tt.filtwav(filterFile)Inputs:
>>> tt.filtwav('models_r06/convolved/*.fits') models_r06/convolved/2H.fits 1.662000 models_r06/convolved/2J.fits 1.235000 models_r06/convolved/2K.fits 2.159000 models_r06/convolved/BB.fits 0.440000 models_r06/convolved/BI.fits 0.790000 models_r06/convolved/BR.fits 0.640000 models_r06/convolved/BU.fits 0.360000 models_r06/convolved/BV.fits 0.550000 models_r06/convolved/I1.fits 3.550000 models_r06/convolved/I2.fits 4.493000 models_r06/convolved/I3.fits 5.731000 models_r06/convolved/I4.fits 7.872000 models_r06/convolved/LP.fits 3.761000 models_r06/convolved/M1.fits 23.680000 # MIPS on Spitzter models_r06/convolved/M2.fits 71.419998 # MIPS on Spitzter models_r06/convolved/M3.fits 155.899994 # MIPS on Spitzter models_r06/convolved/MA.fits 1200.000000 models_r06/convolved/N1.fits 350.000000 models_r06/convolved/N2.fits 442.000000 models_r06/convolved/N3.fits 750.000000 models_r06/convolved/N4.fits 862.000000 models_r06/convolved/S1.fits 12.000000 # IRAS models_r06/convolved/S2.fits 25.000000 # IRAS models_r06/convolved/S3.fits 60.000000 # IRAS models_r06/convolved/S4.fits 100.000000 # IRAS models_r06/convolved/UL.fits 3.547000 models_r06/convolved/UM.fits 4.769000 models_r06/convolved/W1.fits 443.000000 models_r06/convolved/W2.fits 863.000000 models_r06/convolved/XA.fits 8.276000 models_r06/convolved/XC.fits 12.126000 models_r06/convolved/XD.fits 14.649000 models_r06/convolved/XE.fits 21.336000-- ToddHunter - 2015-04-28