200e-16  14.5997  0.0307  -1
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+UVM2     2246    56807.89241  3.1500e-14  8.1600e-16  14.5893  0.0281  -1
+UVM2     2246    56808.55936  3.1700e-14  8.1100e-16  14.5824  0.0278  -1
+UVM2     2246    56808.89233  3.2400e-14  8.3300e-16  14.5587  0.0279  -1
+UVM2     2246    56809.22431  3.0900e-14  8.1000e-16  14.6102  0.0285  -1
+UVM2     2246    56809.89225  3.2700e-14  8.4000e-16  14.5487  0.0279  -1
+UVM2     2246    56810.22523  3.1700e-14  8.6600e-16  14.5824  0.0297  -1
+UVM2     2246    56810.55820  3.0800e-14  8.0900e-16  14.6137  0.0285  -1
+UVM2     2246    56810.89217  3.1900e-14  8.2200e-16  14.5756  0.0280  -1
+UVM2     2246    56811.22315  3.1100e-14  8.5400e-16  14.6032  0.0298  -1
+UVM2     2246    56811.75811  3.2200e-14  9.3800e-16  14.5655  0.0316  -1
+UVM2     2246    56812.02609  3.2200e-14  8.2500e-16  14.5655  0.0278  -1
+UVM2     2246    56812.37406  3.2500e-14  8.5900e-16  14.5554  0.0287  -1
+UVM2     2246    56812.55804  3.2300e-14  8.4500e-16  14.5621  0.0284  -1
+UVM2     2246    56812.89002  3.1400e-14  8.3100e-16  14.5928  0.0287  -1
+UVM2     2246    56813.22899  3.2700e-14  8.3300e-16  14.5487  0.0277  -1
+UVM2     2246    56813.97593  3.3500e-14  8.9300e-16  14.5225  0.0289  -1
+UVM2     2246    56814.37390  3.1700e-14  8.1000e-16  14.5824  0.0277  -1
+UVM2     2246    56814.55689  3.3900e-14  9.0800e-16  14.5096  0.0291  -1
+UVM2     2246    56814.62788  3.3200e-14  8.4100e-16  14.5323  0.0275  -1
+UVM2     2246    56815.69880  3.3400e-14  8.9300e-16  14.5257  0.0290  -1
+UVM2     2246    56815.95778  3.4400e-14  9.5300e-16  14.4937  0.0301  -1
+UVM2     2246    56816.23075  3.5800e-14  9.6900e-16  14.4504  0.0294  -1
+UVM2     2246    56817.62665  3.7300e-14  9.3200e-16  14.4058  0.0271  -1
+UVM2     2246    56818.09561  3.7900e-14  8.9900e-16  14.3885  0.0258  -1
+UVM2     2246    56818.24260  3.7700e-14  9.6900e-16  14.3942  0.0279  -1
+UVM2     2246    56818.70656  3.9300e-14  1.0100e-15  14.3491  0.0279  -1
+UVM2     2246    56818.96154  4.0400e-14  1.1000e-15  14.3191  0.0296  -1
+UVM2     2246    56819.69748  3.7000e-14  9.8400e-16  14.4146  0.0289  -1
+UVM2     2246    56820.22944  4.0000e-14  1.0600e-15  14.3299  0.0288  -1
+UVM2     2246    56820.57641  3.6900e-14  9.4700e-16  14.4175  0.0279  -1
+UVM2     2246    56820.63841  3.7400e-14  1.2100e-15  14.4029  0.0351  -1
+UVM2     2246    56820.69141  3.7400e-14  9.6000e-16  14.4029  0.0279  -1
+UVM2     2246    56821.90631  3.7200e-14  9.6500e-16  14.4087  0.0282  -1
+UVM2     2246    56822.36628  3.6700e-14  1.0300e-15  14.4234  0.0305  -1
+UVM2     2246    56823.15721  3.7400e-14  1.0100e-15  14.4029  0.0293  -1
+UVM2     2246    56823.29720  3.4500e-14  9.4200e-16  14.4905  0.0296  -1
+UVM2     2246    56824.62110  3.6000e-14  9.9400e-16  14.4443  0.0300  -1
+UVM2     2246    56825.36404  3.3900e-14  8.9400e-16  14.5096  0.0286  -1
+UVM2     2246    56825.55403  3.5400e-14  9.0700e-16  14.4626  0.0278  -1
+UVM2     2246    56825.96200  3.5600e-14  1.1400e-15  14.4565  0.0348  -1
+UVM2     2246    56826.43196  3.3400e-14  8.8500e-16  14.5257  0.0288  -1
+UVM2     2246    56826.76494  3.2500e-14  8.6900e-16  14.5554  0.0290  -1
+UVM2     2246    56827.03991  3.2700e-14  9.1800e-16  14.5487  0.0305  -1
+UVM2     2246    56827.43088  3.1700e-14  9.0500e-16  14.5824  0.0310  -1
+UVM2     2246    56827.88585  3.1500e-14  8.1100e-16  14.5893  0.0280  -1
+UVM2     2246    56828.15383  3.2000e-14  8.4600e-16  14.5722  0.0287  -1
+UVM2     2246    56828.42981  3.1100e-14  8.1900e-16  14.6032  0.0286  -1
+UVM2     2246    56828.55880  3.1700e-14  7.6800e-16  14.5824  0.0263  -1
+UVM2     2246    56829.01976  3.1300e-14  8.3600e-16  14.5962  0.0290  -1
+UVM2     2246    56829.81770  3.0700e-14  8.6700e-16  14.6172  0.0307  -1
+UVM2     2246    56830.15268  2.9900e-14  8.5100e-16  14.6459  0.0309  -1
+UVM2     2246    56830.42366  2.9700e-14  8.0700e-16  14.6532  0.0295  -1
\ No newline at end of file
diff --git a/data/STORM_III/raw/separated/2600Å.tab b/data/STORM_III/raw/separated/2600Å.tab
new file mode 100644
index 0000000..4141246
--- /dev/null
+++ b/data/STORM_III/raw/separated/2600Å.tab
@@ -0,0 +1,270 @@
+UVW1     2600    56690.42605  2.3800e-14  5.6200e-16  14.5758  0.0256  -1
+UVW1     2600    56698.35933  2.8600e-14  6.3700e-16  14.3764  0.0242  -1
+UVW1     2600    56699.56236  2.7600e-14  6.3900e-16  14.4150  0.0251  -1
+UVW1     2600    56702.41644  2.7900e-14  6.3000e-16  14.4033  0.0245  -1
+UVW1     2600    56706.48652  2.7700e-14  6.4000e-16  14.4111  0.0251  -1
+UVW1     2600    56708.22756  2.7200e-14  6.2300e-16  14.4309  0.0249  -1
+UVW1     2600    56710.10559  2.6000e-14  6.6100e-16  14.4798  0.0276  -1
+UVW1     2600    56710.16759  2.6500e-14  6.8000e-16  14.4592  0.0279  -1
+UVW1     2600    56710.63659  2.4900e-14  6.2800e-16  14.5268  0.0274  -1
+UVW1     2600    56711.02360  2.4800e-14  6.4700e-16  14.5311  0.0283  -1
+UVW1     2600    56711.35860  2.5400e-14  6.9100e-16  14.5052  0.0295  -1
+UVW1     2600    56712.09261  2.4300e-14  6.1100e-16  14.5533  0.0273  -1
+UVW1     2600    56713.22862  2.5000e-14  6.5400e-16  14.5224  0.0284  -1
+UVW1     2600    56713.51063  2.5200e-14  6.4200e-16  14.5138  0.0277  -1
+UVW1     2600    56713.95863  2.4900e-14  6.5500e-16  14.5268  0.0286  -1
+UVW1     2600    56714.22563  2.3600e-14  6.0700e-16  14.5850  0.0279  -1
+UVW1     2600    56714.51063  2.4200e-14  6.3600e-16  14.5577  0.0285  -1
+UVW1     2600    56715.04264  2.4400e-14  6.3500e-16  14.5488  0.0283  -1
+UVW1     2600    56715.64264  2.4200e-14  6.3100e-16  14.5577  0.0283  -1
+UVW1     2600    56717.03865  2.3400e-14  6.1000e-16  14.5942  0.0283  -1
+UVW1     2600    56718.36766  2.1700e-14  5.4400e-16  14.6761  0.0272  -1
+UVW1     2600    56718.63766  2.0600e-14  5.5000e-16  14.7326  0.0290  -1
+UVW1     2600    56719.71066  2.1700e-14  5.5300e-16  14.6761  0.0277  -1
+UVW1     2600    56720.10066  2.0700e-14  5.7400e-16  14.7274  0.0301  -1
+UVW1     2600    56720.37566  2.1100e-14  5.2700e-16  14.7066  0.0271  -1
+UVW1     2600    56720.63766  2.1100e-14  5.6300e-16  14.7066  0.0290  -1
+UVW1     2600    56720.96766  1.9800e-14  5.1000e-16  14.7756  0.0280  -1
+UVW1     2600    56721.37966  2.0400e-14  5.1600e-16  14.7432  0.0275  -1
+UVW1     2600    56721.56966  1.9900e-14  5.3600e-16  14.7701  0.0292  -1
+UVW1     2600    56722.37066  1.9700e-14  5.0900e-16  14.7811  0.0281  -1
+UVW1     2600    56722.84266  1.8800e-14  5.1200e-16  14.8319  0.0296  -1
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+UVW1     2600    56823.15221  3.2900e-14  8.2900e-16  14.2243  0.0274  -1
+UVW1     2600    56823.29120  3.0100e-14  7.3600e-16  14.3209  0.0265  -1
+UVW1     2600    56824.61610  3.2200e-14  8.1300e-16  14.2476  0.0274  -1
+UVW1     2600    56825.35904  3.1700e-14  7.7900e-16  14.2646  0.0267  -1
+UVW1     2600    56825.54803  3.1000e-14  7.5500e-16  14.2889  0.0264  -1
+UVW1     2600    56825.94900  3.1000e-14  6.6200e-16  14.2889  0.0232  -1
+UVW1     2600    56826.42696  3.0800e-14  7.7400e-16  14.2959  0.0273  -1
+UVW1     2600    56826.75994  3.0000e-14  7.4800e-16  14.3245  0.0271  -1
+UVW1     2600    56827.03392  2.9800e-14  7.3000e-16  14.3317  0.0266  -1
+UVW1     2600    56827.42589  2.8400e-14  7.1300e-16  14.3840  0.0273  -1
+UVW1     2600    56827.87985  2.9100e-14  7.1400e-16  14.3575  0.0266  -1
+UVW1     2600    56828.14883  2.8700e-14  7.1900e-16  14.3726  0.0272  -1
+UVW1     2600    56828.42481  2.9100e-14  7.2800e-16  14.3575  0.0272  -1
+UVW1     2600    56828.54780  2.8900e-14  6.3800e-16  14.3650  0.0240  -1
+UVW1     2600    56829.01376  2.8500e-14  7.1500e-16  14.3802  0.0272  -1
+UVW1     2600    56829.28574  2.8700e-14  7.2300e-16  14.3726  0.0274  -1
+UVW1     2600    56829.81270  2.7800e-14  7.1800e-16  14.4072  0.0280  -1
+UVW1     2600    56830.14668  2.8000e-14  6.9300e-16  14.3994  0.0269  -1
+UVW1     2600    56830.41766  2.7800e-14  6.9500e-16  14.4072  0.0271  -1
\ No newline at end of file
diff --git a/data/STORM_III/raw/separated/3465Å.tab b/data/STORM_III/raw/separated/3465Å.tab
new file mode 100644
index 0000000..b796295
--- /dev/null
+++ b/data/STORM_III/raw/separated/3465Å.tab
@@ -0,0 +1,270 @@
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+Swiftu   3465    56827.03492  2.3300e-14  6.5900e-16  13.9752  0.0307  -1
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+Swiftu   3465    56829.01476  2.3500e-14  6.7900e-16  13.9660  0.0314  -1
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+Swiftu   3465    56830.41866  2.2900e-14  6.5700e-16  13.9940  0.0311  -1
\ No newline at end of file
diff --git a/data/STORM_III/raw/separated/3471Å.tab b/data/STORM_III/raw/separated/3471Å.tab
new file mode 100644
index 0000000..f1a4271
--- /dev/null
+++ b/data/STORM_III/raw/separated/3471Å.tab
@@ -0,0 +1,145 @@
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+u        3471    56693.79261  2.1677e-14  4.4078e-17  14.0493  0.0022  1
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+u        3471    56696.73102  2.2242e-14  4.2376e-17  14.0214  0.0021  1
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+u        3471    56698.61098  2.3247e-14  3.0894e-16  13.9734  0.0144  3
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+u        3471    56700.69467  2.3071e-14  8.8749e-17  13.9817  0.0042  1
+u        3471    56701.56783  2.2122e-14  5.9464e-17  14.0273  0.0029  1
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+u        3471    56703.01636  2.4725e-14  1.5059e-15  13.9065  0.0661  2
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+u        3471    56717.72878  1.9664e-14  6.7365e-17  14.1552  0.0037  1
+u        3471    56717.96153  1.7230e-14  2.0304e-15  14.2986  0.1279  2
+u        3471    56718.66619  1.8798e-14  5.9857e-17  14.2041  0.0035  1
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+u        3471    56728.57170  1.6711e-14  5.6352e-17  14.3318  0.0037  1
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+u        3471    56741.71080  2.1330e-14  1.8256e-15  14.0669  0.0929  2
+u        3471    56742.64716  2.2899e-14  9.6003e-17  13.9898  0.0046  1
+u        3471    56743.59134  2.4381e-14  6.6100e-17  13.9217  0.0029  1
+u        3471    56744.53369  2.4168e-14  7.7558e-17  13.9312  0.0035  1
+u        3471    56744.54996  2.3150e-14  3.5924e-16  13.9780  0.0168  3
+u        3471    56744.69283  2.3546e-14  1.7893e-15  13.9595  0.0825  2
+u        3471    56746.75457  2.5167e-14  1.2237e-15  13.8873  0.0528  2
+u        3471    56748.73710  2.2943e-14  1.7386e-15  13.9877  0.0823  2
+u        3471    56749.74633  2.4360e-14  1.2729e-15  13.9226  0.0567  2
+u        3471    56752.57749  2.5269e-14  6.4085e-17  13.8829  0.0028  1
+u        3471    56753.57247  2.4566e-14  6.3180e-17  13.9135  0.0028  1
+u        3471    56754.55015  2.3137e-14  6.0686e-17  13.9786  0.0028  1
+u        3471    56755.55972  2.2095e-14  7.3075e-17  14.0286  0.0036  1
+u        3471    56755.97603  2.1882e-14  7.4327e-16  14.0391  0.0369  2
+u        3471    56756.50703  2.1821e-14  8.0689e-17  14.0422  0.0040  1
+u        3471    56757.47732  2.4341e-14  4.9831e-16  13.9235  0.0222  3
+u        3471    56759.80043  2.3650e-14  1.3506e-15  13.9548  0.0620  2
+u        3471    56761.59774  2.1636e-14  6.2522e-17  14.0514  0.0031  1
+u        3471    56762.82444  2.1329e-14  1.6439e-15  14.0669  0.0837  2
+u        3471    56763.50473  2.1888e-14  2.8338e-16  14.0388  0.0141  1
+u        3471    56763.74795  2.3439e-14  1.1814e-15  13.9645  0.0547  2
+u        3471    56764.81297  2.1482e-14  1.4967e-15  14.0592  0.0756  2
+u        3471    56765.57793  2.2252e-14  5.3543e-17  14.0209  0.0026  1
+u        3471    56769.51254  2.2955e-14  5.5734e-17  13.9871  0.0026  1
+u        3471    56770.52188  2.3121e-14  6.0531e-17  13.9793  0.0028  1
+u        3471    56771.50651  2.3754e-14  6.1272e-17  13.9500  0.0028  1
+u        3471    56772.49318  2.3681e-14  7.4879e-17  13.9533  0.0034  1
+u        3471    56772.81537  2.2652e-14  1.9586e-15  14.0016  0.0939  2
+u        3471    56774.66833  2.4130e-14  5.4395e-17  13.9329  0.0024  1
+u        3471    56774.75672  2.4380e-14  2.4643e-15  13.9218  0.1097  2
+u        3471    56775.54352  2.4081e-14  7.4272e-17  13.9352  0.0033  1
+u        3471    56777.52540  2.4293e-14  6.2355e-17  13.9256  0.0028  1
+u        3471    56778.49249  2.4166e-14  6.2293e-17  13.9313  0.0028  1
+u        3471    56779.39786  2.4066e-14  4.4166e-16  13.9358  0.0199  3
+u        3471    56779.48233  2.4009e-14  6.5195e-17  13.9384  0.0029  1
+u        3471    56779.82162  2.3922e-14  1.6181e-15  13.9423  0.0734  2
+u        3471    56780.48978  2.4177e-14  6.1525e-17  13.9308  0.0028  1
+u        3471    56780.80668  2.2426e-14  1.7734e-15  14.0125  0.0859  2
+u        3471    56781.77199  2.3784e-14  1.6667e-15  13.9486  0.0761  2
+u        3471    56784.43632  2.3946e-14  6.0840e-17  13.9413  0.0028  1
+u        3471    56785.47099  2.4275e-14  6.2609e-17  13.9264  0.0028  1
+u        3471    56786.47789  2.3891e-14  6.8493e-17  13.9438  0.0031  1
+u        3471    56787.39889  2.3515e-14  6.6317e-17  13.9610  0.0031  1
+u        3471    56794.45879  2.3095e-14  5.8448e-17  13.9805  0.0027  1
+u        3471    56795.44080  2.2944e-14  5.8571e-17  13.9877  0.0028  1
+u        3471    56796.44321  2.2966e-14  6.3198e-17  13.9866  0.0030  1
+u        3471    56797.44590  2.3738e-14  6.0680e-17  13.9507  0.0028  1
+u        3471    56799.43571  2.3687e-14  5.7405e-17  13.9531  0.0026  1
+u        3471    56800.43810  2.3444e-14  5.7183e-17  13.9643  0.0026  1
+u        3471    56801.42262  2.3554e-14  5.8702e-17  13.9592  0.0027  1
+u        3471    56802.43215  2.3802e-14  6.2708e-17  13.9478  0.0029  1
+u        3471    56803.43584  2.3250e-14  6.2057e-17  13.9733  0.0029  1
+u        3471    56805.45570  2.3441e-14  5.6428e-17  13.9644  0.0026  1
+u        3471    56807.43068  2.3017e-14  6.4661e-17  13.9842  0.0031  1
+u        3471    56808.41017  2.3826e-14  5.9285e-17  13.9467  0.0027  1
+u        3471    56809.42509  2.3862e-14  6.2312e-17  13.9451  0.0028  1
+u        3471    56810.42613  2.3786e-14  5.9799e-17  13.9485  0.0027  1
+u        3471    56811.41491  2.3895e-14  5.8996e-17  13.9436  0.0027  1
+u        3471    56812.42491  2.4643e-14  5.9413e-17  13.9101  0.0026  1
+u        3471    56813.39784  2.4209e-14  5.8353e-17  13.9294  0.0026  1
+u        3471    56814.45105  2.4687e-14  5.9653e-17  13.9082  0.0026  1
+u        3471    56815.39866  2.4779e-14  6.1047e-17  13.9041  0.0027  1
+u        3471    56816.40563  2.4829e-14  6.5244e-17  13.9019  0.0029  1
+u        3471    56817.40567  2.5263e-14  6.4339e-17  13.8831  0.0028  1
+u        3471    56818.41048  2.6172e-14  6.0593e-17  13.8448  0.0025  1
+u        3471    56819.41057  2.6952e-14  6.2181e-17  13.8129  0.0025  1
+u        3471    56822.39121  2.6553e-14  6.4630e-17  13.8291  0.0026  1
+u        3471    56823.40216  2.6752e-14  6.5362e-17  13.8210  0.0027  1
+u        3471    56825.40992  2.6878e-14  6.6918e-17  13.8159  0.0027  1
+u        3471    56826.28286  2.4821e-14  5.8906e-16  13.9023  0.0258  3
+u        3471    56826.38771  2.5315e-14  6.9938e-17  13.8809  0.0030  1
+u        3471    56827.39248  2.4980e-14  6.4606e-17  13.8954  0.0028  1
+u        3471    56828.39482  2.5210e-14  5.9082e-17  13.8854  0.0025  1
+u        3471    56829.21472  2.6081e-14  8.0286e-16  13.8485  0.0334  3
+u        3471    56829.40465  2.4573e-14  6.1493e-17  13.9132  0.0027  1
+u        3471    56830.39740  2.4421e-14  5.9538e-17  13.9199  0.0026  1
+u        3471    56832.43166  2.4431e-14  6.0309e-17  13.9195  0.0027  1
+u        3471    56833.39302  2.4515e-14  6.0598e-17  13.9158  0.0027  1
+u        3471    56834.38758  2.4471e-14  6.1341e-17  13.9177  0.0027  1
+u        3471    56835.38907  2.4178e-14  6.3638e-17  13.9308  0.0029  1
+u        3471    56839.32447  2.3537e-14  3.5948e-16  13.9600  0.0166  3
+u        3471    56841.40694  2.4217e-14  6.4234e-17  13.9290  0.0029  1
+u        3471    56843.38678  2.3533e-14  6.3614e-17  13.9601  0.0029  1
+u        3471    56845.40612  2.3291e-14  6.2597e-17  13.9714  0.0029  1
+u        3471    56847.39677  2.2276e-14  6.7848e-17  14.0197  0.0033  1
+u        3471    56858.38334  2.2153e-14  6.5436e-17  14.0258  0.0032  1
+u        3471    56860.38227  2.1712e-14  7.0403e-17  14.0476  0.0035  1
+u        3471    56861.38130  2.2287e-14  6.1222e-17  14.0192  0.0030  1
+u        3471    56862.38184  2.0584e-14  6.0925e-17  14.1055  0.0032  1
+u        3471    56863.38026  2.1421e-14  5.9222e-17  14.0622  0.0030  1
+u        3471    56864.38028  2.1867e-14  6.8518e-17  14.0399  0.0034  1
+u        3471    56865.37923  2.2342e-14  6.8264e-17  14.0165  0.0033  1
\ No newline at end of file
diff --git a/data/STORM_III/raw/separated/4368Å.tab b/data/STORM_III/raw/separated/4368Å.tab
new file mode 100644
index 0000000..f316e67
--- /dev/null
+++ b/data/STORM_III/raw/separated/4368Å.tab
@@ -0,0 +1,151 @@
+B        4368    56691.61532  1.3533e-14  1.0054e-16  14.0618  0.0081  1
+B        4368    56692.59056  1.3947e-14  3.3651e-16  14.0291  0.0262  5
+B        4368    56693.59836  1.3947e-14  4.1356e-16  14.0291  0.0322  5
+B        4368    56694.65251  1.4068e-14  2.4268e-16  14.0197  0.0187  5
+B        4368    56695.65395  1.3933e-14  5.0304e-16  14.0302  0.0392  5
+B        4368    56698.47632  1.4342e-14  4.8547e-17  13.9987  0.0037  1
+B        4368    56699.49150  1.4362e-14  4.2612e-17  13.9972  0.0032  1
+B        4368    56700.51826  1.4354e-14  4.0935e-17  13.9978  0.0031  1
+B        4368    56701.58127  1.4512e-14  3.1488e-17  13.9859  0.0024  1
+B        4368    56705.98773  1.4578e-14  7.0934e-17  13.9810  0.0053  7
+B        4368    56706.99618  1.4669e-14  5.1915e-17  13.9743  0.0038  7
+B        4368    56707.99385  1.4616e-14  8.9918e-17  13.9782  0.0067  7
+B        4368    56708.58501  1.4615e-14  4.3311e-17  13.9783  0.0032  1
+B        4368    56709.56658  1.4465e-14  5.0586e-17  13.9895  0.0038  1
+B        4368    56710.54383  1.4305e-14  1.6089e-16  14.0015  0.0122  5
+B        4368    56710.59783  1.3984e-14  4.3451e-17  14.0262  0.0034  1
+B        4368    56711.60131  1.3934e-14  2.1630e-16  14.0301  0.0169  5
+B        4368    56716.49693  1.3108e-14  9.7109e-17  14.0964  0.0080  1
+B        4368    56721.01552  1.2438e-14  1.4317e-16  14.1534  0.0125  6
+B        4368    56721.49169  1.2196e-14  8.7975e-17  14.1747  0.0078  1
+B        4368    56721.99948  1.1880e-14  1.9795e-16  14.2032  0.0181  6
+B        4368    56722.49166  1.1725e-14  3.6041e-16  14.2175  0.0334  4
+B        4368    56723.46990  1.1728e-14  7.6944e-17  14.2172  0.0071  1
+B        4368    56723.49188  1.1683e-14  3.6284e-16  14.2214  0.0337  4
+B        4368    56724.48738  1.1447e-14  3.5984e-16  14.2435  0.0341  2
+B        4368    56724.62546  1.1488e-14  2.2450e-16  14.2397  0.0212  5
+B        4368    56725.82808  1.1225e-14  2.1278e-16  14.2648  0.0206  8
+B        4368    56726.62712  1.1462e-14  2.1898e-16  14.2421  0.0207  5
+B        4368    56727.47245  1.1691e-14  5.2306e-17  14.2206  0.0049  1
+B        4368    56727.50135  1.1664e-14  3.6530e-16  14.2231  0.0340  4
+B        4368    56727.59782  1.1797e-14  1.8761e-16  14.2108  0.0173  5
+B        4368    56728.62372  1.1524e-14  1.7815e-16  14.2363  0.0168  5
+B        4368    56729.61433  1.1991e-14  2.4086e-16  14.1931  0.0218  5
+B        4368    56730.56953  1.1410e-14  1.6762e-16  14.2471  0.0160  5
+B        4368    56730.92161  1.1838e-14  1.7908e-16  14.2071  0.0164  8
+B        4368    56731.55243  1.1688e-14  4.4052e-16  14.2209  0.0409  3
+B        4368    56731.56761  1.1694e-14  2.2647e-16  14.2204  0.0210  5
+B        4368    56732.46659  1.2054e-14  2.1497e-16  14.1874  0.0194  2
+B        4368    56733.48779  1.2236e-14  2.3509e-16  14.1712  0.0209  4
+B        4368    56733.93324  1.1974e-14  1.5581e-16  14.1947  0.0141  7
+B        4368    56733.96388  1.2041e-14  1.6543e-16  14.1886  0.0149  6
+B        4368    56734.46333  1.2290e-14  3.3607e-16  14.1664  0.0297  3
+B        4368    56735.48402  1.2275e-14  3.3157e-16  14.1677  0.0293  3
+B        4368    56735.56102  1.2448e-14  1.1814e-16  14.1525  0.0103  5
+B        4368    56736.92805  1.3123e-14  1.0324e-16  14.0952  0.0085  7
+B        4368    56736.95796  1.3169e-14  1.0201e-16  14.0914  0.0084  6
+B        4368    56737.45565  1.3222e-14  2.5479e-17  14.0870  0.0021  1
+B        4368    56737.46597  1.3289e-14  2.9127e-16  14.0815  0.0238  3
+B        4368    56737.61136  1.3191e-14  1.2808e-16  14.0896  0.0105  5
+B        4368    56737.92657  1.4122e-14  3.8688e-16  14.0155  0.0297  7
+B        4368    56737.96000  1.3381e-14  7.5688e-17  14.0740  0.0061  6
+B        4368    56738.46522  1.3848e-14  3.0241e-16  14.0368  0.0237  3
+B        4368    56738.46680  1.3626e-14  5.4485e-17  14.0543  0.0043  1
+B        4368    56738.57982  1.3759e-14  1.7609e-16  14.0438  0.0139  5
+B        4368    56739.32863  1.3754e-14  7.1087e-17  14.0442  0.0056  1
+B        4368    56739.59459  1.4194e-14  2.0642e-16  14.0100  0.0158  5
+B        4368    56739.91596  1.4148e-14  8.7402e-17  14.0135  0.0067  7
+B        4368    56739.91835  1.4095e-14  8.5801e-17  14.0176  0.0066  8
+B        4368    56740.57613  1.4382e-14  2.1553e-16  13.9957  0.0163  5
+B        4368    56741.52530  1.4789e-14  2.8850e-16  13.9654  0.0212  5
+B        4368    56741.90709  1.4784e-14  1.2176e-16  13.9658  0.0089  7
+B        4368    56742.46715  1.4723e-14  7.0039e-17  13.9703  0.0052  1
+B        4368    56742.91176  1.4635e-14  1.5910e-16  13.9768  0.0118  7
+B        4368    56744.46040  1.5363e-14  7.2214e-17  13.9241  0.0051  1
+B        4368    56745.85691  1.5290e-14  1.4132e-16  13.9292  0.0100  7
+B        4368    56746.42943  1.5839e-14  2.9556e-16  13.8909  0.0203  5
+B        4368    56748.48947  1.5921e-14  6.7385e-17  13.8853  0.0046  1
+B        4368    56750.36460  1.5726e-14  8.7387e-17  13.8987  0.0060  1
+B        4368    56750.60089  1.5800e-14  2.3698e-16  13.8936  0.0163  5
+B        4368    56751.53726  1.5808e-14  3.7890e-16  13.8931  0.0260  5
+B        4368    56752.43447  1.5575e-14  6.7568e-17  13.9092  0.0047  1
+B        4368    56753.92625  1.4857e-14  1.0478e-16  13.9604  0.0077  7
+B        4368    56754.38909  1.4565e-14  4.9408e-17  13.9820  0.0037  1
+B        4368    56755.44294  1.4296e-14  5.1836e-17  14.0022  0.0039  1
+B        4368    56756.47615  1.4459e-14  2.6981e-16  13.9899  0.0203  5
+B        4368    56756.92239  1.4666e-14  9.8597e-17  13.9745  0.0073  7
+B        4368    56757.36140  1.4404e-14  2.7667e-17  13.9941  0.0021  1
+B        4368    56758.52686  1.4621e-14  2.1318e-16  13.9778  0.0158  5
+B        4368    56760.89217  1.4062e-14  6.3377e-17  14.0201  0.0049  7
+B        4368    56762.45016  1.4043e-14  1.2021e-16  14.0216  0.0093  5
+B        4368    56763.84561  1.4059e-14  5.9986e-17  14.0204  0.0046  7
+B        4368    56764.90845  1.4103e-14  6.1889e-17  14.0170  0.0048  7
+B        4368    56765.32336  1.4025e-14  3.3265e-17  14.0230  0.0026  1
+B        4368    56765.51256  1.4241e-14  1.0891e-16  14.0064  0.0083  5
+B        4368    56766.31096  1.4166e-14  7.8491e-17  14.0121  0.0060  1
+B        4368    56769.85204  1.4676e-14  9.8557e-17  13.9737  0.0073  7
+B        4368    56771.80401  1.4916e-14  9.4539e-17  13.9561  0.0069  7
+B        4368    56772.80764  1.5011e-14  1.0391e-16  13.9492  0.0075  7
+B        4368    56778.46091  1.5328e-14  4.7880e-16  13.9266  0.0339  5
+B        4368    56780.78297  1.4852e-14  9.9827e-17  13.9608  0.0073  7
+B        4368    56784.46680  1.5248e-14  3.4706e-16  13.9322  0.0247  5
+B        4368    56786.75990  1.4982e-14  5.1841e-17  13.9513  0.0038  7
+B        4368    56787.81675  1.4976e-14  5.1772e-17  13.9518  0.0038  7
+B        4368    56788.47011  1.5172e-14  1.2923e-16  13.9377  0.0092  5
+B        4368    56788.81867  1.4932e-14  7.4413e-17  13.9550  0.0054  7
+B        4368    56792.49051  1.4459e-14  4.4731e-16  13.9899  0.0336  5
+B        4368    56793.50159  1.4990e-14  2.4843e-16  13.9508  0.0180  5
+B        4368    56793.83937  1.4805e-14  4.4748e-17  13.9642  0.0033  7
+B        4368    56795.50129  1.4534e-14  1.3504e-16  13.9843  0.0101  5
+B        4368    56795.77306  1.4806e-14  9.9961e-17  13.9642  0.0073  7
+B        4368    56797.47623  1.4879e-14  1.3194e-16  13.9588  0.0096  5
+B        4368    56797.79770  1.5104e-14  1.2052e-16  13.9425  0.0087  7
+B        4368    56798.78371  1.4916e-14  1.2547e-16  13.9561  0.0091  7
+B        4368    56799.35806  1.5146e-14  5.1530e-16  13.9395  0.0369  5
+B        4368    56799.76081  1.4860e-14  9.9486e-17  13.9602  0.0073  7
+B        4368    56800.34275  1.4926e-14  8.7852e-17  13.9554  0.0064  1
+B        4368    56800.45432  1.4974e-14  6.2660e-16  13.9519  0.0454  5
+B        4368    56801.41086  1.4846e-14  7.1092e-17  13.9612  0.0052  1
+B        4368    56801.83263  1.4789e-14  1.1757e-16  13.9654  0.0086  7
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+B        4368    56817.41998  1.5658e-14  1.5331e-16  13.9034  0.0106  5
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+B        4368    56819.43109  1.5868e-14  1.5267e-16  13.8890  0.0104  5
+B        4368    56820.33301  1.6437e-14  4.1742e-17  13.8507  0.0028  1
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+B        4368    56824.37139  1.6258e-14  1.7023e-16  13.8626  0.0114  5
+B        4368    56825.40133  1.6080e-14  2.8948e-17  13.8745  0.0020  1
+B        4368    56825.42925  1.6004e-14  1.5385e-16  13.8797  0.0104  5
+B        4368    56827.39423  1.5485e-14  5.2088e-17  13.9155  0.0037  1
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+B        4368    56831.30382  1.4877e-14  1.0661e-16  13.9590  0.0078  1
+B        4368    56832.29724  1.4858e-14  1.1003e-16  13.9604  0.0080  1
+B        4368    56834.34737  1.5109e-14  1.1089e-16  13.9422  0.0080  1
+B        4368    56834.39826  1.5174e-14  3.6405e-16  13.9375  0.0260  5
+B        4368    56835.40363  1.4935e-14  5.4792e-17  13.9548  0.0040  1
+B        4368    56835.41493  1.5018e-14  3.2152e-16  13.9487  0.0232  5
+B        4368    56836.33550  1.4890e-14  7.5678e-17  13.9580  0.0055  1
+B        4368    56837.33639  1.5005e-14  7.4576e-17  13.9497  0.0054  1
+B        4368    56837.41251  1.5118e-14  2.9164e-16  13.9415  0.0209  5
+B        4368    56839.36431  1.4910e-14  5.1970e-17  13.9566  0.0038  1
+B        4368    56844.35805  1.4689e-14  3.1776e-17  13.9728  0.0023  1
+B        4368    56845.27649  1.4537e-14  4.7387e-17  13.9841  0.0035  1
+B        4368    56846.34947  1.4406e-14  5.1653e-17  13.9939  0.0039  1
+B        4368    56848.29334  1.4209e-14  2.7730e-17  14.0089  0.0021  1
+B        4368    56850.33440  1.4039e-14  4.7049e-17  14.0219  0.0036  1
+B        4368    56851.30912  1.3763e-14  1.7419e-17  14.0435  0.0014  1
+B        4368    56852.28704  1.3853e-14  2.7831e-17  14.0364  0.0022  1
+B        4368    56853.30153  1.3591e-14  3.4540e-17  14.0571  0.0028  1
+B        4368    56854.29472  1.3416e-14  2.9901e-17  14.0712  0.0024  1
+B        4368    56857.27430  1.3984e-14  7.4255e-17  14.0262  0.0058  1
+B        4368    56858.29436  1.4287e-14  3.6334e-17  14.0029  0.0028  1
\ No newline at end of file
diff --git a/data/STORM_III/raw/separated/4392Å.tab b/data/STORM_III/raw/separated/4392Å.tab
new file mode 100644
index 0000000..462d3de
--- /dev/null
+++ b/data/STORM_III/raw/separated/4392Å.tab
@@ -0,0 +1,271 @@
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+Swiftb   4392    56766.10132  1.4500e-14  4.0600e-16  13.9754  0.0304  -1
+Swiftb   4392    56766.38431  1.4100e-14  4.1800e-16  14.0058  0.0322  -1
+Swiftb   4392    56766.72229  1.4600e-14  4.2000e-16  13.9680  0.0312  -1
+Swiftb   4392    56767.38325  1.4900e-14  4.1400e-16  13.9459  0.0302  -1
+Swiftb   4392    56768.44719  1.4500e-14  3.9000e-16  13.9754  0.0292  -1
+Swiftb   4392    56769.38414  1.4700e-14  3.9500e-16  13.9605  0.0292  -1
+Swiftb   4392    56770.51207  1.4700e-14  4.0800e-16  13.9605  0.0301  -1
+Swiftb   4392    56770.84605  1.5100e-14  4.0700e-16  13.9314  0.0293  -1
+Swiftb   4392    56771.17803  1.5000e-14  3.9700e-16  13.9386  0.0287  -1
+Swiftb   4392    56774.72281  1.5600e-14  4.5800e-16  13.8960  0.0319  -1
+Swiftb   4392    56774.92480  1.5100e-14  8.2200e-16  13.9314  0.0591  -1
+Swiftb   4392    56775.57876  1.5200e-14  4.0600e-16  13.9242  0.0290  -1
+Swiftb   4392    56776.03873  1.5300e-14  4.0700e-16  13.9171  0.0289  -1
+Swiftb   4392    56776.23972  1.5300e-14  3.8700e-16  13.9171  0.0275  -1
+Swiftb   4392    56776.97467  1.5800e-14  4.1400e-16  13.8822  0.0284  -1
+Swiftb   4392    56777.37464  1.5600e-14  4.1200e-16  13.8960  0.0287  -1
+Swiftb   4392    56777.52064  1.5000e-14  4.1900e-16  13.9386  0.0303  -1
+Swiftb   4392    56778.58557  1.5200e-14  4.2000e-16  13.9242  0.0300  -1
+Swiftb   4392    56779.37951  1.5600e-14  4.4200e-16  13.8960  0.0308  -1
+Swiftb   4392    56779.85148  1.4800e-14  4.0100e-16  13.9532  0.0294  -1
+Swiftb   4392    56780.25146  1.5100e-14  4.0900e-16  13.9314  0.0294  -1
+Swiftb   4392    56780.52044  1.5800e-14  4.6000e-16  13.8822  0.0316  -1
+Swiftb   4392    56780.58643  1.5700e-14  4.4700e-16  13.8891  0.0309  -1
+Swiftb   4392    56782.09733  1.4600e-14  3.9700e-16  13.9680  0.0295  -1
+Swiftb   4392    56782.30832  1.5400e-14  4.0200e-16  13.9100  0.0283  -1
+Swiftb   4392    56782.70529  1.5200e-14  4.2000e-16  13.9242  0.0300  -1
+Swiftb   4392    56782.97727  1.4900e-14  4.1300e-16  13.9459  0.0301  -1
+Swiftb   4392    56783.36624  1.4900e-14  4.2300e-16  13.9459  0.0308  -1
+Swiftb   4392    56783.56423  1.4800e-14  4.1400e-16  13.9532  0.0304  -1
+Swiftb   4392    56783.96420  1.5700e-14  4.2500e-16  13.8891  0.0294  -1
+Swiftb   4392    56785.04013  1.4900e-14  4.2200e-16  13.9459  0.0308  -1
+Swiftb   4392    56785.56509  1.5100e-14  4.1800e-16  13.9314  0.0301  -1
+Swiftb   4392    56786.36503  1.5600e-14  4.0900e-16  13.8960  0.0285  -1
+Swiftb   4392    56786.97399  1.5400e-14  4.2400e-16  13.9100  0.0299  -1
+Swiftb   4392    56787.30697  1.5300e-14  4.0300e-16  13.9171  0.0286  -1
+Swiftb   4392    56787.71194  1.5000e-14  4.2300e-16  13.9386  0.0306  -1
+Swiftb   4392    56787.98092  1.5400e-14  4.2500e-16  13.9100  0.0300  -1
+Swiftb   4392    56788.30489  1.5300e-14  4.2400e-16  13.9171  0.0301  -1
+Swiftb   4392    56789.04384  1.5200e-14  4.0300e-16  13.9242  0.0288  -1
+Swiftb   4392    56789.30682  1.5200e-14  4.0000e-16  13.9242  0.0286  -1
+Swiftb   4392    56789.64780  1.4800e-14  4.2600e-16  13.9532  0.0313  -1
+Swiftb   4392    56789.96577  1.5300e-14  4.1300e-16  13.9171  0.0293  -1
+Swiftb   4392    56790.63473  1.4600e-14  4.0300e-16  13.9680  0.0300  -1
+Swiftb   4392    56791.03570  1.5200e-14  4.1900e-16  13.9242  0.0299  -1
+Swiftb   4392    56791.63465  1.4800e-14  4.1600e-16  13.9532  0.0305  -1
+Swiftb   4392    56791.96463  1.5300e-14  4.0600e-16  13.9171  0.0288  -1
+Swiftb   4392    56792.36660  1.5500e-14  4.2700e-16  13.9030  0.0299  -1
+Swiftb   4392    56792.50859  1.4700e-14  4.3700e-16  13.9605  0.0323  -1
+Swiftb   4392    56792.84456  1.6300e-14  4.2500e-16  13.8484  0.0283  -1
+Swiftb   4392    56793.84349  1.5300e-14  4.0100e-16  13.9171  0.0285  -1
+Swiftb   4392    56794.24646  1.4900e-14  4.1900e-16  13.9459  0.0305  -1
+Swiftb   4392    56794.69443  1.5100e-14  4.4700e-16  13.9314  0.0321  -1
+Swiftb   4392    56795.36138  1.4800e-14  4.4000e-16  13.9532  0.0323  -1
+Swiftb   4392    56795.89234  1.5000e-14  3.9600e-16  13.9386  0.0287  -1
+Swiftb   4392    56796.89126  1.5200e-14  4.1100e-16  13.9242  0.0294  -1
+Swiftb   4392    56797.22523  1.4900e-14  5.0300e-16  13.9459  0.0367  -1
+Swiftb   4392    56798.50814  1.5700e-14  4.4400e-16  13.8891  0.0307  -1
+Swiftb   4392    56798.89011  1.4700e-14  3.9600e-16  13.9605  0.0292  -1
+Swiftb   4392    56799.22508  1.5000e-14  3.8000e-16  13.9386  0.0275  -1
+Swiftb   4392    56799.56206  1.6100e-14  4.1200e-16  13.8618  0.0278  -1
+Swiftb   4392    56800.02502  1.4800e-14  3.7600e-16  13.9532  0.0276  -1
+Swiftb   4392    56800.36000  1.5200e-14  4.2200e-16  13.9242  0.0301  -1
+Swiftb   4392    56800.82596  1.5300e-14  4.0000e-16  13.9171  0.0284  -1
+Swiftb   4392    56801.09594  1.5200e-14  4.0100e-16  13.9242  0.0286  -1
+Swiftb   4392    56802.09486  1.5400e-14  3.8100e-16  13.9100  0.0269  -1
+Swiftb   4392    56802.62682  1.5500e-14  3.8500e-16  13.9030  0.0270  -1
+Swiftb   4392    56802.88880  1.5600e-14  4.4900e-16  13.8960  0.0312  -1
+Swiftb   4392    56803.62774  1.4900e-14  4.0000e-16  13.9459  0.0291  -1
+Swiftb   4392    56804.22970  1.5700e-14  4.1600e-16  13.8891  0.0288  -1
+Swiftb   4392    56805.08963  1.5200e-14  4.2500e-16  13.9242  0.0304  -1
+Swiftb   4392    56805.97356  1.5400e-14  4.0100e-16  13.9100  0.0283  -1
+Swiftb   4392    56806.36853  1.5200e-14  3.9600e-16  13.9242  0.0283  -1
+Swiftb   4392    56806.88949  1.5400e-14  4.1000e-16  13.9100  0.0289  -1
+Swiftb   4392    56807.22246  1.5600e-14  4.0600e-16  13.8960  0.0283  -1
+Swiftb   4392    56807.88841  1.5300e-14  4.1000e-16  13.9171  0.0291  -1
+Swiftb   4392    56808.55436  1.4700e-14  3.9300e-16  13.9605  0.0290  -1
+Swiftb   4392    56808.88733  1.5500e-14  4.1500e-16  13.9030  0.0291  -1
+Swiftb   4392    56809.22031  1.5200e-14  4.2600e-16  13.9242  0.0304  -1
+Swiftb   4392    56809.88725  1.5900e-14  4.2500e-16  13.8753  0.0290  -1
+Swiftb   4392    56810.22123  1.5800e-14  4.3500e-16  13.8822  0.0299  -1
+Swiftb   4392    56810.55420  1.4900e-14  4.0200e-16  13.9459  0.0293  -1
+Swiftb   4392    56810.88718  1.5800e-14  4.2300e-16  13.8822  0.0291  -1
+Swiftb   4392    56811.22015  1.5200e-14  4.4900e-16  13.9242  0.0321  -1
+Swiftb   4392    56811.75411  1.5200e-14  4.1700e-16  13.9242  0.0298  -1
+Swiftb   4392    56812.02209  1.6300e-14  4.4900e-16  13.8484  0.0299  -1
+Swiftb   4392    56812.37006  1.5800e-14  4.3600e-16  13.8822  0.0300  -1
+Swiftb   4392    56812.55404  1.5500e-14  4.2300e-16  13.9030  0.0296  -1
+Swiftb   4392    56812.88602  1.6000e-14  4.3900e-16  13.8685  0.0298  -1
+Swiftb   4392    56813.22499  1.5600e-14  4.4300e-16  13.8960  0.0308  -1
+Swiftb   4392    56813.55297  1.5600e-14  4.3200e-16  13.8960  0.0301  -1
+Swiftb   4392    56813.97193  1.5900e-14  4.1400e-16  13.8753  0.0283  -1
+Swiftb   4392    56814.36890  1.6100e-14  4.2000e-16  13.8618  0.0283  -1
+Swiftb   4392    56814.55389  1.5800e-14  4.6000e-16  13.8822  0.0316  -1
+Swiftb   4392    56814.62288  1.6100e-14  4.1100e-16  13.8618  0.0277  -1
+Swiftb   4392    56815.69480  1.6000e-14  4.4200e-16  13.8685  0.0300  -1
+Swiftb   4392    56815.95378  1.6200e-14  4.5800e-16  13.8550  0.0307  -1
+Swiftb   4392    56816.22676  1.6100e-14  4.3900e-16  13.8618  0.0296  -1
+Swiftb   4392    56817.62165  1.6700e-14  4.2500e-16  13.8220  0.0276  -1
+Swiftb   4392    56818.08861  1.7100e-14  3.9800e-16  13.7963  0.0253  -1
+Swiftb   4392    56818.23860  1.7200e-14  4.7100e-16  13.7900  0.0297  -1
+Swiftb   4392    56818.70356  1.7800e-14  4.9800e-16  13.7528  0.0304  -1
+Swiftb   4392    56818.95754  1.6600e-14  4.4000e-16  13.8286  0.0288  -1
+Swiftb   4392    56819.69448  1.6500e-14  4.5700e-16  13.8351  0.0301  -1
+Swiftb   4392    56820.22544  1.7200e-14  4.6800e-16  13.7900  0.0295  -1
+Swiftb   4392    56820.57241  1.7900e-14  4.9000e-16  13.7467  0.0297  -1
+Swiftb   4392    56820.63541  1.6900e-14  4.6700e-16  13.8091  0.0300  -1
+Swiftb   4392    56820.68741  1.7300e-14  4.7000e-16  13.7837  0.0295  -1
+Swiftb   4392    56821.90231  1.7100e-14  4.5300e-16  13.7963  0.0288  -1
+Swiftb   4392    56822.36228  1.6700e-14  4.5500e-16  13.8220  0.0296  -1
+Swiftb   4392    56823.15321  1.6600e-14  4.6800e-16  13.8286  0.0306  -1
+Swiftb   4392    56823.29220  1.6700e-14  4.4400e-16  13.8220  0.0289  -1
+Swiftb   4392    56824.61710  1.7300e-14  4.8600e-16  13.7837  0.0305  -1
+Swiftb   4392    56825.36004  1.6500e-14  4.4900e-16  13.8351  0.0295  -1
+Swiftb   4392    56825.54903  1.7800e-14  4.6900e-16  13.7528  0.0286  -1
+Swiftb   4392    56825.95300  1.5900e-14  3.4800e-16  13.8753  0.0238  -1
+Swiftb   4392    56826.42896  1.6500e-14  4.5900e-16  13.8351  0.0302  -1
+Swiftb   4392    56826.76094  1.6400e-14  4.4800e-16  13.8417  0.0297  -1
+Swiftb   4392    56827.03592  1.5900e-14  4.2300e-16  13.8753  0.0289  -1
+Swiftb   4392    56827.42689  1.5800e-14  4.3400e-16  13.8822  0.0298  -1
+Swiftb   4392    56827.88185  1.5700e-14  4.2100e-16  13.8891  0.0291  -1
+Swiftb   4392    56828.14983  1.5900e-14  4.3500e-16  13.8753  0.0297  -1
+Swiftb   4392    56828.42581  1.5700e-14  4.3100e-16  13.8891  0.0298  -1
+Swiftb   4392    56828.55080  1.6100e-14  3.6800e-16  13.8618  0.0248  -1
+Swiftb   4392    56829.01576  1.6100e-14  4.4000e-16  13.8618  0.0297  -1
+Swiftb   4392    56829.81370  1.5600e-14  4.4500e-16  13.8960  0.0310  -1
+Swiftb   4392    56830.14868  1.5400e-14  4.1600e-16  13.9100  0.0293  -1
+Swiftb   4392    56830.41966  1.5500e-14  4.2300e-16  13.9030  0.0296  -1
\ No newline at end of file
diff --git a/data/STORM_III/raw/separated/4775Å.tab b/data/STORM_III/raw/separated/4775Å.tab
new file mode 100644
index 0000000..1625088
--- /dev/null
+++ b/data/STORM_III/raw/separated/4775Å.tab
@@ -0,0 +1,172 @@
+g        4775    56692.94828  1.3913e-14  1.8924e-16  13.8385  0.0148  5
+g        4775    56693.78997  1.3556e-14  1.8012e-17  13.8667  0.0014  1
+g        4775    56694.77052  1.3742e-14  2.7779e-17  13.8519  0.0022  1
+g        4775    56695.70991  1.3882e-14  4.7708e-17  13.8409  0.0037  1
+g        4775    56696.25188  1.3844e-14  3.2561e-16  13.8439  0.0255  2
+g        4775    56696.72838  1.3912e-14  2.0569e-17  13.8386  0.0016  1
+g        4775    56696.98357  1.4217e-14  1.8527e-16  13.8150  0.0141  5
+g        4775    56697.26607  1.4114e-14  2.3255e-16  13.8229  0.0179  2
+g        4775    56697.75721  1.4082e-14  1.4072e-17  13.8254  0.0011  1
+g        4775    56698.25350  1.4228e-14  3.1321e-16  13.8142  0.0239  3
+g        4775    56698.60902  1.4162e-14  8.6627e-17  13.8192  0.0066  4
+g        4775    56698.71785  1.4160e-14  1.6121e-17  13.8194  0.0012  1
+g        4775    56699.71346  1.4234e-14  1.5308e-17  13.8137  0.0012  1
+g        4775    56700.69203  1.4274e-14  8.7576e-17  13.8107  0.0067  1
+g        4775    56701.27577  1.3920e-14  3.9884e-16  13.8379  0.0311  3
+g        4775    56701.56631  1.4389e-14  1.6379e-17  13.8020  0.0012  1
+g        4775    56701.83042  1.4246e-14  1.7690e-16  13.8128  0.0135  5
+g        4775    56702.63876  1.4293e-14  1.5642e-17  13.8092  0.0012  1
+g        4775    56703.01457  1.4306e-14  1.8669e-16  13.8082  0.0142  5
+g        4775    56703.88024  1.4434e-14  1.6975e-16  13.7986  0.0128  5
+g        4775    56704.83738  1.4445e-14  1.7139e-16  13.7977  0.0129  5
+g        4775    56705.95135  1.4395e-14  1.7397e-16  13.8015  0.0131  5
+g        4775    56706.98093  1.4586e-14  1.7364e-16  13.7872  0.0129  5
+g        4775    56708.02307  1.4447e-14  1.8355e-16  13.7976  0.0138  5
+g        4775    56709.25030  1.3975e-14  2.5656e-16  13.8337  0.0199  2
+g        4775    56709.82729  1.4248e-14  1.8702e-16  13.8127  0.0143  5
+g        4775    56710.66228  1.3859e-14  5.3184e-17  13.8427  0.0042  1
+g        4775    56711.68656  1.3688e-14  1.8460e-17  13.8562  0.0015  1
+g        4775    56711.92226  1.3312e-14  2.2319e-16  13.8864  0.0182  5
+g        4775    56712.27343  1.3782e-14  4.5865e-16  13.8488  0.0361  3
+g        4775    56713.26581  1.3760e-14  3.9367e-16  13.8505  0.0311  2
+g        4775    56713.72281  1.3613e-14  1.4292e-17  13.8622  0.0011  1
+g        4775    56713.91844  1.4009e-14  1.8904e-16  13.8310  0.0147  5
+g        4775    56714.72606  1.3515e-14  2.7151e-17  13.8700  0.0022  1
+g        4775    56715.27919  1.3844e-14  3.8556e-16  13.8439  0.0302  2
+g        4775    56715.83090  1.3836e-14  2.1491e-16  13.8445  0.0169  5
+g        4775    56716.65823  1.3199e-14  6.5605e-17  13.8957  0.0054  1
+g        4775    56717.73236  1.2958e-14  2.1769e-17  13.9157  0.0018  1
+g        4775    56717.95168  1.2649e-14  2.6928e-16  13.9419  0.0231  5
+g        4775    56718.66976  1.2930e-14  2.3803e-17  13.9180  0.0020  1
+g        4775    56719.59986  1.2674e-14  2.1553e-16  13.9398  0.0185  4
+g        4775    56720.69136  1.2655e-14  2.2636e-17  13.9414  0.0019  1
+g        4775    56721.71304  1.2395e-14  2.0276e-17  13.9639  0.0018  1
+g        4775    56722.73699  1.2039e-14  2.3331e-17  13.9956  0.0021  1
+g        4775    56723.57046  1.2086e-14  2.1431e-17  13.9913  0.0019  1
+g        4775    56724.52830  1.2153e-14  2.0434e-17  13.9853  0.0018  1
+g        4775    56725.53398  1.1918e-14  1.5730e-17  14.0065  0.0014  1
+g        4775    56726.16793  1.2000e-14  5.2623e-16  13.9991  0.0476  2
+g        4775    56727.14814  1.1863e-14  6.2330e-16  14.0116  0.0570  2
+g        4775    56728.57528  1.2158e-14  1.8774e-17  13.9849  0.0017  1
+g        4775    56729.49420  1.2158e-14  1.8336e-17  13.9849  0.0016  1
+g        4775    56730.53713  1.2266e-14  1.7298e-17  13.9753  0.0015  1
+g        4775    56730.82880  1.1981e-14  4.4497e-16  14.0008  0.0403  5
+g        4775    56733.79888  1.1840e-14  3.5437e-16  14.0137  0.0325  5
+g        4775    56734.47920  1.2550e-14  2.0904e-17  13.9504  0.0018  1
+g        4775    56735.49729  1.2837e-14  2.1313e-17  13.9259  0.0018  1
+g        4775    56735.85749  1.3019e-14  5.3416e-16  13.9106  0.0445  5
+g        4775    56736.50348  1.3646e-14  2.0129e-17  13.8595  0.0016  1
+g        4775    56736.77008  1.2761e-14  3.2299e-16  13.9323  0.0275  5
+g        4775    56740.85991  1.4375e-14  2.3473e-16  13.8030  0.0177  5
+g        4775    56741.61068  1.4150e-14  2.2229e-17  13.8201  0.0017  1
+g        4775    56741.71428  1.4343e-14  2.2541e-16  13.8054  0.0171  5
+g        4775    56743.59490  1.4792e-14  2.2983e-17  13.7720  0.0017  1
+g        4775    56744.69630  1.4929e-14  2.0653e-16  13.7620  0.0150  5
+g        4775    56746.83397  1.5082e-14  2.1297e-16  13.7509  0.0153  5
+g        4775    56747.96827  1.5504e-14  2.2660e-16  13.7209  0.0159  5
+g        4775    56748.75496  1.4933e-14  1.9786e-16  13.7617  0.0144  5
+g        4775    56749.75212  1.5101e-14  1.7202e-16  13.7495  0.0124  5
+g        4775    56751.98056  1.4874e-14  1.8864e-16  13.7660  0.0138  5
+g        4775    56752.58106  1.5141e-14  3.3156e-17  13.7466  0.0024  1
+g        4775    56753.57604  1.4791e-14  3.0625e-17  13.7720  0.0022  1
+g        4775    56754.55372  1.4521e-14  2.7433e-17  13.7920  0.0021  1
+g        4775    56755.56329  1.4049e-14  2.1220e-17  13.8279  0.0016  1
+g        4775    56755.97918  1.4117e-14  1.9640e-16  13.8227  0.0151  5
+g        4775    56756.47692  1.4193e-14  1.5837e-16  13.8169  0.0121  4
+g        4775    56757.13872  1.4389e-14  4.8528e-16  13.8020  0.0366  3
+g        4775    56757.46899  1.4304e-14  9.2279e-17  13.8084  0.0070  4
+g        4775    56758.74467  1.4340e-14  1.8826e-16  13.8057  0.0143  5
+g        4775    56759.88442  1.4032e-14  1.9818e-16  13.8292  0.0153  5
+g        4775    56760.20359  1.3704e-14  5.3731e-16  13.8549  0.0426  3
+g        4775    56761.60131  1.3948e-14  2.0177e-17  13.8358  0.0016  1
+g        4775    56762.46791  1.4264e-14  2.3044e-17  13.8114  0.0018  1
+g        4775    56762.78357  1.4077e-14  1.8136e-16  13.8258  0.0140  5
+g        4775    56763.55634  1.4296e-14  2.1244e-17  13.8090  0.0016  1
+g        4775    56763.75145  1.4258e-14  1.7724e-16  13.8119  0.0135  5
+g        4775    56764.82036  1.4209e-14  1.7879e-16  13.8156  0.0137  5
+g        4775    56765.04045  1.4136e-14  4.1984e-16  13.8212  0.0322  2
+g        4775    56765.58150  1.4012e-14  2.1774e-17  13.8308  0.0017  1
+g        4775    56765.87464  1.4106e-14  1.8453e-16  13.8235  0.0142  5
+g        4775    56770.52544  1.4266e-14  2.9852e-17  13.8113  0.0023  1
+g        4775    56770.77986  1.4534e-14  1.7933e-16  13.7911  0.0134  5
+g        4775    56771.51007  1.4615e-14  3.0284e-17  13.7850  0.0022  1
+g        4775    56772.07535  1.4842e-14  3.7162e-16  13.7683  0.0272  3
+g        4775    56772.49675  1.4555e-14  2.3008e-17  13.7895  0.0017  1
+g        4775    56772.82431  1.4586e-14  1.9940e-16  13.7872  0.0148  5
+g        4775    56773.83144  1.4698e-14  1.8580e-16  13.7789  0.0137  5
+g        4775    56774.75914  1.4682e-14  2.3826e-16  13.7801  0.0176  5
+g        4775    56775.54709  1.4881e-14  2.1703e-17  13.7655  0.0016  1
+g        4775    56776.07454  1.5082e-14  2.6499e-16  13.7509  0.0191  3
+g        4775    56777.02149  1.4913e-14  4.8642e-16  13.7631  0.0354  2
+g        4775    56777.52898  1.4646e-14  2.8533e-17  13.7827  0.0021  1
+g        4775    56778.49606  1.4665e-14  2.6080e-17  13.7813  0.0019  1
+g        4775    56779.39684  1.4657e-14  1.1854e-16  13.7819  0.0088  4
+g        4775    56779.48590  1.4650e-14  2.5238e-17  13.7824  0.0019  1
+g        4775    56779.85156  1.4536e-14  1.9260e-16  13.7909  0.0144  5
+g        4775    56780.49336  1.4625e-14  2.9884e-17  13.7843  0.0022  1
+g        4775    56780.81515  1.4588e-14  1.9312e-16  13.7870  0.0144  5
+g        4775    56781.49256  1.4633e-14  3.0656e-17  13.7837  0.0023  1
+g        4775    56782.44577  1.4378e-14  2.7920e-17  13.8028  0.0021  1
+g        4775    56782.74090  1.4478e-14  2.0494e-16  13.7953  0.0154  5
+g        4775    56783.45060  1.4521e-14  2.8214e-17  13.7920  0.0021  1
+g        4775    56784.43989  1.4749e-14  3.2331e-17  13.7751  0.0024  1
+g        4775    56785.47458  1.4874e-14  2.9764e-17  13.7660  0.0022  1
+g        4775    56786.48146  1.4684e-14  2.8576e-17  13.7799  0.0021  1
+g        4775    56787.40246  1.4572e-14  2.2050e-17  13.7882  0.0016  1
+g        4775    56790.45804  1.4528e-14  2.0048e-17  13.7915  0.0015  1
+g        4775    56793.46403  1.4617e-14  2.0660e-17  13.7849  0.0015  1
+g        4775    56794.46237  1.4552e-14  2.8249e-17  13.7897  0.0021  1
+g        4775    56795.44438  1.4342e-14  3.5353e-17  13.8055  0.0027  1
+g        4775    56796.44679  1.4478e-14  3.7027e-17  13.7953  0.0028  1
+g        4775    56797.44947  1.4572e-14  3.8895e-17  13.7882  0.0029  1
+g        4775    56798.44237  1.4722e-14  3.4131e-17  13.7771  0.0025  1
+g        4775    56799.43929  1.4602e-14  3.2299e-17  13.7860  0.0024  1
+g        4775    56800.44167  1.4572e-14  3.2292e-17  13.7882  0.0024  1
+g        4775    56801.42619  1.4536e-14  3.4059e-17  13.7909  0.0025  1
+g        4775    56802.43572  1.4516e-14  2.1506e-17  13.7924  0.0016  1
+g        4775    56803.43941  1.4539e-14  2.9914e-17  13.7907  0.0022  1
+g        4775    56805.45929  1.4516e-14  3.2424e-17  13.7924  0.0024  1
+g        4775    56806.43246  1.4541e-14  2.3379e-17  13.7906  0.0017  1
+g        4775    56807.43426  1.4455e-14  2.2998e-17  13.7970  0.0017  1
+g        4775    56808.41375  1.4658e-14  2.9973e-17  13.7818  0.0022  1
+g        4775    56809.42867  1.4679e-14  3.0522e-17  13.7803  0.0023  1
+g        4775    56810.42973  1.4644e-14  3.6251e-17  13.7829  0.0027  1
+g        4775    56813.40141  1.4582e-14  3.2039e-17  13.7875  0.0024  1
+g        4775    56814.45463  1.4841e-14  2.8946e-17  13.7684  0.0021  1
+g        4775    56815.40223  1.5082e-14  3.1475e-17  13.7509  0.0023  1
+g        4775    56816.40920  1.5101e-14  3.2889e-17  13.7495  0.0024  1
+g        4775    56816.63585  1.4827e-14  1.7415e-16  13.7694  0.0128  5
+g        4775    56817.40923  1.5363e-14  2.8181e-17  13.7308  0.0020  1
+g        4775    56817.83107  1.5248e-14  2.3106e-16  13.7390  0.0165  5
+g        4775    56818.41406  1.5298e-14  2.5621e-17  13.7354  0.0018  1
+g        4775    56819.41414  1.5733e-14  2.5888e-17  13.7050  0.0018  1
+g        4775    56822.39478  1.5740e-14  2.9131e-17  13.7045  0.0020  1
+g        4775    56823.40574  1.5714e-14  3.3928e-17  13.7063  0.0023  1
+g        4775    56824.39957  1.5753e-14  3.2094e-17  13.7036  0.0022  1
+g        4775    56825.41351  1.5723e-14  3.9410e-17  13.7057  0.0027  1
+g        4775    56826.28815  1.5380e-14  1.2682e-16  13.7296  0.0090  4
+g        4775    56826.39129  1.5305e-14  3.5860e-17  13.7350  0.0025  1
+g        4775    56827.39605  1.5166e-14  3.4523e-17  13.7449  0.0025  1
+g        4775    56828.39839  1.5059e-14  3.1421e-17  13.7525  0.0023  1
+g        4775    56829.40822  1.4761e-14  3.3231e-17  13.7742  0.0024  1
+g        4775    56830.40097  1.4707e-14  3.6825e-17  13.7782  0.0027  1
+g        4775    56831.39238  1.4668e-14  3.3127e-17  13.7811  0.0025  1
+g        4775    56832.43523  1.4649e-14  3.0247e-17  13.7825  0.0022  1
+g        4775    56833.39659  1.4757e-14  2.7630e-17  13.7745  0.0020  1
+g        4775    56834.39117  1.4909e-14  2.7646e-17  13.7634  0.0020  1
+g        4775    56835.39264  1.4627e-14  2.8351e-17  13.7841  0.0021  1
+g        4775    56837.23078  1.4685e-14  1.2595e-16  13.7799  0.0093  4
+g        4775    56839.33334  1.4648e-14  1.0757e-16  13.7826  0.0080  4
+g        4775    56841.41052  1.4786e-14  3.0831e-17  13.7724  0.0023  1
+g        4775    56843.39036  1.4545e-14  3.0295e-17  13.7903  0.0023  1
+g        4775    56845.40969  1.4491e-14  3.1735e-17  13.7943  0.0024  1
+g        4775    56847.40034  1.4168e-14  2.2144e-17  13.8188  0.0017  1
+g        4775    56848.85142  1.3626e-14  5.7652e-16  13.8611  0.0459  3
+g        4775    56858.38691  1.4099e-14  3.3488e-17  13.8241  0.0026  1
+g        4775    56860.38584  1.4178e-14  3.3028e-17  13.8180  0.0025  1
+g        4775    56861.38487  1.4088e-14  2.7736e-17  13.8249  0.0021  1
+g        4775    56862.38541  1.4014e-14  3.5174e-17  13.8306  0.0027  1
+g        4775    56863.38384  1.3759e-14  2.6065e-17  13.8506  0.0021  1
+g        4775    56864.38385  1.3922e-14  3.4208e-17  13.8378  0.0027  1
+g        4775    56864.68837  1.3747e-14  2.4862e-16  13.8515  0.0196  5
+g        4775    56865.38279  1.4296e-14  3.3445e-17  13.8090  0.0025  1
\ No newline at end of file
diff --git a/data/STORM_III/raw/separated/5404Å.tab b/data/STORM_III/raw/separated/5404Å.tab
new file mode 100644
index 0000000..8726b0e
--- /dev/null
+++ b/data/STORM_III/raw/separated/5404Å.tab
@@ -0,0 +1,429 @@
+V        5404    56691.62226  1.2916e-14  3.3940e-17  13.6507  0.0029  1
+V        5404    56692.87101  1.3015e-14  1.1448e-16  13.6424  0.0096  4
+V        5404    56693.82884  1.2926e-14  4.9648e-17  13.6499  0.0042  4
+V        5404    56693.89079  1.3128e-14  7.8165e-17  13.6331  0.0065  2
+V        5404    56697.55422  1.3340e-14  6.8248e-17  13.6157  0.0056  1
+V        5404    56697.82079  1.3304e-14  5.0462e-17  13.6186  0.0041  4
+V        5404    56697.83250  1.3275e-14  8.1106e-17  13.6210  0.0066  2
+V        5404    56698.52913  1.3372e-14  3.9742e-17  13.6131  0.0032  1
+V        5404    56699.46554  1.3480e-14  3.1266e-17  13.6043  0.0025  1
+V        5404    56700.49535  1.3461e-14  3.3659e-17  13.6059  0.0027  1
+V        5404    56700.99825  1.3684e-14  2.3240e-16  13.5880  0.0184  3
+V        5404    56701.03500  1.3498e-14  5.5328e-17  13.6029  0.0045  7
+V        5404    56701.55568  1.3462e-14  2.3048e-17  13.6058  0.0019  1
+V        5404    56701.81640  1.3487e-14  3.5889e-17  13.6038  0.0029  4
+V        5404    56701.82560  1.3423e-14  5.3838e-17  13.6089  0.0044  2
+V        5404    56701.97780  1.3657e-14  2.6880e-16  13.5902  0.0214  3
+V        5404    56702.53063  1.3479e-14  2.8322e-17  13.6044  0.0023  1
+V        5404    56702.80790  1.3469e-14  3.5630e-17  13.6052  0.0029  4
+V        5404    56703.85550  1.3542e-14  6.9003e-17  13.5993  0.0055  2
+V        5404    56704.79649  1.3734e-14  4.3926e-17  13.5841  0.0035  4
+V        5404    56705.85883  1.3553e-14  4.9159e-17  13.5985  0.0039  4
+V        5404    56706.84653  1.3620e-14  4.8569e-17  13.5931  0.0039  4
+V        5404    56707.00720  1.3484e-14  3.5060e-17  13.6040  0.0028  7
+V        5404    56707.88099  1.3807e-14  5.7964e-17  13.5783  0.0046  2
+V        5404    56708.00481  1.3554e-14  3.2078e-17  13.5984  0.0026  7
+V        5404    56708.62443  1.3492e-14  3.5231e-17  13.6034  0.0028  1
+V        5404    56708.88640  1.3725e-14  2.7551e-16  13.5848  0.0218  3
+V        5404    56708.92011  1.3251e-14  8.9579e-17  13.6229  0.0073  10
+V        5404    56709.00386  1.3480e-14  3.0869e-17  13.6043  0.0025  7
+V        5404    56709.58949  1.3372e-14  2.6157e-17  13.6131  0.0021  1
+V        5404    56709.81150  1.3395e-14  3.6601e-17  13.6112  0.0030  4
+V        5404    56709.85646  1.3528e-14  5.4787e-17  13.6005  0.0044  2
+V        5404    56709.96162  1.3615e-14  5.1558e-17  13.5935  0.0041  10
+V        5404    56710.00608  1.3274e-14  3.2491e-17  13.6210  0.0027  7
+V        5404    56710.47224  1.3308e-14  3.7517e-17  13.6183  0.0031  1
+V        5404    56710.92726  1.3328e-14  2.2782e-16  13.6166  0.0186  3
+V        5404    56711.00907  1.3038e-14  5.8200e-17  13.6405  0.0048  10
+V        5404    56711.79904  1.3381e-14  8.1593e-17  13.6123  0.0066  4
+V        5404    56711.89090  1.3472e-14  3.4579e-16  13.6050  0.0279  3
+V        5404    56712.00111  1.3301e-14  5.2106e-17  13.6188  0.0043  10
+V        5404    56712.20821  1.3331e-14  7.2942e-17  13.6164  0.0059  15
+V        5404    56712.91193  1.3439e-14  3.0065e-16  13.6076  0.0243  3
+V        5404    56713.19762  1.3173e-14  6.8688e-17  13.6293  0.0057  16
+V        5404    56713.81538  1.3168e-14  5.8476e-17  13.6297  0.0048  4
+V        5404    56714.21081  1.3016e-14  1.1241e-16  13.6424  0.0094  16
+V        5404    56714.45903  1.2988e-14  3.5496e-17  13.6447  0.0030  1
+V        5404    56715.02663  1.3066e-14  4.8060e-17  13.6382  0.0040  7
+V        5404    56715.54025  1.3198e-14  7.6388e-17  13.6273  0.0063  11
+V        5404    56715.81971  1.2902e-14  7.9930e-17  13.6519  0.0067  4
+V        5404    56715.89472  1.2620e-14  6.4742e-17  13.6759  0.0056  2
+V        5404    56716.00845  1.2929e-14  5.0264e-17  13.6496  0.0042  7
+V        5404    56716.84624  1.2957e-14  8.9603e-17  13.6473  0.0075  4
+V        5404    56716.91669  1.2496e-14  5.8402e-17  13.6866  0.0051  2
+V        5404    56717.90183  1.2463e-14  6.5271e-17  13.6895  0.0057  2
+V        5404    56718.46525  1.2464e-14  4.6730e-17  13.6894  0.0041  1
+V        5404    56722.93956  1.1669e-14  9.7179e-17  13.7610  0.0090  2
+V        5404    56723.44704  1.1586e-14  4.1934e-17  13.7687  0.0039  1
+V        5404    56723.87681  1.1550e-14  1.0313e-16  13.7721  0.0097  2
+V        5404    56725.85614  1.1526e-14  9.3364e-17  13.7744  0.0088  6
+V        5404    56727.44960  1.1572e-14  3.7123e-17  13.7700  0.0035  1
+V        5404    56729.82751  1.1870e-14  1.0180e-16  13.7424  0.0093  2
+V        5404    56730.87228  1.1873e-14  9.0512e-17  13.7421  0.0083  2
+V        5404    56730.91885  1.1930e-14  9.5081e-17  13.7369  0.0087  6
+V        5404    56731.87819  1.1849e-14  2.9290e-16  13.7443  0.0268  3
+V        5404    56731.89093  1.2099e-14  1.1340e-16  13.7217  0.0102  10
+V        5404    56732.49732  1.2056e-14  2.5845e-17  13.7255  0.0023  1
+V        5404    56733.03934  1.1503e-14  2.5934e-16  13.7765  0.0245  3
+V        5404    56733.81337  1.1726e-14  9.1736e-17  13.7557  0.0085  10
+V        5404    56734.87490  1.2002e-14  9.0500e-17  13.7304  0.0082  2
+V        5404    56734.93954  1.2279e-14  1.1022e-16  13.7056  0.0097  10
+V        5404    56735.77574  1.2660e-14  4.8115e-17  13.6725  0.0041  2
+V        5404    56735.94263  1.2559e-14  3.3219e-17  13.6812  0.0029  7
+V        5404    56737.43270  1.2630e-14  1.9183e-17  13.6750  0.0016  1
+V        5404    56738.44405  1.2825e-14  2.7635e-17  13.6584  0.0023  1
+V        5404    56738.99077  1.3007e-14  3.6749e-17  13.6431  0.0031  4
+V        5404    56739.92102  1.3332e-14  8.6062e-17  13.6163  0.0070  6
+V        5404    56739.92692  1.3340e-14  3.9549e-17  13.6157  0.0032  7
+V        5404    56739.95124  1.3337e-14  2.9216e-16  13.6159  0.0238  3
+V        5404    56740.97243  1.3431e-14  5.8499e-17  13.6083  0.0047  4
+V        5404    56741.73763  1.3660e-14  5.4316e-17  13.5899  0.0043  2
+V        5404    56741.87938  1.3491e-14  8.3328e-17  13.6034  0.0067  6
+V        5404    56741.91613  1.3509e-14  4.9037e-17  13.6020  0.0039  7
+V        5404    56741.95456  1.3447e-14  8.0607e-17  13.6070  0.0065  4
+V        5404    56742.44415  1.3660e-14  4.3412e-17  13.5899  0.0035  1
+V        5404    56742.91724  1.3529e-14  6.5057e-17  13.6004  0.0052  10
+V        5404    56742.99643  1.3745e-14  6.8076e-17  13.5832  0.0054  4
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+V        5404    56819.29232  1.4906e-14  1.0717e-16  13.4951  0.0078  5
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+V        5404    56819.81719  1.4911e-14  7.1611e-17  13.4948  0.0052  4
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+V        5404    56820.77850  1.5003e-14  3.0815e-16  13.4881  0.0223  3
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+V        5404    56821.32277  1.4987e-14  1.1388e-16  13.4893  0.0083  14
+V        5404    56821.35255  1.4895e-14  2.5167e-17  13.4959  0.0018  1
+V        5404    56821.74935  1.4662e-14  2.6224e-16  13.5131  0.0194  3
+V        5404    56822.30869  1.4688e-14  4.6507e-17  13.5111  0.0034  1
+V        5404    56822.67602  1.4814e-14  1.0671e-16  13.5019  0.0078  2
+V        5404    56823.34616  1.4730e-14  3.7618e-17  13.5080  0.0028  1
+V        5404    56823.74657  1.4734e-14  2.9445e-16  13.5077  0.0217  3
+V        5404    56824.39947  1.4702e-14  3.0449e-17  13.5101  0.0022  1
+V        5404    56824.73701  1.4709e-14  2.7305e-16  13.5096  0.0202  3
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+V        5404    56825.75026  1.4480e-14  9.3566e-17  13.5266  0.0070  8
+V        5404    56826.64786  1.4180e-14  9.0934e-17  13.5494  0.0070  8
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+V        5404    56826.68166  1.4147e-14  6.7612e-17  13.5519  0.0052  2
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+V        5404    56827.66844  1.4270e-14  1.1259e-16  13.5425  0.0086  8
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+V        5404    56829.74715  1.3971e-14  1.3644e-16  13.5655  0.0106  9
+V        5404    56829.74886  1.3745e-14  5.0278e-17  13.5832  0.0040  8
+V        5404    56830.24214  1.3956e-14  1.3842e-16  13.5666  0.0108  14
+V        5404    56830.30394  1.4003e-14  1.0476e-16  13.5630  0.0081  5
+V        5404    56830.76769  1.3954e-14  5.2657e-17  13.5668  0.0041  10
+V        5404    56831.27387  1.4004e-14  1.2263e-16  13.5629  0.0095  5
+V        5404    56831.28128  1.3797e-14  7.0548e-17  13.5791  0.0056  1
+V        5404    56831.62063  1.3797e-14  1.7446e-16  13.5791  0.0137  13
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+V        5404    56832.27471  1.3788e-14  7.2348e-17  13.5798  0.0057  1
+V        5404    56832.48332  1.3874e-14  1.8162e-16  13.5730  0.0142  13
+V        5404    56832.81541  1.3973e-14  8.3418e-17  13.5653  0.0065  6
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+V        5404    56833.73859  1.3906e-14  2.1709e-16  13.5705  0.0169  3
+V        5404    56833.82215  1.3821e-14  8.4434e-17  13.5772  0.0066  6
+V        5404    56834.32497  1.3824e-14  7.4214e-17  13.5770  0.0058  1
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+V        5404    56835.71455  1.3733e-14  2.7463e-16  13.5841  0.0217  3
+V        5404    56836.31307  1.3756e-14  4.8970e-17  13.5823  0.0039  1
+V        5404    56836.62368  1.3788e-14  1.7179e-16  13.5798  0.0135  13
+V        5404    56836.70019  1.3605e-14  5.5677e-17  13.5943  0.0044  2
+V        5404    56836.72459  1.3916e-14  2.1277e-16  13.5698  0.0166  3
+V        5404    56836.81204  1.3778e-14  1.2697e-16  13.5806  0.0100  9
+V        5404    56837.31374  1.3739e-14  4.8240e-17  13.5837  0.0038  1
+V        5404    56837.62027  1.3859e-14  1.7927e-16  13.5742  0.0140  13
+V        5404    56837.65243  1.3744e-14  5.6353e-17  13.5833  0.0045  2
+V        5404    56837.71889  1.3680e-14  1.8514e-16  13.5883  0.0147  9
+V        5404    56837.76990  1.3859e-14  9.6745e-17  13.5742  0.0076  6
+V        5404    56838.29949  1.3979e-14  1.2039e-16  13.5649  0.0094  5
+V        5404    56838.62409  1.3857e-14  5.8049e-17  13.5744  0.0045  2
+V        5404    56838.75443  1.3610e-14  2.1287e-16  13.5939  0.0170  3
+V        5404    56838.77249  1.3808e-14  8.7214e-17  13.5782  0.0069  6
+V        5404    56839.34175  1.3789e-14  3.3839e-17  13.5797  0.0027  1
+V        5404    56839.47767  1.3825e-14  1.7540e-16  13.5769  0.0138  13
+V        5404    56839.61735  1.3799e-14  5.4273e-17  13.5789  0.0043  2
+V        5404    56839.69027  1.3870e-14  5.1112e-17  13.5734  0.0040  10
+V        5404    56839.72196  1.3744e-14  4.2714e-16  13.5833  0.0337  3
+V        5404    56839.75460  1.3801e-14  9.3899e-17  13.5788  0.0074  6
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+V        5404    56840.60956  1.4005e-14  2.5010e-16  13.5628  0.0194  12
+V        5404    56840.65820  1.3846e-14  6.0476e-17  13.5752  0.0047  2
+V        5404    56840.69592  1.3875e-14  5.7907e-17  13.5730  0.0045  10
+V        5404    56840.71376  1.3753e-14  2.6081e-16  13.5826  0.0206  3
+V        5404    56840.77846  1.3917e-14  8.6240e-17  13.5697  0.0067  6
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+V        5404    56841.72252  1.3807e-14  1.8952e-16  13.5783  0.0149  3
+V        5404    56841.83064  1.3866e-14  8.6008e-17  13.5737  0.0067  6
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+V        5404    56844.68909  1.3672e-14  5.4906e-17  13.5890  0.0044  2
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+V        5404    56848.63379  1.3404e-14  5.7355e-17  13.6105  0.0046  2
+V        5404    56848.71618  1.3505e-14  3.0105e-16  13.6023  0.0242  3
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+V        5404    56850.62787  1.3190e-14  7.4333e-17  13.6279  0.0061  2
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+V        5404    56861.20086  1.3069e-14  7.8444e-17  13.6379  0.0065  5
+V        5404    56861.62344  1.3213e-14  5.3736e-17  13.6260  0.0044  2
+V        5404    56861.67183  1.2989e-14  1.5693e-16  13.6446  0.0131  9
+V        5404    56861.69092  1.2997e-14  9.0132e-16  13.6439  0.0753  3
+V        5404    56862.69153  1.2907e-14  1.5882e-16  13.6515  0.0134  9
+V        5404    56862.70101  1.3267e-14  2.7009e-16  13.6216  0.0221  3
+V        5404    56863.23803  1.2746e-14  1.1061e-16  13.6651  0.0094  5
+V        5404    56863.62566  1.3195e-14  8.8272e-17  13.6275  0.0073  2
+V        5404    56863.70811  1.3322e-14  2.1077e-16  13.6171  0.0172  3
+V        5404    56863.74440  1.3161e-14  2.5621e-16  13.6303  0.0211  6
+V        5404    56864.51044  1.3170e-14  2.6839e-16  13.6296  0.0221  12
+V        5404    56864.62645  1.3110e-14  8.4238e-17  13.6345  0.0070  2
+V        5404    56864.69856  1.3381e-14  5.6120e-16  13.6123  0.0455  3
+V        5404    56865.63124  1.3474e-14  8.9977e-17  13.6048  0.0073  2
+V        5404    56865.69358  1.3945e-14  2.8168e-16  13.5675  0.0219  3
\ No newline at end of file
diff --git a/data/STORM_III/raw/separated/5468Å.tab b/data/STORM_III/raw/separated/5468Å.tab
new file mode 100644
index 0000000..695bdaa
--- /dev/null
+++ b/data/STORM_III/raw/separated/5468Å.tab
@@ -0,0 +1,260 @@
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+Swiftv   5468    56822.36528  1.3300e-14  4.4300e-16  13.5934  0.0362  -1
+Swiftv   5468    56823.15621  1.3500e-14  4.6500e-16  13.5772  0.0374  -1
+Swiftv   5468    56823.29620  1.3100e-14  4.2500e-16  13.6098  0.0352  -1
+Swiftv   5468    56824.62010  1.3700e-14  4.7000e-16  13.5612  0.0372  -1
+Swiftv   5468    56825.36304  1.3200e-14  4.3900e-16  13.6016  0.0361  -1
+Swiftv   5468    56825.55203  1.3600e-14  4.3900e-16  13.5692  0.0350  -1
+Swiftv   5468    56825.96100  1.3300e-14  3.4600e-16  13.5934  0.0282  -1
+Swiftv   5468    56826.43096  1.3600e-14  4.6000e-16  13.5692  0.0367  -1
+Swiftv   5468    56826.76394  1.2900e-14  4.3400e-16  13.6265  0.0365  -1
+Swiftv   5468    56827.03891  1.2900e-14  4.2200e-16  13.6265  0.0355  -1
+Swiftv   5468    56827.42989  1.3300e-14  4.4300e-16  13.5934  0.0362  -1
+Swiftv   5468    56827.88485  1.2800e-14  4.1700e-16  13.6350  0.0354  -1
+Swiftv   5468    56828.15283  1.3200e-14  4.4000e-16  13.6016  0.0362  -1
+Swiftv   5468    56828.42881  1.2800e-14  4.3200e-16  13.6350  0.0366  -1
+Swiftv   5468    56828.55680  1.3100e-14  3.5700e-16  13.6098  0.0296  -1
+Swiftv   5468    56829.01776  1.3100e-14  4.3800e-16  13.6098  0.0363  -1
+Swiftv   5468    56829.81670  1.2500e-14  4.3900e-16  13.6607  0.0381  -1
+Swiftv   5468    56830.15168  1.2500e-14  4.1300e-16  13.6607  0.0359  -1
+Swiftv   5468    56830.42266  1.2600e-14  4.2100e-16  13.6521  0.0363  -1
\ No newline at end of file
diff --git a/data/STORM_III/raw/separated/6175Å.tab b/data/STORM_III/raw/separated/6175Å.tab
new file mode 100644
index 0000000..8d46fc8
--- /dev/null
+++ b/data/STORM_III/raw/separated/6175Å.tab
@@ -0,0 +1,172 @@
+r        6175    56692.94899  1.5638e-14  8.3689e-17  13.1533  0.0058  5
+r        6175    56693.78932  1.5304e-14  1.0731e-17  13.1767  0.0008  1
+r        6175    56694.76987  1.5420e-14  2.3399e-17  13.1685  0.0016  1
+r        6175    56695.70926  1.5483e-14  3.2219e-17  13.1641  0.0023  1
+r        6175    56696.72772  1.5625e-14  1.2458e-17  13.1542  0.0009  1
+r        6175    56696.98428  1.5768e-14  8.7270e-17  13.1443  0.0060  5
+r        6175    56697.75655  1.5663e-14  2.1267e-17  13.1516  0.0015  1
+r        6175    56698.25458  1.5354e-14  6.1018e-16  13.1732  0.0431  2
+r        6175    56698.60973  1.5654e-14  4.0809e-16  13.1522  0.0283  3
+r        6175    56698.71720  1.5598e-14  6.7738e-17  13.1561  0.0047  1
+r        6175    56699.71280  1.5659e-14  1.5572e-17  13.1518  0.0011  1
+r        6175    56700.59946  1.5875e-14  8.5348e-17  13.1370  0.0058  4
+r        6175    56700.69138  1.5835e-14  1.5467e-17  13.1397  0.0011  1
+r        6175    56701.56565  1.5919e-14  2.8327e-17  13.1340  0.0019  1
+r        6175    56701.60387  1.5744e-14  8.8521e-17  13.1460  0.0061  4
+r        6175    56701.83115  1.5886e-14  8.2288e-17  13.1362  0.0056  5
+r        6175    56702.63810  1.5818e-14  4.4214e-17  13.1409  0.0030  1
+r        6175    56703.01559  1.5814e-14  1.0160e-16  13.1411  0.0070  5
+r        6175    56704.83831  1.5908e-14  8.5542e-17  13.1347  0.0058  5
+r        6175    56705.60934  1.5918e-14  6.9049e-17  13.1340  0.0047  4
+r        6175    56705.95231  1.5806e-14  1.0258e-16  13.1417  0.0070  5
+r        6175    56706.98190  1.6095e-14  8.7265e-17  13.1220  0.0059  5
+r        6175    56708.02408  1.5907e-14  1.0073e-16  13.1348  0.0069  5
+r        6175    56709.82822  1.5854e-14  9.9859e-17  13.1384  0.0068  5
+r        6175    56710.66162  1.5756e-14  9.2644e-18  13.1451  0.0006  1
+r        6175    56711.01696  1.5831e-14  1.2148e-16  13.1400  0.0083  5
+r        6175    56711.68589  1.5626e-14  4.4413e-17  13.1541  0.0031  1
+r        6175    56711.92320  1.5708e-14  8.8005e-17  13.1484  0.0061  5
+r        6175    56712.27457  1.5731e-14  3.9074e-16  13.1468  0.0270  2
+r        6175    56713.72216  1.5518e-14  3.1070e-17  13.1617  0.0022  1
+r        6175    56713.91937  1.5678e-14  8.8717e-17  13.1505  0.0061  5
+r        6175    56714.72585  1.5488e-14  3.8702e-17  13.1638  0.0027  1
+r        6175    56715.91254  1.5459e-14  1.4546e-16  13.1658  0.0102  5
+r        6175    56716.64153  1.5424e-14  1.0387e-16  13.1682  0.0073  4
+r        6175    56716.66435  1.5293e-14  2.1776e-17  13.1775  0.0015  1
+r        6175    56717.73301  1.5075e-14  2.2704e-17  13.1931  0.0016  1
+r        6175    56717.95355  1.5415e-14  2.1901e-16  13.1689  0.0154  5
+r        6175    56718.56212  1.5165e-14  2.3539e-16  13.1866  0.0169  4
+r        6175    56718.67042  1.5025e-14  1.8707e-17  13.1967  0.0014  1
+r        6175    56719.60173  1.4275e-14  9.8168e-16  13.2523  0.0747  3
+r        6175    56721.71370  1.4499e-14  1.3701e-17  13.2354  0.0010  1
+r        6175    56722.73766  1.4419e-14  1.2590e-17  13.2414  0.0009  1
+r        6175    56723.57112  1.4425e-14  1.5329e-17  13.2410  0.0012  1
+r        6175    56728.57593  1.4160e-14  1.4365e-17  13.2611  0.0011  1
+r        6175    56729.49486  1.4353e-14  1.4823e-17  13.2464  0.0011  1
+r        6175    56730.53779  1.4283e-14  1.4335e-17  13.2517  0.0011  1
+r        6175    56730.83005  1.4268e-14  1.1391e-16  13.2528  0.0087  5
+r        6175    56732.49418  1.4292e-14  1.0591e-17  13.2510  0.0008  1
+r        6175    56733.48447  1.4443e-14  8.9428e-18  13.2396  0.0007  1
+r        6175    56733.80024  1.4226e-14  1.1349e-16  13.2560  0.0087  5
+r        6175    56734.47987  1.4444e-14  1.0519e-17  13.2395  0.0008  1
+r        6175    56735.49794  1.4529e-14  1.0809e-17  13.2332  0.0008  1
+r        6175    56735.85885  1.4505e-14  1.0589e-16  13.2349  0.0079  5
+r        6175    56736.50413  1.4627e-14  1.2419e-17  13.2259  0.0009  1
+r        6175    56736.77144  1.4674e-14  1.0202e-16  13.2224  0.0075  5
+r        6175    56737.48897  1.4839e-14  1.4325e-17  13.2102  0.0010  1
+r        6175    56740.86129  1.5865e-14  1.4744e-16  13.1376  0.0101  5
+r        6175    56741.61133  1.5566e-14  1.3468e-17  13.1583  0.0009  1
+r        6175    56741.71564  1.5858e-14  1.3840e-16  13.1381  0.0095  5
+r        6175    56742.65138  1.6059e-14  1.2955e-17  13.1244  0.0009  1
+r        6175    56743.57966  1.5969e-14  1.9854e-16  13.1305  0.0135  4
+r        6175    56743.59556  1.6147e-14  1.9892e-17  13.1185  0.0013  1
+r        6175    56744.53792  1.6280e-14  2.0369e-17  13.1096  0.0014  1
+r        6175    56744.56821  1.6281e-14  7.0945e-16  13.1095  0.0473  3
+r        6175    56744.69766  1.6036e-14  1.3328e-16  13.1260  0.0090  5
+r        6175    56746.79678  1.6134e-14  9.6335e-17  13.1194  0.0065  5
+r        6175    56747.95649  1.6145e-14  1.0659e-16  13.1186  0.0072  5
+r        6175    56748.75635  1.6156e-14  1.2206e-16  13.1179  0.0082  5
+r        6175    56749.75350  1.6185e-14  1.0001e-16  13.1160  0.0067  5
+r        6175    56751.98194  1.6167e-14  1.1392e-16  13.1172  0.0077  5
+r        6175    56752.16505  1.6624e-14  5.3472e-16  13.0869  0.0349  2
+r        6175    56755.56395  1.5836e-14  1.6449e-17  13.1396  0.0011  1
+r        6175    56755.98044  1.5916e-14  1.0162e-16  13.1342  0.0069  5
+r        6175    56756.49376  1.6135e-14  7.6601e-16  13.1193  0.0515  3
+r        6175    56756.51125  1.5956e-14  1.5297e-17  13.1314  0.0010  1
+r        6175    56757.13977  1.5944e-14  3.3012e-16  13.1322  0.0225  2
+r        6175    56758.74605  1.5749e-14  1.0607e-16  13.1456  0.0073  5
+r        6175    56759.88579  1.5721e-14  9.6926e-17  13.1475  0.0067  5
+r        6175    56760.20496  1.5828e-14  2.8724e-16  13.1402  0.0197  2
+r        6175    56761.60196  1.5567e-14  1.5441e-17  13.1582  0.0011  1
+r        6175    56762.78497  1.5615e-14  1.2026e-16  13.1549  0.0084  5
+r        6175    56763.75958  1.5439e-14  9.6355e-17  13.1672  0.0068  5
+r        6175    56764.78634  1.5523e-14  1.3817e-16  13.1613  0.0097  5
+r        6175    56765.53891  1.5505e-14  8.8584e-17  13.1626  0.0062  4
+r        6175    56765.58216  1.5581e-14  1.8934e-17  13.1573  0.0013  1
+r        6175    56765.87606  1.5456e-14  2.4446e-16  13.1660  0.0172  5
+r        6175    56766.40033  1.5875e-14  1.1878e-16  13.1370  0.0081  4
+r        6175    56770.52610  1.5830e-14  2.4533e-17  13.1400  0.0017  1
+r        6175    56770.78129  1.5873e-14  9.7424e-17  13.1371  0.0067  5
+r        6175    56771.51073  1.5914e-14  2.4341e-17  13.1343  0.0017  1
+r        6175    56772.49741  1.5963e-14  1.4657e-17  13.1310  0.0010  1
+r        6175    56772.82574  1.5957e-14  1.2014e-16  13.1314  0.0082  5
+r        6175    56773.38818  1.5844e-14  1.6860e-16  13.1391  0.0116  4
+r        6175    56773.83291  1.5962e-14  1.0370e-16  13.1310  0.0071  5
+r        6175    56774.76016  1.6011e-14  1.7435e-16  13.1277  0.0118  5
+r        6175    56775.40551  1.5971e-14  1.5511e-16  13.1304  0.0105  4
+r        6175    56775.54774  1.6173e-14  1.6340e-17  13.1168  0.0011  1
+r        6175    56776.07596  1.6292e-14  1.7462e-16  13.1088  0.0116  2
+r        6175    56777.52964  1.6230e-14  2.3679e-17  13.1129  0.0016  1
+r        6175    56778.49671  1.6277e-14  2.5071e-17  13.1098  0.0017  1
+r        6175    56779.39649  1.5975e-14  5.9869e-16  13.1301  0.0407  3
+r        6175    56779.48655  1.6049e-14  1.9692e-17  13.1251  0.0013  1
+r        6175    56779.85167  1.6001e-14  1.1646e-16  13.1284  0.0079  5
+r        6175    56780.49401  1.6195e-14  2.5443e-17  13.1153  0.0017  1
+r        6175    56780.81477  1.5992e-14  1.1237e-16  13.1290  0.0076  5
+r        6175    56781.49321  1.6092e-14  2.4733e-17  13.1222  0.0017  1
+r        6175    56782.44643  1.5954e-14  2.2719e-17  13.1316  0.0015  1
+r        6175    56782.74049  1.5929e-14  1.2836e-16  13.1333  0.0087  5
+r        6175    56783.45126  1.6069e-14  2.3223e-17  13.1238  0.0016  1
+r        6175    56784.44055  1.6067e-14  2.3882e-17  13.1239  0.0016  1
+r        6175    56785.47523  1.6031e-14  1.9389e-17  13.1263  0.0013  1
+r        6175    56786.48211  1.6077e-14  1.8238e-17  13.1232  0.0012  1
+r        6175    56787.40311  1.5971e-14  1.4984e-17  13.1304  0.0010  1
+r        6175    56790.45869  1.5832e-14  1.4121e-17  13.1399  0.0010  1
+r        6175    56791.43320  1.5833e-14  1.0205e-17  13.1398  0.0007  1
+r        6175    56794.46302  1.5879e-14  2.3947e-17  13.1367  0.0016  1
+r        6175    56795.44503  1.5825e-14  2.9154e-17  13.1404  0.0020  1
+r        6175    56796.44744  1.5777e-14  3.0174e-17  13.1437  0.0021  1
+r        6175    56800.44233  1.6182e-14  2.5827e-17  13.1162  0.0017  1
+r        6175    56801.42684  1.5996e-14  2.1498e-17  13.1287  0.0015  1
+r        6175    56803.44006  1.6003e-14  2.7713e-17  13.1282  0.0019  1
+r        6175    56805.45996  1.5892e-14  2.4104e-17  13.1358  0.0016  1
+r        6175    56806.43311  1.5932e-14  2.0715e-17  13.1331  0.0014  1
+r        6175    56807.43492  1.5852e-14  1.6338e-17  13.1385  0.0011  1
+r        6175    56808.41440  1.6037e-14  2.8909e-17  13.1259  0.0020  1
+r        6175    56809.42932  1.6159e-14  2.2402e-17  13.1177  0.0015  1
+r        6175    56810.43044  1.6153e-14  3.1418e-17  13.1181  0.0021  1
+r        6175    56811.41922  1.6112e-14  2.9053e-17  13.1209  0.0020  1
+r        6175    56813.40213  1.6100e-14  2.9761e-17  13.1217  0.0020  1
+r        6175    56814.45535  1.6200e-14  2.7937e-17  13.1150  0.0019  1
+r        6175    56815.40294  1.6261e-14  2.9174e-17  13.1109  0.0019  1
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+r        6175    56816.40991  1.6279e-14  2.6668e-17  13.1097  0.0018  1
+r        6175    56816.63948  1.6063e-14  9.4249e-17  13.1242  0.0064  5
+r        6175    56817.40994  1.6348e-14  2.6817e-17  13.1051  0.0018  1
+r        6175    56817.83435  1.6135e-14  8.7746e-17  13.1193  0.0059  5
+r        6175    56818.41477  1.6427e-14  2.1216e-17  13.0998  0.0014  1
+r        6175    56819.41485  1.6454e-14  2.0875e-17  13.0981  0.0014  1
+r        6175    56820.48400  1.6782e-14  1.9164e-17  13.0766  0.0012  1
+r        6175    56821.24253  1.7066e-14  1.0637e-16  13.0584  0.0068  4
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+r        6175    56823.40645  1.6921e-14  3.1519e-17  13.0677  0.0020  1
+r        6175    56824.40028  1.6799e-14  3.2749e-17  13.0755  0.0021  1
+r        6175    56825.41423  1.6920e-14  3.1822e-17  13.0677  0.0020  1
+r        6175    56826.29162  1.6503e-14  5.3443e-16  13.0948  0.0352  3
+r        6175    56826.39200  1.6573e-14  3.2291e-17  13.0902  0.0021  1
+r        6175    56827.39678  1.6531e-14  3.1585e-17  13.0930  0.0021  1
+r        6175    56828.39911  1.6394e-14  3.2611e-17  13.1020  0.0022  1
+r        6175    56829.40894  1.6280e-14  3.0643e-17  13.1096  0.0020  1
+r        6175    56830.40168  1.6201e-14  3.4631e-17  13.1149  0.0023  1
+r        6175    56831.39309  1.6149e-14  3.1470e-17  13.1184  0.0021  1
+r        6175    56832.43594  1.6239e-14  3.3012e-17  13.1123  0.0022  1
+r        6175    56833.39730  1.6098e-14  2.7225e-17  13.1218  0.0018  1
+r        6175    56834.39189  1.6105e-14  2.7488e-17  13.1213  0.0019  1
+r        6175    56835.39334  1.6067e-14  3.1663e-17  13.1239  0.0021  1
+r        6175    56837.23411  1.6185e-14  5.3256e-16  13.1160  0.0357  3
+r        6175    56839.33665  1.6228e-14  4.6269e-16  13.1131  0.0310  3
+r        6175    56841.41123  1.6193e-14  2.5718e-17  13.1154  0.0017  1
+r        6175    56842.24558  1.6181e-14  1.2388e-16  13.1162  0.0083  4
+r        6175    56843.21577  1.6074e-14  1.1288e-16  13.1234  0.0076  4
+r        6175    56843.39107  1.6195e-14  3.0966e-17  13.1153  0.0021  1
+r        6175    56845.41039  1.6098e-14  2.8955e-17  13.1218  0.0020  1
+r        6175    56848.85472  1.5512e-14  5.4319e-16  13.1621  0.0380  2
+r        6175    56858.38762  1.5696e-14  3.2211e-17  13.1493  0.0022  1
+r        6175    56860.38654  1.5604e-14  3.2847e-17  13.1557  0.0023  1
+r        6175    56861.38558  1.5824e-14  2.7557e-17  13.1405  0.0019  1
+r        6175    56862.38612  1.5644e-14  2.7052e-17  13.1529  0.0019  1
+r        6175    56863.38454  1.5653e-14  2.8026e-17  13.1522  0.0019  1
+r        6175    56864.38455  1.5683e-14  3.2917e-17  13.1502  0.0023  1
+r        6175    56864.69170  1.5906e-14  1.4082e-16  13.1348  0.0096  5
+r        6175    56865.38350  1.5703e-14  3.0810e-17  13.1488  0.0021  1
+r        6175    56865.63157  1.5934e-14  1.4295e-16  13.1329  0.0097  5
\ No newline at end of file
diff --git a/data/STORM_III/raw/separated/6439Å.tab b/data/STORM_III/raw/separated/6439Å.tab
new file mode 100644
index 0000000..a156caa
--- /dev/null
+++ b/data/STORM_III/raw/separated/6439Å.tab
@@ -0,0 +1,136 @@
+R        6439    56691.57639  1.2656e-14  1.9770e-16  13.2920  0.0170  2
+R        6439    56691.62732  1.2688e-14  1.9957e-17  13.2893  0.0017  1
+R        6439    56692.59183  1.3017e-14  2.3046e-16  13.2615  0.0192  2
+R        6439    56693.59902  1.2992e-14  2.0369e-16  13.2636  0.0170  2
+R        6439    56696.59964  1.2726e-14  2.1202e-16  13.2860  0.0181  2
+R        6439    56697.55904  1.3003e-14  4.2606e-17  13.2627  0.0036  1
+R        6439    56698.54186  1.3226e-14  3.4718e-17  13.2442  0.0029  1
+R        6439    56699.47336  1.3201e-14  2.4990e-17  13.2462  0.0021  1
+R        6439    56700.50066  1.3069e-14  2.4999e-17  13.2572  0.0021  1
+R        6439    56701.56241  1.3122e-14  8.1561e-17  13.2528  0.0067  1
+R        6439    56702.53599  1.3244e-14  2.0330e-17  13.2427  0.0017  1
+R        6439    56708.56677  1.3378e-14  1.8252e-17  13.2318  0.0015  1
+R        6439    56709.54883  1.3261e-14  2.7637e-17  13.2413  0.0023  1
+R        6439    56710.47730  1.3216e-14  2.5764e-17  13.2450  0.0021  1
+R        6439    56710.54491  1.3145e-14  1.5827e-16  13.2509  0.0131  2
+R        6439    56716.47882  1.2580e-14  3.5746e-17  13.2986  0.0031  1
+R        6439    56721.47419  1.2182e-14  2.8510e-17  13.3335  0.0025  1
+R        6439    56723.46431  1.1805e-14  1.8707e-17  13.3676  0.0017  1
+R        6439    56725.87074  1.1738e-14  7.2591e-17  13.3738  0.0067  4
+R        6439    56727.45456  1.1726e-14  2.7081e-17  13.3749  0.0025  1
+R        6439    56730.92366  1.2004e-14  6.2157e-17  13.3494  0.0056  4
+R        6439    56737.43777  1.2511e-14  2.8873e-17  13.3045  0.0025  1
+R        6439    56737.61115  1.2281e-14  1.9671e-16  13.3247  0.0174  2
+R        6439    56738.44885  1.2735e-14  1.9350e-17  13.2853  0.0016  1
+R        6439    56738.58081  1.2461e-14  2.0741e-16  13.3089  0.0181  2
+R        6439    56739.31059  1.2807e-14  2.4071e-17  13.2791  0.0020  1
+R        6439    56739.91574  1.2798e-14  6.4744e-17  13.2799  0.0055  4
+R        6439    56740.57672  1.2562e-14  2.2429e-16  13.3001  0.0194  2
+R        6439    56741.52658  1.3351e-14  2.2079e-16  13.2340  0.0180  2
+R        6439    56741.82441  1.3010e-14  4.1112e-17  13.2621  0.0034  4
+R        6439    56742.44914  1.3194e-14  3.0607e-17  13.2468  0.0025  1
+R        6439    56744.44266  1.3461e-14  2.6464e-17  13.2251  0.0021  1
+R        6439    56746.43072  1.4111e-14  2.0690e-16  13.1739  0.0159  2
+R        6439    56748.47166  1.3902e-14  2.1973e-17  13.1901  0.0017  1
+R        6439    56748.54139  1.4042e-14  1.9608e-16  13.1792  0.0152  2
+R        6439    56750.34601  1.3980e-14  3.1287e-17  13.1840  0.0024  1
+R        6439    56750.60316  1.4055e-14  2.1640e-16  13.1782  0.0167  2
+R        6439    56751.54047  1.3973e-14  2.7890e-16  13.1845  0.0217  2
+R        6439    56752.41420  1.3878e-14  4.2333e-17  13.1919  0.0033  1
+R        6439    56753.36276  1.3772e-14  3.3446e-17  13.2003  0.0026  1
+R        6439    56754.37110  1.3561e-14  3.0466e-17  13.2170  0.0024  1
+R        6439    56755.42524  1.3402e-14  2.2522e-17  13.2298  0.0018  1
+R        6439    56755.88104  1.3350e-14  4.4227e-17  13.2341  0.0036  4
+R        6439    56757.34350  1.3503e-14  2.3647e-17  13.2217  0.0019  1
+R        6439    56758.43907  1.3170e-14  2.7139e-17  13.2488  0.0022  1
+R        6439    56758.52717  1.3227e-14  1.9807e-16  13.2441  0.0163  2
+R        6439    56758.88275  1.3163e-14  3.8922e-17  13.2494  0.0032  4
+R        6439    56763.43422  1.2829e-14  1.4517e-16  13.2773  0.0123  2
+R        6439    56765.51344  1.3025e-14  1.3861e-16  13.2608  0.0116  2
+R        6439    56766.29307  1.3115e-14  2.7510e-17  13.2533  0.0023  1
+R        6439    56774.53089  1.3661e-14  2.0310e-16  13.2091  0.0161  2
+R        6439    56778.80681  1.3226e-14  4.2003e-17  13.2442  0.0034  4
+R        6439    56779.78794  1.3366e-14  4.6196e-17  13.2328  0.0038  4
+R        6439    56780.83251  1.3390e-14  4.0030e-17  13.2308  0.0032  4
+R        6439    56784.46808  1.3433e-14  2.4146e-16  13.2273  0.0195  2
+R        6439    56788.47089  1.3389e-14  1.3393e-16  13.2309  0.0109  2
+R        6439    56791.77006  1.3306e-14  3.4794e-17  13.2376  0.0028  4
+R        6439    56792.79587  1.3046e-14  4.2505e-17  13.2591  0.0035  4
+R        6439    56795.50257  1.2926e-14  1.4580e-16  13.2691  0.0122  2
+R        6439    56797.30490  1.3426e-14  9.4190e-17  13.2279  0.0076  3
+R        6439    56797.47370  1.2947e-14  2.1245e-16  13.2673  0.0178  2
+R        6439    56797.77789  1.3269e-14  4.1007e-17  13.2407  0.0034  4
+R        6439    56798.77420  1.3371e-14  4.1468e-17  13.2324  0.0034  4
+R        6439    56799.36128  1.3553e-14  2.7743e-16  13.2177  0.0222  2
+R        6439    56799.68199  1.3374e-14  4.1311e-17  13.2321  0.0034  4
+R        6439    56800.46101  1.3468e-14  2.5034e-16  13.2245  0.0202  2
+R        6439    56800.77419  1.3392e-14  3.8458e-17  13.2306  0.0031  4
+R        6439    56801.42776  1.3423e-14  2.1901e-17  13.2281  0.0018  1
+R        6439    56802.35871  1.3567e-14  2.3771e-16  13.2166  0.0190  2
+R        6439    56802.35967  1.3358e-14  2.9031e-17  13.2334  0.0024  1
+R        6439    56802.39694  1.3327e-14  8.6631e-17  13.2359  0.0071  3
+R        6439    56803.22259  1.3405e-14  9.1857e-17  13.2296  0.0074  3
+R        6439    56803.34016  1.3438e-14  4.2479e-17  13.2269  0.0034  1
+R        6439    56804.39995  1.3411e-14  2.6984e-17  13.2291  0.0022  1
+R        6439    56804.79155  1.3373e-14  3.8581e-17  13.2322  0.0031  4
+R        6439    56805.43803  1.3355e-14  2.7686e-16  13.2337  0.0225  2
+R        6439    56806.41473  1.3165e-14  1.8669e-16  13.2492  0.0154  2
+R        6439    56807.36620  1.3200e-14  1.6693e-16  13.2463  0.0137  2
+R        6439    56808.74427  1.3216e-14  5.4774e-17  13.2450  0.0045  4
+R        6439    56810.33555  1.3344e-14  4.4912e-17  13.2345  0.0037  1
+R        6439    56811.33532  1.3467e-14  3.5194e-17  13.2246  0.0028  1
+R        6439    56812.79921  1.3474e-14  3.9135e-17  13.2240  0.0032  4
+R        6439    56813.79090  1.3489e-14  3.9439e-17  13.2228  0.0032  4
+R        6439    56814.71648  1.3446e-14  4.2879e-17  13.2263  0.0035  4
+R        6439    56815.40513  1.3513e-14  7.6174e-17  13.2209  0.0061  1
+R        6439    56817.39027  1.3814e-14  2.6763e-17  13.1970  0.0021  1
+R        6439    56817.42227  1.3508e-14  1.5537e-16  13.2213  0.0125  2
+R        6439    56818.37198  1.4322e-14  1.8030e-16  13.1578  0.0137  2
+R        6439    56818.80713  1.4003e-14  5.2857e-17  13.1822  0.0041  4
+R        6439    56819.43218  1.3783e-14  1.4352e-16  13.1994  0.0113  2
+R        6439    56820.81405  1.4233e-14  7.0262e-17  13.1645  0.0054  4
+R        6439    56821.73168  1.4386e-14  6.4871e-17  13.1529  0.0049  4
+R        6439    56822.31349  1.4059e-14  4.0318e-17  13.1779  0.0031  1
+R        6439    56822.40325  1.4163e-14  1.6233e-16  13.1699  0.0124  2
+R        6439    56823.35100  1.4074e-14  1.7815e-17  13.1767  0.0014  1
+R        6439    56824.37249  1.4191e-14  1.5729e-16  13.1677  0.0120  2
+R        6439    56824.37408  1.4163e-14  4.9727e-17  13.1699  0.0038  1
+R        6439    56825.40733  1.4158e-14  4.6769e-17  13.1703  0.0036  1
+R        6439    56825.43322  1.4293e-14  2.8460e-16  13.1600  0.0216  2
+R        6439    56827.37667  1.3926e-14  2.7796e-17  13.1882  0.0022  1
+R        6439    56827.77166  1.3716e-14  4.9506e-17  13.2047  0.0039  4
+R        6439    56828.80116  1.3712e-14  6.0794e-17  13.2050  0.0048  4
+R        6439    56829.30942  1.3782e-14  3.8435e-17  13.1995  0.0030  1
+R        6439    56831.28618  1.3351e-14  4.6522e-17  13.2340  0.0038  1
+R        6439    56831.46575  1.3606e-14  1.9864e-16  13.2134  0.0159  2
+R        6439    56832.27962  1.3442e-14  4.4328e-17  13.2266  0.0036  1
+R        6439    56832.79813  1.3560e-14  4.2153e-17  13.2171  0.0034  4
+R        6439    56833.82408  1.3595e-14  4.2187e-17  13.2143  0.0034  4
+R        6439    56834.32974  1.3460e-14  4.9128e-17  13.2251  0.0040  1
+R        6439    56835.36363  1.3611e-14  3.0915e-17  13.2130  0.0025  1
+R        6439    56836.31791  1.3521e-14  2.9733e-17  13.2202  0.0024  1
+R        6439    56836.81004  1.3378e-14  7.8432e-17  13.2318  0.0064  4
+R        6439    56837.31872  1.3501e-14  3.0099e-17  13.2218  0.0024  1
+R        6439    56837.78895  1.3576e-14  5.0405e-17  13.2158  0.0040  4
+R        6439    56838.76999  1.3577e-14  4.6788e-17  13.2158  0.0037  4
+R        6439    56839.32188  1.3560e-14  2.9911e-17  13.2171  0.0024  1
+R        6439    56839.75186  1.3490e-14  4.3343e-17  13.2227  0.0035  4
+R        6439    56840.78037  1.3640e-14  4.6477e-17  13.2107  0.0037  4
+R        6439    56841.82767  1.3634e-14  8.5268e-17  13.2112  0.0068  4
+R        6439    56844.36368  1.3530e-14  3.3987e-17  13.2195  0.0027  1
+R        6439    56845.28141  1.3433e-14  1.4186e-17  13.2273  0.0011  1
+R        6439    56846.34110  1.3384e-14  1.8309e-17  13.2313  0.0015  1
+R        6439    56846.78281  1.3473e-14  5.2071e-17  13.2241  0.0042  4
+R        6439    56848.29301  1.3091e-14  1.2573e-17  13.2553  0.0010  1
+R        6439    56850.29872  1.3133e-14  1.4752e-17  13.2519  0.0012  1
+R        6439    56852.28364  1.2990e-14  2.5985e-17  13.2637  0.0022  1
+R        6439    56853.31290  1.2927e-14  1.6604e-17  13.2690  0.0014  1
+R        6439    56854.30564  1.2791e-14  1.4360e-17  13.2805  0.0012  1
+R        6439    56855.76047  1.2647e-14  4.4586e-17  13.2928  0.0038  4
+R        6439    56856.24426  1.2705e-14  7.9139e-17  13.2878  0.0068  3
+R        6439    56856.74788  1.2803e-14  5.4792e-17  13.2795  0.0046  4
+R        6439    56857.25654  1.2788e-14  2.6603e-17  13.2808  0.0023  1
+R        6439    56858.29198  1.2980e-14  2.4266e-17  13.2646  0.0020  1
+R        6439    56859.31826  1.3162e-14  3.6186e-17  13.2495  0.0030  1
+R        6439    56861.73946  1.3241e-14  4.0494e-17  13.2430  0.0033  4
+R        6439    56863.74208  1.3262e-14  3.4804e-17  13.2412  0.0028  4
\ No newline at end of file
diff --git a/data/STORM_III/raw/separated/7647Å.tab b/data/STORM_III/raw/separated/7647Å.tab
new file mode 100644
index 0000000..051c693
--- /dev/null
+++ b/data/STORM_III/raw/separated/7647Å.tab
@@ -0,0 +1,178 @@
+i        7647    56692.94969  1.0088e-14  2.3331e-17  13.1650  0.0025  5
+i        7647    56693.78864  9.9818e-15  1.0832e-17  13.1765  0.0012  1
+i        7647    56694.76919  1.0013e-14  1.1449e-17  13.1731  0.0012  1
+i        7647    56695.70858  1.0108e-14  1.0735e-17  13.1628  0.0012  1
+i        7647    56696.72704  1.0136e-14  1.3874e-17  13.1598  0.0015  1
+i        7647    56696.98498  1.0183e-14  3.6828e-17  13.1548  0.0039  5
+i        7647    56697.75587  1.0152e-14  1.5205e-17  13.1581  0.0016  1
+i        7647    56698.71651  1.0214e-14  3.5560e-17  13.1515  0.0038  1
+i        7647    56699.71212  1.0193e-14  3.9285e-17  13.1537  0.0042  1
+i        7647    56700.69069  1.0248e-14  5.7142e-18  13.1479  0.0006  1
+i        7647    56701.56497  1.0255e-14  1.3993e-17  13.1472  0.0015  1
+i        7647    56701.83187  1.0204e-14  5.7308e-17  13.1526  0.0061  5
+i        7647    56702.63742  1.0327e-14  1.3770e-17  13.1396  0.0014  1
+i        7647    56703.01660  1.0295e-14  5.8458e-17  13.1429  0.0062  5
+i        7647    56703.88218  1.0341e-14  2.8286e-17  13.1381  0.0030  5
+i        7647    56704.83926  1.0351e-14  3.3558e-17  13.1370  0.0035  5
+i        7647    56705.61028  1.0381e-14  2.5162e-17  13.1339  0.0026  4
+i        7647    56705.95327  1.0396e-14  3.3394e-17  13.1323  0.0035  5
+i        7647    56706.98285  1.0394e-14  4.1289e-17  13.1325  0.0043  5
+i        7647    56708.02508  1.0421e-14  4.9110e-17  13.1297  0.0051  5
+i        7647    56709.82915  1.0268e-14  4.4325e-17  13.1458  0.0047  5
+i        7647    56710.66093  1.0238e-14  2.1402e-17  13.1490  0.0023  1
+i        7647    56711.01790  1.0269e-14  5.7723e-17  13.1457  0.0061  5
+i        7647    56711.68518  1.0219e-14  1.0382e-17  13.1510  0.0011  1
+i        7647    56711.92414  1.0224e-14  3.3551e-17  13.1504  0.0036  5
+i        7647    56713.72148  1.0099e-14  3.8844e-17  13.1638  0.0042  1
+i        7647    56713.92031  1.0189e-14  3.6202e-17  13.1542  0.0039  5
+i        7647    56714.72560  1.0110e-14  4.7881e-18  13.1626  0.0005  1
+i        7647    56715.91352  1.0075e-14  4.2615e-17  13.1664  0.0046  5
+i        7647    56716.66497  1.0073e-14  1.1921e-17  13.1666  0.0013  1
+i        7647    56717.73364  9.9781e-15  1.0252e-17  13.1769  0.0011  1
+i        7647    56717.95542  1.0032e-14  9.0012e-17  13.1710  0.0097  5
+i        7647    56718.56400  1.0009e-14  8.7718e-17  13.1735  0.0095  4
+i        7647    56718.67105  9.8788e-15  9.7630e-18  13.1877  0.0011  1
+i        7647    56719.60361  9.7784e-15  1.0423e-16  13.1988  0.0116  3
+i        7647    56720.69264  9.7176e-15  1.1920e-17  13.2056  0.0013  1
+i        7647    56721.71433  9.6021e-15  8.6336e-18  13.2186  0.0010  1
+i        7647    56722.73829  9.4266e-15  7.3112e-18  13.2386  0.0008  1
+i        7647    56723.57174  9.4980e-15  1.1062e-17  13.2304  0.0013  1
+i        7647    56724.52211  9.2922e-15  8.2206e-17  13.2542  0.0096  4
+i        7647    56727.61413  9.4276e-15  9.2891e-17  13.2385  0.0107  4
+i        7647    56728.57657  9.4829e-15  7.8424e-18  13.2321  0.0009  1
+i        7647    56729.49550  9.3688e-15  9.8926e-18  13.2453  0.0011  1
+i        7647    56730.53842  9.4341e-15  8.4999e-18  13.2377  0.0010  1
+i        7647    56730.83131  9.3824e-15  5.3844e-17  13.2437  0.0062  5
+i        7647    56731.48995  9.4652e-15  6.6200e-18  13.2342  0.0008  1
+i        7647    56732.49480  9.4518e-15  5.9886e-18  13.2357  0.0007  1
+i        7647    56733.48510  9.4650e-15  6.0471e-18  13.2342  0.0007  1
+i        7647    56733.79272  9.4379e-15  4.1796e-17  13.2373  0.0048  5
+i        7647    56734.48050  9.5518e-15  7.2721e-18  13.2243  0.0008  1
+i        7647    56735.49857  9.5214e-15  6.3674e-18  13.2277  0.0007  1
+i        7647    56735.86020  9.5989e-15  4.2691e-17  13.2189  0.0048  5
+i        7647    56736.50476  9.6861e-15  7.8904e-18  13.2091  0.0009  1
+i        7647    56736.77280  9.7435e-15  4.4905e-17  13.2027  0.0050  5
+i        7647    56737.48960  9.8445e-15  8.4149e-18  13.1915  0.0009  1
+i        7647    56740.86264  1.0188e-14  9.8614e-17  13.1543  0.0105  5
+i        7647    56741.61196  1.0135e-14  9.2777e-18  13.1599  0.0010  1
+i        7647    56741.71700  1.0185e-14  8.7678e-17  13.1546  0.0093  5
+i        7647    56743.58081  1.0336e-14  6.1350e-17  13.1386  0.0064  4
+i        7647    56744.53855  1.0438e-14  1.1527e-17  13.1280  0.0012  1
+i        7647    56744.69903  1.0422e-14  7.8080e-17  13.1296  0.0081  5
+i        7647    56746.79815  1.0507e-14  4.6892e-17  13.1208  0.0048  5
+i        7647    56747.97105  1.0586e-14  1.1607e-16  13.1127  0.0119  5
+i        7647    56748.75772  1.0573e-14  7.3935e-17  13.1140  0.0076  5
+i        7647    56749.75488  1.0654e-14  5.3101e-17  13.1057  0.0054  5
+i        7647    56751.98332  1.0668e-14  6.5078e-17  13.1043  0.0066  5
+i        7647    56752.58234  1.0710e-14  1.3997e-17  13.1000  0.0014  1
+i        7647    56753.57733  1.0614e-14  1.3847e-17  13.1098  0.0014  1
+i        7647    56754.55501  1.0533e-14  1.4189e-17  13.1181  0.0015  1
+i        7647    56755.97913  1.0354e-14  9.8934e-17  13.1367  0.0104  5
+i        7647    56756.51191  1.0333e-14  8.4616e-18  13.1389  0.0009  1
+i        7647    56757.14081  1.0437e-14  1.1788e-16  13.1281  0.0123  2
+i        7647    56757.45963  1.0327e-14  3.9728e-17  13.1396  0.0042  3
+i        7647    56757.55045  1.0455e-14  7.4823e-17  13.1262  0.0078  4
+i        7647    56758.74749  1.0349e-14  5.5304e-17  13.1373  0.0058  5
+i        7647    56759.88717  1.0302e-14  3.1799e-17  13.1422  0.0034  5
+i        7647    56760.20633  1.0226e-14  7.7373e-17  13.1502  0.0082  2
+i        7647    56762.78636  1.0162e-14  3.1825e-17  13.1571  0.0034  5
+i        7647    56763.75427  1.0142e-14  3.1806e-17  13.1592  0.0034  5
+i        7647    56764.78767  1.0157e-14  3.9904e-17  13.1576  0.0043  5
+i        7647    56765.54046  1.0193e-14  2.7080e-17  13.1537  0.0029  4
+i        7647    56765.58278  1.0163e-14  1.0884e-17  13.1569  0.0012  1
+i        7647    56765.87757  1.0130e-14  3.7752e-17  13.1605  0.0040  5
+i        7647    56766.40167  1.0072e-14  3.5624e-17  13.1667  0.0038  4
+i        7647    56770.52673  1.0251e-14  1.3306e-17  13.1476  0.0014  1
+i        7647    56770.78276  1.0251e-14  4.7420e-17  13.1476  0.0050  5
+i        7647    56771.51136  1.0229e-14  1.3331e-17  13.1499  0.0014  1
+i        7647    56772.49803  1.0284e-14  7.4432e-18  13.1441  0.0008  1
+i        7647    56772.82719  1.0347e-14  6.8808e-17  13.1375  0.0072  5
+i        7647    56773.38964  1.0354e-14  5.5093e-17  13.1367  0.0058  4
+i        7647    56773.85849  1.0367e-14  6.5486e-17  13.1354  0.0069  5
+i        7647    56774.76119  1.0403e-14  1.1588e-16  13.1316  0.0121  5
+i        7647    56775.40695  1.0443e-14  5.2589e-17  13.1274  0.0055  4
+i        7647    56775.54837  1.0462e-14  8.2610e-18  13.1255  0.0009  1
+i        7647    56776.07733  1.0515e-14  1.0514e-16  13.1200  0.0109  2
+i        7647    56776.46341  1.0469e-14  7.5634e-18  13.1247  0.0008  1
+i        7647    56777.53027  1.0468e-14  1.2233e-17  13.1248  0.0013  1
+i        7647    56778.49734  1.0451e-14  1.3290e-17  13.1266  0.0014  1
+i        7647    56779.39614  1.0450e-14  4.5924e-17  13.1267  0.0048  3
+i        7647    56779.48718  1.0426e-14  1.0962e-17  13.1292  0.0011  1
+i        7647    56779.85183  1.0364e-14  6.4996e-17  13.1357  0.0068  5
+i        7647    56780.49464  1.0415e-14  1.3409e-17  13.1304  0.0014  1
+i        7647    56780.81441  1.0399e-14  6.0786e-17  13.1320  0.0063  5
+i        7647    56781.49383  1.0414e-14  1.3411e-17  13.1305  0.0014  1
+i        7647    56782.44705  1.0339e-14  1.3211e-17  13.1383  0.0014  1
+i        7647    56782.74020  1.0358e-14  7.5720e-17  13.1363  0.0079  5
+i        7647    56783.45189  1.0321e-14  1.2952e-17  13.1402  0.0014  1
+i        7647    56784.44117  1.0343e-14  1.3026e-17  13.1379  0.0014  1
+i        7647    56785.47585  1.0354e-14  1.1273e-17  13.1367  0.0012  1
+i        7647    56786.48274  1.0373e-14  1.0105e-17  13.1347  0.0011  1
+i        7647    56787.40373  1.0346e-14  8.0860e-18  13.1376  0.0008  1
+i        7647    56790.45933  1.0298e-14  8.9144e-18  13.1426  0.0009  1
+i        7647    56792.40286  1.0234e-14  6.4158e-18  13.1494  0.0007  1
+i        7647    56793.46530  1.0263e-14  1.1514e-17  13.1463  0.0012  1
+i        7647    56794.46365  1.0327e-14  1.3893e-17  13.1396  0.0015  1
+i        7647    56795.44566  1.0266e-14  1.3657e-17  13.1460  0.0014  1
+i        7647    56796.44806  1.0299e-14  1.6216e-17  13.1425  0.0017  1
+i        7647    56797.45075  1.0342e-14  1.5733e-17  13.1380  0.0017  1
+i        7647    56798.44366  1.0344e-14  1.6566e-17  13.1378  0.0017  1
+i        7647    56799.44057  1.0388e-14  1.5688e-17  13.1332  0.0016  1
+i        7647    56800.44296  1.0346e-14  1.2622e-17  13.1376  0.0013  1
+i        7647    56801.42747  1.0319e-14  1.1200e-17  13.1404  0.0012  1
+i        7647    56802.43701  1.0336e-14  9.7995e-18  13.1386  0.0010  1
+i        7647    56803.44075  1.0358e-14  1.4131e-17  13.1363  0.0015  1
+i        7647    56805.46065  1.0323e-14  1.6691e-17  13.1400  0.0018  1
+i        7647    56806.43379  1.0272e-14  1.0286e-17  13.1454  0.0011  1
+i        7647    56807.43561  1.0338e-14  1.2573e-17  13.1384  0.0013  1
+i        7647    56808.41510  1.0356e-14  1.4605e-17  13.1365  0.0015  1
+i        7647    56809.43000  1.0365e-14  1.3149e-17  13.1356  0.0014  1
+i        7647    56810.43131  1.0381e-14  1.4683e-17  13.1339  0.0015  1
+i        7647    56811.42008  1.0372e-14  1.4970e-17  13.1348  0.0016  1
+i        7647    56812.43007  1.0398e-14  1.3380e-17  13.1321  0.0014  1
+i        7647    56813.40298  1.0392e-14  1.7247e-17  13.1328  0.0018  1
+i        7647    56815.40381  1.0443e-14  1.6613e-17  13.1274  0.0017  1
+i        7647    56816.27751  1.0523e-14  2.7328e-17  13.1192  0.0028  4
+i        7647    56816.41077  1.0476e-14  1.4594e-17  13.1240  0.0015  1
+i        7647    56816.64306  1.0492e-14  5.6383e-17  13.1224  0.0058  5
+i        7647    56817.41079  1.0489e-14  1.3550e-17  13.1227  0.0014  1
+i        7647    56817.83763  1.0501e-14  4.6100e-17  13.1214  0.0048  5
+i        7647    56818.41563  1.0635e-14  1.2342e-17  13.1077  0.0013  1
+i        7647    56819.27574  1.0607e-14  2.5741e-17  13.1105  0.0026  4
+i        7647    56819.41571  1.0671e-14  1.3257e-17  13.1040  0.0013  1
+i        7647    56820.48486  1.0676e-14  1.0707e-17  13.1035  0.0011  1
+i        7647    56821.24589  1.0730e-14  3.1384e-17  13.0980  0.0032  4
+i        7647    56822.39635  1.0768e-14  1.1759e-17  13.0942  0.0012  1
+i        7647    56823.40732  1.0863e-14  1.6316e-17  13.0846  0.0016  1
+i        7647    56824.40115  1.0826e-14  1.7377e-17  13.0883  0.0017  1
+i        7647    56825.30887  1.0887e-14  5.3436e-17  13.0822  0.0053  4
+i        7647    56825.41508  1.0888e-14  1.8402e-17  13.0821  0.0018  1
+i        7647    56826.39287  1.0736e-14  1.8112e-17  13.0974  0.0018  1
+i        7647    56827.39764  1.0696e-14  1.6159e-17  13.1014  0.0016  1
+i        7647    56828.39997  1.0676e-14  1.6659e-17  13.1035  0.0017  1
+i        7647    56829.40979  1.0648e-14  1.5916e-17  13.1063  0.0016  1
+i        7647    56830.40255  1.0616e-14  1.8272e-17  13.1096  0.0019  1
+i        7647    56831.39396  1.0419e-14  1.6339e-17  13.1299  0.0017  1
+i        7647    56832.43681  1.0473e-14  1.5363e-17  13.1243  0.0016  1
+i        7647    56833.39816  1.0512e-14  1.6127e-17  13.1203  0.0017  1
+i        7647    56834.39275  1.0565e-14  1.4259e-17  13.1148  0.0015  1
+i        7647    56835.39420  1.0492e-14  1.5334e-17  13.1224  0.0016  1
+i        7647    56839.33995  1.0496e-14  3.9668e-17  13.1219  0.0041  3
+i        7647    56841.41209  1.0514e-14  1.4987e-17  13.1201  0.0015  1
+i        7647    56842.24887  1.0486e-14  4.3920e-17  13.1230  0.0045  4
+i        7647    56843.21908  1.0442e-14  4.1289e-17  13.1275  0.0043  4
+i        7647    56843.39193  1.0488e-14  1.5184e-17  13.1228  0.0016  1
+i        7647    56845.41125  1.0415e-14  1.6418e-17  13.1304  0.0017  1
+i        7647    56847.40192  1.0284e-14  1.2048e-17  13.1441  0.0013  1
+i        7647    56848.39146  1.0219e-14  8.3962e-18  13.1510  0.0009  1
+i        7647    56848.85800  1.0252e-14  6.5753e-17  13.1475  0.0070  2
+i        7647    56850.25912  1.0311e-14  2.9486e-17  13.1412  0.0031  3
+i        7647    56858.38847  1.0166e-14  1.6626e-17  13.1566  0.0018  1
+i        7647    56860.38740  1.0135e-14  1.9027e-17  13.1599  0.0020  1
+i        7647    56861.38644  1.0197e-14  1.3262e-17  13.1533  0.0014  1
+i        7647    56862.38698  1.0128e-14  1.7233e-17  13.1607  0.0018  1
+i        7647    56863.38540  1.0119e-14  1.5518e-17  13.1617  0.0017  1
+i        7647    56864.38541  1.0245e-14  1.8702e-17  13.1482  0.0020  1
+i        7647    56864.69500  1.0165e-14  8.6684e-17  13.1567  0.0093  5
+i        7647    56865.38436  1.0165e-14  1.6406e-17  13.1567  0.0018  1
+i        7647    56865.63489  1.0161e-14  8.8544e-17  13.1572  0.0095  5
\ No newline at end of file
diff --git a/data/STORM_III/raw/separated/8560Å.tab b/data/STORM_III/raw/separated/8560Å.tab
new file mode 100644
index 0000000..4c8967b
--- /dev/null
+++ b/data/STORM_III/raw/separated/8560Å.tab
@@ -0,0 +1,98 @@
+I        8560    56691.57709  8.8307e-15  1.0109e-16  13.0646  0.0124  2
+I        8560    56691.60323  8.7317e-15  2.6959e-17  13.0768  0.0034  1
+I        8560    56692.59282  8.8029e-15  1.2202e-16  13.0680  0.0150  2
+I        8560    56693.60115  8.6513e-15  1.2546e-16  13.0869  0.0157  2
+I        8560    56697.56501  8.9278e-15  2.7706e-17  13.0527  0.0034  1
+I        8560    56698.54819  8.9709e-15  2.5896e-17  13.0475  0.0031  1
+I        8560    56699.47976  8.8364e-15  2.0513e-17  13.0639  0.0025  1
+I        8560    56700.50659  8.8091e-15  2.0570e-17  13.0672  0.0025  1
+I        8560    56701.56881  8.8869e-15  2.4809e-17  13.0577  0.0030  1
+I        8560    56702.54189  8.9436e-15  1.7635e-17  13.0508  0.0021  1
+I        8560    56708.57310  9.0570e-15  4.4293e-17  13.0371  0.0053  1
+I        8560    56709.55489  9.0211e-15  2.1009e-17  13.0414  0.0025  1
+I        8560    56710.48332  8.9276e-15  1.9450e-17  13.0527  0.0024  1
+I        8560    56711.60397  8.7975e-15  1.1322e-16  13.0687  0.0140  2
+I        8560    56714.47459  8.8213e-15  1.8590e-17  13.0657  0.0023  1
+I        8560    56716.48488  8.7184e-15  2.4774e-17  13.0785  0.0031  1
+I        8560    56721.48007  8.3886e-15  2.1876e-17  13.1203  0.0028  1
+I        8560    56723.45793  8.1715e-15  1.9344e-17  13.1488  0.0026  1
+I        8560    56724.62954  8.1366e-15  1.2583e-16  13.1535  0.0168  2
+I        8560    56726.63023  8.2353e-15  2.1025e-16  13.1404  0.0277  2
+I        8560    56727.46070  8.1925e-15  2.1967e-17  13.1460  0.0029  1
+I        8560    56727.60898  8.2072e-15  1.7771e-16  13.1441  0.0235  2
+I        8560    56728.62722  8.2030e-15  1.6299e-16  13.1446  0.0216  2
+I        8560    56729.61621  8.2270e-15  1.4817e-16  13.1415  0.0196  2
+I        8560    56730.57130  8.1727e-15  1.3813e-16  13.1487  0.0184  2
+I        8560    56732.50843  8.1692e-15  1.5562e-17  13.1491  0.0021  1
+I        8560    56737.52364  8.5637e-15  1.4633e-17  13.0979  0.0019  1
+I        8560    56738.45493  8.6607e-15  1.4293e-17  13.0857  0.0018  1
+I        8560    56738.58392  8.4869e-15  1.4230e-16  13.1077  0.0182  2
+I        8560    56739.31690  8.5640e-15  1.9260e-17  13.0979  0.0024  1
+I        8560    56739.59695  8.4919e-15  1.1904e-16  13.1071  0.0152  2
+I        8560    56740.57789  8.7901e-15  9.1033e-17  13.0696  0.0112  2
+I        8560    56741.52756  8.9782e-15  1.0800e-16  13.0466  0.0131  2
+I        8560    56742.45516  8.9150e-15  1.9131e-17  13.0543  0.0023  1
+I        8560    56744.44870  9.0916e-15  1.8332e-17  13.0330  0.0022  1
+I        8560    56746.43172  9.2889e-15  1.1265e-16  13.0097  0.0132  2
+I        8560    56747.45091  9.2676e-15  8.5321e-17  13.0122  0.0100  2
+I        8560    56748.47765  9.2453e-15  1.5179e-17  13.0148  0.0018  1
+I        8560    56748.54210  9.1905e-15  8.8823e-17  13.0212  0.0105  2
+I        8560    56750.35261  9.3011e-15  2.0754e-17  13.0082  0.0024  1
+I        8560    56752.42031  9.3049e-15  2.6117e-17  13.0078  0.0030  1
+I        8560    56753.36861  9.2474e-15  2.4158e-17  13.0145  0.0028  1
+I        8560    56754.37718  9.1760e-15  1.5700e-17  13.0229  0.0019  1
+I        8560    56755.43128  9.0675e-15  3.6559e-17  13.0359  0.0044  1
+I        8560    56757.34955  9.1152e-15  2.2711e-17  13.0302  0.0027  1
+I        8560    56758.44495  8.9878e-15  2.1865e-17  13.0454  0.0026  1
+I        8560    56762.45281  8.6906e-15  1.3725e-16  13.0819  0.0171  2
+I        8560    56763.43267  8.7850e-15  1.6176e-16  13.0702  0.0200  2
+I        8560    56765.31162  8.8668e-15  1.4039e-17  13.0602  0.0017  1
+I        8560    56765.51415  8.7722e-15  8.9637e-17  13.0718  0.0111  2
+I        8560    56766.29921  8.8558e-15  2.0045e-17  13.0615  0.0025  1
+I        8560    56774.53174  9.1516e-15  1.1342e-16  13.0258  0.0135  2
+I        8560    56784.46906  9.1553e-15  1.0877e-16  13.0254  0.0129  2
+I        8560    56793.50084  8.9980e-15  1.4907e-16  13.0442  0.0180  2
+I        8560    56799.36255  9.0840e-15  9.5921e-17  13.0339  0.0115  2
+I        8560    56800.40990  9.1075e-15  5.9837e-17  13.0311  0.0071  1
+I        8560    56800.46214  9.1848e-15  1.2066e-16  13.0219  0.0143  2
+I        8560    56801.43390  8.9689e-15  1.2180e-17  13.0477  0.0015  1
+I        8560    56802.36572  8.9536e-15  1.6556e-17  13.0496  0.0020  1
+I        8560    56803.34639  9.0135e-15  2.3623e-17  13.0423  0.0028  1
+I        8560    56804.40722  8.9891e-15  1.0679e-17  13.0453  0.0013  1
+I        8560    56810.34151  9.0305e-15  2.7017e-17  13.0403  0.0032  1
+I        8560    56811.34127  9.0377e-15  1.8143e-17  13.0394  0.0022  1
+I        8560    56815.37378  9.0767e-15  1.4549e-17  13.0348  0.0017  1
+I        8560    56817.36559  9.1430e-15  2.1517e-17  13.0269  0.0026  1
+I        8560    56817.42263  9.0437e-15  1.0494e-16  13.0387  0.0126  2
+I        8560    56818.37315  9.3889e-15  1.5805e-16  12.9980  0.0183  2
+I        8560    56819.43436  9.1493e-15  1.2155e-16  13.0261  0.0144  2
+I        8560    56820.32712  9.2520e-15  1.2369e-17  13.0140  0.0015  1
+I        8560    56822.31831  9.2788e-15  1.8752e-17  13.0108  0.0022  1
+I        8560    56822.40327  9.3468e-15  9.4074e-17  13.0029  0.0109  2
+I        8560    56823.35584  9.2709e-15  9.3553e-18  13.0118  0.0011  1
+I        8560    56823.39872  9.2605e-15  1.4112e-16  13.0130  0.0165  2
+I        8560    56824.37306  9.2278e-15  7.9565e-17  13.0168  0.0094  2
+I        8560    56824.38000  9.3354e-15  9.5640e-18  13.0042  0.0011  1
+I        8560    56825.35932  9.3370e-15  1.0273e-17  13.0041  0.0012  1
+I        8560    56827.38256  9.2479e-15  1.7244e-17  13.0145  0.0020  1
+I        8560    56829.31543  9.2087e-15  2.2199e-17  13.0191  0.0026  1
+I        8560    56831.29218  9.0190e-15  2.9260e-17  13.0417  0.0035  1
+I        8560    56832.28555  9.0169e-15  2.5419e-17  13.0419  0.0031  1
+I        8560    56834.33571  9.0397e-15  3.1968e-17  13.0392  0.0038  1
+I        8560    56834.40176  9.2469e-15  9.6736e-17  13.0146  0.0114  2
+I        8560    56835.41899  9.1293e-15  1.3811e-16  13.0285  0.0164  2
+I        8560    56836.32377  9.0264e-15  1.7885e-17  13.0408  0.0022  1
+I        8560    56837.32461  9.0328e-15  1.7610e-17  13.0400  0.0021  1
+I        8560    56837.41188  9.1002e-15  1.4680e-16  13.0319  0.0175  2
+I        8560    56839.32787  9.0474e-15  1.8228e-17  13.0383  0.0022  1
+I        8560    56844.37396  9.0582e-15  1.7204e-17  13.0370  0.0021  1
+I        8560    56845.28505  8.9892e-15  1.0768e-17  13.0453  0.0013  1
+I        8560    56846.34474  8.9720e-15  7.5842e-18  13.0474  0.0009  1
+I        8560    56848.28752  8.7447e-15  5.4227e-17  13.0752  0.0067  1
+I        8560    56850.30477  8.8867e-15  1.5854e-17  13.0577  0.0019  1
+I        8560    56852.28952  8.8324e-15  1.0476e-17  13.0644  0.0013  1
+I        8560    56853.31874  8.7353e-15  3.0300e-17  13.0764  0.0038  1
+I        8560    56854.31157  8.6959e-15  2.4919e-17  13.0813  0.0031  1
+I        8560    56857.26244  8.7292e-15  1.6967e-17  13.0771  0.0021  1
+I        8560    56858.29799  8.7835e-15  1.4820e-17  13.0704  0.0018  1
+I        8560    56859.33924  8.8252e-15  1.4181e-17  13.0653  0.0017  1
\ No newline at end of file
diff --git a/data/STORM_III/raw/separated/9157Å.tab b/data/STORM_III/raw/separated/9157Å.tab
new file mode 100644
index 0000000..32eb662
--- /dev/null
+++ b/data/STORM_III/raw/separated/9157Å.tab
@@ -0,0 +1,186 @@
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+z        9157    56864.69970  9.2761e-15  9.0122e-17  12.8649  0.0105  5
+z        9157    56865.38523  9.3475e-15  1.6411e-17  12.8565  0.0019  1
+z        9157    56865.63955  9.2891e-15  9.6014e-17  12.8634  0.0112  5
\ No newline at end of file
diff --git a/data/calibration/combine_curves.sh b/data/calibration/combine_curves.sh
new file mode 100644
index 0000000..d7230c0
--- /dev/null
+++ b/data/calibration/combine_curves.sh
@@ -0,0 +1,14 @@
+
+for line in $(readline)
+do
+    echo \
+
+
+
+
+fd=3
+while IFS='' read -u $fd -r line || [[ -n "$line" ]]; do
+    echo "Text read from file: $line"
+done $fd< "$1"
+
+
diff --git a/data/calibration/lc_drive_1.dat b/data/calibration/lc_drive_1.dat
new file mode 100644
index 0000000..86e0b29
--- /dev/null
+++ b/data/calibration/lc_drive_1.dat
@@ -0,0 +1,201 @@
+# 1 2 						
+      0.00000      11.8346    0.0916311      11.8520    0.0920392
+      1.00000      11.5171    0.0936466      11.6313    0.0921003
+      2.00000      11.6853    0.0926701      11.6957    0.0921849
+      3.00000      11.3192    0.0939709      11.9643    0.0922556
+      4.00000      10.9107    0.0955774      11.7650    0.0923200
+      5.00000      10.9479    0.0963704      11.6677    0.0924218
+      6.00000      10.3560    0.0974515      11.6919    0.0925855
+      7.00000      10.0256     0.100010      11.4352    0.0928581
+      8.00000      9.32688     0.103079      11.2992    0.0932782
+      9.00000      9.05533     0.105088      11.5295    0.0938539
+      10.0000      9.92991     0.100283      11.2463    0.0946021
+      11.0000      10.0773    0.0992539      11.0594    0.0955676
+      12.0000      9.17007     0.106298      10.6793    0.0967491
+      13.0000      8.20408     0.110395      10.5359    0.0979754
+      14.0000      7.95304     0.112638      10.4494    0.0990027
+      15.0000      7.89341     0.112803      10.1798    0.0998917
+      16.0000      7.12456     0.118062      9.82090     0.100963
+      17.0000      7.39933     0.116967      9.67732     0.102398
+      18.0000      7.78264     0.112441      9.21345     0.104175
+      19.0000      8.59596     0.106939      8.89971     0.106255
+      20.0000      8.67170     0.107497      8.40992     0.108486
+      21.0000      8.79939     0.106332      8.11483     0.110458
+      22.0000      9.12812     0.105206      8.02438     0.111699
+      23.0000      9.63422     0.101256      7.85372     0.112031
+      24.0000      10.1258    0.0995095      8.04622     0.111624
+      25.0000      10.8106    0.0964401      8.22570     0.110721
+      26.0000      10.4907    0.0973379      8.39732     0.109430
+      27.0000      9.81701     0.100341      8.49605     0.107793
+      28.0000      10.6162    0.0974127      8.99748     0.105903
+      29.0000      10.0543    0.0996875      9.23480     0.103994
+      30.0000      9.70010     0.101472      9.57122     0.102361
+      31.0000      9.68858     0.101708      9.89263     0.101116
+      32.0000      9.52794     0.101676      9.92097     0.100224
+      33.0000      10.5637    0.0975818      10.0250    0.0997054
+      34.0000      9.78397     0.100777      9.97885    0.0995768
+      35.0000      9.69834     0.101275      10.2150    0.0997341
+      36.0000      9.98752     0.100783      9.81157    0.0999361
+      37.0000      10.2229    0.0990209      10.0589     0.100024
+      38.0000      10.6349    0.0968980      10.0366     0.100073
+      39.0000      10.3095    0.0979626      9.88274     0.100169
+      40.0000      10.8063    0.0951126      9.95001     0.100230
+      41.0000      10.8576    0.0961898      9.93254     0.100073
+      42.0000      11.0283    0.0948915      9.99264    0.0996499
+      43.0000      11.3431    0.0944636      10.0456    0.0990311
+      44.0000      10.9497    0.0950889      10.3528    0.0983430
+      45.0000      11.6750    0.0927961      10.6239    0.0976712
+      46.0000      11.7398    0.0918948      10.7173    0.0970350
+      47.0000      12.0419    0.0910967      10.7174    0.0964282
+      48.0000      12.5050    0.0893909      10.9092    0.0958243
+      49.0000      12.1528    0.0901180      10.9749    0.0951958
+      50.0000      13.2853    0.0876756      11.1911    0.0945360
+      51.0000      13.2477    0.0868076      11.3143    0.0938218
+      52.0000      13.2737    0.0869808      11.3308    0.0930511
+      53.0000      13.4963    0.0860270      11.7891    0.0922236
+      54.0000      13.8486    0.0842024      11.9225    0.0913311
+      55.0000      14.0255    0.0843238      12.2608    0.0904313
+      56.0000      13.2712    0.0862851      12.6589    0.0895612
+      57.0000      12.4751    0.0891995      12.5011    0.0886908
+      58.0000      12.5323    0.0892506      12.9242    0.0878315
+      59.0000      12.8992    0.0885207      13.1266    0.0871161
+      60.0000      13.1741    0.0867656      13.1069    0.0867194
+      61.0000      13.4742    0.0859448      13.3905    0.0866953
+      62.0000      13.5065    0.0856387      13.2695    0.0869285
+      63.0000      13.1393    0.0873250      12.9646    0.0872221
+      64.0000      13.0864    0.0872569      13.0376    0.0873939
+      65.0000      12.4548    0.0895150      13.2114    0.0873804
+      66.0000      11.8028    0.0929012      13.1223    0.0872719
+      67.0000      11.2808    0.0932674      13.1650    0.0871945
+      68.0000      11.3354    0.0933298      13.0233    0.0872570
+      69.0000      11.5443    0.0926183      13.0323    0.0875581
+      70.0000      11.4608    0.0928539      12.9517    0.0881508
+      71.0000      11.4608    0.0938145      12.7212    0.0889497
+      72.0000      10.8992    0.0960987      12.3569    0.0898112
+      73.0000      10.5799    0.0971217      12.2781    0.0906132
+      74.0000      9.82603    0.0997068      11.8600    0.0913149
+      75.0000      10.0442    0.0990730      11.8620    0.0919961
+      76.0000      9.92676     0.100064      11.5508    0.0927538
+      77.0000      10.8805    0.0966226      11.2424    0.0936376
+      78.0000      10.6242    0.0971202      11.2025    0.0946418
+      79.0000      10.3367    0.0983389      10.7453    0.0956645
+      80.0000      10.8152    0.0959204      10.7198    0.0965216
+      81.0000      11.2386    0.0938642      10.5039    0.0970847
+      82.0000      10.9800    0.0950364      10.5691    0.0973957
+      83.0000      11.4681    0.0938408      10.5723    0.0975216
+      84.0000      11.5389    0.0931532      10.4123    0.0974251
+      85.0000      11.2253    0.0931785      10.7288    0.0970937
+      86.0000      11.8083    0.0928413      10.7312    0.0965935
+      87.0000      12.4067    0.0893181      10.8274    0.0959958
+      88.0000      12.2196    0.0910494      10.8779    0.0953716
+      89.0000      12.4784    0.0900115      11.0742    0.0948072
+      90.0000      12.3620    0.0892570      11.4351    0.0942644
+      91.0000      13.2934    0.0867932      11.3226    0.0936637
+      92.0000      12.8009    0.0877081      11.5726    0.0930059
+      93.0000      12.6074    0.0884435      11.5918    0.0923073
+      94.0000      12.2475    0.0903075      11.7779    0.0915448
+      95.0000      11.9577    0.0910858      12.1573    0.0907351
+      96.0000      12.0627    0.0914462      12.5823    0.0899925
+      97.0000      12.1484    0.0910205      12.5467    0.0894718
+      98.0000      12.8625    0.0889425      12.5621    0.0892639
+      99.0000      13.0945    0.0874881      12.3116    0.0893153
+      100.000      12.9432    0.0883140      12.5800    0.0895089
+      101.000      12.7515    0.0889059      12.5499    0.0897063
+      102.000      12.3544    0.0899624      12.4408    0.0897576
+      103.000      13.4878    0.0863317      12.3187    0.0896216
+      104.000      13.5116    0.0861297      12.5760    0.0894072
+      105.000      13.5706    0.0858822      12.4875    0.0892188
+      106.000      13.2511    0.0872667      12.4848    0.0890197
+      107.000      13.1641    0.0866494      12.5287    0.0886937
+      108.000      13.1011    0.0878646      12.8887    0.0882556
+      109.000      12.9774    0.0885943      12.9463    0.0878421
+      110.000      12.7289    0.0888337      13.0328    0.0875591
+      111.000      12.5099    0.0892622      13.2331    0.0874189
+      112.000      12.5295    0.0893067      13.1068    0.0874165
+      113.000      12.5129    0.0896598      13.0240    0.0875312
+      114.000      12.1640    0.0908460      12.8882    0.0877294
+      115.000      12.3755    0.0902718      12.8790    0.0879918
+      116.000      12.7168    0.0894989      12.8959    0.0883059
+      117.000      12.4370    0.0895261      12.6634    0.0886585
+      118.000      12.6094    0.0891641      12.4469    0.0890099
+      119.000      12.6167    0.0891394      12.5087    0.0892957
+      120.000      12.8278    0.0883300      12.5121    0.0894772
+      121.000      12.5393    0.0888684      12.4591    0.0895498
+      122.000      12.9127    0.0879627      12.6241    0.0895314
+      123.000      12.4413    0.0896365      12.5041    0.0894309
+      124.000      13.0784    0.0870958      12.4327    0.0892606
+      125.000      12.9207    0.0879425      12.6621    0.0890624
+      126.000      12.3796    0.0901989      12.7625    0.0888862
+      127.000      11.8530    0.0921237      12.9621    0.0887382
+      128.000      11.0302    0.0940796      12.7621    0.0886156
+      129.000      11.2067    0.0942997      12.9214    0.0886026
+      130.000      11.2179    0.0945982      12.6813    0.0888089
+      131.000      11.0497    0.0947640      12.4662    0.0892739
+      132.000      11.1662    0.0946457      12.2651    0.0899507
+      133.000      10.8700    0.0949281      12.2224    0.0907822
+      134.000      11.0152    0.0948465      12.0585    0.0916789
+      135.000      10.8602    0.0952074      11.7600    0.0924836
+      136.000      11.6466    0.0930853      11.4879    0.0931173
+      137.000      11.7101    0.0921170      11.2207    0.0936178
+      138.000      11.9286    0.0909162      11.3950    0.0940297
+      139.000      11.6441    0.0924392      11.2781    0.0943260
+      140.000      11.7345    0.0927672      11.1356    0.0944032
+      141.000      10.8692    0.0946534      11.4244    0.0942221
+      142.000      10.9138    0.0959359      11.3428    0.0938740
+      143.000      11.1368    0.0949154      11.4297    0.0935168
+      144.000      11.0585    0.0950059      11.3125    0.0933194
+      145.000      11.2426    0.0949456      11.4175    0.0933656
+      146.000      11.9081    0.0912231      11.5941    0.0936050
+      147.000      12.4009    0.0898223      11.4940    0.0939087
+      148.000      13.4207    0.0868470      11.3373    0.0941707
+      149.000      13.8947    0.0847683      11.3460    0.0942463
+      150.000      13.6351    0.0856346      11.1821    0.0939624
+      151.000      14.0538    0.0848999      11.5908    0.0932287
+      152.000      13.6725    0.0854632      11.7160    0.0920930
+      153.000      13.8347    0.0850930      12.2084    0.0907386
+      154.000      14.1285    0.0844082      12.5707    0.0893917
+      155.000      13.7462    0.0854248      12.7879    0.0882323
+      156.000      13.6226    0.0854430      13.0456    0.0873321
+      157.000      14.3598    0.0837400      13.2796    0.0866757
+      158.000      14.9101    0.0818562      13.3855    0.0862314
+      159.000      14.7358    0.0822357      13.4971    0.0859381
+      160.000      15.2094    0.0809918      13.8179    0.0856773
+      161.000      14.6166    0.0830360      13.8419    0.0853203
+      162.000      14.2887    0.0833350      13.8779    0.0848474
+      163.000      14.3504    0.0832084      13.9597    0.0843235
+      164.000      14.5970    0.0823221      14.2008    0.0838318
+      165.000      13.8544    0.0849232      14.4594    0.0834580
+      166.000      13.6646    0.0853847      14.6831    0.0832250
+      167.000      13.3887    0.0869071      14.4196    0.0831116
+      168.000      12.6146    0.0893435      14.4471    0.0831135
+      169.000      12.3948    0.0900204      14.4192    0.0832684
+      170.000      12.6322    0.0891276      14.2742    0.0835975
+      171.000      13.0699    0.0872879      14.0700    0.0841333
+      172.000      13.4310    0.0867458      14.0092    0.0848831
+      173.000      13.0559    0.0880499      13.6975    0.0857398
+      174.000      12.8411    0.0882440      13.5298    0.0865147
+      175.000      12.8093    0.0880553      13.3174    0.0870609
+      176.000      12.7878    0.0882697      13.0970    0.0873768
+      177.000      12.4221    0.0888481      12.8589    0.0875554
+      178.000      12.4004    0.0897559      13.0890    0.0876629
+      179.000      11.9363    0.0921017      13.0661    0.0877491
+      180.000      11.6354    0.0929836      12.9740    0.0878734
+      181.000      11.9204    0.0915473      12.7579    0.0880936
+      182.000      12.0654    0.0916632      12.7115    0.0884856
+      183.000      11.0418    0.0954513      12.7051    0.0890697
+      184.000      11.1620    0.0948989      12.3475    0.0897439
+      185.000      10.8438    0.0963477      12.5288    0.0903871
+      186.000      10.4444    0.0978891      11.9575    0.0910305
+      187.000      10.2987    0.0993244      11.7561    0.0917444
+      188.000      10.2610    0.0978249      11.6216    0.0925205
+      189.000      11.0505    0.0945137      11.4784    0.0933454
+      190.000      11.6235    0.0930578      11.3725    0.0942329
+      191.000      11.4565    0.0934014      11.2604    0.0951098
+      192.000      11.2891    0.0947141      10.8672    0.0957535
+      193.000      11.5836    0.0929899      10.9642    0.0959744
+      194.000      11.2664    0.0935554      11.0822    0.0957947
+      195.000      12.2846    0.0909019      10.9052    0.0954045
+      196.000      11.9529    0.0915996      11.1116    0.0949828
+      197.000      12.0606    0.0915574      11.0937    0.0945913
+      198.000      11.4087    0.0937190      11.1336    0.0941762
+      199.000      11.3703    0.0936658      11.4885    0.0936849
\ No newline at end of file
diff --git a/data/calibration/lc_drive_2.dat b/data/calibration/lc_drive_2.dat
new file mode 100644
index 0000000..e6c883b
--- /dev/null
+++ b/data/calibration/lc_drive_2.dat
@@ -0,0 +1,201 @@
+# 1 2
+      0.00000      11.8346    0.0916311    11.6191    0.0922944								
+      1.00000      11.5171    0.0936466    11.6401    0.0922925								
+      2.00000      11.6853    0.0926701    11.7752    0.0923443								
+      3.00000      11.3192    0.0939709    11.7299    0.0924564								
+      4.00000      10.9107    0.0955774    11.5097    0.0925976								
+      5.00000      10.9479    0.0963704    11.6132    0.0928827								
+      6.00000      10.3560    0.0974515    11.3024    0.0932647								
+      7.00000      10.0256     0.100010    11.3396    0.0937457								
+      8.00000      9.32688     0.103079    11.2390    0.0943776								
+      9.00000      9.05533     0.105088    11.1306    0.0952117								
+      10.0000      9.92991     0.100283    10.8224    0.0961742								
+      11.0000      10.0773    0.0992539    10.6166    0.0969505								
+      12.0000      9.17007     0.106298    10.6107    0.0974986								
+      13.0000      8.20408     0.110395    10.6293    0.0983072								
+      14.0000      7.95304     0.112638    10.2156    0.0995221								
+      15.0000      7.89341     0.112803    9.73226     0.100918								
+      16.0000      7.12456     0.118062    9.57719     0.102450								
+      17.0000      7.39933     0.116967    9.35108     0.104215								
+      18.0000      7.78264     0.112441    9.10679     0.105901								
+      19.0000      8.59596     0.106939    8.85028     0.107149								
+      20.0000      8.67170     0.107497    8.62653     0.107822								
+      21.0000      8.79939     0.106332    8.62642     0.108146								
+      22.0000      9.12812     0.105206    8.68356     0.108223								
+      23.0000      9.63422     0.101256    8.63371     0.108039								
+      24.0000      10.1258    0.0995095    8.54608     0.107478								
+      25.0000      10.8106    0.0964401    8.73538     0.106562								
+      26.0000      10.4907    0.0973379    8.98730     0.105403								
+      27.0000      9.81701     0.100341    9.13563     0.104335								
+      28.0000      10.6162    0.0974127    9.25609     0.103534								
+      29.0000      10.0543    0.0996875    9.52069     0.102766								
+      30.0000      9.70010     0.101472    9.44495     0.102130								
+      31.0000      9.68858     0.101708    9.51522     0.101839								
+      32.0000      9.52794     0.101676    9.77127     0.101801								
+      33.0000      10.5637    0.0975818    9.49540     0.101763								
+      34.0000      9.78397     0.100777    9.73291     0.101451								
+      35.0000      9.69834     0.101275    9.87817     0.101135								
+      36.0000      9.98752     0.100783    9.91361     0.100981								
+      37.0000      10.2229    0.0990209    9.68331     0.100850								
+      38.0000      10.6349    0.0968980    9.92354     0.100553								
+      39.0000      10.3095    0.0979626    10.0572     0.100052								
+      40.0000      10.8063    0.0951126    10.0019    0.0995339								
+      41.0000      10.8576    0.0961898    10.4107    0.0989405								
+      42.0000      11.0283    0.0948915    10.2195    0.0984326								
+      43.0000      11.3431    0.0944636    10.3752    0.0979184								
+      44.0000      10.9497    0.0950889    10.5510    0.0974279								
+      45.0000      11.6750    0.0927961    10.5743    0.0969687								
+      46.0000      11.7398    0.0918948    10.6968    0.0964735								
+      47.0000      12.0419    0.0910967    10.7102    0.0959205								
+      48.0000      12.5050    0.0893909    11.0114    0.0953374								
+      49.0000      12.1528    0.0901180    11.1946    0.0946548								
+      50.0000      13.2853    0.0876756    11.4126    0.0939756								
+      51.0000      13.2477    0.0868076    11.5006    0.0931625								
+      52.0000      13.2737    0.0869808    11.7754    0.0922933								
+      53.0000      13.4963    0.0860270    11.8208    0.0915008								
+      54.0000      13.8486    0.0842024    12.0433    0.0907127								
+      55.0000      14.0255    0.0843238    12.3065    0.0898517								
+      56.0000      13.2712    0.0862851    12.6127    0.0890129								
+      57.0000      12.4751    0.0891995    12.7979    0.0884341								
+      58.0000      12.5323    0.0892506    12.9170    0.0882075								
+      59.0000      12.8992    0.0885207    12.9120    0.0881862								
+      60.0000      13.1741    0.0867656    12.8311    0.0881851								
+      61.0000      13.4742    0.0859448    12.7626    0.0881101								
+      62.0000      13.5065    0.0856387    12.7355    0.0878867								
+      63.0000      13.1393    0.0873250    13.0566    0.0876199								
+      64.0000      13.0864    0.0872569    13.0858    0.0874587								
+      65.0000      12.4548    0.0895150    13.0637    0.0873841								
+      66.0000      11.8028    0.0929012    13.0861    0.0874815								
+      67.0000      11.2808    0.0932674    13.0060    0.0878146								
+      68.0000      11.3354    0.0933298    12.9712    0.0883388								
+      69.0000      11.5443    0.0926183    12.5439    0.0888727								
+      70.0000      11.4608    0.0928539    12.4093    0.0893995								
+      71.0000      11.4608    0.0938145    12.4806    0.0898754								
+      72.0000      10.8992    0.0960987    12.3604    0.0903651								
+      73.0000      10.5799    0.0971217    12.0862    0.0909874								
+      74.0000      9.82603    0.0997068    11.8622    0.0917078								
+      75.0000      10.0442    0.0990730    11.6181    0.0925784								
+      76.0000      9.92676     0.100064    11.4071    0.0934934								
+      77.0000      10.8805    0.0966226    11.1830    0.0943190								
+      78.0000      10.6242    0.0971202    11.1317    0.0948683								
+      79.0000      10.3367    0.0983389    10.9994    0.0952581								
+      80.0000      10.8152    0.0959204    10.9458    0.0956412								
+      81.0000      11.2386    0.0938642    10.8370    0.0959216								
+      82.0000      10.9800    0.0950364    10.8813    0.0959776								
+      83.0000      11.4681    0.0938408    10.7914    0.0959262								
+      84.0000      11.5389    0.0931532    10.9591    0.0957781								
+      85.0000      11.2253    0.0931785    10.9570    0.0955378								
+      86.0000      11.8083    0.0928413    10.9655    0.0952682								
+      87.0000      12.4067    0.0893181    10.9320    0.0950437								
+      88.0000      12.2196    0.0910494    11.2019    0.0946086								
+      89.0000      12.4784    0.0900115    11.3075    0.0941209								
+      90.0000      12.3620    0.0892570    11.6161    0.0935998								
+      91.0000      13.2934    0.0867932    11.5208    0.0930414								
+      92.0000      12.8009    0.0877081    11.8408    0.0923417								
+      93.0000      12.6074    0.0884435    11.8717    0.0916413								
+      94.0000      12.2475    0.0903075    12.2988    0.0910673								
+      95.0000      11.9577    0.0910858    12.2204    0.0907331								
+      96.0000      12.0627    0.0914462    12.1921    0.0905923								
+      97.0000      12.1484    0.0910205    12.3528    0.0905090								
+      98.0000      12.8625    0.0889425    12.0825    0.0904795								
+      99.0000      13.0945    0.0874881    12.4668    0.0903701								
+      100.000      12.9432    0.0883140    12.4346    0.0900769								
+      101.000      12.7515    0.0889059    12.3796    0.0897671								
+      102.000      12.3544    0.0899624    12.5202    0.0895736								
+      103.000      13.4878    0.0863317    12.5757    0.0894434								
+      104.000      13.5116    0.0861297    12.6180    0.0891776								
+      105.000      13.5706    0.0858822    12.7767    0.0887577								
+      106.000      13.2511    0.0872667    12.8706    0.0883716								
+      107.000      13.1641    0.0866494    12.9153    0.0881182								
+      108.000      13.1011    0.0878646    12.8901    0.0879767								
+      109.000      12.9774    0.0885943    12.7327    0.0878869								
+      110.000      12.7289    0.0888337    12.7027    0.0878951								
+      111.000      12.5099    0.0892622    12.9023    0.0879652								
+      112.000      12.5295    0.0893067    12.5219    0.0880903								
+      113.000      12.5129    0.0896598    12.8855    0.0882725								
+      114.000      12.1640    0.0908460    12.7330    0.0884813								
+      115.000      12.3755    0.0902718    12.7486    0.0887067								
+      116.000      12.7168    0.0894989    12.5972    0.0888843								
+      117.000      12.4370    0.0895261    12.4674    0.0889902								
+      118.000      12.6094    0.0891641    12.4738    0.0890517								
+      119.000      12.6167    0.0891394    12.6992    0.0890661								
+      120.000      12.8278    0.0883300    12.5417    0.0890795								
+      121.000      12.5393    0.0888684    12.6013    0.0890221								
+      122.000      12.9127    0.0879627    12.5916    0.0889154								
+      123.000      12.4413    0.0896365    12.8299    0.0888197								
+      124.000      13.0784    0.0870958    12.7512    0.0887938								
+      125.000      12.9207    0.0879425    12.5866    0.0887361								
+      126.000      12.3796    0.0901989    12.4179    0.0887046								
+      127.000      11.8530    0.0921237    12.5870    0.0888264								
+      128.000      11.0302    0.0940796    12.5530    0.0891488								
+      129.000      11.2067    0.0942997    12.3623    0.0896120								
+      130.000      11.2179    0.0945982    12.3279    0.0901279								
+      131.000      11.0497    0.0947640    11.9809    0.0907484								
+      132.000      11.1662    0.0946457    12.0043    0.0913478								
+      133.000      10.8700    0.0949281    11.9215    0.0917708								
+      134.000      11.0152    0.0948465    11.6957    0.0921635								
+      135.000      10.8602    0.0952074    11.6358    0.0925593								
+      136.000      11.6466    0.0930853    11.6102    0.0929324								
+      137.000      11.7101    0.0921170    11.6009    0.0931846								
+      138.000      11.9286    0.0909162    11.3637    0.0932140								
+      139.000      11.6441    0.0924392    11.5453    0.0931127								
+      140.000      11.7345    0.0927672    11.6310    0.0930099								
+      141.000      10.8692    0.0946534    11.6498    0.0929859								
+      142.000      10.9138    0.0959359    11.5116    0.0931559								
+      143.000      11.1368    0.0949154    11.3889    0.0934403								
+      144.000      11.0585    0.0950059    11.4075    0.0937308								
+      145.000      11.2426    0.0949456    11.4605    0.0939602								
+      146.000      11.9081    0.0912231    11.3968    0.0941584								
+      147.000      12.4009    0.0898223    11.3791    0.0940522								
+      148.000      13.4207    0.0868470    11.4023    0.0936119								
+      149.000      13.8947    0.0847683    11.4626    0.0928532								
+      150.000      13.6351    0.0856346    11.7726    0.0918876								
+      151.000      14.0538    0.0848999    11.8860    0.0908862								
+      152.000      13.6725    0.0854632    12.3626    0.0899696								
+      153.000      13.8347    0.0850930    12.6226    0.0892446								
+      154.000      14.1285    0.0844082    12.6672    0.0886200								
+      155.000      13.7462    0.0854248    12.8877    0.0880803								
+      156.000      13.6226    0.0854430    13.1534    0.0876651								
+      157.000      14.3598    0.0837400    13.2155    0.0872913								
+      158.000      14.9101    0.0818562    13.3516    0.0868580								
+      159.000      14.7358    0.0822357    13.5676    0.0862905								
+      160.000      15.2094    0.0809918    13.5670    0.0857415								
+      161.000      14.6166    0.0830360    13.7193    0.0851997								
+      162.000      14.2887    0.0833350    13.9514    0.0847309								
+      163.000      14.3504    0.0832084    14.0792    0.0843901								
+      164.000      14.5970    0.0823221    14.1623    0.0841140								
+      165.000      13.8544    0.0849232    14.1813    0.0838890								
+      166.000      13.6646    0.0853847    14.3739    0.0837793								
+      167.000      13.3887    0.0869071    14.2389    0.0838496								
+      168.000      12.6146    0.0893435    14.1678    0.0840449								
+      169.000      12.3948    0.0900204    13.7964    0.0844776								
+      170.000      12.6322    0.0891276    13.7969    0.0850506								
+      171.000      13.0699    0.0872879    13.8489    0.0855851								
+      172.000      13.4310    0.0867458    13.3145    0.0859592								
+      173.000      13.0559    0.0880499    13.4777    0.0861804								
+      174.000      12.8411    0.0882440    13.4562    0.0863861								
+      175.000      12.8093    0.0880553    13.4848    0.0865908								
+      176.000      12.7878    0.0882697    13.2427    0.0867433								
+      177.000      12.4221    0.0888481    13.4245    0.0869263								
+      178.000      12.4004    0.0897559    13.1066    0.0871799								
+      179.000      11.9363    0.0921017    12.8802    0.0875366								
+      180.000      11.6354    0.0929836    12.9258    0.0880713								
+      181.000      11.9204    0.0915473    12.8621    0.0886917								
+      182.000      12.0654    0.0916632    12.3253    0.0892224								
+      183.000      11.0418    0.0954513    12.4277    0.0896876								
+      184.000      11.1620    0.0948989    12.3010    0.0903323								
+      185.000      10.8438    0.0963477    12.0204    0.0910145								
+      186.000      10.4444    0.0978891    11.9316    0.0917006								
+      187.000      10.2987    0.0993244    11.7388    0.0924303								
+      188.000      10.2610    0.0978249    11.4519    0.0932371								
+      189.000      11.0505    0.0945137    11.5353    0.0939014								
+      190.000      11.6235    0.0930578    11.2657    0.0942116								
+      191.000      11.4565    0.0934014    11.3133    0.0942556								
+      192.000      11.2891    0.0947141    11.1743    0.0942119								
+      193.000      11.5836    0.0929899    11.1667    0.0942004								
+      194.000      11.2664    0.0935554    11.3246    0.0941764								
+      195.000      12.2846    0.0909019    11.2522    0.0941263								
+      196.000      11.9529    0.0915996    11.3115    0.0939087								
+      197.000      12.0606    0.0915574    11.2917    0.0936196								
+      198.000      11.4087    0.0937190    11.5174    0.0933944								
+      199.000      11.3703    0.0936658    11.4705    0.0933883								
diff --git a/inputfiles/HST1367_i_7647 b/inputfiles/HST1367_i_7647
new file mode 100644
index 0000000..d72f2da
--- /dev/null
+++ b/inputfiles/HST1367_i_7647
@@ -0,0 +1,10 @@
+2
+data/STORM_III/lightcurves/Δt≔0.01/HST1367.lc 0
+data/STORM_III/lightcurves/Δt≔0.01/i_7647.lc 0
+8 0.005 0.019 0.0425 0.07 0.11 0.17 0.24 0.4 0.603
+0
+1 1 1 0.49 0.42 0.30 0.36 1.2 -0.60 -0.89 -0.72 2.39 -0.74 -1.5 -1 -3.1 -1.3 -2.3 -2 -1.9 -1.6 -2.6 -2 -1.3 -1.8 -2.6 -1.2 0 -2 -2.7 -1.3 0.63
+0:0 0
+0
+0
+100 50 50 mcmc.dat
\ No newline at end of file
diff --git a/inputfiles/calibration/calibration_2bins b/inputfiles/calibration/calibration_2bins
new file mode 100644
index 0000000..4581814
--- /dev/null
+++ b/inputfiles/calibration/calibration_2bins
@@ -0,0 +1,10 @@
+2
+data/calibration/lc_drive.dat 0
+data/calibration/lc_1.dat 0
+2 5e-3 5e-2 5e-1
+0
+1 1 1 1 1 1 1 1
+0:1 1
+0
+0
+100 50 50 mcmc.dat
diff --git a/inputfiles/calibration b/inputfiles/calibration/calibration_3bins
similarity index 75%
rename from inputfiles/calibration
rename to inputfiles/calibration/calibration_3bins
index 8f999aa..8fd1871 100644
--- a/inputfiles/calibration
+++ b/inputfiles/calibration/calibration_3bins
@@ -1,5 +1,5 @@
 2
-data/calibration/lc_drive.dat 
+data/calibration/lc_drive.dat 0
 data/calibration/lc_1.dat 0
 3 5e-3 1e-2 5e-2 5e-1
 0
diff --git a/inputfiles/calibration/calibration_final b/inputfiles/calibration/calibration_final
new file mode 100644
index 0000000..32866c4
--- /dev/null
+++ b/inputfiles/calibration/calibration_final
@@ -0,0 +1,10 @@
+2
+data/calibration/lc_drive.dat 0
+data/calibration/lc_2.dat 0
+8 0.005 0.019 0.0425 0.07 0.11 0.17 0.24 0.4 0.603
+0
+1 1 1 0.49 0.42 0.30 0.36 1.2 -0.60 -0.89 -0.72 2.39 -0.74 -1.5 -1 -3.1 -1.3 -2.3 -2 -1.9 -1.6 -2.6 -2 -1.3 -1.8 -2.6 -1.2 0 -2 -2.7 -1.3 0.63
+0:0 0
+0
+0
+100 50 50 mcmc.dat
\ No newline at end of file
diff --git a/inputfiles/calibration/calibration_final_onefile b/inputfiles/calibration/calibration_final_onefile
new file mode 100644
index 0000000..0982186
--- /dev/null
+++ b/inputfiles/calibration/calibration_final_onefile
@@ -0,0 +1,9 @@
+1
+data/calibration/lc_drive_2.dat 0
+8 0.005 0.019 0.0425 0.07 0.11 0.17 0.24 0.4 0.603
+0
+1 1 1 0.49 0.42 0.30 0.36 1.2 -0.60 -0.89 -0.72 2.39 -0.74 -1.5 -1 -3.1 -1.3 -2.3 -2 -1.9 -1.6 -2.6 -2 -1.3 -1.8 -2.6 -1.2 0 -2 -2.7 -1.3 0.63
+0 1
+0
+0
+100 50 50 mcmc.dat
\ No newline at end of file
diff --git a/inputfiles/default b/inputfiles/default
deleted file mode 100644
index 2a2ccc0..0000000
--- a/inputfiles/default
+++ /dev/null
@@ -1,10 +0,0 @@
-1                             
-../data/calibration/lc_1.dat 0                      
-3 1e-5 1e-4 5e-4 1e-3         
-1                             
-1 1 1 1 1 1 1 1 1 1 1 1       
-0 1                           
-0                             
-0                             
-100 50 50 mcmc.dat            
-
diff --git a/notes/Fourier Frequencies b/notes/Fourier Frequencies
new file mode 100644
index 0000000..f0bc1e6
--- /dev/null
+++ b/notes/Fourier Frequencies	
@@ -0,0 +1,134 @@
+
+
+HST1367 time boundaries (days)
+──────────────────────────────
+56691.5
+56865.9
+
+
+
+
+Fourier Quantification
+──────────────────────
+(56865.9 - 56691.5) days = 174.4 days
+
+1 / (174.4 days) = ~0.00573394495412844037 days⁻¹
+
+	First 27 Frequencies
+	────────────────────
+	Linear					Log₁₀
+	0.00573394495412844037 	-2.24154648059654841582
+	0.01146788990825688074 	-1.94051648493256722061
+	0.01720183486238532111 	-1.76442522587688597853
+	0.02293577981651376148 	-1.63948648926858602540
+	0.02866972477064220185 	-1.54257647626052961104
+	0.03440366972477064222 	-1.46339523021290478332
+	0.04013761467889908259 	-1.39644844058229158511
+	0.04587155963302752296 	-1.33845649360460483018
+	0.05160550458715596333 	-1.28730397115722354124
+	0.0573394495412844037 	-1.24154648059654841582
+	0.06307339449541284407 	-1.20015379543832337508
+	0.06880733944954128444 	-1.16236523454892358810
+	0.07454128440366972481 	-1.12760312828971164662
+	0.08027522935779816518 	-1.09541844491831038990
+	0.08600917431192660555 	-1.06545522154086717374
+	0.09174311926605504592 	-1.03742649794062363497
+	0.09747706422018348629 	-1.01109755921827448728
+	0.10321100917431192666 	-0.98627397549324234602
+	0.10894495412844036703 	-0.96279287964371945429
+	0.1146788990825688074 	-0.94051648493256722061
+	0.12041284403669724777 	-0.91932718586262914782
+	0.12614678899082568814 	-0.89912379977434217986
+	0.13188073394495412851 	-0.87981864457895553696
+	0.13761467889908256888 	-0.86133523888494239289
+	0.14334862385321100925 	-0.84360647192451080625
+	0.14908256880733944962 	-0.82657313262573045141
+	0.15481651376146788999 	-0.81018271643756110394
+
+Calibration Bins
+────────────────
+min		max		binned #
+0.005	0.019	3
+	0.00573394495412844037 	-2.24154648059654841582
+	0.01146788990825688074 	-1.94051648493256722061
+	0.01720183486238532111 	-1.76442522587688597853
+0.019	0.0425	4
+	0.02293577981651376148 	-1.63948648926858602540
+	0.02866972477064220185 	-1.54257647626052961104
+	0.03440366972477064222 	-1.46339523021290478332
+	0.04013761467889908259 	-1.39644844058229158511
+0.0425	0.07	5
+	0.04587155963302752296 	-1.33845649360460483018
+	0.05160550458715596333 	-1.28730397115722354124
+	0.0573394495412844037 	-1.24154648059654841582
+	0.06307339449541284407 	-1.20015379543832337508
+	0.06880733944954128444 	-1.16236523454892358810
+0.07	0.11	7
+	0.07454128440366972481 	-1.12760312828971164662
+	0.08027522935779816518 	-1.09541844491831038990
+	0.08600917431192660555 	-1.06545522154086717374
+	0.09174311926605504592 	-1.03742649794062363497
+	0.09747706422018348629 	-1.01109755921827448728
+	0.10321100917431192666 	-0.98627397549324234602
+	0.10894495412844036703 	-0.96279287964371945429
+0.11	0.17	8
+	0.1146788990825688074 	-0.94051648493256722061
+	0.12041284403669724777 	-0.91932718586262914782
+	0.12614678899082568814 	-0.89912379977434217986
+	0.13188073394495412851 	-0.87981864457895553696
+	0.13761467889908256888 	-0.86133523888494239289
+	0.14334862385321100925 	-0.84360647192451080625
+	0.14908256880733944962 	-0.82657313262573045141
+	0.15481651376146788999 	-0.81018271643756110394
+0.17	0.24	12 (30-41)
+0.24	0.4		28 (42-69)
+0.4		0.603   36 (70-105)
+	
+	
+Binning Regimes to Try on i band Lightcurve
+───────────────────────────────────────────
+With lowest frequency
+    0.0049999999     0.018619375    
+    0.018619375     0.044733049     
+    0.044733049     0.069336227     
+    0.069336227      0.10747115    
+    0.10747115      0.16658029      
+    0.16658029      0.25819945      
+    0.25819945      0.40020915      
+    0.40020915      0.62032418      
+
+Next, let’s remove that lowest frequency
+
+    0.0099999998     0.024025000    
+    0.024025000     0.037238750     
+    0.037238750     0.057720063     
+    0.057720063     0.089466098    
+    0.089466098      0.13867245     
+    0.13867245      0.21494230     
+    0.21494230      0.33316057      
+    0.33316057      0.51639889      
+
+
+
+	
+	
+	
+	
+	
+	
+	
+	
+	
+	
+	
+	
+	
+	
+	
+	
+	
+	
+	
+	
+	
+
diff --git a/notes/log b/notes/log
new file mode 100644
index 0000000..4b40778
--- /dev/null
+++ b/notes/log
@@ -0,0 +1,56 @@
+Week 1:
+
+Given four papers to read
+Giving version 1 of the psdlag program
+spent some time exploring the program
+mostly read papers
+
+
+Week 2:
+Given calibration data in the form of light curves produced by a known transfer function
+
+Did things, forgot to keep track
+
+Week 3-4ish:
+A script has been created to read an output file from psdlag and then plot the source and reprocessed power spectral densities, the cross-correlation power spectral density, and the phase difference between the signals.
+
+scripts/power_lag_plot.pl
+
+
+2016-
+
+
+2016-07-11
+
+	created gnuplot templates for plotting the timelags and power spectrum densities
+		- still need to modify this for points to be evenly spaced in log space
+
+2016-07-14
+	Ran through results with simple error checking and determined they made sense.
+	Project can continue.
+
+2016-07-15
+	Determined frequency binning for PSD and timelags:
+
+		0.0049999999	0.018619375
+		0.018619375		0.044733049
+		0.044733049		0.069336227
+		0.069336227		0.10747115
+		0.10747115		0.16658029
+		0.16658029		0.25819945
+		0.25819945		0.40020915
+		0.40020915		0.62032418
+
+
+
+2016-07-18
+	Ran first MCMC run, need to analyze results
+
+2016-07-25
+	Determined MC is definitely the way to go, with at least 1000 simulations.
+
+	Setup scripts to run simulations on Thor, one simulation per processor.
+
+2016-07-26
+	MC giving huge errors. Putting together plots to send to Dr. Zoghbi.
+
diff --git a/plot.sh b/plot.sh
new file mode 100644
index 0000000..9139da3
--- /dev/null
+++ b/plot.sh
@@ -0,0 +1,5 @@
+#!/usr/bin/env bash
+
+# This metascript uses the available plotting scripts to produce a list of
+# document-rady plots.
+
diff --git a/pres/final_notes b/pres/final_notes
new file mode 100644
index 0000000..d23312c
--- /dev/null
+++ b/pres/final_notes
@@ -0,0 +1,10 @@
+Intro
+
+    continuum reverberation mapping
+    tradiational approach
+    our approach
+
+results
+
+future work
+
diff --git a/pres/report/report.tex b/pres/report/report.tex
new file mode 100644
index 0000000..ae6e7aa
--- /dev/null
+++ b/pres/report/report.tex
@@ -0,0 +1,29 @@
+\begin{abstract}
+
+\section{Introduction}
+
+The study of Active Galactic Nuclei (AGN) promises to inform many fields of physical science. As a standard candle, AGN could provide structural information about the universe beyond the supernova horizon, allowing a better measurement of cosmological parameters such as the Expansion Coefficient. 
+
+    \subsection{continuing research}
+
+    \endsubsection
+
+    \subsection{Traditional Reverberation Mapping}
+
+    \endsubsection
+
+    \subsection{Maximum Likelihood Method}
+
+    \endsubsection
+
+\endsection
+
+\section{Results}
+
+\endsection
+
+\section{Future Work}
+
+\endsection
+
+
diff --git a/scripts/analyze_lightcurves.sh b/scripts/analyze_lightcurves.sh
new file mode 100755
index 0000000..effb9b0
--- /dev/null
+++ b/scripts/analyze_lightcurves.sh
@@ -0,0 +1,168 @@
+#!/usr/bin/env bash
+
+timestep="0.1"
+ref_band='1367Å'
+# Parameters for HST(𝛌＝1367Å) from MC results
+refpsd_params="6.533e-02 -9.694e-02 -1.175e+00 -1.525e+00 -2.166e+00 -2.492e+00 -3.258e+00 -9.328e+00"
+# Parameters for HST(𝛌＝1367Å) from covariance matrix estimates
+#refpsd_params="7.376e-02 -1.976e-01 -1.182e+00 -1.521e+00 -2.144e+00 -2.503e+00 -3.580e+00 -1.233e+01"
+
+
+error_type="1"
+# error types:
+#   0 for covariance matrix, 1 for likelihood function, 2 for monte carlo
+case $error_type in
+    "0") err_src="CM";;
+    "1") err_src="LF";;
+    "2") err_src="MC";;
+esac
+
+mkdir -p analyses
+
+host=$1
+
+if [[ "$(hostname)" == "thor.cs.wmich.edu" ]]
+then
+    host="thor"
+fi
+
+if [[ $host == "thor" ]]
+then
+    echo Setting up Thor environment.
+    mkdir -p thor
+    mkdir -p thor/arguments
+    if [[ -e thor/submissions ]]; then echo "" >> thor/submissions; fi
+    date >> thor/submissions
+    echo "─────────────────────────────" >> thor/submissions
+fi
+
+lc_dir="data/STORM_III/lightcurves/Δt＝${timestep}"
+echo Using lightcurves in $lc_dir.
+ref_curve="${lc_dir}/${ref_band}.lc"
+
+for echo_curve in $lc_dir/*
+do
+    # Determine band and inputs for band
+    echo_band=$(basename $echo_curve|sed 's@\(.*\)\.lc@\1@')
+    echo -n $(date "+%R")\: Running psdlag for $echo_band against $ref_band.
+    outputfile="analyses/${ref_band}_≺_${echo_band}_{Δt＝${timestep};σ∊${err_src}}"
+    case $echo_band in
+        "4775Å")
+            initial_params="$refpsd_params -9.745e-01 -1.384e+00 -2.748e+00 -3.305e+00 -3.314e+00 -3.389e+00 -4.198e+00 -4.465e+00 -4.700e-01 -7.487e-01 -2.046e+00 -2.428e+00 -2.953e+00 -3.086e+00 -3.761e+00 -4.290e+00 9.862e-02 3.899e-01 8.650e-01 5.516e-01 2.228e-01 9.508e-01 -2.872e-01 9.059e-02"
+        ;;
+        "1158Å")
+            initial_params="$refpsd_params 2.504e-01 3.210e-02 -9.091e-01 -1.226e+00 -1.790e+00 -2.227e+00 -2.929e+00 -5.131e+00 1.618e-01 -8.299e-02 -1.045e+00 -1.370e+00 -1.952e+00 -2.338e+00 -3.075e+00 -3.742e+00 -3.301e-02 -7.353e-02 -7.112e-02 -5.938e-02 -4.394e-02 -1.756e-01 -2.617e-01 -1.425e-01"
+        ;;
+        "1367Å")
+            initial_params="$refpsd_params 6.534e-02 -9.689e-02 -1.175e+00 -1.525e+00 -2.167e+00 -2.492e+00 -3.258e+00 -1.115e+01 6.378e-02 -9.676e-02 -1.181e+00 -1.520e+00 -2.180e+00 -2.532e+00 -3.129e+00 -3.794e+00 -8.535e-05 -5.126e-04 -4.066e-04 1.080e-03 -2.682e-03 -5.462e-03 -2.209e-02 1.564e-01"
+        ;;
+        "1479Å")
+            initial_params="$refpsd_params 0.2e+00 -0.2e+00 -1.0e+00 -1.500e+00 -2.0e+00 -2.6e+00 -3.4e+00 -6e+00 9.340e-02 -8.842e-02 -1.173e+00 -1.512e+00 -2.113e+00 -2.547e+00 -3.178e+00 -7.000e+00 -6.035e-02 2.044e-02 1.258e-01 5.764e-02 2.580e-02 -4.737e-02 9.579e-02 6.411e-01"
+        ;;
+        "1746Å")
+            initial_params="$refpsd_params 3.934e-02 -1.638e-01 -1.339e+00 -1.655e+00 -2.232e+00 -2.793e+00 -4.025e+00 -1.072e+01 5.043e-02 -1.304e-01 -1.254e+00 -1.587e+00 -2.207e+00 -2.693e+00 -3.209e+00 -3.820e+00 3.040e-02 2.438e-02 1.450e-01 1.458e-01 1.772e-01 2.076e-01 -1.390e-01 3.929e-01"
+        ;;
+        "7647Å")
+            initial_params="$refpsd_params -1.510e+00 -1.872e+00 -3.441e+00 -3.722e+00 -4.794e+00 -4.336e+00 -4.644e+00 -5.061e+00 -7.402e-01 -9.967e-01 -2.393e+00 -2.723e+00 -3.701e+00 -3.742e+00 -4.317e+00 -4.979e+00 2.638e-01 7.095e-01 1.297e+00 1.035e+00 9.267e-01 8.982e-01 7.318e-01 9.092e-01"
+        ;;
+        "8560Å")
+            initial_params="$refpsd_params -1.497e+00 -1.786e+00 -3.555e+00 -4.203e+00 -3.826e+00 -4.096e+00 -4.304e+00 -1.109e+01 -7.542e-01 -9.532e-01 -2.397e+00 -3.214e+00 -3.008e+00 -3.489e+00 -4.353e+00 -4.061e+00 1.675e-01 7.660e-01 1.781e+00 1.971e+00 6.572e-01 8.520e-01 2.405e+00 -1.573e-01"
+        ;;
+        "6175Å")
+            initial_params="$refpsd_params -1.372e+00 -1.786e+00 -3.214e+00 -3.558e+00 -4.416e+00 -4.366e+00 -4.559e+00 -4.586e+00 -6.693e-01 -5.998e+00 -2.390e+00 -2.927e+00 -3.411e+00 -3.731e+00 -3.930e+00 -7.000e+00 2.905e-01 1.835e+00 8.760e-01 1.476e+00 -3.280e-01 1.439e+00 9.464e-02 1.532e+00"
+        ;;
+        "6439Å")
+            initial_params="$refpsd_params -1.287e+00 -1.580e+00 -2.879e+00 -3.569e+00 -3.661e+00 -4.471e+00 -3.998e+00 -5.001e+00 -6.583e-01 -6.900e+00 -2.060e+00 -2.727e+00 -2.954e+00 -3.654e+00 -4.406e+00 -5.203e+00 1.448e-01 -1.004e-01 9.754e-01 1.140e+00 9.284e-01 1.116e+00 2.092e+00 8.634e-01"
+        ;;
+        #"4392Å")
+         #   initial_params="$refpsd_params -5.0e-01 -1.0e+00 -2.7e+00 -3.7e+00 -4.5e+00 -5.5e+00 -6.5e+00 -7.5e+00 -2.515e-01 -6.156e-01 -1.924e+00 -2.417e+00 -3.053e+00 -6.570e+00 -3.450e+00 -6.333e+00 0.027 0.187189 0.39349 0.479216 0.405156 0.313996 0.486696 0.502917"
+            # old params initial_params="$refpsd_params -5.0e-01 -1.0e+00 -2.7e+00 -3.7e+00 -4.5e+00 -5.5e+00 -6.5e+00 -7.5e+00 -2.515e-01 -6.156e-01 -1.924e+00 -2.417e+00 -3.053e+00 -6.570e+00 -3.450e+00 -6.333e+00 7.408e-02 3.089e-01 5.057e-01 4.831e-01 5.779e-01 6.277e-01 4.823e-01 6.110e-01"
+        #;;
+        "3465Å")
+            initial_params="$refpsd_params -3.178e-01 -7.537e-01 -2.402e+00 -2.624e+00 -3.413e+00 -3.834e+00 -1.236e+01 -1.071e+01 -1.376e-01 -4.256e-01 -1.794e+00 -2.123e+00 -2.817e+00 -3.029e+00 -4.139e+00 -7.000e+00 7.840e-02 3.388e-01 6.616e-01 1.349e-01 9.909e-01 8.538e-01 1.397e+00 6.412e-01"
+        ;;
+        "5468Å")
+            initial_params="$refpsd_params -8.802e-01 -1.441e+00 -2.883e+00 -4.483e+00 -1.045e+01 -1.041e+01 -1.042e+01 -1.078e+01 -4.161e-01 -7.660e-01 -2.020e+00 -3.201e+00 -2.699e+00 -2.859e+00 -3.653e+00 -5.501e+00 1.900e-02 3.560e-01 1.007e+00 -1.270e+00 1.858e+00 9.302e-01 -1.502e+00 2.653e+00"
+        ;;
+        "3471Å")
+            initial_params="$refpsd_params -4.289e-01 -9.017e-01 -2.581e+00 -2.751e+00 -2.764e+00 -3.105e+00 -3.298e+00 -3.870e+00 -1.925e-01 -5.093e-01 -1.923e+00 -2.204e+00 -2.568e+00 -2.768e+00 -3.425e+00 -5.982e+00 1.646e-01 4.376e-01 7.202e-01 9.556e-01 4.856e-01 2.985e-01 -6.801e-01 -2.416e+00"
+        ;;
+        "2246Å")
+            initial_params="$refpsd_params -1.003e-01 -5.235e-01 -1.863e+00 -2.159e+00 -2.718e+00 -3.504e+00 -1.139e+01 -4.119e+00 -2.431e-02 -3.121e-01 -1.526e+00 -1.877e+00 -2.455e+00 -2.945e+00 -3.356e+00 -3.602e+00 -1.862e-04 1.380e-01 2.475e-01 3.102e-02 6.040e-01 -1.488e-01 1.171e+00 1.617e+00"
+        ;;
+        "2600Å")
+            initial_params="$refpsd_params -2.417e-01 -7.027e-01 -1.998e+00 -2.387e+00 -2.912e+00 -4.026e+00 -1.069e+01 -9.716e+00 -9.867e-02 -4.150e-01 -1.587e+00 -1.963e+00 -2.539e+00 -3.274e+00 -3.460e+00 -6.742e+00 6.734e-02 1.353e-01 3.585e-01 1.587e-01 2.959e-01 1.111e-01 2.364e-01 7.463e-01"
+        ;;
+        #"1928Å")
+         #   initial_params="$refpsd_params -1.648e-01 -4.974e-01 -1.686e+00 -1.897e+00 -2.600e+00 -2.801e+00 -3.814e+00 -9.888e+00 -6.101e-02 -3.020e-01 -1.433e+00 -1.756e+00 -2.485e+00 -2.799e+00 -3.317e+00 -7.000e+00 4.622e-02 1.473e-01 2.243e-01 1.606e-01 2.365e-01 1.484e-01 8.110e-01 9.689e-01"
+        #;;
+        "5404Å")
+            initial_params="$refpsd_params -1.044e+00 -1.457e+00 -2.848e+00 -2.957e+00 -4.232e+00 -4.052e+00 -4.515e+00 -5.250e+00 -5.026e-01 -7.878e-01 -2.118e+00 -2.276e+00 -3.421e+00 -3.378e+00 -4.224e+00 -4.930e+00 9.142e-02 4.814e-01 8.550e-01 3.929e-01 3.975e-01 5.311e-01 -1.203e-01 -2.905e+00"
+        ;;
+        "9157Å")
+            initial_params="$refpsd_params -1.852e+00 -2.156e+00 -3.798e+00 -4.146e+00 -4.577e+00 -4.338e+00 -4.672e+00 -5.230e+00 -9.339e-01 -1.131e+00 -2.536e+00 -3.031e+00 -4.060e+00 -3.801e+00 -4.139e+00 -5.151e+00 2.445e-01 8.131e-01 1.621e+00 1.460e+00 2.909e-01 6.991e-01 -5.831e-01 -2.921e-01"
+        ;;
+        *)
+            echo -n  " Unknown band: using default parameters."
+            initial_params="$refpsd_params -9.745e-01 -1.384e+00 -2.748e+00 -3.305e+00 -3.314e+00 -3.389e+00 -4.198e+00 -4.465e+00 -4.700e-01 -7.487e-01 -2.046e+00 -2.428e+00 -2.953e+00 -3.086e+00 -3.761e+00 -4.290e+00 9.862e-02 3.899e-01 8.650e-01 5.516e-01 2.228e-01 9.508e-01 -2.872e-01 9.059e-02"
+        ;;
+    esac
+
+    # Write temporary input file
+    echo "2" > tmp.psdlagargs
+    echo "$ref_curve 0" >> tmp.psdlagargs
+    echo "$echo_curve 0" >> tmp.psdlagargs
+    echo "8 0.0049999999 0.018619375 0.044733049 0.069336227 0.10747115 0.16658029 0.25819945 0.40020915 0.62032418" >> tmp.psdlagargs
+    echo "0" >> tmp.psdlagargs
+    echo "$initial_params" >> tmp.psdlagargs
+    echo "0:0 0" >> tmp.psdlagargs
+    echo $error_type >> tmp.psdlagargs
+    echo "0" >> tmp.psdlagargs
+	# Use 10x num vars for walkers
+    echo -n "1000 50 400 mcmc_${echo_band}.dat" >> tmp.psdlagargs
+
+    # Run psdlag with inputs
+    if [[ $host == "thor" || "$(hostname)" == "thor.cs.wmich.edu" ]]
+    then
+        echo_band_noUTF=$(echo $echo_band|
+            #sed 's|𝛌||g'|
+            #sed 's|(|_|g'|
+            #sed 's|)|_|g'|
+            sed 's|＝||g'|
+            sed 's|Å|A|g')
+        outputfile_noUTF=$(echo $outputfile|
+            #sed 's|𝛌||g'|
+            #sed 's|(|_|g'|
+            #sed 's|)|_|g'|
+            sed 's|＝||g'|
+            sed 's|[{}]|_|g'|
+            sed 's|;|_|'|
+            sed 's|Å|A|g'|
+            sed 's|_≺_|_|g'|
+            sed 's|σ∊|err|')
+        if [[ -e $outputfile_noUTF ]]
+        then
+            echo -n " Analysis already exists; skipping."
+        else
+            argsfile="thor/arguments/$echo_band_noUTF.args"
+            submitscript="thor/${echo_band_noUTF}.pbs"
+            cp tmp.psdlagargs $argsfile
+            cat scripts/templates/thor_submit.pbs|
+                sed "s@%NAME%@psdlag_${echo_band_noUTF}@g"|
+                sed "s@%ARGSFILE%@${argsfile}@g"|
+                sed "s@%ROOTDIR%@$(pwd)@g"|
+                sed "s@%OUTPUTFILE%@${outputfile_noUTF}@g" > $submitscript
+            cd thor
+            qsub $(basename $submitscript) >> submissions
+            cd ..
+        fi
+    else
+        if [[ -e $outputfile ]]
+        then
+            echo -n " Analysis already exists; skipping."
+        else
+            time bin/psdlag tmp.psdlagargs > $outputfile
+        fi
+    fi
+    echo ""
+done
diff --git a/scripts/create_tables.pl b/scripts/create_tables.pl
new file mode 100755
index 0000000..8b27fc0
--- /dev/null
+++ b/scripts/create_tables.pl
@@ -0,0 +1,286 @@
+#!/usr/bin/env perl
+
+
+use utf8;
+use Encode qw(encode decode);
+use feature 'say';
+use locale;
+use Switch;
+
+use constant PI    => 4 * atan2(1, 1);
+
+# Enables various levels of output.
+our $verbose=1;
+our $debug=0;
+
+
+# This section locates the output data of interest in a
+# psdlab output file.
+if (!${^UTF8LOCALE}) {
+    say encode($charset,"You are not using UTF-8 encoding. :(");
+}
+my $charset=$ENV{LANG};
+our $outputfilename=decode($charset,$ARGV[0]);
+open $outputfile,'<',$outputfilename or die $!;
+
+my $star_linenum_1=0;
+my $star_linenum_2=0;
+my $star_bytenum_1=0;
+my $star_bytenum_2=0;
+
+my $linenum=0;
+while(<$outputfile>) {
+    $linenum++;
+    if ($_ =~ /^\*+$/) {
+        $star_linenum_1=$star_linenum_2;
+        $star_linenum_2 = $linenum;
+        $star_bytenum_1=$star_bytenum_2;
+        #$star_bytenum_2 = $outputfile.tell();
+        $star_bytenum_2 = tell($outputfile);
+    }
+}
+if ($debug) {
+    say encode($charset,"Final set found between lines "),
+        $star_linenum_1,
+        " and ",
+        $star_linenum_2;
+
+    say encode($charset,"Final set found between bytes "),
+        $star_bytenum_1,
+        " and ",
+        $star_bytenum_2;
+}
+seek($outputfile,$star_bytenum_1,0);
+<$outputfile>;
+$bin_bounds_line = <$outputfile>;
+#say $bin_bounds_line;
+$bin_bounds_line =~ s/^#\s*(.*)$/$1/;
+#$line =~ /
+@bin_bounds = split /\s/,$bin_bounds_line;
+if ($debug) {
+    print encode($charset,"Found bin boundaries: ");
+    foreach (@bin_bounds) {print encode($charset,"$_ ");}
+    say encode($charset," ");
+}
+
+
+
+# This captures the frequency bins.
+our %function_bin = ();
+
+my $upper_bound = 0;
+my $lower_bound = 0;
+foreach (@bin_bounds) {
+    $lower_bound = $upper_bound;
+    $upper_bound = $_;
+    if ($lower_bound == 0) {next}
+    my $μ = ($upper_bound + $lower_bound)/2;
+    my $Δ = ($upper_bound - $lower_bound)/2;
+    #say ($μ,":",$Δ);
+    # push(@freq_coords_mean,$μ);
+    # push(@freq_coords_err,$Δ);
+    $function_bin{$μ} = {"Δ" => $Δ};
+}
+
+$numbins = keys %function_bin;
+say encode($charset,"$numbins frequency bins captured in output.");
+
+if($verbose) {
+    say encode($charset,"freq μ        freq σ");
+    while (each %function_bin ) {
+        say encode($charset,
+            sprintf("%f      %f",$_,$function_bin{$_}{"Δ"}));
+    }
+}
+
+
+
+
+=pod
+
+    This section collects the various quantities. The mode counter
+increments to designate the data being captured.
+
+=cut
+
+if ($verbose) {
+    say encode($charset,"");
+    say encode($charset,
+        "           New Curve -- Reference Curve PSD");
+    say encode($charset,
+        "──────────────────────────────────────────────────");
+}
+
+foreach ( sort { $a <=> $b } keys %function_bin ) {
+    my $μ = my $σ = <$outputfile>;
+    $μ =~ s/^([\-\+e0-9\.]+)+\s+[\-\+e0-9\.]+\s*$/$1/;
+    $σ =~ s/^[\-\+e0-9\.]+\s+([\-\+e0-9\.]+)\s*$/$1/;
+#    if ($μ<0) {
+#        $μ = abs($μ);
+#        $function_bin{$_}{"φdiff_μ"} = PI;
+#    }
+    $function_bin{$_}{"ref_PSD_μ"} = $μ;
+    $function_bin{$_}{"ref_PSD_σ"} = $σ;
+    if ($verbose) {
+        say encode($charset,
+            "freq = " .
+            sprintf('%.3f',$_) .
+            ": μ = " .
+            sprintf('%10.3e',$μ) .
+            "; σ = " .
+            sprintf('%10.3e',$σ));
+    }
+}
+
+if ($verbose) {
+    say encode($charset,"");
+    say encode($charset,
+        "          New Curve -- Reverberating Curve PSD");
+    say encode($charset,
+        "──────────────────────────────────────────────────");
+}
+foreach ( sort { $a <=> $b } keys %function_bin ) {
+    my $μ = my $σ = <$outputfile>;
+    $μ =~ s/^([\-\+e0-9\.]+)+\s+[\-\+e0-9\.]+\s*$/$1/;
+    $σ =~ s/^[\-\+e0-9\.]+\s+([\-\+e0-9\.]+)\s*$/$1/;
+#    if ($μ<0) {
+#        $μ = abs($μ);
+#        $function_bin{$_}{"φdiff_μ"} = PI;
+#    }
+    $function_bin{$_}{"echo_PSD_μ"} = $μ;
+    $function_bin{$_}{"echo_PSD_σ"} = $σ;
+    if ($verbose) {
+        say encode($charset,
+            "freq = " .
+            sprintf('%.3f',$_) .
+            ": μ = " .
+            sprintf('%10.3e',$μ) .
+            "; σ = " .
+            sprintf('%10.3e',$σ));
+    }
+}
+
+
+if ($verbose) {
+    say encode($charset,"");
+    say encode($charset,
+        "          New Curve -- Cross Spectrum PSD");
+    say encode($charset,
+        "──────────────────────────────────────────────────");
+}
+foreach ( sort { $a <=> $b } keys %function_bin ) {
+    my $μ = my $σ = <$outputfile>;
+    $μ =~ s/^([\-\+e0-9\.]+)+\s+[\-\+e0-9\.]+\s*$/$1/;
+    $σ =~ s/^[\-\+e0-9\.]+\s+([\-\+e0-9\.]+)\s*$/$1/;
+#    if ($μ<0) {
+#        $μ = abs($μ);
+#        $function_bin{$_}{"φdiff_μ"} = PI;
+#    }
+    $function_bin{$_}{"crsspctrm_PSD_μ"} = $μ;
+    $function_bin{$_}{"crsspctrm_PSD_σ"} = $σ;
+    if ($verbose) {
+        say encode($charset,
+            "freq = " .
+            sprintf('%.3f',$_) .
+            ": μ = " .
+            sprintf('%10.3e',$μ) .
+            "; σ = " .
+            sprintf('%10.3e',$σ));
+    }
+}
+
+if ($verbose) {
+    say encode($charset,"");
+    say encode($charset,
+        "         New Curve -- Phase Difference φ");
+    say encode($charset,
+        "──────────────────────────────────────────────────");
+}
+foreach ( sort { $a <=> $b } keys %function_bin ) {
+    my $μ = my $σ = <$outputfile>;
+    $μ =~ s/^([\-\+e0-9\.]+)+\s+[\-\+e0-9\.]+\s*$/$1/;
+    $σ =~ s/^[\-\+e0-9\.]+\s+([\-\+e0-9\.]+)\s*$/$1/;
+    if (exists $function_bin{$_}{"φdiff_μ"}) {
+        $μ = $function_bin{$_}{"φdiff_μ"} + $μ;
+    }
+    $function_bin{$_}{"φdiff_μ"} = $μ;
+    $function_bin{$_}{"φdiff_σ"} = $σ;
+    if ($verbose) {
+        say encode($charset,
+            "freq = " .
+            sprintf('%.3f',$_) .
+            ": μ = " .
+            sprintf('%10.3e',$μ) .
+            "; σ = " .
+            sprintf('%10.3e',$σ));
+    }
+    $μ = $μ/(2*PI*$_);
+    $σ = $σ/(2*PI*$_);
+    $function_bin{$_}{"timelag_μ"} = $μ;
+    $function_bin{$_}{"timelag_σ"} = $σ;
+    if ($verbose) {
+        say encode($charset,
+            "     timelag: μ = " .
+            sprintf('%10.3e',$μ) .
+            "; σ = " .
+            sprintf('%10.3e',$σ)) .
+            "\n";
+    }
+}
+
+say encode($charset,"");
+
+if($debug) {
+    while (each %function_bin ) {
+        print encode($charset,$_ . ": ");
+        while ( my ($key,$value) = each %{$function_bin{$_}} ) {
+            print encode($charset,$key . " => " . $value . ";  ");
+        }
+        say encode($charset,"");
+    }
+}
+
+close($outputfile);
+
+open($datafile,'>',"tmp.refPSD") or die $!;
+while( each %function_bin) {
+    say $datafile
+        $_ . " " .
+        $function_bin{$_}{"ref_PSD_μ"} . " " .
+        $function_bin{$_}{"Δ"} . " " .
+        $function_bin{$_}{"ref_PSD_σ"};
+}
+close($datafile);
+
+open($datafile,'>',"tmp.echoPSD") or die $!;
+while( each %function_bin) {
+    say $datafile
+        $_ . " " .
+        $function_bin{$_}{"echo_PSD_μ"} . " " .
+        $function_bin{$_}{"Δ"} . " " .
+        $function_bin{$_}{"echo_PSD_σ"};
+}
+close($datafile);
+
+open($datafile,'>',"tmp.crsspctrmPSD") or die $!;
+while( each %function_bin) {
+    say $datafile
+        $_ . " " .
+        $function_bin{$_}{"crsspctrm_PSD_μ"} . " " .
+        $function_bin{$_}{"Δ"} . " " .
+        $function_bin{$_}{"crsspctrm_PSD_σ"};
+}
+close($datafile);
+
+open($datafile,'>',"tmp.timelag") or die $!;
+while( each %function_bin) {
+    say $datafile
+        $_ . " " .
+        $function_bin{$_}{"timelag_μ"} . " " .
+        $function_bin{$_}{"Δ"} . " " .
+        $function_bin{$_}{"timelag_σ"};
+}
+close($datafile);
+
+
+#open($datafile,'>',"tmp.echoPSD") or die $!;
diff --git a/scripts/fix_swapped_names.sh b/scripts/fix_swapped_names.sh
new file mode 100755
index 0000000..09a6ab9
--- /dev/null
+++ b/scripts/fix_swapped_names.sh
@@ -0,0 +1,13 @@
+#!/usr/bin/env bash
+
+for analysis in analyses/*
+do
+    # Grab and determine labels of analyses
+    ref_band=$(basename $analysis|sed 's|[^≻]*_≻_\([^≻_]*\)_[^_]*_[^_]*|\1|')
+    echo_band=$(basename $analysis|sed 's|\([^≻]*\)_≻_[^≻_]*_[^_]*_[^_]*|\1|')
+    timestep_str=$(basename $analysis|sed 's|[^≻]*_≻_[^≻_]*_\([^_]*\)_σ∊[CLM][MFC]|\1|')
+    err_str=$(basename $analysis|sed 's|[^≻]*_≻_[^≻_]*_[^_]*_\(σ∊[CLM][MFC]\)|\1|')
+
+    newname="${ref_band}_≺_${echo_band}_${timestep_str}_${err_str}"
+    mv $analysis analyses/$newname
+done
\ No newline at end of file
diff --git a/scripts/fix_thor_names.sh b/scripts/fix_thor_names.sh
new file mode 100755
index 0000000..782bb82
--- /dev/null
+++ b/scripts/fix_thor_names.sh
@@ -0,0 +1,22 @@
+#!/usr/bin/env bash
+
+
+for analysis in analyses/*A_*
+do
+    if [[ $analysis == "analyses/*A_*" ]]; then continue; fi
+    echo Restoring unicode characters to $analysis.
+    new_name=$(basename $analysis|
+        #sed 's|_\([0-9]\+\)|\(𝛌＝\1|g'|
+        sed 's|_|≺|'|
+        sed 's|A|Å|'|
+        sed 's|A_|Å%|'|
+        sed 's|_|{|'|
+        sed 's|_|;|'|
+        sed 's|_||g'|
+        #sed 's|_|}|'|
+        sed 's|≺|_≺_|'|
+        sed 's|Å%|Å_|'|
+        sed 's|Δt|Δt＝|'|
+        sed 's|err|σ∊|')}
+    mv $analysis analyses/$new_name
+done
\ No newline at end of file
diff --git a/scripts/gaussian_fit.py b/scripts/gaussian_fit.py
new file mode 100644
index 0000000..45741e7
--- /dev/null
+++ b/scripts/gaussian_fit.py
@@ -0,0 +1,74 @@
+import numpy as np
+from astropy.modeling import models, fitting
+
+# Using Models
+
+# The astropy.modeling package defines a number of models that are collected under a single namespace as astropy.modeling.models. Models behave like parametrized functions:
+
+from astropy.modeling import models
+g = models.Gaussian1D(amplitude=1.2, mean=0.9, stddev=0.5)
+print(g)
+# Model: Gaussian1D
+# Inputs: ('x',)
+# Outputs: ('y',)
+# Model set size: 1
+# Parameters:
+#     amplitude mean stddev
+#     --------- ---- ------
+#           1.2  0.9    0.5
+#
+# Model parameters can be accessed as attributes:
+
+g.amplitude
+# Parameter('amplitude', value=1.2)
+g.mean
+# Parameter('mean', value=0.9)
+g.stddev
+# Parameter('stddev', value=0.5)
+
+# and can also be updated via those attributes:
+
+g.amplitude = 0.8
+g.amplitude
+# Parameter('amplitude', value=0.8)
+
+# Models can be evaluated by calling them as functions:
+
+g(0.1)
+# 0.22242984036255528
+g(np.linspace(0.5, 1.5, 7))
+# array([ 0.58091923,  0.71746405,  0.7929204 ,  0.78415894,  0.69394278,
+#         0.54952605,  0.3894018 ])
+
+
+
+import numpy as np
+import matplotlib.pyplot as plt
+from astropy.modeling import models, fitting
+
+# Generate fake data
+np.random.seed(0)
+x = np.linspace(-5., 5., 200)
+y = 3 * np.exp(-0.5 * (x - 1.3)**2 / 0.8**2)
+y += np.random.normal(0., 0.2, x.shape)
+
+# Fit the data using a box model
+t_init = models.Trapezoid1D(amplitude=1., x_0=0., width=1., slope=0.5)
+fit_t = fitting.LevMarLSQFitter()
+t = fit_t(t_init, x, y)
+
+# Fit the data using a Gaussian
+g_init = models.Gaussian1D(amplitude=1., mean=0, stddev=1.)
+fit_g = fitting.LevMarLSQFitter()
+g = fit_g(g_init, x, y)
+
+# Plot the data with the best-fit model
+plt.figure(figsize=(8,5))
+plt.plot(x, y, 'ko')
+plt.plot(x, t(x), label='Trapezoid')
+plt.plot(x, g(x), label='Gaussian')
+plt.xlabel('Position')
+plt.ylabel('Flux')
+plt.legend(loc=2)
+
+plt.show()
\ No newline at end of file
diff --git a/scripts/heasoft_to_psdlag.pl b/scripts/heasoft_to_psdlag.pl
new file mode 100755
index 0000000..d77458f
--- /dev/null
+++ b/scripts/heasoft_to_psdlag.pl
@@ -0,0 +1,88 @@
+#!/usr/bin/env perl
+
+use feature say;
+use utf8;
+
+# reads (stdin) a heasoft data set (single filter) as compiled circa 2016 by Cackett @ WSU
+# outputs (stdout) a lightcurve in psdlag circa 2016 Zoghbi @ UMY input format
+
+# smallest time interval.
+my $Δt = 0.1;
+# normalization factor based on inspection of data
+my $flux_norm = 1e14;
+
+# This sets up the capture widths for rounding
+# Add two because need to read more digits for rounding
+$keep_digits = -1 * log10($Δt) + 2;
+$throw_digits = 5 - $keep_digits;
+
+# Skip first line -- shouldn't need this for a single filter
+<>;
+
+#Start file  Δt and with number of light curves (1)
+my $linetoprint="# $Δt 1";
+
+my $num_avg = 0;
+while (<>) {
+    $_ =~ /^.{8}\s+([0-9]{4})\s+([0-9]{5})\.([0-9]{$keep_digits})[0-9]{$throw_digits}(.*)/;
+    # wavelength/label, characteristic, mantissa, everything else
+    ($λ, $t𝓃, $t𝜀, $vals) = ($1,$2,$3,$4);
+
+    # Round t according to Δt
+    if (!(chop($t𝜀) == 0) + chop($t𝜀) > 5) {$t𝜀++;}
+    if ($t𝜀 >= (1/$Δt)) {
+        $t𝓃 += 1;
+        $t𝜀 = 0;
+    }
+    if ($Δt >= 1) { $t𝜀 = 0; }
+
+    # Print all values for this line
+    # say "$λ\t$t𝓃.$t𝜀$vals";
+
+    # Compile variables for this line
+    ($flux_μ,$flux_σ,$mag_μ,$mag_σ,$telescope_id) = split ' ',$vals;
+    $flux_μ *= $flux_norm;
+    $flux_σ *= $flux_norm;
+
+    # Print light curve for psdlag
+    # This mess to average measurements occuring at the same time coordinate
+    $this_t = "$t𝓃.$t𝜀";
+    if ($this_t == $last_t) {
+    	if ($heap_count == 1) {
+    	        if ( $linetoprint =~ /([0-9\.]+)\s+([0-9e\-\.]+)\s+([0-9e\-\.]+)/ ) {
+    	            our $new_flux_μ = $flux_μ + $2;
+    	            # Could just take the max error
+    	            # $new_flux_σ = max($flux_σ,$3);
+    	            # Average error seems fair
+    	            our $new_flux_σ = $flux_σ + $3;
+    	            $num_avg++;
+    	        }
+    	        else { die "Malformed data"; }
+    	}
+    	else {
+    		$new_flux_μ += $flux_μ;
+    		$new_flux_σ += $flux_σ;
+    	}
+        $heap_count++;
+    }
+    else {
+    	if ($heap_count > 1) {
+	    	$new_flux_μ /= $heap_count;
+	    	$new_flux_σ /= $heap_count;
+	    	$linetoprint="$last_t  $new_flux_μ  $new_flux_σ";
+	        our $heap_count = 1;
+    	}
+        say $linetoprint;
+        $linetoprint = "$this_t  $flux_μ  $flux_σ";
+    }
+    $last_t = $this_t;
+}
+say $linetoprint;
+
+
+sub max ($$) { $_[$_[0] < $_[1]] }
+sub min ($$) { $_[$_[0] > $_[1]] }
+sub log10 {
+  my $n = shift;
+  return log($n)/log(10);
+}
diff --git a/scripts/lc_plot.pl b/scripts/lc_plot.pl
new file mode 100755
index 0000000..a0a4f63
--- /dev/null
+++ b/scripts/lc_plot.pl
@@ -0,0 +1,211 @@
+#!/usr/bin/env perl
+
+
+use utf8;
+use Encode qw(encode decode);
+use feature 'say';
+use locale;
+use Switch;
+
+# This needs to be rewritten to plot the lightcurves instead
+# of the PSD and timelags.
+
+
+# Enables debug output.
+our $debug=0;
+our #verbose=0;
+
+
+
+# This section locate the output data of interest in a
+# psdlab output file.
+if (!${^UTF8LOCALE}) {
+    say encode($charset,"You are not using UTF-8 encoding. :(");
+}
+my $charset=$ENV{LANG};
+our $outputfilename=decode($charset,$ARGV[0]);
+open $outputfile,'<',$outputfilename or die $!;
+
+my $star_linenum_1=0;
+my $star_linenum_2=0;
+my $star_bytenum_1=0;
+my $star_bytenum_2=0;
+
+my $linenum=0;
+while(<$outputfile>) {
+    $linenum++;
+    if ($_ =~ /^\*+$/) {
+        $star_linenum_1=$star_linenum_2;
+        $star_linenum_2 = $linenum; 
+        $star_bytenum_1=$star_bytenum_2;
+        #$star_bytenum_2 = $outputfile.tell(); 
+        $star_bytenum_2 = tell($outputfile); 
+    }
+}
+if ($debug) {
+    say encode($charset,"Final set found between lines "),
+        $star_linenum_1,
+        " and ",
+        $star_linenum_2;
+
+    say encode($charset,"Final set found between bytes "),
+        $star_bytenum_1,
+        " and ",
+        $star_bytenum_2;
+}
+seek($outputfile,$star_bytenum_1,0);
+<$outputfile>;
+$bin_bounds_line = <$outputfile>;
+#say $bin_bounds_line;
+$bin_bounds_line =~ s/^#\s*(.*)$/$1/;
+#$line =~ /
+@bin_bounds = split /\s/,$bin_bounds_line;
+if ($debug) {
+    print encode($charset,"Found bin boundaries: ");
+    foreach (@bin_bounds) {print encode($charset,"$_ ");}
+    say encode($charset," ");
+}
+
+
+# These arrays store the various quantities of interest.
+@freq_coords_avg = [];
+@freq_coords_err = [];
+@power_curve_source_avg = [];
+@power_curve_source_err = [];
+@power_curve_reprocessed_avg = [];
+@power_curve_reprocessed_err = [];
+@cross_correlation_power_curve_avg = [];
+@cross_correlation_power_curve_err = [];
+@phase_difference_avg = [];
+@phase_difference_err = [];
+@time_lag_avg = [];
+@time_lag_err = [];
+
+# This captures the frequency bins.
+my $upper_bound = 0;
+my $lower_bound = 0;
+foreach (@bin_bounds) {
+    $lower_bound = $upper_bound;
+    $upper_bound = $_;
+    if ($lower_bound == 0) {next}
+    my $avg = ($upper_bound + $lower_bound)/2;
+    my $err = ($upper_bound - $lower_bound)/2;
+    #say ($avg,":",$err);
+    push(@freq_coords_avg,$avg);
+    push(@freq_coords_err,$err);
+}
+
+$numbins = scalar @freq_coords_avg;
+say encode($charset,"$numbins frequency boundaries captured.");
+
+
+# This section collects the various quantities. The mode counter
+# increments to designate the data being captured.
+my $mode=0;
+while (<$outputfile>) {
+    next if $_ =~ /\*+/;
+    switch ($mode) {
+        case 0 { 
+            my $avg = my $err = $_;
+            $avg =~ s/^([\-\+e0-9\.]+)+\s+[\-\+e0-9\.]+\s*$/$1/;
+            $err =~ s/^[\-\+e0-9\.]+\s+([\-\+e0-9\.]+)\s*$/$1/;
+            if ($debug) {
+                say encode($charset,"");
+                say encode($charset,"                    New Bin");
+                say encode($charset,"─────────────────────────────────────────────────");
+                print encode($charset,"Driving light curve power from $_");
+                say encode($charset,"Average: $avg, Err: $err");
+            }
+            push(@power_curve_source_avg,$avg);
+            push(@power_curve_source_err,$err);
+            $mode++;
+        }
+        case 1 { 
+            my $avg = my $err = $_;
+            $avg =~ s/^([\-\+e0-9\.]+)+\s+[\-\+e0-9\.]+\s*$/$1/;
+            $err =~ s/^[\-\+e0-9\.]+\s+([\-\+e0-9\.]+)\s*$/$1/;
+            if ($debug) {
+                print encode($charset,"Reprocessed light curve power from $_");
+                say encode($charset,"Average: $avg, Err: $err");
+            }
+            push(@power_curve_reprocessed_avg,$avg);
+            push(@power_curve_reprocessed_err,$err);
+            $mode++;
+        }
+        case 2 { 
+            my $avg = my $err = $_;
+            $avg =~ s/^([\-\+e0-9\.]+)+\s+[\-\+e0-9\.]+\s*$/$1/;
+            $err =~ s/^[\-\+e0-9\.]+\s+([\-\+e0-9\.]+)\s*$/$1/;
+            if ($debug) {
+                print encode($charset,"Cross-Correlation from $_");
+                say encode($charset,"Average: $avg, Err: $err");
+            }
+            push(@cross_correlation_power_curve_avg,$avg);
+            push(@cross_correlation_power_curve_err,$err);
+            $mode++;
+        }
+        case 3 { 
+            my $avg = my $err = $_;
+            $avg =~ s/^([\-\+e0-9\.]+)\s+[\-\+e0-9\.]+\s*$/$1/;
+            $err =~ s/^[\-\+e0-9\.]+\s+([\-\+e0-9\.]+)\s*$/$1/;
+            if ($debug) {
+                print encode($charset,"Phase different from $_");
+                say encode($charset,"Average: $avg, Err: $err");
+            }
+            push(@phase_difference_avg,$avg);
+            push(@phase_difference_err,$err);
+            $mode = 0;
+        }                 
+    } # End switch
+}
+
+$numvals = scalar @power_curve_source_avg;
+say encode($charset,"Saved $numvals sets of quantities.");
+
+
+
+
+
+
+
+
+
+=pod
+    Tried to set up the program to find the boundaries on its own.
+    Should return to complete the program this way; just a syntax
+    problem.
+
+#while (<$outputfile>) {say $_;}
+#while (<$outputfile> =~ /^[^#]+(.*)$/) {}
+#if (eof($outputfile)) {
+#    say encode($charset,"Could not scrape values of energy bin boundaries.");
+#    exit 9;
+#}
+=cut
+
+
+
+
+
+
+#$thing = <$outputfile>;
+#say encode($charset,"$thing");
+
+
+
+
+
+
+
+# switch ($val) {
+#             case 1      { print encode($charset,"number 1" )}
+#             case "a"    { print encode($charset,"string a" )}
+#             case [1..10,42] { print encode($charset,"number in list" )}
+#             case (@array)   { print encode($charset,"number in list" )}
+#             case /\w+/  { print encode($charset,"pattern" )}
+#             case qr/\w+/    { print encode($charset,"pattern" )}
+#             case (%hash)    { print encode($charset,"entry in hash" )}
+#             case (\%hash)   { print encode($charset,"entry in hash" )}
+#             case (\&sub)    { print encode($charset,"arg to subroutine" )}
+#             else        { print encode($charset,"previous case not true" )}
+#         }
\ No newline at end of file
diff --git a/scripts/plot_MC_against_CM.sh b/scripts/plot_MC_against_CM.sh
new file mode 100755
index 0000000..60d85a2
--- /dev/null
+++ b/scripts/plot_MC_against_CM.sh
@@ -0,0 +1,83 @@
+for table in analyses/tables/timelag*MC}.tab
+do
+    #ref_band=$(basename $table|sed 's|\([^≺]*\)_≺_[^≺_]*_[^_]*_[^_]*\.tab|\1|')
+    #echo_band=$(basename $table|sed 's|[^≺]*_≺_\([^≺_]*\)_[^_]*_[^_]*\.tab|\1|')
+    CMtable=$(echo $table|sed 's|∊MC|∊CM|')
+
+    ref_band=$(basename $table|sed 's|timelag_\([^≺]*\)[_ ]≺[_ ][^≺_ ]*[_ ]{[^_ ]*}.tab|\1|')
+    echo_band=$(basename $table|sed 's|timelag_[^≺]*[_ ]≺[_ ]\([^≺_ ]*\)[_ ]{[^_ ]*}.tab|\1|')
+    if [[ $ref_band == $echo_band ]]; then continue; fi
+    err_str=$(basename $table|sed 's|timelag_[^≺]*[_ ]≺[_ ][^≺_ ]*[_ ]{[^_ ]*\(σ∊[CLM][MFC]\)}.tab|\1|')
+
+    echo Plotting timelag error fit comparison for $echo_band.
+
+    plotfile=analyses/plots/timelag_${echo_band}_error_compare.png
+
+    echo_band_noUTF=$(echo $echo_band|
+        sed 's|𝛌|λ|g'|
+        #sed 's|(|_|g'|
+        #sed 's|)|_|g'|
+        sed 's|＝|=|g'|
+        sed 's|Å|A|g')
+
+    ref_band_noUTF=$(echo $ref_band|
+        sed 's|𝛌|λ|g'|
+        #sed 's|(|_|g'|
+        #sed 's|)|_|g'|
+        sed 's|＝|=|g'|
+        sed 's|Å|A|g')
+
+    cat scripts/templates/timelag_compare.gp|
+        sed "s|%TITLE|Error Fit Comparison for Time Lag of $echo_band|"|
+        sed "s|%SUBTITLE|referred to ${ref_band}, as reported by Fausnaugh et. al, STORM III, 2016|"|
+        sed "s|%FILE1|$table|"|
+        sed "s|%LABEL1|${echo_band} Delay with Monte Carlo Fit|"|
+        sed "s|%FILE2|$CMtable|"|
+        sed "s|%LABEL2|${echo_band} Delay with Covariance Matrix Fit|"|
+        sed "s|%YMIN|$ymin|"|sed "s|%YMAX|$ymax|"|
+        sed "s|%OUTPUTFILE|$plotfile|" > tmp.gp
+    gnuplot tmp.gp
+
+done
+
+
+for table in analyses/tables/PSD*MC}.tab
+do
+    #ref_band=$(basename $table|sed 's|\([^≺]*\)_≺_[^≺_]*_[^_]*_[^_]*\.tab|\1|')
+    #echo_band=$(basename $table|sed 's|[^≺]*_≺_\([^≺_]*\)_[^_]*_[^_]*\.tab|\1|')
+    CMtable=$(echo $table|sed 's|∊MC|∊CM|')
+
+    echo_band=$(basename $table|sed 's|PSD[_ ]\([0-9]\{4\}\)Å[_ ]{[^_ ]*}.tab|\1Å|')
+    if [[ $ref_band == $echo_band ]]; then continue; fi
+    err_str=$(basename $table|sed 's|PSD[ _]\([0-9]\{4\}\)Å[_ ]{[^_ ]*\(σ∊[CLM][MFC]\)}.tab|\1|')
+
+    echo Plotting PSD error fit comparison for $echo_band.
+
+    plotfile=analyses/plots/PSD_${echo_band}_error_compare.png
+
+    echo_band_noUTF=$(echo $echo_band|
+        sed 's|𝛌|λ|g'|
+        #sed 's|(|_|g'|
+        #sed 's|)|_|g'|
+        sed 's|＝|=|g'|
+        sed 's|Å|A|g')
+
+    ref_band_noUTF=$(echo $ref_band|
+        sed 's|𝛌|λ|g'|
+        #sed 's|(|_|g'|
+        #sed 's|)|_|g'|
+        sed 's|＝|=|g'|
+        sed 's|Å|A|g')
+
+    cat scripts/templates/psd_freq.gp|
+        sed "s|%TITLE|Error Fit Comparison for PSD of $echo_band|"|
+        sed "s|%SUBTITLE|as reported by Fausnaugh et. al, STORM III, 2016|"|
+        sed "s|%FILE1|$table|"|
+        sed "s|%LABEL1|${echo_band} PSD with Monte Carlo Fit|"|
+        sed "s|%FILE2|$CMtable|"|
+        sed "s|%LABEL2|${echo_band} PSD with Covariance Matrix Fit|"|
+        sed "s|%YMIN|$ymin|"|sed "s|%YMAX|$ymax|"|
+        sed "s|%OUTPUTFILE|$plotfile|" > tmp.gp
+    gnuplot tmp.gp
+
+done
\ No newline at end of file
diff --git a/scripts/plot_psdlag.sh b/scripts/plot_psdlag.sh
new file mode 100755
index 0000000..56ef823
--- /dev/null
+++ b/scripts/plot_psdlag.sh
@@ -0,0 +1,77 @@
+#!/usr/bin/env bash
+
+ywindow_of () {
+    max=0
+    min=-5
+    while read line
+#    for line in $(cat $1)
+    do
+        read xval yval xerr yerr <<< $line
+        echo $xval $yval
+        if (bc <<< "$yval > $max"); then max=$yval; fi
+        if (bc <<< "$yval < $min"); then min=$yval; fi
+#    done
+    done < $1
+    echo $(bc <<< "$min - 0.5") $(bc <<< "$max + 0.5")
+}
+
+mkdir -p analyses/tables
+mkdir -p analyses/plots
+
+
+# Restore the proper naming conventions for any files stripped
+# of their unicode characters.
+scripts/fix_thor_names.sh
+
+# Create tables and plot the power spectra distributions and time lags.
+for analysis in analyses/*
+do
+    # Grab and determine labels of analyses, skip if over the same band.
+    ref_band=$(basename $analysis|sed 's|\([^≺]*\)[_ ]≺[_ ][^≺_ ]*[_ ]{[^_ ]*}|\1|')
+    echo_band=$(basename $analysis|sed 's|[^≺]*[_ ]≺[_ ]\([^≺_ ]*\)[_ ]{[^_ ]*}|\1|')
+    if [[ $ref_band == $echo_band ]]; then continue; fi
+    err_str=$(basename $analysis|sed 's|[^≺]*[_ ]≺[_ ][^≺_ ]*[_ ]{[^_ ]*;\(σ∊[CLM][MFC]\)}|\1|')
+
+    # Prepare files
+    echo "Plotting PSD and time lags for $echo_band, referred to ${ref_band}."
+    echoPSD_tabfile=analyses/tables/PSD_${echo_band}_\{${err_str}\}.tab
+    refPSD_tabfile=analyses/tables/PSD_${ref_band}_\{${err_str}\}.tab
+    timelag_tabfile=analyses/tables/timelag_${ref_band}_≺_${echo_band}_\{${err_str}\}.tab
+    PSD_plotfile=analyses/plots/PSD_${ref_band}_≺_${echo_band}_\{${err_str}\}.png
+    timelag_plotfile=analyses/plots/timelag_${ref_band}_≺_${echo_band}_\{${err_str}\}.png
+
+    # Output curves to temporary files using perl script, move tables to
+    # permanent location. This just assumes there are no conflicts.
+    scripts/create_tables.pl $analysis > /dev/null
+    mv tmp.echoPSD $echoPSD_tabfile
+    mv tmp.refPSD $refPSD_tabfile
+    mv tmp.timelag $timelag_tabfile
+
+
+    # Plot PSD and save using gnuplot
+#    read ymin ymax <<< $(ywindow_of $timelag_tabfile)
+#    echo $ymin $ymax
+    cat scripts/templates/psd_freq.gp|
+        sed "s|%TITLE|Power Spectrum for Lightcurves $echo_band and $ref_band|"|
+        sed "s|%SUBTITLE|as reported by Fausnaugh et. al, STORM III, 2016|"|
+        sed "s|%FILE1|$refPSD_tabfile|"|
+        sed "s|%LABEL1|${ref_band} PSD|"|
+        sed "s|%FILE2|$echoPSD_tabfile|"|
+        sed "s|%LABEL2|${echo_band} PSD|"|
+        sed "s|%YMIN|$ymin|"|sed "s|%YMAX|$ymax|"|
+        sed "s|%OUTPUTFILE|$PSD_plotfile|" > tmp.gp
+    gnuplot tmp.gp
+
+    # Plot time lags and save using gnuplot
+#    read ymin ymax <<< $(ywindow_of $timelag_tabfile)
+    cat scripts/templates/timelag_freq.gp|
+        sed "s|%TITLE|Time Lag for Lightcurve $echo_band relative to $ref_band|"|
+        sed "s|%SUBTITLE|as reported by Fausnaugh et. al, STORM III, 2016|"|
+        sed "s|%FILE1|$timelag_tabfile|"|
+        sed "s|%LABEL1|${echo_band} Lag|"|
+        sed "s|%YMIN|$ymin|"|sed "s|%YMAX|$ymax|"|
+        sed "s|%OUTPUTFILE|$timelag_plotfile|" > tmp.gp
+    gnuplot tmp.gp
+done
+
+rm tmp.*
diff --git a/scripts/prep_lcfiles.sh b/scripts/prep_lcfiles.sh
new file mode 100755
index 0000000..2aa4d8c
--- /dev/null
+++ b/scripts/prep_lcfiles.sh
@@ -0,0 +1,12 @@
+#!/usr/bin/env bash
+
+timestep="0.1"
+newdir="data/STORM_III/lightcurves/Δt＝${timestep}"
+mkdir -p $newdir
+
+for file in data/STORM_III/raw/separated/*
+do
+    newfile=$(basename $file|sed 's@\(.*\)\.tab@\1.lc@')
+    scripts/heasoft_to_psdlag.pl < $file > ${newdir}/${newfile}
+    echo Processed $file
+done
\ No newline at end of file
diff --git a/scripts/spec.py b/scripts/spec.py
new file mode 100644
index 0000000..2ed8c78
--- /dev/null
+++ b/scripts/spec.py
@@ -0,0 +1,5688 @@
+"""
+Suite to reduce spectroscopic data.
+
+subfunctions:
+   calibrate
+      setheaders -- exptime, gain, readnoise, etc.
+      makeflat -- make median flat and noisy pixel map
+      makedark -- make median dark, and estimate  noise in each pixel.
+   clean -- clean and replace bad pixels
+   extract
+      trace -- trace spectral orders
+      makeprofile -- compute mean spectral PSF (a spline) for an order
+      fitprofile -- fit given spline-PSF to a spectral cross-section
+
+Utilities:
+   pickloc   
+   fitPSF
+"""
+# 2010-07-02 10:56 IJC: Began the great endeavor.
+
+
+
+try:
+    from astropy.io import fits as pyfits
+except:
+    import pyfits
+
+import matplotlib.pyplot as plt
+import numpy as np
+from scipy import optimize, interpolate
+import pdb
+
+
+[docs]
+class baseObject:
+    """Empty object container.
+    """
+    def __init__(self):
+        return
+
+
+########
+# Utilities to add in a separate cohesive package at a later date:
+
+from analysis import polyfitr, stdr, binarray, gaussian, egaussian
+import analysis as an
+from nsdata import bfixpix
+
+########
+# Parameters to put in a GUI:
+gain = 5 # e-/ADU
+readnoise = 25 # e-
+
+
+#########
+
+[docs]
+def message(text):
+    """Display a message; for now, with text."""
+    from sys import stdout
+
+    print text
+    stdout.flush()
+
+[docs]
+def pickloc(ax=None, zoom=10):
+    """
+    :INPUTS:
+       ax   : (axes instance) -- axes in which to pick a location
+
+       zoom : int -- zoom radius for target confirmation
+            : 2-tuple -- (x,y) radii for zoom confirmation.
+    """
+    # 2011-04-29 19:26 IJC: 
+    # 2011-09-03 20:59 IJMC: Zoom can now be a tuple; x,y not cast as int.
+
+    pickedloc = False
+    if ax is None:
+        ax = plt.gca()
+
+    axlimits = ax.axis()
+
+    if hasattr(zoom, '__iter__') and len(zoom)>1:
+        xzoom, yzoom = zoom
+    else:
+        xzoom = zoom
+        yzoom = zoom
+
+    while not pickedloc:
+        ax.set_title("click to select location")
+        ax.axis(axlimits)
+
+        x = None
+        while x is None:  
+            selectevent = plt.ginput(n=1,show_clicks=False)
+            if len(selectevent)>0: # Prevent user from cancelling out.
+                x,y = selectevent[0]
+
+        #x = x.astype(int)
+        #y = y.astype(int)
+
+        
+        if zoom is not None:
+            ax.axis([x-xzoom,x+xzoom,y-yzoom,y+yzoom])
+
+        ax.set_title("you selected xy=(%i,%i)\nclick again to confirm, or press Enter/Return to try again" %(x,y)  )
+        plt.draw()
+
+        confirmevent = plt.ginput(n=1,show_clicks=False)
+        if len(confirmevent)>0:  
+            pickedloc = True
+            loc = confirmevent[0]
+
+    return loc
+
+[docs]
+def fitTophat(vec, err=None, verbose=False, guess=None):
+    """Fit a 1D tophat function to an input data vector.
+
+    Return the fit, and uncertainty estimates on that fit.
+    
+    SEE ALSO: :func:`analysis.gaussian`"""
+    xtemp = np.arange(1.0*len(vec))
+
+    if guess is None: # Make some educated guesses as to the parameters:
+        pedestal = 0.5 * (0.8*np.median(vec) + 0.2*(vec[0]+vec[1]))
+        area = (vec-pedestal).sum()
+        centroid = (vec*xtemp).sum()/vec.sum()
+        if centroid<0:
+            centroid = 1.
+        elif centroid>len(vec):
+            centroid = len(vec)-2.
+
+        sigma = area/vec[int(centroid)]/np.sqrt(2*np.pi)
+        if sigma<=0:
+            sigma = 0.01
+
+        guess = [area,sigma,centroid,pedestal]
+
+    if verbose: 
+        print 'Gaussian guess parameters>>', guess
+    if err is None:
+        fit, fitcov = optimize.leastsq(egaussian, guess, args=(xtemp, vec), full_output=True)[0:2]
+        pc.resfunc
+
+
+        fit, fitcov = optimize.leastsq(egaussian, guess, args=(xtemp, vec), full_output=True)[0:2]
+    else:
+        fit, fitcov = optimize.leastsq(egaussian, guess, args=(xtemp, vec, err), full_output=True)[0:2]
+
+    if fitcov is None: # The fitting was really bad!
+        fiterr = np.abs(fit)
+    else:
+        fiterr = np.sqrt(np.diag(fitcov))
+
+    if verbose:
+        print 'Best-fit parameters>>', fit
+        f = plt.figure()
+        ax = plt.axes()
+        plt.plot(xtemp, vec, 'o', \
+                     xtemp, gaussian(fit, xtemp), '-', \
+                     xtemp, gaussian(guess, xtemp), '--')
+
+    return fit, fiterr
+
+[docs]
+def fitGaussian(vec, err=None, verbose=False, guess=None):
+    """Fit a Gaussian function to an input data vector.
+
+    Return the fit, and uncertainty estimates on that fit.
+    
+    SEE ALSO: :func:`analysis.gaussian`"""
+    # 2012-12-20 13:28 IJMC: Make a more robust guess for the centroid.
+    xtemp = np.arange(1.0*len(vec))
+
+    if guess is None: # Make some educated guesses as to the parameters:
+        pedestal = (0.8*np.median(vec) + 0.2*(vec[0]+vec[1]))
+        area = (vec-pedestal).sum()
+        centroid = ((vec-pedestal)**2*xtemp).sum()/((vec-pedestal)**2).sum()
+        if centroid<0:
+            centroid = 1.
+        elif centroid>len(vec):
+            centroid = len(vec)-2.
+        #pdb.set_trace()
+        sigma = area/vec[int(centroid)]/np.sqrt(2*np.pi)
+        if sigma<=0:
+            sigma = .01
+
+        guess = [area,sigma,centroid,pedestal]
+
+    if err is None:
+        err = np.ones(vec.shape, dtype=float)
+
+    badvals = True - (np.isfinite(xtemp) * np.isfinite(err) * np.isfinite(vec))
+    vec[badvals] = np.median(vec[True - badvals])
+    err[badvals] = vec[True - badvals].max() * 1e9
+
+    if verbose: 
+        print 'Gaussian guess parameters>>', guess
+
+    if not np.isfinite(xtemp).all():
+        pdb.set_trace()
+    if not np.isfinite(vec).all():
+        pdb.set_trace()
+    if not np.isfinite(err).all():
+        pdb.set_trace()
+
+    try:
+        fit, fitcov = optimize.leastsq(egaussian, guess, args=(xtemp, vec, err), full_output=True)[0:2]
+    except:
+        pdb.set_trace()
+
+    if fitcov is None: # The fitting was really bad!
+        fiterr = np.abs(fit)
+    else:
+        fiterr = np.sqrt(np.diag(fitcov))
+
+    #pdb.set_trace()
+    if verbose:
+        print 'Best-fit parameters>>', fit
+        f = plt.figure()
+        ax = plt.axes()
+        plt.plot(xtemp, vec, 'o', \
+                     xtemp, gaussian(fit, xtemp), '-', \
+                     xtemp, gaussian(guess, xtemp), '--')
+
+    return fit, fiterr
+
+
+
+[docs]
+def fitGaussiann(vec, err=None, verbose=False, guess=None, holdfixed=None):
+    """Fit a Gaussian function to an input data vector.
+
+    Return the fit, and uncertainty estimates on that fit.
+    
+    SEE ALSO: :func:`analysis.gaussian`"""
+    from phasecurves import errfunc
+    from analysis import fmin
+
+    xtemp = np.arange(1.0*len(vec))
+
+    if guess is None: # Make some educated guesses as to the parameters:
+        holdfixed = None
+
+        pedestal = 0.5 * (0.8*np.median(vec) + 0.2*(vec[0]+vec[1]))
+        area = (vec-pedestal).sum()
+        centroid = (vec*xtemp).sum()/vec.sum()
+        if centroid<0:
+            centroid = 1.
+        elif centroid>len(vec):
+            centroid = len(vec)-2.
+
+        sigma = area/vec[int(centroid)]/np.sqrt(2*np.pi)
+        if sigma<=0:
+            sigma = 0.01
+
+        guess = [area,sigma,centroid,pedestal]
+
+    if err is None:
+        err = np.ones(vec.shape, dtype=float)
+
+    badvals = True - (np.isfinite(xtemp) * np.isfinite(err) * np.isfinite(vec))
+    vec[badvals] = np.median(vec[True - badvals])
+    err[badvals] = vec[True - badvals].max() * 1e9
+
+    if verbose: 
+        print 'Gaussian guess parameters>>', guess
+
+    if not np.isfinite(xtemp).all():
+        pdb.set_trace()
+    if not np.isfinite(vec).all():
+        pdb.set_trace()
+    if not np.isfinite(err).all():
+        pdb.set_trace()
+
+    try:
+        #fit, fitcov = optimize.leastsq(egaussian, guess, args=(xtemp, vec, err), full_output=True)[0:2]
+        fitargs = (gaussian, xtemp, vec, 1./err**2)
+        fit = fmin(errfunc, guess, args=fitargs, full_output=True, disp=False, holdfixed=holdfixed)[0]
+        fitcov = None
+    except:
+        pdb.set_trace()
+
+    if fitcov is None: # The fitting was really bad!
+        fiterr = np.abs(fit)
+    else:
+        fiterr = np.sqrt(np.diag(fitcov))
+
+    if verbose:
+        print 'Best-fit parameters>>', fit
+        f = plt.figure()
+        ax = plt.axes()
+        plt.plot(xtemp, vec, 'o', \
+                     xtemp, gaussian(fit, xtemp), '-', \
+                     xtemp, gaussian(guess, xtemp), '--')
+
+    return fit, fiterr
+
+[docs]
+def fit2Gaussian(vec, err=None, verbose=False, guess=None, holdfixed=None):
+    """Fit two Gaussians simultaneously to an input data vector.
+
+    :INPUTS:
+      vec : sequence
+        1D array or list of values to fit to
+
+      err : sequence
+        uncertainties on vec
+
+      guess : sequence
+        guess parameters: [area1, sigma1, cen1, area2, sig2, cen2, constant].
+        Note that parameters are in pixel units.
+
+      holdfixed : sequence
+        parameters to hold fixed in analysis, _IF_ guess is passed in.
+
+
+    SEE ALSO: :func:`analysis.gaussian`, :func:`fitGaussian`"""
+
+    # 2012-03-14 14:33 IJMC: Created
+
+    from tools import sumfunc
+    from phasecurves import errfunc
+    from analysis import fmin  # Don't use SciPy, because I want keywords!
+
+
+    xtemp = np.arange(1.0*len(vec))
+    if err is None:
+        err = np.ones(xtemp.shape)
+    else:
+        err = np.array(err, copy=False)
+    
+    if guess is None: # Make some semi-educated guesses as to the parameters:
+        holdfixed = None
+
+        pedestal = 0.5 * (0.8*np.median(vec) + 0.2*(vec[0]+vec[1]))
+        area = (vec-pedestal).sum()
+        centroid = (vec*xtemp).sum()/vec.sum()
+        if centroid<0:
+            centroid = 1.
+        elif centroid>len(vec):
+            centroid = len(vec)-2.
+
+        sigma = area/vec[int(centroid)]/np.sqrt(2*np.pi)
+        if sigma<=0:
+            sigma = 0.01
+
+        guess1 = [area/2.,sigma/1.4,centroid+xtemp.size/5.]
+        guess2 = [area/2.,sigma/1.4,centroid-xtemp.size/5.]
+        guess = guess1 + guess2 + [pedestal]
+
+
+    ## Testing:
+    #mod = sumfunc(guess, gaussian, gaussian, 3, 4, args1=(xtemp,), args2=(xtemp,)) 
+    #fitargs = (sumfunc, gaussian, gaussian, 3, 4, (xtemp,), (xtemp,), None, vec, 1./err**2)
+    #fitkw = dict(useindepvar=False, testfinite=False)
+    #chisq = errfunc(guess, *fitargs, **fitkw)
+    #thisfit = fmin(errfunc, guess, args=fitargs, kw=fitkw, full_output=True)
+    #mod2 = sumfunc(thisfit[0], gaussian, gaussian, 3, 4, args1=(xtemp,), args2=(xtemp,)) 
+
+    if verbose: 
+        print '2-Gaussian guess parameters>>', guess
+
+    fitargs = (sumfunc, gaussian, gaussian, 3, 4, (xtemp,), (xtemp,), vec, 1./err**2)
+    fitkw = dict(useindepvar=False, testfinite=False)
+    fit = fmin(errfunc, guess, args=fitargs, kw=fitkw, full_output=True, disp=False, holdfixed=holdfixed)
+
+    if verbose:
+        model = sumfunc(fit[0], gaussian, gaussian, 3, 4, args1=(xtemp,), args2=(xtemp,)) 
+        model1 = gaussian(fit[0][0:3], xtemp)
+        model2 = gaussian(fit[0][3:6], xtemp)
+        print 'Best-fit parameters>>', fit[0]
+        f = plt.figure()
+        ax = plt.axes()
+        plt.plot(xtemp, vec, 'o', \
+                     xtemp, model, '--')
+        plt.plot(xtemp, model1+fit[0][6], ':')
+        plt.plot(xtemp, model2+fit[0][6], ':')
+
+    return fit[0]
+
+[docs]
+def fitPSF(ec, guessLoc, fitwidth=20, verbose=False, sigma=5, medwidth=6, err_ec=None):
+    """
+    Helper function to fit 1D PSF near a given region.  Assumes
+    spectrum runs horizontally across the frame!
+
+    ec : 2D numpy array
+        echellogram array, with horizontal dispersion direction
+    guessLoc : 2-tuple
+       A slight misnomer for this (x,y) tuple: y is a guess and will
+         be fit, but x is the coordinate at which the fitting takes
+         place.
+    fitwidth : int
+       width of cross-dispersion direction to use in fitting
+    medwidth : int
+       number of columns to average over when fitting a profile
+    verbose : bool
+       verbosity/debugging printout flag
+
+    sigma : scalar
+       sigma scale for clipping bad values
+    """
+    # 2010-08-24 22:00 IJC: Added sigma option
+    # 2010-11-29 20:54 IJC: Added medwidth option
+    # 2011-11-26 23:17 IJMC: Fixed bug in computing "badval"
+    # 2012-04-27 05:15 IJMC: Now allow error estimates to be passed in.
+    # 2012-04-28 08:48 IJMC: Added better guessing for initial case.
+
+    if verbose<0:
+        verbose = False
+
+    ny, nx = ec.shape
+
+    x = guessLoc[0].astype(int)
+    y = guessLoc[1].astype(int)
+
+    # Fit the PSF profile at the initial, selected location:
+    ymin = max(y-fitwidth/2, 0)
+    ymax = min(y+fitwidth/2, ny)
+    xmin = max(x-medwidth/2, 0)
+    xmax = min(x+medwidth/2, nx)
+    if verbose:
+        message("Sampling: ec[%i:%i,%i:%i]"%(ymin,ymax,xmin,xmax))
+
+    firstSeg = np.median(ec[ymin:ymax,xmin:xmax],1)
+    if verbose:
+        print firstSeg
+
+    if err_ec is None:
+        ey = stdr(firstSeg, sigma)
+        badval = abs((firstSeg-np.median(firstSeg))/ey) > sigma
+        err = np.ones(firstSeg.shape, float)
+        err[badval] = 1e9
+    else:
+        err = np.sqrt((err_ec[ymin:ymax,xmin:xmax]**2).mean(1))
+        err[True - np.isfinite(err)] = err[np.isfinite(err)].max() * 1e9
+
+    guessAmp = (an.wmean(firstSeg, 1./err**2) - np.median(firstSeg)) * fitwidth
+    if not np.isfinite(guessAmp):
+        pdb.set_trace()
+
+    #fit, efit = fitGaussian(firstSeg, verbose=verbose, err=err, guess=[guessAmp[0], 5, fitwidth/2., np.median(firstSeg)])
+    fit, efit = fitGaussian(firstSeg, verbose=verbose, err=err, guess=None)
+    newY = ymin+fit[2]
+    err_newY = efit[2]
+    if verbose:
+        message("Initial position: (%3.2f,%3.2f)"%(x,newY))
+
+    return x, newY, err_newY
+
+
+[docs]
+def traceorders(filename, pord=5, dispaxis=0, nord=1, verbose=False, ordlocs=None, stepsize=20, fitwidth=20, plotalot=False, medwidth=6, xylims=None, uncertainties=None, g=gain, rn=readnoise, badpixelmask=None, retsnr=False, retfits=False):
+    """
+    Trace spectral orders for a specified filename.
+
+    filename : str OR 2D array
+        full path and filename to a 2D echelleogram FITS file, _OR_
+        a 2D numpy array representing such a file.
+
+    OPTIONAL INPUTS:
+    pord : int
+        polynomial order of spectral order fit
+
+    dispaxis : int
+        set dispersion axis: 0 = horizontal and  = vertical
+    
+    nord : int
+        number of spectral orders to trace.
+
+    ordlocs : (nord x 2) numpy array
+        Location to "auto-click" and 
+
+    verbose: int
+        0,1,2; whether (and how much) to print various verbose debugging text
+
+    stepsize : int
+        number of pixels to step along the spectrum while tracing
+
+    fitwidth : int
+        number of pixels to use when fitting a spectrum's cross-section
+        
+    medwidth : int
+        number of columns to average when fitting profiles to echelleograms
+        
+    plotalot : bool
+        Show pretty plot?  If running in batch mode (using ordlocs)
+        default is False; if running in interactive mode (ordlocs is
+        None) default is True.
+
+    xylims : 4-sequence
+        Extract the given subset of the data array: [xmin, xmax, ymin, ymax]
+
+    uncertainties : str OR 2D array
+        full path and filename to a 2D uncertainties FITS file, _OR_
+        a 2D numpy array representing such a file.
+        
+        If this is set, 'g' and 'rn' below are ignored.  This is
+        useful if, e.g., you are analyzing data which have already
+        been sky-subtracted, nodded on slit, or otherwise altered.
+        But note that this must be the same size as the input data!
+
+    g : scalar > 0
+      Detector gain, electrons per ADU (for setting uncertainties)
+    
+    rn : scalar > 0
+      Detector read noise, electrons (for setting uncertainties)
+    
+    retsnr : bool
+      If true, also return the computed S/N of the position fit at
+      each stepped location.
+    
+    retfits : bool
+      If true, also return the X,Y positions at each stepped location.
+    
+
+    :RETURNS:
+       (nord, pord) shaped numpy array representing the polynomial
+         coefficients for each order (suitable for use with np.polyval)
+
+    :NOTES:
+
+      If tracing fails, a common reason can be that fitwidth is too
+      small.  Try increasing it!
+    """
+    # 2010-08-31 17:00 IJC: If best-fit PSF location goes outside of
+    #     the 'fitwidth' region, nan values are returned that don't
+    #     cause the routine to bomb out quite so often.
+    # 2010-09-08 13:54 IJC: Updated so that going outside the
+    #     'fitwidth' region is determined only in the local
+    #     neighborhood, not in relation to the (possibly distant)
+    #     initial guess position.
+    # 2010-11-29 20:56 IJC: Added medwidth option
+    # 2010-12-17 09:32 IJC: Changed color scaling options; added xylims option.
+
+    # 2012-04-27 04:43 IJMC: Now perform _weighted_ fits to the measured traces!
+
+    global gain
+    global readnoise
+
+    if verbose < 0:
+        verbose = 0
+
+    if not g==gain:
+        message("Setting gain to " + str(g))
+        gain = g
+
+    if not rn==readnoise:
+        message("Setting readnoise to " + str(rn))
+        readnoise = rn
+
+    if ordlocs is not None:
+        ordlocs = np.array(ordlocs, copy=False)
+        if ordlocs.ndim==1:
+            ordlocs = ordlocs.reshape(1, ordlocs.size)
+        autopick = True
+    else:
+        autopick = False
+
+
+    plotalot = (not autopick) or plotalot
+    if isinstance(filename, np.ndarray):
+        ec = filename.copy()
+    else:
+        try:
+            ec = pyfits.getdata(filename)
+        except:
+            message("Could not open filename %s" % filename)
+            return -1
+
+
+    if isinstance(uncertainties, np.ndarray):
+        err_ec = uncertainties.copy()
+    else:
+        try:
+            err_ec = pyfits.getdata(uncertainties)
+        except:
+            err_ec = np.sqrt(ec * gain + readnoise**2)
+
+    if dispaxis<>0:
+        ec = ec.transpose()
+        err_ec = err_ec.transpose()
+        if verbose: message("Took transpose of echelleogram to rotate dispersion axis")
+    else:
+        pass
+
+    if xylims is not None:
+        try:
+            ec = ec[xylims[0]:xylims[1], xylims[2]:xylims[3]]
+            err_ec = err_ec[xylims[0]:xylims[1], xylims[2]:xylims[3]]
+        except:
+            message("Could not extract subset: ", xylims)
+            return -1
+
+    if badpixelmask is None:
+        badpixelmask = np.zeros(ec.shape, dtype=bool)
+    else:
+        if not hasattr(badpixelmask, 'shape'):
+            badpixelmask = pyfits.getdata(badpixelmask)
+        if xylims is not None:
+            badpixelmask = badpixelmask[xylims[0]:xylims[1], xylims[2]:xylims[3]]
+
+        
+    err_ec[badpixelmask.nonzero()] = err_ec[np.isfinite(err_ec)].max() * 1e9
+
+    try:
+        ny, nx = ec.shape
+    except:
+        message("Echellogram file %s does not appear to be 2D: exiting." % filename)
+        return -1
+
+    if plotalot:
+        f = plt.figure()
+        ax = plt.axes()
+        plt.imshow(ec, interpolation='nearest',aspect='auto')
+        sortedvals = np.sort(ec.ravel())
+        plt.clim([sortedvals[nx*ny*.01], sortedvals[nx*ny*.99]])
+        #plt.imshow(np.log10(ec-ec.min()+np.median(ec)),interpolation='nearest',aspect='auto')
+        ax.axis([0, nx, 0, ny])
+
+    orderCoefs = np.zeros((nord, pord+1), float)
+    position_SNRs = []
+    xyfits = []
+    if not autopick:
+
+        ordlocs = np.zeros((nord, 2),float)
+        for ordernumber in range(nord):
+            message('Selecting %i orders; please click on order %i now.' % (nord, 1+ordernumber))
+            plt.figure(f.number)
+            guessLoc = pickloc(ax, zoom=fitwidth)
+            ordlocs[ordernumber,:] = guessLoc
+            ax.plot([guessLoc[0]],[guessLoc[1]], '*k')
+            ax.axis([0, nx, 0, ny])
+            plt.figure(f.number)
+            plt.draw()
+            if verbose:
+                message("you picked the location: ")
+                message(guessLoc)
+
+    for ordernumber in range(nord):
+        guessLoc = ordlocs[ordernumber,:]
+        xInit, yInit, err_yInit = fitPSF(ec, guessLoc, fitwidth=fitwidth,verbose=verbose, medwidth=medwidth, err_ec=err_ec)
+        if plotalot:
+            ax.plot([xInit],[yInit], '*k')
+            ax.axis([0, nx, 0, ny])
+            plt.figure(f.number)
+            plt.draw()
+
+        if verbose:
+            message("Initial (fit) position: (%3.2f,%3.2f)"%(xInit,yInit))
+
+        # Prepare to fit PSFs at multiple wavelengths.
+
+        # Determine the other positions at which to fit:
+        xAbove = np.arange(1, np.ceil(1.0*(nx-xInit)/stepsize))*stepsize + xInit
+        xBelow = np.arange(-1,-np.ceil((1.+xInit)/stepsize),-1)*stepsize + xInit
+        nAbove = len(xAbove)
+        nBelow = len(xBelow)
+        nToMeasure = nAbove + nBelow + 1
+        iInit = nBelow
+
+        if verbose:
+            message("Going to measure PSF at the following %i locations:"%nToMeasure )
+            message(xAbove)
+            message(xBelow)
+        
+        # Measure all positions "above" the initial selection:
+        yAbove = np.zeros(nAbove,float)
+        err_yAbove = np.zeros(nAbove,float)
+        lastY = yInit
+        for i_meas in range(nAbove):
+            guessLoc = xAbove[i_meas], lastY
+
+            thisx, thisy, err_thisy = fitPSF(ec, guessLoc, fitwidth=fitwidth, verbose=verbose-1, medwidth=medwidth, err_ec=err_ec)
+            if abs(thisy - yInit)>fitwidth/2:
+                thisy = yInit
+                err_thisy = yInit
+                lastY = yInit
+            else:
+                lastY = thisy.astype(int)
+            yAbove[i_meas] = thisy
+            err_yAbove[i_meas] = err_thisy
+            if verbose:
+                print thisx, thisy
+            if plotalot and not np.isnan(thisy):
+                #ax.plot([thisx], [thisy], 'xk')
+                ax.errorbar([thisx], [thisy], [err_thisy], fmt='xk')
+
+        # Measure all positions "below" the initial selection:
+        yBelow = np.zeros(nBelow,float)
+        err_yBelow = np.zeros(nBelow,float)
+        lastY = yInit
+        for i_meas in range(nBelow):
+            guessLoc = xBelow[i_meas], lastY
+            thisx, thisy, err_thisy = fitPSF(ec, guessLoc, fitwidth=fitwidth, verbose=verbose-1, medwidth=medwidth, err_ec=err_ec)
+            if abs(thisy-lastY)>fitwidth/2:
+                thisy = np.nan
+            else:
+                lastY = thisy.astype(int)
+            yBelow[i_meas] = thisy
+            err_yBelow[i_meas] = err_thisy
+            if verbose:
+                print thisx, thisy
+            if plotalot and not np.isnan(thisy):
+                ax.errorbar([thisx], [thisy], [err_thisy], fmt='xk')
+    
+        # Stick all the fit positions together:
+        yPositions = np.concatenate((yBelow[::-1], [yInit], yAbove))
+        err_yPositions = np.concatenate((err_yBelow[::-1], [err_yInit], err_yAbove))
+        xPositions = np.concatenate((xBelow[::-1], [xInit], xAbove))
+
+        if verbose:
+            message("Measured the following y-positions:")
+            message(yPositions)
+
+        theseTraceCoefs = polyfitr(xPositions, yPositions, pord, 3, \
+                                       w=1./err_yPositions**2, verbose=verbose)
+        orderCoefs[ordernumber,:] = theseTraceCoefs
+        # Plot the traces
+        if plotalot:
+            ax.plot(np.arange(nx), np.polyval(theseTraceCoefs,np.arange(nx)), '-k')
+            ax.plot(np.arange(nx), np.polyval(theseTraceCoefs,np.arange(nx))+fitwidth/2, '--k')
+            ax.plot(np.arange(nx), np.polyval(theseTraceCoefs,np.arange(nx))-fitwidth/2, '--k')
+            ax.axis([0, nx, 0, ny])   
+            plt.figure(f.number)
+            plt.draw()
+
+        if retsnr:
+            position_SNRs.append(yPositions / err_yPositions)
+        if retfits:
+            xyfits.append((xPositions, yPositions))
+    
+    # Prepare for exit and return:
+    ret = (orderCoefs,)
+    if retsnr:
+        ret = ret + (position_SNRs,)
+    if retfits:
+        ret = ret + (xyfits,)
+    if len(ret)==1:
+        ret = ret[0]
+    return  ret
+
+
+[docs]
+def makeprofile(filename, trace, **kw): #dispaxis=0, fitwidth=20, verbose=False, oversamp=4, nsigma=20, retall=False, neg=False, xylims=None, rn=readnoise, g=gain, extract_radius=10, bkg_radii=[15, 20], bkg_order=0, badpixelmask=None, interp=False):
+    """
+    Make a spatial profile from a spectrum, given its traced location.
+    We interpolate the PSF at each pixel to a common reference frame,
+    and then average them.
+
+    filename : str _OR_ 2D numy array
+       2D echellogram
+
+    trace : 1D numpy array
+        set of polynomial coeficients of order (P-1)
+
+    dispaxis : int
+        set dispersion axis: 0 = horizontal and  = vertical
+    
+    fitwidth : int
+        Total width of extracted spectral sub-block. WIll always be
+        increased to at least twice the largest value of bkg_radii.
+
+    neg : bool scalar
+        set True for a negative spectral trace
+
+    nsigma : scalar
+        Sigma-clipping cut for bad pixels (beyond read+photon
+        noise). Set it rather high, and feel free to experiment with
+        this parameter!
+
+    xylims : 4-sequence
+        Extract the given subset of the data array: [xmin, xmax, ymin, ymax]
+
+    retall : bool
+        Set True to output several additional parameters (see below)
+
+    rn : scalar
+        Read noise (electrons)
+
+    g : scalar
+        Detector gain (electrons per data unit)
+
+    bkg_radii : 2-sequence
+        Inner and outer radius for background computation and removal;
+        measured in pixels from the center of the profile.
+
+    bkg_order : int > 0
+        Polynomial order of background trend computed in master spectral profile
+
+    interp : bool
+        Whether to (bi-linearly) interpolate each slice to produce a
+        precisely-centered spectral profile (according to the input
+        'trace').  If False, slices will only be aligned to the
+        nearest pixel.
+
+
+    OUTPUT:
+       if retall:
+         a spline-function that interpolates pixel locations onto the mean profile
+
+         a stack of data slices
+
+         estimates of the uncertainties
+
+         good pixel flag
+
+         list of splines
+
+       else:
+         a spline-function that interpolates pixel locations onto the mean profile    
+    """
+    from scipy import signal
+    #import numpy as np
+    # 2010-12-17 10:22 IJC: Added xylims option
+    # 2012-03-15 06:34 IJMC: Updated documentation.
+    # 2012-04-24 16:43 IJMC: Now properly update gain and readnoise,
+    #                        as neccessary.  Much better flagging of
+    #                        bad pixels.  Fixed interpolation on RHS.
+    # 2012-08-19 17:53 IJMC: Added dispaxis option.
+    # 2012-09-05 10:17 IJMC: Made keyword options into a dict.
+
+    global gain
+    global readnoise
+
+
+    # Parse inputs:
+    names = ['dispaxis', 'fitwidth', 'verbose', 'oversamp', 'nsigma', 'retall', 'neg', 'xylims', 'rn', 'g', 'extract_radius', 'bkg_radii', 'bkg_order', 'badpixelmask', 'interp']
+    defaults = [0, 20, False, 4, 20, False, False, None, readnoise, gain, 10, [15, 20], 0, None, False]
+    for n,d in zip(names, defaults):
+        #exec('%s = [d, kw["%s"]][kw.has_key("%s")]' % (n, n, n))
+        exec('%s = kw["%s"] if kw.has_key("%s") else d' % (n, n, n))
+
+    if fitwidth < (bkg_radii[1]*2):
+        fitwidth = bkg_radii[1]*2
+
+
+    if not g==gain:
+        message("Setting gain to " + str(g))
+        gain = g
+
+    if not rn==readnoise:
+        message("Setting readnoise to " + str(rn))
+        readnoise = rn
+
+
+    if verbose:
+        f = plt.figure()
+        ax = plt.axes()
+
+    # Check whether we have a filename, or an array:
+    if isinstance(filename, np.ndarray):
+        ec = filename.copy()
+    else:
+        try:
+            ec = pyfits.getdata(filename)
+        except:
+            message("Could not open filename %s" % filename)
+            return -1
+
+    if neg:
+        ec *= -1
+        
+    if xylims is not None:
+        try:
+            ec = ec[xylims[0]:xylims[1], xylims[2]:xylims[3]]
+            if verbose:
+                message("Extracted subset: " + str(xylims))
+        except:
+            message("Could not extract subset: " + str(xylims))
+            return -1
+
+    if badpixelmask is None:
+        badpixelmask = np.zeros(ec.shape, dtype=bool)
+    else:
+        if not hasattr(badpixelmask, 'shape'):
+            badpixelmask = pyfits.getdata(badpixelmask)
+        if xylims is not None:
+            badpixelmask = badpixelmask[xylims[0]:xylims[1], xylims[2]:xylims[3]]
+
+
+
+
+    trace = np.array(trace,copy=True)
+    if len(trace.shape)>1:
+        if verbose:
+            message("Multi-order spectrum input...")
+        #rets = []
+        rets = [makeprofile(filename, thistrace, dispaxis=dispaxis, fitwidth=fitwidth,verbose=verbose,oversamp=oversamp,nsigma=nsigma,retall=retall, neg=neg, xylims=xylims, rn=rn, g=g, extract_radius=extract_radius, bkg_radii=bkg_radii, bkg_order=bkg_order,interp=interp, badpixelmask=badpixelmask) for thistrace in trace]
+
+        return rets
+
+    if dispaxis==1:
+        if verbose: message("Transposing spectra and bad pixel mask...")
+        ec = ec.transpose()
+        badpixelmask = badpixelmask.transpose()
+
+    if verbose:
+        message("Making a spatial profile...")
+
+    ny, nx = ec.shape
+    pord = len(trace) - 1
+    if verbose:
+        message("Echellogram of size (%i,%i)" % (nx,ny))
+        message("Polynomial of order %i (i.e., %i coefficients)" % (pord, pord+1))
+
+    xPositions = np.arange(nx)
+    yPositions = np.polyval(trace, xPositions)
+    yPixels = yPositions.astype(int)
+
+    # Placeholder for a more comprehensive workaround:
+    if (yPixels.max()+fitwidth/2)>=ny:
+        message("Spectrum may be too close to the upper boundary; try decreasing fitwidth")
+    if (yPixels.min()-fitwidth/2)<0:
+        message("Spectrum may be too close to the lower boundary; try decreasing fitwidth")
+    
+    yProfile = np.linspace(-fitwidth/2, fitwidth/2-1,fitwidth*oversamp)
+    #yProfile2 = np.linspace(-fitwidth/2, fitwidth/2-1,(fitwidth+1)*oversamp)
+    profileStack = np.zeros((nx,fitwidth),float)
+    badpixStack  = np.zeros((nx,fitwidth),bool)
+    profileSplineStack = np.zeros((nx,(fitwidth)*oversamp),float)
+    xProf = np.arange(fitwidth, dtype=float) - fitwidth/2.
+    xProf2 = np.arange(ny, dtype=float)
+
+
+    # Loop through to extract the spectral cross-sections at each pixel:
+    for i_pix in range(nx):
+        xi = xPositions[i_pix]
+        yi = yPixels[i_pix]
+
+        ymin = max(yi-fitwidth/2, 0)
+        ymax = min(yi+fitwidth/2, ny)
+        # Ensure that profile will always be "fitwidth" wide:
+        if (ymax-ymin)<fitwidth and ymin==0:
+            ymax = fitwidth
+        elif (ymax-ymin)<fitwidth and ymax==ny:
+            ymin = ymax - fitwidth
+
+        profile = ec[ymin:ymax,xi]
+        if interp:
+            profile = np.interp(xProf + yPositions[i_pix], xProf2[ymin:ymax], profile)
+
+        if verbose:
+            print 'i_pix, xi, yi, y0, ymin, ymax', i_pix, xi, yi, yPositions[i_pix], ymin, ymax
+
+        try:
+            profileStack[i_pix] = profile.copy() * gain
+            badpixStack[i_pix] = badpixelmask[ymin:ymax, xi]
+        except:
+            print "Busted!!!!"
+            print "profileStack[i_pix].shape, profile.shape, gain", \
+                profileStack[i_pix].shape, profile.shape, gain
+            stop
+
+    # Use the profile stack to flag bad pixels in the interpolation process:
+    medianProfile = np.median(profileStack,0)
+    scaledProfileStack = 0*profileStack
+    myMatrix = np.linalg.pinv(np.vstack((medianProfile, np.ones(profileStack.shape[1]))).transpose())
+    for ii in range(profileStack.shape[0]):
+        these_coefs = np.dot(myMatrix, profileStack[ii])
+        scaledProfileStack[ii] = (profileStack[ii] - these_coefs[1]) / these_coefs[0]
+
+    #import pylab as py
+    #pdb.set_trace()
+
+
+    #########
+    # Flag all the bad pixels:
+    #########
+
+    # Hot Pixels should be 1-pixel wide:
+    nFilt = 3
+    filteredProfileStack = signal.medfilt2d(profileStack, nFilt)
+    errorFiltStack = np.sqrt(filteredProfileStack + readnoise**2)
+    errorFiltStack[badpixStack] = errorFiltStack.max() * 1e9
+
+    goodPixels = (True - badpixStack) * np.abs((profileStack - filteredProfileStack) / errorFiltStack) < nsigma
+    badPixels = True - goodPixels
+    #cleaned_profileStack  = ns.bfixpix(profStack, badpix, retdat=True)
+
+    if False:  # Another (possibly worse?) way to do it:
+        stdProfile = stdr(scaledProfileStack, nsigma=nsigma, axis=0)  #profileStack.std(0)
+        goodPixels = (scaledProfileStack-medianProfile.reshape(1,fitwidth)) /  \
+            stdProfile.reshape(1,fitwidth)<=nsigma
+        goodPixels *= profileStack>=0
+
+    goodColumns = np.ones(goodPixels.shape[0],bool)
+    errorStack = np.sqrt(profileStack + readnoise**2)
+    errorStack[badPixels] = profileStack[goodPixels].max() * 1e9
+    
+    # Loop through and fit weighted splines to each cross-section
+    for i_pix in range(nx):
+        xi = xPositions[i_pix]
+        yi = yPixels[i_pix]
+        ymin = max(yi-fitwidth/2, 0)
+        ymax = min(yi+fitwidth/2, ny)
+        # Ensure that profile will always be "fitwidth" wide:
+        if (ymax-ymin)<fitwidth and ymin==0:
+            ymax = fitwidth
+        elif (ymax-ymin)<fitwidth and ymax==ny:
+            ymin = ymax - fitwidth
+
+        profile = profileStack[i_pix]
+        index = goodPixels[i_pix]
+
+        ytemp = np.arange(ymin,ymax)-yPositions[i_pix]
+        if verbose>1: 
+            print "ii, index.sum()>>",i_pix,index.sum()
+            print "xi, yi>>",xi,yi
+        if verbose: 
+            print "ymin, ymax>>",ymin,ymax
+            print "ytemp.shape, profile.shape, index.shape>>",ytemp.shape, profile.shape, index.shape
+        if index.sum()>fitwidth/2:
+            ydata_temp = np.concatenate((ytemp[index], ytemp[index][-1] + np.arange(1,6)))
+            pdata_temp = np.concatenate((profile[index], [profile[index][-1]]*5))
+            profileSpline = interpolate.UnivariateSpline(ydata_temp, pdata_temp, k=3.0,s=0.0)
+            profileSplineStack[i_pix,:] = profileSpline(yProfile)
+        else:
+            if verbose: message("not enough good pixels in segment %i" % i_pix)
+            profileSplineStack[i_pix,:] = 0.0
+            goodColumns[i_pix] = False
+
+    finalYProfile = np.arange(fitwidth)-fitwidth/2
+    finalSplineProfile = np.median(profileSplineStack[goodColumns,:],0)
+    ## Normalize the absolute scaling of the final Profile
+    #xProfile = np.arange(-50.*fitwidth, 50*fitwidth)/100.
+    #profileParam = fitGaussian(finalSplineProfile, verbose=True)
+    #profileScaling = profileParam[0]*.01
+    #print 'profileScaling>>', profileScaling
+
+    backgroundAperture = (np.abs(yProfile) > bkg_radii[0]) * (np.abs(yProfile) < bkg_radii[1])
+    #pdb.set_trace()
+    backgroundFit = np.polyfit(yProfile[backgroundAperture], finalSplineProfile[backgroundAperture], bkg_order)
+
+    extractionAperture = (np.abs(yProfile) < extract_radius)
+    normalizedProfile = finalSplineProfile - np.polyval(backgroundFit, yProfile)
+    normalizedProfile *= oversamp / normalizedProfile[extractionAperture].sum()
+
+    finalSpline = interpolate.UnivariateSpline(yProfile, 
+                      normalizedProfile,   k=3.0, s=0.0)
+
+    
+
+    if verbose:
+        ax.plot(yProfile, finalSplineProfile, '--',linewidth=2)
+        plt.draw()
+    
+    if retall==True:
+        ret =  finalSpline, profileStack, errorStack, goodPixels,profileSplineStack
+    else:
+        ret = finalSpline
+
+    return ret
+
+def makeFitModel(param, spc,profile, xtemp=None):
+    scale, background, shift = param[0:3]
+    npix = len(spc)
+    if xtemp is None:
+        xtemp = np.arange(-npix/2,npix/2,dtype=float)
+    model = scale*profile(xtemp-shift)+background
+    return model
+
+
+[docs]
+def profileError(param, spc, profile, w, xaxis=None, retresidual=False):
+    "Compute the chi-squared error on a spectrum vs. a profile "
+    # 2012-04-25 12:59 IJMC: Slightly optimised (if retresidual is False)
+
+        #scale, background, shift = param[0:3]
+        #npix = len(spc)
+        #xtemp = np.arange(-npix/2,npix/2,dtype=float)
+    interpolatedProfile = makeFitModel(param,spc,profile, xtemp=xaxis) #scale*profile(xtemp-shift)+background
+    #wresiduals = 
+    #wresiduals = (w*((spc-interpolatedProfile))**2).sum()
+    if retresidual:
+        ret = (w**0.5) * (spc - interpolatedProfile)
+    else:
+        ret = (w * (spc - interpolatedProfile)**2).sum()
+
+    return ret
+
+
+[docs]
+def fitprofile(spc, profile, w=None,verbose=False, guess=None, retall=False):
+    """Fit a spline-class spatial profile to a spectrum cross-section
+    """
+    # 2010-11-29 14:14 IJC: Added poor attempt at catching bad pixels
+
+    import analysis as an
+    import numpy as np
+
+    spc = np.array(spc)
+    try:
+        npix = len(spc)
+    except:
+        npix = 0
+
+    if w is None:
+        w = np.ones(spc.shape)
+    else:
+        w[True - np.isfinite(w)] = 0.
+
+    if guess is None:
+        guessParam = [1., 0., 0.]
+    else:
+        guessParam = guess
+
+    if verbose>1:
+        #message("profileError>>"+str(profileError))
+        #message("guessParam>>"+str(guessParam))
+        #message("spc>>"+str(spc))
+        message("w>>"+str(w))
+
+    good_index = w<>0.0
+    xaxis = np.arange(-npix/2,npix/2,dtype=float) # take outside of fitting loop
+    bestFit = optimize.fmin_powell(profileError, guessParam, args=(spc,profile,w * good_index, xaxis),disp=verbose, full_output=True)
+    best_bic = bestFit[1] + len(bestFit[0]) * np.log(good_index.sum())
+    keepCleaning = True
+
+    if False:
+        while keepCleaning is True:
+            if verbose: print "best bic is: ", best_bic
+            #w_residuals = profileError(bestFit[0], spc, profile, w, retresidual=True)
+            #good_values = (abs(w_residuals)<>abs(w_residuals).max())
+            diffs = np.hstack(([0], np.diff(spc)))
+            d2 = diffs * (-np.ones(len(spc)))**np.arange(len(spc))
+            d3 = np.hstack((np.vstack((d2[1::], d2[0:len(spc)-1])).mean(0), [0]))
+            good_values = abs(d3)<>abs(d3).max()
+            good_index *= good_values
+            xaxis = np.arange(-npix/2,npix/2,dtype=float) # take outside of fitting loop
+            latestFit = optimize.fmin_powell(profileError, guessParam, args=(spc,profile,w * good_index, xaxis),disp=verbose, full_output=True)
+            latest_bic = latestFit[1] + len(latestFit[0]) * np.log(good_index.sum())
+            if latest_bic < best_bic:
+                best_bic = latest_bic
+                bestFit = latestFit
+                keepCleaning = True
+            else:
+                keepCleaning = False
+
+            if good_index.any() is False:
+                keepCleaning = False
+            #good_index = good_index * an.removeoutliers(w_residuals, 5, retind=True)[1]
+
+    if verbose:
+        message("initial guess chisq>>%3.2f" % profileError(guessParam, spc, profile,w,xaxis))
+        message("final fit chisq>>%3.2f" % profileError(bestFit[0], spc, profile,w, xaxis))
+
+    if retall:
+        ret = bestFit
+    else:
+        ret = bestFit[0]
+
+    return ret
+
+    
+
+[docs]
+def fitProfileSlices(splineProfile, profileStack, stdProfile, goodPixels,verbose=False, bkg_radii=None, extract_radius=None):
+    """Fit a given spatial profile to a spectrum
+
+    Helper function for :func:`extractSpectralProfiles`
+
+    """
+
+    npix, fitwidth = profileStack.shape
+    stdProfile = stdProfile.copy()
+    goodPixels[np.isnan(stdProfile) + stdProfile==0] = False
+    stdProfile[True - goodPixels] = 1e9
+    #varPData = stdProfile**2
+
+    if extract_radius is None:
+        extract_radius = fitwidth
+
+    x2 = np.arange(npix)
+    x = np.arange(-fitwidth/2, fitwidth/2)
+    extractionAperture = np.abs(x) < extract_radius
+    nextract = extractionAperture.sum()
+
+    fitparam = np.zeros((npix, 3),float)
+    fitprofiles = np.zeros((npix,nextract),float)
+    tempx = np.arange(-fitwidth/2,fitwidth/2,dtype=float)
+    
+    # Start out by getting a rough estimate of any additional bad
+    # pixels (to flag):
+
+    #backgroundAperture = (np.abs(x) > bkg_radii[0]) * (np.abs(x) < bkg_radii[1])
+    #background = an.wmean(profileStack[:, backgroundAperture], (goodPixels/varPData)[:, backgroundAperture], axis=1)
+    #badBackground = True - np.isfinite(background)
+    #background[badBackground] = 0.
+
+    #subPData = profileStack - background
+    #standardSpectrum = an.wmean(subPData[:, extractionAperture], (goodPixels/varPData)[:,extractionAperture], axis=1) * extractionAperture.sum()
+
+    if False:
+        varStandardSpectrum = an.wmean(varPData[:, extractionAperture], goodPixels[:, extractionAperture], axis=1) * extractionAperture.sum()
+
+        badSpectrum = True - np.isfinite(standardSpectrum)
+        standardSpectrum[badSpectrum] = 1.
+        varStandardSpectrum[badSpectrum] = varStandardSpectrum[True - badSpectrum].max() * 1e9
+
+        mod = background + standardSpectrum * splineProfile(x) * (splineProfile(x).sum() / splineProfile(x[extractionAperture]).sum())
+
+
+
+
+    anyNewBadPixels = True
+    nbp0 = goodPixels.size - goodPixels.sum()
+    gp0 = goodPixels.copy()
+
+    while anyNewBadPixels is True:
+        for ii in range(npix):
+            if verbose>1:
+                print "*****In fitProfileSlices, pixel %i/%i" % (ii+1, npix)
+            if ii>0: # take the median of the last several guesses
+                gindex = [max(0,ii-2), min(npix, ii+2)]
+                guess = np.median(fitparam[gindex[0]:gindex[1]], 0)
+            else:
+                guess = None
+
+            if verbose>1:
+                message("ii>>%i"%ii)
+                message("goodPixels>>"+str(goodPixels.astype(float)[ii,:]))
+                message("stdProfile>>"+str(stdProfile[ii,:]))
+
+            thisfit = fitprofile(profileStack[ii,extractionAperture], splineProfile, (goodPixels.astype(float)/stdProfile**2)[ii,extractionAperture],verbose=verbose, guess=guess, retall=True)
+            bestfit = thisfit[0]
+            bestchi = thisfit[1]
+
+            if verbose>1:
+                print "this fit: ",bestfit
+            fitparam[ii,:] = bestfit
+            fitprofiles[ii,:] = makeFitModel(bestfit, profileStack[ii,extractionAperture],splineProfile) 
+            if verbose>1:
+                print "finished pixel %i/%i" % (ii+1,npix)
+                
+            
+        deviations = (fitprofiles - profileStack[:, extractionAperture])/stdProfile[:, extractionAperture]
+        for kk in range(nextract):
+            kk0 = extractionAperture.nonzero()[0][kk]
+            thisdevfit = an.polyfitr(x2, deviations[:, kk], 1, 3)
+            theseoutliers = np.abs((deviations[:, kk] - np.polyval(thisdevfit, x2))/an.stdr(deviations, 3)) > 5
+            goodPixels[theseoutliers, kk0] = False
+            
+        nbp = goodPixels.size - goodPixels.sum()
+        if nbp <= nbp0:
+            anyNewBadPixels = False
+        else:
+            nbp0 = nbp
+
+    for ii in range(fitparam.shape[1]):
+        fitparam[:,ii] = bfixpix(fitparam[:,ii], goodPixels[:,extractionAperture].sum(1) <= (nextract/2.), retdat=True)
+
+    return fitparam, fitprofiles
+
+
+
+
+
+
+[docs]
+def extractSpectralProfiles(args, **kw):
+    """
+    Extract spectrum 
+
+    :INPUTS:
+       args : tuple or list
+           either a tuple of (splineProfile, profileStack, errorStack,
+           profileMask), or a list of such tuples (from makeprofile).
+             
+    :OPTIONS:
+       bkg_radii : 2-sequence
+         inner and outer radii to use in computing background
+
+       extract_radius : int
+         radius to use for both flux normalization and extraction
+
+    :RETURNS:
+       3-tuple:
+          [0] -- spectrum flux (in electrons)
+
+          [1] -- background flux
+
+          [2] -- best-fit pixel shift
+
+    :EXAMPLE:
+      ::
+        out = spec.traceorders('aug16s0399.fits',nord=7)
+
+        out2 = spec.makeprofile('aug16s0399.fits',out,retall=True)
+
+        out3 = spec.extractSpectralProfiles(out2)
+
+    :SEE_ALSO:
+      :func:`optimalExtract`
+
+    :NOTES:
+      Note that this is non-optimal with highly tilted or curved
+      spectra, for the reasons described by Marsh (1989) and Mukai
+      (1990).
+    """
+    # 2010-08-24 21:24 IJC: Updated comments
+
+    if kw.has_key('verbose'):
+        verbose = kw['verbose']
+    else:
+        verbose = False
+
+    if kw.has_key('bkg_radii'):
+        bkg_radii = kw['bkg_radii']
+    else:
+        bkg_radii = [15, 20]
+        if verbose: message("Setting option 'bkg_radii' to: " + str(bkg_radii))
+
+    if kw.has_key('extract_radius'):
+        extract_radius = kw['extract_radius']
+    else:
+        extract_radius = 10
+
+
+    #print 'starting...', args[0].__class__, isinstance(args[0],tuple)#, len(*arg)
+    fitprofiles = []
+    if isinstance(args,list): # recurse with each individual set of arguments
+        #nord = len(arg[0])
+        spectrum = []
+        background = []
+        pixshift = []
+        for ii, thesearg in enumerate(args):
+            if verbose: print "----------- Iteration %i/%i" % (ii+1, len(args)) 
+            #print 'recursion loop', thesearg.__class__, verbose, kw
+            tempout = extractSpectralProfiles(thesearg, **kw)
+            spectrum.append(tempout[0])
+            background.append(tempout[1])
+            pixshift.append(tempout[2])
+            fitprofiles.append(tempout[3])
+        spectrum = np.array(spectrum)
+        background = np.array(background)
+        pixshift = np.array(pixshift)
+    else:
+        #print 'actual computation', args.__class__, verbose, kw
+        splineProfile = args[0]
+        profileStack = args[1]
+        errorStack = args[2]
+        profileMask = args[3]
+
+
+        #print splineProfile.__class__, profileStack.__class__, errorStack.__class__, profileMask.__class__
+        #print profileStack.shape, errorStack.shape, profileMask.shape
+        fps = dict()
+        if kw.has_key('verbose'): fps['verbose'] = kw['verbose']
+        if kw.has_key('bkg_radii'): fps['bkg_radii'] = kw['bkg_radii']
+        if kw.has_key('extract_radius'): fps['extract_radius'] = kw['extract_radius']
+        fitparam, fitprofile = fitProfileSlices(splineProfile, profileStack, errorStack, profileMask, **fps)
+        spectrum = fitparam[:,0]
+        background = fitparam[:,1]
+        pixshift = fitparam[:,2]
+        fitprofiles.append(fitprofile)
+
+    ret = (spectrum, background, pixshift, fitprofiles)
+
+    return  ret
+
+
+
+[docs]
+def gaussint(x):
+    """ 
+    :PURPOSE:
+        Compute the integral from -inf to x of the normalized Gaussian
+
+    :INPUTS:
+        x : scalar
+            upper limit of integration
+
+    :NOTES:
+        Designed to copy the IDL function of the same name.
+        """
+    # 2011-10-07 15:41 IJMC: Created
+
+    from scipy.special import erf
+
+    scalefactor = 1./np.sqrt(2)
+    return 0.5 + 0.5 * erf(x * scalefactor)
+
+[docs]
+def slittrans(*varargin):
+    """+
+     :NAME:
+         slittrans
+
+     :PURPOSE:
+         Compute flux passing through a slit assuming a gaussian PSF.
+
+     :CATEGORY:
+         Spectroscopy
+
+     :CALLING SEQUENCE:
+         result = slittrans(width,height,fwhm,xoffset,yoffset,CANCEL=cancel)
+
+     :INPUTS:
+         width   - Width of slit.
+         height  - Height of slit.
+         fwhm    - Full-width at half-maximum of the gaussian image.
+         xoffset - Offset in x of the image from the center of the slit.
+         yoffset - Offset in y of the image from the center of the slit.
+
+         Note: the units are arbitrary but they must be the same across
+               all of the input quantities.  
+
+     KEYWORD PARAMETERS:
+         CANCEL - Set on return if there is a problem
+
+     OUTPUTS:
+         Returned is the fraction of the total gaussian included in the slit.
+
+     EXAMPLE:
+         result = slittrans(0.3,15,0.6,0,0)
+
+         Computes the fraction of the flux transmitted through a slit 
+         0.3x15 arcseconds with a PSF of 0.6 arcseconds FWHM.  The PSF is
+         centered on the slit.
+
+     MODIFICATION HISTORY:
+         Based on M Buie program,  1991 Mar., Marc W. Buie, Lowell Observatory
+         Modified 2000 Apr., M. Cushing to include y offsets.
+         2011-10-07 15:45 IJMC: Converted to Python
+         2011-11-14 16:29 IJMC: Rewrote to use :func:`erf` rather than
+                                :func:`gaussint`
+         """
+    #function slittrans,width,height,fwhm,xoffset,yoffset,CANCEL=cancel
+
+    from scipy.special import erf
+
+    cancel = 0
+    n_params = len(varargin)
+
+    if n_params <> 5:
+        print 'Syntax - result = slittrans(width,height,fwhm,xoffset,yoffset,',
+        print '                            CANCEL=cancel)'
+        cancel = 1
+        return cancel
+
+    width, height, fwhm, xoffset, yoffset = varargin[0:5]
+
+    #cancel = cpar('slittrans',width,1,'Width',[2,3,4,5],0)
+    #if cancel then return,-1
+    #cancel = cpar('slittrans',height,2,'Height',[2,3,4,5],0)
+    #if cancel then return,-1
+    #cancel = cpar('slittrans',fwhm,3,'FWHM',[2,3,4,5],0)
+    #if cancel then return,-1
+    #cancel = cpar('slittrans',xoffset,4,'Xoffset',[2,3,4,5],0)
+    #if cancel then return,-1
+    #cancel = cpar('slittrans',yoffset,5,'Yoffset',[2,3,4,5],0)
+    #if cancel then return,-1
+
+    #  Go ahead
+
+    a = 0.5 * width 
+    b = 0.5 * height 
+
+    #s = fwhm/(np.sqrt(8.0*np.log(2.0)))  
+    #slit = ( 1.0 - gaussint( -(a+xoffset)/s ) - gaussint( -(a-xoffset)/s ) ) * \
+    #       ( 1.0 - gaussint( -(b+yoffset)/s ) - gaussint( -(b-yoffset)/s ) )
+
+    invs2 = (np.sqrt(4.0*np.log(2.0)))/fwhm    # sigma * sqrt(2)
+    
+    slit4 = 0.25 * (  (erf( -(a+xoffset)*invs2) + erf( -(a-xoffset)*invs2) ) ) * \
+                   (  (erf( -(b+yoffset)*invs2) + erf( -(b-yoffset)*invs2) ) )
+       
+
+    #print 'smin/max,',slit.min(), slit5.max()
+    #print 'smin/max,',slit4.min(), slit4.max()
+
+     
+    return slit4
+
+
+[docs]
+def atmosdisp(wave, wave_0, za, pressure, temp, water=2., fco2=0.0004, obsalt=0.):
+    """:NAME:
+         atmosdisp
+
+     PURPOSE:
+         Compute the atmosperic dispersion relative to lambda_0.     
+
+     CATEGORY:
+         Spectroscopy
+
+     CALLING SEQUENCE:
+         result = atmosdisp(wave,wave_0,za,pressure,temp,[water],[obsalt],$
+                            CANCEL=cancel)
+
+     INPUTS:
+         wave     - wavelength in microns
+         wave_0   - reference wavelength in microns
+         za       - zenith angle of object [in degrees]
+         pressure - atmospheric pressure in mm of Hg
+         temp     - atmospheric temperature in degrees C
+
+     OPTIONAL INPUTS:
+         water    - water vapor pressure in mm of Hg.
+         fco2     - relative concentration of CO2 (by pressure)
+         obsalt    - The observatory altitude in km.
+
+     KEYWORD PARAMETERS:
+         CANCEL - Set on return if there is a problem
+
+     OUTPUTS:
+         Returns the atmospheric disperion in arcseconds.      
+
+     PROCEDURE:
+         Computes the difference between the dispersion at two
+         wavelengths.  The dispersion for each wavelength is derived from
+         Section 4.3 of Green's "Spherical Astronomy" (1985).
+
+     EXAMPLE:
+
+
+
+     MODIFICATION HISTORY:
+         2000-04-05 - written by M. Cushing, Institute for Astronomy, UH
+         2002-07-26 - cleaned up a bit.
+         2003-10-20 - modified formula - WDV
+         2011-10-07 15:51 IJMC: Converted to Python, with some unit conversions
+    -"""
+
+    #function atmosdisp,wave,wave_0,za,pressure,temp,water,obsalt,CANCEL=cancel
+    from nsdata import nAir
+
+    # Constants
+
+    mmHg2pa = 101325./760.      # Pascals per Torr (i.e., per mm Hg)
+    rearth = 6378.136e6 #6371.03    # mean radius of earth in km [Allen's]
+    hconst = 2.926554e-2    # R/(mu*g) in km/deg K,  R=gas const=8.3143e7
+                                    # mu=mean mol wght. of atm=28.970, g=980.665
+    tempk  = temp + 273.15
+    pressure_pa = pressure * mmHg2pa
+    water_pp = water/pressure   # Partial pressure
+    hratio = (hconst * tempk)/(rearth + obsalt)
+
+    # Compute index of refraction
+
+    nindx  = nAir(wave,P=pressure_pa,T=tempk,pph2o=water_pp, fco2=fco2)
+    nindx0 = nAir(wave_0,P=pressure_pa,T=tempk,pph2o=water_pp, fco2=fco2)
+
+    # Compute dispersion
+
+    acoef  = (1. - hratio)*(nindx - nindx0)
+    bcoef  = 0.5*(nindx*nindx - nindx0*nindx0) - (1. + hratio)*(nindx - nindx0)
+
+    tanz   = np.tan(np.deg2rad(za))
+    disp   = 206265.*tanz*(acoef + bcoef*tanz*tanz)
+
+    #print nindx
+    #print nindx0
+    #print acoef
+    #print bcoef
+    #print tanz
+    #print disp
+    return disp
+
+
+
+[docs]
+def parangle(HA, DEC, lat):
+    """
+    +
+     NAME:
+         parangle
+
+     PURPOSE:
+         To compute the parallactic angle at a given position on the sky.
+
+     CATEGORY:
+         Spectroscopy    
+
+     CALLING SEQUENCE:
+         eta, za = parangle(HA, DEC, lat)
+
+     INPUTS:
+         HA  - Hour angle of the object, in decimal hours (0,24)
+         DEC - Declination of the object, in degrees
+         lat - The latitude of the observer, in degrees
+
+     KEYWORD PARAMETERS:
+         CANCEL - Set on return if there is a problem
+
+     OUTPUTS:
+         eta - The parallactic angle
+         za  - The zenith angle
+
+     PROCEDURE:
+         Given an objects HA and DEC and the observers latitude, the
+         zenith angle and azimuth are computed.  The law of cosines
+         then gives the parallactic angle.  
+
+     EXAMPLE:
+         NA
+
+
+     MODIFICATION HISTORY:
+         2000-04-05 - written by M. Cushing, Institute for Astronomy,UH
+         2002-08-15 - cleaned up a bit.
+         2003-10-21 - changed to pro; outputs zenith angle as well - WDV
+         2011-10-07 17:58 IJMC: Converted to Python
+-"""
+
+  #pro parangle, HA, DEC, lat, eta, za, CANCEL=cancel
+
+    cancel = 0
+    d2r = np.deg2rad(1.)
+    r2d = np.rad2deg(1.)
+
+    #  If HA equals zero then it is easy.
+    HA = HA % 24
+    #  Check to see if HA is greater than 12.
+    if hasattr(HA, '__iter__'):
+        HA = np.array(HA, copy=False)
+        HAind = HA > 12
+        if HAind.any():
+            HA[HAind] = 24. - HA[HAind]
+    else:
+        if HA>12.:
+            HA = 24. - HA
+
+    HA = HA*15.
+
+    #  Determine Zenith angle and Azimuth
+    cos_za = np.sin(lat*d2r) * np.sin(DEC*d2r) + \
+             np.cos(lat*d2r) * np.cos(DEC*d2r) * np.cos(HA*d2r)
+    za     = np.arccos(cos_za) * r2d
+    cos_az = (np.sin(DEC*d2r) - np.sin(lat*d2r)*np.cos(za*d2r)) / \
+             (np.cos(lat*d2r) * np.sin(za*d2r))
+    az     = np.arccos(cos_az)*r2d
+
+    if hasattr(az, '__iter__'):
+        azind = az==0
+        if azind.any() and DEC<lat:
+            az[azind] = 180.
+    else:
+        if az==0. and DEC<lat:
+            az = 180.
+
+    tan_eta = np.sin(HA*d2r)*np.cos(lat*d2r) / \
+              (np.cos(DEC*d2r)*np.sin(lat*d2r) - \
+               np.sin(DEC*d2r)*np.cos(lat*d2r)*np.cos(HA*d2r))
+    eta     = np.arctan(tan_eta)*r2d
+
+    if hasattr(eta, '__iter__'):
+        etaind = eta < 0
+        ezind = (eta==0) * (az==0)
+        zaind = za > 90
+        if etaind.any():
+            eta[etaind] += 180.
+        elif ezind.any():
+            eta[ezind] = 180.
+        if zaind.any():
+            eta[zaind] = np.nan
+    else:
+        if eta < 0:
+            eta += 180.
+        elif eta==0 and az==0:
+            eta = 180.
+        if za>90:
+            eta = np.nan
+
+    HA = HA/15.0
+
+    return eta, za
+
+[docs]
+def lightloss(objfile, wguide, seeing, press=None, water=None, temp=None, fco2=None, wobj=None, dx=0, dy=0, retall=False):
+    """    +
+     NAME:
+         lightloss
+
+     PURPOSE:
+         To determine the slit losses from a spectrum.
+
+     CATEGORY:
+         Spectroscopy     
+
+     CALLING SEQUENCE:
+         ### TBD lightloss, obj, std, wguide, seeing, out, CANCEL=cancel
+
+     INPUTS:
+         obj    - FITS file of the object spectrum
+         wguide - wavelength at which guiding was done
+         seeing - seeing FWHM at the guiding wavelength
+
+     OPTIONAL INPUTS:
+         press -  mm Hg typical value (615 for IRTF, unless set)
+         water -  mm Hg typical value (2 for IRTF, unless set)
+         temp  -  deg C typical value (0 for IRTF, unless set)
+         fco2  -  relative concentration of CO2 (0.004, unless set)
+         wobj  -  wavelength scale for data
+         dx    -  horizontal offset of star from slit center
+         dy    -  vertical offset of star from slit center
+         retall-  whether to return much diagnostic info, or just lightloss.
+
+     NOTES:
+         'seeing', 'dx', and 'dy' should all be in the same units, and
+         also the same units used to define the slit dimensions in the
+         obj FITS file header
+
+     KEYWORD PARAMETERS:
+         CANCEL - Set on return if there is a problem
+
+     OUTPUTS:
+         array : fractional slit loss at each wavelength value
+        OR:
+         tuple of arrays: (slitloss, disp_obj, diff, fwhm, dx_obj, dy_obj)
+
+
+     PROCEDURE:
+         Reads a Spextool FITS file.
+
+     EXAMPLE:
+         None
+
+     REQUIREMENTS:
+         :doc:`phot`
+         :doc:`pyfits`
+
+     MODIFICATION HISTORY:
+         2003-10-21 - Written by W D Vacca 
+         2011-10-07 20:19 IJMC: Converted to Python, adapted for single objects
+         2011-10-14 14:01 IJMC: Added check for Prism mode (has
+                    different slit dimension keywords) and different
+                    pyfits header read mode.
+         2011-11-07 15:53 IJMC: Added 'retall' keyword
+         
+    -
+    """
+    #pro lightloss, objfile, stdfile, wguide, seeing, outfile,CANCEL=cancel
+
+
+    try:
+        from astropy.io import fits as pyfits
+    except:
+        import pyfits
+    
+    from phot import hms, dms
+
+    r2d = np.rad2deg(1)
+
+    # --- Open input files
+
+    obj    = pyfits.getdata(objfile)
+    objhdu = pyfits.open(objfile)
+    #objhdr = pyfits.getheader(objfile)
+    objhdu.verify('silentfix')
+    objhdr = objhdu[0].header
+    if wobj is None:
+        try:
+            wobj   = obj[:,0,:]
+            #fobj   = obj[:,1,:]
+            #eobj   = obj[:,2,:]
+        except:
+            wobj   = obj[0,:]
+            #fobj   = obj[1,:]
+            #eobj   = obj[2,:]
+
+    # --- Read header keywords
+
+    tele       = objhdr['TELESCOP']
+    
+    try:
+        slitwd     = objhdr['SLTW_ARC']
+        slitht     = objhdr['SLTH_ARC']
+    except: # for SpeX prism mode:
+        slitwd, slitht = map(float, objhdr['SLIT'].split('x'))
+
+    #xaxis      = objhdr['XAXIS']
+    #xunits     = objhdr['XUNITS']
+    #yaxis      = objhdr['YAXIS']
+    #yunits     = objhdr['YUNITS']
+
+    posang_obj = objhdr['POSANGLE']
+    HA_objstr  = objhdr['HA']
+    DEC_objstr = objhdr['DEC']
+
+    # --- Process keywords
+
+    #coord_str  = HA_objstr + ' ' + DEC_objstr
+    #get_coords, coords, InString=coord_str
+    HA_obj, DEC_obj = dms(HA_objstr), dms(DEC_objstr)
+
+    if posang_obj<0.0:
+        posang_obj += 180.
+    if posang_obj >= 180.0:
+        posang_obj -= 180.0
+
+    if tele=='NASA IRTF':
+       obsdeg  = 19.0
+       obsmin  = 49.0
+       obssec  = 34.39
+       obsalt  = 4.16807    # observatory altitude in km
+       teldiam = 3.0        # diameter of primary in meters
+       if press is None:
+           press   = 615.0      # mm Hg typical value
+       if water is None:
+           water   = 2.0        # mm Hg typical value
+       if temp is None:
+           temp    = 0.0        # deg C typical value   
+    else:
+       print 'Unknown Telescope - stopping!'
+       return
+
+    if fco2 is None:
+        fco2 = 0.0004
+
+    obslat  = dms('%+02i:%02i:%02i' % (obsdeg, obsmin, obssec))
+
+    # --- Compute Parallactic Angle
+    pa_obj, za_obj = parangle(HA_obj, DEC_obj, obslat)
+    dtheta_obj = posang_obj - pa_obj
+    #print posang_obj, pa_obj, dtheta_obj, za_obj
+
+    #print posang_obj, pa_obj, dtheta_obj, HA_obj
+
+    # --- Compute Differential Atmospheric Dispersion
+    disp_obj = atmosdisp(wobj, wguide, za_obj, press, temp, \
+                             water=water, obsalt=obsalt, fco2=fco2)
+
+    # --- Compute FWHM at each input wavelength
+    diff     = 2.0*1.22e-6*3600.0*r2d*(wobj/teldiam)    # arcsec
+    fwhm     = (seeing*(wobj/wguide)**(-0.2))
+    fwhm[fwhm < diff] = diff[fwhm < diff]
+
+    # --- Compute Relative Fraction of Light contained within the slit
+    dx_obj   = (disp_obj*np.sin(dtheta_obj/r2d)) + dx
+    dy_obj   = (disp_obj*np.cos(dtheta_obj/r2d)) + dy
+
+    slitloss  = slittrans(slitwd,slitht,fwhm,dx_obj,dy_obj)
+
+    #debug_check = lightloss2(wobj, slitwd, slitht, posang_obj/57.3, pa_obj/57.3, za_obj/57.3, wguide, seeing, retall=retall)
+
+    if retall:
+        return  (slitloss, disp_obj, diff, fwhm, dx_obj, dy_obj)
+    else:
+        return slitloss
+
+
+
+[docs]
+def lightloss2(wobj, slitwd, slitht, slitPA, targetPA, zenith_angle, wguide, seeing, press=615., water=2., temp=0., fco2=0.004, obsalt=4.16807, teldiam=3., dx=0, dy=0, retall=False, ydisp=None, xdisp=None, fwhm=None):
+    """    +
+     NAME:
+         lightloss2
+
+     PURPOSE:
+         To determine the slit losses from an observation (no FITS file involved)
+
+     CATEGORY:
+         Spectroscopy     
+
+     CALLING SEQUENCE:
+         ### TBD lightloss, obj, std, wguide, seeing, out, CANCEL=cancel
+
+     INPUTS:
+         wobj  -  wavelength scale for data
+         slitwd - width of slit, in arcsec
+         slitht - height of slit, in arcsec
+         slitPA - slit Position Angle, in radians
+         targetPA - Parallactic Angle at target, in radians
+         zenith_angle - Zenith Angle, in radians
+         wguide - wavelength at which guiding was done
+         seeing - seeing FWHM at the guiding wavelength
+
+     OPTIONAL INPUTS:
+         press -  mm Hg typical value (615, unless set)
+         water -  mm Hg typical value (2 , unless set)
+         temp  -  deg C typical value (0 , unless set)
+         fco2  -  relative concentration of CO2 (0.004, unless set)
+         obsalt-  observatory altitude, in km
+         teldiam- observatory limiting aperture diameter, in m
+         dx    -  horizontal offset of star from slit center
+         dy    -  vertical offset of star from slit center
+         retall-  whether to return much diagnostic info, or just lightloss.
+
+         ydisp - The position of the spectrum in the slit at all
+                 values of wobj.  This should be an array of the same
+                 size as wobj, with zero corresponding to the vertical
+                 middle of the slit and positive values tending toward
+                 zenith.  In this case xdisp will be computed as XXXX
+                 rather than from the calculated atmospheric
+                 dispersion; dx and dy will also be ignored.
+
+         fwhm - Full-width at half-maximum of the spectral trace, at
+                all values of wobj.  This should be an array of the
+                same size as wobj, measured in arc seconds.
+
+
+     NOTES:
+         'slitwidth', 'slitheight', 'seeing', 'dx', 'dy', and 'fwhm'
+         (if used) should all be in the same units: arc seconds.
+
+     OUTPUTS:
+         array : fractional slit loss at each wavelength value
+        OR:
+         tuple of arrays: (slitloss, disp_obj, diff, fwhm, dx_obj, dy_obj)
+
+
+     PROCEDURE:
+         All input-driven.  For the SpeXTool-version analogue, see
+         :func:`lightloss`
+
+     EXAMPLE:
+         import numpy as np
+         import spec
+         w = np.linspace(.5, 2.5, 100) # Wavelength, in microns
+         d2r = np.deg2rad(1.)
+         #targetPA, za = spec.parangle(1.827, 29.67*d2r, lat=20.*d2r)
+         targetPA, za = 105.3, 27.4
+         slitPA = 90. * d2r
+
+         spec.lightloss2(w, 3., 15., slitPA, targetPA*d2r, za*d2r, 2.2, 1.0)
+
+     REQUIREMENTS:
+         :doc:`phot`
+
+     MODIFICATION HISTORY:
+         2003-10-21 - Written by W D Vacca 
+         2011-10-07 20:19 IJMC: Converted to Python, adapted for single objects
+         2011-10-14 14:01 IJMC: Added check for Prism mode (has
+                    different slit dimension keywords) and different
+                    pyfits header read mode.
+         2011-11-07 15:53 IJMC: Added 'retall' keyword
+         2011-11-07 21:17 IJMC: Cannibalized from SpeXTool version
+         2011-11-25 15:06 IJMC: Added ydisp and fwhm options.
+    -
+    """
+    #pro lightloss, objfile, stdfile, wguide, seeing, outfile,CANCEL=cancel
+
+
+    try:
+        from astropy.io import fits as pyfits
+    except:
+        import pyfits
+    
+    from phot import hms, dms
+
+    r2d = np.rad2deg(1)
+    d2r = np.deg2rad(1.)
+
+    if slitPA<0.0:
+        slitPA += np.pi
+    if slitPA >= np.pi:
+        slitPA -= np.pi
+
+    # --- Compute Parallactic Angle
+    dtheta_obj = slitPA - targetPA
+    #print slitPA, targetPA, dtheta_obj, zenith_angle
+
+    # --- Compute FWHM at each input wavelength
+    diff     = 2.0*1.22e-6*3600.0*r2d*(wobj/teldiam)    # arcsec
+    if fwhm is None:
+        fwhm     = (seeing*(wobj/wguide)**(-0.2))
+
+    fwhm[fwhm < diff] = diff[fwhm < diff]
+
+    if ydisp is None or xdisp is None:
+        # --- Compute Differential Atmospheric Dispersion
+        disp_obj = atmosdisp(wobj, wguide, zenith_angle*r2d, press, temp, \
+                                 water=water, obsalt=obsalt, fco2=fco2)
+
+
+        if ydisp is None:
+            dy_obj   = (disp_obj*np.cos(dtheta_obj)) + dy
+        else:
+            dy_obj = ydisp
+
+        if xdisp is None:
+            dx_obj   = (disp_obj*np.sin(dtheta_obj)) + dx
+        else:
+            dx_obj = xdisp
+
+    else:
+        dx_obj = np.array(xdisp, copy=False)
+        dy_obj = np.array(ydisp, copy=False)
+        if retall:
+            disp_obj = (dy_obj - dy) / np.cos(dtheta_obj)
+
+#    if xdisp is None and ydisp is None:
+#        guide_index = np.abs(wobj - wguide).min() == np.abs(wobj-wguide)
+#        dy = ydisp[guide_index].mean()
+#        dy_obj = ydisp
+#        dx_obj = (dy_obj - dy) * np.tan(dtheta_obj)
+        
+    # --- Compute Relative Fraction of Light contained within the slit
+    slitloss  = slittrans(slitwd, slitht, fwhm, dx_obj, dy_obj)
+
+    if retall:
+        return slitloss, disp_obj, diff, fwhm, dx_obj, dy_obj
+    else:
+        return slitloss
+
+
+[docs]
+def humidpressure(RH, T):
+    """    +
+     NAME:
+         humidpressure
+
+     PURPOSE:
+         To convert relative humidity into a H2O vapor partial pressure
+
+     CATEGORY:
+         Spectroscopy     
+
+     CALLING SEQUENCE:
+         humidpressure(RH, 273.15)
+
+     INPUTS:
+         RH  - relative humidity, in percent
+         T   - temperature, in Kelvin
+
+     OUTPUTS:
+         h2o_pp : water vapor partial pressure, in Pascals
+
+     PROCEDURE:
+         As outlined in Butler (1998): "Precipitable Water at KP", MMA
+         Memo 238 (which refers in turn to Liebe 1989, "MPM - An
+         Atmospheric Millimeter-Wave Propagation Model").  Liebe
+         claims that this relation has an error of <0.2% from -40 C to
+         +40 C.
+
+     EXAMPLE:
+         None
+
+     MODIFICATION HISTORY:
+         2011-10-08 17:08 IJMC: Created.
+    -
+    """
+
+    # units of Pa
+    #theta =  300./T
+    #return 2.408e11 * RH * np.exp(-22.64*theta) * (theta*theta*theta*theta)
+
+    theta_mod = 6792./T  # theta * 22.64
+    return 9.1638 * RH * np.exp(-theta_mod) * \
+        (theta_mod*theta_mod*theta_mod*theta_mod)
+
+
+[docs]
+def runlblrtm(lamrange, pwv=2., zang=0., alt=4.2, co2=390, res=200, dotrans=True, dorad=False,
+              pwv_offset=4.,
+              verbose=False, _save='/Users/ianc/temp/testatmo.mat',
+              _wd='/Users/ianc/proj/atmo/aerlbl_v12.0_package/caltech_wrapper_v02/',
+              scriptname='runtelluric.m', 
+              command = '/Applications/Octave.app/Contents/Resources/bin/octave'):
+    """
+    Run LBLRTM to compute atmospheric transmittance and/or radiance.
+
+    :INPUTS:
+      lamrange : 2-sequence
+        approximate minimum and maximum wavelengths
+
+    :OPTIONS:
+      pwv : float
+        mm of Precipitable Water Vapor above observation site.  If
+        negative, then the value abs(pwv_offset-pwv) will be used instead.
+
+      pwv_offset : float
+        Only used if (pwv < 0); see above for description.
+        
+      zang : float
+        observation angle from zenith, in degrees
+
+      alt : float
+        Observation elevation, in km.
+
+      co2 : float
+        CO2 concentration in ppm by volume.  Concentration is assumed
+        to be uniform throughout the atmosphere.
+
+      res : float
+        approximate spectral resolution desired
+      
+      dotrans : bool
+        compute atmospheric transmittance
+
+      dorad : bool
+        compute atmospheric radiance.  NOT CURRENTLY WORKING
+
+      _save : str
+        path where temporary MAT save file will be stored
+
+      _wd : str
+        path where MATLAB wrapper scripts for LBLRTM are located
+
+      scriptname : str
+        filename for temporary MATLAB/OCTAVE script (saved after exit)
+
+      command : str
+        path to MATLAB/OCTAVE executable
+
+    :OUTPUTS:
+       A 2- or 3-tuple: First element is wavelength in microns, second
+       element is transmittance (if requested). Radiance will (if
+       requested) always be the last element, and in f_nu units: W/cm2/sr/(cm^-1)
+
+    :REQUIREMENTS:
+       SciPy
+
+       OCTAVE or MATLAB
+
+       `LBLRTM  <http://rtweb.aer.com/lblrtm_code.html>`_
+
+       D. Feldman's set of MATLAB `wrapper scripts
+       <http://www.mathworks.com/matlabcentral/fileexchange/6461-lblrtm-wrapper-version-0-2/>`_
+    """
+    # 2011-10-13 13:59 IJMC: Created.
+    # 2011-10-19 23:35 IJMC: Added scipy.__version__ check for MAT IO
+    # 2011-10-25 17:00 IJMC: Added pwv_offset option.
+    # 2011-11-07 08:47 IJMC: Now path(path..) uses _wd input option; call --wd option.
+    # 2012-07-20 21:25 IJMC: Added 'alt' option for altitude.
+    # 2012-09-16 15:13 IJMC: Fixed for 'dotrans' and 'dorad'
+    # 2014-02-17 18:48 IJMC: Specified approximate units of radiance output.
+
+    import os
+    from scipy.io import loadmat
+    from scipy import __version__
+
+    # Define variables:
+    
+    def beforev8(ver):
+        v1, v2, v3 = ver.split('.')
+        if v1==0 and v2<8:
+            before = True
+        else:
+            before = False
+        return before
+
+    if pwv<0:
+        pwv = np.abs(pwv_offset - pwv)
+    #lamrange = [1.12, 2.55] # microns
+    #res = 200;  # lambda/dlambda
+    
+
+    # Try to delete old files, if possible.
+    if os.path.isfile(_save):
+        #try: 
+        #    os.remove(_save)
+        #except:
+        while os.path.isfile(_save):
+            _save = _save.replace('.mat', '0.mat')
+
+    if os.path.isfile(scriptname):
+        #try: 
+        #    os.remove(scriptname)
+        #except:
+        while os.path.isfile(scriptname):
+            scriptname = scriptname.replace('.m', '0.m')
+
+    aerlbl_dir = '/'.join(_wd.split('/')[0:-2]) + '/'
+            
+    # Make the matlab script:
+    matlines = []
+    matlines.append("_path = '%s';\n" % _wd )
+    matlines.append("_save = '%s';\n" % _save )
+    matlines.append("_dir0 = pwd;\n")
+    matlines.append("path(path, '%s')\n" % _wd )
+    matlines.append("lamrange = [%1.5f, %1.5f];\n" % (lamrange[0], lamrange[1]))
+    if verbose:
+        matlines.append("pwd\n")
+        matlines.append("wt = which('telluric_simulator')\n")
+        matlines.append("strfind(path, 'caltech')\n")
+    matlines.append("tran_out = telluric_simulator(lamrange, '--dotran %i', '--dorad %i', '--R %s', '--alt %1.4f', '--pwv %1.4f', '--co2 %1.1f', '--zang %1.3f', '--verbose %i', '--wd %s');\n" % (int(dotrans), int(dorad), res, alt, pwv, co2, zang, verbose, aerlbl_dir) )
+    matlines.append("save('-v6', _save, 'tran_out');\n")
+
+    # Write script to disk, and execute it:
+    f = open(scriptname, 'w')
+    f.writelines(matlines)
+    f.close()
+    os.system('%s %s' % (command, scriptname))
+
+    # Open the MAT file and extract the desired output:
+#    try:
+    if os.path.isfile(_save):
+        mat = loadmat(_save)
+    else:
+        trycount = 1
+        print "Saved file '%s' could not be loaded..." % _save
+        while trycount < 5:
+            os.system('%s %s' % (command, scriptname))
+            try:
+                mat = loadmat(_save)
+                trycount = 10
+            except:
+                trycount += 1
+        if trycount < 10: # never successfully loaded the file.
+            pdb.set_trace()
+
+    if beforev8(__version__):
+        w = mat['tran_out'][0][0].wavelength.ravel()
+    else:
+        w = mat['tran_out']['wavelength'][0][0].ravel()
+    if dotrans:
+        if beforev8(__version__):
+            t = mat['tran_out'][0][0].transmittance.ravel()
+        else:
+            t = mat['tran_out']['transmittance'][0][0].ravel()
+    if dorad:
+        if beforev8(__version__):
+            r = mat['tran_out'][0][0].radiance.ravel()
+        else:
+            r = mat['tran_out']['radiance'][0][0].ravel()
+
+    os.remove(scriptname)
+    os.remove(_save)
+
+    if dotrans:
+        ret = (w, t)
+    else:
+        ret = (w,)
+    if dorad:
+        ret = ret + (r,)
+
+    return ret
+
+[docs]
+def zenith(ra, dec, ha, obs):
+    """ Compute zenith angle (in degrees) for an observation.
+
+    :INPUTS:
+      ra : str
+         Right Ascension of target, in format: HH:MM:SS.SS
+
+      dec : str
+         Declination of target, in format: +ddd:mm:ss
+
+      ha : str
+         Hour Angle of target, in format: +HH:MM:SS.SS
+
+      obs : str
+         Name of observatory site (keck, irtf, lick, lapalma, ctio,
+         andersonmesa, mtgraham, kpno) or a 3-tuple containing
+         (longitude_string, latitude_string, elevation_in_meters)
+
+         
+    :OUTPUTS:
+      Zenith angle, in degrees, for the specified observation
+
+    :REQUIREMENTS:
+      :doc:`phot`
+
+      Numpy
+         """
+    # 2011-10-14 09:43 IJMC: Created
+
+    import phot
+
+    #observer = ephem.Observer()
+    if obs=='lick':
+        obs_long, obs_lat = '-121:38.2','37:20.6'
+        obs_elev = 1290
+    elif obs=='keck':
+        obs_long, obs_lat = '-155:28.7','19:49.7'
+    obs_elev = 4160
+    elif obs=='irtf':
+        obs_long, obs_lat = '-155:28:21.3', '19:49:34.8'
+    obs_elev = 4205
+    elif obs=='lapalma':
+        obs_long, obs_lat = '17:53.6','28:45.5'
+    obs_elev = 4160
+    elif obs=='ctio':
+        obs_long, obs_lat = '-70:48:54','-30:9.92'
+    obs_elev = 2215
+    elif obs=='andersonmesa':  # 
+        obs_long, obs_lat = '-111:32:09', '30:05:49'
+    obs_elev = 2163
+    elif obs=='mtgraham':
+        obs_long, obs_lat = '-109:53:23', '32:42:05'
+        obs_elev = 3221
+    elif obs=='kpno':
+        obs_long, obs_lat = '-111:25:48', '31:57:30'
+        obs_elev = 2096
+    elif len(obs)==3:
+        obs_long, obs_lat, obs_elev = obs
+    else:
+        print "Unknown or imparseable observatory site."
+        return -1
+
+    lat = phot.dms(obs_lat) * np.pi/180.
+    long = phot.dms(obs_long) * np.pi/180.
+    ra = (phot.hms(ra))  * np.pi/180.
+    dec= (phot.dms(dec)) * np.pi/180.
+
+    # Compute terms for coordinate conversion
+    if hasattr(ha, '__iter__'):
+        zang = []
+        for ha0 in ha:
+            har = (phot.hms(ha0))  * np.pi/180.
+            term1 = np.sin(lat)*np.sin(dec)+np.cos(lat)*np.cos(dec)*np.cos(har)
+            term2 = np.cos(lat)*np.sin(dec)-np.sin(lat)*np.cos(dec)*np.cos(har)
+            term3 = -np.cos(dec)*np.sin(har)
+            rad = np.abs(term3 +1j*term2)
+            az = np.arctan2(term3,term2)
+            alt = np.arctan2(term1, rad)
+            zang.append(90. - (alt*180./np.pi))
+    else:
+        har = (phot.hms(ha0))  * np.pi/180.
+        term1 = np.sin(lat)*np.sin(dec)+np.cos(lat)*np.cos(dec)*np.cos(har)
+        term2 = np.cos(lat)*np.sin(dec)-np.sin(lat)*np.cos(dec)*np.cos(har)
+        term3 = -np.cos(dec)*np.sin(har)
+        rad = np.abs(term3 +1j*term2)
+        az = np.arctan2(term3,term2)
+        alt = np.arctan2(term1, rad)
+        zang = 90. - (alt*180./np.pi)
+        
+    ## Compute airmass
+    #z = pi/2. - alt
+    #airmass = 1./(np.cos(z) + 0.50572*(96.07995-z*180./pi)**(-1.6364))
+    
+    return zang
+
+
+
+
+[docs]
+def spexsxd_scatter_model(dat, halfwid=48, xlims=[470, 1024], ylims=[800, 1024], full_output=False, itime=None):
+    """Model the scattered light seen in SpeX/SXD K-band frames.
+
+    :INPUTS:
+       dat : str or numpy array
+         filename of raw SXD frame to be corrected, or a Numpy array
+         containing its data.
+
+    :OPTIONS:
+       halfwid : int
+         half-width of the spectral orders.  Experience shows this is
+         approximately 48 pixels.  This value is not fit!
+
+       xlims : list of length 2
+         minimum and maximum x-pixel values to use in the fitting
+
+       ylims : list of length 2
+         minimum and maximum y-pixel values to use in the fitting
+
+       full_output : bool
+         whether to output only model, or the tuple (model, fits, chisq, nbad)
+
+       itime : float
+         integration time, in seconds, with which to scale the initial
+         guesses
+
+    :OUTPUT:
+       scatter_model : numpy array
+         Model of the scattered light component, for subtraction or saving.
+
+      OR:
+
+       scatter_model, fits, chis, nbad
+
+    :REQUIREMENTS:
+       :doc:`pyfits`, :doc:`numpy`, :doc:`fit_atmo`, :doc:`analysis`, :doc:`phasecurves`
+
+    :TO_DO_LIST:
+       I could stand to be more clever in modeling the scattered light
+       components -- perhaps fitting for the width, or at least
+       allowing the width to be non-integer.
+    """
+    # 2011-11-10 11:10 IJMC: Created
+
+    import analysis as an
+    import phasecurves as pc
+
+    try:
+        from astropy.io import fits as pyfits
+    except:
+        import pyfits
+    
+
+    ############################################################
+    # Define some helper functions:
+    ############################################################
+
+
+
+    def tophat(param, x):
+        """Grey-pixel tophat function with set width
+        param: [cen_pix, amplitude, background]
+        x : must be array of ints, arange(0, size-1)
+        returns the model."""     
+        # 2011-11-09 21:37 IJMC: Created
+        intpix, fracpix = int(param[0]), param[0] % 1
+        th = param[1] * ((-halfwid <= (x - intpix)) * ((x - intpix) < halfwid))
+        #        th =  * th.astype(float)
+        if (intpix >= halfwid) and ((intpix - halfwid) < x.size):
+            th[intpix - halfwid] = param[1]*(1. - fracpix)
+        if (intpix < (x.size - halfwid)) and ((intpix + halfwid) >= 0):
+            th[intpix + halfwid] = param[1]*fracpix
+        return th + param[2]
+
+    def tophat2g(param, x, p0prior=None):
+        """Grey-pixel double-tophat plus gaussian
+        param: [cen_pix1, amplitude1, cen_pix2, amplitude2, g_area, g_sigma, g_center, background]
+        x : must be ints, arange(0, size-1)
+        returns the model."""     # 2011-11-09 21:37 IJMC: Created
+        #th12 = 
+        #th2 = 
+        #gauss = 
+#        if p0prior is not None:
+#            penalty = 
+        return tophat([param[0], param[1], 0], x) + \
+            tophat([param[2], param[3], 0], x) + \
+            gaussian(param[4:7], x) + param[7]
+
+    ############################################################
+    # Parse inputs
+    ############################################################
+    halfwid = int(halfwid)
+    if isinstance(dat, np.ndarray):
+        if itime is None: 
+            itime = 1.
+    else:
+        if itime is None:
+            try:
+                itime = pyfits.getval(dat, 'ITIME')
+            except:
+                itime = 1.
+        dat = pyfits.getdata(dat)
+
+    nx, ny = dat.shape
+
+    scatter_model = np.zeros((nx, ny), dtype=float)
+    chis, fits, nbad = [], [], []
+    iivals = np.arange(xlims[1]-1, xlims[0], -1, dtype=int)
+
+    position_offset = 850 - ylims[0]
+    est_coefs = np.array([ -5.02509772e-05,   2.97212397e-01,  -7.65702234e+01])
+    estimated_position = np.polyval(est_coefs, iivals) + position_offset
+    estimated_error = 0.5
+
+    # to hold scattered light position fixed, rather than fitting for
+    # that position, uncomment the following line:
+    #holdfixed = [0] 
+    holdfixed = None
+
+    ############################################################
+    # Start fitting
+    ############################################################
+    for jj, ii in enumerate(iivals):
+        col = dat[ylims[0]:ylims[1], ii]
+        ecol = np.ones(col.size, dtype=float)
+        x = np.arange(col.size, dtype=float)
+        if len(fits)==0:
+            all_guess = [175 + position_offset, 7*itime, \
+                             70 + position_offset, 7*itime, \
+                             250*itime, 5, 89 + position_offset, 50]
+        else:
+            all_guess = fits[-1]
+        all_guess[0] = estimated_position[jj]
+        model_all = tophat2g(all_guess, x)
+        res = (model_all - col)
+        badpix = np.abs(res) > (4*an.stdr(res, nsigma=4))
+        ecol[badpix] += 1e9
+
+        fit = an.fmin(pc.errfunc, all_guess, args=(tophat2g, x, col, 1./ecol**2), full_output=True, maxiter=1e4, maxfun=1e4, disp=False, kw=dict(testfinite=False), holdfixed=holdfixed)
+        best_params = fit[0].copy()
+        res = tophat2g(best_params, x) - col
+        badpix = np.abs(res) > (4*an.stdr(res, nsigma=4))
+        badpix[((np.abs(np.abs(x - best_params[0]) - 48.)) < 2) + \
+                   ((np.abs(np.abs(x - best_params[2]) - 48.)) < 2)] = False
+        badpix += (np.abs(res) > (20*an.stdr(res, nsigma=4)))
+        ecol = np.ones(col.size, dtype=float)
+        ecol[badpix] += 1e9
+        best_chisq = pc.errfunc(best_params, tophat2g, x, col, 1./ecol**2)
+
+        # Make sure you didn't converge on the wrong model:
+        for this_offset in ([-2, 0, 2]):
+            this_guess = fit[0].copy()
+            this_guess[2] += this_offset
+            this_guess[0] = estimated_position[jj]
+            #pc.errfunc(this_guess, tophat2g, x, col, 1./ecol**2)
+            this_fit = an.fmin(pc.errfunc, this_guess, args=(tophat2g, x, col, 1./ecol**2), full_output=True, maxiter=1e4, maxfun=1e4, disp=False, kw=dict(testfinite=False), holdfixed=holdfixed)
+            #print this_offset1, this_offset2, this_fit[1]
+            if this_fit[1] < best_chisq:
+                best_chisq = this_fit[1]
+                best_params = this_fit[0].copy()
+
+        fits.append(best_params)
+        chis.append(best_chisq)
+        nbad.append(badpix.sum())
+        mod2 = tophat2g(best_params, x)
+        scatter_model[ylims[0]:ylims[1], ii]  = tophat(list(best_params[0:2])+[0], x)
+
+    if full_output:
+        return scatter_model, fits, chis, nbad
+    else:
+        return scatter_model
+
+
+
+[docs]
+def spexsxd_scatter_fix(fn1, fn2, **kw):
+    """ Fix scattered light in SpeX/SXD K-band and write a new file.
+
+    :INPUTS:
+       fn1 : str
+         file to be fixed
+
+       fn2 : str
+         new filename of fixed file.
+
+    :OPTIONS:
+       clobber : bool
+          whether to overwrite existing FITS files
+       
+       Other options will be passed to :func:`spexsxd_scatter_model`
+
+    :OUTPUTS:
+       status : int
+         0 if a problem, 1 if everything is Okay
+    """
+    # 2011-11-10 13:34 IJMC: Created
+
+
+    try:
+        from astropy.io import fits as pyfits
+    except:
+        import pyfits
+    
+
+    if kw.has_key('clobber'):
+        clobber = kw.pop('clobber')
+    else:
+        clobber = False
+
+    try:
+        dat0 = pyfits.getdata(fn1)
+        hdr0 = pyfits.getheader(fn1)
+    except:
+        print "Could not read one of data or header from %s" % fn1
+        return 0
+
+    try:
+        hdr0.update('SCAT_FIX', 1, 'SXD Scattered light fixed by spexsxd_scatter_fix()')
+    except:
+        print "Could not update header properly."
+        return 0
+
+    try:
+        if hdr0.has_key('ITIME') and not kw.has_key('itime'):
+            kw['itime'] = hdr0['ITIME']
+        mod = spexsxd_scatter_model(dat0, **kw)
+    except:
+        print "Could not not model scattered light with spexsxd_scatter_model()"
+        return 0
+
+    try:
+        if not isinstance(mod, np.ndarray):
+            mod = mod[0]
+        pyfits.writeto(fn2, dat0 - mod, header=hdr0, clobber=clobber, output_verify='ignore')
+    except:
+        print "Could not write updated file to %s; clobber is %s" % (fn2, clobber)
+        return 0
+
+    return 1
+    
+        
+    
+
+[docs]
+def tophat2(param, x):
+    """Grey-pixel tophat function with set width
+    param: [cen_pix, amplitude, background]
+    newparam: [amplitude, full width, cen_pix, background]
+    x : must be array of ints, arange(0, size-1)
+    returns the model."""     
+    # 2011-11-09 21:37 IJMC: Created
+    import analysis as an
+    
+    oversamp = 100.
+
+    x2 = np.linspace(x[0], x[-1], (x.size - 1)*oversamp + 1)
+    halfwid = np.round(param[1] / 2.).astype(int)
+    halfwid = param[1] / 2.
+    intpix, fracpix = int(param[2]), param[2] % 1
+    intwid, fracwid = int(halfwid), halfwid % 1
+    th2 = 0.0 + param[0] * ((-halfwid <= (x2 - param[2])) * ((x2 - param[2]) < halfwid))
+
+    th = an.binarray(np.concatenate((th2, np.zeros(oversamp-1))), oversamp)
+
+    return th + param[3]
+
+
+[docs]
+def tophat_alt(param, x):
+    """Standard tophat function (alternative version).
+
+    :INPUTS:
+      p : sequence
+        p[0] -- Amplitude
+        p[1] -- full width dispersion
+        p[2] -- central offset (mean location)
+        p[3] -- vertical offset (OPTIONAL)
+
+      x : scalar or sequence
+        values at which to evaluate function
+
+    :OUTPUTS:
+      y : scalar or sequence
+        1.0 where |x| < 0.5, 0.5 where |x| = 0.5, 0.0 otherwise.
+        """
+    # 2012-04-11 12:50 IJMC: Created
+    if len(param)==3:
+        vertical_offset = 0.
+    elif len(param)>3:
+        vertical_offset = param[3]
+    else:
+        print "Input `param` to function `tophat` requires length > 3."
+        return -1
+
+    amplitude, width, center = param[0:3]
+
+    absx = np.abs((x - center) )
+    if hasattr(x, '__iter__'):
+        ret = np.zeros(x.shape, float)
+
+        #ind1 = absx < (0.5*width)
+        #ret[ind1] = 1.0
+
+        i0 = np.searchsorted(x, center-width/2)
+        i1 = np.searchsorted(x, center+width/2)
+        ret[i0:i1] = 1.
+
+        #ind2 = absx ==(0.5*width)
+        #ret[ind2] = 0.5
+
+    else:
+        if absx < 0.5:
+            ret = 1.0
+        elif absx==0.5:
+            ret = 0.5
+        else:
+            ret = 0.
+
+    return amplitude * ret + vertical_offset
+
+
+[docs]
+def model_resel(param, x):
+    """Model a spectral resolution element.
+
+    :INPUTS:
+      param : sequence
+      
+        param[0, 1, 2] - amplitude, sigma, and central location of
+        Gaussian line profile (cf. :func:`analysis.gaussian`).
+
+        param[3, 4, 5] - amplitude, width, and central location of
+        top-hat-like background (cf. :func:`tophat`).
+
+        param[6::] - additional (constant or polynomial) background
+        components, for evaluation with :func:`numpy.polyval`
+
+      x : sequence
+        Values at which to evaluate model function (i.e., pixels).
+        Typically 1D.
+    
+    :OUTPUTS:
+      line : NumPy array
+         model of the resolution element, of same shape as `x`.
+
+    :DESCRIPTION:
+      
+      Model a spectral resolution element along the spatial
+      direction.  This consists of a (presumably Gaussian) line
+      profile superimposed on the spectral trace's top-hat-like
+      background, with an additional constant (or polynomial)
+      out-of-echelle-order background component.
+    """
+    # 2012-04-11 12:57 IJMC: Created
+    # 2012-04-12 13:29 IJMC: Try to be more clever and save time in
+    #                        the back2 polyval call.
+
+    lineprofile = gaussian(param[0:3], x)
+    back1 = tophat(param[3:6], x)
+
+    if hasattr(param[6::], '__iter__') and len(param[6::])>1:
+        back2 = np.polyval(param[6::], x)
+    else:
+        back2 = param[6::]
+
+    return lineprofile + back1 + back2
+
+[docs]
+def add_partial_pixel(x0, y0, z0, z):
+    """
+    :INPUTS:
+      x0 : int or sequence of ints
+        first index of z at which data will be added
+
+      y0 : int or sequence of ints
+        second index of z at which data will be added
+
+      z0 : scalar or sequence
+        values which will be added to z
+
+      z : 2D NumPy array
+        initial data, to which partial-pixels will be added
+    """
+    # 2012-04-11 14:24 IJMC: Created
+    nx, ny = z.shape[0:2]
+    x = np.arange(nx)
+    y = np.arange(ny)
+    dx, dy = 1, 1
+    ret = np.array(z, copy=True)
+
+    if hasattr(x0, '__iter__'):
+        if len(x0)==len(y0) and len(x0)==len(z0):
+            for x00, y00, z00 in zip(x0, y0, z0):
+                ret = add_partial_pixel(x00, y00, z00, ret)
+        else:
+            print "Inputs x0, y0, and z0 must have the same length!"
+            ret = -1
+
+            if False:
+                if hasattr(x0, '__iter__'):
+                    x0 = np.array(x0, copy=False)
+                    y0 = np.array(y0, copy=False)
+                    z0 = np.array(z0, copy=False)
+                else:
+                    x0 = np.array([x0])
+                    y0 = np.array([y0])
+                    z0 = np.array([z0])
+
+                ix0 = np.tile(np.vstack((np.floor(x0), np.floor(x0)+1)).astype(int), (2,1))
+                iy0 = np.tile(np.vstack((np.floor(y0), np.floor(y0)+1)).astype(int), (2,1))
+
+                xfrac = x0 % 1
+                yfrac = y0 % 1
+
+                weights0 = np.vstack([(1. - xfrac) * (1. - yfrac), \
+                               xfrac * (1. - yfrac), \
+                               (1. - xfrac) *  yfrac, \
+                               xfrac * yfrac])
+
+                for ii in range(ix0.shape[1]):
+                    print ii, weights0[:,ii]*z0[ii]
+                    ret[ix0[:,ii], iy0[:,ii]] = ret[ix0[:,ii], iy0[:,ii]] + weights0[:,ii]*z0[ii]
+
+    else:
+        ix0 = map(int, [np.floor(x0), np.floor(x0)+1]*2)
+        iy0 = map(int, [np.floor(y0)]*2 + [np.floor(y0)+1]*2)
+
+        xfrac = x0 % 1
+        yfrac = y0 % 1
+        weights0 = [(1. - xfrac) * (1. - yfrac), \
+                       xfrac * (1. - yfrac), \
+                       (1. - xfrac) *  yfrac, \
+                       xfrac * yfrac]
+        ix = []
+        iy = []
+        weights = []
+        for ii in range(4):
+            #print ix0[ii], iy0[ii], weights0[ii]
+            if ix0[ii]>=0 and ix0[ii]<nx and iy0[ii]>=0 and iy0[ii]<ny:
+                ix.append(ix0[ii]), iy.append(iy0[ii])
+                weights.append(weights0[ii])
+
+        npix = len(ix)
+        if npix>0:
+            sumweights = sum(weights)
+            for ii in range(npix):
+                #print ix[ii], iy[ii], weights[ii]
+                ret[ix[ii], iy[ii]] += weights[ii] * z0
+
+
+    return ret
+
+
+[docs]
+def modelSpectralTrace(param, shape=None, nscat=None, npw=None, npy=None, noy=None, now=None, ndist=None, x=None, y=None, transpose=False):
+    """Model a raw spectral trace!
+
+    :INPUTS:
+
+    NOTE that most inputs should be in the _rectified_ frame.
+    
+      Trace background pedestal level : 1D array
+
+      Width of background pedestarl level : scalar (for now)
+
+      Center of trace : 1D array
+
+      Offset of object spectrum, relative to center : scalar
+
+      width of 1D PSF : scalar
+
+      Area of 1D psf : 1D array
+
+      Distortion (x and y, somehow???)
+
+      Scattered light background : scalar (for now)
+    """
+    # 2012-04-11 17:50 IJMC: Created
+
+    ########################################
+    # Parse various inputs:
+    ########################################
+    param = np.array(param, copy=False)
+    nx, ny = shape
+
+    # Construct pixel vectors:
+    if x is None:
+        x = np.arange(nx)
+    if y is None:
+        y = np.arange(ny)
+
+    def makevec(input):
+        if not hasattr(input, '__iter__'):
+            ret = [input]*ny 
+            #ret = np.polyval([input], y)
+        elif len(input)==1:
+            ret = [input[0]]*ny 
+            #ret = np.polyval(input, y)
+        else:
+            ret = np.polyval(input, y)
+        return ret
+
+    # Define all the parameters:
+    pedestal_level = param[0:ny]
+    obj_flux = param[ny:ny*2]
+    pedestal_width = param[ny*2:ny*2+npw]
+    obj_fwhm = param[ny*2+npw:ny*2+npw+now]
+    spectrum_yparam = param[ny*2+npw+now : ny*2+npw+now+npy]
+    obj_yparam = param[ny*2+npw+now+npy : ny*2+npw+now+npy+noy]
+    disp_distort = param[ny*2+npw+now+npy+noy : ny*2+npw+now+npy+noy+ndist]
+    scat_param = list(param[-ndist:])
+
+    
+    pedestal_width = makevec(pedestal_width)
+    obj_fwhm = makevec(obj_fwhm)
+    obj_yloc = makevec(obj_yparam) #, mode='cheby')
+    spectrum_yloc = makevec(spectrum_yparam) #, mode='cheby')
+
+    ########################################
+    # Generate spectral model
+    ########################################
+
+    rectified_model = np.zeros((nx, ny), float)
+
+    for ii in range(ny):
+        obj_param = [obj_flux[ii], obj_fwhm[ii], obj_yloc[ii]]
+        bkg_param = [pedestal_level[ii], pedestal_width[ii], spectrum_yloc[ii]]
+        oneDparam = obj_param + bkg_param + scat_param
+        rectified_model[:, ii] =  model_resel(oneDparam, x)
+
+        #ypts = x
+        #xpts = ii + np.polyval(disp_distort, x-obj_yloc[ii])
+        #distorted_model = spec.add_partial_pixel(ypts, xpts, oneD, model)
+
+    interp_model = 0*rectified_model
+
+    if False:
+        # One way to do it:
+        newxcoords = (y - np.polyval(disp_distort, (x.reshape(nx,1)-obj_yloc.reshape(1,ny))))
+
+        meshmod = interpolate.RectBivariateSpline(x, y, rectified_model, kx=1, ky=1, s=0.)
+        for ii in range(nx):
+            interp_model[ii,:] = meshmod(x[ii], newxcoords[ii,:])
+
+    else:
+        # An alternate (not entirely equivalanet) approach, about equally fast:
+        shifts = np.polyval(disp_distort, x)
+        intshifts = np.floor(shifts)
+        minshift = intshifts.min()
+        shifts -= minshift
+        intshifts -= minshift
+
+        for ii in range(nx):
+            kern = np.zeros(intshifts.max() - intshifts.min()+2, float)
+            kern[intshifts[ii]] += 1. - (shifts[ii] - intshifts[ii])
+            kern[intshifts[ii]+1] += (shifts[ii] - intshifts[ii])
+            interp_model[ii] = np.convolve(rectified_model[ii], kern, 'same')
+
+
+    return interp_model
+
+
+[docs]
+def makeSpexSlitlessSky(skyfns, scatcen=[980, 150], scatdim=[60, 300]):
+    """
+    Generate a normalized Sky frame from SpeX slitless spectroscopy data.
+
+    :INPUTS:
+      skyfns : list of strs
+        Filenames of slitless-spectroscopy sky frames
+        
+      scatcen : 2-sequence
+        center of region to use in median-normalizing the frame.
+
+      scatdim : 2-sequence
+        full width of region to use in median-normalizing the frame.
+
+    :OUTPUTS:
+      (sky, skyHeader)
+    """
+    # 2012-04-19 09:38 IJMC: Created
+    import phot
+
+    nsky = len(skyfns)
+
+    skyscat = phot.subreg2(skyfns, center=scatcen, dim=scatdim)
+    normfact = np.median(skyscat.reshape(nsky, np.prod(scatdim)), 1)
+
+    hdr = pyfits.getheader(skyfns[0])
+
+    skyframe = np.zeros((hdr['naxis1'], hdr['naxis2']), float)
+    for skyfn, factor in zip(skyfns, normfact):
+        skyframe += (pyfits.getdata(skyfn) / factor / nsky)
+
+    hdr.update('SKYRGCEN', str(scatcen))
+    hdr.update('SKYRGDIM', str(scatdim))
+    hdr.update('SKYNOTE', 'sky frame, median=1 in SKYRG')
+
+    return skyframe, hdr
+    
+
+
+[docs]
+def resamplespec(w1, w0, spec0, oversamp=100):
+    """
+    Resample a spectrum while conserving flux density.
+
+    :INPUTS:
+      w1 : sequence
+        new wavelength grid (i.e., center wavelength of each pixel)
+
+      w0 : sequence
+        old wavelength grid (i.e., center wavelength of each pixel)
+
+      spec0 : sequence
+        old spectrum (e.g., flux density or photon counts)
+
+      oversamp : int
+        factor by which to oversample input spectrum prior to
+        rebinning.  The worst fractional precision you achieve is
+        roughly 1./oversamp.
+
+    :NOTE: 
+      Format is the same as :func:`numpy.interp`
+      
+    :REQUIREMENTS:
+      :doc:`tools`
+
+    """
+    from tools import errxy
+
+    # 2012-04-25 18:40 IJMC: Created
+    nlam = len(w0)
+    x0 = np.arange(nlam, dtype=float)
+    x0int = np.arange((nlam-1.)*oversamp + 1., dtype=float)/oversamp
+    w0int = np.interp(x0int, x0, w0)
+    spec0int = np.interp(w0int, w0, spec0)/oversamp
+
+    # Set up the bin edges for down-binning
+    maxdiffw1 = np.diff(w1).max()
+    w1bins = np.concatenate(([w1[0] - maxdiffw1], 
+                             .5*(w1[1::] + w1[0:-1]), \
+                                 [w1[-1] + maxdiffw1]))
+    # Bin down the interpolated spectrum:
+    junk, spec1, junk2, junk3 = errxy(w0int, spec0int, w1bins, xmode=None, ymode='sum', xerr=None, yerr=None)
+
+    return spec1
+
+
+
+
+#wcoords = spec.wavelengthMatch(mastersky0, wsky, thissky, ethissky, guess=wcoef1, order=None)
+
+[docs]
+def wavelengthMatch(spectrum, wtemplate, template, etemplate, guess=None, nthread=1):
+    """
+    Determine dispersion solution for a spectrum, from a template.
+
+    :INPUTS:
+      spectrum : 1D sequence
+        Spectrum for which a wavelength solution is desired.
+
+      wtemplate : 1D sequence
+        Known wavelength grid of a template spectrum.
+
+      template : 1D sequence
+        Flux (e.g.) levels of the template spectrum with known
+        wavelength solution.
+
+      etemplate : 1D sequence
+        Uncertainties on the template values.  This can be important
+        in a weighted fit!
+
+    :OPTIONS:
+      guess : sequence
+        Initial guess for the wavelength solution.  This is very
+        helpful, if you have it!  The guess should be a sequence
+        containing the set of Chebychev polynomial coefficients,
+        followed by a scale factor and DC offset (to help in scaling
+        the template).
+
+        If guess is None, attempt to fit a simple linear dispersion relation.
+
+      order : int > 0
+        NOT YET IMPLEMENTED! BUT EVENTUALLY: if guess is None, this
+        sets the polynomial order of the wavelength solution.
+
+        FOR THE MOMENT: if guess is None, return a simple linear
+        solution.  This is likely to fail entirely for strongly
+        nonlinear dispersion solutions or poorly mismatched template
+        and spectrum.
+
+      nthread : int > 0
+        Number of processors to use for MCMC searching.
+
+    :RETURNS:
+      (wavelength, wavelength_polynomial_coefficients, full_parameter_set)
+
+    :NOTES:
+      This implementation uses a rather crude MCMC sampling approach
+      to sample parameters space and 'home in' on better solutions.
+      There is probably a way to do this that is both faster and more
+      optimal...
+
+      Note that if 'spectrum' and 'template' are of different lengths,
+      the longer one will be trimmed at the end to make the lengths match.
+
+    :REQUIREMENTS:
+      `emcee  <http://TBD>`_
+      
+    """
+    #2012-04-25 20:53 IJMC: Created
+    # 2012-09-23 20:17 IJMC: Now spectrum & template can be different length.
+    # 2013-03-09 17:23 IJMC: Added nthread option
+
+    import emcee
+    import phasecurves as pc
+
+    nlam_s = len(spectrum)
+    nlam_t = len(template)
+
+    if nlam_s <= nlam_t:
+        template = np.array(template, copy=True)[0:nlam_s]
+        etemplate = np.array(etemplate, copy=True)[0:nlam_s]
+        nlam = nlam_s
+        spectrum_trimmed = False        
+    else: # nlam_s > nlam_t:
+        spectrum0 = np.array(spectrum, copy=True)
+        spectrum = spectrum0[0:nlam_t]
+        wtemplate = np.array(wtemplate, copy=True)[0:nlam_t]
+        nlam = nlam_t
+        spectrum_trimmed = True
+
+    # Create a normalized vector of coordinates for computing
+    # normalized polynomials:
+    dx0 = 1. / (nlam - 1.)
+    x0n = dx0 * np.arange(nlam) - 1.
+    #x0n = 2*np.arange(nlam, dtype=float) / (nlam - 1.) - 1
+
+
+
+    if guess is None:
+        # Start with a simple linear wavelength solution.
+        guess = [np.diff(wtemplate).mean() * len(template)/2, np.mean(wtemplate), np.median(template)/np.median(spectrum), 1.]
+    
+    # Define arguments for use by fitting routines:
+    fitting_args = (makemodel, x0n, spectrum, wtemplate, template, 1./etemplate**2)
+
+    # Try to find an initial best fit:
+    bestparams = an.fmin(pc.errfunc, guess, args=fitting_args, disp=False)
+    
+    # Initial fit is likely a local minimum, so explore parameter
+    # space using an MCMC approach.
+    ndim = len(guess)
+    nwalkers = ndim * 50
+    sampler = emcee.EnsembleSampler(nwalkers, ndim, pc.lnprobfunc, args=fitting_args, threads=nthread)
+
+    # Initialize the sampler with various starting positions:
+    e_params1 = np.vstack((np.array(guess)/10., np.zeros(ndim) + .01)).max(0)
+    e_params2 = np.vstack((bestparams/10., np.zeros(ndim) + .01)).max(0)
+    p0 = np.vstack(([guess, bestparams], \
+           [np.random.normal(guess, e_params1) for ii in xrange(nwalkers/2-1)], \
+           [np.random.normal(bestparams, e_params2) for ii in xrange(nwalkers/2-1)]))
+
+    # Run the sampler for a while:
+    pos, prob, state = sampler.run_mcmc(p0, 300) # Burn-in
+
+    bestparams = sampler.flatchain[np.nonzero(sampler.lnprobability.ravel()==sampler.lnprobability.ravel().max())[0][0]]
+    
+    # Optimize the latest set of best parameters.
+    bestparams = an.fmin(pc.errfunc, bestparams, args=fitting_args, disp=False)
+
+    dispersionSolution = bestparams[0:-2]
+    if spectrum_trimmed:
+        x0n_original = dx0 * np.arange(nlam_s) - 1.
+        wavelengths = np.polyval(dispersionSolution, x0n_original)
+    else:
+        wavelengths = np.polyval(dispersionSolution, x0n)
+
+    return dispersionSolution, wavelengths, bestparams
+
+[docs]
+def makemodel(params, xvec, specvec, wtemplate):
+    """Helper function for :func:`wavelengthMatch`: generate a scaled,
+    interpolative model of the template."""
+    wcoef = params[0:-2]
+    scale, offset = params[-2::]
+    neww = np.polyval(wcoef, xvec)
+    return offset + scale * np.interp(wtemplate, neww, specvec, left=0, right=0)
+
+
+[docs]
+def normalizeSpecFlat(flatdat, nspec=1, minsep=50, median_width=51, readnoise=40, badpixelmask=None, traces=None):
+    """Trace and normalize a spectroscopic flat field frame.
+
+    :INPUTS:
+      flatdat : 2D NumPy array
+        Master, unnormalized flat frame: assumed to be measured in
+        photoelectrons (for computing uncertainties).
+
+      nspec : int
+        Number of spectral orders to find and normalize
+
+      minsep : int
+        Minimum separation, in pixels, between spectral orders that
+        will be found.
+
+      median_width : int
+        Width of median-filter kernel used to compute the low-
+
+      readnoise : scalar
+        Detector read noise, in electrons.  For computing uncertainties.
+
+      badpixelmask : 2D NumPy array
+        bad pixel mask: 1 at bad pixel locations, 0 elsewhere.
+
+      traces : 2D NumPy Array 
+        (nord, pord) shaped numpy array representing the polynomial
+         coefficients for each order (suitable for use with
+         np.polyval), as produced by :func:`traceorders`
+    """
+    # 2012-04-28 06:22 IJMC: Created
+    # 2012-07-24 21:04 IJMC: Now, as a final step, all bad indices are set to unity.
+    # 2014-12-17 20:07 IJMC: Added 'traces' option
+
+    import analysis as an
+    from scipy import signal
+
+    if badpixelmask is None:
+        badpixelmask = np.zeros(flatdat.shape, bool)
+
+    # Enforce positivity and de-weight negative flux values:
+    e_flatdat = np.sqrt(flatdat + readnoise**2)
+    badindices = ((flatdat<=0) + badpixelmask).nonzero()
+    e_flatdat[badindices] = flatdat[badindices] * 1e9
+    flatdat[badindices] = 1.
+
+    # Find spectral orders, using derivatives (will probably fail if
+    # spec. overlaps the edge!):
+    ordvec = an.meanr(flatdat, axis=1, nsigma=3)
+
+    filtvec = signal.medfilt(ordvec, 9)
+    dvec1 = np.diff(filtvec)
+    dvec2 = -np.diff(filtvec)
+    dvec1[dvec1<0] = 0.
+    dvec2[dvec2<0] = 0.
+
+    x1 = np.arange(dvec1.size)
+    available1 = np.ones(dvec1.size, dtype=bool)
+    available2 = np.ones(dvec1.size, dtype=bool)
+
+    pos1 = []
+    pos2 = []
+    for ii in range(nspec):
+        thisx1 = x1[dvec1==dvec1[available1].max()][0]
+        available1[np.abs(x1 - thisx1) < minsep] = False
+        pos1.append(thisx1)
+        thisx2 = x1[dvec2==dvec2[available2].max()][0]
+        available2[np.abs(x1 - thisx2) < minsep] = False
+        pos2.append(thisx2)
+
+    limits = np.array(zip(np.sort(pos1), np.sort(pos2)))
+    # Generate and normalize the spectral traces:
+    masterflat = np.ones(flatdat.shape, dtype=float)
+
+    if traces is not None:
+        nx = flatdat.shape[1]
+        xvec = np.arange(nx)
+        ymat = np.tile(np.arange(flatdat.shape[0]), (nx, 1)).T
+
+    for ii in range(nspec):
+        if traces is None:
+            profvec = np.median(flatdat[limits[ii,0]:limits[ii,1], :], axis=0)
+            e_profvec = np.sqrt(an.wmean(flatdat[limits[ii,0]:limits[ii,1], :], 1./e_flatdat[limits[ii,0]:limits[ii,1], :]**2, axis=0) / np.diff(limits[ii]))[0]
+            e_profvec[e_profvec <= 0] = profvec.max()*1e9
+            smooth_prof = signal.medfilt(profvec, median_width)
+            masterflat[limits[ii,0]:limits[ii,1], :] = flatdat[limits[ii,0]:limits[ii,1], :] / smooth_prof
+        else:
+            traceloc = np.polyval(traces[ii], xvec)
+            limind = (limits[:,1] > traceloc.mean()).nonzero()[0][0]
+            order_ind_2d = ((ymat - traceloc) > (limits[limind,0] - traceloc.mean())) * ((ymat - traceloc) < (limits[limind,1] - traceloc.mean()))
+            profvec = np.array([np.median(flatdat[order_ind_2d[:,jj], jj]) for jj in xrange(nx)])
+            smooth_prof = signal.medfilt(profvec, median_width)
+            for jj in xrange(nx):
+                masterflat[order_ind_2d[:,jj],jj] = flatdat[order_ind_2d[:,jj],jj] / smooth_prof[jj]
+                
+
+        # Ideally, we would do some sort of weighted fitting here.  Instead,
+        # for now, just take a running median:
+
+    masterflat[badindices] = 1.
+
+    return masterflat
+
+
+[docs]
+def optspecextr_idl(frame, gain, readnoise, x1, x2, idlexec, clobber=True, tempframefn='tempframe.fits', specfn='tempspec.fits', scriptfn='temp_specextract.pro', IDLoptions="adjfunc='adjgauss', adjoptions={center:1,centerfit:1,centerdeg:3}, bgdeg=3", inmask=None):
+    """Run optimal spectral extraction in IDL; pass results to Python.
+
+    :INPUTS:
+      frame : str
+        filename, or 2D Numpy Array, or list of filenames containing
+        frames from which spectra will be extracted.  This should be
+        in units of ADU (not electrons) for the noise properties to
+        come out properly.  
+
+        Also, the spectral trace must run vertically across the frame.
+
+      gain : scalar
+        Detector gain, in electrons / ADU
+
+      readnoise : scalar
+        Detector read noise, in electrons
+
+      x1, x2 : ints, or lists of ints
+        Start and stop indices of the spectral trace across the frame.
+        If multiple frames are input and a single x1/x2 is input, the
+        same value will be used for each frame.  Note however that
+        multiple x1/x2 can also be input (one for each frame).
+
+      idlexec : str
+        Path to the IDL executable.  OPTSPECEXTR.PRO and its
+        associated files must be in your IDL path.  If set to None,
+        then it will be set to: os.popen('which idl').read().strip()
+
+    :OPTIONS:
+      clobber : bool
+        Whether to overwrite files when writing input data to TEMPFRAMFN.
+
+      tempframefn : str
+        If input 'frame' is an array, it will be written to this
+        filename in order to pass it to IDL.
+
+      specfn : str
+        IDL will write the spectral data to this filename in order to
+        pass it back to Python.
+
+      scriptfn : str
+        Filename in which the short IDL script will be written.
+
+      IDLoptions : str
+         Options to pass to OPTSPECEXTR.PRO. For example:
+         "adjfunc='adjgauss', adjoptions={center:1,centerfit:1,centerdeg:3}, bgdeg=3"
+
+         Note that this Python code will break if you _don't_ trace
+         the spectrum (adjoptions, etc.); this is an area for future
+         work if I ever use a spectrograph with straight traces.
+
+       inmask : None or str
+         Name of the good pixel mask for OPTSPECEXTR.PRO.  Equal to 1
+         for good pixels, and 0 for bad pixels.
+
+    :OUTPUTS:
+      For each input frame, a list of four items:
+        [0] -- Extracted spectrum, ADU per pixel
+        [1] -- Uncertainty (1 sigma) of extracted spectrum
+        [2] -- Location of trace (in pixels) across the frame
+        [3] -- Width of trace across the frame
+
+    :NOTES:
+      Note that this more closely follows Horne et al. than does
+      :func:`optimalExtract`, and is faster than both that function
+      and (especially!) :func:`extractSpectralProfiles`.  The only
+      downside (if it is one) is that this function requires IDL.
+
+    :TO-DO:
+      Add options for user input of a variance frame, or of sky variance.
+
+      Allow more flexibility (tracing, input/output options, etc.)
+      
+    :REQUIREMENTS:
+       IDL
+
+       `OPTSPECEXTR  <http://physics.ucf.edu/~jh/ast/software.html>`_
+
+    """
+    # 2012-08-18 16:36 IJMC: created
+    # 2012-08-19 09:39 IJMC: Added 'inmask' option.
+
+    import os
+
+    try:
+        from astropy.io import fits as pyfits
+    except:
+        import pyfits
+    
+
+    # Put the input frames in the proper format:
+    if isinstance(frame, np.ndarray):
+        frameisfilename = False
+        if frame.ndim==2:
+            frames = [frame]
+        elif frame.ndim==1:
+            print "Input array should be 2D or 3D -- no telling what will happen next!"
+        else:
+            frames = frame
+    else:
+        frameisfilename = True
+        if isinstance(frame, str):
+            frames = [frame]
+        else:
+            frames = frame
+        
+    if not hasattr(x1, '__iter__'):
+        x1 = [x1] * len(frames)
+    if not hasattr(x2, '__iter__'):
+        x2 = [x2] * len(frames)
+    
+    if idlexec is None:
+        idlexec = os.popen('which idl').read().strip()
+
+
+    # Loop through all files:
+    specs = []
+    ii = 0
+    for frame in frames:
+        if frameisfilename:
+            tempframefn = frame
+        else:
+            pyfits.writeto(tempframefn, frame, clobber=clobber)
+
+        # Prepare the temporary IDL script:
+        idlcmds = []
+        idlcmds.append("frame = readfits('%s')\n" % tempframefn)
+        idlcmds.append("gain = %1.3f\n" % gain)
+        idlcmds.append("readnoise = %i\n" % readnoise)
+        idlcmds.append("varim = abs(frame) / gain + readnoise^2\n")
+        idlcmds.append("x1 = %i & x2 = %i\n" % (x1[ii],x2[ii]))
+        if inmask is not None:
+            idlcmds.append("inmask = readfits('%s')\n" % inmask)
+            IDLoptions += ', inmask=inmask'
+        idlcmds.append("spec = optspecextr(frame, varim, readnoise, gain, x1, x2, adjparms=adjparm, opvar=opvar, %s)\n" % IDLoptions)
+        idlcmds.append("spec_err_loc_width = [[spec], [sqrt(opvar)], [adjparm.traceest], [adjparm.widthest]]\n")
+        idlcmds.append("writefits,'%s', spec_err_loc_width\n" % (specfn))
+        idlcmds.append("exit\n")
+
+            
+        
+
+
+        # Write it to disk, and execute it.
+        f = open(scriptfn, 'w')
+        f.writelines(idlcmds)
+        f.close()
+        os.system('%s %s' % (idlexec, scriptfn))
+
+        # Read the spectrum into Python, and iterate.
+        spec = pyfits.getdata(specfn)
+        specs.append(spec)
+        ii += 1
+
+    # Clean up after ourselves:
+    if not frameisfilename and os.path.isfile(tempframefn):
+        os.remove(tempframefn)
+    if os.path.isfile(specfn):
+        os.remove(specfn)
+    if os.path.isfile(scriptfn):
+        os.remove(scriptfn)
+
+    # If only one file was run, we don't need to return a list.
+    if ii==1:  
+        specs = specs[0]
+
+    return specs
+
+
+[docs]
+def optimalExtract(*args, **kw):
+    """
+    Extract spectrum, following Horne 1986.
+
+    :INPUTS:
+       data : 2D Numpy array
+         Appropriately calibrated frame from which to extract
+         spectrum.  Should be in units of ADU, not electrons!
+
+       variance : 2D Numpy array
+         Variances of pixel values in 'data'.
+
+       gain : scalar
+         Detector gain, in electrons per ADU
+
+       readnoise : scalar
+         Detector readnoise, in electrons.
+
+    :OPTIONS:
+       goodpixelmask : 2D numpy array
+         Equals 0 for bad pixels, 1 for good pixels
+
+       bkg_radii : 2- or 4-sequence
+         If length 2: inner and outer radii to use in computing
+         background. Note that for this to be effective, the spectral
+         trace should be positions in the center of 'data.'
+         
+         If length 4: start and end indices of both apertures for
+         background fitting, of the form [b1_start, b1_end, b2_start,
+         b2_end] where b1 and b2 are the two background apertures, and
+         the elements are arranged in strictly ascending order.
+
+       extract_radius : int or 2-sequence
+         radius to use for both flux normalization and extraction.  If
+         a sequence, the first and last indices of the array to use
+         for spectral normalization and extraction.
+
+
+       dispaxis : bool
+         0 for horizontal spectrum, 1 for vertical spectrum
+
+       bord : int >= 0
+         Degree of polynomial background fit.
+
+       bsigma : int >= 0
+         Sigma-clipping threshold for computing background.
+
+       pord : int >= 0
+         Degree of polynomial fit to construct profile.
+
+       psigma : int >= 0
+         Sigma-clipping threshold for computing profile.
+
+       csigma : int >= 0
+         Sigma-clipping threshold for cleaning & cosmic-ray rejection.
+
+       finite : bool
+         If true, mask all non-finite values as bad pixels.
+
+       nreject : int > 0
+         Number of pixels to reject in each iteration.
+             
+    :RETURNS:
+       3-tuple:
+          [0] -- spectrum flux (in electrons)
+
+          [1] -- uncertainty on spectrum flux
+
+          [1] -- background flux
+
+
+    :EXAMPLE:
+      ::
+
+
+    :SEE_ALSO:
+      :func:`superExtract`.
+
+    :NOTES:
+      Horne's classic optimal extraction algorithm is optimal only so
+      long as the spectral traces are very nearly aligned with
+      detector rows or columns.  It is *not* well-suited for
+      extracting substantially tilted or curved traces, for the
+      reasons described by Marsh 1989, Mukai 1990.  For extracting
+      such spectra, see :func:`superExtract`.
+    """
+
+    # 2012-08-20 08:24 IJMC: Created from previous, low-quality version.
+    # 2012-09-03 11:37 IJMC: Renamed to replace previous, low-quality
+    #                        version. Now bkg_radii and extract_radius
+    #                        can refer to either a trace-centered
+    #                        coordinate system, or the specific
+    #                        indices of all aperture edges. Added nreject.
+
+
+    from scipy import signal
+
+    # Parse inputs:
+    frame, variance, gain, readnoise = args[0:4]
+
+    # Parse options:
+    if kw.has_key('goodpixelmask'):
+        goodpixelmask = np.array(kw['goodpixelmask'], copy=True).astype(bool)
+    else:
+        goodpixelmask = np.ones(frame.shape, dtype=bool)
+
+    if kw.has_key('dispaxis'):
+        if kw['dispaxis']==1:
+            frame = frame.transpose()
+            variance = variance.transpose()
+            goodpixelmask = goodpixelmask.transpose()
+
+    if kw.has_key('verbose'):
+        verbose = kw['verbose']
+    else:
+        verbose = False
+
+    if kw.has_key('bkg_radii'):
+        bkg_radii = kw['bkg_radii']
+    else:
+        bkg_radii = [15, 20]
+        if verbose: message("Setting option 'bkg_radii' to: " + str(bkg_radii))
+
+    if kw.has_key('extract_radius'):
+        extract_radius = kw['extract_radius']
+    else:
+        extract_radius = 10
+        if verbose: message("Setting option 'extract_radius' to: " + str(extract_radius))
+
+    if kw.has_key('bord'):
+        bord = kw['bord']
+    else:
+        bord = 1
+        if verbose: message("Setting option 'bord' to: " + str(bord))
+
+    if kw.has_key('bsigma'):
+        bsigma = kw['bsigma']
+    else:
+        bsigma = 3
+        if verbose: message("Setting option 'bsigma' to: " + str(bsigma))
+
+    if kw.has_key('pord'):
+        pord = kw['pord']
+    else:
+        pord = 2
+        if verbose: message("Setting option 'pord' to: " + str(pord))
+
+    if kw.has_key('psigma'):
+        psigma = kw['psigma']
+    else:
+        psigma = 4
+        if verbose: message("Setting option 'psigma' to: " + str(psigma))
+
+    if kw.has_key('csigma'):
+        csigma = kw['csigma']
+    else:
+        csigma = 5
+        if verbose: message("Setting option 'csigma' to: " + str(csigma))
+
+    if kw.has_key('finite'):
+        finite = kw['finite']
+    else:
+        finite = True
+        if verbose: message("Setting option 'finite' to: " + str(finite))
+
+    if kw.has_key('nreject'):
+        nreject = kw['nreject']
+    else:
+        nreject = 100
+        if verbose: message("Setting option 'nreject' to: " + str(nreject))
+
+    if finite:
+        goodpixelmask *= (np.isfinite(frame) * np.isfinite(variance))
+
+    
+    variance[True-goodpixelmask] = frame[goodpixelmask].max() * 1e9
+    nlam, fitwidth = frame.shape
+
+    xxx = np.arange(-fitwidth/2, fitwidth/2)
+    xxx0 = np.arange(fitwidth)
+    if len(bkg_radii)==4: # Set all borders of background aperture:
+        backgroundAperture = ((xxx0 > bkg_radii[0]) * (xxx0 <= bkg_radii[1])) + \
+            ((xxx0 > bkg_radii[2]) * (xxx0 <= bkg_radii[3]))
+    else: # Assume trace is centered, and use only radii.
+        backgroundAperture = (np.abs(xxx) > bkg_radii[0]) * (np.abs(xxx) <= bkg_radii[1])
+
+    if hasattr(extract_radius, '__iter__'):
+        extractionAperture = (xxx0 > extract_radius[0]) * (xxx0 <= extract_radius[1])
+    else:
+        extractionAperture = np.abs(xxx) < extract_radius
+
+    nextract = extractionAperture.sum()
+    xb = xxx[backgroundAperture]
+
+    #Step3: Sky Subtraction
+    if bord==0: # faster to take weighted mean:
+        background = an.wmean(frame[:, backgroundAperture], (goodpixelmask/variance)[:, backgroundAperture], axis=1)
+    else:
+        background = 0. * frame
+        for ii in range(nlam):
+            fit = an.polyfitr(xb, frame[ii, backgroundAperture], bord, bsigma, w=(goodpixelmask/variance)[ii, backgroundAperture], verbose=verbose-1)
+            background[ii, :] = np.polyval(fit, xxx)
+
+    # (my 3a: mask any bad values)
+    badBackground = True - np.isfinite(background)
+    background[badBackground] = 0.
+    if verbose and badBackground.any():
+        print "Found bad background values at: ", badBackground.nonzero()
+
+    skysubFrame = frame - background
+
+
+    #Step4: Extract 'standard' spectrum and its variance
+    standardSpectrum = nextract * an.wmean(skysubFrame[:, extractionAperture], goodpixelmask[:, extractionAperture], axis=1) 
+    varStandardSpectrum = nextract * an.wmean(variance[:, extractionAperture], goodpixelmask[:, extractionAperture], axis=1)
+
+    # (my 4a: mask any bad values)
+    badSpectrum = True - (np.isfinite(standardSpectrum))
+    standardSpectrum[badSpectrum] = 1.
+    varStandardSpectrum[badSpectrum] = varStandardSpectrum[True - badSpectrum].max() * 1e9
+
+
+    #Step5: Construct spatial profile; enforce positivity & normalization
+    normData = skysubFrame / standardSpectrum
+    varNormData = variance / standardSpectrum**2
+
+
+    # Iteratively clip outliers
+    newBadPixels = True
+    iter = -1
+    if verbose: print "Looking for bad pixel outliers in profile construction."
+    xl = np.linspace(-1., 1., nlam)
+
+    while newBadPixels:
+        iter += 1
+
+
+        if pord==0: # faster to take weighted mean:
+            profile = np.tile(an.wmean(normData, (goodpixelmask/varNormData), axis=0), (nlam,1))
+        else:
+            profile = 0. * frame
+            for ii in range(fitwidth):
+                fit = an.polyfitr(xl, normData[:, ii], pord, np.inf, w=(goodpixelmask/varNormData)[:, ii], verbose=verbose-1)
+                profile[:, ii] = np.polyval(fit, xl)
+
+        if profile.min() < 0:
+            profile[profile < 0] = 0.
+        profile /= profile.sum(1).reshape(nlam, 1)
+
+        #Step6: Revise variance estimates 
+        modelData = standardSpectrum * profile + background
+        variance = (np.abs(modelData)/gain + (readnoise/gain)**2) / \
+            (goodpixelmask + 1e-9) # Avoid infinite variance
+
+        outlierSigmas = (frame - modelData)**2/variance
+        if outlierSigmas.max() > psigma**2:
+            maxRejectedValue = max(psigma**2, np.sort(outlierSigmas[:, extractionAperture].ravel())[-nreject])
+            worstOutliers = (outlierSigmas>=maxRejectedValue).nonzero()
+            goodpixelmask[worstOutliers] = False
+            newBadPixels = True
+            numberRejected = len(worstOutliers[0])
+        else:
+            newBadPixels = False
+            numberRejected = 0
+
+        if verbose: print "Rejected %i pixels on this iteration " % numberRejected
+
+        #Step5: Construct spatial profile; enforce positivity & normalization
+        varNormData = variance / standardSpectrum**2
+
+    if verbose: print "%i bad pixels found" % iter
+
+
+    # Iteratively clip Cosmic Rays
+    newBadPixels = True
+    iter = -1
+    if verbose: print "Looking for bad pixel outliers in optimal extraction."
+    while newBadPixels:
+        iter += 1
+
+        #Step 8: Extract optimal spectrum and its variance
+        gp = goodpixelmask * profile
+        denom = (gp * profile / variance)[:, extractionAperture].sum(1)
+        spectrum = ((gp * skysubFrame  / variance)[:, extractionAperture].sum(1) / denom).reshape(nlam, 1)
+        varSpectrum = (gp[:, extractionAperture].sum(1) / denom).reshape(nlam, 1)
+
+
+        #Step6: Revise variance estimates 
+        modelData = spectrum * profile + background
+        variance = (np.abs(modelData)/gain + (readnoise/gain)**2) / \
+            (goodpixelmask + 1e-9) # Avoid infinite variance
+
+
+        #Iterate until worse outliers are all identified:
+        outlierSigmas = (frame - modelData)**2/variance
+        if outlierSigmas.max() > csigma**2:
+            maxRejectedValue = max(csigma**2, np.sort(outlierSigmas[:, extractionAperture].ravel())[-nreject])
+            worstOutliers = (outlierSigmas>=maxRejectedValues).nonzero()
+            goodpixelmask[worstOutliers] = False
+            newBadPixels = True
+            numberRejected = len(worstOutliers[0])
+        else:
+            newBadPixels = False
+            numberRejected = 0
+
+        if verbose: print "Rejected %i pixels on this iteration " % numberRejected
+
+
+    if verbose: print "%i bad pixels found" % iter
+
+    ret = (spectrum, varSpectrum, profile, background, goodpixelmask)
+
+    return  ret
+
+
+
+[docs]
+def superExtract(*args, **kw):
+    """
+    Optimally extract curved spectra, following Marsh 1989.
+
+    :INPUTS:
+       data : 2D Numpy array
+         Appropriately calibrated frame from which to extract
+         spectrum.  Should be in units of ADU, not electrons!
+
+       variance : 2D Numpy array
+         Variances of pixel values in 'data'.
+
+       gain : scalar
+         Detector gain, in electrons per ADU
+
+       readnoise : scalar
+         Detector readnoise, in electrons.
+
+    :OPTIONS:
+       trace : 1D numpy array
+         location of spectral trace.  If None, :func:`traceorders` is
+         invoked.
+
+       goodpixelmask : 2D numpy array
+         Equals 0 for bad pixels, 1 for good pixels
+
+       npoly : int
+         Number of profile polynomials to evaluate (Marsh's
+         "K"). Ideally you should not need to set this -- instead,
+         play with 'polyspacing' and 'extract_radius.' For symmetry,
+         this should be odd.
+
+       polyspacing : scalar
+         Spacing between profile polynomials, in pixels. (Marsh's
+         "S").  A few cursory tests suggests that the extraction
+         precision (in the high S/N case) scales as S^-2 -- but the
+         code slows down as S^2.
+
+       pord : int
+         Order of profile polynomials; 1 = linear, etc.
+
+       bkg_radii : 2-sequence
+         inner and outer radii to use in computing background
+
+       extract_radius : int
+         radius to use for both flux normalization and extraction
+
+       dispaxis : bool
+         0 for horizontal spectrum, 1 for vertical spectrum
+
+       bord : int >= 0
+         Degree of polynomial background fit.
+
+       bsigma : int >= 0
+         Sigma-clipping threshold for computing background.
+
+       tord : int >= 0
+         Degree of spectral-trace polynomial (for trace across frame
+         -- not used if 'trace' is input)
+
+       csigma : int >= 0
+         Sigma-clipping threshold for cleaning & cosmic-ray rejection.
+
+       finite : bool
+         If true, mask all non-finite values as bad pixels.
+
+       qmode : str ('fast' or 'slow')
+         How to compute Marsh's Q-matrix.  Valid inputs are
+         'fast-linear', 'slow-linear', 'fast-nearest,' 'slow-nearest,'
+         and 'brute'.  These select between various methods of
+         integrating the nearest-neighbor or linear interpolation
+         schemes as described by Marsh; the 'linear' methods are
+         preferred for accuracy.  Use 'slow' if you are running out of
+         memory when using the 'fast' array-based methods.  'Brute' is
+         both slow and inaccurate, and should not be used.
+         
+       nreject : int
+         Number of outlier-pixels to reject at each iteration. 
+
+       retall : bool
+         If true, also return the 2D profile, background, variance
+         map, and bad pixel mask.
+             
+    :RETURNS:
+       object with fields for:
+         spectrum
+
+         varSpectrum
+
+         trace
+
+
+    :EXAMPLE:
+      ::
+
+        import spec
+        import numpy as np
+        import pylab as py
+
+        def gaussian(p, x):
+           if len(p)==3:
+               p = concatenate((p, [0]))
+           return (p[3] + p[0]/(p[1]*sqrt(2*pi)) * exp(-(x-p[2])**2 / (2*p[1]**2)))
+
+        # Model some strongly tilted spectral data:
+        nx, nlam = 80, 500
+        x0 = np.arange(nx)
+        gain, readnoise = 3.0, 30.
+        background = np.ones(nlam)*10
+        flux =  np.ones(nlam)*1e4
+        center = nx/2. + np.linspace(0,10,nlam)
+        FWHM = 3.
+        model = np.array([gaussian([flux[ii]/gain, FWHM/2.35, center[ii], background[ii]], x0) for ii in range(nlam)])
+        varmodel = np.abs(model) / gain + (readnoise/gain)**2
+        observation = np.random.normal(model, np.sqrt(varmodel))
+        fitwidth = 60
+        xr = 15
+
+        output_spec = spec.superExtract(observation, varmodel, gain, readnoise, polyspacing=0.5, pord=1, bkg_radii=[10,30], extract_radius=5, dispaxis=1)
+
+        py.figure()
+        py.plot(output_spec.spectrum.squeeze() / flux)
+        py.ylabel('(Measured flux) / (True flux)')
+        py.xlabel('Photoelectrons')
+        
+
+
+    :TO_DO:
+      Iterate background fitting and reject outliers; maybe first time
+      would be unweighted for robustness.
+
+      Introduce even more array-based, rather than loop-based,
+      calculations.  For large spectra computing the C-matrix takes
+      the most time; this should be optimized somehow.
+
+    :SEE_ALSO:
+
+    """
+
+    # 2012-08-25 20:14 IJMC: Created.
+    # 2012-09-21 14:32 IJMC: Added error-trapping if no good pixels
+    #                      are in a row. Do a better job of extracting
+    #                      the initial 'standard' spectrum.
+
+    from scipy import signal
+    from pylab import *
+    from nsdata import imshow, bfixpix
+
+
+
+    # Parse inputs:
+    frame, variance, gain, readnoise = args[0:4]
+
+    frame    = gain * np.array(frame, copy=False)
+    variance = gain**2 * np.array(variance, copy=False)
+    variance[variance<=0.] = readnoise**2
+
+    # Parse options:
+    if kw.has_key('verbose'):
+        verbose = kw['verbose']
+    else:
+        verbose = False
+    if verbose: from time import time
+
+
+    if kw.has_key('goodpixelmask'):
+        goodpixelmask = kw['goodpixelmask']
+        if isinstance(goodpixelmask, str):
+            goodpixelmask = pyfits.getdata(goodpixelmask).astype(bool)
+        else:
+            goodpixelmask = np.array(goodpixelmask, copy=True).astype(bool)
+    else:
+        goodpixelmask = np.ones(frame.shape, dtype=bool)
+
+
+    if kw.has_key('dispaxis'):
+        dispaxis = kw['dispaxis']
+    else:
+        dispaxis = 0
+
+    if dispaxis==0:
+        frame = frame.transpose()
+        variance = variance.transpose()
+        goodpixelmask = goodpixelmask.transpose()
+
+
+    if kw.has_key('pord'):
+        pord = kw['pord']
+    else:
+        pord = 2
+
+    if kw.has_key('polyspacing'):
+        polyspacing = kw['polyspacing']
+    else:
+        polyspacing = 1
+
+    if kw.has_key('bkg_radii'):
+        bkg_radii = kw['bkg_radii']
+    else:
+        bkg_radii = [15, 20]
+        if verbose: message("Setting option 'bkg_radii' to: " + str(bkg_radii))
+
+    if kw.has_key('extract_radius'):
+        extract_radius = kw['extract_radius']
+    else:
+        extract_radius = 10
+        if verbose: message("Setting option 'extract_radius' to: " + str(extract_radius))
+
+    if kw.has_key('npoly'):
+        npoly = kw['npoly']
+    else:
+        npoly = 2 * int((2.0 * extract_radius) / polyspacing / 2.) + 1
+
+    if kw.has_key('bord'):
+        bord = kw['bord']
+    else:
+        bord = 1
+        if verbose: message("Setting option 'bord' to: " + str(bord))
+
+    if kw.has_key('tord'):
+        tord = kw['tord']
+    else:
+        tord = 3
+        if verbose: message("Setting option 'tord' to: " + str(tord))
+
+    if kw.has_key('bsigma'):
+        bsigma = kw['bsigma']
+    else:
+        bsigma = 3
+        if verbose: message("Setting option 'bsigma' to: " + str(bsigma))
+
+    if kw.has_key('csigma'):
+        csigma = kw['csigma']
+    else:
+        csigma = 5
+        if verbose: message("Setting option 'csigma' to: " + str(csigma))
+
+    if kw.has_key('qmode'):
+        qmode = kw['qmode']
+    else:
+        qmode = 'fast'
+        if verbose: message("Setting option 'qmode' to: " + str(qmode))
+
+    if kw.has_key('nreject'):
+        nreject = kw['nreject']
+    else:
+        nreject = 100
+        if verbose: message("Setting option 'nreject' to: " + str(nreject))
+
+    if kw.has_key('finite'):
+        finite = kw['finite']
+    else:
+        finite = True
+        if verbose: message("Setting option 'finite' to: " + str(finite))
+
+
+    if kw.has_key('retall'):
+        retall = kw['retall']
+    else:
+        retall = False
+
+
+    if finite:
+        goodpixelmask *= (np.isfinite(frame) * np.isfinite(variance))
+
+    variance[True-goodpixelmask] = frame[goodpixelmask].max() * 1e9
+    nlam, fitwidth = frame.shape
+
+    # Define trace (Marsh's "X_j" in Eq. 9)
+    if kw.has_key('trace'):
+        trace = kw['trace']
+    else:
+        trace = None
+
+    if trace is None:
+        trace = 5
+    if not hasattr(trace, '__iter__'):
+        if verbose: print "Tracing not fully tested; dispaxis may need adjustment."
+        #pdb.set_trace()
+        tracecoef = traceorders(frame, pord=trace, nord=1, verbose=verbose-1, plotalot=verbose-1, g=gain, rn=readnoise, badpixelmask=True-goodpixelmask, dispaxis=dispaxis, fitwidth=min(fitwidth, 80))
+        trace = np.polyval(tracecoef.ravel(), np.arange(nlam))
+
+    
+    #xxx = np.arange(-fitwidth/2, fitwidth/2)
+    #backgroundAperture = (np.abs(xxx) > bkg_radii[0]) * (np.abs(xxx) < bkg_radii[1])
+    #extractionAperture = np.abs(xxx) < extract_radius
+    #nextract = extractionAperture.sum()
+    #xb = xxx[backgroundAperture]
+
+    xxx = np.arange(fitwidth) - trace.reshape(nlam,1)
+    backgroundApertures = (np.abs(xxx) > bkg_radii[0]) * (np.abs(xxx) <= bkg_radii[1])
+    extractionApertures = np.abs(xxx) <= extract_radius
+
+    nextracts = extractionApertures.sum(1)
+
+    #Step3: Sky Subtraction
+    background = 0. * frame
+    for ii in range(nlam):
+        if goodpixelmask[ii, backgroundApertures[ii]].any():
+            fit = an.polyfitr(xxx[ii,backgroundApertures[ii]], frame[ii, backgroundApertures[ii]], bord, bsigma, w=(goodpixelmask/variance)[ii, backgroundApertures[ii]], verbose=verbose-1)
+            background[ii, :] = np.polyval(fit, xxx[ii])
+        else:
+            background[ii] = 0.
+
+    background_at_trace = np.array([np.interp(0, xxx[j], background[j]) for j in range(nlam)])
+
+    # (my 3a: mask any bad values)
+    badBackground = True - np.isfinite(background)
+    background[badBackground] = 0.
+    if verbose and badBackground.any():
+        print "Found bad background values at: ", badBackground.nonzero()
+
+    skysubFrame = frame - background
+
+
+    # Interpolate and fix bad pixels for extraction of standard
+    # spectrum -- otherwise there can be 'holes' in the spectrum from
+    # ill-placed bad pixels.
+    fixSkysubFrame = bfixpix(skysubFrame, True-goodpixelmask, n=8, retdat=True)
+
+    #Step4: Extract 'standard' spectrum and its variance
+    standardSpectrum = np.zeros((nlam, 1), dtype=float)
+    varStandardSpectrum = np.zeros((nlam, 1), dtype=float)
+    for ii in range(nlam):
+        thisrow_good = extractionApertures[ii] #* goodpixelmask[ii] * 
+        standardSpectrum[ii] = fixSkysubFrame[ii, thisrow_good].sum()
+        varStandardSpectrum[ii] = variance[ii, thisrow_good].sum()
+
+
+    spectrum = standardSpectrum.copy()
+    varSpectrum = varStandardSpectrum
+
+    # Define new indices (in Marsh's appendix):
+    N = pord + 1
+    mm = np.tile(np.arange(N).reshape(N,1), (npoly)).ravel()
+    nn = mm.copy()
+    ll = np.tile(np.arange(npoly), N)
+    kk = ll.copy()
+    pp = N * ll + mm
+    qq = N * kk + nn
+
+    jj = np.arange(nlam)  # row (i.e., wavelength direction)
+    ii = np.arange(fitwidth) # column (i.e., spatial direction)
+    jjnorm = np.linspace(-1, 1, nlam) # normalized X-coordinate
+    jjnorm_pow = jjnorm.reshape(1,1,nlam) ** (np.arange(2*N-1).reshape(2*N-1,1,1))
+
+    # Marsh eq. 9, defining centers of each polynomial:
+    constant = 0.  # What is it for???
+    poly_centers = trace.reshape(nlam, 1) + polyspacing * np.arange(-npoly/2+1, npoly/2+1) + constant
+
+
+    # Marsh eq. 11, defining Q_kij    (via nearest-neighbor interpolation)
+    #    Q_kij =  max(0, min(S, (S+1)/2 - abs(x_kj - i)))
+    if verbose: tic = time() 
+    if qmode=='fast-nearest': # Array-based nearest-neighbor mode.
+        if verbose: tic = time()
+        Q = np.array([np.zeros((npoly, fitwidth, nlam)), np.array([polyspacing * np.ones((npoly, fitwidth, nlam)), 0.5 * (polyspacing+1) - np.abs((poly_centers - ii.reshape(fitwidth, 1, 1)).transpose(2, 0, 1))]).min(0)]).max(0)
+
+    elif qmode=='slow-linear': # Code is a mess, but it works.
+        invs = 1./polyspacing
+        poly_centers_over_s = poly_centers / polyspacing
+        xps_mat = poly_centers + polyspacing
+        xms_mat = poly_centers - polyspacing
+        Q = np.zeros((npoly, fitwidth, nlam), dtype=float)
+        for i in range(fitwidth):
+            ip05 = i + 0.5
+            im05 = i - 0.5
+            for j in range(nlam):
+                for k in range(npoly):
+                    xkj = poly_centers[j,k]
+                    xkjs = poly_centers_over_s[j,k]
+                    xps = xps_mat[j,k] #xkj + polyspacing
+                    xms = xms_mat[j,k] # xkj - polyspacing
+
+                    if (ip05 <= xms) or (im05 >= xps):
+                        qval = 0.
+                    elif (im05) > xkj:
+                        lim1 = im05
+                        lim2 = min(ip05, xps)
+                        qval = (lim2 - lim1) * \
+                            (1. + xkjs - 0.5*invs*(lim1+lim2))
+                    elif (ip05) < xkj:
+                        lim1 = max(im05, xms)
+                        lim2 = ip05
+                        qval = (lim2 - lim1) * \
+                            (1. - xkjs + 0.5*invs*(lim1+lim2))
+                    else:
+                        lim1 = max(im05, xms)
+                        lim2 = min(ip05, xps)
+                        qval = lim2 - lim1 + \
+                            invs * (xkj*(-xkj + lim1 + lim2) - \
+                                        0.5*(lim1*lim1 + lim2*lim2))
+                    Q[k,i,j] = max(0, qval)
+
+
+    elif qmode=='fast-linear': # Code is a mess, but it's faster than 'slow' mode
+        invs = 1./polyspacing
+        xps_mat = poly_centers + polyspacing
+        Q = np.zeros((npoly, fitwidth, nlam), dtype=float)
+        for j in range(nlam):
+            xkj_vec = np.tile(poly_centers[j,:].reshape(npoly, 1), (1, fitwidth))
+            xps_vec = np.tile(xps_mat[j,:].reshape(npoly, 1), (1, fitwidth))
+            xms_vec = xps_vec - 2*polyspacing
+
+            ip05_vec = np.tile(np.arange(fitwidth) + 0.5, (npoly, 1))
+            im05_vec = ip05_vec - 1
+            ind00 = ((ip05_vec <= xms_vec) + (im05_vec >= xps_vec))
+            ind11 = ((im05_vec > xkj_vec) * (True - ind00))
+            ind22 = ((ip05_vec < xkj_vec) * (True - ind00))
+            ind33 = (True - (ind00 + ind11 + ind22)).nonzero()
+            ind11 = ind11.nonzero()
+            ind22 = ind22.nonzero()
+
+            n_ind11 = len(ind11[0])
+            n_ind22 = len(ind22[0])
+            n_ind33 = len(ind33[0])
+
+            if n_ind11 > 0:
+                ind11_3d = ind11 + (np.ones(n_ind11, dtype=int)*j,)
+                lim2_ind11 = np.array((ip05_vec[ind11], xps_vec[ind11])).min(0)
+                Q[ind11_3d] = ((lim2_ind11 - im05_vec[ind11]) * invs * \
+                                   (polyspacing + xkj_vec[ind11] - 0.5*(im05_vec[ind11] + lim2_ind11)))
+            
+            if n_ind22 > 0:
+                ind22_3d = ind22 + (np.ones(n_ind22, dtype=int)*j,)
+                lim1_ind22 = np.array((im05_vec[ind22], xms_vec[ind22])).max(0)
+                Q[ind22_3d] = ((ip05_vec[ind22] - lim1_ind22) * invs * \
+                                   (polyspacing - xkj_vec[ind22] + 0.5*(ip05_vec[ind22] + lim1_ind22)))
+            
+            if n_ind33 > 0:
+                ind33_3d = ind33 + (np.ones(n_ind33, dtype=int)*j,)
+                lim1_ind33 = np.array((im05_vec[ind33], xms_vec[ind33])).max(0)
+                lim2_ind33 = np.array((ip05_vec[ind33], xps_vec[ind33])).min(0)
+                Q[ind33_3d] = ((lim2_ind33 - lim1_ind33) + invs * \
+                                   (xkj_vec[ind33] * (-xkj_vec[ind33] + lim1_ind33 + lim2_ind33) - 0.5*(lim1_ind33*lim1_ind33 + lim2_ind33*lim2_ind33)))
+            
+
+    elif qmode=='brute': # Neither accurate, nor memory-frugal.
+        oversamp = 4.
+        jj2 = np.arange(nlam*oversamp, dtype=float) / oversamp
+        trace2 = np.interp(jj2, jj, trace)
+        poly_centers2 = trace2.reshape(nlam*oversamp, 1) + polyspacing * np.arange(-npoly/2+1, npoly/2+1, dtype=float) + constant
+        x2 = np.arange(fitwidth*oversamp, dtype=float)/oversamp
+        Q = np.zeros((npoly, fitwidth, nlam), dtype=float)
+        for k in range(npoly):
+            Q[k,:,:] = an.binarray((np.abs(x2.reshape(fitwidth*oversamp,1) - poly_centers2[:,k]) <= polyspacing), oversamp)
+
+        Q /= oversamp*oversamp*2
+
+    else:  # 'slow' Loop-based nearest-neighbor mode: requires less memory
+        if verbose: tic = time()
+        Q = np.zeros((npoly, fitwidth, nlam), dtype=float)
+        for k in range(npoly):
+            for i in range(fitwidth):
+                for j in range(nlam):
+                    Q[k,i,j] = max(0, min(polyspacing, 0.5*(polyspacing+1) - np.abs(poly_centers[j,k] - i)))
+
+    if verbose: print '%1.2f s to compute Q matrix (%s mode)' % (time() - tic, qmode)
+        
+
+    # Some quick math to find out which dat columns are important, and
+    #   which contain no useful spectral information:
+    Qmask = Q.sum(0).transpose() > 0
+    Qind = Qmask.transpose().nonzero()
+    Q_cols = [Qind[0].min(), Qind[0].max()]
+    nQ = len(Qind[0])
+    Qsm = Q[:,Q_cols[0]:Q_cols[1]+1,:]
+
+    # Prepar to iteratively clip outliers
+    newBadPixels = True
+    iter = -1
+    if verbose: print "Looking for bad pixel outliers."
+    while newBadPixels:
+        iter += 1
+        if verbose: print "Beginning iteration %i" % iter
+
+
+        # Compute pixel fractions (Marsh Eq. 5):
+        #     (Note that values outside the desired polynomial region
+        #     have Q=0, and so do not contribute to the fit)
+        #E = (skysubFrame / spectrum).transpose()
+        invEvariance = (spectrum**2 / variance).transpose()
+        weightedE = (skysubFrame * spectrum / variance).transpose() # E / var_E
+        invEvariance_subset = invEvariance[Q_cols[0]:Q_cols[1]+1,:]
+
+        # Define X vector (Marsh Eq. A3):
+        if verbose: tic = time()
+        X = np.zeros(N * npoly, dtype=float)
+        X0 = np.zeros(N * npoly, dtype=float)
+        for q in qq:
+            X[q] = (weightedE[Q_cols[0]:Q_cols[1]+1,:] * Qsm[kk[q],:,:] * jjnorm_pow[nn[q]]).sum() 
+        if verbose: print '%1.2f s to compute X matrix' % (time() - tic)
+
+        # Define C matrix (Marsh Eq. A3)
+        if verbose: tic = time()
+        C = np.zeros((N * npoly, N*npoly), dtype=float)
+
+        buffer = 1.1 # C-matrix computation buffer (to be sure we don't miss any pixels)
+        for p in pp:
+            qp = Qsm[ll[p],:,:]
+            for q in qq:
+                #  Check that we need to compute C:
+                if np.abs(kk[q] - ll[p]) <= (1./polyspacing + buffer):
+                    if q>=p: 
+                        # Only compute over non-zero columns:
+                        C[q, p] = (Qsm[kk[q],:,:] * qp * jjnorm_pow[nn[q]+mm[p]] * invEvariance_subset).sum() 
+                    if q>p:
+                        C[p, q] = C[q, p]
+
+
+        if verbose: print '%1.2f s to compute C matrix' % (time() - tic)
+
+        ##################################################
+        ##################################################
+        # Just for reference; the following is easier to read, perhaps, than the optimized code:
+        if False: # The SLOW way to compute the X vector:
+            X2 = np.zeros(N * npoly, dtype=float)
+            for n in nn:
+                for k in kk:
+                    q = N * k + n
+                    xtot = 0.
+                    for i in ii:
+                        for j in jj:
+                            xtot += E[i,j] * Q[k,i,j] * (jjnorm[j]**n) / Evariance[i,j]
+                    X2[q] = xtot
+
+            # Compute *every* element of C (though most equal zero!)
+            C = np.zeros((N * npoly, N*npoly), dtype=float)
+            for p in pp:
+                for q in qq:
+                    if q>=p:
+                        C[q, p] = (Q[kk[q],:,:] * Q[ll[p],:,:] * (jjnorm.reshape(1,1,nlam)**(nn[q]+mm[p])) / Evariance).sum()
+                    if q>p:
+                        C[p, q] = C[q, p]
+        ##################################################
+        ##################################################
+
+        # Solve for the profile-polynomial coefficients (Marsh Eq. A)4: 
+        if np.abs(np.linalg.det(C)) < 1e-10:
+            Bsoln = np.dot(np.linalg.pinv(C), X)
+        else:
+            Bsoln = np.linalg.solve(C, X)
+
+        Asoln = Bsoln.reshape(N, npoly).transpose()
+
+        # Define G_kj, the profile-defining polynomial profiles (Marsh Eq. 8)
+        Gsoln = np.zeros((npoly, nlam), dtype=float)
+        for n in range(npoly):
+            Gsoln[n] = np.polyval(Asoln[n,::-1], jjnorm) # reorder polynomial coef.
+
+
+        # Compute the profile (Marsh eq. 6) and normalize it:
+        if verbose: tic = time()
+        profile = np.zeros((fitwidth, nlam), dtype=float)
+        for i in range(fitwidth):
+            profile[i,:] = (Q[:,i,:] * Gsoln).sum(0)
+
+        #P = profile.copy() # for debugging 
+        if profile.min() < 0:
+            profile[profile < 0] = 0. 
+        profile /= profile.sum(0).reshape(1, nlam)
+        profile[True - np.isfinite(profile)] = 0.
+        if verbose: print '%1.2f s to compute profile' % (time() - tic)
+
+        #Step6: Revise variance estimates 
+        modelSpectrum = spectrum * profile.transpose()
+        modelData = modelSpectrum + background
+        variance0 = np.abs(modelData) + readnoise**2
+        variance = variance0 / (goodpixelmask + 1e-9) # De-weight bad pixels, avoiding infinite variance
+
+        outlierVariances = (frame - modelData)**2/variance
+
+        if outlierVariances.max() > csigma**2:
+            newBadPixels = True
+            # Base our nreject-counting only on pixels within the spectral trace:
+            maxRejectedValue = max(csigma**2, np.sort(outlierVariances[Qmask])[-nreject])
+            worstOutliers = (outlierVariances>=maxRejectedValue).nonzero()
+            goodpixelmask[worstOutliers] = False
+            numberRejected = len(worstOutliers[0])
+            #pdb.set_trace()
+        else:
+            newBadPixels = False
+            numberRejected = 0
+        
+        if verbose: print "Rejected %i pixels on this iteration " % numberRejected
+
+            
+        # Optimal Spectral Extraction: (Horne, Step 8)
+        fixSkysubFrame = bfixpix(skysubFrame, True-goodpixelmask, n=8, retdat=True)
+        spectrum = np.zeros((nlam, 1), dtype=float)
+        #spectrum1 = np.zeros((nlam, 1), dtype=float)
+        varSpectrum = np.zeros((nlam, 1), dtype=float)
+        goodprof =  profile.transpose() #* goodpixelmask
+        for ii in range(nlam):
+            thisrow_good = extractionApertures[ii] #* goodpixelmask[ii]
+            denom = (goodprof[ii, thisrow_good] * profile.transpose()[ii, thisrow_good] / variance0[ii, thisrow_good]).sum()
+            if denom==0:
+                spectrum[ii] = 0.
+                varSpectrum[ii] = 9e9
+            else:
+                spectrum[ii] = (goodprof[ii, thisrow_good] * skysubFrame[ii, thisrow_good] / variance0[ii, thisrow_good]).sum() / denom
+                #spectrum1[ii] = (goodprof[ii, thisrow_good] * modelSpectrum[ii, thisrow_good] / variance0[ii, thisrow_good]).sum() / denom
+                varSpectrum[ii] = goodprof[ii, thisrow_good].sum() / denom
+            #if spectrum.size==1218 and ii>610:
+            #    pdb.set_trace()
+
+        #if spectrum.size==1218: pdb.set_trace()
+
+    ret = baseObject()
+    ret.spectrum = spectrum
+    ret.raw = standardSpectrum
+    ret.varSpectrum = varSpectrum
+    ret.trace = trace
+    ret.units = 'electrons'
+    ret.background = background_at_trace
+
+    ret.function_name = 'spec.superExtract'
+
+    if retall:
+        ret.profile_map = profile
+        ret.extractionApertures = extractionApertures
+        ret.background_map = background
+        ret.variance_map = variance0
+        ret.goodpixelmask = goodpixelmask
+        ret.function_args = args
+        ret.function_kw = kw
+
+    return  ret
+
+
+         
+
+
+[docs]
+def spextractor(frame, gain, readnoise, framevar=None, badpixelmask=None, mode='superExtract', trace=None, options=None, trace_options=None, verbose=False):
+    """Extract a spectrum from a frame using one of several methods.
+
+    :INPUTS:
+      frame : 2D Numpy array or filename
+        Contains a single spectral trace.
+
+      gain : None or scalar
+        Gain of data contained in 'frame;' i.e., number of collected
+        photoelectrons equals frame * gain.
+
+      readnoise : None or scalar
+        Read noise of detector, in electrons.
+
+      framevar : None, 2D Numpy array, or filename
+        Variance of values in 'frame.'
+
+        If and only if framevar is None, use gain and readnoise to
+        compute variance.
+
+      badpixelmask : None, 2D Numpy array, or filename
+        Mask of bad pixels in 'frame.'  Bad pixels are set to 1, good
+        pixels are set to 0.
+
+      mode : str
+        Which spectral extraction mode to use.  Options are:
+        
+          superExtract -- see :func:`superExtract`
+
+          optimalExtract -- see :func:`optimalExtract`
+
+          spline -- see :func:`extractSpectralProfiles`
+                    Must also input trace_options.
+
+      trace : None, or 1D Numpy Array
+        Spectral trace location: fractional pixel index along the
+        entire spectral trace.  If None, :func:`traceorders` will be
+        called using the options in 'trace_options.'
+
+      options : None or dict
+        Keyword options to be passed to the appropriate spectral
+        extraction algorithm. Note that you should be able to pass the
+        same sets of parameters to :func:`superExtract` and
+        :func:`optimalExtract` (the necessary parameter sets overlap,
+        but are not identical).
+
+      trace_options : None or dict
+        Keyword options to be passed to :func:`traceorders` (if no
+        trace is input, or if mode='spline')
+
+
+    :RETURNS:
+      spectrum, error or variance of spectrum, sky background, ...
+      
+    :NOTES:
+      When 'optimalextract' is used: if len(bkg_radii)==2 then the
+      background apertures will be reset based on the median location
+      of the trace.  If extract_radius is a singleton, it will be
+      similarly redefined.
+
+    :EXAMPLE:
+     ::
+
+      frame = pyfits.getdata('spectral_filename.fits')
+      gain, readnoise = 3.3, 30
+      output = spec.spextractor(frame, gain, readnoise, mode='superExtract', \
+               options=dict(bord=2, bkg_radii=[20, 30], extract_radius=15, \
+               polyspacing=1./3, pord=5, verbose=True, trace=trace, \
+               qmode='slow-linear'))
+
+      output2 = spec.spextractor(frame, gain, readnoise, mode='optimalExtract', \
+                options=dict(bkg_radii=[20,30], extract_radius=15, bord=2, \
+                bsigma=3, pord=3, psigma=8, csigma=5, verbose=1))
+
+
+        """
+    # 2012-09-03 11:12 IJMC: Created
+    from tools import array_or_filename
+
+    # Parse inputs:
+    frame    = array_or_filename(frame)
+    framevar = array_or_filename(framevar, noneoutput=np.abs(frame) / gain + (readnoise / gain)**2)
+
+    if options is None:
+        options = dict(dispaxis=0)
+
+
+    if verbose and not options.has_key('verbose'):
+        options['verbose'] = verbose
+
+    if trace is None:
+        if trace_options is None:
+            trace_options = dict(pord=2)
+        if not trace_options.has_key('dispaxis'):
+            if options.has_key('dispaxis'): trace_options['dispaxis'] = options['dispaxis']
+
+        trace_coef = traceorders(frame, **trace_options) 
+        trace = np.polyval(trace_coef.ravel(), np.arange(frame.shape[1-options['dispaxis']]))
+
+    options['trace'] = trace
+
+    ##################################################
+    # Extract spectrum in one of several ways:
+    ##################################################
+    if mode.lower()=='superextract':
+        ret = superExtract(frame, framevar, gain, readnoise, **options)
+
+    elif mode.lower()=='spline':
+        # First, set things up:
+        try:
+            trace_coef += 0.0
+        except:
+            trace_coef = np.polyfit(np.arange(trace.size), trace, trace_options['pord']).reshape(1, trace_options['pord']+1)
+        trace_options['retall'] = True
+
+        if not trace_options.has_key('bkg_radii'):
+            if options.has_key('bkg_radii') and len(options['bkg_radii'])==2:
+                trace_options['bkg_radii'] = options['bkg_radii']
+        if not trace_options.has_key('extract_radius'):
+            if options.has_key('extract_radius') and not hasattr(options['extract_radius'], '__iter__'):
+                trace_options['extract_radius'] = options['extract_radius']
+
+        prof = makeprofile(frame, trace_coef, **trace_options)
+
+        ret = extractSpectralProfiles(prof, **trace_options)
+
+
+    elif mode.lower()=='optimalextract':
+        # First, re-define bkg_radii and extract_radius (if necessary):
+        options = dict().update(options) # prevents alterations of options
+        if options.has_key('bkg_radii') and len(options['bkg_radii'])==2:
+            t0 = np.median(trace)
+            bkg_radii = [t0-options['bkg_radii'][1], t0-options['bkg_radii'][0],
+                         t0+options['bkg_radii'][0], t0+options['bkg_radii'][1]]
+            options['bkg_radii'] = bkg_radii
+            if verbose: print "Re-defining background apertures: ", bkg_radii
+
+        if options.has_key('extract_radius') and \
+                (not hasattr(options['extract_radius'], '__iter__') or \
+                     (len(options['extract_radius'])==1)):
+            extract_radius = [t0 - options['extract_radius'], \
+                                  t0 + options['extract_radius']]
+            options['extract_radius'] = extract_radius
+            if verbose: print "Re-defining extraction aperture: ", extract_radius
+
+        ret = optimalExtract(frame, framevar, gain, readnoise, **options)
+
+    else:
+        print "No valid spectral extraction mode specified!"
+        ret = -1
+
+
+    return ret
+
+
+[docs]
+def scaleSpectralSky_pca(frame, skyframes, variance=None, mask=None, badpixelmask=None, npca=3, gain=3.3, readnoise=30):
+    """
+    Use PCA and blank sky frames to subtract
+
+    frame : str or NumPy array
+      data frame to subtract sky from. Assumed to be in ADU, not
+      electrons (see gain and readnoise)
+    
+    npca : int
+      number of PCA components to remove
+
+    f0 = pyfits.getdata(odome.procsci[0])
+    mask = pyfits.getdata(odome._proc + 'skyframes_samplemask.fits').astype(bool)
+    badpixelmask = pyfits.getdata( odome.badpixelmask).astype(bool)
+
+
+    The simplest way to fit sky to a 'frame' containing bright
+    spectral is to include the spectral-trace regions in 'mask' but
+    set the 'variance' of those regions extremely high (to de-weight
+    them in the least-squares fit).
+
+    To use for multi-object data, consider running multiple times
+    (once per order)
+
+    Returns the best-fit sky frame as determined from the first 'npca'
+    PCA components.
+    """
+    # 2012-09-17 16:41 IJMC: Created
+
+    from scipy import sparse
+    from pcsa import pca
+
+    # Parse inputs
+    if not isinstance(frame, np.ndarray):
+        frame = pyfits.getdata(frame)
+
+    if variance is None:
+        variance = np.abs(frame)/gain + (readnoise/gain)**2
+    else:
+        variance = np.array(variance, copy=False)
+    weights = 1./variance
+
+    nx, ny = frame.shape
+    n_tot = nx*ny
+
+    if badpixelmask is None:
+        badpixelmask = np.zeros((nx, ny), dtype=bool)
+    elif isinstance(badpixelmask, str):
+        badpixelmask = pyfits.getdata(badpixelmask).astype(bool)
+    else:
+        badpixelmask = np.array(badpixelmask).astype(bool)
+    weights[badpixelmask] = 0.
+
+
+    if mask is None:
+        mask = np.ones(frame.shape, dtype=bool)
+        n_elem = n_tot
+    else:
+        n_elem = mask.astype(bool).sum()
+    maskind = mask.nonzero()
+
+    if isinstance(skyframes, np.ndarray) and skyframes.ndim==3:
+        pass
+    else:
+        skyframes = np.array([pyfits.getdata(fn) for fn in skyframes])
+
+    skyframes_ind = np.array([sf[maskind] for sf in skyframes])
+
+    frame_ind = frame[maskind]
+    pcaframes = np.zeros((npca, n_elem), dtype=np.float32)
+    iter = 0
+
+    pcas = []
+    for jj in range(npca):
+        pcas.append(pca(skyframes_ind, None, ord=jj+1))
+    pcaframes[0] = pcas[0][0][0]
+    for jj in range(1, npca):
+        pcaframes[jj] = (pcas[jj][0] - pcas[jj-1][0])[0]
+
+#    del pcas
+
+    svecs = sparse.csr_matrix(pcaframes).transpose()
+    fitcoef, efitcoef = an.lsqsp(svecs, frame_ind, weights[maskind])
+    skyvalues = (fitcoef.reshape(npca, 1) * pcaframes).sum(0)
+    #eskyvalues = (np.diag(efitcoef).reshape(npca, 1) * pcaframes).sum(0)
+
+    skyframe = np.zeros((nx, ny), dtype=float)
+    skyframe[maskind] = skyvalues
+    return skyframe
+
+[docs]
+def scaleSpectralSky_dsa(subframe, variance=None, badpixelmask=None, nk=31, pord=1, nmed=3, gain=3.3, readnoise=30, dispaxis=0, spatial_index=None):
+    """
+    Use difference-imaging techniques to subtract moderately tilted
+    sky background.  Doesn't work so well!
+
+    subframe : NumPy array
+      data subframe containing sky data to be subtracted (and,
+      perhaps, an object's spectral trace). Assumed to be in ADU, not
+      electrons (see gain and readnoise)
+    
+    variance : None or NumPy array
+      variance of each pixel in 'subframe'
+
+    nmed : int
+      size of 2D median filter
+
+    pord : int
+      degree of spectral tilt.  Keep this number low!
+
+    nk : int
+      Number of kernel pixels in :func:`dia.dsa`
+
+    nmed : int
+      Size of window for 2D median filter (to reject bad pixels, etc.)
+
+    dispaxis : int
+        set dispersion axis: 0 = horizontal and 1 = vertical
+
+    gain, readnoise : ints
+       If 'variance' is None, these are used to estimate the uncertainties.
+
+    spatial_index : None, or 1D NumPy array of type bool
+       Which spatial rows (if dispaxis=0) to use when fitting the tilt
+       of sky lines across the spectrum.  If you want to use all, set
+       to None.  If you want to ignore some (e.g., because there's a
+       bright object's spectrum there) then set those rows' elements
+       of spatial_index to 'False'.
+
+    :NOTES:
+       Note that (in my experience!) this approach works better when
+       you set all weights to unity, rather than using the suggested
+       (photon + read noise) variances.
+    
+
+    Returns the best-fit sky frame.
+    """
+    # 2012-09-17 16:41 IJMC: Created
+
+    from scipy import signal
+    import dia
+
+    # Parse inputs
+    if not isinstance(subframe, np.ndarray):
+        subframe = pyfits.getdata(subframe)
+
+    if variance is None:
+        variance = np.abs(subframe)/gain + (readnoise/gain)**2
+    else:
+        variance = np.array(variance, copy=False)
+    weights = 1./variance
+
+    if badpixelmask is None:
+        badpixelmask = np.zeros(subframe.shape, dtype=bool)
+    elif isinstance(badpixelmask, str):
+        badpixelmask = pyfits.getdata(badpixelmask).astype(bool)
+    else:
+        badpixelmask = np.array(badpixelmask).astype(bool)
+    weights[badpixelmask] = 0.
+
+    if dispaxis==1:
+        subframe = subframe.transpose()
+        variance = variance.transpose()
+        weights = weights.transpose()
+        badpixelmask = badpixelmask.transpose()
+
+    sub = subframe
+    if nmed > 1:
+        ss = signal.medfilt2d(sub, nmed)
+    else:
+        ss = sub.copy()
+    ref = np.median(ss, axis=0)
+
+    n, nlam = ss.shape
+    if spatial_index is None:
+        spatial_index = np.arange(n)
+    else:
+        spatial_index = np.array(spatial_index, copy=False).nonzero()
+
+
+    gaussianpar = np.zeros((n, 4))
+    for ii in range(n):
+        test = dia.dsa(ref, ss[ii], nk, w=weights[ii], noback=True)
+        gaussianpar[ii] = fitGaussian(test[1])[0]
+
+    position_fit = an.polyfitr(np.arange(n)[spatial_index], gaussianpar[:,2][spatial_index], pord, 3)
+    positions = np.polyval(position_fit, np.arange(n))
+    width_fit = np.median(gaussianpar[:,1])
+
+    skyframe = 0*ss
+    testDC_spec = np.ones(nlam)
+    testx = np.arange(nk, dtype=float)
+    lsqfits = np.zeros((n,2))
+    for ii in range(n):
+        testgaussian = gaussian([1, width_fit, positions[ii], 0.], testx)
+        testgaussian /= testgaussian.sum()
+        testspectrum = dia.rconvolve1d(ref, testgaussian)
+        skyframe[ii] = testspectrum
+
+    if dispaxis==1:
+        skyframe = skyframe.transpose()
+
+    return skyframe
+
+
+
+[docs]
+def scaleSpectralSky_cor(subframe, badpixelmask=None, maxshift=20, fitwidth=2, pord=1, nmed=3, dispaxis=0, spatial_index=None, refpix=None, tord=2):
+    """
+    Use cross-correlation to subtract tilted sky backgrounds.
+
+    subframe : NumPy array
+      data subframe containing sky data to be subtracted (and,
+      perhaps, an object's spectral trace).
+    
+    badpixelmask : None or NumPy array
+      A boolean array, equal to zero for good pixels and unity for bad
+      pixels.  If this is set, the first step will be a call to
+      :func:`nsdata.bfixpix` to interpolate over these values.
+      
+    nmed : int
+      size of 2D median filter for pre-smoothing.
+
+    pord : int
+      degree of spectral tilt.  Keep this number low!
+
+    maxshift : int
+      Maximum acceptable shift.  NOT YET IMPLEMENTED!
+
+    fitwidth : int
+      Maximum radius (in pixels) for fitting to the peak of the
+      cross-correlation.
+      
+    nmed : int
+      Size of window for 2D median filter (to reject bad pixels, etc.)
+
+    dispaxis : int
+        set dispersion axis: 0 = horizontal and 1 = vertical
+
+    spatial_index : None, or 1D NumPy array of type *bool*
+       Which spatial rows (if dispaxis=0) to use when fitting the tilt
+       of sky lines across the spectrum.  If you want to use all, set
+       to None.  If you want to ignore some (e.g., because there's a
+       bright object's spectrum there) then set those rows' elements
+       of spatial_index to 'False'.
+
+    refpix : scalar
+       Pixel along spatial axis to which spectral fits should be
+       aligned; if a spectral trace is present, one should set
+       "refpix" to the location of the trace.
+
+    tord : int
+       Order of polynomial fits along spatial direction in aligned
+       2D-spectral frame, to account for misalignments or
+       irregularities of tilt direction.
+
+    :RETURNS:
+      a model of the sky background, of the same shape as 'subframe.'
+    """
+    # 2012-09-22 17:04 IJMC: Created
+    # 2012-12-27 09:53 IJMC: Edited to better account for sharp edges
+    #                        in backgrounds.
+
+    from scipy import signal
+    from nsdata import bfixpix
+
+    # Parse inputs
+    if not isinstance(subframe, np.ndarray):
+        subframe = pyfits.getdata(subframe)
+
+    if badpixelmask is None:
+        pass
+    else:
+        badpixelmask = np.array(badpixelmask).astype(bool)
+        subframe = bfixpix(subframe, badpixelmask, retdat=True)
+
+    if dispaxis==1:
+        subframe = subframe.transpose()
+
+    # Define necessary variables and vectors:
+    npix, nlam = subframe.shape
+    if spatial_index is None:
+        spatial_index = np.ones(npix, dtype=bool)
+    else:
+        spatial_index = np.array(spatial_index, copy=False)
+    if refpix is None:
+        refpix = npix/2.
+
+    lampix = np.arange(nlam)
+    tpix = np.arange(npix)
+    alllags = np.arange(nlam-maxshift*2) - np.floor(nlam/2 - maxshift)
+
+    # Median-filter the input data:
+    if nmed > 1:
+        ssub = signal.medfilt2d(subframe, nmed)
+    else:
+        ssub = subframe.copy()
+    ref = np.median(ssub, axis=0)
+
+
+    #allcor = np.zeros((npix, nlam-maxshift*2))
+    shift = np.zeros(npix, dtype=float)
+    for ii in tpix:
+        # Cross-correlate to measure alignment at each row:
+        cor = np.correlate(ref[maxshift:-maxshift], signal.medfilt(ssub[ii], nmed)[maxshift:-maxshift], mode='same')
+        # Measure offset of each row:
+        maxind = alllags[(cor==cor.max())].mean()
+        fitind = np.abs(alllags - maxind) <= fitwidth
+        quadfit = np.polyfit(alllags[fitind], cor[fitind], 2)
+        shift[ii] = -0.5 * quadfit[1] / quadfit[0]
+
+    shift_polyfit = an.polyfitr(tpix[spatial_index], shift[spatial_index], pord, 3) #, w=weights)
+    refpos = np.polyval(shift_polyfit, refpix)
+    #pdb.set_trace()
+    fitshift = np.polyval(shift_polyfit, tpix) - refpos
+
+    # Interpolate each row to a common frame to create an improved reference:
+    newssub = np.zeros((npix, nlam))
+    for ii in tpix:
+        newssub[ii] = np.interp(lampix, lampix+fitshift[ii], ssub[ii])
+
+    #pdb.set_trace()
+    newref = np.median(newssub[spatial_index,:], axis=0)
+
+    tfits = np.zeros((nlam, tord+1), dtype=float)
+    newssub2 = np.zeros((npix, nlam))
+    for jj in range(nlam):
+        tfits[jj] = an.polyfitr(tpix, newssub[:,jj], tord, 3)
+        newssub2[:, jj] = np.polyval(tfits[jj], tpix)
+
+
+    # Create the final model of the sky background:
+    skymodel = np.zeros((npix, nlam), dtype=float)
+    shiftmodel = np.zeros((npix, nlam), dtype=float)
+    for ii in tpix:
+        #skymodel[ii] = np.interp(lampix, lampix-fitshift[ii], newref)
+        skymodel[ii] = np.interp(lampix, lampix-fitshift[ii], newssub2[ii])
+        shiftmodel[ii] = np.interp(lampix, lampix+fitshift[ii], ssub[ii])
+
+    #pdb.set_trace()
+
+    if dispaxis==1:
+        skymodel = skymodel.transpose()
+
+    return skymodel, shiftmodel, newssub, newssub2
+
+
+
+
+
+[docs]
+def defringe_sinusoid(subframe, badpixelmask=None, nmed=5, dispaxis=0, spatial_index=None, period_limits=[20, 100], retall=False, gain=None, readnoise=None, bictest=False, sinonly=False):
+    """
+    Use simple fitting to subtract fringes and sky background.
+
+    subframe : NumPy array
+      data subframe containing sky data to be subtracted (and,
+      perhaps, an object's spectral trace). Assumed to be in ADU, not
+      electrons (see gain and readnoise)
+    
+    nmed : int
+      Size of window for 2D median filter (to reject bad pixels, etc.)
+
+    dispaxis : int
+        set dispersion axis: 0 = horizontal and 1 = vertical
+
+    spatial_index : None, or 1D NumPy array of type bool
+       Which spatial rows (if dispaxis=0) to use when fitting the tilt
+       of sky lines across the spectrum.  If you want to use all, set
+       to None.  If you want to ignore some (e.g., because there's a
+       bright object's spectrum there) then set those rows' elements
+       of spatial_index to 'False'.
+
+     period_limits : 2-sequence
+       Minimum and maximum periods (in pixels) of fringe signals to
+       accept as 'valid.' Resolution elements with best-fit periods
+       outside this range will only be fit by a linear trend.
+
+     gain : scalar
+       Gain of detector, in electrons per ADU (where 'subframe' is in
+       units of ADUs).
+
+     readnoise : scalar
+       Readnoise of detector, in electrons (where 'subframe' is in
+       units of ADUs).
+
+     bictest : bool
+       If True, use 'gain' and 'readnoise' to compute the Bayesian
+       Information Criterion (BIC) for each fit; a sinusoid is only
+       removed if BIC(sinusoid fit) is lower than BIC(constant fit).
+
+     sinonly : bool
+       If True, the output "model 2D spectrum" will only contain the
+       sinusoidal component.  Otherwise, it will contain DC and
+       linear-trend terms.
+
+    :NOTES:
+       Note that (in my experience!) this approach works better when
+       you set all weights to unity, rather than using the suggested
+       (photon + read noise) variances.
+
+    :REQUIREMENTS:
+       :doc:`analysis` (for :func:`analysis.fmin`)
+
+       :doc:`phasecurves` (for :func:`phasecurves.errfunc`)
+
+       :doc:`lomb` (for Lomb-Scargle periodograms)
+
+       SciPy 'signal' module (for median-filtering)
+    
+
+    Returns the best-fit sky frame.
+    """
+    # 2012-09-19 16:22 IJMC: Created
+    # 2012-12-16 15:11 IJMC: Made some edits to fix bugs, based on outlier indexing.
+
+    from scipy import signal
+    import lomb
+    from analysis import fmin # scipy.optimize.fmin would also be O.K.
+    from phasecurves import errfunc
+
+    twopi = np.pi*2
+
+    fudgefactor = 1.5
+
+    def ripple(params, x):  # Sinusoid + constant offset
+        if params[1]==0:
+            ret = 0.0
+        else:
+            ret = params[0] * np.cos(twopi*x/params[1] - params[2]) + params[3]
+        return ret
+
+    def linripple(params, x): # Sinusoid + linear trend
+        if params[1]==0:
+            ret = 0.0
+        else:
+            ret = params[0] * np.cos(twopi*x/params[1] - params[2]) + params[3] + params[4]*x
+        return ret
+
+    # Parse inputs
+    if not isinstance(subframe, np.ndarray):
+        subframe = pyfits.getdata(subframe)
+
+    if gain is None: gain = 1
+    if readnoise is None: readnoise = 1
+
+    if badpixelmask is None:
+        badpixelmask = np.zeros(subframe.shape, dtype=bool)
+    elif isinstance(badpixelmask, str):
+        badpixelmask = pyfits.getdata(badpixelmask).astype(bool)
+    else:
+        badpixelmask = np.array(badpixelmask).astype(bool)
+
+    if dispaxis==1:
+        subframe = subframe.transpose()
+        badpixelmask = badpixelmask.transpose()
+
+    sub = subframe
+    if nmed > 1:
+        sdat = signal.medfilt2d(sub, nmed)
+    else:
+        sdat = sub.copy()
+
+    if bictest:
+        var_sdat = np.abs(sdat)/gain + (readnoise/gain)**2
+
+    npix, nlam = sdat.shape
+    if spatial_index is None:
+        spatial_index = np.ones(npix, dtype=bool)
+    else:
+        spatial_index = np.array(spatial_index, copy=False).astype(bool)
+
+    ##############################
+
+    periods = np.logspace(0.5, np.log10(npix), npix*2)
+
+    x = np.arange(npix)
+    allfits = np.zeros((nlam, 5))
+
+    for jj in range(nlam):
+        vec = sdat[:, jj].copy()
+        this_index = spatial_index * (True - badpixelmask[:, jj])
+
+        #if jj==402: pdb.set_trace()
+        if this_index.sum() > 1:
+            # LinFit the data;  exclude values inconsistent with a sinusoid:
+            linfit = an.polyfitr(x[this_index], vec[this_index], 1, 3)
+            linmodel = (x) * linfit[0] + linfit[1]
+            vec2 = vec - (linmodel - linfit[1])
+            maxexcursion = an.dumbconf(vec2[this_index], .683)[0] * (fudgefactor / .88)
+            index = this_index * (np.abs(vec2 - np.median(vec2[this_index])) <= (6*maxexcursion))
+
+            # Use Lomb-Scargle to find strongest period:
+            #lsp = lomb.lomb(vec2[index], x[index], twopi/periods)
+            freqs, lsp = lomb.fasper(x[index], vec2[index], 12., 0.5)
+            guess_period = (1./freqs[lsp==lsp.max()]).mean()
+            # If the best-fit period is within our limits, fit it:
+            if (guess_period <= period_limits[1]) and (guess_period >= period_limits[0]):
+                #periods2 = np.arange(guess_period-1, guess_period+1, 0.02)
+                #lsp2 = lomb.lomb(vec2[index], x[index], twopi/periods2)
+                #guess_period = periods2[lsp2==lsp2.max()].mean()
+                guess_dc = np.median(vec2[index])
+                guess_amp = an.dumbconf(vec2[index], .683, mid=guess_dc)[0] / 0.88
+                guess = [guess_amp, guess_period, np.pi, guess_dc]
+                if bictest:
+                    w = 1./var_sdat[:,jj][index]
+                else:
+                    w = np.ones(index.sum())
+
+                fit = fmin(errfunc, guess, args=(ripple, x[index], vec2[index], w), full_output=True, disp=False)
+                guess2 = np.concatenate((fit[0], [linfit[0]]))
+                fit2 = fmin(errfunc, guess2, args=(linripple, x[index], vec[index], w), full_output=True, disp=False)
+                if bictest:
+                    ripple_model = linripple(fit2[0], x)
+                    bic_ripple = (w*(vec - ripple_model)[index]**2).sum() + 6*np.log(index.sum())
+                    bic_linfit = (w*(vec - linmodel)[index]**2).sum() + 2*np.log(index.sum())
+                    #if jj==546:
+                    #    pdb.set_trace()
+                    if bic_ripple >= bic_linfit:
+                        fit2 = [[0, 1e9, 0, linfit[1], linfit[0]], 0]
+
+                
+
+            else: # Clearly not a valid ripple -- just use the linear fit.
+                fit2 = [[0, 1e11, 0, linfit[1], linfit[0]], 0]
+
+        else: # *NO* good pixels!
+            #linfit = an.polyfitr(x[spatial_index], vec[spatial_index], 1, 3)
+            
+            fit2 = [[0, 1e11, 0, np.median(vec[spatial_index]), 0], 0]
+
+        allfits[jj] = fit2[0]
+
+
+    # Median filter the resulting coefficients
+    if nmed > 1: # No median-filtering
+        newfits = np.array([signal.medfilt(fits, nmed) for fits in allfits.transpose()]).transpose()
+        newfits[:, 2] = allfits[:,2] # Don't smooth phase
+    else:
+        newfits = allfits
+
+    # Generate the model sky pattern
+    skymodel = np.zeros((npix, nlam))
+    for jj in range(nlam):
+        if sinonly:
+            coef = newfits[jj].copy()
+            coef[3:] = 0.
+        else:
+            coef = newfits[jj]
+        skymodel[:, jj] = linripple(coef, x)
+
+
+    if dispaxis==1:
+        skymodel = skymodel.transpose()
+
+
+    if retall:
+        ret = skymodel, allfits
+    else:
+        ret = skymodel
+
+    return ret
+
+
+
+#
+
+[docs]
+def makexflat(subreg, xord, nsigma=3, minsnr=10, minfrac=0.5, niter=1):
+    """Helper function for XXXX.
+
+    :INPUTS:
+      subreg : 2D NumPy array
+        spectral subregion, containing spectral background, sky,
+        and/or target flux measurements.
+
+      xord : scalar or sequence
+        Order of polynomial by which each ROW will be normalized. If
+        niter>0, xord can be a sequence of length (niter+1).  A good
+        approach for, e.g., spectral dome flats is to set niter=1 and
+        xord=[15,2].
+
+      nsigma : scalar
+        Sigma-clipping level for calculation of column-by-column S/N
+
+      minsnr : scalar
+        Minimum S/N value to use when selecting 'good' columns for
+        normalization.
+
+      minfrac : scalar, 0 < minfrac < 1
+        Fraction of columns to use, selected by highest S/N, when
+        selecting 'good' columns for normalization.
+
+      niter : int
+        Number of iterations.  If set to zero, do not iterate (i.e.,
+        run precisely once through.)
+
+    :NOTES:
+      Helper function for functions XXXX
+    """
+    # 2013-01-20 14:20 IJMC: Created
+
+    ny,  nx = subreg.shape
+    xall = np.arange(2048.)
+    subreg_new = subreg.copy()
+
+    iter = 0
+    if not hasattr(xord, '__iter__'): xord = [xord]*(niter+1)
+    while iter <= niter:
+        snr = an.snr(subreg_new, axis=0, nsigma=nsigma)
+        ind = (snr > np.sort(snr)[-int(minfrac*snr.size)]) * (snr > minsnr)
+        xxx = ind.nonzero()[0]
+        norm_subreg = subreg[:,ind] / np.median(subreg[:,ind], 0)
+        coefs = np.array([an.polyfitr(xxx, row, xord[iter], 3) for row in norm_subreg])
+        xflat = np.array([np.polyval(coef0, xall) for coef0 in coefs])
+        iter += 1
+        subreg_new = subreg / xflat
+    return xflat
+
+
+
+[docs]
+def make_spectral_flats(sky, domeflat, subreg_corners, badpixelmask=None, xord_pix=[15,2], xord_sky=[2,1], yord=2, minsnr=5, minfrac_pix=0.7, minfrac_sky=0.5, locs=None, nsigma=3):
+    """
+    Construct appropriate corrective frames for multi-object
+    spectrograph data.  Specifically: corrections for irregular slit
+    widths, and pixel-by-pixel detector sensitivity variations.
+
+    sky : 2D NumPy array
+       Master spectral sky frame, e.g. from median-stacking many sky
+       frames or masking-and-stacking dithered science spectra frames.
+       This frame is used to construct a map to correct science frames
+       (taken with the identical slit mask!) for irregular sky
+       backgrounds resulting from non-uniform slit widths (e.g.,
+       Keck/MOSFIRE).
+
+       Note that the dispersion direction should be 'horizontal'
+       (i.e., parallel to rows) in this frames.
+
+    domeflat : 2D NumPy array
+       Master dome spectral flat, e.g. from median-stacking many dome
+       spectra.  This need not be normalized in the dispersion or
+       spatial directions. This frame is used to construct a flat map
+       of the pixel-to-pixel variations in detector sensitivity.
+
+       Note that the dispersion direction should be 'horizontal'
+       (i.e., parallel to rows) in this frames.
+
+    subreg_corners : sequence of 2- or 4-sequences
+        Indices (or merely starting- and ending-rows) for each
+        subregion of interest in 'sky' and 'domeflat' frames.  Syntax
+        should be that of :func:`tools.extractSubregion`, or such that
+        subreg=sky[subreg_corners[0]:subreg_corners[1]]
+
+    badpixelmask : 2D NumPy array, or str
+        Nonzero for any bad pixels; these will be interpolated over
+        using :func:`nsdata.bfixpix`.
+
+    xord_pix : sequence
+        Polynomial orders for normalization in dispersion direction of
+        pixel-based flat (dome flat) on successive iterations; see
+        :func:`makexflat`.
+
+    xord_sky : sequence
+        Polynomial orders for normalization in dispersion direction of
+        slit-based correction (sky frame) on successive iterations;
+        see :func:`makexflat`.
+
+    yord : scalar
+        Polynomial order for normalization of pixel-based (dome) flat
+        in spatial direction.
+
+    locs : None, or sequence
+        Row-index in each subregion of the location of the
+        spectral-trace-of-interest.  Only used for rectifying of sky
+        frame; leaving this at None will have at most a mild
+        effect. If not None, should be the same length as
+        subreg_corners.  If subreg_corners[0] = [800, 950] then
+        locs[0] might be set to, e.g., 75 if the trace lies in the
+        middle of the subregion.
+
+
+    :RETURNS:
+        wideslit_skyflat, narrowslit_domeflat
+
+    :EXAMPLE:
+     ::
+
+
+        try:
+            from astropy.io import fits as pyfits
+        except:
+            import pyfits
+        
+        import spec
+        import ir
+
+        obs = ir.initobs('20121010')
+        sky = pyfits.getdata(obs._raw + 'masktersky.fits')
+        domeflat = pyfits.getdata(obs._raw + 'mudflat.fits')
+        allinds = [[7, 294], [310, 518], [532, 694], [710, 960], [976, 1360], [1378, 1673], [1689, 2022]]
+        locs = [221, 77, 53, 88, 201, 96, 194]
+
+        skycorrect, pixcorrect = spec.make_spectral_flats(sky, domeflat, allinds, obs.badpixelmask, locs=locs)
+    """
+    # 2013-01-20 14:40 IJMC: Created
+    from tools import extractSubregion
+    from nsdata import bfixpix
+    
+    # Check inputs:
+    if not isinstance(sky, np.ndarray):
+        sky = pyfits.getdata(sky)
+    if not isinstance(domeflat, np.ndarray):
+        domeflat = pyfits.getdata(domeflat)
+
+    ny0, nx0 = sky.shape
+
+    if badpixelmask is None:
+        badpixelmask = np.zeros((ny0, nx0), dtype=bool)
+    if not isinstance(badpixelmask, np.ndarray):
+        badpixelmask = pyfits.getdata(badpixelmask)
+
+    # Correct any bad pixels:
+    if badpixelmask.any():
+        bfixpix(sky, badpixelmask)
+        bfixpix(domeflat, badpixelmask)
+
+    wideslit_skyflat = np.ones((ny0, nx0), dtype=float)
+    narrowslit_domeflat = np.ones((ny0, nx0), dtype=float)
+
+    # Loop through all subregions specified:
+    for jj in range(len(subreg_corners)):
+        # Define extraction & alignment indices:
+        specinds = np.array(subreg_corners[jj]).ravel().copy()
+        if len(specinds)==2:
+            specinds = np.concatenate(([0, nx0], specinds))
+        if locs is None:
+            loc = np.mean(specinds[2:4])
+        else:
+            loc = locs[jj]
+  
+        skysub = extractSubregion(sky, specinds, retall=False)
+        domeflatsub = extractSubregion(domeflat, specinds, retall=False)
+        badsub = extractSubregion(badpixelmask, specinds, retall=False)
+        ny, nx = skysub.shape
+        yall = np.arange(ny)
+  
+        # Normalize Dome Spectral Flat in X-direction (rows):
+        xflat_dome = makexflat(domeflatsub, xord_pix, minsnr=minsnr, minfrac=minfrac_pix, niter=len(xord_pix)-1, nsigma=nsigma)
+        ymap = domeflatsub*0.0
+        xsubflat = domeflatsub / np.median(domeflatsub, 0) / xflat_dome
+  
+        # Normalize Dome Spectral Flat in Y-direction (columns):
+        ycoefs = [an.polyfitr(yall, xsubflat[:,ii], yord, nsigma) for ii in range(nx)]
+        yflat = np.array([np.polyval(ycoef0, yall) for ycoef0 in ycoefs]).transpose()
+        pixflat = domeflatsub / (xflat_dome * yflat * np.median(domeflatsub, 0))
+        bogus = (pixflat< 0.02) + (pixflat > 50)
+        pixflat[bogus] = 1.
+  
+        # Normalize Sky spectral Flat in X-direction (rows):
+        askysub = scaleSpectralSky_cor(skysub/pixflat, badsub, pord=2, refpix=loc, nmed=1)
+        xflat_sky = makexflat(askysub[1], xord_sky, minsnr=minsnr, minfrac=minfrac_sky, niter=len(xord_sky)-1, nsigma=nsigma)
+  
+        wideslit_skyflat[specinds[2]:specinds[3], specinds[0]:specinds[1]] = xflat_sky
+        narrowslit_domeflat[specinds[2]:specinds[3], specinds[0]:specinds[1]] = pixflat
+  
+        print "Done with subregion %i" % (jj+1)
+    
+    return wideslit_skyflat, narrowslit_domeflat
+
+
+[docs]
+def calibrate_stared_mosfire_spectra(scifn, outfn, skycorrect, pixcorrect, subreg_corners, **kw):
+    """Correct non-dithered WIDE-slit MOSFIRE spectral frames for:
+          pixel-to-pixel nonuniformities (i.e., traditional flat-fielding)
+          
+          detector nonlinearities
+
+          tilted spectral lines
+
+          non-uniform slit widths (which cause non-smooth backgrounds) 
+
+       Note that the dispersion direction should be 'horizontal'
+       (i.e., parallel to rows) in this frame.
+
+    :INPUTS:
+       scifn : str
+         Filename of raw, uncalibrated science frame (in ADUs, not electrons)
+
+       outfn : str
+         Name into which final, calibrated file  should be written.
+
+       skycorrect : str or 2D NumPy array
+         Slitloss correction map (i.e., for slits of nonuniform width),
+         such as generated by :func:`make_spectral_flats`.
+
+       pixcorrect : str or 2D NumPy array
+         Pixel-by-pixel sensitivity correction map (i.e., flat field),
+         such as generated by :func:`make_spectral_flats`.
+
+       subreg_corners : sequence of 2- or 4-sequences
+         Indices (or merely starting- and ending-rows) for each
+         subregion of interest in 'sky' and 'domeflat' frames.  Syntax
+         should be that of :func:`tools.extractSubregion`, or such that
+         subreg=sky[subreg_corners[0]:subreg_corners[1]]
+
+       linearize : bool
+         Whether to linearity-correct the data.
+
+         If linearizing: linearity correction is computed & applied
+         AFTER applying the pixel-by-pixel (flatfield) correction, but
+         BEFORE the slitloss (sky) correction.
+
+
+
+       bkg_radii : 2-sequence
+           Inner and outer radius for background computation and removal;
+           measured in pixels from the center of the profile.  The values 
+           [11,52] seems to work well for MOSFIRE K-band spectra.
+
+    locs : None, or sequence
+        Row-index in each subregion of the location of the
+        spectral-trace-of-interest.  Only used for rectifying of sky
+        frame; leaving this at None will have at most a mild
+        effect. If not None, should be the same length as
+        subreg_corners.  If subreg_corners[0] = [800, 950] then
+        locs[0] might be set to, e.g., 75 if the trace lies in the
+        middle of the subregion.
+
+       gain: scalar
+           Detector gain, in electrons per ADU
+
+       readnoise: scalar
+           Detector readnoise, in electrons
+
+      polycoef : str, None, or sequence
+        Polynomial coefficients for detector linearization: see
+        :func:`ir.linearity_correct` and :func:`ir.linearity_mosfire`.
+
+      unnormalized_flat : str or 2D NumPy array
+        If not 'None', this is used to define the subregion header
+        keywords ('subreg0', 'subreg1', etc.) for each slit's
+        two-dimensional spectrum.  These keywords are required by much
+        of my extraction machinery!
+
+
+    :EXAMPLE:
+      ::
+
+
+        try:
+            from astropy.io import fits as pyfits
+        except:
+            import pyfits
+        
+        import spec
+        import ir
+
+        obs = ir.initobs('20121010')
+        allinds = [[7, 294], [310, 518], [532, 694], [710, 960], [976, 1360], [1378, 1673], [1689, 2022]]
+        locs = [221, 77, 53, 88, 201, 96, 194]
+        unnorm_flat_fn = obs._raw + 'mudflat.fits'
+        if False:
+           sky = pyfits.getdata(obs._raw + 'masktersky.fits')
+           unnorm_domeflat = pyfits.getdata(unnorm_flat_fn)
+           skycorrect, pixcorrect = spec.make_spectral_flats(sky, unnorm_domeflat, allinds, badpixelmask=obs.badpixelmask, locs=locs)
+        else:
+           skycorrect = obs._raw + 'skycorrect.fits'
+           pixcorrect = obs._raw + 'pixcorrect.fits'
+
+        linearcoef='/Users/ianc/proj/transit/data/mosfire_linearity/linearity/mosfire_linearity_cnl_coefficients_bic-optimized.fits'
+        rawfn = obs.rawsci
+        outfn = obs.procsci
+        spec.calibrate_stared_mosfire_spectra(rawfn, outfn, skycorrect, pixcorrect, allinds, linearize=True, badpixelmask=obs.badpixelmask, locs=locs, polycoef=linearcoef, verbose=True, clobber=True, unnormalized_flat=unnorm_flat_fn)
+
+    :NOTES:
+      This routine is *slow*, mostly because of the call to
+      :func:`defringe_sinusoid`.  Consider running multiple processes
+      in parallel, to speed things up!
+
+    :SEE_ALSO:
+      :func:`ir.mosfire_speccal`, :func:`defringe_sinusoid`
+    """
+    # 2013-01-20 16:17 IJMC: Created.
+    # 2013-01-25 15:59 IJMC: Fixed various bugs relating to
+    #                        determinations of subregion boundaries.
+    
+
+    try:
+        from astropy.io import fits as pyfits
+    except:
+        import pyfits
+    
+    from nsdata import bfixpix
+    import ir
+    import os
+    from tools import extractSubregion, findRectangles
+    import sys
+
+    #pdb.set_trace()
+
+    if not isinstance(scifn, str) and hasattr(scifn, '__iter__'):
+        for scifn0, outfn0 in zip(scifn, outfn):
+            calibrate_stared_mosfire_spectra(scifn0, outfn0, skycorrect, pixcorrect, subreg_corners, **kw)
+
+    else:
+        # Set defaults:
+        names = ['linearize', 'bkg_radii', 'gain', 'readnoise', 'polycoef', 'verbose', 'badpixelmask', 'locs', 'clobber', 'unnormalized_flat']
+        defaults = [False, [11, 52], 2.15, 21.1, None, False, None, None, False, None]
+        for n,d in zip(names, defaults):
+            exec('%s = kw["%s"] if kw.has_key("%s") else d' % (n, n, n))
+
+
+        # Check inputs:
+        scihdr = pyfits.getheader(scifn)
+        sci = pyfits.getdata(scifn)
+        exptime = scihdr['TRUITIME']
+        ncoadd  = scihdr['COADDONE'] 
+        nread   = scihdr['READDONE']
+
+        if not isinstance(pixcorrect, np.ndarray):
+            scihdr.update('SLITFLAT', pixcorrect)
+            pixcorrect = pyfits.getdata(pixcorrect)
+        else:
+            scihdr.update('PIXFLAT', 'User-specified data array')
+
+        if not isinstance(skycorrect, np.ndarray):
+            scihdr.update('SLITFLAT', skycorrect)
+            skycorrect = pyfits.getdata(skycorrect)
+        else:
+            scihdr.update('SLITFLAT', 'User-specified data array')
+
+        if unnormalized_flat is not None:
+            if not isinstance(unnormalized_flat, np.ndarray):
+                scihdr.update('MUDFLAT', unnormalized_flat)
+                unnormalized_flat = pyfits.getdata(unnormalized_flat)
+            else:
+                scihdr.update('MUDFLAT', 'User-specified data array')
+
+        ny0, nx0 = sci.shape
+
+        if badpixelmask is None:
+            badpixelmask = np.zeros((ny0, nx0), dtype=bool)
+        if not isinstance(badpixelmask, np.ndarray):
+            badpixelmask = pyfits.getdata(badpixelmask)
+
+        # Correct any bad pixels:
+        if badpixelmask.any():
+            bfixpix(sci, badpixelmask)
+
+        if linearize:
+            # If you edit this section, be sure to update the same
+            # section in ir.mosfire_speccal!
+            fmap = ir.makefmap_mosfire() 
+            sci_lin, liniter = ir.linearity_correct(sci/pixcorrect, nread-1, ncoadd, 1.45, exptime, fmap, ir.linearity_mosfire, verbose=verbose, linfuncargs=dict(polycoef=polycoef), retall=True)
+            ir.hdradd(scihdr, 'COMMENT', 'Linearity-corrected by calibrate_stared_mosfire_spectra.')
+            ir.hdradd(scihdr, 'LIN_ITER', liniter)
+            if isinstance(polycoef, str):
+                ir.hdradd(scihdr, 'LINPOLY', os.path.split(polycoef)[1])
+            else:
+                if isinstance(polycoef, np.ndarray) and polycoef.ndim==3:
+                    ir.hdradd(scihdr, 'LINPOLY', 'user-input array used for linearity correction.')
+                else:
+                    ir.hdradd(scihdr, 'LINPOLY', str(polycoef))
+
+        else:
+            sci_lin = sci/pixcorrect
+            ir.hdradd(scihdr, 'COMMENT', 'NOT linearity-corrected.')
+
+
+        newframe = np.zeros((ny0, nx0), dtype=float)
+
+
+        # Loop through all subregions specified:
+        for jj in range(len(subreg_corners)):
+            # Define extraction & alignment indices:
+            specinds = np.array(subreg_corners[jj]).ravel().copy()
+            if len(specinds)==2:
+                specinds = np.concatenate(([0, nx0], specinds))
+            if locs is None:
+                loc = np.mean(specinds[2:4])
+            else:
+                loc = locs[jj]
+
+            # Prepare various necessities:
+            scisub = extractSubregion(sci, specinds, retall=False)
+            badsub = extractSubregion(badpixelmask, specinds, retall=False)
+            slitflat = extractSubregion(skycorrect, specinds, retall=False)
+            ny, nx = scisub.shape
+            yall = np.arange(ny)
+
+
+            # Define subregion boundaries from the flat-field frame.
+            # Ideally, this should go *outside* the loop (no need to
+            # re-calculate this for every file...)
+            if unnormalized_flat is not None:
+                samplemask, boundaries = ir.spectral_subregion_mask(unnormalized_flat)
+                samplemask *= (unnormalized_flat > 500)
+                samplemask_corners = findRectangles(samplemask, minsepy=42, minsepx=150)
+                nspec = samplemask_corners.shape[0]
+                for kk in range(nspec):
+                    ir.hdradd(scihdr, 'SUBREG%i' % kk, str(samplemask_corners[kk]))
+                ir.hdradd(scihdr, 'NSUBREG', nspec)
+
+
+
+            # Align the subregion, so that sky lines run along detector columns:
+            aligned_scisub = scaleSpectralSky_cor(scisub/slitflat, badsub, pord=2, refpix=loc, nmed=1)
+
+            # Model the sky background, using linear trends plus a sinusoid:
+            if locs is None:
+                spatind = np.ones(ny, dtype=bool)
+            else:
+                spatind = (np.abs(np.arange(ny) - loc) > bkg_radii[0]) * (np.abs(np.arange(ny) - loc) < bkg_radii[1])
+
+            sscisub = defringe_sinusoid(aligned_scisub[1], badpixelmask=badsub, period_limits=[9,30], gain=gain, readnoise=readnoise, bictest=False, spatial_index=spatind, nmed=1)
+
+            # Compute the corrected subregion:
+            aligned_flattened_skycorrected_subregion = \
+                (aligned_scisub[1] - sscisub) * slitflat + aligned_scisub[3]
+
+            #try2 = (aligned_scisub[1] - sscisub) * slitflat + aligned_scisub
+
+            newframe[specinds[2]:specinds[3],specinds[0]:specinds[1]] = aligned_flattened_skycorrected_subregion
+
+            print "Done with subregion %i" % (jj+1)
+
+        scihdr.update('MOSCAL', 'Calibrated by calibrate_stared_mosfire_spectra')
+        
+        pyfits.writeto(outfn, newframe.astype(float), scihdr, clobber=clobber)
+
+    return 
+
+
+
+[docs]
+def reconstitute_gmos_roi(infile, outfile, **kw):
+    """Convert GMOS frames taken with custom ROIs into standard FITS frames.
+
+    :INPUTS:
+      in : string or sequence of strings
+        Input filename or filenames.
+
+      out : string or sequence of strings
+        Output filename or filenames.
+
+    :OPTIONS:
+      clobber : bool
+        Passed to PyFITS; whether to overwrite existing files.
+        """
+    # 2013-03-28 21:37 IJMC: Created at the summit of Mauna Kea
+
+    import ir
+
+    def trimlims(instr):
+        trimchars = '[]:,'
+        for trimchar in trimchars:
+            instr = instr.replace(trimchar, ' ')
+        return map(float, instr.strip().split())
+        
+    
+    # Handle recursive case:
+    if not isinstance(infile, str):
+        for thisin, thisout in zip(infile, outfile):
+            reconstitute_gmos_roi(thisin, thisout, **kw)
+        return
+
+
+    # Handle non-recursive (single-file) case:
+    file = pyfits.open(infile)
+    hdr = file[0].header
+    nroi = hdr['detnroi']
+    biny, binx = map(int, file[1].header['ccdsum'].split())
+
+    detsize = trimlims(hdr['detsize'])
+    binning = 2
+    frame = np.zeros(((detsize[3]-detsize[2]+1)/binx, (detsize[1]-detsize[0]+1)/biny), dtype=int)
+    print frame.shape
+    
+    # Read in the coordinates for each ROI:
+    roi_xys = np.zeros((nroi, 4), dtype=int)
+    for ii in range(1, nroi+1):
+        roi_xys[ii-1, 0] = (hdr['detro%ix' % ii] - 1) 
+        roi_xys[ii-1, 1] = (hdr['detro%ix' % ii] + hdr['detro%ixs' % ii] - 1) 
+        roi_xys[ii-1, 2] = (hdr['detro%iy' % ii] - 1) /binx
+        roi_xys[ii-1, 3] = (hdr['detro%iy' % ii] - 1)/binx + hdr['detro%iys' % ii] - 1
+        
+    # Read in the C
+    nhdus = len(file) - 1
+    detsecs = np.zeros((nhdus, 4), dtype=int)
+    datasecs = np.zeros((nhdus, 4), dtype=int)
+    biassecs = np.zeros((nhdus, 4), dtype=int)
+    for ii in range(nhdus):
+        detsecs[ii] = trimlims(file[ii+1].header['detsec'])
+        datasecs[ii] = trimlims(file[ii+1].header['datasec'])
+        biassecs[ii] = trimlims(file[ii+1].header['biassec'])
+        di2 = (detsecs[ii,0]-1)/biny, detsecs[ii,1]/biny, (detsecs[ii,2]-1)/binx, detsecs[ii,3]/binx
+        thisdat = file[ii+1].data
+        #pdb.set_trace()
+        if biassecs[ii,0]==1025:
+            frame[di2[2]:di2[3], di2[0]:di2[1]] = thisdat[:, :-32]
+        elif biassecs[ii,0]==1:
+            frame[di2[2]:di2[3], di2[0]:di2[1]] = thisdat[:, 32:]
+        else:
+            print 'bombed. bummer!  I should have written a better function'        
+
+    file.close()
+
+    hdr = pyfits.getheader(infile)
+    for ii in range(nroi):
+        ir.hdradd(hdr, 'SUBREG%i' % ii, '[%i %i %i %i]' % tuple(roi_xys[ii]))
+
+    pyfits.writeto(outfile, frame, hdr, **kw)
+
+    return #detsecs, datasecs, biassecs
+
+[docs]
+def rotationalProfile(delta_epsilon, delta_lam):
+    """Compute the rotational profile of a star, assuming solid-body
+    rotation and linear limb darkening.
+
+    This uses Eq. 18.14 of Gray's Photospheres, 2005, 3rd Edition.
+
+    :INPUTS:
+
+      delta_epsilon : 2-sequence
+
+        [0] : delta_Lambda_L = lambda * V * sin(i)/c; the rotational
+              displacement at the stellar limb.
+
+        [1] : epsilon, the linear limb darkening coefficient, used in
+              the relation I(theta) = I0 + epsilon * (cos(theta) - 1).
+
+        [2] : OPTIONAL! The central location of the profile (otherwise
+              assumed to be located at delta_lam=0).
+
+      delta_lam : scalar or sequence
+        Wavelength minus offset: Lambda minus lambda_0.  Grid upon
+        which computations will be done.
+
+    :EXAMPLE:
+      ::
+
+        import pylab as py
+        import spec
+
+        dlam = py.np.linspace(-2, 2, 200) # Create wavelength grid
+        profile = spec.rotationalProfile([1, 0.6], dlam)
+
+        py.figure()
+        py.plot(dlam, profile)
+    """
+    # 2013-05-26 10:37 IJMC: Created.
+
+    delta_lambda_L, epsilon = delta_epsilon[0:2]
+    if len(delta_epsilon)>2:  # optional lambda_offset
+        lamdel2 = 1. - ((delta_lam - delta_epsilon[2])/delta_lambda_L)**2
+    else:
+        lamdel2 = 1. - (delta_lam/delta_lambda_L)**2
+    
+    if not hasattr(delta_lam, '__iter__'):
+        delta_lam = np.array([delta_lam])
+
+    ret = (4*(1.-epsilon) * np.sqrt(lamdel2) + np.pi*epsilon*lamdel2) / \
+        (2*np.pi * delta_lambda_L * (1. - epsilon/3.))    
+
+    ret[lamdel2<0] = 0.
+
+    return ret
+
+
+[docs]
+def modelline(param, prof2, dv):
+  """Generate a rotational profile, convolve it with a second input
+  profile, normalize it (simply), and return.
+  
+  :INPUTS:
+    param : 1D sequence
+      param[0:3]  -- see :func:`rotationalProfile`
+      param[4]  -- multiplicative scaling factor
+
+    dv : velocity grid
+
+    prof2 : second input profile
+  """
+  # 2013-08-07 09:55 IJMC: Created
+  nk = dv.size
+  rot_model = rotationalProfile(param[0:3], dv)  
+  conv_rot_model = np.convolve(rot_model, prof2, 'same')
+  norm_conv_rot_model = conv_rot_model - (conv_rot_model[0] + (conv_rot_model[-1] - conv_rot_model[0])/(nk - 1.) * np.arange(nk))  
+  return norm_conv_rot_model * param[3]
\ No newline at end of file
diff --git a/scripts/templates/psd_freq.gp b/scripts/templates/psd_freq.gp
new file mode 100644
index 0000000..9b595e7
--- /dev/null
+++ b/scripts/templates/psd_freq.gp
@@ -0,0 +1,29 @@
+# This method assumes y values are in logarithm units already.
+
+set terminal pngcairo
+set output "%OUTPUTFILE"
+set termoption dash
+set termopt enhanced
+
+set title "%TITLE\n{/*0.7 %SUBTITLE}"
+set xlabel "Temporal Frequency [days^{-1}]"
+set xrange [0.005:0.620]
+
+# Use this if you want to show PSD directly on a log axis
+# set logscale xy
+# set ylabel "PSD"
+# set yrange [%YMIN:%YMAX]
+# plot '%FILE1' using 1:(10**($2+2)):3:4 with xyerrorbars title "%LABEL1", \
+# '%FILE2' using 1:(10**($2+2)):3:4 with xyerrorbars title "%LABEL2"
+
+# Use this if you want to show the log value of the PSD
+set logscale x
+set ylabel "log_{10} PSD"
+#set yrange [%YMIN:%YMAX]
+#set yrange [-5.5:0.5]
+
+set key outside
+set key center bottom
+
+plot '%FILE1' using 1:2:3:4 with xyerrorbars title "%LABEL1", \
+'%FILE2' using 1:2:3:4 with xyerrorbars title "%LABEL2"
diff --git a/scripts/templates/thor_submit.pbs b/scripts/templates/thor_submit.pbs
new file mode 100644
index 0000000..1878de9
--- /dev/null
+++ b/scripts/templates/thor_submit.pbs
@@ -0,0 +1,9 @@
+#!/bin/sh -login
+#PBS -l mem=64GB
+#PBS -l nodes=1:ppn=1:research
+#PBS -l walltime=48:00:00
+#PBS -N "%NAME%"
+#PBS -j oe
+
+cd %ROOTDIR%
+time ~/psdlag-agn/bin/psdlag '%ROOTDIR%/%ARGSFILE%' > '%ROOTDIR%/%OUTPUTFILE%'
diff --git a/scripts/templates/timelag_compare.gp b/scripts/templates/timelag_compare.gp
new file mode 100644
index 0000000..446b60a
--- /dev/null
+++ b/scripts/templates/timelag_compare.gp
@@ -0,0 +1,22 @@
+set terminal pngcairo
+set termoption dash
+set termopt enhanced
+set output "%OUTPUTFILE"
+set encoding utf8
+
+set title "%TITLE\n{/*0.7 %SUBTITLE}"
+set xlabel "Temporal Frequency [days^{-1}]"
+set xrange [0.005:0.620]
+set logscale x
+
+set ylabel "Lag [days]"
+#set yrange [%YMIN:%YMAX]
+#set yrange [-5:7]
+set key outside
+set key center bottom
+
+# Draw line at origin
+set arrow from 0.005,0 to 0.620,0 nohead lt 3 lc rgb 'black'
+set pointsize 0
+plot '%FILE1' using 1:2:3:4 with xyerrorbars title "%LABEL1", \
+"%FILE2" using 1:2:3:4 with xyerrorbars title "%LABEL2"
diff --git a/scripts/templates/timelag_freq.gp b/scripts/templates/timelag_freq.gp
new file mode 100644
index 0000000..19217eb
--- /dev/null
+++ b/scripts/templates/timelag_freq.gp
@@ -0,0 +1,17 @@
+set terminal png
+set termoption dash
+set output "%OUTPUTFILE"
+
+set title "%TITLE\n{/*0.7 %SUBTITLE}"
+set xlabel "Temporal Frequency [days^{-1}]"
+set xrange [0.005:0.620]
+set logscale x
+
+set ylabel "Lag [days]"
+#set yrange [%YMIN:%YMAX]
+
+# Draw line at origin
+set arrow from 0.005,0 to 0.603,0 nohead lt 3 lc rgb 'black'
+
+
+plot '%FILE1' using 1:2:3:4 with xyerrorbars title "%LABEL1"
diff --git a/scripts/tophat_fft.pl b/scripts/tophat_fft.pl
new file mode 100755
index 0000000..74ac733
--- /dev/null
+++ b/scripts/tophat_fft.pl
@@ -0,0 +1,26 @@
+#!/usr/bin/env perl
+
+use feature say;
+use utf8; 
+use PDL::FFT;
+
+my $PI = 3.1415926539;
+my $N = 64;
+my ($series, $other_series);
+for (my $k=0; $k<$N; $k++) {
+  $series->[$k] = sin(4*$k*$PI/$N) + cos(6*$k*$PI/$N);
+}
+my $fft = new Math::FFT($series);
+my $coeff = $fft->rdft();
+my $spectrum = $fft->spctrm;
+my $original_data = $fft->invrdft($coeff);
+
+for (my $k=0; $k<$N; $k++) {
+  $other_series->[$k] = sin(16*$k*$PI/$N) + cos(8*$k*$PI/$N);
+}
+my $other_fft = $fft->clone($other_series);
+my $other_coeff = $other_fft->rdft();
+my $correlation = $fft->correl($other_fft);
+
+
+sub top_hat 
\ No newline at end of file
diff --git a/src/.smhist b/src/.smhist
deleted file mode 100644
index 439f404..0000000
--- a/src/.smhist
+++ /dev/null
@@ -1,47 +0,0 @@
-data check.dat
-read f 1
-read pow 2
-limits f pow
-box
-ptype 10 3
-points f pow
-quit
-data check.dat
-read f 1
-read pow 2
-limits f pow
-ptype 10 3
-points f pow
-box
-quit
-data check.dat
-read f 1
-read pow 2
-limits f pow
-box
-ptype 10 3
-points f pow
-set lgf = log10(f)
-set lgf = alog10(f)
-set lgf = lg(f)
-erase
-box
-erase
-limits lgf pow
-points lgf pow
-box
-erase
-limits lgf 0. 3.
-box
-points lgf pow
-quit
-data check.dat
-read 1 f
-read f 1
-read pow 2
-set lgf = lg(f)
-limits lgf pow
-box
-ptype 10 3
-points lgf pow
-quit
diff --git a/src/README b/src/README
deleted file mode 100644
index f095c4e..0000000
--- a/src/README
+++ /dev/null
@@ -1,32 +0,0 @@
-
-
-
-To compile, modify the file makefile to so that libdir and incdir point to where
-alglib library and header files are installed respectively, then run: make
-
-
-FILES:
-- README: this file
-- makefile: a make file for compiling
-- main.cpp: the main file, that process the input file, print help message if
-  needed and reads the light curves files. check readLC to see the light curve
-  format.
-- mod.cpp: contains the class mod, which is a base class that uses the likelihood method. This on its own does nothing, but it is meant to be inherited by others, e.g. psd , lag , lag10
-  etc ... It is here where the likelihood is calculated, and maximized.
-
-- psd.cpp: a child class of mod to calculate psds. psd10 is similar by it
-  calculate the log10 of psd. This is better than calculating the psd directly.
-
-- lag.cpp: a child class of mod to calculate cross spectra and phase lags.
-  similar to psd. there is also lag10 that calcualte the log10 of the cross
-  spectrum plus phase lag.
-
-- psdlag.cpp: a child class of mod to calculate psd and cross spec and phase
-  lag at the same time.
-
-- inc: contains the header files.
-
-- inc/mod.hpp: the header for mod.cpp. This file has the Mod class also, which
-  is a class that holds multiple mod's used for fitting multiple light curve
-  simultaneusly, each mod fits for one light curve, and the total log-likelihood
-  is the sum of individual likelihood (i.e. product of probabilities)
diff --git a/src/archive/newsrc.tar b/src/archive/newsrc.tar
new file mode 100644
index 0000000..cbc313c
Binary files /dev/null and b/src/archive/newsrc.tar differ
diff --git a/src/archive/oldsrc.tar b/src/archive/oldsrc.tar
new file mode 100644
index 0000000..8f1eca2
Binary files /dev/null and b/src/archive/oldsrc.tar differ
diff --git a/src/check.dat b/src/check.dat
deleted file mode 100644
index 0dd0f5a..0000000
--- a/src/check.dat
+++ /dev/null
@@ -1,6 +0,0 @@
-2.5e-5	4.681
-5.5e-5	3.358
-8.5e-5	2.911
-1.5e-4	2.650
-2.5e-4	1.864
-3.5e-4	0.605
diff --git a/src/fittedlags.dat b/src/fittedlags.dat
deleted file mode 100644
index a48c299..0000000
--- a/src/fittedlags.dat
+++ /dev/null
@@ -1,6 +0,0 @@
-1.022e-01 9.694e-01
-3.260e-01 9.192e-01
-5.118e-01 9.776e-01
-3.254e-01 9.269e-01
-1.000e+00 9.659e-01
-1.000e+00 9.891e-01
diff --git a/src/alglibinternal.cpp b/src/inc/alglib/alglibinternal.cpp
similarity index 100%
rename from src/alglibinternal.cpp
rename to src/inc/alglib/alglibinternal.cpp
diff --git a/src/alglibinternal.h b/src/inc/alglib/alglibinternal.h
similarity index 100%
rename from src/alglibinternal.h
rename to src/inc/alglib/alglibinternal.h
diff --git a/src/alglibmisc.cpp b/src/inc/alglib/alglibmisc.cpp
similarity index 100%
rename from src/alglibmisc.cpp
rename to src/inc/alglib/alglibmisc.cpp
diff --git a/src/alglibmisc.h b/src/inc/alglib/alglibmisc.h
similarity index 100%
rename from src/alglibmisc.h
rename to src/inc/alglib/alglibmisc.h
diff --git a/src/ap.cpp b/src/inc/alglib/ap.cpp
similarity index 100%
rename from src/ap.cpp
rename to src/inc/alglib/ap.cpp
diff --git a/src/ap.h b/src/inc/alglib/ap.h
similarity index 100%
rename from src/ap.h
rename to src/inc/alglib/ap.h
diff --git a/src/dataanalysis.cpp b/src/inc/alglib/dataanalysis.cpp
similarity index 100%
rename from src/dataanalysis.cpp
rename to src/inc/alglib/dataanalysis.cpp
diff --git a/src/dataanalysis.h b/src/inc/alglib/dataanalysis.h
similarity index 100%
rename from src/dataanalysis.h
rename to src/inc/alglib/dataanalysis.h
diff --git a/src/diffequations.cpp b/src/inc/alglib/diffequations.cpp
similarity index 100%
rename from src/diffequations.cpp
rename to src/inc/alglib/diffequations.cpp
diff --git a/src/diffequations.h b/src/inc/alglib/diffequations.h
similarity index 100%
rename from src/diffequations.h
rename to src/inc/alglib/diffequations.h
diff --git a/src/fasttransforms.cpp b/src/inc/alglib/fasttransforms.cpp
similarity index 100%
rename from src/fasttransforms.cpp
rename to src/inc/alglib/fasttransforms.cpp
diff --git a/src/fasttransforms.h b/src/inc/alglib/fasttransforms.h
similarity index 100%
rename from src/fasttransforms.h
rename to src/inc/alglib/fasttransforms.h
diff --git a/src/integration.cpp b/src/inc/alglib/integration.cpp
similarity index 100%
rename from src/integration.cpp
rename to src/inc/alglib/integration.cpp
diff --git a/src/integration.h b/src/inc/alglib/integration.h
similarity index 100%
rename from src/integration.h
rename to src/inc/alglib/integration.h
diff --git a/src/interpolation.cpp b/src/inc/alglib/interpolation.cpp
similarity index 100%
rename from src/interpolation.cpp
rename to src/inc/alglib/interpolation.cpp
diff --git a/src/interpolation.h b/src/inc/alglib/interpolation.h
similarity index 100%
rename from src/interpolation.h
rename to src/inc/alglib/interpolation.h
diff --git a/src/linalg.cpp b/src/inc/alglib/linalg.cpp
similarity index 100%
rename from src/linalg.cpp
rename to src/inc/alglib/linalg.cpp
diff --git a/src/linalg.h b/src/inc/alglib/linalg.h
similarity index 100%
rename from src/linalg.h
rename to src/inc/alglib/linalg.h
diff --git a/src/optimization.cpp b/src/inc/alglib/optimization.cpp
similarity index 100%
rename from src/optimization.cpp
rename to src/inc/alglib/optimization.cpp
diff --git a/src/optimization.h b/src/inc/alglib/optimization.h
similarity index 100%
rename from src/optimization.h
rename to src/inc/alglib/optimization.h
diff --git a/src/solvers.cpp b/src/inc/alglib/solvers.cpp
similarity index 100%
rename from src/solvers.cpp
rename to src/inc/alglib/solvers.cpp
diff --git a/src/solvers.h b/src/inc/alglib/solvers.h
similarity index 100%
rename from src/solvers.h
rename to src/inc/alglib/solvers.h
diff --git a/src/specialfunctions.cpp b/src/inc/alglib/specialfunctions.cpp
similarity index 100%
rename from src/specialfunctions.cpp
rename to src/inc/alglib/specialfunctions.cpp
diff --git a/src/specialfunctions.h b/src/inc/alglib/specialfunctions.h
similarity index 100%
rename from src/specialfunctions.h
rename to src/inc/alglib/specialfunctions.h
diff --git a/src/statistics.cpp b/src/inc/alglib/statistics.cpp
similarity index 100%
rename from src/statistics.cpp
rename to src/inc/alglib/statistics.cpp
diff --git a/src/statistics.h b/src/inc/alglib/statistics.h
similarity index 100%
rename from src/statistics.h
rename to src/inc/alglib/statistics.h
diff --git a/src/stdafx.h b/src/inc/alglib/stdafx.h
similarity index 100%
rename from src/stdafx.h
rename to src/inc/alglib/stdafx.h
diff --git a/src/inc/def.hpp b/src/inc/def.hpp
index ef14350..84fa165 100644
--- a/src/inc/def.hpp
+++ b/src/inc/def.hpp
@@ -8,7 +8,7 @@
 #ifndef DEF_HPP_
 #define DEF_HPP_
 
-#include <ap.h>
+#include <alglib/ap.h>
 #include <iostream>
 #include <iomanip>
 
@@ -25,7 +25,7 @@ public:
 	int len; double dt; vec lc,lce,t;
 	lcurve(int n=1){t.setlength(n); lc.setlength(n); lce.setlength(n) ;len=n;dt=1.0;}
 	lcurve(vec& t_,vec& lc_,vec& lce_,double dt_){t=t_;lc=lc_;lce=lce_;len=t.length();dt=dt_;}
-	void demean(){double m=0;int i;for(i=0;i<len;i++){m+=lc[i];} m/=len;for(i=0;i<len;i++){lc[i]-=m;}}
+	double demean(){double m=0;int i;for(i=0;i<len;i++){m+=lc[i];} m/=len;for(i=0;i<len;i++){lc[i]-=m;}; return m;}
 	friend ostream& operator<<(ostream&o,lcurve&l){ o << setprecision(12);
 			for(int i=0;i<l.len;i++){o << l.t[i] << " " << l.lc[i] << " " << l.lce[i] << endl;}
 			return(o);}
diff --git a/src/inc/lag_rms.hpp b/src/inc/lag_rms.hpp
new file mode 100644
index 0000000..5c933e1
--- /dev/null
+++ b/src/inc/lag_rms.hpp
@@ -0,0 +1,58 @@
+/*
+ * lag.hpp
+ *
+ *  Created on: Jun 1, 2013
+ *      Author: azoghbi
+ */
+
+#ifndef LAG_RMS_HPP_
+#define LAG_RMS_HPP_
+
+#include "mod.hpp"
+
+class lag_rms : public mod {
+
+	int		n1;
+	vec		p1,p2;
+	ivec	icx,iphi;
+	double	mean1,mean2,mean1sq,mean2sq,mean12;
+
+	void	_C( vec );
+	void	_dC( vec , int );
+	void	_dCx( vec , int );
+	void	_dPhi( vec , int );
+
+
+public:
+	lag_rms( lcurve , lcurve , vec , vec );
+	virtual ~lag_rms();
+
+	void	step_pars( int , vec& , vec& );
+	void	print_pars( vec& , vec& );
+};
+
+// ----------------------------------- //
+
+class lag10_rms : public mod {
+
+	int		n1;
+	vec		p1,p2;
+	ivec	icx,iphi;
+	double	mean1,mean2,mean1sq,mean2sq,mean12;
+
+	void	_C( vec );
+	void	_dC( vec , int );
+	void	_dCx( vec , int );
+	void	_dPhi( vec , int );
+
+
+public:
+	lag10_rms( lcurve , lcurve , vec , vec );
+	virtual ~lag10_rms();
+
+	void	step_pars( int , vec& , vec& );
+	void	print_pars( vec& , vec& );
+	void	what_pars( int& , int& );
+};
+
+#endif /* LAG_HPP_ */
diff --git a/src/inc/main.hpp b/src/inc/main.hpp
index ffc4e4f..72571d0 100644
--- a/src/inc/main.hpp
+++ b/src/inc/main.hpp
@@ -16,8 +16,11 @@
 
 #include "def.hpp"
 #include "psd.hpp"
+#include "psd_rms.hpp"
 #include "lag.hpp"
+#include "lag_rms.hpp"
 #include "psdlag.hpp"
+#include "psdlag_rms.hpp"
 #include "mcmc.hpp"
 
 
@@ -27,14 +30,28 @@ void readLC( vector<vector<lcurve> >&, string , int , int , int , bool );
 
 
 double mcmc_lag10( vec& x ,void*ptr ){
-	int i,np = x.length(); for(i=0;i<np/2;i++){if(x[i]>7){x[i]=7;}if(x[i]<-7){x[i]=-7;}}
-	Mod<lag10> *mod = (Mod<lag10>*) ptr; double logl=mod->loglikelihood(x);
+	int i,hnp,np = x.length();hnp=np/2; double phi,twopi=2*M_PI;
+	for(i=0;i<hnp;i++){
+		if(x[i]>7){x[i]=7;}if(x[i]<-7){x[i]=-7;}
+		phi = x[i+hnp]+M_PI;
+		while(phi>twopi){phi -= twopi;}
+		while(phi<0){phi += twopi;}
+		x[i+hnp] = phi-M_PI;
+	}
+	Mod<lag10_rms> *mod = (Mod<lag10_rms>*) ptr; double logl=mod->loglikelihood(x);
 	return logl;
 }
 
 double mcmc_psdlag10( vec& x ,void*ptr ){
-	int i,np = x.length(); for(i=0;i<np/2;i++){if(x[i]>7){x[i]=7;}if(x[i]<-7){x[i]=-7;}}
-	Mod<psdlag10> *mod = (Mod<psdlag10>*) ptr; double logl=mod->loglikelihood(x);
+	int i,hnp,np = x.length();hnp=np/2; double phi,twopi=2*M_PI;
+	for(i=0;i<hnp;i++){
+		if(x[i]>7){x[i]=7;}if(x[i]<-7){x[i]=-7;}
+		phi = x[i+hnp]+M_PI;
+		while(phi>twopi){phi -= twopi;}
+		while(phi<0){phi += twopi;}
+		x[i+hnp] = phi-M_PI;
+	}
+	Mod<psdlag10_rms> *mod = (Mod<psdlag10_rms>*) ptr; double logl=mod->loglikelihood(x);
 	return logl;
 }
 
diff --git a/src/inc/mcmc.hpp b/src/inc/mcmc.hpp
index ef50efb..7cfb0e4 100644
--- a/src/inc/mcmc.hpp
+++ b/src/inc/mcmc.hpp
@@ -8,8 +8,8 @@
 #ifndef MCMC_HPP_
 #define MCMC_HPP_
 
-#include <ap.h>
-#include <alglibmisc.h>
+#include <alglib/ap.h>
+#include <alglib/alglibmisc.h>
 #include <fstream>
 #include <iostream>
 #include <iomanip>
diff --git a/src/inc/mod.hpp b/src/inc/mod.hpp
index 008dbe6..fa24844 100644
--- a/src/inc/mod.hpp
+++ b/src/inc/mod.hpp
@@ -9,8 +9,8 @@
 #define MOD_HPP_
 
 #include "def.hpp"
-#include <specialfunctions.h>
-#include <solvers.h>
+#include <alglib/specialfunctions.h>
+#include <alglib/solvers.h>
 
 class mod {
 
@@ -125,7 +125,8 @@ public:
 			// ---------- //
 			vector<int> ic,iv; sing = 0;
 			for( i=0; i <npar ; i++ ){ if( fabs(grad[i]) < 1e-5 ){ sing=1;ic.push_back(i); }else { iv.push_back(i);}}
-			if( sing == 1 and iv.size()!=0 ){
+			//if( sing == 1 and iv.size()!=0 ){
+			if( sing == 1 ){
 				np = iv.size();
 				vec	g; vec2 h,hi,iii; g.setlength(np); h.setlength(np,np); hi.setlength(np,np);iii.setlength(np,np);
 				for( i=0 ; i<np ; i++ ){
@@ -184,15 +185,19 @@ public:
 		vec		tmppars,dpar,grad,prevp; vec2 hess,hessi,ii;
 		tmppars.setlength(npar); dpar.setlength(npar); grad.setlength(npar); prevp.setlength(npar);
 		hess.setlength( npar , npar );ii.setlength(npar,npar);hessi.setlength(npar,npar);
-		vector<int> ic,iv; np = npar - 1;
-		vec	g; vec2 h,hi,iii; g.setlength(np); h.setlength(np,np); hi.setlength(np,np);iii.setlength(np,np);
-		ic.push_back(k);
+		vector<int> ic,iv;
+		vec	g; vec2 h,hi,iii;
+
+		dlikelihood( pars , loglike , grad , hess );
+		if( fabs(grad[k])<1e-5 ) { return loglike;}
 
 		for( i=0 ; i<npar ; i++ ){
 			tmppars[i] = pars[i]; dpar[i] = 0.01*pars[i]; ii(i,i)=1;for(j=0;j<i;j++){ii(i,j)=0;ii(j,i)=0;}
 			prevp[i] = pars[i];
-			if(i==k){continue;} iv.push_back(i);
+			if(i==k or fabs(grad[i])<1e-5 ){ic.push_back(i);}else{ iv.push_back(i); }
 		}
+		np = npar - ic.size();
+		g.setlength(np); h.setlength(np,np); hi.setlength(np,np);iii.setlength(np,np);
 
 
 		for( n=1 ; n<= nmax ; n++ ){
@@ -241,8 +246,8 @@ public:
 		errors( pars , errs2 , -1 );
 	}
 
-	void	errors( vec& pars , vec& errs , int sign=-1){
-		double		tol = 1e-3 , delchi = 1;
+	void	errors( vec& pars , vec& errs , int sign=1){
+		double		tol = 1e-2 , delchi = 1;
 		int			i,ip,ip1,ip2;
 		double		pu,pd,phalf=0,bestlike,tmplike,dl;
 		vec			pars0,errs0,tmpp; pars0.setlength(npar);errs0.setlength(npar);tmpp.setlength(npar);
@@ -253,6 +258,7 @@ public:
 
 		for( ip=ip1 ; ip<ip2 ; ip++ ){
 			cout << endl << "******* par " << ip << " *******" << endl << endl;
+			if( errs0[ip]>10 ) { continue ;}
 			pd	=	pars[ip];
 			for( i=0 ;i<npar ; i++ ){tmpp[i] = pars0[i]; }
 
diff --git a/src/inc/psd_rms.hpp b/src/inc/psd_rms.hpp
new file mode 100644
index 0000000..dce3e40
--- /dev/null
+++ b/src/inc/psd_rms.hpp
@@ -0,0 +1,41 @@
+/*
+ * psd.hpp
+ *
+ *  Created on: May 31, 2013
+ *      Author: azoghbi
+ */
+
+#ifndef PSD_RMS_HPP_
+#define PSD_RMS_HPP_
+
+#include "mod.hpp"
+
+class psd_rms: public mod {
+
+	double	mean,mean2;
+
+	void	_C( vec );
+	void	_dC( vec , int );
+
+public:
+	psd_rms( lcurve , vec );
+	virtual ~psd_rms();
+};
+
+// ---------------------- //
+
+class psd10_rms: public mod {
+
+	double	mean,mean2;
+
+	void	_C( vec );
+	void	_dC( vec , int );
+
+public:
+	psd10_rms( lcurve , vec );
+	virtual ~psd10_rms();
+
+	void	step_pars( int , vec& , vec& );
+};
+
+#endif /* PSD_RMS_HPP_ */
diff --git a/src/inc/psdlag_rms.hpp b/src/inc/psdlag_rms.hpp
new file mode 100644
index 0000000..81efdf8
--- /dev/null
+++ b/src/inc/psdlag_rms.hpp
@@ -0,0 +1,56 @@
+/*
+ * psdlag.hpp
+ *
+ *  Created on: Jun 1, 2013
+ *      Author: azoghbi
+ */
+
+#include "mod.hpp"
+
+#ifndef PSDLAG_RMS_HPP_
+#define PSDLAG_RMS_HPP_
+
+class psdlag_rms : public mod {
+
+	int		n1;
+	ivec	ip1,ip2,icx,iphi;
+	double	mean1,mean2,mean1sq,mean2sq,mean12;
+
+	void	_C( vec );
+	void	_dC( vec , int );
+	void	_dP1( vec , int );
+	void	_dP2( vec , int );
+	void	_dCx( vec , int );
+	void	_dPhi( vec , int );
+
+public:
+	psdlag_rms( lcurve , lcurve , vec );
+	virtual ~psdlag_rms();
+
+	void	step_pars( int , vec& , vec& );
+	void	print_pars( vec& , vec& );
+};
+
+class psdlag10_rms : public mod {
+
+	int		n1;
+	ivec	ip1,ip2,icx,iphi;
+	double	mean1,mean2,mean1sq,mean2sq,mean12;
+
+	void	_C( vec );
+	void	_dC( vec , int );
+	void	_dP1( vec , int );
+	void	_dP2( vec , int );
+	void	_dCx( vec , int );
+	void	_dPhi( vec , int );
+
+public:
+	psdlag10_rms( lcurve , lcurve , vec );
+	virtual ~psdlag10_rms();
+
+	void	step_pars( int , vec& , vec& );
+	void	print_pars( vec& , vec& );
+	void	what_pars( int&, int& );
+};
+
+#endif /* PSDLAG_RMS_HPP_ */
diff --git a/src/lag_rms.cpp b/src/lag_rms.cpp
new file mode 100644
index 0000000..9ac355a
--- /dev/null
+++ b/src/lag_rms.cpp
@@ -0,0 +1,230 @@
+/*
+ * lag.cpp
+ *
+ *  Created on: Jun 1, 2013
+ *      Author: azoghbi
+ */
+
+#include "inc/lag_rms.hpp"
+
+lag_rms::lag_rms( lcurve lc1 , lcurve lc2 , vec fqL , vec pars ) {
+
+	// ----------- initial parameters ------------ //
+	n1		=	lc1.len;
+	n		=	n1 + lc2.len;
+	dt		=	lc1.dt;
+	// ------------------------------------------ //
+
+
+	// ----------- light curve setup ------------ //
+	setlc();
+	mean1	=	lc1.demean(); mean2	=	lc2.demean();
+	mean1sq	=	mean1*mean1; mean2sq = mean2*mean2; mean12 = mean1*mean2;
+	int		i;
+	for( i=0 ; i<n1 ; i++ ){
+		lc[i]	=	lc1.lc[i];
+		lce[i]	=	lc1.lce[i]*lc1.lce[i];
+		t[i]	=	lc1.t[i];
+	}
+	for( i=n1 ; i<n ; i++ ){
+		lc[i]	=	lc2.lc[i-n1];
+		lce[i]	=	lc2.lce[i-n1]*lc2.lce[i-n1];
+		t[i]	=	lc2.t[i-n1];
+	}
+	// ------------------------------------------ //
+
+	// ----------- parent initilizer ------------ //
+	init( fqL , 2 );
+	// ------------------------------------------ //
+
+	// --------- constants and indices ---------- //
+	p1.setlength(nfq); p2.setlength(nfq); icx.setlength(nfq); iphi.setlength(nfq);
+	for( i=0 ; i<nfq ; i++ ){
+		p1[i]	=	pars[i] * mean1sq;
+		p2[i]	=	pars[i+nfq] * mean2sq;
+		icx[i]	=	i;
+		iphi[i]	=	i+nfq;
+	}
+	// ------------------------------------------ //
+
+}
+
+lag_rms::~lag_rms() {}
+
+void lag_rms::_C( vec p ){
+	int	i,j,k; double d;
+	for( i=0 ; i<nfq ; i++ ){ p[icx[i]] *= mean12;}
+	for( i=0 ; i<n1 ; i++ ){
+		d = 0; for( k=0 ; k<nfq ; k++ ){ d += p1[k]*Cfq[k](i,i); } d += lce[i]; C(i,i) = d;
+		for( j=0 ; j<i ; j++ ){ d = 0; for( k=0 ; k<nfq ; k++ ){ d += p1[k]*Cfq[k](i,j); } C(i,j) = d;}
+	}
+	for( i=n1 ; i<n ; i++ ){
+		d = 0; for( k=0 ; k<nfq ; k++ ){ d += p2[k]*Cfq[k](i,i); } d += lce[i]; C(i,i) = d;
+		for( j=n1 ; j<i ; j++ ){ d = 0; for( k=0 ; k<nfq ; k++ ){ d += p2[k]*Cfq[k](i,j); } C(i,j) = d;}
+		for( j=0 ; j<n1 ; j++ ){ d = 0; for( k=0 ; k<nfq ; k++ ){
+			d += p[icx[k]] * ( Cfq[k](i,j)*cos(p[iphi[k]]) - Sfq[k](i,j)*sin(p[iphi[k]])); } C(i,j) = d;}
+	}
+}
+
+void lag_rms::_dC( vec p , int k ){
+	if( k<nfq ){	_dCx( p , k );
+	}else{			_dPhi( p , k-nfq );}
+}
+
+void lag_rms::_dCx( vec p , int k ){
+	int	i,j; double phi=p[iphi[k]];
+	for( i=0 ; i<n1 ; i++ ){ for( j=0 ; j<=i ; j++ ){ dC(i,j) = 0; dC(j,i) = 0;} }
+	for( i=n1 ; i<n ; i++ ){
+		for( j=n1 ; j<=i ; j++ ){ dC(i,j) = 0; dC(j,i) = 0;}
+		for( j=0 ; j<n1 ; j++ ){
+			dC(i,j) = ( Cfq[k](i,j)*cos(phi) - Sfq[k](i,j)*sin(phi) ); dC(j,i) = dC(i,j);}
+	}
+}
+
+void lag_rms::_dPhi( vec p , int k ){
+	int	i,j; double cx=p[k]*mean12,phi=p[iphi[k]];
+	for( i=0 ; i<n1 ; i++ ){ for( j=0 ; j<=i ; j++ ){ dC(i,j) = 0; dC(j,i) = 0;} }
+	for( i=n1 ; i<n ; i++ ){
+		for( j=n1 ; j<=i ; j++ ){ dC(i,j) = 0; dC(j,i) = 0;}
+		for( j=0 ; j<n1 ; j++ ){
+			dC(i,j) = cx * ( -Cfq[k](i,j)*sin(phi) - Sfq[k](i,j)*cos(phi) ); dC(j,i) = dC(i,j);}
+	}
+}
+
+
+void lag_rms::step_pars( int n , vec& dpar , vec& pars ){
+	for( int i=0 ; i<npar ; i++ ){
+		if( dpar[i]>3000 ){dpar[i] = 3000;} if( dpar[i]<-3000 ){dpar[i] = -3000;}
+		pars[i] += dpar[i]/((n<10)?10:1);
+	}
+}
+
+
+void lag_rms::print_pars( vec& pars , vec& errs ){
+	for( int i=0 ; i<nfq ; i++ ){
+		if( pars[i]<0 ){ pars[i]*=-1; pars[i+nfq] += M_PI;}
+		while( pars[i+nfq] > M_PI ){ pars[i+nfq] -= 2*M_PI; }
+		while( pars[i+nfq] <-M_PI ){ pars[i+nfq] += 2*M_PI; }
+	}
+	mod::print_pars( pars , errs );
+}
+
+
+// ****************************************** //
+
+
+lag10_rms::lag10_rms( lcurve lc1 , lcurve lc2 , vec fqL , vec pars ) {
+
+	// ----------- initial parameters ------------ //
+	n1		=	lc1.len;
+	n		=	n1 + lc2.len;
+	dt		=	lc1.dt;
+	// ------------------------------------------ //
+
+
+	// ----------- light curve setup ------------ //
+	setlc();
+	mean1	=	lc1.demean(); mean2	=	lc2.demean();
+	mean1sq	=	mean1*mean1; mean2sq = mean2*mean2; mean12 = mean1*mean2;
+	int		i;
+	for( i=0 ; i<n1 ; i++ ){
+		lc[i]	=	lc1.lc[i];
+		lce[i]	=	lc1.lce[i]*lc1.lce[i];
+		t[i]	=	lc1.t[i];
+	}
+	for( i=n1 ; i<n ; i++ ){
+		lc[i]	=	lc2.lc[i-n1];
+		lce[i]	=	lc2.lce[i-n1]*lc2.lce[i-n1];
+		t[i]	=	lc2.t[i-n1];
+	}
+	// ------------------------------------------ //
+
+	// ----------- parent initilizer ------------ //
+	init( fqL , 2 );
+	// ------------------------------------------ //
+
+	// --------- constants and indices ---------- //
+	p1.setlength(nfq); p2.setlength(nfq); icx.setlength(nfq); iphi.setlength(nfq);
+	for( i=0 ; i<nfq ; i++ ){
+		p1[i]	=	pow(10,pars[i]) * mean1sq;
+		p2[i]	=	pow(10,pars[i+nfq]) * mean2sq;
+		icx[i]	=	i;
+		iphi[i]	=	i+nfq;
+	}
+	// ------------------------------------------ //
+
+}
+
+lag10_rms::~lag10_rms() {}
+
+void lag10_rms::_C( vec p ){
+	int	i,j,k; double d; for(i=0;i<nfq;i++){p[icx[i]] = pow(10,p[i]) * mean12;}
+	for( i=0 ; i<n1 ; i++ ){
+		d = 0; for( k=0 ; k<nfq ; k++ ){ d += p1[k]*Cfq[k](i,i); }
+		d += f1*p1[0]*Cfq2[0](i,i) + f2*p1[nfq-1]*Cfq2[2](i,i);
+		d += lce[i]; C(i,i) = d;
+		for( j=0 ; j<i ; j++ ){ d = 0; for( k=0 ; k<nfq ; k++ ){ d += p1[k]*Cfq[k](i,j); }
+		d += f1*p1[0]*Cfq2[0](i,j) + f2*p1[nfq-1]*Cfq2[2](i,j);
+		C(i,j) = d;}
+	}
+	for( i=n1 ; i<n ; i++ ){
+		d = 0; for( k=0 ; k<nfq ; k++ ){ d += p2[k]*Cfq[k](i,i); }
+		d += f1*p2[0]*Cfq2[0](i,i) + f2*p2[nfq-1]*Cfq2[2](i,i);
+		d += lce[i]; C(i,i) = d;
+		for( j=n1 ; j<i ; j++ ){ d = 0; for( k=0 ; k<nfq ; k++ ){ d += p2[k]*Cfq[k](i,j); }
+		d += f1*p2[0]*Cfq2[0](i,j) + f2*p2[nfq-1]*Cfq2[2](i,j);
+		C(i,j) = d;}
+		for( j=0 ; j<n1 ; j++ ){ d = 0; for( k=0 ; k<nfq ; k++ ){
+			d += p[icx[k]] * ( Cfq[k](i,j)*cos(p[iphi[k]]) - Sfq[k](i,j)*sin(p[iphi[k]])); }
+		d += f1*p[icx[0]] * ( Cfq2[0](i,j)*cos(p[iphi[0]]) - Sfq2[0](i,j)*sin(p[iphi[0]]) );
+		d += f2*p[icx[nfq-1]] * ( Cfq2[2](i,j)*cos(p[iphi[nfq-1]]) - Sfq2[2](i,j)*sin(p[iphi[nfq-1]]) );
+		C(i,j) = d;}
+	}
+}
+
+void lag10_rms::_dC( vec p , int k ){
+	if( k<nfq ){	_dCx( p , k );
+	}else{			_dPhi( p , k-nfq );}
+}
+
+void lag10_rms::_dCx( vec p , int k ){
+	int	i,j; double phi=p[iphi[k]],cx = log(10)*pow(10,p[k])*mean12;
+	for( i=0 ; i<n1 ; i++ ){ for( j=0 ; j<=i ; j++ ){ dC(i,j) = 0; dC(j,i) = 0;} }
+	for( i=n1 ; i<n ; i++ ){
+		for( j=n1 ; j<=i ; j++ ){ dC(i,j) = 0; dC(j,i) = 0;}
+		for( j=0 ; j<n1 ; j++ ){
+			dC(i,j) = cx * ( Cfq[k](i,j)*cos(phi) - Sfq[k](i,j)*sin(phi) ); dC(j,i) = dC(i,j);}
+	}
+}
+
+void lag10_rms::_dPhi( vec p , int k ){
+	int	i,j; double cx=pow(10,p[k])*mean12,phi=p[iphi[k]];
+	for( i=0 ; i<n1 ; i++ ){ for( j=0 ; j<=i ; j++ ){ dC(i,j) = 0; dC(j,i) = 0;} }
+	for( i=n1 ; i<n ; i++ ){
+		for( j=n1 ; j<=i ; j++ ){ dC(i,j) = 0; dC(j,i) = 0;}
+		for( j=0 ; j<n1 ; j++ ){
+			dC(i,j) = cx * ( -Cfq[k](i,j)*sin(phi) - Sfq[k](i,j)*cos(phi) ); dC(j,i) = dC(i,j);}
+	}
+}
+
+
+void lag10_rms::step_pars( int n , vec& dpar , vec& pars ){
+	for( int i=0 ; i<npar ; i++ ){
+		if( dpar[i]>3 ){dpar[i] = 3;} if( dpar[i]<-3 ){dpar[i] = -3;}
+		//pars[i] += dpar[i];
+		pars[i] += dpar[i]/((n<5)?10:1);
+	}
+}
+
+
+void lag10_rms::print_pars( vec& pars , vec& errs ){
+	for( int i=0 ; i<nfq ; i++ ){
+		while( pars[i+nfq] > M_PI ){ pars[i+nfq] -= 2*M_PI; }
+		while( pars[i+nfq] <-M_PI ){ pars[i+nfq] += 2*M_PI; }
+	}
+	mod::print_pars( pars , errs );
+}
+
+void lag10_rms::what_pars( int& ip1 , int& ip2 ){
+	ip1 = nfq; ip2 = npar;
+}
diff --git a/src/main.cpp b/src/main.cpp
index de883de..a17e05c 100644
--- a/src/main.cpp
+++ b/src/main.cpp
@@ -35,10 +35,9 @@ int main( int argc , char* argv[] ){
 void do_work( char* fname ){
 
 	/************   Reading the input file  ***********/
-
 	ifstream fp(fname);
-	string line,mcmcfile; stringstream ss;
-	int i,nfiles,nfq,mode,npar,bin1,bin2,fit_type,nrun,nburn,nwk; bool strict;
+	string line,mcmcfile,colon; stringstream ss;
+	int i,nfiles,nfq,mode,npar,bin1,bin2,fit_type,nrun,nburn,nwk,ref; bool strict;
 	getline(fp,line);ss.str(line); ss >> nfiles;								ss.clear();
 	vector<string> files(nfiles);ivec secL;secL.setlength(nfiles);for(i=0;i<nfiles;i++){
 	getline(fp,line); ss.str(line); ss >> files[i] >> secL[i];					ss.clear();}
@@ -47,7 +46,12 @@ void do_work( char* fname ){
 	getline(fp,line); ss.str(line); ss >> mode;									ss.clear();
 	npar = (mode==0)?4*nfq:nfq; vec pars; pars.setlength(npar);
 	getline(fp,line); ss.str(line); for(i=0;i<npar;i++){ss >> pars[i];}			ss.clear();
-	getline(fp,line); ss.str(line); ss >> bin1 >> bin2;							ss.clear();
+	getline(fp,line); ss.str(line); ss >> ref >> colon >> bin2;			ss.clear();
+	if(colon.compare(0,1,":") == 0 ){
+		bin1	=	atoi(colon.substr(1).c_str());
+	}else{
+		bin1	=	ref; ref = -10; bin2 = atoi(colon.c_str());
+	}
 	if(mode!=0){bin1 = (mode==-1)?-1:(mode-1);bin2=-1;}
 	getline(fp,line); ss.str(line); ss >> fit_type;								ss.clear();
 	getline(fp,line); ss.str(line); ss >> i; strict	= i!=0;						ss.clear();
@@ -56,47 +60,59 @@ void do_work( char* fname ){
 	/*********   END Reading the input file  **********/
 
 	/**********  Read the light curves  **************/
-	vector<vector<lcurve> > LC;
-	for(i=0;i<nfiles;i++) readLC( LC , files[i] , secL[i] , bin1 , bin2 , strict );
+	vector<vector<lcurve> > LC,LC_ref;
+	if ( ref == -10 ){
+		for(i=0;i<nfiles;i++)  readLC( LC , files[i] , secL[i] , bin1 , bin2 , strict );
+	}else {
+		for(i=0;i<nfiles;i++) {
+			if( i!=ref ) { readLC( LC , files[i] , secL[i] , bin2 , -1 , strict ); }
+		}
+		readLC( LC_ref , files[ref] , secL[ref] , bin1 , -1 , strict );
+	}
 	/********  END Read the light curves  ************/
 
-
 	if( mode > 0 or mode==-1 ){
 		vec errs; errs.setlength(nfq);
 		vector<lcurve> lc1; for( i=0 ; i<int(LC.size()) ; i++ ){ lc1.push_back( LC[i][0]); }
-		Mod<psd10>	p1( lc1 , fqL );
+		Mod<psd10_rms>	p1( lc1 , fqL );
 
 		p1.optimize( pars , errs );
 		if( fit_type==1 ) p1.errors( pars , errs );
-	}else if( mode == 0 or mode==-1 ){
+	}else if( mode == 0 ){
 		vec ec,e1,e2,errs; ec.setlength(2*nfq);e1.setlength(nfq);e2.setlength(nfq);errs.setlength(4*nfq);
-		vector<lcurve> lc1,lc2; for( i=0 ; i<int(LC.size()) ; i++ ){ lc1.push_back( LC[i][0]); lc2.push_back( LC[i][1]); }
-		Mod<psd10>	p1( lc1 , fqL ), p2( lc2 , fqL );
+		vector<lcurve> lc1,lc2;
+		if( ref==-10 ){ // standard way of getting reference
+			for( i=0 ; i<int(LC.size()) ; i++ ){ lc1.push_back( LC[i][0]); lc2.push_back( LC[i][1]); }
+		}else{
+			for( i=0 ; i<int(LC_ref.size()) ; i++ ){ lc2.push_back( LC[i][0]);}
+			for( i=0 ; i<int(LC.size()) ; i++ ){ lc1.push_back( LC_ref[i][0]);}
+		}
+		Mod<psd10_rms>	p1( lc1 , fqL ), p2( lc2 , fqL );
 		vec		pars1,pars2;pars1.setlength(nfq);pars2.setlength(nfq);for(i=0;i<nfq;i++){pars1[i]=pars[i];pars2[i]=pars[i+nfq];}
 
 		p1.optimize( pars1 , e1 );
 		p2.optimize( pars2 , e2 );
 		if( fit_type==1 ) {
-			p1.errors_avg( pars1 , e1 );
-			p2.errors_avg( pars2 , e2 );
+			p1.errors( pars1 , e1 );
+			p2.errors( pars2 , e2 );
 		}
 
 		vec		pc; pc.setlength(2*nfq);
 		for(i=0;i<nfq;i++){pc[i]=pars[i+2*nfq];pc[i+nfq]=pars[i+3*nfq];pars[i]=pars1[i];pars[i+nfq]=pars2[i];pc[i]=(pars1[i]+pars2[i])/2;}
-		Mod<lag10>		l( lc1 , lc2 , fqL , pars );
+		Mod<lag10_rms>		l( lc1 , lc2 , fqL , pars );
 		l.optimize( pc , ec );
-		if( fit_type==1 ) l.errors_avg( pc , ec );
+		if( fit_type==1 ) l.errors( pc , ec );
 		if( fit_type==2 ) {
 			mcmc mc( 2*nfq , mcmc_lag10 , (void*)&l );
 			mc.nrun=nrun; mc.nburn=nburn; mc.nwk=nwk;
-			mc.run( pc , errs , mcmcfile.c_str() );
+			mc.run( pc , ec , mcmcfile.c_str() );
 		}
 
 		for(i=0;i<nfq;i++){ pars[i+2*nfq]=pc[i]; pars[i+3*nfq]=pc[i+nfq];}
 		for(i=0;i<nfq;i++){ errs[i] = e1[i]; errs[i+nfq] = e2[i]; errs[i+2*nfq]=ec[i]; errs[i+3*nfq]=ec[i+nfq];}
 
-		Mod<psdlag10>	pl( lc1 , lc2 , fqL );
-		if( fit_type==3 ) pl.errors_avg( pars , errs );
+		Mod<psdlag10_rms>	pl( lc1 , lc2 , fqL );
+		if( fit_type==3 ) pl.errors( pars , errs );
 		if( fit_type==4 ) {
 			mcmc mc( 4*nfq , mcmc_psdlag10 , (void*)&pl );
 			mc.nrun=nrun; mc.nburn=nburn; mc.nwk=nwk;
@@ -161,6 +177,7 @@ void readLC( vector<vector<lcurve> >&LC , string fname , int secL , int b1 , int
 	/*		Initialize file stream, define some variables		*/
 	ifstream fp(fname.c_str()); string line,sdum; stringstream ss;
 	int i,j,nlc,n,nsec,ns,sl; double dt;
+
 	/*		read dt from the description line 					*/
 	getline(fp,line); ss.str(line); ss >> sdum >> dt >> nlc; ss.clear();
 
diff --git a/src/makefile b/src/makefile
index b2b2670..08bd484 100644
--- a/src/makefile
+++ b/src/makefile
@@ -1,8 +1,13 @@
 
-
-incdir='/home/caes/science/psdlag-agn/src/'
-#incdir='/Users/othoulrich/science/psdlag-agn/src'
-
-
+# original
+#libdir='/eos/azoghbi/soft/usr/lib'
+#incdir='/eos/azoghbi/soft/usr/include'
+# most systems
+#libdir='/home/caes/science/psdlag-agn/src/inc'
+#incdir='/home/caes/science/psdlag-agn/src/inc'
+# thor
+libdir='/home/research/xaw5719/psdlag-agn/src/inc'
+alglibdir='/home/research/xaw5719/psdlag-agn/src/inc/alglib'
+incdir='/home/research/xaw5719/psdlag-agn/src/inc'
 psdlag:
-	g++ *cpp -o psdlag -O3 -Wall  -I${incdir}
+	g++ *cpp inc/alglib/*cpp -o psdlag -O3 -Wall -I${incdir} -L${libdir} -L${alglibdir} -I${alglibdir}
diff --git a/src/mod.cpp b/src/mod.cpp
index 1987f22..c2dc7b0 100644
--- a/src/mod.cpp
+++ b/src/mod.cpp
@@ -32,7 +32,8 @@ void mod::init( vec fqL , int fac ){
 	Cfq2.resize( 3 );Sfq2.resize( 3 );
 	for( i=0 ; i<3 ; i++ ){ Cfq2[i].setlength( n , n ); Sfq2[i].setlength( n , n );}
 	vec		fqL2;fqL2.setlength(4); fqL2[0] = 1e-1*fqL[0]; fqL2[1] = fqL[0]; fqL2[2] = fqL[nfq]; fqL2[3] = 10*fqL[nfq];
-	f1 = 2; f2 = 0.5;
+	//f1 = 1.5; f2 = 0.6;
+	f1 = 0.5; f2 = .0;
 	_CSfq( fqL2 , Cfq2 , Sfq2 );
 	// ------------------------------------------ //
 
diff --git a/src/psd_rms.cpp b/src/psd_rms.cpp
new file mode 100644
index 0000000..0c90a2e
--- /dev/null
+++ b/src/psd_rms.cpp
@@ -0,0 +1,108 @@
+/*
+ * psd.cpp
+ *
+ *  Created on: May 31, 2013
+ *      Author: azoghbi
+ */
+
+#include "inc/psd_rms.hpp"
+
+psd_rms::psd_rms( lcurve inlc , vec fqL ) {
+
+	// ----------- initial parameters ------------ //
+	n		=	inlc.len;
+	dt		=	inlc.dt;
+	// ------------------------------------------ //
+
+
+	// ----------- light curve setup ------------ //
+	setlc();
+	mean	=	inlc.demean();
+	mean2	=	mean*mean;
+	int		i;
+	for( i=0 ; i<n ; i++ ){
+		lc[i]	=	inlc.lc[i];
+		lce[i]	=	inlc.lce[i]*inlc.lce[i];
+		t[i]	=	inlc.t[i];
+	}
+	// ------------------------------------------ //
+
+	// ----------- parent initilizer ------------ //
+	init( fqL , 1 );
+	// ------------------------------------------ //
+}
+
+psd_rms::~psd_rms() {}
+
+void psd_rms::_C( vec p ){
+	int	i,j,k; double d;
+	for( i=0 ; i<npar ; i++ ){ p[i] *= mean2; }
+	for( i=0 ; i<n ; i++ ){
+		d = 0; for( k=0 ; k<nfq ; k++ ){ d += p[k]*Cfq[k](i,i); }
+		d += f1*p[0]*Cfq2[0](i,i) + f2*p[nfq-1]*Cfq2[2](i,i);
+		d += lce[i]; C(i,i) = d;
+		for( j=0 ; j<i ; j++ ){ d = 0; for( k=0 ; k<nfq ; k++ ){ d += p[k]*Cfq[k](i,j); }
+		d += f1*p[0]*Cfq2[0](i,j) + f2*p[nfq-1]*Cfq2[2](i,j);
+		C(i,j) = d;}
+	}
+}
+
+void psd_rms::_dC( vec p , int k ){
+	int	i,j;
+	for( i=0 ; i<n ; i++ ){ for( j=0 ; j<=i ; j++ ){ dC(i,j) = Cfq[k](i,j); dC(j,i) = dC(i,j);} }
+}
+
+
+// ********************************************** //
+// ********************************************** //
+
+psd10_rms::psd10_rms( lcurve inlc , vec fqL ) {
+
+	// ----------- initial parameters ------------ //
+	n		=	inlc.len;
+	dt		=	inlc.dt;
+	// ------------------------------------------ //
+
+
+	// ----------- light curve setup ------------ //
+	setlc();
+	mean		=	inlc.demean();
+	mean2	=	mean*mean;
+	int		i;
+	for( i=0 ; i<n ; i++ ){
+		lc[i]	=	inlc.lc[i];
+		lce[i]	=	inlc.lce[i]*inlc.lce[i];
+		t[i]	=	inlc.t[i];
+	}
+	// ------------------------------------------ //
+
+	// ----------- parent initilizer ------------ //
+	init( fqL , 1 );
+	// ------------------------------------------ //
+}
+
+psd10_rms::~psd10_rms() {}
+
+void psd10_rms::_C( vec p ){
+	int	i,j,k; double d; for(i=0;i<npar;i++){p[i]=pow(10,p[i])*mean2;}
+	for( i=0 ; i<n ; i++ ){
+		d = 0; for( k=0 ; k<nfq ; k++ ){ d += p[k]*Cfq[k](i,i); }
+		d += f1*p[0]*Cfq2[0](i,i) + f2*p[nfq-1]*Cfq2[2](i,i);
+		d += lce[i]; C(i,i) = d;
+		for( j=0 ; j<i ; j++ ){ d = 0; for( k=0 ; k<nfq ; k++ ){ d += p[k]*Cfq[k](i,j); }
+		d += f1*p[0]*Cfq2[0](i,j) + f2*p[nfq-1]*Cfq2[2](i,j);
+		C(i,j) = d;}
+	}
+}
+
+void psd10_rms::_dC( vec p , int k ){
+	int	i,j; double d = log(10)*pow(10,p[k])*mean2;
+	for( i=0 ; i<n ; i++ ){ for( j=0 ; j<=i ; j++ ){ dC(i,j) = d*Cfq[k](i,j); dC(j,i) = dC(i,j);} }
+}
+
+void psd10_rms::step_pars( int n , vec& dpar , vec& pars ){
+	for( int i=0 ; i<npar ; i++ ){
+		if( dpar[i]>3 ){dpar[i] = 3;} if( dpar[i]<-3 ){dpar[i] = -3;}
+		pars[i] += dpar[i];
+	}
+}
diff --git a/src/psdlag_rms.cpp b/src/psdlag_rms.cpp
new file mode 100644
index 0000000..1f78912
--- /dev/null
+++ b/src/psdlag_rms.cpp
@@ -0,0 +1,255 @@
+/*
+ * psdlag.cpp
+ *
+ *  Created on: Jun 1, 2013
+ *      Author: azoghbi
+ */
+
+#include "inc/psdlag_rms.hpp"
+
+psdlag_rms::psdlag_rms( lcurve lc1, lcurve lc2 , vec fqL ) {
+
+	// ----------- initial parameters ------------ //
+	n1		=	lc1.len;
+	n		=	n1 + lc2.len;
+	dt		=	lc1.dt;
+	// ------------------------------------------ //
+
+
+	// ----------- light curve setup ------------ //
+	setlc();
+	mean1	=	lc1.demean(); mean2	=	lc2.demean();
+	mean1sq	=	mean1*mean1; mean2sq = mean2*mean2; mean12 = mean1*mean2;
+	int		i;
+	for( i=0 ; i<n1 ; i++ ){
+		lc[i]	=	lc1.lc[i];
+		lce[i]	=	lc1.lce[i]*lc1.lce[i];
+		t[i]	=	lc1.t[i];
+	}
+	for( i=n1 ; i<n ; i++ ){
+		lc[i]	=	lc2.lc[i-n1];
+		lce[i]	=	lc2.lce[i-n1]*lc2.lce[i-n1];
+		t[i]	=	lc2.t[i-n1];
+	}
+	// ------------------------------------------ //
+
+	// ----------- parent initilizer ------------ //
+	init( fqL , 4 );
+	// ------------------------------------------ //
+
+	// --------- constants and indices ---------- //
+	ip1.setlength(nfq); ip2.setlength(nfq); icx.setlength(nfq); iphi.setlength(nfq);
+	for( i=0 ; i<nfq ; i++ ){
+		ip1[i]	=	i;
+		ip2[i]	=	i+nfq;
+		icx[i]	=	i+2*nfq;
+		iphi[i]	=	i+3*nfq;
+	}
+	// ------------------------------------------ //
+
+}
+
+psdlag_rms::~psdlag_rms() {}
+
+void psdlag_rms::_C( vec p ){
+	int	i,j,k; double d;
+	for( i=0 ; i<nfq ; i++ ){ p[ip1[i]] *= mean1sq; p[ip2[i]] *= mean2sq; p[icx[i]] *= mean12;}
+	for( i=0 ; i<n1 ; i++ ){
+		d = 0; for( k=0 ; k<nfq ; k++ ){ d += p[ip1[k]]*Cfq[k](i,i); } d += lce[i]; C(i,i) = d;
+		for( j=0 ; j<i ; j++ ){ d = 0; for( k=0 ; k<nfq ; k++ ){ d += p[ip1[k]]*Cfq[k](i,j); } C(i,j) = d;}
+	}
+	for( i=n1 ; i<n ; i++ ){
+		d = 0; for( k=0 ; k<nfq ; k++ ){ d += p[ip2[k]]*Cfq[k](i,i); } d += lce[i]; C(i,i) = d;
+		for( j=n1 ; j<i ; j++ ){ d = 0; for( k=0 ; k<nfq ; k++ ){ d += p[ip2[k]]*Cfq[k](i,j); } C(i,j) = d;}
+		for( j=0 ; j<n1 ; j++ ){ d = 0; for( k=0 ; k<nfq ; k++ ){
+			d += p[icx[k]] * ( Cfq[k](i,j)*cos(p[iphi[k]]) - Sfq[k](i,j)*sin(p[iphi[k]])); } C(i,j) = d;}
+	}
+}
+
+void psdlag_rms::_dC( vec p , int k ){
+	if( k<nfq ){			_dP1( p , k );
+	}else if( k<2*nfq ){	_dP2( p , k%nfq );
+	}else if( k<3*nfq ){	_dCx( p , k%nfq );
+	}else{					_dPhi( p , k%nfq );}
+}
+
+void psdlag_rms::_dP1( vec p , int k ){
+	int	i,j;
+	for( i=0 ; i<n1 ; i++ ){ for( j=0 ; j<=i ; j++ ){ dC(i,j) = Cfq[k](i,j); dC(j,i) = dC(i,j);} }
+	for( i=n1 ; i<n ; i++ ){
+		for( j=n1 ; j<=i ; j++ ){ dC(i,j) = 0; dC(j,i) = 0;}
+		for( j=0 ; j<n1 ; j++ ){ dC(i,j) = 0; dC(j,i) = dC(i,j);}
+	}
+}
+
+void psdlag_rms::_dP2( vec p , int k ){
+	int	i,j;
+	for( i=0 ; i<n1 ; i++ ){ for( j=0 ; j<=i ; j++ ){ dC(i,j) = 0; dC(j,i) = dC(i,j);} }
+	for( i=n1 ; i<n ; i++ ){
+		for( j=n1 ; j<=i ; j++ ){ dC(i,j) = Cfq[k](i,j); dC(j,i) = dC(i,j);}
+		for( j=0 ; j<n1 ; j++ ){ dC(i,j) = 0; dC(j,i) = dC(i,j);}
+	}
+}
+
+void psdlag_rms::_dCx( vec p , int k ){
+	int	i,j; double phi=p[iphi[k]];
+	for( i=0 ; i<n1 ; i++ ){ for( j=0 ; j<=i ; j++ ){ dC(i,j) = 0; dC(j,i) = 0;} }
+	for( i=n1 ; i<n ; i++ ){
+		for( j=n1 ; j<=i ; j++ ){ dC(i,j) = 0; dC(j,i) = 0;}
+		for( j=0 ; j<n1 ; j++ ){
+			dC(i,j) = ( Cfq[k](i,j)*cos(phi) - Sfq[k](i,j)*sin(phi) ); dC(j,i) = dC(i,j);}
+	}
+}
+
+void psdlag_rms::_dPhi( vec p , int k ){
+	int	i,j; double cx=p[icx[k]]*mean12,phi=p[iphi[k]];
+	for( i=0 ; i<n1 ; i++ ){ for( j=0 ; j<=i ; j++ ){ dC(i,j) = 0; dC(j,i) = 0;} }
+	for( i=n1 ; i<n ; i++ ){
+		for( j=n1 ; j<=i ; j++ ){ dC(i,j) = 0; dC(j,i) = 0;}
+		for( j=0 ; j<n1 ; j++ ){
+			dC(i,j) = cx * ( -Cfq[k](i,j)*sin(phi) - Sfq[k](i,j)*cos(phi) ); dC(j,i) = dC(i,j);}
+	}
+}
+
+void psdlag_rms::step_pars( int n , vec& dpar , vec& pars ){
+	for( int i=0 ; i<npar ; i++ ){
+		if( dpar[i]>3000 ){dpar[i] = 3000;} if( dpar[i]<-3000 ){dpar[i] = -3000;}
+		pars[i] += dpar[i]/((n<10)?10:1);
+	}
+}
+
+
+void psdlag_rms::print_pars( vec& pars , vec& errs ){
+	for( int i=0 ; i<nfq ; i++ ){
+		if( pars[i+2*nfq]<0 ){ pars[i+2*nfq]*=-1; pars[i+3*nfq] += M_PI;}
+		while( pars[i+3*nfq] > M_PI ){ pars[i+3*nfq] -= 2*M_PI; }
+		while( pars[i+3*nfq] <-M_PI ){ pars[i+3*nfq] += 2*M_PI; }
+		mod::print_pars( pars , errs );
+	}
+}
+
+// ++++++++++++++++++++++++++++++++++++++++++++++++++++++ //
+
+psdlag10_rms::psdlag10_rms( lcurve lc1, lcurve lc2 , vec fqL ) {
+
+	// ----------- initial parameters ------------ //
+	n1		=	lc1.len;
+	n		=	n1 + lc2.len;
+	dt		=	lc1.dt;
+	// ------------------------------------------ //
+
+
+	// ----------- light curve setup ------------ //
+	setlc();
+	mean1	=	lc1.demean(); mean2	=	lc2.demean();
+	mean1sq	=	mean1*mean1; mean2sq = mean2*mean2; mean12 = mean1*mean2;
+	int		i;
+	for( i=0 ; i<n1 ; i++ ){
+		lc[i]	=	lc1.lc[i];
+		lce[i]	=	lc1.lce[i]*lc1.lce[i];
+		t[i]	=	lc1.t[i];
+	}
+	for( i=n1 ; i<n ; i++ ){
+		lc[i]	=	lc2.lc[i-n1];
+		lce[i]	=	lc2.lce[i-n1]*lc2.lce[i-n1];
+		t[i]	=	lc2.t[i-n1];
+	}
+	// ------------------------------------------ //
+
+	// ----------- parent initilizer ------------ //
+	init( fqL , 4 );
+	// ------------------------------------------ //
+
+	// --------- constants and indices ---------- //
+	ip1.setlength(nfq); ip2.setlength(nfq); icx.setlength(nfq); iphi.setlength(nfq);
+	for( i=0 ; i<nfq ; i++ ){
+		ip1[i]	=	i;
+		ip2[i]	=	i+nfq;
+		icx[i]	=	i+2*nfq;
+		iphi[i]	=	i+3*nfq;
+	}
+	// ------------------------------------------ //
+
+}
+
+psdlag10_rms::~psdlag10_rms() {}
+
+void psdlag10_rms::_C( vec p ){
+	int	i,j,k; double d; for(i=0;i<nfq;i++){ p[ip1[i]] = pow(10,p[ip1[i]])*mean1sq; p[ip2[i]] = pow(10,p[ip2[i]])*mean2sq; p[icx[i]] = pow(10,p[icx[i]])*mean12;}
+	for( i=0 ; i<n1 ; i++ ){
+		d = 0; for( k=0 ; k<nfq ; k++ ){ d += p[ip1[k]]*Cfq[k](i,i); } d += lce[i]; C(i,i) = d;
+		for( j=0 ; j<i ; j++ ){ d = 0; for( k=0 ; k<nfq ; k++ ){ d += p[ip1[k]]*Cfq[k](i,j); } C(i,j) = d;}
+	}
+	for( i=n1 ; i<n ; i++ ){
+		d = 0; for( k=0 ; k<nfq ; k++ ){ d += p[ip2[k]]*Cfq[k](i,i); } d += lce[i]; C(i,i) = d;
+		for( j=n1 ; j<i ; j++ ){ d = 0; for( k=0 ; k<nfq ; k++ ){ d += p[ip2[k]]*Cfq[k](i,j); } C(i,j) = d;}
+		for( j=0 ; j<n1 ; j++ ){ d = 0; for( k=0 ; k<nfq ; k++ ){
+			d += p[icx[k]] * ( Cfq[k](i,j)*cos(p[iphi[k]]) - Sfq[k](i,j)*sin(p[iphi[k]])); } C(i,j) = d;}
+	}
+}
+
+void psdlag10_rms::_dC( vec p , int k ){
+	if( k<nfq ){			_dP1( p , k );
+	}else if( k<2*nfq ){	_dP2( p , k%nfq );
+	}else if( k<3*nfq ){	_dCx( p , k%nfq );
+	}else{					_dPhi( p , k%nfq );}
+}
+
+void psdlag10_rms::_dP1( vec p , int k ){
+	int	i,j; double p1 = log(10)*pow(10,p[k])*mean1sq;
+	for( i=0 ; i<n1 ; i++ ){ for( j=0 ; j<=i ; j++ ){ dC(i,j) = p1 * Cfq[k](i,j); dC(j,i) = dC(i,j);} }
+	for( i=n1 ; i<n ; i++ ){
+		for( j=n1 ; j<=i ; j++ ){ dC(i,j) = 0; dC(j,i) = 0;}
+		for( j=0 ; j<n1 ; j++ ){ dC(i,j) = 0; dC(j,i) = dC(i,j);}
+	}
+}
+
+void psdlag10_rms::_dP2( vec p , int k ){
+	int	i,j; double p2 = log(10) * pow(10,p[ip2[k]])*mean2sq;
+	for( i=0 ; i<n1 ; i++ ){ for( j=0 ; j<=i ; j++ ){ dC(i,j) = 0; dC(j,i) = dC(i,j);} }
+	for( i=n1 ; i<n ; i++ ){
+		for( j=n1 ; j<=i ; j++ ){ dC(i,j) = p2 * Cfq[k](i,j); dC(j,i) = dC(i,j);}
+		for( j=0 ; j<n1 ; j++ ){ dC(i,j) = 0; dC(j,i) = dC(i,j);}
+	}
+}
+
+void psdlag10_rms::_dCx( vec p , int k ){
+	int	i,j; double phi=p[iphi[k]], cx=log(10)*pow(10,p[icx[k]])*mean12;
+	for( i=0 ; i<n1 ; i++ ){ for( j=0 ; j<=i ; j++ ){ dC(i,j) = 0; dC(j,i) = 0;} }
+	for( i=n1 ; i<n ; i++ ){
+		for( j=n1 ; j<=i ; j++ ){ dC(i,j) = 0; dC(j,i) = 0;}
+		for( j=0 ; j<n1 ; j++ ){
+			dC(i,j) = cx * ( Cfq[k](i,j)*cos(phi) - Sfq[k](i,j)*sin(phi) ); dC(j,i) = dC(i,j);}
+	}
+}
+
+void psdlag10_rms::_dPhi( vec p , int k ){
+	int	i,j; double cx=pow(10,p[icx[k]])*mean12,phi=p[iphi[k]];
+	for( i=0 ; i<n1 ; i++ ){ for( j=0 ; j<=i ; j++ ){ dC(i,j) = 0; dC(j,i) = 0;} }
+	for( i=n1 ; i<n ; i++ ){
+		for( j=n1 ; j<=i ; j++ ){ dC(i,j) = 0; dC(j,i) = 0;}
+		for( j=0 ; j<n1 ; j++ ){
+			dC(i,j) = cx * ( -Cfq[k](i,j)*sin(phi) - Sfq[k](i,j)*cos(phi) ); dC(j,i) = dC(i,j);}
+	}
+}
+
+void psdlag10_rms::step_pars( int n , vec& dpar , vec& pars ){
+	for( int i=0 ; i<npar ; i++ ){
+		if( dpar[i]>3 ){dpar[i] = 3;} if( dpar[i]<-3 ){dpar[i] = -3;}
+		//pars[i] += dpar[i];
+		pars[i] += dpar[i]/((n<5)?10:1);
+	}
+}
+
+
+void psdlag10_rms::print_pars( vec& pars , vec& errs ){
+	for( int i=0 ; i<nfq ; i++ ){
+		while( pars[i+3*nfq] > M_PI ){ pars[i+3*nfq] -= 2*M_PI; }
+		while( pars[i+3*nfq] <-M_PI ){ pars[i+3*nfq] += 2*M_PI; }
+	}
+	mod::print_pars( pars , errs );
+}
+
+void psdlag10_rms::what_pars( int& ip1 , int& ip2 ){
+	ip1 = 3*nfq; ip2 = 4*nfq;
+}