diff --git a/Brown_Walker_table_1.dat.orig b/Brown_Walker_table_1.dat.orig new file mode 100644 index 0000000..4aafc53 --- /dev/null +++ b/Brown_Walker_table_1.dat.orig @@ -0,0 +1,100 @@ +LMC 05h23m34.6s -69d45m22s 323 0.20 0.05 SFD_98 +SMC 00h52m38.0s -72d48m01s 190 0.10 0.05 SFD_98 +M31 00h42m44.5s +41d16m09s 95 0.17 0.05 SFD_98 +M82 9h55m52.43s 69d40m46.93s 20 0.16 0.05 Dalcanton_etal_2009 +Arp244 12h01m53.17s -18d52m37.92s 15 0.11 0.01 Brown_Walker_2020 +ESO138-G10 16h59m02.952s -60d12m57.67s 12 0.55 0.05 Brown_Walker_2020 +ESO287-G40 21h37m28.1842s -47d02m08.8331s 7 0.065 0.01 Brown_Walker_2020 +ESO317-32 10h28m01.6186s -42d06m38.7541s 12 0.33 0.03 Brown_Walker_2020 +ESO509-IG064 13h34m39.3s -23d40m50s 15 0.3 0.02 Brown_Walker_2020 +IC208 2h08m27.736s 6d23m41.53s 12 0.13 0.01 Brown_Walker_2020 +IC2574 10h28m23.6205s 68d24m43.4414s 15 0.08 0.01 Brown_Walker_2020 +IC5249 22h47m06.262s -64d49m55.42s 15 0.08 0.01 Brown_Walker_2020 +KUG0647+311 6h50m36.832s 31d07m00.6s 15 0.31 0.03 Brown_Walker_2020 +M33 01h33m50.8900s +30d39m36.800s 30 0.14 0.01 Brown_Walker_2020 +M51 13h29m52.698s 47d11m42.93s 15 0.05 0.01 Brown_Walker_2020 +M61 12h21m54.9275s 4d28m25.5883s 15 0.05 0.01 Brown_Walker_2020 +M66 11h20m15.026s 12d59m28.64s 15 0.07 0.01 Brown_Walker_2020 +M74 1h36m41.772s 15d47m00.46s 15 0.16 0.02 Brown_Walker_2020 +M83 13h37m00.919s -29d51m56.74s 15 0.12 0.01 Brown_Walker_2020 +M106/NGC4258 12h18m57.5046s +47d18m14.303 35 0.045 0.005 Brown_Walker_2020 +MCG-01-07-004 2h23m13.2516s -4d31m01.5168s 7 0.07 0.05 Brown_Walker_2020 +MCG-02-24-027 9h28m59.256s -14d48m27.25s 7 0.17 0.01 Brown_Walker_2020 +MCG-02-30-003 11h33m10.5799s -10d13m43.7361s 10 0.09 0.01 Brown_Walker_2020 +MCG+10-19-1 12h54m49.706s 58d52m56.46s 7 0.025 0.005 Brown_Walker_2020 +NGC088 00h21m22.12s -48d38m24.6s 20 0.045 0.005 Brown_Walker_2020 +NGC0584 1h31m20.755s -6d52m05.02s 15 0.1 0.01 Brown_Walker_2020 +NGC1097 2h46m19.059s -30d16m29.68s 15 0.05 0.01 Brown_Walker_2020 +NGC1313 3h18m16.046s -66d29m53.74s 20 0.1 0.05 Brown_Walker_2020 +NGC134 0h30m21.893s -33d14m43.26s 20 0.03 0.01 Brown_Walker_2020 +NGC1365 3h33m36.458s -36d08m26.37s 20 0.03 0.01 Brown_Walker_2020 +NGC1371 3h35m01.351s -24d55m59.19s 10 0.05 0.01 Brown_Walker_2020 +NGC1448 3h44m31.915s -44d38m41.38s 10 0.025 0.01 Brown_Walker_2020 +NGC2315 7h02m33.038s 50d35m26.18s 7 0.2 0.02 Brown_Walker_2020 +NGC2357 7h17m40.981s 23d21m24.28s 10 0.15 0.02 Brown_Walker_2020 +NGC2442 7h36m23.84s -69d31m51.0s 7 0.55 0.05 Brown_Walker_2020 +NGC253 00h47m33.120s -25d17m17.59s 200.045 0.005 Brown_Walker_2020 +NGC2577 8h22m43.45s 22d33m11.1408s 10 0.12 0.01 Brown_Walker_2020 +NGC2615 8h34m33.358s -2d32m48.57s 10 0.075 0.005 Brown_Walker_2020 +NGC2668 8h49m22.57s 36d42m37.53s 10 0.085 0.005 Brown_Walker_2020 +NGC2748 9h13m43.037s 76d28m31.23s 5 0.06 0.01 Brown_Walker_2020 +NGC2811 9h16m11.1s -16d18m45.78s 12 0.12 0.01 Brown_Walker_2020 +NGC3034 9h55m52.43s 69d40m46.93s 15 0.2 0.05 Brown_Walker_2020 +NGC3521 11h05m48.5676s -0d02m09.2282s 15 0.11 0.02 Brown_Walker_2020 +NGC3556 11h11m30.967s 55d40m26.84s 20 0.025 0.005 Brown_Walker_2020 +NGC3627 11h20m15.026s 12d59m28.64s 15 0.07 0.01 Brown_Walker_2020 +NGC3690 11h28m31.326s 58d33m41.8s 10 0.035 0.01 Brown_Walker_2020 +NGC383 1h07m24.9587s 32d24m45.214s 14 0.16 0.02 Brown_Walker_2020 +NGC3953 11h53m49.0088s 52d19m36.4738s 15 0.06 0.01 Brown_Walker_2020 +NGC4080 12h04m51.804s 26d59m33.43s 12 0.055 0.005 Brown_Walker_2020 +NGC4214 12h15m39.174s 36d19m36.8s 10 0.06 0.005 Brown_Walker_2020 +NGC4303 12h21m54.9275s 4d28m25.5883s 15 0.05 0.005 Brown_Walker_2020 +NGC4569 12h36m49.816s 13d09m46.33s 7 0.12 0.01 Brown_Walker_2020 +NGC4594 12h39m59.4319s -11d37m22.9954s 10 0.135 0.005 Brown_Walker_2020 +NGC4631 12h42m08.009s 32d32m29.44s 10 0.045 0.005 Brown_Walker_2020 +NGC4666 12h45m08.59s -00d27m42.8s 10 0.065 0.01 Brown_Walker_2020 +NGC4736 12h50m53.148s 41d07m12.55s 15 0.035 0.01 Brown_Walker_2020 +NGC5055 13h15m49.2739s 42d01m45.7261s 15 0.035 0.01 Brown_Walker_2020 +NGC5177 13h29m24.269s 11d47m49.55s 5 0.08 0.01 Brown_Walker_2020 +NGC5221 13h34m55.909s 13d49m57.14s 10 0.065 0.01 Brown_Walker_2020 +NGC5490 14h09m57.33s 17d32m43.53s 10 0.065 0.005 Brown_Walker_2020 +NGC613 1h34m18.235s -29d25m06.56s 15 0.045 0.005 Brown_Walker_2020 +NGC6166 16h28m38.2444s 39d33m04.2318s 10 0.025 0.005 Brown_Walker_2020 +NGC634 1h38m18.679s 35d21m53.47s 10 0.12 0.01 Brown_Walker_2020 +NGC6479 17h48m21.5875s 54d08m56.4765s 7 0.095 0.01 Brown_Walker_2020 +NGC6822 19h44m56.199s -14d47m51.29s 20 0.55 0.05 Brown_Walker_2020 +NGC7187 22h02m44.4954s -32d48m11.439s 10 0.08 0.01 Brown_Walker_2020 +NGC7259 22h23m05.5451s -28d57m17.4766s 7 0.045 0.005 Brown_Walker_2020 +NGC7371 22h46m03.744s -11d00m04.3327s 7 0.15 0.005 Brown_Walker_2020 +NGC7552 23h16m10.767s -42d35m05.39s 12 0.03 0.005 Brown_Walker_2020 +NGC7653 23h24m49.3612s 15d16m32.1419s 7 0.18 0.02 Brown_Walker_2020 +NGC7793 23h57m49.7534s -32d35m27.7083s 12 0.035 0.01 Brown_Walker_2020 +NGC88 0h21m22.132s -48d38m24.28s 20 0.04 0.01 Brown_Walker_2020 +NGC918 2h25m50.7911s 18d29m46.3842s 20 0.7 0.1 Brown_Walker_2020 +PGC071943 23h37m44.414s -47d30m22.92s 10 0.03 0.005 Brown_Walker_2020 +PGC2692384 18h32m24.016s 66d53m43s 15 0.16 0.02 Brown_Walker_2020 +PGC29010 10h01m26.5223s 36d40m16.6648s 8 0.03 0.005 Brown_Walker_2020 +PGC68345 22h14m03.018s -26d56m15.77s 10 0.06 0.005 Brown_Walker_2020 +PGC83768 13h02m35.193s 27d26m21.38s 12 0.025 0.005 Brown_Walker_2020 +PGC9204 2h25m28.346s -25d38m16.46s 10 0.035 0.005 Brown_Walker_2020 +SDSSJ161609.48+383245.0 16h16m09.485s 38d32m45.09s 7 0.03 0.005 Brown_Walker_2020 +UGC09113 14h14m14.762s 35d25m23.83s 15 0.045 0.005 Brown_Walker_2020 +UGC09386 14h34m52.7783s 40d44m52.8518s 15 0.035 0.005 Brown_Walker_2020 +UGC10064 15h51m13.2752s 25d42m06.784s 13 0.16 0.01 Brown_Walker_2020 +UGC10214 16h06m03.94s 55d25m31.33s 5 0.02 0.005 Brown_Walker_2020 +UGC10685 17h04m50.999s 12d55m29.64s 10 0.24 0.02 Brown_Walker_2020 +UGC11501 19h58m37.031s 2d36m10.62s 10 0.37 0.05 Brown_Walker_2020 +UGC11797 21h43m20.1605s 43d34m34.644s 10 1.25 0.1 Brown_Walker_2020 +UGC12640 23h30m56.799s 15d29m25.96s 10 0.17 0.01 Brown_Walker_2020 +UGC12846 23h55m46.0248s 18d25m33.6036s 10 0.09 0.01 Brown_Walker_2020 +UGC12850 23h56m06.16s 29d22m40.44s 10 0.14 0.015 Brown_Walker_2020 +UGC2855 3h48m20.731s 70d07m58.37s 7 1.9 0.2 Brown_Walker_2020 +UGC402 0h39m18.612s 3d57m08.87s 10 0.065 0.005 Brown_Walker_2020 +UGC4179 8h02m05.9609s 0d48m32.742s 5 0.14 0.02 Brown_Walker_2020 +UGC5055 9h30m11.7493s 55d51m08.6863s 8 0.07 0.02 Brown_Walker_2020 +UGC5378 10h00m31.9918s 4d24m25.6711s 5 0.065 0.005 Brown_Walker_2020 +UGC5460 10h08m09.197s 51d50m40.2504s 5 0.02 0.005 Brown_Walker_2020 +UGC5623 10h23m48.6038s 33d48m28.7892s 9 0.055 0.005 Brown_Walker_2020 +UGC6483 11h29m02.358s 17d13m55.15s 7 0.065 0.005 Brown_Walker_2020 +UGC7848 12h40m57.433s 63d31m11.3s 5 0.035 0.005 Brown_Walker_2020 +UGC8713 13h47m01.2595s 33d53m36.9528s 10 0.05 0.005 Brown_Walker_2020 \ No newline at end of file diff --git a/Brown_Walker_table_1.tex.orig b/Brown_Walker_table_1.tex.orig new file mode 100644 index 0000000..6ab022f --- /dev/null +++ b/Brown_Walker_table_1.tex.orig @@ -0,0 +1,141 @@ +%% The values (usually only l,r and c) in the last part of +%% \begin{deluxetable}{} command tell LaTeX how many columns +%% there are and how to align them. +\begin{deluxetable*}{llrrrr} + +%% Keep a portrait orientation + +%% Over-ride the default font size +%% Use Default (12pt) + +%% Use \tablewidth{?pt} to over-ride the default table width. +%% If you are unhappy with the default look at the end of the +%% *.log file to see what the default was set at before adjusting +%% this value. + +%% This is the title of the table. +\tablecaption{Table 1} + +%% This command over-rides LaTeX's natural table count +%% and replaces it with this number. LaTeX will increment +%% all other tables after this table based on this number +\tablenum{1} + +%% The \tablehead gives provides the column headers. It +%% is currently set up so that the column labels are on the +%% top line and the units surrounded by ()s are in the +%% bottom line. You may add more header information by writing +%% another line between these lines. For each column that requries +%% extra information be sure to include a \colhead{text} command +%% and remember to end any extra lines with \\ and include the +%% correct number of &s. +\tablehead{\colhead{Galaxy Name} & \colhead{RA} & \colhead{Dec} & \colhead{Radius} & \colhead{A$_V$} & \colhead{Err} \\ +\colhead{} & \colhead{(H M S)} & \colhead{(D M S)} & \colhead{(arcsec)} & \colhead{(mag)} & \colhead{(mag)} } + +%% All data must appear between the \startdata and \enddata commands +\startdata +Arp244 & 12h01m53.17s & -18d52m37.92s & 15 & 0.11 & 0.01 \\ +ESO138-G10 & 16h59m02.952s & -60d12m57.67s & 12 & 0.55 & 0.05 \\ +ESO287-G40 & 21h37m28.1842s & -47d02m08.8331s & 7 & 0.065 & 0.01 \\ +ESO317-32 & 10h28m01.6186s & -42d06m38.7541s & 12 & 0.33 & 0.03 \\ +ESO509-IG064 & 13h34m39.3s & -23d40m50s & 15 & 0.3 & 0.02 \\ +IC208 & 2h08m27.736s & 6d23m41.53s & 12 & 0.13 & 0.01 \\ +IC2574 & 10h28m23.6205s & 68d24m43.4414s & 15 & 0.08 & 0.01 \\ +IC5249 & 22h47m06.262s & -64d49m55.42s & 15 & 0.08 & 0.01 \\ +KUG0647+311 & 6h50m36.832s & 31d07m00.6s & 15 & 0.31 & 0.03 \\ +M51 & 13h29m52.698s & 47d11m42.93s & 15 & 0.05 & 0.01 \\ +M61 & 12h21m54.9275s & 4d28m25.5883s & 15 & 0.05 & 0.01 \\ +M66 & 11h20m15.026s & 12d59m28.64s & 15 & 0.07 & 0.01 \\ +M74 & 1h36m41.772s & 15d47m00.46s & 15 & 0.16 & 0.02 \\ +M82 & 9h55m52.43s & 69d40m46.93s & 20 & 0.2 & 0.05 \\ +M83 & 13h37m00.919s & -29d51m56.74s & 15 & 0.12 & 0.01 \\ +MCG-01-07-004 & 2h23m13.2516s & -4d31m01.5168s & 7 & 0.07 & 0.05 \\ +MCG-02-24-027 & 9h28m59.256s & -14d48m27.25s & 7 & 0.17 & 0.01 \\ +MCG-02-30-003 & 11h33m10.5799s & -10d13m43.7361s & 10 & 0.09 & 0.01 \\ +MCG+10-19-1 & 12h54m49.706s & 58d52m56.46s & 7 & 0.025 & 0.005 \\ +NGC0584 & 1h31m20.755s & -6d52m05.02s & 15 & 0.1 & 0.01 \\ +NGC1097 & 2h46m19.059s & -30d16m29.68s & 15 & 0.05 & 0.01 \\ +NGC1313 & 3h18m16.046s & -66d29m53.74s & 20 & 0.1 & 0.05 \\ +NGC134 & 0h30m21.893s & -33d14m43.26s & 20 & 0.03 & 0.01 \\ +NGC1365 & 3h33m36.458s & -36d08m26.37s & 20 & 0.03 & 0.01 \\ +NGC1371 & 3h35m01.351s & -24d55m59.19s & 10 & 0.05 & 0.01 \\ +NGC1448 & 3h44m31.915s & -44d38m41.38s & 10 & 0.025 & 0.01 \\ +NGC2315 & 7h02m33.038s & 50d35m26.18s & 7 & 0.2 & 0.02 \\ +NGC2357 & 7h17m40.981s & 23d21m24.28s & 10 & 0.15 & 0.02 \\ +NGC2577 & 8h22m43.45s & 22d33m11.1408s & 10 & 0.12 & 0.01 \\ +NGC2615 & 8h34m33.358s & -2d32m48.57s & 10 & 0.075 & 0.005 \\ +NGC2668 & 8h49m22.57s & 36d42m37.53s & 10 & 0.085 & 0.005 \\ +NGC2748 & 9h13m43.037s & 76d28m31.23s & 5 & 0.06 & 0.01 \\ +NGC2811 & 9h16m11.1s & -16d18m45.78s & 12 & 0.12 & 0.01 \\ +NGC3034 & 9h55m52.43s & 69d40m46.93s & 15 & 0.2 & 0.05 \\ +NGC3521 & 11h05m48.5676s & -0d02m09.2282s & 15 & 0.11 & 0.02 \\ +NGC3556 & 11h11m30.967s & 55d40m26.84s & 20 & 0.025 & 0.005 \\ +NGC3627 & 11h20m15.026s & 12d59m28.64s & 15 & 0.07 & 0.01 \\ +NGC3690 & 11h28m31.326s & 58d33m41.8s & 10 & 0.035 & 0.01 \\ +NGC383 & 1h07m24.9587s & 32d24m45.214s & 14 & 0.16 & 0.02 \\ +NGC3953 & 11h53m49.0088s & 52d19m36.4738s & 15 & 0.06 & 0.01 \\ +NGC4038 & 12h01m53.002s & -18d52m03.32s & 15 & 0.11 & 0.01 \\ +NGC4080 & 12h04m51.804s & 26d59m33.43s & 12 & 0.055 & 0.005 \\ +NGC4214 & 12h15m39.174s & 36d19m36.8s & 10 & 0.06 & 0.005 \\ +NGC4303 & 12h21m54.9275s & 4d28m25.5883s & 15 & 0.05 & 0.005 \\ +NGC4569 & 12h36m49.816s & 13d09m46.33s & 7 & 0.12 & 0.01 \\ +NGC4594 & 12h39m59.4319s & -11d37m22.9954s & 10 & 0.135 & 0.005 \\ +NGC4631 & 12h42m08.009s & 32d32m29.44s & 10 & 0.045 & 0.005 \\ +NGC4736 & 12h50m53.148s & 41d07m12.55s & 15 & 0.035 & 0.01 \\ +NGC5055 & 13h15m49.2739s & 42d01m45.7261s & 15 & 0.035 & 0.01 \\ +NGC5177 & 13h29m24.269s & 11d47m49.55s & 5 & 0.08 & 0.01 \\ +NGC5194 & 13h29m52.698s & 47d11m42.93s & 20 & 0.05 & 0.01 \\ +ngc5195 & 13h29m59.59s & 47d15m58.06s & 20 & 0.05 & 0.01 \\ +NGC5221 & 13h34m55.909s & 13d49m57.14s & 10 & 0.065 & 0.01 \\ +NGC5490 & 14h09m57.33s & 17d32m43.53s & 10 & 0.065 & 0.005 \\ +NGC613 & 1h34m18.235s & -29d25m06.56s & 15 & 0.045 & 0.005 \\ +NGC6166 & 16h28m38.2444s & 39d33m04.2318s & 10 & 0.025 & 0.005 \\ +NGC634 & 1h38m18.679s & 35d21m53.47s & 10 & 0.12 & 0.01 \\ +NGC6479 & 17h48m21.5875s & 54d08m56.4765s & 7 & 0.095 & 0.01 \\ +NGC6822 & 19h44m56.199s & -14d47m51.29s & 20 & 0.55 & 0.05 \\ +NGC7187 & 22h02m44.4954s & -32d48m11.439s & 10 & 0.08 & 0.01 \\ +NGC7259 & 22h23m05.5451s & -28d57m17.4766s & 7 & 0.045 & 0.005 \\ +NGC7371 & 22h46m03.744s & -11d00m04.3327s & 7 & 0.15 & 0.005 \\ +NGC7552 & 23h16m10.767s & -42d35m05.39s & 12 & 0.03 & 0.005 \\ +NGC7653 & 23h24m49.3612s & 15d16m32.1419s & 7 & 0.18 & 0.02 \\ +NGC7793 & 23h57m49.7534s & -32d35m27.7083s & 12 & 0.035 & 0.01 \\ +NGC88 & 0h21m22.132s & -48d38m24.28s & 20 & 0.04 & 0.01 \\ +NGC918 & 2h25m50.7911s & 18d29m46.3842s & 20 & 0.7 & 0.1 \\ +PGC071943 & 23h37m44.414s & -47d30m22.92s & 10 & 0.03 & 0.005 \\ +PGC2692384 & 18h32m24.016s & 66d53m43s & 15 & 0.16 & 0.02 \\ +PGC29010 & 10h01m26.5223s & 36d40m16.6648s & 8 & 0.03 & 0.005 \\ +PGC68345 & 22h14m03.018s & -26d56m15.77s & 10 & 0.06 & 0.005 \\ +PGC83768 & 13h02m35.193s & 27d26m21.38s & 12 & 0.025 & 0.005 \\ +PGC9204 & 2h25m28.346s & -25d38m16.46s & 10 & 0.035 & 0.005 \\ +SDSSJ161609.48+383245.0 & 16h16m09.485s & 38d32m45.09s & 7 & 0.03 & 0.005 \\ +UGC09113 & 14h14m14.762s & 35d25m23.83s & 15 & 0.045 & 0.005 \\ +UGC09386 & 14h34m52.7783s & 40d44m52.8518s & 15 & 0.035 & 0.005 \\ +UGC10064 & 15h51m13.2752s & 25d42m06.784s & 13 & 0.16 & 0.01 \\ +UGC10214 & 16h06m03.94s & 55d25m31.33s & 5 & 0.02 & 0.005 \\ +UGC10685 & 17h04m50.999s & 12d55m29.64s & 10 & 0.24 & 0.02 \\ +UGC11501 & 19h58m37.031s & 2d36m10.62s & 10 & 0.37 & 0.05 \\ +UGC11797 & 21h43m20.1605s & 43d34m34.644s & 10 & 1.25 & 0.1 \\ +UGC12640 & 23h30m56.799s & 15d29m25.96s & 10 & 0.17 & 0.01 \\ +UGC12846 & 23h55m46.0248s & 18d25m33.6036s & 10 & 0.09 & 0.01 \\ +UGC12850 & 23h56m06.16s & 29d22m40.44s & 10 & 0.14 & 0.015 \\ +UGC2855 & 3h48m20.731s & 70d07m58.37s & 7 & 1.9 & 0.2 \\ +UGC402 & 0h39m18.612s & 3d57m08.87s & 10 & 0.065 & 0.005 \\ +UGC4179 & 8h02m05.9609s & 0d48m32.742s & 5 & 0.14 & 0.02 \\ +UGC5055 & 9h30m11.7493s & 55d51m08.6863s & 8 & 0.07 & 0.02 \\ +UGC5378 & 10h00m31.9918s & 4d24m25.6711s & 5 & 0.065 & 0.005 \\ +UGC5460 & 10h08m09.197s & 51d50m40.2504s & 5 & 0.02 & 0.005 \\ +UGC5623 & 10h23m48.6038s & 33d48m28.7892s & 9 & 0.055 & 0.005 \\ +UGC6483 & 11h29m02.358s & 17d13m55.15s & 7 & 0.065 & 0.005 \\ +UGC7848 & 12h40m57.433s & 63d31m11.3s & 5 & 0.035 & 0.005 \\ +UGC8713 & 13h47m01.2595s & 33d53m36.9528s & 10 & 0.05 & 0.005 \\ +\enddata + +%% Include any \tablenotetext{key}{text}, \tablerefs{ref list}, +%% or \tablecomments{text} between the \enddata and +%% \end{deluxetable} commands + +%% No \tablecomments indicated + +%% No \tablerefs indicated + +\end{deluxetable} \ No newline at end of file diff --git a/Brown_Walker_table_1_SN.tex b/Brown_Walker_table_1_SN.tex index 3e8b7c1..e013763 100644 --- a/Brown_Walker_table_1_SN.tex +++ b/Brown_Walker_table_1_SN.tex @@ -7,97 +7,97 @@ #M82 & 9h55m52.43s & 69d40m46.93s & 20 & 0.2 & 0.05 \\ my value # AB=0.25 from Dalcanton 0.25*3.1/4.1*0.86 M82 & 9h55m52.43s & 69d40m46.93s & 20 & 0.16\tablenotekey{(b)} & 0.05 & \nodata \\ -Arp244 & 12h01m53.17s & -18d52m37.92s & 15 & 0.11 & 0.01 & SNe 1921A, 1974E, 2004gt, 2007sr, 2013dk \\ -ESO138-G10 & 16h59m02.952s & -60d12m57.67s & 12 & 0.55 & 0.05 & SN2013by \\ -ESO287-G40 & 21h37m28.1842s & -47d02m08.8331s & 7 & 0.065 & 0.01 & SNe 2009lc, 2013fy\\ -ESO317-32 & 10h28m01.6186s & -42d06m38.7541s & 12 & 0.33 & 0.03 & SN2017ghs\\ -ESO509-IG064 & 13h34m39.3s & -23d40m50s & 15 & 0.3 & 0.02 & SN2016eiy\\ -IC208 & 2h08m27.736s & 6d23m41.53s & 12 & 0.13 & 0.01 & SNe 2003G, 2017glq \\ -IC2574 & 10h28m23.6205s & 68d24m43.4414s & 15 & 0.08 & 0.01 & \nodata \\ -IC5249 & 22h47m06.262s & -64d49m55.42s & 15 & 0.08 & 0.01 & SN2011cb \\ -KUG0647+311 & 6h50m36.832s & 31d07m00.6s & 15 & 0.31 & 0.03 & SN2016asf \\ -M51 & 13h29m52.698s & 47d11m42.93s & 15 & 0.05 & 0.01 & SNe 1945A, 1994I, 2005cs, 2011dh\\ -M61 & 12h21m54.9275s & 4d28m25.5883s & 15 & 0.05 & 0.01 & SNe 1926A, 1964F, 1961I, 1999gn, 2006ov, 2008in, 2014dt\\ -M66 & 11h20m15.026s & 12d59m28.64s & 15 & 0.07 & 0.01 & SNe 1973R, 1989B, 1997bs, 2009hd, 2016cok \\ -M74 & 1h36m41.772s & 15d47m00.46s & 15 & 0.16 & 0.02 & SNe 2002ap, 2003gd, 2013ej, PS15blm \\ -M82 & 9h55m52.43s & 69d40m46.93s & 20 & 0.2 & 0.05 & SNe 2004am, 2008iz, 2014J, 2019aji\\ -M83 & 13h37m00.919s & -29d51m56.74s & 15 & 0.12 & 0.01 & SNe 1923A, 1945B, 1950B, 1957D, 1968L, 1983N \\ -MCG-01-07-004 & 2h23m13.2516s & -4d31m01.5168s & 7 & 0.07 & 0.05 & SNe ASASSN-15od, SCP06R12, PS1-10g \\ -MCG-02-24-027 & 9h28m59.256s & -14d48m27.25s & 7 & 0.17 & 0.01 & SN2011at \\ -MCG-02-30-003 & 11h33m10.5799s & -10d13m43.7361s & 10 & 0.09 & 0.01 & SNe 2003ee, 2017hm \\ -MCG+10-19-1 & 12h54m49.706s & 58d52m56.46s & 7 & 0.025 & 0.005 & PTF10icb \\ -NGC0584 & 1h31m20.755s & -6d52m05.02s & 15 & 0.1 & 0.01 & SN2016fng\\ -NGC1097 & 2h46m19.059s & -30d16m29.68s & 15 & 0.05 & 0.01 & SNe 1992bd, 1999eu, 2003B\\ -NGC1313 & 3h18m16.046s & -66d29m53.74s & 20 & 0.1 & 0.05 & SNe 1962M, 1978K \\ -NGC134 & 0h30m21.893s & -33d14m43.26s & 20 & 0.03 & 0.01 & SN2009gj \\ -NGC1365 & 3h33m36.458s & -36d08m26.37s & 20 & 0.03 & 0.01 & SNe 1957C, 1983V, 2001du, 2012fr \\ -NGC1371 & 3h35m01.351s & -24d55m59.19s & 10 & 0.05 & 0.01 & SN2005ke \\ -NGC1448 & 3h44m31.915s & -44d38m41.38s & 10 & 0.025 & 0.01 & SNe 1983S, 2001el, 2003hn, 2014df \\ -NGC2315 & 7h02m33.038s & 50d35m26.18s & 7 & 0.2 & 0.02 & SN2011ay \\ -NGC2357 & 7h17m40.981s & 23d21m24.28s & 10 & 0.15 & 0.02 & SNe 2010bj, 2015I \\ -NGC2577 & 8h22m43.45s & 22d33m11.1408s & 10 & 0.12 & 0.01 & SN2007ax \\ -NGC2615 & 8h34m33.358s & -2d32m48.57s & 10 & 0.075 & 0.005 & SN2014ao \\ -NGC2668 & 8h49m22.57s & 36d42m37.53s & 10 & 0.085 & 0.005 & SN2003je \\ -NGC2748 & 9h13m43.037s & 76d28m31.23s & 5 & 0.06 & 0.01 & SNe 1985A, 2013ff, PS15jf, 2017gkk \\ -NGC2811 & 9h16m11.1s & -16d18m45.78s & 12 & 0.12 & 0.01 & SNe 2005am, 2018jzo \\ -NGC3034 & 9h55m52.43s & 69d40m46.93s & 15 & 0.2 & 0.05 & SNe 2004am, 2008iz, 2014J, 2019ajl \\ -NGC3521 & 11h05m48.5676s & -0d02m09.2282s & 15 & 0.11 & 0.02 & \nodata \\ -NGC3556 & 11h11m30.967s & 55d40m26.84s & 20 & 0.025 & 0.005 & SN1969B \\ -NGC3627 & 11h20m15.026s & 12d59m28.64s & 15 & 0.07 & 0.01 & SNe 1973R, 1989B, 1997bs, 2009hd, 2016cok \\ -NGC3690 & 11h28m31.326s & 58d33m41.8s & 10 & 0.035 & 0.01 & SNe 1992bu, 1993G, 1998T, 1999D, 2005U, 2010O, 2010P, 2019lqo \\ -NGC383 & 1h07m24.9587s & 32d24m45.214s & 14 & 0.16 & 0.02 & SNe 2000dk, 2015ar, 2016sx, 2017hle \\ -NGC3953 & 11h53m49.0088s & 52d19m36.4738s & 15 & 0.06 & 0.01 & SNe 2001dp, 2006bp \\ -# duplicate NGC4038 & 12h01m53.002s & -18d52m03.32s & 15 & 0.11 & 0.01 & \\ -NGC4080 & 12h04m51.804s & 26d59m33.43s & 12 & 0.055 & 0.005 & MASTER OT J120451.50+265946.6 \\ -NGC4214 & 12h15m39.174s & 36d19m36.8s & 10 & 0.06 & 0.005 & SNe 1954A, 2010U \\ -# M61 duplicate NGC4303 & 12h21m54.9275s & 4d28m25.5883s & 15 & 0.05 & 0.005 & \\ -NGC4569 & 12h36m49.816s & 13d09m46.33s & 7 & 0.12 & 0.01 & \nodata \\ -NGC4594 & 12h39m59.4319s & -11d37m22.9954s & 10 & 0.135 & 0.005 & SNe 1997bl, PS15akv \\ -NGC4631 & 12h42m08.009s & 32d32m29.44s & 10 & 0.045 & 0.005 & \nodata \\ -NGC4736 & 12h50m53.148s & 41d07m12.55s & 15 & 0.035 & 0.01 & \nodata \\ -NGC5055 & 13h15m49.2739s & 42d01m45.7261s & 15 & 0.035 & 0.01 & SN1971I \\ -NGC5177 & 13h29m24.269s & 11d47m49.55s & 5 & 0.08 & 0.01 & SN2010cr \\ -# M51 duplicate NGC5194 & 13h29m52.698s & 47d11m42.93s & 20 & 0.05 & 0.01 & \\ -# ngc5195 & 13h29m59.59s & 47d15m58.06s & 20 & 0.05 & 0.01 & \\ -NGC5221 & 13h34m55.909s & 13d49m57.14s & 10 & 0.065 & 0.01 & SN1970P, SN2008ez, PS1-14ea, SN2016bln \\ -NGC5490 & 14h09m57.33s & 17d32m43.53s & 10 & 0.065 & 0.005 & SN1997cn, SN2003aq, SN2005I, SN2015bo, SN2016ccm \\ -NGC613 & 1h34m18.235s & -29d25m06.56s & 15 & 0.045 & 0.005 & SN2016gkg \\ -NGC6166 & 16h28m38.2444s & 39d33m04.2318s & 10 & 0.025 & 0.005 & SN1997cq, SN2009eu, PS15aot, SN2018ccl, SN2019gqd \\ -NGC634 & 1h38m18.679s & 35d21m53.47s & 10 & 0.12 & 0.01 & SN2006Q, SN2008A \\ -NGC6479 & 17h48m21.5875s & 54d08m56.4765s & 7 & 0.095 & 0.01 & SN2007cl, SN2009ay \\ -NGC6822 & 19h44m56.199s & -14d47m51.29s & 20 & 0.55 & 0.05 & \nodata \\ -NGC7187 & 22h02m44.4954s & -32d48m11.439s & 10 & 0.08 & 0.01 & SN2017gah \\ -NGC7259 & 22h23m05.5451s & -28d57m17.4766s & 7 & 0.045 & 0.005 & SN2009ip, SN2014dq, SMT16jyu \\ -NGC7371 & 22h46m03.744s & -11d00m04.3327s & 7 & 0.15 & 0.005 & LSQ13cux, PS15bgt \\ -NGC7552 & 23h16m10.767s & -42d35m05.39s & 12 & 0.03 & 0.005 & SN2017bzc \\ -NGC7653 & 23h24m49.3612s & 15d16m32.1419s & 7 & 0.18 & 0.02 & SN2015bf, SN2018cjk \\ -NGC7793 & 23h57m49.7534s & -32d35m27.7083s & 12 & 0.035 & 0.01 & SN2008bk \\ -NGC88 & 0h21m22.132s & -48d38m24.28s & 20 & 0.04 & 0.01 & SN1994Z, ASASSN-15ut \\ -NGC918 & 2h25m50.7911s & 18d29m46.3842s & 20 & 0.7 & 0.1 & SN2009js, SN2011ek \\ -PGC071943 & 23h37m44.414s & -47d30m22.92s & 10 & 0.03 & 0.005 & \nodata \\ -PGC2692384 & 18h32m24.016s & 66d53m43s & 15 & 0.16 & 0.02 & SN2011hj \\ -PGC29010 & 10h01m26.5223s & 36d40m16.6648s & 8 & 0.03 & 0.005 & SN2012ak, PS15ahw \\ -PGC68345 & 22h14m03.018s & -26d56m15.77s & 10 & 0.06 & 0.005 & SN2010bv, SN2016dgt \\ -PGC83768 & 13h02m35.193s & 27d26m21.38s & 12 & 0.025 & 0.005 & SN1962I, SN1991Q, SN2003do, SN2012da \\ -PGC9204 & 2h25m28.346s & -25d38m16.46s & 10 & 0.035 & 0.005 & SN2014cp \\ -SDSSJ161609.48+383245.0 & 16h16m09.485s & 38d32m45.09s & 7 & 0.03 & 0.005 & SN2013eh \\ -UGC09113 & 14h14m14.762s & 35d25m23.83s & 15 & 0.045 & 0.005 & \nodata \\ -UGC09386 & 14h34m52.7783s & 40d44m52.8518s & 15 & 0.035 & 0.005 & SN2017daf \\ -UGC10064 & 15h51m13.2752s & 25d42m06.784s & 13 & 0.16 & 0.01 & SN2009dc, SN2019fee \\ -UGC10214 & 16h06m03.94s & 55d25m31.33s & 5 & 0.02 & 0.005 & SN2002lk, SN2007cu, SN2008dq, PS1-10acx, PS1-11agk \\ -UGC10685 & 17h04m50.999s & 12d55m29.64s & 10 & 0.24 & 0.02 & SN2010hw, SN2013cj \\ -UGC11501 & 19h58m37.031s & 2d36m10.62s & 10 & 0.37 & 0.05 & SN2011dn \\ -UGC11797 & 21h43m20.1605s & 43d34m34.644s & 10 & 1.25 & 0.1 & SN2004ca, SN2015N, SN2018dfy \\ -UGC12640 & 23h30m56.799s & 15d29m25.96s & 10 & 0.17 & 0.01 & SN2011ef, SN2019ssi \\ -UGC12846 & 23h55m46.0248s & 18d25m33.6036s & 10 & 0.09 & 0.01 & SN2007od \\ -UGC12850 & 23h56m06.16s & 29d22m40.44s & 10 & 0.14 & 0.015 & SN2014ek, CSSJ235535.6+291220 \\ -UGC2855 & 3h48m20.731s & 70d07m58.37s & 7 & 1.9 & 0.2 & SN2014dg \\ -UGC402 & 0h39m18.612s & 3d57m08.87s & 10 & 0.065 & 0.005 & ASASSN-15qc, SN2016hsq \\ -UGC4179 & 8h02m05.9609s & 0d48m32.742s & 5 & 0.14 & 0.02 & SN2006jd \\ -UGC5055 & 9h30m11.7493s & 55d51m08.6863s & 8 & 0.07 & 0.02 & SN2014R \\ -UGC5378 & 10h00m31.9918s & 4d24m25.6711s & 5 & 0.065 & 0.005 & SN2007S \\ -UGC5460 & 10h08m09.197s & 51d50m40.2504s & 5 & 0.02 & 0.005 & SN2011ht, SN2015as \\ -UGC5623 & 10h23m48.6038s & 33d48m28.7892s & 9 & 0.055 & 0.005 & SN2010ks \\ -UGC6483 & 11h29m02.358s & 17d13m55.15s & 7 & 0.065 & 0.005 & SN2013hh \\ -UGC7848 & 12h40m57.433s & 63d31m11.3s & 5 & 0.035 & 0.005 & SN2006bv \\ -UGC8713 & 13h47m01.2595s & 33d53m36.9528s & 10 & 0.05 & 0.005 & SN2012cp \\ \ No newline at end of file +Arp244 & 12h01m53.17s & -18d52m37.92s & 15 & 0.112 & 0.007 & SNe 1921A, 1974E, 2004gt, 2007sr, 2013dk \\ +ESO138-G10 & 16h59m02.952s & -60d12m57.67s & 12 & 0.574 & 0.023 & SN2013by \\ +ESO287-G40 & 21h37m28.1842s & -47d02m08.8331s & 7 & 0.067 & 0.004 & SNe 2009lc, 2013fy\\ +ESO317-32 & 10h28m01.6186s & -42d06m38.7541s & 12 & 0.329 & 0.014 & SN2017ghs\\ +ESO509-IG064 & 13h34m39.3s & -23d40m50s & 15 & 0.292 & 0.026 & SN2016eiy\\ +IC208 & 2h08m27.736s & 6d23m41.53s & 12 & 0.135 0.004 & & SNe 2003G, 2017glq \\ +IC2574 & 10h28m23.6205s & 68d24m43.4414s & 15 & 0.094 & 0.010 & \nodata \\ +IC5249 & 22h47m06.262s & -64d49m55.42s & 15 & 0.087 & 0.004 & SN2011cb \\ +KUG0647+311 & 6h50m36.832s & 31d07m00.6s & 15 & 0.334 & 0.027 & SN2016asf \\ +M51 & 13h29m52.698s & 47d11m42.93s & 15 & 0.076 & 0.013 & SNe 1945A, 1994I, 2005cs, 2011dh\\ +M61 & 12h21m54.9275s & 4d28m25.5883s & 15 & 0.051 & 0.003 & SNe 1926A, 1964F, 1961I, 1999gn, 2006ov, 2008in, 2014dt\\ +M66 & 11h20m15.026s & 12d59m28.64s & 15 & 0.07 & 0.008 & SNe 1973R, 1989B, 1997bs, 2009hd, 2016cok \\ +M74 & 1h36m41.772s & 15d47m00.46s & 15 & 0.171 & 0.010 & SNe 2002ap, 2003gd, 2013ej, PS15blm \\ +M82 & 9h55m52.43s & 69d40m46.93s & 20 & 0.224 & 0.092 & SNe 2004am, 2008iz, 2014J, 2019aji\\ +M83 & 13h37m00.919s & -29d51m56.74s & 15 & 0.127 & 0.011 & SNe 1923A, 1945B, 1950B, 1957D, 1968L, 1983N \\ +MCG-01-07-004 & 2h23m13.2516s & -4d31m01.5168s & 7 & 0.071 & 0.002 & SNe ASASSN-15od, SCP06R12, PS1-10g \\ +MCG-02-24-027 & 9h28m59.256s & -14d48m27.25s & 7 & 0.172 & 0.004 & SN2011at \\ +MCG-02-30-003 & 11h33m10.5799s & -10d13m43.7361s & 10 & 0.095 & 0.003 & SNe 2003ee, 2017hm \\ +MCG+10-19-1 & 12h54m49.706s & 58d52m56.46s & 7 & 0.028 & 0.003 & PTF10icb \\ +NGC0584 & 1h31m20.755s & -6d52m05.02s & 15 & 0.102 & 0.006 & SN2016fng\\ +NGC1097 & 2h46m19.059s & -30d16m29.68s & 15 & 0.054 & 0.003 & SNe 1992bd, 1999eu, 2003B\\ +NGC1313 & 3h18m16.046s & -66d29m53.74s & 20 & 0.222 & 0.063 & SNe 1962M, 1978K \\ +NGC134 & 0h30m21.893s & -33d14m43.26s & 20 & 0.03 & 0.004 & SN2009gj \\ +NGC1365 & 3h33m36.458s & -36d08m26.37s & 20 & 0.034 0.004 & SNe 1957C, 1983V, 2001du, 2012fr \\ +NGC1371 & 3h35m01.351s & -24d55m59.19s & 10 & 0.054 & 0.006 & SN2005ke \\ +NGC1448 & 3h44m31.915s & -44d38m41.38s & 10 & 0.037 & 0.003 & SNe 1983S, 2001el, 2003hn, 2014df \\ +NGC2315 & 7h02m33.038s & 50d35m26.18s & 7 & 0.215 & 0.008 & SN2011ay \\ +NGC2357 & 7h17m40.981s & 23d21m24.28s & 10 & 0.164 & 0.014 & SNe 2010bj, 2015I \\ +NGC2577 & 8h22m43.45s & 22d33m11.1408s & 10 & 0.124 & 0.008 & SN2007ax \\ +NGC2615 & 8h34m33.358s & -2d32m48.57s & 10 & 0.077 & 0.004 & SN2014ao \\ +NGC2668 & 8h49m22.57s & 36d42m37.53s & 10 & 0.088 & 0.003 & SN2003je \\ +NGC2748 & 9h13m43.037s & 76d28m31.23s & 5 & 0.071 & 0.005 & SNe 1985A, 2013ff, PS15jf, 2017gkk \\ +NGC2811 & 9h16m11.1s & -16d18m45.78s & 12 & 0.123 & 0.005 & SNe 2005am, 2018jzo \\ +NGC3034 & 9h55m52.43s & 69d40m46.93s & 15 & 0.31 & 0.090 & SNe 2004am, 2008iz, 2014J, 2019ajl \\ +NGC3521 & 11h05m48.5676s & -0d02m09.2282s & 15 & 0.121 & 0.018 & \nodata \\ +NGC3556 & 11h11m30.967s & 55d40m26.84s & 20 & 0.029 & 0.008 & SN1969B \\ +NGC3627 & 11h20m15.026s & 12d59m28.64s & 15 & 0.07 & 0.008 & SNe 1973R, 1989B, 1997bs, 2009hd, 2016cok \\ +NGC3690 & 11h28m31.326s & 58d33m41.8s & 10 & 0.045 & 0.001 & SNe 1992bu, 1993G, 1998T, 1999D, 2005U, 2010O, 2010P, 2019lqo \\ +NGC383 & 1h07m24.9587s & 32d24m45.214s & 14 & 0.162 & 0.007 & SNe 2000dk, 2015ar, 2016sx, 2017hle \\ +NGC3953 & 11h53m49.0088s & 52d19m36.4738s & 15 & 0.079 & 0.015 & SNe 2001dp, 2006bp \\ +# duplicate NGC4038 & 12h01m53.002s & -18d52m03.32s & 15 & 0.113 & 0.007 & \\ +NGC4080 & 12h04m51.804s & 26d59m33.43s & 12 & 0.055 & 0.002 & MASTER OT J120451.50+265946.6 \\ +NGC4214 & 12h15m39.174s & 36d19m36.8s & 10 & 0.057 & 0.005 & SNe 1954A, 2010U \\ +# M61 duplicate NGC4303 & 12h21m54.9275s & 4d28m25.5883s & 15 & 0.051 & 0.003 & \\ +NGC4569 & 12h36m49.816s & 13d09m46.33s & 7 & 0.125 & 0.008 & \nodata \\ +NGC4594 & 12h39m59.4319s & -11d37m22.9954s & 10 & 0.13 & 0.005 & SNe 1997bl, PS15akv \\ +NGC4631 & 12h42m08.009s & 32d32m29.44s & 10 & 0.046 & 0.003 & \nodata \\ +NGC4736 & 12h50m53.148s & 41d07m12.55s & 15 & 0.039 & 0.006 & \nodata \\ +NGC5055 & 13h15m49.2739s & 42d01m45.7261s & 15 & 0.0415 & 0.005 & SN1971I \\ +NGC5177 & 13h29m24.269s & 11d47m49.55s & 5 & 0.085 & 0.004 & SN2010cr \\ +# M51 duplicate NGC5194 & 13h29m52.698s & 47d11m42.93s & 20 & 0.06 & 0.011 & \\ +# ngc5195 & 13h29m59.59s & 47d15m58.06s & 20 & 0.058 & 0.013 & \\ +NGC5221 & 13h34m55.909s & 13d49m57.14s & 10 & 0.068 & 0.003 & SN1970P, SN2008ez, PS1-14ea, SN2016bln \\ +NGC5490 & 14h09m57.33s & 17d32m43.53s & 10 & 0.066 & 0.003 & SN1997cn, SN2003aq, SN2005I, SN2015bo, SN2016ccm \\ +NGC613 & 1h34m18.235s & -29d25m06.56s & 15 & 0.046 & 0.004 & SN2016gkg \\ +NGC6166 & 16h28m38.2444s & 39d33m04.2318s & 10 & 0.025 & 0.002 & SN1997cq, SN2009eu, PS15aot, SN2018ccl, SN2019gqd \\ +NGC634 & 1h38m18.679s & 35d21m53.47s & 10 & 0.12 & 0.003 & SN2006Q, SN2008A \\ +NGC6479 & 17h48m21.5875s & 54d08m56.4765s & 7 & 0.103 & 0.005 & SN2007cl, SN2009ay \\ +NGC6822 & 19h44m56.199s & -14d47m51.29s & 20 & 0.551 & 0.045 & \nodata \\ +NGC7187 & 22h02m44.4954s & -32d48m11.439s & 10 & 0.085 & 0.003 & SN2017gah \\ +NGC7259 & 22h23m05.5451s & -28d57m17.4766s & 7 & 0.045 & 0.002 & SN2009ip, SN2014dq, SMT16jyu \\ +NGC7371 & 22h46m03.744s & -11d00m04.3327s & 7 & 0.145 & 0.005 & LSQ13cux, PS15bgt \\ +NGC7552 & 23h16m10.767s & -42d35m05.39s & 12 & 0.035 & 0.004 & SN2017bzc \\ +NGC7653 & 23h24m49.3612s & 15d16m32.1419s & 7 & 0.177 & 0.009 & SN2015bf, SN2018cjk \\ +NGC7793 & 23h57m49.7534s & -32d35m27.7083s & 12 & 0.041 & 0.004 & SN2008bk \\ +NGC88 & 0h21m22.132s & -48d38m24.28s & 20 & 0.048 & 0.003 & SN1994Z, ASASSN-15ut \\ +NGC918 & 2h25m50.7911s & 18d29m46.3842s & 20 & 0.702 & 0.049 & SN2009js, SN2011ek \\ +PGC071943 & 23h37m44.414s & -47d30m22.92s & 10 & 0.033 & 0.003 & \nodata \\ +PGC2692384 & 18h32m24.016s & 66d53m43s & 15 & 0.16 & 0.02 & SN2011hj \\ +PGC29010 & 10h01m26.5223s & 36d40m16.6648s & 8 & 0.18 & 0.018 & SN2012ak, PS15ahw \\ +PGC68345 & 22h14m03.018s & -26d56m15.77s & 10 & 0.033 & 0.004 & SN2010bv, SN2016dgt \\ +PGC83768 & 13h02m35.193s & 27d26m21.38s & 12 & 0.057 & 0.004 & SN1962I, SN1991Q, SN2003do, SN2012da \\ +PGC9204 & 2h25m28.346s & -25d38m16.46s & 10 & 0.025 & 0.002 & SN2014cp \\ +SDSSJ161609.48+383245.0 & 16h16m09.485s & 38d32m45.09s & 7 & 0.034 & 0.001 & SN2013eh \\ +UGC09113 & 14h14m14.762s & 35d25m23.83s & 15 & 0.038 & 0.005 & \nodata \\ +UGC09386 & 14h34m52.7783s & 40d44m52.8518s & 15 & 0.047 & 0.009 & SN2017daf \\ +UGC10064 & 15h51m13.2752s & 25d42m06.784s & 13 & 0.173 & 0.008 & SN2009dc, SN2019fee \\ +UGC10214 & 16h06m03.94s & 55d25m31.33s & 5 & 0.023 & 0.001 & SN2002lk, SN2007cu, SN2008dq, PS1-10acx, PS1-11agk \\ +UGC10685 & 17h04m50.999s & 12d55m29.64s & 10 & 0.239 & 0.014 & SN2010hw, SN2013cj \\ +UGC11501 & 19h58m37.031s & 2d36m10.62s & 10 & 0.376 & 0.068 & SN2011dn \\ +UGC11797 & 21h43m20.1605s & 43d34m34.644s & 10 & 1.248 & 0.069 & SN2004ca, SN2015N, SN2018dfy \\ +UGC12640 & 23h30m56.799s & 15d29m25.96s & 10 & 0.169 & 0.008 & SN2011ef, SN2019ssi \\ +UGC12846 & 23h55m46.0248s & 18d25m33.6036s & 10 & 0.094 & 0.006 & SN2007od \\ +UGC12850 & 23h56m06.16s & 29d22m40.44s & 10 & 0.143 & 0.010 & SN2014ek, CSSJ235535.6+291220 \\ +UGC2855 & 3h48m20.731s & 70d07m58.37s & 7 & 1.947 & 0.038 & SN2014dg \\ +UGC402 & 0h39m18.612s & 3d57m08.87s & 10 & 0.067 & 0.003 & ASASSN-15qc, SN2016hsq \\ +UGC4179 & 8h02m05.9609s & 0d48m32.742s & 5 & 0.147 & 0.019 & SN2006jd \\ +UGC5055 & 9h30m11.7493s & 55d51m08.6863s & 8 & 0.086 & 0.006 & SN2014R \\ +UGC5378 & 10h00m31.9918s & 4d24m25.6711s & 5 & 0.067 & 0.005 & SN2007S \\ +UGC5460 & 10h08m09.197s & 51d50m40.2504s & 5 & 0.022 & 0.003 & SN2011ht, SN2015as \\ +UGC5623 & 10h23m48.6038s & 33d48m28.7892s & 9 & 0.055 & 0.006 & SN2010ks \\ +UGC6483 & 11h29m02.358s & 17d13m55.15s & 7 & 0.069 & 0.003 & SN2013hh \\ +UGC7848 & 12h40m57.433s & 63d31m11.3s & 5 & 0.039 & 0.001 & SN2006bv \\ +UGC8713 & 13h47m01.2595s & 33d53m36.9528s & 10 & 0.054 & 0.004 & SN2012cp \\ \ No newline at end of file diff --git a/Brown_Walker_table_1_SN.tex.orig b/Brown_Walker_table_1_SN.tex.orig new file mode 100644 index 0000000..3e8b7c1 --- /dev/null +++ b/Brown_Walker_table_1_SN.tex.orig @@ -0,0 +1,103 @@ +LMC & 05h23m34.6s & -69d45m22s & 323 & 0.20\tablenotekey{(a)} & 0.05 & \nodata \\ +# 0.075*3.1*0.86 +SMC & 00h52m38.0s & -72d48m01s & 190 & 0.10\tablenotekey{(a)} & 0.05 & \nodata \\ +# 0.037*3.1**0.86 +M31 & 00h42m44.5s & +41d16m09s & 95 & 0.17\tablenotekey{(a)} & 0.05 & \nodata \\ +#0.062*3.1**0.86 +#M82 & 9h55m52.43s & 69d40m46.93s & 20 & 0.2 & 0.05 \\ my value +# AB=0.25 from Dalcanton 0.25*3.1/4.1*0.86 +M82 & 9h55m52.43s & 69d40m46.93s & 20 & 0.16\tablenotekey{(b)} & 0.05 & \nodata \\ +Arp244 & 12h01m53.17s & -18d52m37.92s & 15 & 0.11 & 0.01 & SNe 1921A, 1974E, 2004gt, 2007sr, 2013dk \\ +ESO138-G10 & 16h59m02.952s & -60d12m57.67s & 12 & 0.55 & 0.05 & SN2013by \\ +ESO287-G40 & 21h37m28.1842s & -47d02m08.8331s & 7 & 0.065 & 0.01 & SNe 2009lc, 2013fy\\ +ESO317-32 & 10h28m01.6186s & -42d06m38.7541s & 12 & 0.33 & 0.03 & SN2017ghs\\ +ESO509-IG064 & 13h34m39.3s & -23d40m50s & 15 & 0.3 & 0.02 & SN2016eiy\\ +IC208 & 2h08m27.736s & 6d23m41.53s & 12 & 0.13 & 0.01 & SNe 2003G, 2017glq \\ +IC2574 & 10h28m23.6205s & 68d24m43.4414s & 15 & 0.08 & 0.01 & \nodata \\ +IC5249 & 22h47m06.262s & -64d49m55.42s & 15 & 0.08 & 0.01 & SN2011cb \\ +KUG0647+311 & 6h50m36.832s & 31d07m00.6s & 15 & 0.31 & 0.03 & SN2016asf \\ +M51 & 13h29m52.698s & 47d11m42.93s & 15 & 0.05 & 0.01 & SNe 1945A, 1994I, 2005cs, 2011dh\\ +M61 & 12h21m54.9275s & 4d28m25.5883s & 15 & 0.05 & 0.01 & SNe 1926A, 1964F, 1961I, 1999gn, 2006ov, 2008in, 2014dt\\ +M66 & 11h20m15.026s & 12d59m28.64s & 15 & 0.07 & 0.01 & SNe 1973R, 1989B, 1997bs, 2009hd, 2016cok \\ +M74 & 1h36m41.772s & 15d47m00.46s & 15 & 0.16 & 0.02 & SNe 2002ap, 2003gd, 2013ej, PS15blm \\ +M82 & 9h55m52.43s & 69d40m46.93s & 20 & 0.2 & 0.05 & SNe 2004am, 2008iz, 2014J, 2019aji\\ +M83 & 13h37m00.919s & -29d51m56.74s & 15 & 0.12 & 0.01 & SNe 1923A, 1945B, 1950B, 1957D, 1968L, 1983N \\ +MCG-01-07-004 & 2h23m13.2516s & -4d31m01.5168s & 7 & 0.07 & 0.05 & SNe ASASSN-15od, SCP06R12, PS1-10g \\ +MCG-02-24-027 & 9h28m59.256s & -14d48m27.25s & 7 & 0.17 & 0.01 & SN2011at \\ +MCG-02-30-003 & 11h33m10.5799s & -10d13m43.7361s & 10 & 0.09 & 0.01 & SNe 2003ee, 2017hm \\ +MCG+10-19-1 & 12h54m49.706s & 58d52m56.46s & 7 & 0.025 & 0.005 & PTF10icb \\ +NGC0584 & 1h31m20.755s & -6d52m05.02s & 15 & 0.1 & 0.01 & SN2016fng\\ +NGC1097 & 2h46m19.059s & -30d16m29.68s & 15 & 0.05 & 0.01 & SNe 1992bd, 1999eu, 2003B\\ +NGC1313 & 3h18m16.046s & -66d29m53.74s & 20 & 0.1 & 0.05 & SNe 1962M, 1978K \\ +NGC134 & 0h30m21.893s & -33d14m43.26s & 20 & 0.03 & 0.01 & SN2009gj \\ +NGC1365 & 3h33m36.458s & -36d08m26.37s & 20 & 0.03 & 0.01 & SNe 1957C, 1983V, 2001du, 2012fr \\ +NGC1371 & 3h35m01.351s & -24d55m59.19s & 10 & 0.05 & 0.01 & SN2005ke \\ +NGC1448 & 3h44m31.915s & -44d38m41.38s & 10 & 0.025 & 0.01 & SNe 1983S, 2001el, 2003hn, 2014df \\ +NGC2315 & 7h02m33.038s & 50d35m26.18s & 7 & 0.2 & 0.02 & SN2011ay \\ +NGC2357 & 7h17m40.981s & 23d21m24.28s & 10 & 0.15 & 0.02 & SNe 2010bj, 2015I \\ +NGC2577 & 8h22m43.45s & 22d33m11.1408s & 10 & 0.12 & 0.01 & SN2007ax \\ +NGC2615 & 8h34m33.358s & -2d32m48.57s & 10 & 0.075 & 0.005 & SN2014ao \\ +NGC2668 & 8h49m22.57s & 36d42m37.53s & 10 & 0.085 & 0.005 & SN2003je \\ +NGC2748 & 9h13m43.037s & 76d28m31.23s & 5 & 0.06 & 0.01 & SNe 1985A, 2013ff, PS15jf, 2017gkk \\ +NGC2811 & 9h16m11.1s & -16d18m45.78s & 12 & 0.12 & 0.01 & SNe 2005am, 2018jzo \\ +NGC3034 & 9h55m52.43s & 69d40m46.93s & 15 & 0.2 & 0.05 & SNe 2004am, 2008iz, 2014J, 2019ajl \\ +NGC3521 & 11h05m48.5676s & -0d02m09.2282s & 15 & 0.11 & 0.02 & \nodata \\ +NGC3556 & 11h11m30.967s & 55d40m26.84s & 20 & 0.025 & 0.005 & SN1969B \\ +NGC3627 & 11h20m15.026s & 12d59m28.64s & 15 & 0.07 & 0.01 & SNe 1973R, 1989B, 1997bs, 2009hd, 2016cok \\ +NGC3690 & 11h28m31.326s & 58d33m41.8s & 10 & 0.035 & 0.01 & SNe 1992bu, 1993G, 1998T, 1999D, 2005U, 2010O, 2010P, 2019lqo \\ +NGC383 & 1h07m24.9587s & 32d24m45.214s & 14 & 0.16 & 0.02 & SNe 2000dk, 2015ar, 2016sx, 2017hle \\ +NGC3953 & 11h53m49.0088s & 52d19m36.4738s & 15 & 0.06 & 0.01 & SNe 2001dp, 2006bp \\ +# duplicate NGC4038 & 12h01m53.002s & -18d52m03.32s & 15 & 0.11 & 0.01 & \\ +NGC4080 & 12h04m51.804s & 26d59m33.43s & 12 & 0.055 & 0.005 & MASTER OT J120451.50+265946.6 \\ +NGC4214 & 12h15m39.174s & 36d19m36.8s & 10 & 0.06 & 0.005 & SNe 1954A, 2010U \\ +# M61 duplicate NGC4303 & 12h21m54.9275s & 4d28m25.5883s & 15 & 0.05 & 0.005 & \\ +NGC4569 & 12h36m49.816s & 13d09m46.33s & 7 & 0.12 & 0.01 & \nodata \\ +NGC4594 & 12h39m59.4319s & -11d37m22.9954s & 10 & 0.135 & 0.005 & SNe 1997bl, PS15akv \\ +NGC4631 & 12h42m08.009s & 32d32m29.44s & 10 & 0.045 & 0.005 & \nodata \\ +NGC4736 & 12h50m53.148s & 41d07m12.55s & 15 & 0.035 & 0.01 & \nodata \\ +NGC5055 & 13h15m49.2739s & 42d01m45.7261s & 15 & 0.035 & 0.01 & SN1971I \\ +NGC5177 & 13h29m24.269s & 11d47m49.55s & 5 & 0.08 & 0.01 & SN2010cr \\ +# M51 duplicate NGC5194 & 13h29m52.698s & 47d11m42.93s & 20 & 0.05 & 0.01 & \\ +# ngc5195 & 13h29m59.59s & 47d15m58.06s & 20 & 0.05 & 0.01 & \\ +NGC5221 & 13h34m55.909s & 13d49m57.14s & 10 & 0.065 & 0.01 & SN1970P, SN2008ez, PS1-14ea, SN2016bln \\ +NGC5490 & 14h09m57.33s & 17d32m43.53s & 10 & 0.065 & 0.005 & SN1997cn, SN2003aq, SN2005I, SN2015bo, SN2016ccm \\ +NGC613 & 1h34m18.235s & -29d25m06.56s & 15 & 0.045 & 0.005 & SN2016gkg \\ +NGC6166 & 16h28m38.2444s & 39d33m04.2318s & 10 & 0.025 & 0.005 & SN1997cq, SN2009eu, PS15aot, SN2018ccl, SN2019gqd \\ +NGC634 & 1h38m18.679s & 35d21m53.47s & 10 & 0.12 & 0.01 & SN2006Q, SN2008A \\ +NGC6479 & 17h48m21.5875s & 54d08m56.4765s & 7 & 0.095 & 0.01 & SN2007cl, SN2009ay \\ +NGC6822 & 19h44m56.199s & -14d47m51.29s & 20 & 0.55 & 0.05 & \nodata \\ +NGC7187 & 22h02m44.4954s & -32d48m11.439s & 10 & 0.08 & 0.01 & SN2017gah \\ +NGC7259 & 22h23m05.5451s & -28d57m17.4766s & 7 & 0.045 & 0.005 & SN2009ip, SN2014dq, SMT16jyu \\ +NGC7371 & 22h46m03.744s & -11d00m04.3327s & 7 & 0.15 & 0.005 & LSQ13cux, PS15bgt \\ +NGC7552 & 23h16m10.767s & -42d35m05.39s & 12 & 0.03 & 0.005 & SN2017bzc \\ +NGC7653 & 23h24m49.3612s & 15d16m32.1419s & 7 & 0.18 & 0.02 & SN2015bf, SN2018cjk \\ +NGC7793 & 23h57m49.7534s & -32d35m27.7083s & 12 & 0.035 & 0.01 & SN2008bk \\ +NGC88 & 0h21m22.132s & -48d38m24.28s & 20 & 0.04 & 0.01 & SN1994Z, ASASSN-15ut \\ +NGC918 & 2h25m50.7911s & 18d29m46.3842s & 20 & 0.7 & 0.1 & SN2009js, SN2011ek \\ +PGC071943 & 23h37m44.414s & -47d30m22.92s & 10 & 0.03 & 0.005 & \nodata \\ +PGC2692384 & 18h32m24.016s & 66d53m43s & 15 & 0.16 & 0.02 & SN2011hj \\ +PGC29010 & 10h01m26.5223s & 36d40m16.6648s & 8 & 0.03 & 0.005 & SN2012ak, PS15ahw \\ +PGC68345 & 22h14m03.018s & -26d56m15.77s & 10 & 0.06 & 0.005 & SN2010bv, SN2016dgt \\ +PGC83768 & 13h02m35.193s & 27d26m21.38s & 12 & 0.025 & 0.005 & SN1962I, SN1991Q, SN2003do, SN2012da \\ +PGC9204 & 2h25m28.346s & -25d38m16.46s & 10 & 0.035 & 0.005 & SN2014cp \\ +SDSSJ161609.48+383245.0 & 16h16m09.485s & 38d32m45.09s & 7 & 0.03 & 0.005 & SN2013eh \\ +UGC09113 & 14h14m14.762s & 35d25m23.83s & 15 & 0.045 & 0.005 & \nodata \\ +UGC09386 & 14h34m52.7783s & 40d44m52.8518s & 15 & 0.035 & 0.005 & SN2017daf \\ +UGC10064 & 15h51m13.2752s & 25d42m06.784s & 13 & 0.16 & 0.01 & SN2009dc, SN2019fee \\ +UGC10214 & 16h06m03.94s & 55d25m31.33s & 5 & 0.02 & 0.005 & SN2002lk, SN2007cu, SN2008dq, PS1-10acx, PS1-11agk \\ +UGC10685 & 17h04m50.999s & 12d55m29.64s & 10 & 0.24 & 0.02 & SN2010hw, SN2013cj \\ +UGC11501 & 19h58m37.031s & 2d36m10.62s & 10 & 0.37 & 0.05 & SN2011dn \\ +UGC11797 & 21h43m20.1605s & 43d34m34.644s & 10 & 1.25 & 0.1 & SN2004ca, SN2015N, SN2018dfy \\ +UGC12640 & 23h30m56.799s & 15d29m25.96s & 10 & 0.17 & 0.01 & SN2011ef, SN2019ssi \\ +UGC12846 & 23h55m46.0248s & 18d25m33.6036s & 10 & 0.09 & 0.01 & SN2007od \\ +UGC12850 & 23h56m06.16s & 29d22m40.44s & 10 & 0.14 & 0.015 & SN2014ek, CSSJ235535.6+291220 \\ +UGC2855 & 3h48m20.731s & 70d07m58.37s & 7 & 1.9 & 0.2 & SN2014dg \\ +UGC402 & 0h39m18.612s & 3d57m08.87s & 10 & 0.065 & 0.005 & ASASSN-15qc, SN2016hsq \\ +UGC4179 & 8h02m05.9609s & 0d48m32.742s & 5 & 0.14 & 0.02 & SN2006jd \\ +UGC5055 & 9h30m11.7493s & 55d51m08.6863s & 8 & 0.07 & 0.02 & SN2014R \\ +UGC5378 & 10h00m31.9918s & 4d24m25.6711s & 5 & 0.065 & 0.005 & SN2007S \\ +UGC5460 & 10h08m09.197s & 51d50m40.2504s & 5 & 0.02 & 0.005 & SN2011ht, SN2015as \\ +UGC5623 & 10h23m48.6038s & 33d48m28.7892s & 9 & 0.055 & 0.005 & SN2010ks \\ +UGC6483 & 11h29m02.358s & 17d13m55.15s & 7 & 0.065 & 0.005 & SN2013hh \\ +UGC7848 & 12h40m57.433s & 63d31m11.3s & 5 & 0.035 & 0.005 & SN2006bv \\ +UGC8713 & 13h47m01.2595s & 33d53m36.9528s & 10 & 0.05 & 0.005 & SN2012cp \\ \ No newline at end of file diff --git a/Graphs/M31e.png b/Graphs/M31e.png index cf68f27..bf6c684 100644 Binary files a/Graphs/M31e.png and b/Graphs/M31e.png differ diff --git a/Graphs/M33e.png b/Graphs/M33e.png index c176982..2074e6f 100644 Binary files a/Graphs/M33e.png and b/Graphs/M33e.png differ diff --git a/Graphs/M51e.png b/Graphs/M51e.png index 0a7712c..1edb9d6 100644 Binary files a/Graphs/M51e.png and b/Graphs/M51e.png differ diff --git a/Graphs/M61e.png b/Graphs/M61e.png index cad9a55..ce8bd79 100644 Binary files a/Graphs/M61e.png and b/Graphs/M61e.png differ diff --git a/Graphs/M83e.png b/Graphs/M83e.png index 0911f51..e2a31f2 100644 Binary files a/Graphs/M83e.png and b/Graphs/M83e.png differ diff --git a/Graphs/NGC1316e.png b/Graphs/NGC1316e.png index 1e0e242..5edded9 100644 Binary files a/Graphs/NGC1316e.png and b/Graphs/NGC1316e.png differ diff --git a/Graphs/NGC134e.png b/Graphs/NGC134e.png index 1da5e95..c6a2f63 100644 Binary files a/Graphs/NGC134e.png and b/Graphs/NGC134e.png differ diff --git a/Graphs/NGC1371e.png b/Graphs/NGC1371e.png index 35222f2..b0fca1f 100644 Binary files a/Graphs/NGC1371e.png and b/Graphs/NGC1371e.png differ diff --git a/Graphs/NGC1448e.png b/Graphs/NGC1448e.png index ff849aa..99bf01a 100644 Binary files a/Graphs/NGC1448e.png and b/Graphs/NGC1448e.png differ diff --git a/Graphs/NGC147e.png b/Graphs/NGC147e.png index aed8004..9aa5c6c 100644 Binary files a/Graphs/NGC147e.png and b/Graphs/NGC147e.png differ diff --git a/Graphs/NGC4258e.png b/Graphs/NGC4258e.png index 8c47cd1..9e9c00b 100644 Binary files a/Graphs/NGC4258e.png and b/Graphs/NGC4258e.png differ diff --git a/Graphs/NGC45e.png b/Graphs/NGC45e.png index 9830f35..eddf389 100644 Binary files a/Graphs/NGC45e.png and b/Graphs/NGC45e.png differ diff --git a/Graphs/NGC55e.png b/Graphs/NGC55e.png index 8bfb193..b767632 100644 Binary files a/Graphs/NGC55e.png and b/Graphs/NGC55e.png differ diff --git a/Graphs/NGC88e.png b/Graphs/NGC88e.png index 6fed3e8..de7efc1 100644 Binary files a/Graphs/NGC88e.png and b/Graphs/NGC88e.png differ diff --git a/Graphs/NGC918e.png b/Graphs/NGC918e.png index f85c087..c64b579 100644 Binary files a/Graphs/NGC918e.png and b/Graphs/NGC918e.png differ diff --git a/Pictures/M31.png b/Pictures/M31.png index 77998e0..07b5003 100644 Binary files a/Pictures/M31.png and b/Pictures/M31.png differ diff --git a/Pictures/M51.png b/Pictures/M51.png index 66adfd9..38445c6 100644 Binary files a/Pictures/M51.png and b/Pictures/M51.png differ diff --git a/Pictures/M61.png b/Pictures/M61.png index 2246ff4..d2d0a74 100644 Binary files a/Pictures/M61.png and b/Pictures/M61.png differ diff --git a/Pictures/M83.png b/Pictures/M83.png index de5f389..8af2148 100644 Binary files a/Pictures/M83.png and b/Pictures/M83.png differ diff --git a/Pictures/NGC1316.png b/Pictures/NGC1316.png index 14f440c..3d55799 100644 Binary files a/Pictures/NGC1316.png and b/Pictures/NGC1316.png differ diff --git a/Pictures/NGC134.png b/Pictures/NGC134.png index ec1e287..9ec932e 100644 Binary files a/Pictures/NGC134.png and 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index a6d01c0..268a4b9 100644 Binary files a/Pictures/NGC88.png and b/Pictures/NGC88.png differ diff --git a/Pictures/NGC918.png b/Pictures/NGC918.png index 0867905..c22df4b 100644 Binary files a/Pictures/NGC918.png and b/Pictures/NGC918.png differ diff --git a/Program/AVcomp.png b/Program/AVcomp.png new file mode 100644 index 0000000..cadac5f Binary files /dev/null and b/Program/AVcomp.png differ diff --git a/Program/Arp244_radeccomparison.png b/Program/Arp244_radeccomparison.png new file mode 100644 index 0000000..2282eaf Binary files /dev/null and b/Program/Arp244_radeccomparison.png differ diff --git a/Program/Brown_Walker_table_1.dat b/Program/Brown_Walker_table_1.dat index 6655d02..ec3c3e7 100644 --- a/Program/Brown_Walker_table_1.dat +++ b/Program/Brown_Walker_table_1.dat @@ -1,94 +1,100 @@ -Arp244 12h01m53.17s -18d52m37.92s 15 0.11 0.01 -ESO138-G10 16h59m02.952s -60d12m57.67s 12 0.55 0.05 -ESO287-G40 21h37m28.1842s -47d02m08.8331s 7 0.065 0.01 -ESO317-32 10h28m01.6186s 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+ "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Program/a_v=tab b/Program/a_v=tab new file mode 100644 index 0000000..9134457 --- /dev/null +++ b/Program/a_v=tab @@ -0,0 +1,43 @@ +Arcminute,North,East,South,West +0.0,0.056,0.056,0.056,0.056 +1.0,0.056,0.056,0.056,0.056 +2.0,0.056,0.056,0.056,0.056 +3.0,0.056,0.056,0.056,0.055 +4.0,0.056,0.056,0.056,0.055 +5.0,0.056,0.056,0.056,0.055 +6.0,0.056,0.056,0.055,0.054 +7.0,0.056,0.057,0.055,0.054 +8.0,0.056,0.058,0.055,0.053 +9.0,0.057,0.058,0.053,0.055 +10.0,0.057,0.06,0.053,0.055 +11.0,0.057,0.061,0.053,0.056 +12.0,0.06,0.061,0.052,0.058 +13.0,0.061,0.062,0.052,0.058 +14.0,0.061,0.061,0.052,0.061 +15.0,0.063,0.061,0.051,0.061 +16.0,0.065,0.058,0.049,0.061 +17.0,0.065,0.055,0.049,0.063 +18.0,0.067,0.055,0.047,0.063 +19.0,0.068,0.053,0.046,0.063 +20.0,0.068,0.052,0.046,0.062 +21.0,0.07,0.052,0.045,0.061 +22.0,0.068,0.053,0.044,0.061 +23.0,0.068,0.053,0.044,0.059 +24.0,0.067,0.053,0.044,0.058 +25.0,0.066,0.055,0.043,0.058 +26.0,0.066,0.056,0.043,0.059 +27.0,0.064,0.056,0.043,0.06 +28.0,0.062,0.06,0.041,0.06 +29.0,0.062,0.058,0.041,0.06 +30.0,0.061,0.058,0.041,0.061 +31.0,0.06,0.059,0.04,0.062 +32.0,0.06,0.058,0.04,0.062 +33.0,0.06,0.058,0.04,0.062 +34.0,0.059,0.056,0.04,0.06 +35.0,0.059,0.055,0.04,0.06 +36.0,0.06,0.055,0.04,0.06 +37.0,0.059,0.054,0.042,0.059 +38.0,0.059,0.055,0.042,0.059 +39.0,0.059,0.055,0.042,0.057 +40.0,0.06,0.056,0.043,0.057 +,,,, diff --git a/Program/annulus.py b/Program/annulus.py index 374a4ee..47ca41f 100644 --- a/Program/annulus.py +++ b/Program/annulus.py @@ -1,11 +1,17 @@ -def annulus(distance,ra,dec): +moved into utilities def annulus(distance,ra,dec): +#def annulus(): import math import sys from astropy import units as u from astropy import coordinates from astropy.coordinates import Angle,ICRS,SkyCoord - print('does this work?') + +# ra='12h00m00.0s' +# dec='00d00m00.00s' +# distance=60.0 + degreedistance=distance/60.0 + """ Gets 360 coordinates a specified distance away from the center of the galaxy @@ -16,40 +22,22 @@ def annulus(distance,ra,dec): Outputs: ring_coords: list of coordinates *distance* arcminutes away from the center of the specified galaxy - #angle is from astropy.coordinates - """ ring_coords = [None]*360 directions = [i for i in range(360)] # directions = [0, 90, 180, 270, 360] - coord=SkyCoord(ra+' '+dec) - print(coord) + inputcoord=SkyCoord(ra+' '+dec) +# print(inputcoord) for direction in directions: - print(direction) - decli = coord.dec.arcminute+distance*math.cos(direction*2.0*3.14159/360.0) - #print(math.cos(direction*2.0*3.14159/360.0)) - print(decli) - decl = Angle(decli,u.arcminute) - decl = Angle(decl.to_string(unit=u.degree),u.degree) - coord = SkyCoord(ra=coord.ra, dec=decl) - #print(coord) - # converting from arcminutes into right ascension seconds - # 24 h x 60 m/h x 60 s/m = 86400 sec - # 360 deg x 60 arcmin/deg x 60 arsec/arcmin = 1296000 arcsec - # in on arcminute = 60 arcsec x 86400 / 1296000 - ds = distance*4*math.sin(direction*2*3.14159/360.0) - ds/=math.cos(math.radians(coord.dec.degree)) - h = coord.ra.hms.h - m = coord.ra.hms.m - s = coord.ra.hms.s+ds - (s,m,h) = timeFix(s,m,h) #keep time within allowed range - - rad = Angle((h,m,s), unit = u.hour) - rad = Angle(rad.to_string(unit=u.hour),u.hour) - #print(SkyCoord(ra=rad, dec=decl)) - ring_coords[direction] = SkyCoord(ra=rad, dec=decl) +# print(direction) + newdec=inputcoord.dec+Angle(degreedistance*math.cos(direction*2.0*3.14159/360.0),unit=u.degree) +# print(newdec) + newra=inputcoord.ra+Angle(math.cos(newdec.value*2.0*3.14159/360.0)*degreedistance*math.sin(direction*2.0*3.14159/360.0),u.degree) +# print(newra) + + ring_coords[direction] = SkyCoord(ra=newra, dec=newdec) # print(ring_coords) diff --git a/Program/comparemaps.py b/Program/comparemaps.py new file mode 100644 index 0000000..816b9c7 --- /dev/null +++ b/Program/comparemaps.py @@ -0,0 +1,161 @@ +from __future__ import print_function + +import matplotlib +import matplotlib.pyplot as plt +import numpy as np + +import astropy.units as units +from astropy.coordinates import SkyCoord +from astropy.coordinates import Angle +from astroquery.irsa_dust import IrsaDust + +import healpy as hp +import pandas as pd +import warnings +warnings.filterwarnings('ignore') + +from dustmaps.sfd import SFDQuery +from dustmaps.planck import PlanckQuery +from dustmaps.bayestar import BayestarQuery + +#import getAVbest2 +#import getAVbest +import dustmaps.planck +#dustmaps.planck.fetch() + + +inFile = 'Brown_Walker_table_1.dat' +inTable = pd.read_csv(inFile,header=None,delimiter=' ') +names = inTable.iloc[:,0] +allra = inTable.iloc[:,1] +alldec = inTable.iloc[:,2] +distances = inTable.iloc[:,3] +cardinalAV = inTable.iloc[:,4] +cardinalAVspread = inTable.iloc[:,5] +n=len(inTable.index) + +print(cardinalAV) + +SF2011AVs = [None]*n +SFDqueryAVs = [None]*n +BW2021AVs = [None]*n +BW20212AVs = [None]*n +PlanckAVs = [None]*n +HIAVs = [None]*n +index = [i for i in range(n)] + + +for j in index: + + + ra0, dec0 = allra[j], alldec[j] + + sourceCoords = SkyCoord(ra0,dec0,frame='icrs') + print("sourceCoords ", sourceCoords) + + sfd = SFDQuery() + # this conversion puts Av on the Schlafly system + Av_sfd = 2.742 * sfd(sourceCoords) + print("av_sfd good") + planck = PlanckQuery() + Av_planck = 3.1 * planck(sourceCoords) + print("av_planck good") + + AVtable = IrsaDust.get_extinction_table(sourceCoords,show_progress = False) + AV=AVtable['A_SandF'][2] + print("av_sandf good") + + SF2011AVs[j] = AV + SFDqueryAVs[j] = Av_sfd + # BW2021AVs[j],err,source = getAVbest(SourceCoords) + # BW20212AVs[j],err,source = getAVbest2(SourceCoords) + PlanckAVs[j] = Av_planck + + # https://nbviewer.jupyter.org/github/DanielLenz/ebv_tools/blob/master/examples.ipynb + ebv_map = hp.read_map('/Users/pbrown/Desktop/SN/github/ebv_tools/ebv_lhd.hpx.fits', verbose=False) + nside = hp.get_nside(ebv_map) + npix = hp.nside2npix(nside) + ordering = 'ring' + + s_gal = sourceCoords.galactic + glon = s_gal.l.value + glat = s_gal.b.value + pix = hp.ang2pix(nside, glon, glat, lonlat=True) + + # get reddening for these pixels + ebv_los = ebv_map[pix] + # the conversion puts it on the schlafley system + HIAVs[j] = ebv_los*2.742 + print("av_hi good") + + imagename=names[j]+'_radeccomparison.png' + + ra0 = Angle(ra0) + dec0 = Angle(dec0) + + print(ra0, dec0) + + ra = np.arange(ra0.degree - 2., ra0.degree + 2., 0.05) + dec = np.arange(dec0.degree - 2., dec0.degree + 2., 0.05) + ra, dec = np.meshgrid(ra, dec) + coords = SkyCoord(ra*units.deg, dec*units.deg, frame='icrs') + + + + sfd = SFDQuery() + # this conversion puts Av on the Schlafly system + Av_sfd = 2.742 * sfd(coords) + + planck = PlanckQuery() + Av_planck = 3.1 * planck(coords) + +#bayestar = BayestarQuery(max_samples=1) +#Av_bayestar = 2.742 * bayestar(coords) + + fig = plt.figure(figsize=(8,4), dpi=150) + + for k,(Av,title) in enumerate([(Av_sfd, 'SFD'), + (Av_planck, 'Planck')]): + + + ax = fig.add_subplot(1,2,k+1) + ax.imshow( + np.sqrt(Av)[::,::-1], + vmin=0., + vmax=0.5, + origin='lower', + interpolation='nearest', + cmap='binary', + aspect='equal' + ) + ax.axis('off') + ax.set_title(title) + + fig.subplots_adjust(wspace=0., hspace=0.) + plt.savefig(imagename, dpi=150) + +x=cardinalAV[0:n] + +for j in index: + print(names[j], x[j], SF2011AVs[j], SFDqueryAVs[j], PlanckAVs[j], HIAVs[j]) + +plt.clf() +fig = plt.figure(figsize=(4,4), dpi=150) +plt.plot(x,SF2011AVs, color = '#00429d', marker = "o", linestyle='none', label = "SF2011") +plt.plot(x,SFDqueryAVs, color = '#73a2c6', marker = ".", linestyle='none', label = "SFDquery") +plt.plot(x,PlanckAVs, color = '#f4777f', marker = "x", linestyle='none', label = "Planck") +plt.plot(x,HIAVs, color = '#93003a', marker = "H", linestyle='none', label = "HI") +#plt.axvline(x=majAxis[j]) +plt.xlabel("Annular A$_V$") +plt.ylabel("A$_V$") +plt.legend(loc='bottom right', shadow=True) +# plt.suptitle("A$_V$ Values by Arcminute") +ax.set_aspect('equal', adjustable='box') +ax.set_xscale('log') +plt.xlim(0.01,3) +ax.set_yscale('log') +plt.ylim(0.01,3) +plt.savefig('AVcomp.png', dpi=150) + + + diff --git a/Program/comparemapvalues.py b/Program/comparemapvalues.py new file mode 100644 index 0000000..131428d --- /dev/null +++ b/Program/comparemapvalues.py @@ -0,0 +1,81 @@ +from __future__ import print_function + +import matplotlib +import matplotlib.pyplot as plt +import numpy as np + +import astropy.units as units +from astropy.coordinates import SkyCoord +from astropy.coordinates import Angle + +from dustmaps.sfd import SFDQuery +from dustmaps.planck import PlanckQuery +from dustmaps.bayestar import BayestarQuery + +import dustmaps.planck +#dustmaps.planck.fetch() + +''' +l0, b0 = (121.174405, -21.572936) +imagename='M31comparison.png' + +l0, b0 = (138.320313, 68.842121) +imagename='NGC4258comparison.png' + +## Galactic coordinates for M33 +l0, b0 = (133.610161, -31.330587) +imagename='M33comparison.png' + +l = np.arange(l0 - 1., l0 + 1., 0.05) +b = np.arange(b0 - 1., b0 + 1., 0.05) +l, b = np.meshgrid(l, b) +coords = SkyCoord(l*units.deg, b*units.deg, + distance=1.*units.kpc, frame='galactic') +''' + +ra0, dec0 = '12h18m57.5046s', '+47d18m14.303s' +imagename='NGC4258radeccomparison.png' + +ra0 = Angle(ra0) +dec0 = Angle(dec0) + +print(ra0, dec0) + +ra = np.arange(ra0.degree - 2., ra0.degree + 2., 0.05) +dec = np.arange(dec0.degree - 2., dec0.degree + 2., 0.05) +ra, dec = np.meshgrid(ra, dec) +coords = SkyCoord(ra*units.deg, dec*units.deg, frame='icrs') + + + +sfd = SFDQuery() +Av_sfd = 2.742 * sfd(coords) +print(Av_sfd) +planck = PlanckQuery() +Av_planck = 3.1 * planck(coords) + +#bayestar = BayestarQuery(max_samples=1) +#Av_bayestar = 2.742 * bayestar(coords) + +fig = plt.figure(figsize=(8,4), dpi=150) + +for k,(Av,title) in enumerate([(Av_sfd, 'SFD'), + (Av_planck, 'Planck')]): +#, +# (Av_bayestar, 'Bayestar') + + ax = fig.add_subplot(1,2,k+1) + ax.imshow( + np.sqrt(Av)[::,::-1], + vmin=0., + vmax=0.5, + origin='lower', + interpolation='nearest', + cmap='binary', + aspect='equal' + ) + ax.axis('off') + ax.set_title(title) + +fig.subplots_adjust(wspace=0., hspace=0.) +plt.savefig(imagename, dpi=150) diff --git a/Program/dust map postage stamps.ipynb b/Program/dust map postage stamps.ipynb new file mode 100644 index 0000000..7e83b7d --- /dev/null +++ b/Program/dust map postage stamps.ipynb @@ -0,0 +1,281 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading data to '/Users/pbrown/opt/miniconda3/lib/python3.7/site-packages/dustmaps/data/bayestar/bayestar2019.h5' ...\n", + "Downloading https://dataverse.harvard.edu/api/access/datafile/3424724 ...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "694.1 MiB of 694.2 MiB | 8.4 MiB/s |################# | 99% | ETA: 0:00:00" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "done importing\n" + ] + } + ], + "source": [ + "from __future__ import print_function\n", + "import matplotlib.pyplot as plt \n", + "import numpy as np\n", + "\n", + "import astropy.units as units \n", + "from astropy.coordinates import SkyCoord \n", + "from astropy.coordinates import Angle \n", + "from astroquery.irsa_dust import IrsaDust\n", + "\n", + "import healpy as hp \n", + "import pandas as pd \n", + "\n", + "from dustmaps.sfd import SFDQuery \n", + "from dustmaps.planck import PlanckQuery \n", + "from dustmaps.bayestar import BayestarQuery\n", + "import dustmaps.bayestar\n", + "dustmaps.bayestar.fetch()\n", + "\n", + "\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "print('done importing')\n", + "#import getAVbest2\n", + "\n", + "#import getAVbest import dustmaps.planck\n", + "\n", + "#dustmaps.planck.fetch() print('done with importing')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "done\n" + ] + } + ], + "source": [ + "name='UGC2855'\n", + "Coord = SkyCoord.from_name(name, frame = 'icrs')\n", + "print(Coord)\n", + "ra0=Coord.ra\n", + "dec0=Coord.dec\n", + "print('done')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ra = np.arange(ra0.degree - 2., ra0.degree + 2., 0.05)\n", + "dec = np.arange(dec0.degree - 2., dec0.degree + 2., 0.05)\n", + "ra, dec = np.meshgrid(ra, dec)\n", + "coords = SkyCoord(ra*units.deg, dec*units.deg, frame='icrs')\n", + "\n", + "sfd = SFDQuery()\n", + "# this conversion puts Av on the Schlafly system\n", + "Av_sfd = 2.742 * sfd(coords)\n", + "\n", + "planck = PlanckQuery()\n", + "Av_planck = 3.1 * planck(coords)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bayestar = BayestarQuery(max_samples=1)\n", + "Av_bayestar = 2.742 * bayestar(coords)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ebv_map = hp.read_map('$SNFOLDER/github/ebv_tools/ebv_lhd.hpx.fits', verbose=False)\n", + "nside = hp.get_nside(ebv_map)\n", + "npix = hp.nside2npix(nside)\n", + "ordering = 'ring'\n", + "\n", + "\n", + "s_gal = coords.galactic\n", + "glon = s_gal.l.value\n", + "glat = s_gal.b.value\n", + "pix = hp.ang2pix(nside, glon, glat, lonlat=True)\n", + "ebv_los = ebv_map[pix]\n", + "Av_HI = ebv_los*2.742\n", + "print('done loading')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.6020184\n" + ] + } + ], + "source": [ + "maxboth=max(np.amax(Av_planck),np.amax(Av_sfd))\n", + "print(maxboth)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(8,4), dpi=150)\n", + "\n", + "for k,(Av,title) in enumerate([(Av_sfd, 'SFD'),\n", + " (Av_planck, 'Planck'),\n", + " (Av_bayestar, 'Bayestar')]):\n", + "\n", + " ax = fig.add_subplot(1,3,k+1)\n", + " ax.imshow(\n", + " np.sqrt(Av)[::,::-1],\n", + " vmin=0.,\n", + " vmax=maxboth,\n", + " origin='lower',\n", + " interpolation='nearest',\n", + " cmap='binary',\n", + " aspect='equal'\n", + " )\n", + " ax.axis('off')\n", + " ax.set_title(title)\n", + "\n", + "fig.subplots_adjust(wspace=0., hspace=0.)\n", + "plt.savefig(imagename, dpi=150)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Program/dustmapimages.ipynb b/Program/dustmapimages.ipynb new file mode 100644 index 0000000..09be5a1 --- /dev/null +++ b/Program/dustmapimages.ipynb @@ -0,0 +1,19441 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: AstropyDeprecationWarning: astropy.extern.six will be removed in 4.0, use the six module directly if it is still needed [astropy.extern.six]\n" + ] + } + ], + "source": [ + "from __future__ import print_function\n", + "\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import astropy.units as units\n", + "from astropy.coordinates import SkyCoord\n", + "from astropy.coordinates import Angle\n", + "from astroquery.irsa_dust import IrsaDust\n", + "\n", + "import healpy as hp\n", + "import pandas as pd\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "from dustmaps.sfd import SFDQuery\n", + "from dustmaps.planck import PlanckQuery\n", + "from dustmaps.bayestar import BayestarQuery\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import os" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "os.listdir(\"/Users/pbrown/Desktop/SN/github/ebv_tools/\")\n", + "ebv_map = hp.read_map('/Users/pbrown/Desktop/SN/github/ebv_tools/ebv_lhd.hpx.fits', verbose=False)\n", + "nside = hp.get_nside(ebv_map)\n", + "npix = hp.nside2npix(nside)\n", + "ordering = 'ring'\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "424828\n", + "0.015596184879541397\n" + ] + } + ], + "source": [ + "name='NGC4258'\n", + "coord = SkyCoord.from_name(name, frame = 'icrs')\n", + "s_gal = coord.galactic\n", + "glon = s_gal.l.value\n", + "glat = s_gal.b.value\n", + "# get pixel numbers\n", + "galpix = hp.ang2pix(nside, glon, glat, lonlat=True)\n", + "print(galpix)\n", + "# get reddening for these pixels\n", + "ebv_los = ebv_map[galpix]\n", + "print(ebv_los)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "imagename=name+'_4comparison.png'\n", + "ra0 = coord.ra\n", + "dec0 = coord.dec\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "ra = np.arange(ra0.degree - 2., ra0.degree + 2., 0.05)\n", + "dec = np.arange(dec0.degree - 2., dec0.degree + 2., 0.05)\n", + "\n", + "ragrid, decgrid = np.meshgrid(ra, dec)\n", + "coords = SkyCoord(ragrid*units.deg, decgrid*units.deg, frame='icrs')\n", + "\n", + "sfd = SFDQuery()\n", + "# this conversion puts Av on the Schlafly system\n", + "Av_sfd = 2.742 * sfd(coords)\n", + "\n", + "planck = PlanckQuery()\n", + "Av_planck = 3.1 * planck(coords)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading pixel_info ...\n", + "Loading samples ...\n", + "Loading best_fit ...\n", + "Replacing NaNs in reliable distance estimates ...\n", + "Sorting pixel_info ...\n", + "Extracting hp_idx_sorted and data_idx at each nside ...\n", + " nside = 64\n", + " nside = 128\n", + " nside = 256\n", + " nside = 512\n", + " nside = 1024\n", + "t = 72.431 s\n", + " pix_info: 2.654 s\n", + " samples: 34.014 s\n", + " best: 20.956 s\n", + " nan: 0.938 s\n", + " sort: 13.366 s\n", + " idx: 0.502 s\n", + "done with Bayestar\n" + ] + } + ], + "source": [ + "\n", + "coords = SkyCoord(ragrid*units.deg, decgrid*units.deg,\n", + " distance=1000000.*units.kpc, frame='galactic')\n", + "bayestar = BayestarQuery(max_samples=1)\n", + "Av_bayestar = 2.742 * bayestar(coords)\n", + "print('done with Bayestar')\n", + "\n", + "Av_HI=0*Av_sfd" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.17722937874495984\n" + ] + } + ], + "source": [ + "maxboth=max(np.amax(Av_planck),np.amax(Av_sfd))\n", + "print(maxboth)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(80, 80)\n" + ] + } + ], + "source": [ + "print(np.shape(Av_HI))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "374805\n", + "\n", + "376539\n", + "\n", + "378276\n", + "\n", + "380017\n", + "\n", + "381763\n", + "\n", + "383512\n", + "\n", + "385265\n", + "\n", + "387022\n", + "\n", + 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these pixels\n", + "ebv_los = ebv_map[pix]\n", + "for i in range(len(dec)-1):\n", + " for j in range(len(ra)-1):\n", + " stepcoord = SkyCoord(ra[i]*units.deg, dec[j]*units.deg, frame='icrs')\n", + " print(stepcoord)\n", + " stepgal = stepcoord.galactic\n", + " steplon = stepgal.l.value\n", + " steplat = stepgal.b.value\n", + " # get pixel numbers\n", + " steppix = hp.ang2pix(nside, steplon, steplat, lonlat=True)\n", + " print(steppix)\n", + " ebv=ebv_map[steppix]\n", + " Av_HI[j,i]=ebv*2.742\n", + " #print(ebv)\n", + "#AV_HI=2.742*ebv_los\n", + "#print('done with AV_HI')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.03678199 0.03678199 0.03670816 ... 0.03486471 0.03546592 0. ]\n", + " [0.03670947 0.03699267 0.03699003 ... 0.03504511 0.03546592 0. ]\n", + " [0.03685114 0.03699267 0.03701967 ... 0.03524185 0.03524185 0. ]\n", + " ...\n", + " [0.06578277 0.06578277 0.06367689 ... 0.03999896 0.03999896 0. ]\n", + " [0.06818204 0.06697419 0.06697419 ... 0.04024665 0.03978885 0. ]\n", + " [0. 0. 0. ... 0. 0. 0. ]]\n" + ] + } + ], + "source": [ + "print(Av_HI)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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8K9WwVALtOFIXvXtRk2aVBMOSlMTbDKVS9SIZbTqOBHYvcbLk+qGmGWe0iXdNJb03i7LudT1HM8ZHVIY3j6L2V9JE1XpEqauejRs3usenp6eHOmZmNjs76x5X5ldG+rfSL2oZiuhevONqQq7yrIwSUJWkXq/OUd2iemSsS2qitEcZj1H9vLGuznGlTTNe52UkWCvrZnTfGX2orBPRucraq64HXjpzNLeUOReJ6uel2pd4vcM7tAAAAACAKrGhBQAAAABUiQ0tAAAAAKBKbGgBAAAAAFUaKRSqZBhT10FPyofPuw5/yuKFBaihMOMIafJ0XY/og/G1jgWPEuJQ8r4zAikyzlXmgDoelXVFHXt9CafzqKFLSlhLyfvu+jmgtKkSMhSJgpSU/lKC87Zu3eqeOzEx4R731gQvaMRMC4XKeM5Fdc4Iv8kItFHucWpqSqpHxlo46viNrhf1izc+MsZBNFeUNUxpZzN/HETnRvVQgr0USiiXWc69eGVH40Bpj4gSLqg+372yM/pFDUT0rhm16TiCT/e5/livDgAAAADAw8SGFgAAAABQJTa0AAAAAIAqsaEFAAAAAFSJDS0AAAAAoEojpRxnJFCWTCjO0HX9MtL2lHPVflFSzDLaTkneXer8UfUlUbakru9FTe/11hvlXLUeir6kf0cyUuYzxkdGSqM6FzP6JiNJWDE3Nzf0uUpCrrqeev2lpHJGZUcpvdH4UNojSuBUylBSVKNUZSWJWJ0XGcm03viI2i6iXFNJlVXaTr2eV7aS0K1eTxHNrWj9WbnywJfw0blR2d68UO9FSfVVniPe/WWVrZSRkdyd8XxSk5mV53hUttcHSlK1WXfPT96hBQAAAABUiQ0tAAAAAKBKbGgBAAAAAFViQwsAAAAAqBIbWgAAAABAlYqkHJdKyMxQMgkzI3ktqoeXHjaOBOCMJL+IV79xjJu+JFtnyKiHMp+jNLuu1wQluTgjWbzvSesZ6bYZSqYtl1zfvOMZyY3K9dS2U8pQUmXVenj9oqYZe20dnRslo6oJvqNS1sJo7Ebt5JWtpsZ77aTO+4xvuhj1XjLWA7X9lVTfqGxlTJf6lovofDUp2RPNw0ipb9BQv7FDGWNRfynp8BmU57gyptXrKXM8SocfBe/QAgAAAACqxIYWAAAAAFAlNrQAAAAAgCqxoQUAAAAAVGmkUCjlg8F9D4XKkBGSFYVzeOdH50b16EvoUkZ4S8kgpVLtlBHWkBHIMo56lFoTMu4lQ0aI0jjCwTLqnRHQkRGol0EJacpouyh8RQl0UtZ7tR4ZoXIZASlePaLQmei56F1TfYYqAUYRZdyoQU9KGd69qIExyhhT7rtkCJIiKttrj5JrqVK2EvBm5t9j1N/RnCsVyKe8pjbz6630oZkf9KT2i1eGGj6ZESLlzXH1maqMJyXoKSNUcX+8QwsAAAAAqBIbWgAAAABAldjQAgAAAACqxIYWAAAAAFAlNrQAAAAAgCr1NuU4IzWtVEKmkmy21PnKuUrS2MTEhHvca9OMOmfJSOTN4I2bkmm647hHT1QPpT2U42qybdfjoy/Ju+r1up67Sh9ktF3GuMkoexz34omSRz1RW2Q8B0ol3kZll0wHjZ633jWVZOwsXv3URGRlXY/KzkjfVeaWkshbauxG5yvzMKKkni91vkd5Nivjv08yXmsr1JR0TzRuvDmgJgNnpKF7ZShpxmZacvQoeIcWAAAAAFAlNrQAAAAAgCqxoQUAAAAAVIkNLQAAAACgSmxoAQAAAABVGj2WzeGlV2WkGqrphRlJs6USTaN7iRLPvPPVpFmvjMnJSffc6elp93hGe3SdlFeyzl2nGZdM3s0oQ0nKG0ebKomaaqKgwmunqB7q2pmR0l2KmsSq3LtSRsZ6n9Gm0Rjz6hElEUfHV61adcCxKAG/ZDJwxusBT0YyrZqQ693j6tWr3XN37do1dNnKOIjOV9vUu/eMcZDRt0o9onEXlRHNF4XX/tH1lHEavf5T6pyR9h5RUpjVdvbWq2gsKet3RkK0mvocrbOekv2lpLJHycXevY/jW2uGwTu0AAAAAIAqsaEFAAAAAFSJDS0AAAAAoEpsaAEAAAAAVSoSCuXJCOHI+PB0ySCJ6AP9nqgMJeBAvRclrCG6FyW8omQIknJ+yXGjhFdkBBipMtrDOx6dq4SpdB3EFomupwTGZISpqLpeO0uK6uGNm5JhXV2HvEX34t13FLISzTnveHRuRphbRHl2KSEwGXMumhdRqIt3fhSmEt2LsiZHvPbIWNfVgDzlNYXyDFWDnpTrKWuK8gxQn2fRPXqi+inP5uhevONqe2Q8s715pK4THrU9lNC2rp+f0bqktEf0HFECRzP2USVe5/EOLQAAAACgSmxoAQAAAABVYkMLAAAAAKgSG1oAAAAAQJXY0AIAAAAAqjRSynFG8mvXSqaSRfetJDSWTAZWksaU1LSo7JKURDa1DCU5MFKqPdT77joxuOuU2IjSt1HapJIIm5HGrVJSNfuSZqxS7qXvSc6eaOx5xzdu3OieGyXSe2mW6rrupXsqqbkR9bnjtUfJBPwouVh5hippohmvmdR1zBPVWfl2g4zXNiXHkkdJvFXrkZHmryQiZ7xGUNPQvfpFdY7GmJK23PVanzGm1X5Rys5I445ktGmplPoDykwvEQAAAACADrChBQAAAABUiQ0tAAAAAKBKbGgBAAAAAFViQwsAAAAAqFKRlONSSWPjSKsslRKrluullUVlROlyJZNVFSXroaRQ9v1eSsmYt2qdM5J3lbbOGB/KPIpSITPGh5rA2edU30hGAmdJGcnnw5YbiRI/lbInJibcc5U5oK6nGUnJShkRJfEz4xkapU8r5Srtoa5BSpsq40btFyXtVGl/Zd0suaYoY11NF1bXBI93zeh6ynElsdxMG9MRZZ1W5n7Ga5WS3zgRtZPXpkrKdERtD69NS8w53qEFAAAAAFSJDS0AAAAAoEpsaAEAAAAAVWJDCwAAAACoUpFQqJIfflbqoei6zkoIipl/j9GHuJUyur7vqB7L6XpmWlhGyT4vFVqlXk8JSSg5JjP6RalfdK4SxBHVTw2LKiWjv0q2tUcNCuk6FEopW+lvdSx5bTo7Ozv0udFxNfzGOx5dLwo98e59cnJSqkdGOJUnKkO5l4w1OZIRcBX1eamQJmU9VdfevryGUcJ2lP5WAweV0FLledv165qMcyPqvPBEgVrKNTP2bWrgl7L2joJ3aAEAAAAAVWJDCwAAAACoEhtaAAAAAECV2NACAAAAAKrEhhYAAAAAUKWRUo5LUlIl+5K23DU1NS1K8uuLrpMDl/v4iKj3XSpptus0Y/XckvXLmItqYq0nI01UmbcZyZfjSC0vRUkk9RJ2zeJkyampqYdfsYchI+E8ukcl5XhiYsI97rV1lCIcpfpmzBelPTJe2ygJ1lG5UbJ1168pvOsp6ckqpV+ieZjxLQbROI3my7DXM9PW2KgeGUqm7I4q4zVTxrdcRKJzvbUw6sPl8Hq43zscAAAAAAACbGgBAAAAAFViQwsAAAAAqBIbWgAAAABAldjQAgAAAACqNFLKcUbapJpcnFG2cq6SNhlR0sOWU4Jn3/WlrUvWoy+pw0qiY6RU/UomjEYyEjgjSrtmpJQq95KR9DiONEYlEVahpHKqfeWVHbW/8kxTUoTN/HaamZkZ+npmfv2i9ojuRVmDonvx0o/VlF2vX6Lk0YxnQ8k5rqS5RvXw2iO6nld2lEgdjQMlqTcjAT8j5Thq01KpvlHbZaQqK9QyMhLEFRn7F2X8KinwZuWezWrbKfcyCt6hBQAAAABUiQ0tAAAAAKBKbGgBAAAAAFViQwsAAAAAqFKRUCglyKPGUCg1WKNkAIxCuZeMQBw1NCWjfhnjpuvwoYzr9SX8SaH2lXJ+1+M0muNKIEvWOuGVkxEME8kIpMiQMT5KzlFP1HZKuEl03Au/UUNkvDaNypicnBy6DDVISSkjYxwoz/Io1CWihAGVHI/eNdU5q7S10l/K6w+1jUoFzWXUQx3TSh9mtH801r17VAOuZmdnhz43WoMygoa8epd8vak8m6OxpMzbjL2Y8syJyijx+oB3aAEAAAAAVWJDCwAAAACoEhtaAAAAAECV2NACAAAAAKrEhhYAAAAAUKWRUo4jpRJGMxLFVBmJhMq5StJYyURftX4l733U66l17jrluGTZSipkZBzzblgZ6YNqeygpwiXHaddKphb3OZ28JGUtjNoo6pedO3cOfW6UUDw1NTV0GcocWLNmjXtulJI5PT3tHveoSc4K716iOkd9q6YiD1u2koQbidouI2XXS7E185NpJyYm3HO9sqNylfGYkY6rPouU9VR5NqjpwsOWq5ahfhuItwZF5yopu+p6pTzfM9KFFeNIfVbqEVHWq1HwDi0AAAAAoEpsaAEAAAAAVWJDCwAAAACoEhtaAAAAAECV2NACAAAAAKpUJOU4g5LOmpG8ViqZOaIm0XWdAloycbVkAm1GGRlprn1JBs6oh1eG2qYZYy9jnitKpvp6879k6nbJ/ippuacfK30ePTOi1Fwv/VV97nhlRImaMzMz7nHvmhnPFzXVN+O5k5FM6yWPRm2qvOZR66ykH0e8VFk1mXbUpFj1eeEdV5OnvTqr9VBe40ZtpKShR/fo9WGpsbEU7x6jeaGktWfMC6X9o+NRGdF8UV4jZLxWUdaajPTkjDF2QJnpJQIAAAAA0AE2tAAAAACAKrGhBQAAAABUiQ0tAAAAAKBKbGgBAAAAAFXqbcqxos8plmZamldGwmtGGrQqIwnXS2pT76VUKmpGkmJfkqMj40jTHbXscdy3kk45jr4tdU01iXXU60XUpEclATI6t9S4zhi/UeLkxMTE0NeLjnvtEV1PKSODl2hqlvP8U5JHSyaMRmV7czFKmY7K9lJvoznupV1HSj6zS/VhxrxQeW2acT0lXdvMHwclvxlCeVaqabrK667odbnyOlQ5nvEMiean8tpSHR9K4n5G+vEoeIcWAAAAAFAlNrQAAAAAgCqxoQUAAAAAVIkNLQAAAACgSkVCoTICPjJ0HSKT8cFs9ZqjKhkUFSkZoJMhI1igVChUX9oogzoOMoLHSgU6ZZTRlzVBlRHqUvKZ4Z2vhj+VCjZS7lutQ6nxEQWTRMeVoBDl3Lm5Ofe4siaX6lczrT2icaCUMY6wNK/eXsjQUkZ93kb3HQVZZTzfPep880Lb1DHtUYN5vPOjcRdRXuMq7aSMO5USbJQRNKc++7w+UNcJpa2jMrywOXXdVMIFR8E7tAAAAACAKrGhBQAAAABUiQ0tAAAAAKBKbGgBAAAAAFViQwsAAAAAqFKRlGNF1ym7UepXlHhWKoF2HAmlGSmxJftLSfIrmYqqpCBmKJm8Peq50flqqm/GeM9IRyx1vYyE4ox+eTjljCojuTiDmlysnJvRX6OK1sfovjPW08nJyaHLVZJiowTaKGnTu0e1Hl4Z0blRUq+XQpux1kTptsrrlajtontR+iW6R69spQ8jSmKzus546arqWMpYU5S0a2Vty3iNm5EurD4XvHooa5hKSdlVU5+VsR7do3JNpV+i+y6Z+N7Vs5J3aAEAAAAAVWJDCwAAAACoEhtaAAAAAECV2NACAAAAAKrEhhYAAAAAUKWxpxx3LSO1TlWq7Ixk1Ywy1HK6TspTlUo5zmg7tWzl3JIJr10nwo4jRbwUtb9KphV6MhLiFer9KSmefUmO9to0I81Y5aXeRumbSrqt2ode2VEycJTi6ZVRcu1V6hHJSKqOUo49at96lPRYM60PlPbwErrNtKRq5XWGOqa9sjOS2tUx3fXrDyUNXW0PpQ8y2lopu2SSdsazIZrjynwZ9/OTd2gBAAAAAFViQwsAAAAAqBIbWgAAAABAldjQAgAAAACqNFIoVMaHybGvUuFKpWUETnnUUChFqQApteyMc6MP6XvHMz64X/JD/n0a155S9as1yEoJ4igZGqHMjZKBIBm8+k1MTAx9bkRdP7xQkaiMmZmZoeuhUoKGlGAjNcinVADX1NSUe1y5b3Vd8u4lGmNeOJiZFo4U9YsyxryAmqjcjPVgenp66LKjekShXF5/jSOcVBnTfQl3LPkaxuuXqI2UMDE1uMmb+8q8iM5Xnxs4C48AACAASURBVCNe2Wo9PCVeR/EOLQAAAACgSmxoAQAAAABVYkMLAAAAAKgSG1oAAAAAQJXY0AIAAAAAqkTKcc9kpPpmJL2px736qfeScY/e8b4kl6qUBMnouJeUp5bhKdm3CmU8qvqeOK6kVqpprsr1lPNLphmrCb6KUv0bJVwqfRgl4SplRAmtnr6sp0qasVpG1KbKeqrMzygdVEnRV1NslQTlkq8plHOV9Fjl/tQ1wis7SvlW0p3V56cyx6MyvHorcygqW10zvfOjhOiob5Xk6GheKONUmfsZ41Q5NxIllke8e1HaP0LKMQAAAAAA89jQAgAAAACqxIYWAAAAAFAlNrQAAAAAgCqxoQUAAAAAVOmQSznOSGjsU6Kpcq6SjJiRVKgk8y11viKjfqUSpdUySvVXRspxRElHVBI8IyXHaV+oYyyjH/siYw4oyZdKPTIoa2F0rkJJXDUrN5bU63nHoz6JEoO9a0Ypqsp9R20aleGljEZ1zkhyjnj1UxNyM9ZO75pKmmvUFlHfRmnEnpLf6lDqtYqaau3VQ03dVtYmJV245Ov1jNcZGetmRnJxRHn2Ke0Rza2oDCUdfhS8QwsAAAAAqBIbWgAAAABAldjQAgAAAACqxIYWAAAAAFClkUKhFCWDVzI+XF8q9CO6ZsnrjUPGB/pLhTGNI6xh1HL7xKu32nZdB351Xa56f8qaEJVdMkyia12Hf6j91XVgiUIJCIrGTMkgMS84JQpTUUJWtm/fLpXhBZmoQVtKO/UlsC4KQfLqkRHYqPZtqQC0qJ0nJiYOOKaGIO3atevhV+wgZXuicZfxbM6Y+966ogauefWO1rboHr3ANTU8TlnrlbU3Old5jpcMIY149VbGY3S8xOs53qEFAAAAAFSJDS0AAAAAoEpsaAEAAAAAVWJDCwAAAACoEhtaAAAAAECVRko5LplcrCiZVluqjJJpyyX7JSo7I/2x6/HUl9ThjDTMiNemJdtZSTAsqWQaoJLAqdy32kZRaqJX75IpthnJhl2nzEeUtSmjziXTXDPSnZX7ju5FSdNVrFq1yj0epYZ69xKdG80tr97R3IrKnpqaOuBYRgKt2qYZKeneNb2kWTP/vs38fonu27uemmKrtFNUtpfgG11PuRcl5TuirrHKOq2sH1F7RHPLO66sKdH5Sh+a+fNCXeuVMZYxDzP6VkkhV9Kd1XqMgndoAQAAAABVYkMLAAAAAKgSG1oAAAAAQJXY0AIAAAAAqsSGFgAAAABQpZFSjpGvZPqxcj21HkoappKUp6bcZVDK7jqxOSMlNqNu6n13nTge6UvyuZI+rba1l4ipll0yFdmTkRBfsh6ejKTejFTwjPaIkjYzkliVFFtlHERJmxnppRnjf3Jy0j3u3ePOnTvdc6N+mZiYGLoeUXt47VdyPCqpt0ryq5q4GqUwe5RE2Kido/tWkl+VflG/iUKph9IvUXsoqc/qPMxYv5X1qmQ6uVeG+hoo43W5sk5EMvp2qOuklwgAAAAAQAfY0AIAAAAAqsSGFgAAAABQJTa0AAAAAIAqEQqFFBmhEd4Hx7NCq0allpsRzDNsuWoZETXsRaGEVyhllAzJUsapEjCh6su4yZiLan+VCjArGSqnyFg3M8aBUnbGepAxbyNKuEkUchMd9+49mvtRWJEXEhQF6JQMYfSOR3UuOT+9tlbGaRTypIS2Rf1dcm1TzlXGtFq2cj2FGuiZ8XxX+lx5vVMynCqjD5VxGoXVRbx7j+aL0l8lwh15hxYAAAAAUCU2tAAAAACAKrGhBQAAAABUiQ0tAAAAAKBKbGgBAAAAAFVaFinHGcmIfUm97IuSiaYlk18zkvK8VLeSqZyKvtQjEiX2KSnHGWmuyhhTE4oz0rgzKEmgJesRUepRMiG0zzLSOiPKOhaNpYx6eImYaqK6t66o89Y7riaxetQ2ihKNPUq6bUbfqu3h9W1U5ygZVTlXSZmOKIm1SgJtdN9Kf0cmJiZGrsfMzIx7XOlDpT2iOmckEWe8tlSSetXnlvItF9Fxr/2i9le+xUCth5L63HWS9v54hxYAAAAAUCU2tAAAAACAKrGhBQAAAABUiQ0tAAAAAKBKbGgBAAAAAFWqKuWYNOPuZKTIqSlmyvkZ6adRUltGynFfUn0zylBSfTN0nY6t9ss4EoM9Sv36fi8KdS5mrPml2klZg0r2oZoYrPBSXqP7VhJhS64TUUKr0i/KeMxIDVXTfpV1fW5uzj3uJcVG9xKlyirpx974yFi/1bbz2kMdj0o9lPTvqB7R3FLKiChlKGUrSf4RtW+9sqOEaIWaaJ9xj6XWGnWNVb4FZRS8QwsAAAAAqBIbWgAAAABAldjQAgAAAACqxIYWAAAAAFClsYdCLaeQpuV0L5GMD/pnhHYo9cgIkSkZLJMRtFVy7JUKxFFDNLzzM4LHSobtjGNNyLhHL+RDCW9RKcFtUahFhj6HZKljyTs/IyAlooSNZISKqPXICB30yvbmylL1UCgBS1NTU+650X1nzK2M8eG1X9SmSpBSxrNSDWlSzvXqp85xbxxEZURtWipsNSMEU12vPBnzUB1j3vEoWC0j/Cl6NkfX9CjtpI4Z5TXJKHiHFgAAAABQJTa0AAAAAIAqsaEFAAAAAFSJDS0AAAAAoEpsaAEAAAAAVRop5fhQSPVdTjJS9ZTz1RRbpYyMlMGuk5kjGW2qKJkwWlJGUl6fk4vV+ZJxL0oZakpmqTTijPTSjHHT9bwomeYayVirPSXTlpX0+igdVLmXmZkZ93g0XxRRGStXHvjSTU13zkgyV9JtJyYmhi5DSTlWZaRue+NXfZ3htbW61iupzxGvrceRYuvVO6pHlN7r3UvUHko7efNtqXp4966uB6VShzO+9SPqW6WtS3xrAu/QAgAAAACqxIYWAAAAAFAlNrQAAAAAgCqxoQUAAAAAVIkNLQAAAACgSiOlHB+q+pL62idem0TtNDs76x73Ut3UxL6uKWNBTaEsVY+SydYRJQVROZ6RAKym/vUh8XYppdpJpaSoZtSv69TnDMr6VjI1VDmurmPKs6HkvPVE7a8kjI4jOd1LVo6SWKN78eZiVI+MpFilTaN1Qkl3VuqhpNWa+Wubcq5ZTvsrKcddv5ZSU2y9e1GTo71+jNK1M54tavL8qDLWTXWcZqQcd6XfuwUAAAAAAAJsaAEAAAAAVWJDCwAAAACoEhtaAAAAAECVCIVCMRkBJF74xVJleAEAXQe9REqFKy11XPlAvxLAEJ2rhCT0pV9USptmlNH3dlLGnhrC452vhsAoug6hUoI41EAQJbws6hcl2CUjsCQShfZ0TZm3JdvDmwPRszLqQ+94NA4mJyfd416/RGNamftR2+3atWvociPT09ND1cFMC4tavXq1e65y3+q9dB3CE/WL0oeKjPAhdX56ZUfjQA0TG/Z6Zn79orkcleG9Hi4Z6hfxyo7WiVHwDi0AAAAAoEpsaAEAAAAAVWJDCwAAAACoEhtaAAAAAECV2NACAAAAAKpEynHPZCTDjaPsDEr9okQ2L3XOS3pbqgxFyTbNqF9Gaqt3XEkzLlmPjHRhVal+yUozzkgl9IxjvnipjhkJixnpoCXbo1TZGYmfShq6SilDTczOKMNrv4zUUJWSYB2liXrHozqvXOm/VFTGR1S2Vw8l5VhN3J+dnR3q2FJle3WOUqaVNFc1Ibrrbw9QUo7VFFtlTGekH0d9qzxzuk7Fj8qI0o+9e1S/JcDrRyWdv0u8QwsAAAAAqBIbWgAAAABAldjQAgAAAACqxIYWAAAAAFAlNrQAAAAAgCqRcjwmfU8zHkdamZICp6TtqQl1UaKjUsao545DRrpwyTIUGWOp1PWyyo7qPe6kwYdDSc7NSGlUlerfviclK22tXC9K5VTW3oiSCKukO5v57ZGRmJ2R3J1RdlSukvar9qH3zQQZKdPT09Pu8YwUbO+bEzJSpqPrKWOs5HqglBG1szI+onuJ1g/v/MnJSakMr97eGF2K11/q81opI+Kld6sp9WvWrBn6ehnjYxS8QwsAAAAAqBIbWgAAAABAldjQAgAAAACqxIYWAAAAAFClZR0KVWM4imK5hT+VCl5RAgTMzGZmZg44FgUZRIEDSgCJGvqjlKGIylACKboOyRpHoJMScDUOythTqGVkBDp5ZWSEnnS9BkW80A4zf85FbRfNT6W/orK9tVMNQfLOL1lnhRL+VLLsjGeAyis7I/wmelZG4UhKe3jHvee1mRYcqQQ3mfmhUKtXr3bPjV5/KG2nhHVF50b18M5XgtXMcoL3POqap9Sj1Osas5xn36jXM/PbIxoHyvyMzlUCrkqMGd6hBQAAAABUiQ0tAAAAAKBKbGgBAAAAAFViQwsAAAAAqBIbWgAAAABAlXqbctyXhOK+JAnX2B4ZyY1qCmWplNKMlLuSacYlE5FLtWnXCZ6qkqm5StkZ6cIlU1SjMrw0RSV5dKnzPUpbd53WmSGjD6N2VhI4M+oRtXOUwOlR05YVStkZbZpBXae9+qnPVS8VOepDLxk4EpXhXS+qc5R+7PVt9G0FUXKxdy/RmI6Si5V1OqKkoUfz0zuekWSuXM/Mv/eo7SJeH2SkPqvPBW+cqs9apYwoLX96enro60Vle3Mj45sGSDkGAAAAAGAeG1oAAAAAQJXY0AIAAAAAqsSGFgAAAABQJTa0AAAAAIAqFUk57ksir6LGOi83ShqpUkYGL3HOLE6X89L5oiTFiHcv6jjNKGPYcrMo9St5L1HZSqJjyXaKKH1eMv04g7ImRMe9/opSPDNSqRUZSY8ZCdFKqmmUDholkippuko9MvpEHf99nhfqOPDaOmqPjG8VUNKP16xZ456rpCpHdfaupyQwm/nzVl1jM9Zp7x7VJHmPkhAdic5V7jEqIyOFPKIkR2ckmZd8vaM8X9Q0aE/0DPDmV4m1lHdoAQAAAABVYkMLAAAAAKgSG1oAAAAAQJXY0AIAAAAAqjRSKFRfAhL6omR7dB3uUzJERilDCXoxy/mQfgYvvEIJkDIzm5qaGvp6JcN9ug42yriXknNRCeJQAlki4+jDjD5XwjWUkKaM8BV1XSm1/irU62WshUqgVkQZS0qdS7Z/RrBOSaWet+o6ltG3u3btGvp63nN19erV7rnr1693j3shN6tWrXLPjZ7NGet3RgimR52fXv3UUChlLEVBRV5bK4FEKiVETQ0LLFWGGpLltbUaiOidH4WoRfNFud4oeIcWAAAAAFAlNrQAAAAAgCqxoQUAAAAAVIkNLQAAAACgSmxoAQAAAABVGinluEZdJxGXvGbX1ystI9FUSVYtmf6opGF6yY1mWrpcX/o8I7muL/cSUeqXkaQ4jjTXjH5UylDSM0vKSI8tdT2VmnY6bBnqmue1U5R0qqSaqu1fMjF1VMo6oVLuW72e8qzMmC9eGnE0lqJnpdfWavtHz2yPkkwbpcRG9+L1rVrnbdu2DV3G9PS0e9wTpU9H48C7F3XMeGMhupdoXmQkrUdjUuH1l5JmHB1X10HlORKVnfEsGgbv0AIAAAAAqsSGFgAAAABQJTa0AAAAAIAqsaEFAAAAAFSJDS0AAAAAoEqHXMrxoUBJhlMTCUsmGCrJr0oZUcKamn6sUNKWI16iYFS3jFS9DONI5O26XCWNW7lmxliPdJ1abKYlG2akhUe8uRHdi3K8ZJsq960kRKvtmTHGlLGupB+rc85rp4ykZPX54h1Xx4HSpsr4KPl8V2R8A0HEaw+1D2dnZw84lrE+RimxUfqxd+9REnGUcnzfffcdcGzHjh3uuRs3bnSPeynMXhuZxf3l3aP6ukZJBlao6b3euInOjcZNlGztUZ6fSiLyUmV7lDWoxGtW3qEFAAAAAFSJDS0AAAAAoEpsaAEAAAAAVWJDCwAAAACo0iEXCqWGHZW8pqJU/bLCn5SQipLBE0pgj/KB/pIBMEp7RIEPURlTU1MHHMv4MH7J+VJSn+dirZQ2Vdu/L4FOpUKhorCRjNAfjxIuY6aFSCntrz4DvHoroUtmOeEwSrBUtM569ctYUzLKUAKkouMZZUS8to7m0M6dO4cuN5oXUbCRd49RGVF7RGV7ojGtPMvvuece9/hDDz10wLEokEgJL1PDh5Tgt6gMr35RGJYS0hS1RzT2Sj1z1LVNqYfyfMnolxJBc7xDCwAAAACoEhtaAAAAAECV2NACAAAAAKrEhhYAAAAAUCU2tAAAAACAKh1yKccRJXFrHCmnJRLBzHLSjKPzS6YZZ5TddT+qKaVKwmjES1LMSEuN9CUBOCPNvNScyypbTSv0lOyvjLK9e1GSFJVyxyFKyVSSJZV1oi9puhky1jH1OaKkuSoJqFEfKm3dl9clXX9jhNJXZn79ZmZm3HOVPoyScCNKYnN0L8pYOuqoo4Y+Ht1L9C0L3vlqEq5nHN8u4aVVq2M6Ix0+g5LGrZShrptdrRO8QwsAAAAAqBIbWgAAAABAldjQAgAAAACqxIYWAAAAAFAlNrQAAAAAgCqRcvwwjCMhs1RyYEaacXR+X1Ji+5L+qMpIHfao6clKPTKSmTNEdc4YN13PfzXNuOv6Re3k9YGS1hkdz7jvvqQcKwm5UfJoRsqx0h7R3Oo6QTxjXVfvxTs/SqaNePeu1sPrc7U9lLmltEfJJG2l7IxxqpSRUedoLCnHlb4y89eg6Nw1a9a4x7t+fRrx6hGl+pYcS16bqq+NMr45RJmfSvp0yddXo+AdWgAAAABAldjQAgAAAACqxIYWAAAAAFAlNrQAAAAAgCoRClWJcX/Y+mAyPsA+6vUyzjXLCbNRzh1Hf3n6EuiUIboXr28zgif6EjJk1n04jxIaViroLEtf+lEJ88gIbctYqzPWt4yglox1LCN4TGnrqFwlqEWVsU4o95JxPeV1RiSjHko4mBLGpITELXVcUWo8RmUodVYDnbzj0TyM7sWr99zcnFQPr8+jcaCs35OTk+7xqE0z1quM10zKa+pR9PvVBQAAAAAAATa0AAAAAIAqsaEFAAAAAFSJDS0AAAAAoEpsaAEAAAAAVSLleBkqmTLYl0TekinHnigRry/tUTLZM0OptM6+tH8kIy0165qlyih5j17/KgmXqr6kyZdKF1aTgb2y1f72ys4oQ01mzkji9+qhrr1dzy0liVWdWxn34iW0KvedkbgaUdojOndmZsY9PjExccCxKNU3Y24pMtY2NVE9o18yyij12lKdW8q9eGPJTFubMtojGr9dfYMG79ACAAAAAKrEhhYAAAAAUCU2tAAAAACAKrGhBQAAAABUiQ0tAAAAAKBKpBwfQpSUx1LXW+qapZJfo7S4kmmpXafvZqT7ZVDKVupcsh6RjKTNQznNuFT91MREbyyodSvZj8OKxnTJBMmMZHxvnkd1jtaEjHTbvqzJXqqvWrdSKdgZbao+b0d9XVJyLpd8rTI7O3vAsejbAKLjyjM0KsObi3Nzc0OXG4nqpnzjgfrtCMqYVvowI0VY5dVPTanPWHu986P2UOZLidd/vEMLAAAAAKgSG1oAAAAAQJXY0AIAAAAAqsSGFgAAAABQJUKhDnFZwU1dB26UDMTpcyhUybCjjHoobeqFoyxVhhIikBGEkhEKVVJGYElfAtoyRGEXSn91Ha4WzQGPEpiURbnHjEC4qAzv/Iw1KDo3Gkve8ageSrCOct8R9XmWEbzkhfYo89BMC65RQnjUMeZRx4dyPaUeSpDPOF67rVw5/FaiZD2UtV5ZezNC26LxqBxXy/DqHQVteSFlZlowY1S2V4bS/sPiHVoAAAAAQJXY0AIAAAAAqsSGFgAAAABQJTa0AAAAAIAqsaEFAAAAAFTpkEs5HkcaqafrVOBaZaUwd82rx6HQ5yWTd73japKlUg8llVMdd6XSYyN9mRdqv2QkFJeadxltmpFcrNyfmiwZpVZ6lOTXkvMlo9yu50vGOIjKUBJyIyXXK+/8kmMpI3E1Iy3fo44Drx5qqrJX9sTEhHtu1C9eondURsYzOOLVT0kbNyv3TQiRjCTziDI+lPVDeS4sdc1svEMLAAAAAKgSG1oAAAAAQJXY0AIAAAAAqsSGFgAAAABQJTa0AAAAAIAqVZVy3JekzsihkGJbo3EkJStJeX2njGulrTMSAjNScyMZybsZMu6lLzISUJeTKC1SSaeMEloz2q7UvFXXZI+ShKuWsXKl/9LI66+o/TPWwoiaMurxrpnRplHbKWmzSvKrcm4kav+oPZRvMVDatGSivTLW1cRsZSxFY1d5rpb8JgSvPUp+a0LGnFPaSb2ed/6uXbvcc6O+9fqgxLOdd2gBAAAAAFViQwsAAAAAqBIbWgAAAABAldjQAgAAAACqNFIoVMlQna4t9/ARVUaQkjo+SoUPlSxDKbtk4INCrUfGNTPao+tApyhMouv1rWSfR4E2XhBE1B7K8b6P366faRmhMxEvoCMjEESdF30JTvGogUJe/TJCiZTrRcYRKKTUTwkDiq6njMdIRtCZ0tZqKNGo1HspNX6V/jbTwpii55Z3zageSpibGpKlUNq/1DpolhM0p4xpQqEAAAAAAJjHhhYAAAAAUCU2tAAAAACAKrGhBQAAAABUiQ0tAAAAAKBKI6Ucd40kYnShzwnFKiXRsWsZqahq2aWuF8lI3VZ56Y1ROmKU9KhQUhq77luznLlb6tmTkZYa6TqZOTrupZdmUBNovfpFdVPKjtJBo/bwylDTS0smKyvXKzUvlOT0qA4lv5FBqUe0xiplKM9KtU+UZ0BUtjcH1H6ZnZ094Ji6digpx4qMhGh13VQoKcxqSv3ExMQBx6I+nJubc493tXfjHVoAAAAAQJXY0AIAAAAAqsSGFgAAAABQJTa0AAAAAIAqsaEFAAAAAFSpqpRjjJ+S/qimUGYk9vUlobgvidylkk5LJgP3JYVZTWksdT11HnnH1bI9ajpiRj+OIyW6D9fz7ltJS43KiERJoBkp2Mp4VBI/lWTPSEaqbCRjbmUkoGasY0rKq1rnUftc7SvvvtXUaK/OSkqsWobSRupcVtojI107qod3j1FqbkY9orbOWPMUGfMwqrN3fjSWSiZpZ8y5YfAOLQAAAACgSmxoAQAAAABVYkMLAAAAAKgSG1oAAAAAQJWKhEIpAUGjlluDvoTc1EgNxClVhmq59/lyv78sSpiH2qZKUIsanOIdV0OhMmSUXeNY9eq8cqX/uM4I/SkZhJJRthd0E92fEooTlaEEcGXMW2Uum2mhYRn1iChjTBmnJQJj9lJCbjKCckoGjGXUQ6HUIxqPUdCT96xU1zzvmlEZXT9b1HW6VJipGiylzM+orT3ROBgF79ACAAAAAKrEhhYAAAAAUCU2tAAAAACAKrGhBQAAAABUiQ0tAAAAAKBKRVKOPbUmFGcolfpcq74kF2ck1PVdqXFGavlwMtI6laRTs3IJnBlJyZFS8zaLV7+M8RulYJdKeY3KVdK41bKV8RjxEjijNGMlMTijnTPGgVpGX9bOPryOyZhDGfcR1SNjbct45kT1K9WHakKxN5+jJNyMlF2FmkTstWm0XinjRk1D98qempoa+npmcb09SoJ1CbxDCwAAAACoEhtaAAAAAECV2NACAAAAAKrEhhYAAAAAUCU2tAAAAACAKnWWchzpOq2vD6l8Zvp996XekYwky1JlRPqerNq1ru+xL0nJJe+7VFqtWdn2UxIWM5KS+96PpZ5TSjqlkiIclaHqOm1ZOV/tk1L3Mo6UY6UMpeySaeiRjOd+KRltp8hYYzP6UKWUMTExMfS5q1atco9PTk66x5U0+oxEXuXccfSLx0uMz7peyRT9oa7fyVUAAAAAAEjGhhYAAAAAUCU2tAAAAACAKrGhBQAAAABUiQ0tAAAAAKBKI6Uc9yWJTqEmjfWFkjq6nPQlFbXGsR5ZTuOm5L30JfV5HAnA3jXVpEIlVbbrflQTYUvN/4xETeWZpt6HkrYc8VI1ozJKpoIrMl4nKG2dkQKqrh+lxrSa2jpqkrBabl/mxajXM9NSprtO081IMo9ESb0KZT2N2mjFihXu8ZmZmQOOzc7OuudG9+2lQUfJzBFlXEfp0+o1PRn9NYx+PD0AAAAAABCxoQUAAAAAVIkNLQAAAACgSmxoAQAAAABVGikUajmpMSyqxjpn6TrQKSNU51CwnMZeX+5lHMFXGaFQyvWyzvd49e7L/FSCSaI6R8EkpeoRhZiowSkeJYBEDTvy6h3VTSk7uu+oTb3j45gXGWFpo14vKltp05JhTFGYTakwpnG8VvFE80IpOyOsS6W038qV/vZH6Vul7G3btklleNeMgpui+1ae2RkhdtFxb45mvJ444DrpJQIAAAAA0AE2tAAAAACAKrGhBQAAAABUiQ0tAAAAAKBKbGgBAAAAAFUi5fgguk7D7Drp7VAwjoTi5d6P40jl7PP1DgVqImxGImYpfZlbSttlJFmOYy3MSLNUUp+VOkcptkpyrtpGStnRuUqbRmUoyaPR8YxvG1DGuneukkSsXi+ijEclFVxNSlbqXXIsKUny6j0qMvrWe86pa69XxoYNG9xzM9agjGdAdHx2dnbosseNd2gBAAAAAFViQwsAAAAAqBIbWgAAAABAldjQAgAAAACqxIYWAAAAAFClZZ1y3Je0WkXJ9E2SX4eTkdzYdT1Kykir7UvKMWtCvr4nentljyPtV+GlVmYki2fMiyhRc+XK0V9OZCRmK+mgkegeM8aSUr+obC9FNUpLjXjnR9fLSLeNyhh1bqljWmn/KNndu2/1/jLShaP6eZRxGo2l6F68eqj90pfnyNzc3NDnKvNCTXv37kX9pgGPkrq91PkeJQ29xDOVd2gBAAAAAFViQwsAAAAAqBIbWgAAAABAldjQAgAAAACq1NtQqL4EofSlHoqM4I++BBL1XV8CpBQZ4TJdn5ulxnHd9wCjSF/qkaGrUIu9vCCOjJCbjOCPqB5RmIoSTqVec9jrRWVkBNdEATpK+Mo45rhyTSWMRg3lUtp02J9fluLTEgAAIABJREFU6rhXdkad1fmZEfymhFNFlGApJeAqColTysjol5KvhzPmULRueuNUCayKZASMZSjxWox3aAEAAAAAVWJDCwAAAACoEhtaAAAAAECV2NACAAAAAKrEhhYAAAAAUKXOUo5LJkIu9zRNValEyKXOx8H1ve1KJhT3/d6xr1KpvhmJk2rZyvX68izJSEBV+tC7XnR+yWdDRtnRvSiUcaqkGZv56aVRwqhy30qarirqF6/e6vW6nnOl1jY1kVdJMs+YFxllKCnpGeMuI0G85HoV3WPGepXxOl7pg6gML616ZmZGup6XzhylYI+Cd2gBAAAAAFViQwsAAAAAqBIbWgAAAABAldjQAgAAAACqxIYWAAAAAFClkWKmMpLhDoX044wkuq6vN4704+WehFtyPJZsu+XeLzhQ1+tQ1wnFfXk2RLz2yEiyjGSUoVDXFK9+aqqsUkbJNU9Jty11PTO/PdQU7CidWeGVkZGGnnFu188+JUXYzO+XqK8yUmUzUreVflHnuKdkmnHW+Z6MtlZMTk66x72kaTVR3buXKMF6FLxDCwAAAACoEhtaAAAAAECV2NACAAAAAKrEhhYAAAAAUKXRPyU+pEMh/Cmi1C/jA+x9CTjAvg7l9vfG5KHcHoq+h++VlBHs0nXAValyo3OjcI2u55xSthIeYpYTeqIEGCnPUPVelHCqjLEb1c+75tzcnFR2RrDLqAE6yv2ZlZsX6uuujKAzL+hJbc9S4X0qJQhPoT4XMl6vZwS/eeer64Qy1qenp93jSvCYMhdLjCXeoQUAAAAAVIkNLQAAAACgSmxoAQAAAABVYkMLAAAAAKgSG1oAAAAAQJVGSjleTsnFJa+npHkdCgnFNd7LOOrcl2RaEorz9T25uM9jT5WRnumd33V6sprmOmp6rFnOfStlRKm53j1G9911MmpGAnNGX2VcM2q7jNclyj1mXE8pQx0z3jjNWFOiJG5ljGXMi4xxUHJMK/XLSBdW20MpQ0kuVudFxtrrUdc873wvoXtUvEMLAAAAAKgSG1oAAAAAQJXY0AIAAAAAqsSGFgAAAABQJTa0AAAAAIAq5cdM9Vxf0jsxmkMhZTcj0bGUkkmnShnj0JeE4kNhLSvV50oqpFqGIupDLxUyI3m0ZOJnlJI5MTFxwLEoATUqW0lMjRI4vQRZtU0zjCPReFhRe0Tpu8q9KPNFSX6NKCmqap9E7aFQ5qeScF5yTCuJ6hlrrNoeShkKdb1SrlmynbxxqqQZm/nzKBr/437Nyju0AAAAAIAqsaEFAAAAAFSJDS0AAAAAoEpsaAEAAAAAVTrkQqHGEQKh6DqUpC9BO5G+1w/DUcJllDIiXYcT9KUMHMgbCyUDnRRKyEpGiFrJEBMlKEQN/VHuZdzBJHtlBNdkjFOlHlGQUlQPrx/VAJ1oLAxLHdOjXs9MC9bJuG/lHkuuE0o4lUqpd8a5yj1m3HfJ17JKeyjPnCzefCEUCgAAAACAeWxoAQAAAABVYkMLAAAAAKgSG1oAAAAAQJXY0AIAAAAAqtTblGMvEaxkImHXabrq9bpOPy5JSZ0rmf6I8cpIY8wqu1QZ4ygbD1/ULxlpoqNSk4GVc0uup3Nzc0OfW+O6rqaDZiS+Z/CumZHmGp2rpFJnvEZQ0lyjuilpxsrakaXUayY1QTxjTHv9Fd1LlKjunR/1oTJv1WTgqH4eZfxG18u4l4znS9TW3liYnZ0dutxh8Q4tAAAAAKBKbGgBAAAAAFViQwsAAAAAqBIbWgAAAABAldjQAgAAAACq1NuUY4+ampaRLlcqfVBNkeu7viQ3Lick4e6rz+3R57od6jISivuQaKymuWakH496bnR+1+mgS11TKdt7pqnXU56LSpprREk1VVNUV64c/SWkktSrJBQrZYxDqddM6mtLZa1R2rpk+nTG+Ih4Sb1RnScmJtzjXv3UdbrUc0tNMs9I+u5qzvEOLQAAAACgSmxoAQAAAABVYkMLAAAAAKgSG1oAAAAAQJWqCoVSdR3oVLIeXQcsjSPkRglJUEML+qBkm5Ysu89tOg4EQMGs7LzICGkqFSqiXM/Mb6eo7TKClGZnZ0euR0b4TdfPffVZOer1zPz+isJlMuqn/HxGGJYiYyxFlPpltH9GgJEaMKbMz5Jj2gs6KxnIF9XPC2NSx2nG+pERGuZRAgCHxTu0AAAAAIAqsaEFAAAAAFSJDS0AAAAAoEpsaAEAAAAAVWJDCwAAAACo0rJOOfb0JXG478mxav36kvyqJCWXul4NZStKJgoCpWWsCaXGdcYcz5ifJVNelXOVhFb1XqKyFV2nPkeU5FH1+LDXi44rqa1mftqpkpCrpqV6dY7qFt2LUueIUkbU/so1lXRhNcVWGUvKOIhE52as9UqbRonqyr0o6dgZa1jJNO6I1x4lXt/yDi0AAAAAoEpsaAEAAAAAVWJDCwAAAACo0iH3GVqgFn/6p39q73//+83M7IYbbljy3H//93+35z//+WZm9qY3vcle9KIXLfzd7/7u79qnP/1pO+644+yzn/1suQoDAJa1Sy65xD74wQ+6fzc1NWVHH320nXrqqXbBBRfYqaee2nHt+uXjH/+4bdu2zZ7xjGfYSSedNO7qAMsaG1oAAABIjjzyyIU/t21rDz30kG3atMk2bdpkV1xxhb3mNa+xiy66aIw1HK9PfOITds8999ixxx7LhhYobFlvaPuSRnwoJL92nQKqXK/GtOBaZfQXsJSuk4HN+r2+KemUarqwV0ZG4qqSmqtSElAjURKrUr+M9FIlmTYjvXSYPlz8c5/61Kf2+bvdu3fbLbfcYu9617vs1ltvtUsuucSe8pSn2BOf+MSURF4loVhJzI7GTMaY3vt3wyTfqonNGemxSnK3Mm+jflHqkZH+HVH6Vm2PjCTzjDKUNlWeDWp7eDKS9Q+4fnqJAAAAOKSsWLHCTj31VHv729++cOy6664bY40AHCqW9Tu0AAAA6M4P//AP2+GHH25btmyxnTt37vN3c3Nz9i//8i/25S9/2W666Sa7//77bcuWLbZu3Tp77GMfa+edd549+9nPPuAdo/e97332sY99zB796Efbxz72sfDa27dvtxe/+MW2c+dO++3f/u2FbIm9pqen7VOf+pR94QtfsO9+97u2fft227Bhg5188sl2wQUX2Omnn+6WOz09bZdddplde+21duedd9qOHTts/fr1tnHjRnv84x9vZ555pp111llmduDnjN/ylrfYW97yln3K+8pXvrLw5zvuuMO+9KUv2T/8wz/YXXfdZffff7+tXLnSTjjhBHv6059ur3zlK23jxo1uvV7ykpfYPffcY69//evtrLPOso9+9KP2xS9+0e6++27buXOnXXrppXbccceF7QUsF2xoAQAAkOK+++6zLVu2mJnZIx/5yH3+7hvf+Ib9yq/8ysJ/T05O2sTEhG3evNmuv/56u/766+3aa6+1N77xjfv8CuMLX/hC+4u/+Au7/fbb7cYbb7Qf//Efd6995ZVX2s6dO23dunV29tln7/N33/ve9+zXf/3X7Xvf+56ZDX7Ncu3atfbggw/addddZ9ddd529+MUvtl/7tV/b5+d27Nhhv/ALv2C33Xbbws+tW7fOtm7daps3b7bbb7/dbrjhhoUN7erVq+3II4+0zZs32549e2zt2rU2NTUVttfFF19s99xzzz5lb9u2zb71rW/Zt771Lbviiivsj//4j+1Rj3pUWMaWLVvs537u5+zOO++0iYkJW7VqVXgusByxoQUAAMBIdu/ebTfffLO9613vMjOzI444ws4999x9zpmcnLRzzjnHzjnnHHvc4x5nP/RDP2RN09hDDz1kn//85+0DH/iAXXPNNXbqqafay172soWfO/744+2pT32qffWrX7XLL7883NB++tOfNjOz5z3vefts6rZu3WoXX3yx3X333fbkJz/ZXvOa19gTnvAEm5yctK1bt9rf/u3f2p/92Z/Z3/zN39gjH/lIe8UrXrHws5/4xCfstttusw0bNthv/MZv2BlnnGGTk5O2Z88ee+CBB+yf//mf7cYbb1w4/8ILL7QLL7zQXvrSl9o999xjr3vd6w54p3ixk08+2S688EI7/fTT7ZhjjrGJiQmbnZ21G2+80d73vvfZzTffbG984xvtQx/6UFjGBz/4QVu9erW99a1vtTPOOMNWrlxp9957r23YsCH8GWA5YUOLXut7mFBXQU/7/5/m/e0fwBDV6+HUN6MPMgIf+qLv9T4Uwse6Vmr8ZoRrRLz6RddTwofUIKtS4zEj2EsJH4qogU5ekE/UdkqwjhJQExmmTRefs/jr4fbs2WNbt2613bt329q1a+2cc86xiy66yNasWWO7d+9eqMfJJ59sJ5988gH127Bhg7385S+3o446yn7nd37HLr300n02lWZmF1xwgX31q1+1a665xl73utfZ+vXr9/n7W2+91f7t3/7NzMxe8IIX7NN+H/7wh+3uu++2Jz3pSfaOd7zDVq4cvPzdvXu3rV+/3l71qlfZ8ccfb7/1W79lH/nIR+ylL33pQt1uuukmMzP7qZ/6KXvWs5610NaHHXaYHX300XbuuefaeeedF7ZZ0zQH9MPidvy93/u9hfL2mpqastNOO81OOeUUe9nLXma33nrrAe9MLy5zenra/uRP/sQe97jHLRw79thj3esdjDq3vLEejbuJiQn3eEYQnlePjPWqL2ueEt5nVi7UNlrblPW0xOsoQqGACjzwwANL/rN58+ZxVxEAcAh58MEHF/7ZvHnzwgZy165dtn37dnvwwQflMp/+9Keb2eC71e+///4D/u6YY46x6elp+9znPnfAz+59d/aUU06xH/3RH1043ratXXHFFWY22JTu3czu75nPfKatXbvWNm/ebLfeeuvC8XXr1pnZ4DnctTVr1thP/MRPmJnt8y7w/p761Kfus5kFDjW8QwtU4IYbbjCz+P/E3XXXXUv+ShMAAJm++MUv7vPf09PTduedd9pll11mn/nMZ+yf/umf7A1veIM94xnP2Oe8HTt22Cc/+Un7yle+YnfccYdt27bN5ubmDij/vvvus6OOOmrhv1esWGEveMEL7JJLLrFPf/rT9vKXv3zh73bu3GlXXXWVmZmdf/75+5Rz++2320MPPWRmZm9961uXfAd+b4jVPffcY49//OPNbLCRvuqqq+yyyy6zzZs323Oe8xw79dRTw6Cmh+PLX/6yfe5zn7NbbrnFHnzwQdu1a9cB59x3333hz5966qlpdQFqxIYWAAAAI5mamrKTTjrJfvM3f9Meeughu+666+xtb3ubPelJT1p4l/POO++0iy++2O69996Fn1u1apWtW7duYaO5951db1N3/vnn24c//GH79re/bTfddJM94QlPMDOzq6++2rZv327r1q2zZz/72fv8zOJ3eof9babF1z7nnHPslltusb/+67+2q6++2q6++mozMzvxxBPttNNOs+c///kL9VDt2bPH3vSmN9mVV165cGzFihW2YcOGhXeSt23bZjMzMwckRi92xBFHPKzrA8sFG1oAAACkOf/88+26666zbdu22Ve/+lV7znOeY2Zmb3vb2+zee++14447zl772tfaU57ylH2Ci3bv3m3PfOYzzcz/jaSjjz7azjzzTLv22mvt8ssvX9hI7v114+c+97m2atWqfX528Wf7PvWpT9mRRx65T5ne55r31mWvX/qlX7KXvOQlds0119jXv/51++Y3v2mbNm2yTZs22WWXXWaveMUr7OKLL5baaG+9r7zySluxYoW9+tWvtnPPPddOOOGEfd5FfsMb3mCf//znlywnugfgUMFnaAEAAJBmcSDR3XffbWZm3//+9xcClt7whjfYWWeddUAK7zCfu73gggvM7Afvyn7729+2m2++2cwGX++zv8Ub2O985zvinfzAiSeeaD/zMz9jf/RHf2RXXHGFvf/971/YfH/84x+36667Ti5z8a9JX3TRRfaIRzzigF+JfjifRQYONbxDexAZ6ZbLKeE1Q433Pu7k2HFfH/Uomb6LH8hoI+VdlYwkS3VseCm7UZ2V9iiVvrmUKIFTKVu5ZhQ8pIjaepzJ84vP8eq3t50Xf95z9erVZmb7/JrxSSed5Jb/j//4j/tcy6vTaaedZieeeKJt2rTJrrrqKrv99tvNbBAGtbfcxf39mMc8xtauXWvbt2+3q6++2k477bTwnhZban6uWLHCnvjEJ9pb3vIWe9nLXmbf//737frrr7czzzxz4Zy9G9Ol5sbeNnnsYx/rjtEdO3bYN7/5zYPW9WB/rybkepQ5FFGSgdUUYa9+agq5co9KSn3G8yLjGaB+A0bGuq70yyh4hxYAAABpFn8mdG/67t7P0ZqZ3XbbbQf8zPbt2+0jH/nIQctummbhXdrLLrts4ddxvXdnzQb/c2Hv1+p89rOfXTIt2Mxsy5Yt+/z3zMxMeO6KFSsWvopm/xfpa9euNbPBd+BG9p7zrW99y/37D33oQ7Zjx44l6wuADS0AAAASPPDAA/aBD3xg4Wt1Tj75ZDvllFPMzOzRj360HXPMMWZm9va3v32fr8a56aab7Bd/8ReX3Pwt9vznP98mJyftO9/5jm3dutXWr1+/8Dldz6tf/Wo74YQTbPfu3farv/qr9pd/+Zf2H//xHwt/v23bNvv7v/97e/Ob32yvfe1r9/nZiy66yN75znfa1772tX2Cme677z575zvfaZs2bTIzs6c97Wn7/NyP/MiPmJnZNddcs5CyvL/TTz/dzMwuv/xy++QnP2mzs7NmNmjHd7/73faxj33MDj/88KHaBDiU8SvHAAAAkOz/9TgzMzO2bdu2hf9+zGMeY7//+7+/8KuITdPYxRdfbK9//evtu9/9rv38z/+8rVq1yswGqcKrVq2yP/iDP7Bf/uVfPui1Dz/8cDvrrLMW3p197nOfa1NTU+H5GzZssHe+8532+te/3m677TZ7z3veY+95z3ts/fr1tmfPHtu+ffvCuSeeeOI+P7t161a79NJL7dJLL7WmaWzdunU2Nze3z+b2la98pT31qU/d5+de9KIX2ZVXXmnf+MY37LzzzrMjjjhi4VfRL7vsMjMze9WrXmXXXHON3XHHHfaHf/iH9o53vMPWrl1r27Zts7Zt7YILLrCZmZmF79EF4GNDCwAAAMn+YUUrV660I4880n7sx37MfvInf9LOPffchV/H3euMM86w9773vfbnf/7n9o1vfMOmp6ftyCOPtLPPPtt++qd/2h71qEcNff1nP/vZCxvaF73oRQc9//jjj7dLLrnErrrqKvu7v/s7+9d//VfbsmWLHXbYYXb88cfbSSedZGeeeeY+n4M1M3vzm99s119/vd1www1211132YMPPmi7//927t/FsuQ8+PiZHxiM2KRBeCIPCJzsOtiFwcbQy2KBDB0IgelQTCAHm5kBB/sPOHEgI4cTGMMkBruzDRoc2h2OFiRZixV4YbIFQztYr8DGM7zJ+6K31VWj+/Sp59x67v18wquzdevWqXNnai76vn69PHr0aHnvvfeW733ve8uTJ09uvd8HH3yw/PCHP1z+/u//fvnFL36xXF9f3/r/E77zzjvL8+fPl7/7u79b/vmf/3n5j//4j+XBgwfLBx98sHzve99bvvOd7yx/+Zd/ufOawLG6t+b/qPzZZ59NXQ7Jig/tI2hxSCp+9n1EcmYJ88z8HJFjlr3XMvPclmVZ3n///Z039tXV1c4fZkQQpOd///d/m6+3Yh4jolCZsaOeraNQkehJ9HOPWKfWekSjP63POGKMXT/fX//1Xy8XFxfL7//+7y/Pnz/faR6t9xsRTOqJho0iY0SuHTGPEVGoyPdH9LO0InaZUahITGz2KFTk+kgMq3d979o//MM/vPOX20H/QqsufHfWaf9a92D2v8xzGDL/gORuMuvVvb9kRirMvb8IzrI/IuXREX9RHXGgjZRHo3/5b72eWWbu7Y/WIWQXX3/99XJ5ebksy7L86Z/+6a31PqS/w0T22D7+cSRykIzc72gJd8Q/sLT2b3SPRtYjIno4j3z3ZlbqW2OP+MeRXycKBQBACf/zP/+z/M3f/M3y9ddfL7/zO7/z1hgUcBwO+hdaAADq+4d/+IflH//xH5f//M//XP77v/97WZZl+fM///Nb/z9d4Pg40AIAMLX/+q//Wr788svlt37rt5bf+73fW77//e8v3/72t/c9LWACDrQAAEztBz/4wfKDH/xg39MAJuRAe+RmCidsPZcR/yf4rYMnM92viKrz5jfb+tmY5VncWjSiMaI6HAmkjKgfZ5acW3pzHhF0GhFWy4q69IwICkXDY60xetXtrNBW79nauh7bMyIo1Hu/1mfvvV9kHiO+JyKl6mVp36/ePYx8n46IU434bov+GdAae8QYUVsFTkWhAAAAKMmBFgAAgJIcaAEAACjJgRYAAICSHGgBAAAo6d7K0tRhZyUBYHfRJGS5P0Ovrq5Wj3F6ejpgJsfr888/3/naTz/9dOdrP/nkk7tMZyeROUe8evUqdP0vfvGLna999uxZdDoMdHFxEbr+/Pw8aSZ5Is/F9fV1aOyTk5Odr3333XdDY7dcXl42Xz87O4sMc+essl9oAQAAKMmBFgAAgJIcaAEAACjJgRYAAICSHGgBAAAoKaVy/OMf/3jNmBRz7147SrZybw3Tm0fr9a0/S3Tc1vW9MUZ8lsi1r1+/3vnanjdv3qRdP+LarLVblv79ilzber137f377X/PjIyRKWutI98HUd/+9rdLVo6fP3/efP29995bPXZW0bhXBx1R69xaVgF4WXLXY5ZyccTjx49vvTZijXpF3hHl3UH12KbWPczcM9FycUTFyvGIanxP0Zq8yjEAAADHxYEWAACAkhxoAQAAKMmBFgAAgJIe7nsCzGlEBGbEGCNCLZGATmZgaYRIsCfyWWb53L1QUS/S1Lo+GpaKiKxHdP+PiEJFro3e861F9unacd/2+ixxu6he6GmtzNBIZhzpkETWqWIkqxVuWpZ2LKp37QiZAaNe6GmtQ3qGKkaeMhWNPG3GL7QAAACU5EALAABASQ60AAAAlORACwAAQEkOtAAAAJSkcnxEZqmXjrB1AXVEEbY1Rq/qG/ksVQvFLZlF2cj8ovclUjmOjNGbR0+kgh2RWUTOfG4PSa9e+i//8i+rx/7www+br48o5B5SdTWLNbrp+vq6+fqIonFrT4+oGX/jG99ovj6iZvzOO+80X6+4b5SLb1IuHscvtAAAAJTkQAsAAEBJDrQAAACU5EALAABASQ60AAAAlJRSOc4q0HLTMZQ9Z/+Mkb0+82cZUXfOfMZ782sViiPXjtKqEc+yD3qfO1Jyjs75kL7vM+9XZqW0Vy5eq2JZdXa98muvvptVis28t71y8VojStw9vXLxCL1ycZbMdYK3iXyvrNmnfqEFAACgJAdaAAAASnKgBQAAoCQHWgAAAEpKiUK1RKMz3N3Wa7qP2NGhf8bI85IZdBqxzr34UCTotHVwKhp0GjG/rD2WuXd9f+9fVvxpWQSg9m1E/Ony8rL5+uPHj1eP3Qq4XF1drR739PR09RgRI+JPZ2dnzddHrEeP0BMz2fefF36hBQAAoCQHWgAAAEpyoAUAAKAkB1oAAABKcqAFAACgpJTKsfLlePsoCc8sUqbd2oh7NaJQPMIsheLIerTm1rv2bWOvlfnMzr7HfF8dn0hxNVrDVHO9qVcuXitznbcuF4/QKxdnyVyjyDPneWOkrfaTX2gBAAAoyYEWAACAkhxoAQAAKMmBFgAAgJIcaAEAACjp3srCZ/M//uyzz25fOEmBdhaZFdBZCrlbl04zS6xbr2lkjOj7jRi7VxJe+36vX7/e+dre2JG59cbY+tpRInu6d23Wc3sM5eOPPvoo9CE///zzqf9gVDu9qVWKzVyjETXjrUu9I/SKvPbjTdFa+K6sM3t0578o+IUWAACAkhxoAQAAKMmBFgAAgJIcaAEAACjpYcagxxqAmiV6MiIMU1HmZ8la00hsqnd95NpR7t+//W9hvRhTb37RANSuWnNbltj8Zo+lZc5vxNiH9L2yDyOiLFsHjCrKiupkO6TQE3fXep4FtZjNixcvdr726dOnd34fv9ACAABQkgMtAAAAJTnQAgAAUJIDLQAAACU50AIAAFBSSuX4kKh1six5JeHouCMKyiMKxVmV3ej7RT5L5D1HrH/m2mWWkltjH9L3YHT9Mz97ZnlU1fSmEZXdrDW9vLxsvj6iZnxxcdF8/fz8PGXsEWt0fX29eoxZXF1dNV8/PT1Neb9jfu6V3bfx/Pnz5uu//du/vfFMbvILLQAAACU50AIAAFCSAy0AAAAlOdACAABQkgMtAAAAJakc/1+HVPFkNyPueauy26uotl7PrNW25va269fqvV+vOvzgwYNbr/XmFikX99a09X69sSPXLkvevY3aeo8dw/dm1vPC/mUWUHvl4hF65eJZx32bSNG4VQzu1YIzq9a9cjFjjbiH7KZXLh7h6dOnaWP///xCCwAAQEkOtAAAAJTkQAsAAEBJDrQAAACUdG9l8KL5H//4xz9eM2aqY4iYHIMR93GGvRB9/lrXjxhjxLWRYFJ0jN69ev369c5jR9+zZcTa9T7LiHsbcSjP0NuMWL/IZ/zoo4+iC7J6gr1wSmbwaGZbr8eI+NPjx4+br2eGjbKCO7/85S+br3/rW99aPXYrADV7/KkXreKmzPvIr4yIP3388ccDZtJ1579U+IUWAACAkhxoAQAAKMmBFgAAgJIcaAEAACjJgRYAAICSHu57ApGCZLQaCsty+PsjUs3tXZ9Z071/v/3vZq26cO/arec3olA8woh7mzmP2VWdd1aBtqqK69ErF0dECsXRwmvr+t4698rFI2RVgDOLt8rF21At3k5yuXgTfqEFAACgJAdaAAAASnKgBQAAoCQHWgAAAEpyoAUAAKCkeyvrnM3/+LOam2OPAAAgAElEQVTPPlsz5sGJrHHVKmeWEetRcU17eyayl0aMER07ax6tIvKytO9t79ro2GvH6O27Ee+XWX1uqfgM7cNHH30UWqjPP/989Y2sWALNrBa/evVq52vPzs5Wv1/mZ4lUjqNaY4yoGT99+nT1GOxmRO2aw3V5eXnrtRHfecnu/JcNv9ACAABQkgMtAAAAJTnQAgAAUJIDLQAAACU93OqNolGRVvSkN8bWgZQRRFZ207u3h7J+mfGn2UXu4YMHD5qvR2JMEffvt/+trxd06l3fEpnfoe9/2iqGXTLjSC2R+FPUPkJPa2XOWehpG1s/Q9TSijzxK36hBQAAoCQHWgAAAEpyoAUAAKAkB1oAAABKcqAFAACgpJTKcaTA2bu29Xqv+NkrjLauj1ZDRxRkFUl/M2t0U2Q/7qP+HXk+R1R9I/OIrsfDh7e/Bns14xEi31fRz1K1eH2sKtaMM/XWY0Td8+zs7NZrI6qyn376afP1Tz75ZPXYPSPm/ejRo1uvnZ6erh63d69a63/MRtzDrYvZs39fXVxc3Hrt/Px89bhXV1fN1zOflxGO7ZnzCy0AAAAlOdACAABQkgMtAAAAJTnQAgAAUJIDLQAAACWlVI5bFc8R9dKoEQXUETVX5dGbjrVoPGIfHPpeij5Dke+ayHPbm8eDBw9CY7dkFpQj31ewhawyambBs1cunlmrWjxKZomVX8msCI+oKm+tVS0epVcuHjHGV199tXrslqrV4h/96Ec7X/vs2bM7v49faAEAACjJgRYAAICSHGgBAAAoyYEWAACAklKiUC1ZkaeoaNBphMjYI+JUx+qQYlMV98Es6x8NOrUiTZFrl6V9D6LrIUC3X/tYu15U5PT0dOOZrJcZtMmKoYyIP33yyScDZhIzIgDV2mMj4k+PHz9ePcYh6cWYsp6XEfGnzGc5U2veI9bj5z//eej63/3d39352qqhpxn5hRYAAICSHGgBAAAoyYEWAACAkhxoAQAAKMmBFgAAgJI2qxz3ZJZRR5RHI2OMqGRG5pf5WWaXNe/ovV1bpt1HxXZEdXtrsxTOIwXlXhG5V1B+/fr16nls/X01u2P4jIy3j3LxWpllbOXim0aUc7NULRS3ZH6WaLk4Qrn4pmfPnm3yPn6hBQAAoCQHWgAAAEpyoAUAAKAkB1oAAABKcqAFAACgpJTK8Szl3BE11xFl2sj7ZZrlvoywdbF5RAk3a84jKraZe3rE/DLHGPGM94rGa+3jmY2sxyy2nt+o98ss03JTq0w7omZ8cXHRfP38/Hz12FdXV83XR+yb1tgjasaHVNkdwXrc1HteWqJrd319HZ3OTj7++OOUcbP1vj9aIt8pL168CM3j6dOnoevvyi+0AAAAlORACwAAQEkOtAAAAJTkQAsAAEBJ91bGLZr/8U9/+tM1Yy7Lsn3QKTJ2ZoBkRJxnxPvNYpbQ09ZGBJ0i18++DyJGfJboGJEoVGTs3rhZ79e7fpb9sY/v3og//uM/jn6pzLGwBbUiT1G96EwkXBP16NGjtLGziJfxNlnPS+azUnFPRyJPUV988UXa2MEo1J3/Yu4XWgAAAEpyoAUAAKAkB1oAAABKcqAFAACgJAdaAAAASnq47wmMKM1uXasd8X5b14x7eu83e9V05kLxCFvfl/v32/+2lVlbPiSt9YuUiEe8X+Z7Zu7HWcbYx9gVRevCvZLw1rLmER33+vp652sjJdZoAfXk5CR0/Qwiey9yX168eBGaR6TamjXnqs7Pz3e+dpY9XfU7L/L9MaJy/G//9m+rx1jDL7QAAACU5EALAABASQ60AAAAlORACwAAQEkOtAAAAJS098rxCJkVylbdU/VyOxUr2CNE9li0QHvo65FpxDy2LliP2B9bf8cuy5hi8+wF5VlEK54zyCyJZo4dKY9GVKwWL0ve3ouWiyMqPi+ZIuXiiMw9fUj3MOs7ZVn2Xy6O8AstAAAAJTnQAgAAUJIDLQAAACU50AIAAFDS3qNQxxDcaBkR5tlHqCXLse6DWUTX/5BiO1kBrmiMacQY9+/f/jfKEXGlnsj8Mu93ZOxZ9t2yxMIkmaGiY5AVgbm+vk4ZN1vWfsqM7WSN/eTJk5Rxl2VZXr16tfO10XtycXGx87WRcNPl5WVoHmdnZ6Hrt9R7PkcEp7b+Tp49/vT9739/wEzuzi+0AAAAlORACwAAQEkOtAAAAJTkQAsAAEBJDrQAAACUtFnluFeWzKzpRqqhkTH2YZZ5ZNm6qrwPs5SBs/bSLHu06jxmLhRHa8uR9xvxGbd+Lo7h+2p2mTXdiuXizOKqcvFNkXLxCJGacVS0aDyDzOdTTf6mfZeLI/xCCwAAQEkOtAAAAJTkQAsAAEBJDrQAAACU5EALAABASSmV40gtckRZMuv9mNcsldG1+2mWanFvDM/WTZmfMeu7sFVPXpbtC8qZNePMgnJkHrPLrNVmalVeZ6+Rvnz5svn66enpxjOZQ+9+rd2T+6g7f+Mb31g99uPHj2+9NqJmfH5+3nx9RM3466+/Xj1GhJrxdkbUjPe9pn6hBQAAoCQHWgAAAEpyoAUAAKAkB1oAAABKSolCZaka4og4hs84QmudRoSitl7/aOhs60jTLGMcqxEhvBHvufX79d5zlr00yzyWpWboaUT8Zh96oadjlRWB2Ufoaa1W5GmUXuhphK1DT9Sy79BThF9oAQAAKMmBFgAAgJIcaAEAACjJgRYAAICSHGgBAAAoqVTlmOMzolzcs3WpdJaC8ps3b269Fp1bVv04er+zatcjzF4ovn+//e+Zrf0Reb/ee/beL1L0jo7Rml/k8+3L9fX1rddG1Fl7xcrMsUcUjf/93/995/cbYUTN+E/+5E8GzKStdb8y12PE2FdXVyljZ+67Xl0463nJvIcjasaZteUsp6enaWP39nTme2apVDPu8QstAAAAJTnQAgAAUJIDLQAAACU50AIAAFCSAy0AAAAl3VtZ3Gz+xz/5yU/WjHkUti7ezmSWCm3WPYiMO2IOI6qtkdJsdIzZRcrAI/Zu5pq2ro9+ltevX4fesyWyJyOfsTfuLGv6ne98J7RBrq6uVj80FYuaf/VXf5U29ne/+920sSvKrJf2Kq9rzb6nt65Mj9CrPs9cLt5HRXjEnp59/87g8vKy+frZ2dmd/5LlF1oAAABKcqAFAACgJAdaAAAASnKgBQAAoKSH+57AseoFWSqGdTJDOZGxK65dVOsz9tYoMxY1YowR93brSNMII579yOeOvt/9+7v/O2dvj7XGiIbHWvPufZbIZxxxv7PDdqIiN2WFnl6+fBm6/smTJztfG4kEtSJDo8aexcnJyb6nEBa9LzObOf60LHmBsUyzxKlm//OiF4AazS+0AAAAlORACwAAQEkOtAAAAJTkQAsAAEBJDrQAAACUlFI5zqqXHkPFdpaq7yz12Fnu+SyfJWuM6Lgzf5aeWWrcEdFxR3xvtgrFkZpx7/reZ3nw4MHOY0Teryc6RuafRbPXKbNkVYuXJV4ujsiqC1esFi+LcjF9FavFy5L3nVx1PUY4Ozvb5H38QgsAAEBJDrQAAACU5EALAABASQ60AAAAlORACwAAQEkpleOIrDroMeitXWYZeJbqcETFIm/v2lk+S0VbF4qjti6cR95vljWKOtZa/qGJVIAjleNvfetbd5nOTiJV02hZ9fLycudrtyqM/iaRuvAs1edZ5jGLzFLvsRbfGccvtAAAAJTkQAsAAEBJDrQAAACU5EALAABASXuPQkVEwySt+Mc+QkpZjiH+dOifcfbPN8MaRWUGjGaJI239PdZ7vzdv3qweOxLrytyP9++3/313xGfkpszYziyhp4hI5GkmkdDTLISebsra0yJPbM0vtAAAAJTkQAsAAEBJDrQAAACU5EALAABASQ60AAAAlJRSOY6UQDOrlZF5zFI/rliVHeGQPnfks0T33Yjya2SMWZ6LrevCs9SMRxhxD/fxPR15z0ihOPP9qCVaYo0UYSNjRyvHZ2dnoetn8OjRo31PgZVOTk72PYWpZFa+VaLj/KkMAABASQ60AAAAlORACwAAQEkOtAAAAJTkQAsAAEBJKZVjfrNDqvrObua1js6tdf2IMaJGlMxHlISzqs9VjViPlpmfoWXJnV+raDz7ehyzzDpo1tgVq8XLolx8CJSLb8oqF6sW39Rb53fffffOY/qFFgAAgJIcaAEAACjJgRYAAICSHGgBAAAoabMo1OwRjdnnd+h6YZ7Z78sM8xuxdpnrH4kuZYWiRo0xy5qOMMv8eu+XtfcyI2WwhUi4JhJZyQriLEvNKE7WOme6uroKXT9LFCprrZ8/fx6ax4cffpgyj6jLy8udr41E5SLjRsfO/P7YhV9oAQAAKMmBFgAAgJIcaAEAACjJgRYAAICSHGgBAAAoabPK8SxFzZ7M+c3yGY/VzHtvH3PLqg5n1mMzq8Mj5pE1RmZlOlIXjo5x//7tfyt98+ZNb4pNsxSKI/OAu7i4uGi+PqKiqlx8075LrHcRLRfPIHOdo+XiiKxycbQuPMvYlZ4Xv9ACAABQkgMtAAAAJTnQAgAAUJIDLQAAACU50AIAAFBSSuVYAfImlcy7G1Ev3boYHKnHZtq6oBy9V7NUh2e2jwr2iEJxxOz7tKW1RtmitcmsWmdUpNAaqeZWXY9e0Xit6Odrrd/19XVojNa97d3DEZ/7/Px89RgRmXsmqx57cnKSMm626N7b1XvvvZcybrazs7Odr41UjiPjLovKMQAAAKRzoAUAAKAkB1oAAABKcqAFAACgpJQo1CERdLpp9khTxNb3dsT7ZY3R0xt75hhTdG6tzzgiVJQZO8q8hyM+4+vXr3ceI2KWNR2lUnDj/4lEnqIqrkdW5GlZcqNEWRGezPUY4Z/+6Z9uvfbs2bPV42bu3Yqhp6z9tSw1Q0/RGNMsY1fiF1oAAABKcqAFAACgJAdaAAAASnKgBQAAoCQHWgAAAEpSOd6TWWrBM9dqR8kso85QbD6k8us+9mPkPUfMb0ShOFNW9Tlq66J35hjH7PT0dOdrI6XYzAJwporzjtzDWSrHP/rRj1aPkVkuHjF2ay99+umnq8f97ne/u3qMqMgei5TWI+Meg8vLy9D1jx8/TprJeH6hBQAAoCQHWgAAAEpyoAUAAKAkB1oAAABKcqAFAACgJJXjO9hH8TNi9irniHppyyzrHzFLobj3fluPwTqzPAP379/+t9I3b96sHre3lyLP0Yj92Pp8xyKzGlqxAFxxzstSs/46olzcklUcXpYx1efe2COKxp988smt10asxxdffNF8/enTp6vHrrh3M0XLxTPI+N483j+VAQAAKM2BFgAAgJIcaAEAACjJgRYAAICSUqJQWRGYaPBkRNBmxPtlhVpmj+1kBmpmid9kBa62fr8Re2n2/XgMIvsjEvGKBr9mfj579hF66oUxWlGWqvGhSGAm8hmj4ZpZ1i9rHldXV6HrI2GdEZGgnvPz852vjUShnj17tvO1s+ylEeO2Ik+jvHz5Mm3s6P5tOaRY1IsXL3a+9pvf/ObO156dnYXmkfnsj+YXWgAAAEpyoAUAAKAkB1oAAABKcqAFAACgJAdaAAAASrq3skbZ/I9/9rOfrRlzKiNqna0xZq+D9swyv6z7kmnmtZtlP2aWzLc28/2OXj9ijOi1b9682XmMEYXiEXvpj/7oj6KDzLFJBpi9hjlL5ThiRPm15+TkJGXciuucKfO5yFrrSGG35+nTp83XZ68Zb12Yz1zrEbL2b2/c8/PzO/9B7BdaAAAASnKgBQAAoCQHWgAAAEpyoAUAAKCkh/uewOwioZBe3ES45jebZR6ZRuyDEes0y36cOew1SyRrFtH1GPG9+fDh7T+eetf2Xm/FoqL3cMQYx2xEUGXrUEtF0VBOJPZSca0rfr5Z5tGTGR/KCj3NHqvryVzrLPtea7/QAgAAUJIDLQAAACU50AIAAFCSAy0AAAAlOdACAABQ0r2Vtcbmf/yzn/1szZjDjChtRmSWLyNjz17gnGV+s8xja7Ps9WNd/1lECsXRuvCIeWQZUfnujfEHf/AH0cHLPQS9kmVWzThz7NmrstyUVVG1Dw7DiP3x8uXLW69VLA6PcnFxkTLu+fl573+68x/QfqEFAACgJAdaAAAASnKgBQAAoCQHWgAAAEpyoAUAAKCkh/ueQMSIOuWI94zUQd92PfsVubfcnTW9u8zvlMj3ae/ayPx6c75/v/3vqpHPOGKdRswjKrPqO0JWVXbEuFlz4zDM8gxFRPf0LJ8xMu+t59yqFrO7Vo24Vz5+S7l4E36hBQAAoCQHWgAAAEpyoAUAAKAkB1oAAABK2nsUah+hp5YRgZRILCoaGpk5TlU1+lN13mtF9lJkjUas5z7uySzfQRGROe/je6I19j7WuRd6WjtG1e+OzJDS7GPPEtCZxeXl5a3Xzs7OVo+bGTqreA8rxssqznlZxgSgnjx5MmAmt11dXTVfPz09XT12ZqSptReyxl2Wdc+4X2gBAAAoyYEWAACAkhxoAQAAKMmBFgAAgJIcaAEAACgppXJcsRo6i0h5dOuaMbXMvg9mmV/k2cqU9Z7RcbMKytH7nVUXHrHOo+5VxWrrCNHPHamuHuuatqrFo8xcsD6k+/23f/u3oet/+MMfJs0kJuseZO7pETXjXrl4hF65eK2Zn+W1/EILAABASQ60AAAAlORACwAAQEkOtAAAAJTkQAsAAEBJKZXjYxUpX+6jUDyiPLr2/bLfM2Lr9RhhlvnNMo8s0c83c9l9lrnNVBeOjL2Pvd4qXJ6fn28+j4jM2uwhlWwjetXQV69erR777Oxs5/eL6N2rfRdQ96W3Hn/xF3+x8Ux2l/m8zV4ubhlRMz45OWm+PqJm3PuzIeuZy/yeWMMvtAAAAJTkQAsAAEBJDrQAAACU5EALAABASaJQRUQCRlvHTWaJzkTNEDaaYQ7LMiaC1Btjls84wtZho9kjSPfvt/9NtDVG9Ptqa/uYx4ggSGZsJysOE42snJ6epsxjFpn3sBV/ijrWKNfW/uzP/ix0fWvfjIhyzXK/o3t3lvBYLwAVMXsEsGXf+8YvtAAAAJTkQAsAAEBJDrQAAACU5EALAABASQ60AAAAlKRyPJmtC8WRSmnPiEJu1Mzl3FnmljmPrT/jiPebpaabaesKc2SM6HdbZB6ZBeXWd2T2/m8VLkcUPPdRoYwWjVsiNddZzF4ubpl9TWefX0S0aNzSWo9ZSr89WXs304hqcc/sNeOsZ+7y8rL5+pr94RdaAAAASnKgBQAAoCQHWgAAAEpyoAUAAKAkB1oAAABKUjkuLFrwbF2fWevcutgctfV6bG329R9h5qrvPsYYYURdeO24xzDGsowpXFYsv2ZWQzNlFWQz72FkzvvYSxX3b5bMtbDON83+XGTJ/Cy9cvFW/EILAABASQ60AAAAlORACwAAQEkOtAAAAJQkClVEVqgl+n6tsfcRsxkRdBqxThUDS7Pspcj7bb3HZgkHZX7uWeY3Yoz799v/NjsiWjXLMz57mKQXWRkRgNr6s4+IP80SeprF7Pt3a1nrMfs69/buiHlXfC4yZe2FEfGns7OzATO5yS+0AAAAlORACwAAQEkOtAAAAJTkQAsAAEBJDrQAAACUpHJ8gDILxZExIsXafZREt679HpIRZdqs/diTWVveR+n7UBxDybmqrGro7CXWnlnm3ZpHZj0W3ibre+Lq6ip0/aNHj2699uWXXzavPT8/33nc6Oc71mcuo1wc4RdaAAAASnKgBQAAoCQHWgAAAEpyoAUAAKAkB1oAAABKurey3tr8j//1X/91zZhsaES9NzrG1u+5j8+YNcbM9vH5sirHUTPXdPdR6T3WMvD7778f/eBTfCmMqJRmlj1btdPT09O094Nj1qsLn5ycrB771atXzdffeeed1WO3+J6Y01uq7Hf+y4NfaAEAACjJgRYAAICSHGgBAAAoyYEWAACAkh5u9UaZwZhegKT1nscaK+mJrsfWEZ7e+7XGnmWPRcaIzjlrjJ4R9zvzHh5SSClLZP2z33PreTDe1vEnYL3MZ6sXemKsy8vL0PVnZ2dJM2kbERdcwy+0AAAAlORACwAAQEkOtAAAAJTkQAsAAEBJDrQAAACUlFI5zqzNrn2/WSqqhyS6RhULxVlG7K/MPTqiiOwZurvMNZ2lRLx1SZvbjrVc3KtyZq7HCK15zz7nLFXvYZYRz9vJyUnz9RE14155d8S8T09PV4+xtd7+nbkcPaJmfH19PWAmN/mFFgAAgJIcaAEAACjJgRYAAICSHGgBAAAoyYEWAACAklIqx1vbugR6zKXNmQvFI2rGI+7tDPXkqg7pWYkaUWs/9PWLPltZz/6hr/NIrfLoPsrHI8qcWWae2z5Yj/F65eIReuXitSpWi5clb/9mrfMoGeXiCL/QAgAAUJIDLQAAACU50AIAAFCSAy0AAAAlpUShMiM8WfPgpq3XLnMfRN9zy/c7pD06yz08pDWNGvHdu3bc6NizGBHlYv9mCQplzuPdd99NGbcX6xoR58laj6y1qGpE/Km3pplrPXMA6sWLF83Xnzx5snrsfaz1WiPiTxmRMr/QAgAAUJIDLQAAACU50AIAAFCSAy0AAAAlOdACAABQUkrlOGLrWmTv/UYUP3tjvHnzZudrI6XYaEn0kMqckbpt5r0dcV8O3Qw16bfJerb2UX1u2cd+9FxwF7OUTntF3lZ5dPaaca9cPIJy8TZm32MV9crFIxzrmvZkFI1b/EILAABASQ60AAAAlORACwAAQEkOtAAAAJTkQAsAAEBJe68cjzCiGjpLbTlS2W3Vk3vXRucxS5F061JsxffLHGNrVfdj1hizrwfbiZR3ZzdLzbint6YjarNZ92tEzbhXIx3xuXtjnJ+frx47S+YzpxC9jRE146dPnw6YyfZae2zE/uh9f2fWuHfhF1oAAABKcqAFAACgJAdaAAAASnKgBQAAoKRpo1CzRJpaekGW169fh66PuH9/9397yIxkzRyjGTG36Nqtfc/Mdc4cIys4tY/9NSLS1Ho+R4TfolpjjNjTWz8Xx27fcY1DlBXLmSXC0wu19GJRvQBURMV9mjXnzLWYZY8dkoqhp4rP25b8QgsAAEBJDrQAAACU5EALAABASQ60AAAAlORACwAAQEkpleNWEXNELXXEPEaMER13RBH2zZs3O18bUbVGOqLEuvb9erIKwCPeb9R7jqhuz1IXjowR+R7rXRsZo7fOW++xWb4nKtbX30a1crzMImzF2uyImnHvc0f279Zrl/lsZY09Yo16c5tl715eXjZfPzs7S3m/2WvGFevYs/655RdaAAAASnKgBQAAoCQHWgAAAEpyoAUAAKAkB1oAAABKSqkcR4qTW1deo7XlrWu6W1c8M2uuFY2ocY9Yu8j7HVIJN1oGzhojc01HfH9kVbdnEpn31ns92yxFUg5X1h6zd8ebtSr7Nr2aceT6rPJxtpkr2D0Va8u/zi+0AAAAlORACwAAQEkOtAAAAJTkQAsAAEBJKVGoWYwIycwcVBkRkKoQSNnV7Pdw6/vSCxu1ZMbIZpnHCJHgVHTfZQXooms3y/OSZaY9NiKWcXV11Xz99PR09dhZegGSYwgKXVxc3Hrt/Px89bj7WNMRY7fmPcs+6M1jRECnNXbWuNmiAaiWx48fD5jJtma/X63vmhHvN8vz+ev8QgsAAEBJDrQAAACU5EALAABASQ60AAAAlORACwAAQEkHUTmO1Ckzq6H7KJJubfb5ZRmxx2Zeu+jcsj5L5jwi9yWz9Bv5/lAovmnmZ2ikXrl4ZiOKnxW1SqKjHOuajrB1iXX2mvTWKlaLR8m6X5nfNZl7LPI9tmYefqEFAACgJAdaAAAASnKgBQAAoCQHWgAAAEpyoAUAAKCkg6gcR0QLxSOqmlkV1RFzq1jk7Zml8Lr12o0o4Wbt8977RccYoeKePmbHcL9G1IxPT08HzKQtq7I7e7W1d1++/PLL1WOfn5/fem3EOs++pj1Z8+6t6Yj3y5pz1Xs4omj86tWrW6+NWI+K+2BZxhSNW981mfZdZfcLLQAAACU50AIAAFCSAy0AAAAlOdACAABQ0tFFoXoiAZIRIaURIZ8RZgkKjTAiSrS1faxd5j7NGmNrmYGrEfsxc09nRezYXVboKTPaUTFoE41yPXr06NZrmVGuimuaOed9R2fYXSvyNMqx7oOtI0+jbPU95hdaAAAASnKgBQAAoCQHWgAAAEpyoAUAAKAkB1oAAABKureyXtn8j3/605+uGfPgbF0I3boqG625Zs1vlhLrLFXfWeZR0T5KxLPUj2d5jir64IMPog9d2mKPKIFWrOz2Pvf19fXqsb/44otbrz19+nT1uLPrrWnW/jiGvfvixYtbr1XdS5eXl6vHODs7u/WaKvs6rYp7ZpV9kDv/xdUvtAAAAJTkQAsAAEBJDrQAAACU5EALAABASQ60AAAAlPRw3xM4Bodem82sGY8orkYrzJExRsyD/Zq56jvz3JjbIVU8s2qnrWrxscgsyGapuKdbNePotRXrx61qMeu0qsUVROa9psLsF1oAAABKcqAFAACgJAdaAAAASnKgBQAAoKS9R6EiQaHM+BC72XqtM99P6IlRsuJNI4JmPRWDU9H1iPw5cmhaIY41wY2RepGQk5OT1WP3Yke//OUvV4/95MmT1WNkiQZjImu9dYxp9vhTa4+9fPkyNMY3v/nNUdO54fLysvn6iEhT79maOQA1+17KtPX3/b6jVX6hBQAAoCQHWgAAAEpyoAUAAKAkB1oAAABKcqAFAACgpJTK8dYFTvXju7NGu7FO9c1etz3mIm9L5JnLunZZ5rkH+y5I/iaZ8+vVVdeauVr8NiPW+pjrr78ua38ty5iaca9cPGKMx48frx57a8e6d2ep1Ed98cUXO1+75jP6hRYAAICSHGgBAAAoyYEWAACAkhxoAQAAKMmBFgAAgJJSKsf8Zr3S5ixFTR3abR8AAARdSURBVGBeI74/fAftZh+F8xEV25mLmNG5RSq05+fnq8fYWmYheuZ9sCztzz5izlvf7xE143feeaf5+ldffbV67BF6a5pVHT7WmnFVkZpxBr/QAgAAUJIDLQAAACU50AIAAFCSAy0AAAAlHUQUah/RjrVmCa/05lFxTaOO4TPyKyJIu4k8FyPWznO4m9njPj0j5t0LPUW0AjOZ4aBjDT2N+Ny9MU5OTlaPvbVe6GkWI56Bi4uLW6+NeGY5DE+fPt3kffxCCwAAQEkOtAAAAJTkQAsAAEBJDrQAAACU5EALAABASfdWViqb//FPfvKTNWOGqWRup+JaV5wz440o8mYWkWefX9b7RerTsz/L77//fnSCEtv/n15xtVUozny/iOvr69VjfPXVV83Xz87OVo+dqbV+0fX4+c9/vvO1H3744c7X9vbMiHveGntE3blXr768vFw9dm8vtQrFPZnl4hcvXtx6bas67oxa+zTre3Ayd/5D3i+0AAAAlORACwAAQEkOtAAAAJTkQAsAAEBJDrQAAACU9HDfExihV9qcvYhZUaRqOsv6j5hzZI9lvt+IMWa5L4y3dj+OeL/oe2Zduyxj1sPzss6IquzMTk5Omq+/evVq45msl3mvIjXjnhGV161Lsb1y8QhZFewRNeNWtfiYHfr34Az8QgsAAEBJDrQAAACU5EALAABASQ60AAAAlHQQUajZox2ZUZaW2ddjZtF7tfbejtgbmXOuuJf2saZZYxySEQGpKPdxOyOiJ9fX1wNmUs/jx483f88R96sVWHr+/PnqcT/++OPVY2xtRPypd09GxJ96Y0cCUJlho6dPn6aNvbWsZ2sW0c+31WfxCy0AAAAlOdACAABQkgMtAAAAJTnQAgAAUJIDLQAAACWlVI4PvaI6i9nXrrcPZp/3lqwR2aJ7aUTVN/KekWsVh+tRLr67Xk00qxqaWSOtWC4eIbMMnDX2iHEPqVo8wszV4mXJ3adb8QstAAAAJTnQAgAAUJIDLQAAACU50AIAAFCSAy0AAAAlpVSOWzKrrSNKsZljRKjb7tcsFdVZSuGzV5iz7lfv8424L7PssZ7WvGefM7dFqpUjCpy9Ma6urnYe4/T0dPU8ttb73LNUQ3vrP2KtLy8vb702oma8dd05KuveZu6lfYwdsfX31Qj26Vz8QgsAAEBJDrQAAACU5EALAABASQ60AAAAlLRZFGofRoRMto7OHIPWmh7zeqwV3aPWmqjonpnle/MYYlazxId6Din0NItIaCsyxldffbV63J6Z92nm3DL3UtbYmXOeeR8sy/zza5kl7LVvfqEFAACgJAdaAAAASnKgBQAAoCQHWgAAAEpyoAUAAKCke8dQgQQAAODw+IUWAACAkhxoAQAAKMmBFgAAgJIcaAEAACjJgRYAAICSHGgBAAAoyYEWAACAkhxoAQAAKMmBFgAAgJIcaAEAACjJgRYAAICSHGgBAAAoyYEWAACAkhxoAQAAKOn/ACtV1i2tQ1mTAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#fig = plt.figure(figsize=(8,8), dpi=150)\n", + "\n", + "#for k,(Av,title) in enumerate([(Av_sfd, 'SFD'),\n", + "# (Av_planck, 'Planck'),\n", + "# # (AV_HI, 'HI'),\n", + "# (Av_bayestar, 'Bayestar')]):\n", + "# ax = fig.add_subplot(2,2,k+1)\n", + "fig = plt.figure(figsize=(8,8), dpi=150)\n", + "\n", + "for k,(Av,title) in enumerate([(Av_sfd, 'SFD'),\n", + " (Av_planck, 'Planck'),\n", + " (Av_HI, 'HI'),\n", + " (Av_bayestar, 'Bayestar')]):\n", + " ax = fig.add_subplot(2,2,k+1)\n", + " ax.imshow(\n", + " np.sqrt(Av)[::,::-1],\n", + " vmin=0.,\n", + " vmax=1.,\n", + " origin='lower',\n", + " interpolation='nearest',\n", + " cmap='binary',\n", + " aspect='equal'\n", + " )\n", + " ax.axis('off')\n", + " ax.set_title(title)\n", + "\n", + "fig.subplots_adjust(wspace=0., hspace=0.)\n", + "plt.savefig(imagename, dpi=150)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Program/dustmaploops.ipynb b/Program/dustmaploops.ipynb new file mode 100644 index 0000000..0ec36a7 --- /dev/null +++ b/Program/dustmaploops.ipynb @@ -0,0 +1,323 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: AstropyDeprecationWarning: astropy.extern.six will be removed in 4.0, use the six module directly if it is still needed [astropy.extern.six]\n" + ] + } + ], + "source": [ + "from __future__ import print_function\n", + "\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import astropy.units as units\n", + "from astropy.coordinates import SkyCoord\n", + "from astropy.coordinates import Angle\n", + "from astroquery.irsa_dust import IrsaDust\n", + "\n", + "import healpy as hp\n", + "import pandas as pd\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "from dustmaps.sfd import SFDQuery\n", + "from dustmaps.planck import PlanckQuery\n", + "from dustmaps.bayestar import BayestarQuery\n", + "\n", + "#import getAVbest2\n", + "#import getAVbest\n", + "import dustmaps.planck\n", + "#dustmaps.planck.fetch()\n", + "print('done with importing')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "inFile = 'Brown_Walker_table_1.dat'\n", + "inTable = pd.read_csv(inFile,header=None,delimiter=' ')\n", + "names = inTable.iloc[:,0]\n", + "allra = inTable.iloc[:,1]\n", + "alldec = inTable.iloc[:,2]\n", + "distances = inTable.iloc[:,3]\n", + "cardinalAV = inTable.iloc[:,4]\n", + "cardinalAVspread = inTable.iloc[:,5]\n", + "n=len(inTable.index)\n", + " \n", + "#print(cardinalAV)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(4,4), dpi=150)\n", + "ax = fig.add_subplot(111)\n", + "plt.xlabel(\"Annular A$_V$\")\n", + "plt.ylabel(\"A$_V$\")\n", + "plt.legend(loc='bottom right', shadow=True)\n", + "# plt.suptitle(\"A$_V$ Values by Arcminute\")\n", + "ax.set_aspect('equal', adjustable='box')\n", + "ax.set_xscale('log')\n", + "xlim=[0.3,3]\n", + "ax.set_yscale('log')\n", + "ylim=3\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "name='UGC2855'\n", + "#name='SMC'\n", + "#print(names)\n", + "index=np.where(names==name)\n", + "print(index)\n", + "print(names[90])\n", + "index=90\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "imagename=name+'_radeccomparison.png'\n", + "print(allra[index])\n", + "ra0 = Angle(allra[index])\n", + "dec0 = Angle(alldec[index])\n", + "print(ra0, dec0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ra = np.arange(ra0.degree - 2., ra0.degree + 2., 0.05)\n", + "dec = np.arange(dec0.degree - 2., dec0.degree + 2., 0.05)\n", + "ra, dec = np.meshgrid(ra, dec)\n", + "coords = SkyCoord(ra*units.deg, dec*units.deg, frame='icrs')\n", + "\n", + "sfd = SFDQuery()\n", + "# this conversion puts Av on the Schlafly system\n", + "Av_sfd = 2.742 * sfd(coords)\n", + "\n", + "planck = PlanckQuery()\n", + "Av_planck = 3.1 * planck(coords)\n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "maxboth=max(np.amax(Av_planck),np.amax(Av_sfd))\n", + "print(maxboth)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(8,4), dpi=150)\n", + "\n", + "for k,(Av,title) in enumerate([(Av_sfd, 'SFD'),\n", + " (Av_planck, 'Planck')]):\n", + "\n", + "\n", + " ax = fig.add_subplot(1,2,k+1)\n", + " ax.imshow(\n", + " np.sqrt(Av)[::,::-1],\n", + " vmin=0.,\n", + " vmax=maxboth,\n", + " origin='lower',\n", + " interpolation='nearest',\n", + " cmap='binary',\n", + " aspect='equal'\n", + " )\n", + " ax.axis('off')\n", + " ax.set_title(title)\n", + "\n", + "fig.subplots_adjust(wspace=0., hspace=0.)\n", + "plt.savefig(imagename, dpi=150)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# now loop through everything" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "inFile = 'Brown_Walker_table_1.dat'\n", + "inTable = pd.read_csv(inFile,header=None,delimiter=' ')\n", + "names = inTable.iloc[:,0]\n", + "allra = inTable.iloc[:,1]\n", + "alldec = inTable.iloc[:,2]\n", + "distances = inTable.iloc[:,3]\n", + "cardinalAV = inTable.iloc[:,4]\n", + "cardinalAVspread = inTable.iloc[:,5]\n", + "n=len(inTable.index)\n", + " \n", + "print(cardinalAV)\n", + "\n", + "SF2011AVs = [None]*n\n", + "SFDqueryAVs = [None]*n\n", + "BW2021AVs = [None]*n\n", + "BW20212AVs = [None]*n\n", + "PlanckAVs = [None]*n\n", + "HIAVs = [None]*n\n", + "index = [i for i in range(n)]\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for j in index:\n", + "\n", + "\n", + " ra0, dec0 = allra[j], alldec[j]\n", + " \n", + " sourceCoords = SkyCoord(ra0,dec0,frame='icrs')\n", + " print(\"sourceCoords \", sourceCoords)\n", + "\n", + " sfd = SFDQuery()\n", + " # this conversion puts Av on the Schlafly system\n", + " Av_sfd = 2.742 * sfd(sourceCoords)\n", + " print(\"av_sfd good\")\n", + " planck = PlanckQuery()\n", + " Av_planck = 3.1 * planck(sourceCoords)\n", + "\n", + " AVtable = IrsaDust.get_extinction_table(sourceCoords,show_progress = False)\n", + " AV=AVtable['A_SandF'][2]\n", + "\n", + " SF2011AVs[j] = AV\n", + " SFDqueryAVs[j] = Av_sfd\n", + " # BW2021AVs[j],err,source = getAVbest(SourceCoords)\n", + " # BW20212AVs[j],err,source = getAVbest2(SourceCoords)\n", + " PlanckAVs[j] = Av_planck\n", + "\n", + " # https://nbviewer.jupyter.org/github/DanielLenz/ebv_tools/blob/master/examples.ipynb\n", + " ebv_map = hp.read_map('/Users/pbrown/Desktop/SN/github/ebv_tools/ebv_lhd.hpx.fits', verbose=False)\n", + " nside = hp.get_nside(ebv_map)\n", + " npix = hp.nside2npix(nside)\n", + " ordering = 'ring'\n", + "\n", + " s_gal = sourceCoords.galactic\n", + " glon = s_gal.l.value\n", + " glat = s_gal.b.value\n", + " pix = hp.ang2pix(nside, glon, glat, lonlat=True)\n", + " \n", + " # get reddening for these pixels\n", + " ebv_los = ebv_map[pix]\n", + " # the conversion puts it on the schlafley system\n", + " HIAVs[j] = ebv_los*2.742\n", + "\n", + " imagename=names[j]+'_radeccomparison.png'\n", + "\n", + " ra0 = Angle(ra0)\n", + " dec0 = Angle(dec0)\n", + "\n", + "\n", + "x=cardinalAV[0:n]\n", + "\n", + "for j in index:\n", + " print(names[j], x[j], SF2011AVs[j], SFDqueryAVs[j], PlanckAVs[j], HIAVs[j])\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.clf()\n", + "fig = plt.figure(figsize=(4,4), dpi=150)\n", + "ax.loglog()\n", + "plt.plot(x,SF2011AVs, color = '#00429d', marker = \"o\", linestyle='none', label = \"SF2011\")\n", + "plt.plot(x,SFDqueryAVs, color = '#73a2c6', marker = \".\", linestyle='none', label = \"SFDquery\")\n", + "plt.plot(x,PlanckAVs, color = '#f4777f', marker = \"x\", linestyle='none', label = \"Planck\")\n", + "plt.plot(x,HIAVs, color = '#93003a', marker = \"H\", linestyle='none', label = \"HI\")\n", + "#plt.axvline(x=majAxis[j])\n", + "plt.xlabel(\"Annular A$_V$\")\n", + "plt.ylabel(\"A$_V$\")\n", + "plt.legend(loc='bottom right', shadow=True)\n", + "# plt.suptitle(\"A$_V$ Values by Arcminute\")\n", + "ax.set_aspect('equal', adjustable='box')\n", + "#ax.set_xscale('log')\n", + "plt.xlim(0,0.1)\n", + "#ax.set_yscale('log')\n", + "plt.ylim(0,0.1)\n", + "plt.savefig('AVcomp.png', dpi=150)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Program/getAVbest.py b/Program/getAVbest.py new file mode 100644 index 0000000..7134113 --- /dev/null +++ b/Program/getAVbest.py @@ -0,0 +1,52 @@ +#!/usr/bin/python3.7 +def getAVbest(inputcoordinates): + "Coordinates are input as a single string. Output is the recommended Av value for MW reddening, error, and reference" + from astropy.coordinates import SkyCoord + from astropy.coordinates import Angle, Latitude, Longitude + from astroquery.irsa_dust import IrsaDust + + import astropy.units as u + import pandas as pd + import numpy as np + import math + import sys + + inputcoordinates = sys.argv[1] + testCoords = SkyCoord(inputcoordinates,frame='fk5') + + #print('\n-----\nReading input files...') + inFile = 'Brown_Walker_table_1.dat' + inTable = pd.read_csv(inFile,header=None,delimiter=' ') + ra = Angle(inTable.iloc[:,1]) + dec = Angle(inTable.iloc[:,2]) + sourceCoords = SkyCoord(ra,dec,frame='fk5') + + #print('Calculating separation from table coordinates') + separations = testCoords.separation(sourceCoords).arcminute + # compare to the distances in the table + within = np.less(separations,inTable.iloc[:,3]) + + # Are any of the input coordinates within the tabulated distance + # of the coordinates in the table? + correctedAV = np.where(within,inTable.iloc[:,4],None) #get calculated value + fix=any(within) + #print('fix?',fix) + + if fix: + AV = next((item for item in correctedAV if item is not None),None) + correctedAVerr = np.where(within,inTable.iloc[:,5],None) #get calculated val + newAVerr = next((item for item in correctedAVerr if item is not None),None) + AVerr = math.sqrt((newAVerr)**2+(AV*0.1)**2) + sources=np.where(within,inTable.iloc[:,6],None) + source = next((item for item in sources if item is not None),None)+",S_F_2011" + if not fix: + AVtable = IrsaDust.get_extinction_table(testCoords,show_progress = False) + AV=AVtable['A_SandF'][2] + AVerr = AV*0.1 + source = 'S_F_2011' + + print(AV, AVerr, source) + return(AV, AVerr, source); + +#if __name__ == "__main__": +getAVbest(input) \ No newline at end of file diff --git a/Program/getAVbest2.py b/Program/getAVbest2.py new file mode 100644 index 0000000..214ad4d --- /dev/null +++ b/Program/getAVbest2.py @@ -0,0 +1,53 @@ +#!/usr/bin/python3.7 +def getAVbest2(inputcoordinates): + print(inputcoordinates) + "Coordinates are input as a single string. Output is the recommended Av value for MW reddening, error, and reference" + from astropy.coordinates import SkyCoord + from astropy.coordinates import Angle, Latitude, Longitude + from astroquery.irsa_dust import IrsaDust + + import astropy.units as u + import pandas as pd + import numpy as np + import math + import sys + + inputcoordinates = sys.argv[0] + testCoords = SkyCoord(inputcoordinates,frame='fk5') + + #print('\n-----\nReading input files...') + inFile = 'Brown_Walker_table_1_rev2.dat' + inTable = pd.read_csv(inFile,header=None,delimiter=' ') + ra = Angle(inTable.iloc[:,1]) + dec = Angle(inTable.iloc[:,2]) + sourceCoords = SkyCoord(ra,dec,frame='fk5') + + #print('Calculating separation from table coordinates') + separations = testCoords.separation(sourceCoords).arcminute + # compare to the distances in the table + within = np.less(separations,inTable.iloc[:,3]) + + # Are any of the input coordinates within the tabulated distance + # of the coordinates in the table? + correctedAV = np.where(within,inTable.iloc[:,4],None) #get calculated value + fix=any(within) + #print('fix?',fix) + + if fix: + AV = next((item for item in correctedAV if item is not None),None) + correctedAVerr = np.where(within,inTable.iloc[:,5],None) #get calculated val + newAVerr = next((item for item in correctedAVerr if item is not None),None) + AVerr = math.sqrt((newAVerr)**2+(AV*0.1)**2) + sources=np.where(within,inTable.iloc[:,6],None) + source = next((item for item in sources if item is not None),None)+",S_F_2011" + if not fix: + AVtable = IrsaDust.get_extinction_table(testCoords,show_progress = False) + AV=AVtable['A_SandF'][2] + AVerr = AV*0.1 + source = 'S_F_2011' + + print(AV, AVerr, source) + return(AV, AVerr, source); + +#if __name__ == "__main__": +getAVbest2(input) \ No newline at end of file diff --git a/Program/m31 b/Program/m31 new file mode 100644 index 0000000..ce00b3f --- /dev/null +++ b/Program/m31 @@ -0,0 +1,43 @@ +Arcminute,North,East,South,West +0.0,1.862,1.862,1.862,1.862 +1.0,1.973,1.701,1.862,1.79 +2.0,1.99,1.701,1.387,1.79 +3.0,1.99,1.602,1.272,1.504 +4.0,1.94,1.398,1.272,1.419 +5.0,1.683,1.398,1.058,1.419 +6.0,1.683,1.231,0.955,1.224 +7.0,1.444,1.168,0.955,0.993 +8.0,1.334,1.168,0.927,0.993 +9.0,1.334,1.134,0.934,0.939 +10.0,1.029,1.105,0.934,0.875 +11.0,0.964,1.105,0.94,0.875 +12.0,0.964,1.058,0.975,0.861 +13.0,0.927,1.021,0.975,0.842 +14.0,0.923,1.021,0.969,0.842 +15.0,0.923,0.974,0.898,0.754 +16.0,0.94,0.843,0.898,0.581 +17.0,0.975,0.843,0.878,0.581 +18.0,0.975,0.787,0.677,0.461 +19.0,0.884,0.59,0.677,0.383 +20.0,0.81,0.59,0.586,0.383 +21.0,0.81,0.444,0.597,0.303 +22.0,0.802,0.426,0.597,0.276 +23.0,0.543,0.426,0.492,0.276 +24.0,0.543,0.332,0.415,0.245 +25.0,0.512,0.304,0.415,0.225 +26.0,0.369,0.304,0.386,0.225 +27.0,0.369,0.277,0.318,0.224 +28.0,0.338,0.258,0.318,0.216 +29.0,0.28,0.258,0.318,0.216 +30.0,0.28,0.255,0.287,0.215 +31.0,0.248,0.243,0.287,0.214 +32.0,0.24,0.243,0.281,0.214 +33.0,0.24,0.238,0.268,0.21 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+36.0,0.064,0.059,0.059,0.072 +37.0,0.064,0.059,0.059,0.072 +38.0,0.063,0.058,0.057,0.072 +39.0,0.062,0.058,0.056,0.072 +40.0,0.062,0.058,0.056,0.07 +,,,, diff --git a/Program/ngc55 b/Program/ngc55 new file mode 100644 index 0000000..b6166da --- /dev/null +++ b/Program/ngc55 @@ -0,0 +1,43 @@ +Arcminute,North,East,South,West +0.0,0.041,0.041,0.041,0.041 +1.0,0.041,0.041,0.039,0.04 +2.0,0.042,0.04,0.038,0.039 +3.0,0.044,0.041,0.038,0.039 +4.0,0.044,0.041,0.037,0.04 +5.0,0.044,0.04,0.038,0.04 +6.0,0.045,0.039,0.038,0.04 +7.0,0.045,0.039,0.039,0.039 +8.0,0.045,0.038,0.039,0.038 +9.0,0.045,0.036,0.039,0.038 +10.0,0.045,0.036,0.04,0.038 +11.0,0.044,0.036,0.04,0.037 +12.0,0.043,0.038,0.04,0.037 +13.0,0.043,0.038,0.041,0.037 +14.0,0.041,0.038,0.042,0.037 +15.0,0.04,0.041,0.042,0.037 +16.0,0.04,0.041,0.043,0.038 +17.0,0.039,0.039,0.044,0.039 +18.0,0.038,0.039,0.044,0.039 +19.0,0.038,0.039,0.042,0.04 +20.0,0.038,0.038,0.04,0.041 +21.0,0.038,0.037,0.04,0.041 +22.0,0.038,0.037,0.038,0.042 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0000000..d3da1d3 --- /dev/null +++ b/Program/ngc918 @@ -0,0 +1,43 @@ +Arcminute,North,East,South,West +0.0,0.945,0.945,0.945,0.945 +1.0,0.958,0.938,0.947,0.945 +2.0,0.958,0.91,0.947,0.984 +3.0,0.956,0.91,0.895,0.996 +4.0,0.947,0.89,0.873,1.059 +5.0,0.947,0.87,0.873,1.059 +6.0,0.94,0.87,0.815,1.067 +7.0,0.902,0.838,0.797,1.069 +8.0,0.902,0.818,0.797,1.069 +9.0,0.862,0.818,0.749,1.029 +10.0,0.843,0.778,0.733,0.993 +11.0,0.843,0.763,0.733,0.993 +12.0,0.827,0.763,0.705,0.915 +13.0,0.797,0.738,0.699,0.86 +14.0,0.797,0.731,0.699,0.86 +15.0,0.792,0.731,0.681,0.799 +16.0,0.789,0.719,0.666,0.75 +17.0,0.789,0.711,0.667,0.75 +18.0,0.774,0.711,0.658,0.715 +19.0,0.765,0.695,0.642,0.684 +20.0,0.765,0.69,0.642,0.684 +21.0,0.765,0.69,0.639,0.672 +22.0,0.738,0.665,0.647,0.645 +23.0,0.738,0.645,0.647,0.645 +24.0,0.709,0.645,0.638,0.638 +25.0,0.703,0.614,0.644,0.621 +26.0,0.703,0.583,0.644,0.621 +27.0,0.662,0.583,0.647,0.615 +28.0,0.605,0.556,0.652,0.615 +29.0,0.605,0.531,0.652,0.615 +30.0,0.588,0.524,0.653,0.615 +31.0,0.554,0.5,0.654,0.617 +32.0,0.554,0.498,0.654,0.617 +33.0,0.515,0.498,0.661,0.617 +34.0,0.509,0.49,0.664,0.622 +35.0,0.533,0.495,0.664,0.622 +36.0,0.517,0.495,0.664,0.621 +37.0,0.52,0.494,0.667,0.63 +38.0,0.52,0.499,0.671,0.63 +39.0,0.524,0.499,0.671,0.632 +40.0,0.519,0.491,0.679,0.645 +,,,, diff --git a/Program/runsinglegalaxybyname.py b/Program/runsinglegalaxybyname.py index fe97519..ab720cc 100644 --- a/Program/runsinglegalaxybyname.py +++ b/Program/runsinglegalaxybyname.py @@ -125,8 +125,6 @@ - - galaxy_name='NGC4258' picSaver('..', '12h18m57.5046s', '+47d18m14.303s', galaxy_name) a_v=tableFill(40, '12h18m57.5046s', '+47d18m14.303s', galaxy_name,'ngc258') @@ -183,12 +181,6 @@ a_v=tableFill(40, '00h42m44.5s', '+41d16m09s', galaxy_name,'m31') graphMaker('..', a_v, galaxy_name) -galaxy_name='NGC88' -picSaver('..', '00h21m22.12s', '-48d38m24.6s', galaxy_name) -a_v=tableFill(40, '00h21m22.12s', '-48d38m24.6s', galaxy_name,'ngc88') -graphMaker('..', a_v, galaxy_name) - - @@ -212,9 +204,9 @@ graphMaker('..', a_v, galaxy_name) -galaxy_name='NGC88' -picSaver('..', '00h21m22.12s', '-48d38m24.6s', galaxy_name) -a_v=tableFill(40, '00h21m22.12s', '-48d38m24.6s', galaxy_name,'ngc88') +galaxy_name='NGC918' +picSaver('..', '02h25m50.84s', '+18d29m46.5s ', galaxy_name) +a_v=tableFill(40, '02h25m50.84s', '+18d29m46.5s ', galaxy_name,'ngc918') graphMaker('..', a_v, galaxy_name) diff --git a/Program/test.py b/Program/test.py deleted file mode 100644 index e0369f7..0000000 --- a/Program/test.py +++ /dev/null @@ -1 +0,0 @@ -print('test') \ No newline at end of file diff --git a/Program/updatetable.py b/Program/updatetable.py new file mode 100644 index 0000000..46c15a7 --- /dev/null +++ b/Program/updatetable.py @@ -0,0 +1,110 @@ + + +def getAV(coord): + from astroquery.irsa_dust import IrsaDust + from astropy.coordinates import Angle,ICRS,SkyCoord +# print(coord) +# C = SkyCoord(coord, frame = 'icrs') +# print(C) + table = IrsaDust.get_extinction_table(coord,show_progress = False) +# print(table['A_SandF'][2]) + return (table['A_SandF'][2]) + + +def annulusmedian(distance,ra_central,dec_central): + from astropy.coordinates import Angle,ICRS,SkyCoord + import numpy as np + import math + from astropy import units as u + from astropy import coordinates +# print('starting annulusmedian ', distance, ra_central, dec_central) +# ra='12h00m00.0s' +# dec='00d00m00.00s' +# distance=60.0 + degreedistance=distance/60.0 + + """ + Gets 360 coordinates a specified distance away from the center of the galaxy + Inputs: + distance: distance in arcminutes + ra_central: right ascension component of the coordinate + dec_central: declination component of the coordinate + Outputs: + ring_coords: list of coordinates *distance* arcminutes away from the center of the specified galaxy + + """ + + ring_coords = [None]*360 + directions = [i for i in range(360)] + AVloop = [i for i in range(360)] + +# print('just before skycoord ',ra_central, ' ', dec_central) + + inputcoord=SkyCoord(ra_central,dec_central,frame='fk5') +# print(inputcoord) + + for direction in directions: +# print(direction) + newdec=inputcoord.dec+Angle(degreedistance*math.cos(direction*2.0*3.14159/360.0),unit=u.degree) +# print(newdec) + newra=inputcoord.ra+Angle(math.cos(newdec.value*2.0*3.14159/360.0)*degreedistance*math.sin(direction*2.0*3.14159/360.0),u.degree) +# print(newra) + + ring_coords[direction] = SkyCoord(ra=newra, dec=newdec) +# print(ring_coords) +# print(ring_coords) + AVloop[direction]=getAV(ring_coords[direction]) + + AVmedian=np.median(AVloop) + AVstddev=np.std(AVloop) + + return AVmedian, AVstddev; #performs transformation of initial coordinate into cardinal coordinates + + +def updatetable(): + print("updating the table") + import math + import sys + from astropy import units as u + from astropy import coordinates + from astropy.coordinates import Angle,ICRS,SkyCoord + import numpy as np + import pandas as pd + + inFile = 'Brown_Walker_table_1.dat' + inTable = pd.read_csv(inFile,header=None,delimiter=' ') + names = inTable.iloc[:,0] + ra = Angle(inTable.iloc[:,1]) + dec = Angle(inTable.iloc[:,2]) + distances = inTable.iloc[:,3] + cardinalAV = inTable.iloc[:,4] + cardinalAVspread = inTable.iloc[:,5] + sourceCoords = SkyCoord(ra,dec,frame='fk5') + + + n=len(inTable.index) + + medianAVs = [None]*n + stddevAVs = [None]*n + index = [i for i in range(n)] + + + for j in index: + i=j+50 + coords=sourceCoords[i] +# print('i') +# print(coords) +# print(distances[i]) +# print(cardinalAV[i]) + med, stddev=annulusmedian(distances[i],ra[i], dec[i]) + + medianAVs[i]=med + stddevAVs[i]=stddev + print(cardinalAV[i], cardinalAVspread[i], med, stddev) + + +# this didn't work, so the screen output was copied into the table np.savetxt("Brown_Walker_table_1_rev1.dat", inTable.iloc[:,0],inTable.iloc[:,1],inTable.iloc[:,2],inTable.iloc[:,3], medianAVs, stddevAVs, delimiter=" ",fmt='%s') + + +if __name__ == "__main__": + updatetable() diff --git a/Program/utilities.py b/Program/utilities.py index 317cf03..c6c1176 100644 --- a/Program/utilities.py +++ b/Program/utilities.py @@ -14,6 +14,9 @@ import matplotlib #matplotlib.use("Tkag") import matplotlib.pyplot as plt +from matplotlib import lines +#help(plt.plot) + import numpy as np @@ -29,6 +32,54 @@ from progress.bar import ChargingBar,FillingCirclesBar +def annulus(distance,ra,dec): +#def annulus(): + import math + import sys + from astropy import units as u + from astropy import coordinates + from astropy.coordinates import Angle,ICRS,SkyCoord + + +# ra='12h00m00.0s' +# dec='00d00m00.00s' +# distance=60.0 + degreedistance=distance/60.0 + + + """ + Gets 360 coordinates a specified distance away from the center of the galaxy + Inputs: + distance: distance in arcminutes + ra: right ascension component of the coordinate + dec: declination component of the coordinate + Outputs: + ring_coords: list of coordinates *distance* arcminutes away from the center of the specified galaxy + + """ + + ring_coords = [None]*360 + directions = [i for i in range(360)] +# directions = [0, 90, 180, 270, 360] + inputcoord=SkyCoord(ra+' '+dec) +# print(inputcoord) + + for direction in directions: +# print(direction) + newdec=inputcoord.dec+Angle(degreedistance*math.cos(direction*2.0*3.14159/360.0),unit=u.degree) +# print(newdec) + newra=inputcoord.ra+Angle(math.cos(newdec.value*2.0*3.14159/360.0)*degreedistance*math.sin(direction*2.0*3.14159/360.0),u.degree) +# print(newra) + + ring_coords[direction] = SkyCoord(ra=newra, dec=newdec) + +# print(ring_coords) + + + return ring_coords; #performs transformation of initial coordinate into cardinal coordinates + + + def csv_to_ascii(inFile,outFile): """ Converts csv file to ascii file @@ -327,6 +378,132 @@ def tableFill(distance, ra, dec, gal_name,file_obj): return(a_v) #returns LIST of a_v values +def tableFillplanck(distance, ra, dec, gal_name,file_obj): + """ + Saves a table to Excel with Av values up to [distance] away from the center [ra, dec] in the four cardinal directions. + Inputs: + distance: distance from center in arcminutes + ra: radial component of central coordinate + dec: declination component of central coordinate + gal_name: name of current galaxy + Outputs: + a_v: list of lists of Av values at each arcminute away from center + -size(a_v) = [[Av * 4]*distance] + + IN THE PROCESS OF CLEANING THIS UP + Things I think I don't need: + Table + DEAL WITH THIS LATER: 11/1/18 + DEALING WITH THIS NOW: 11/20/18 + """ + a_v = [None]*(distance+1) + curVal = [None]*4 #n = 0, e = 1, s = 2, w = 3 + #get values for each arcminute + print('\nGetting Av values for',gal_name) + # bar = ChargingBar('Fetching', max = distance+1) + for arc_minute in range(0,distance+1): + cardinals = fourCoord_pb(arc_minute, ra, dec) + for i in range(0,4): + C = coordinates.SkyCoord(cardinals[i], frame = 'icrs') + table = IrsaDust.get_extinction_table(C,show_progress = False) + curVal[i] = (table['A_SandF'][2]) + a_v[arc_minute] = tuple(curVal) + # bar.next() + #bar.finish() + print('Generating table') + + n = [gal_name] + namesTable = Table([n], names=('n')) + final_name = namesTable.to_pandas() + + av_table = Table(None) + Am = Column(name = 'Arcminute') + North = Column(name = 'North') + East = Column(name = 'East') + South = Column(name = 'South') + West = Column(name = 'West') + av_table.add_columns([Am,North, East, South, West]) + # print(a_v) + for arcminute in range(0,len(a_v)): + av_table.add_row() + av_table[arcminute][0] = arcminute + for i in range(0,4): + av_table[arcminute][i+1] = a_v[arcminute][i] + av_table.add_row() + # print(av_table) + + for i in range(0,5): #this adds a blank line to the table to separate queries + av_table[arcminute+1][i] = None + + final_vals = av_table.to_pandas() + from pandas import ExcelWriter + final_name.to_csv(file_obj, header =False, index = False) + final_vals.to_csv(file_obj, header =True, index = False, sep = ',') + + return(a_v) #returns LIST of a_v values + + + +def tableFill360(distance, ra, dec, gal_name,file_obj): + """ + Saves a table to Excel with Av values up to [distance] away from the center [ra, dec] in the 360 degree angle directions. + Inputs: + distance: distance from center in arcminutes + ra: radial component of central coordinate + dec: declination component of central coordinate + gal_name: name of current galaxy + Outputs: + a_v: list of lists of Av values at each arcminute away from center + -size(a_v) = [[Av * 360]*distance] + + """ + a_v = [None]*(distance+1) + curVal = [None]*360 #n = 0, e = 1, s = 2, w = 3 + #get values for each arcminute + print('\nGetting Av values for',gal_name) + # bar = ChargingBar('Fetching', max = distance+1) + for arc_minute in range(0,distance+1): + cardinals = fourCoord_pb(arc_minute, ra, dec) + for i in range(0,4): + C = coordinates.SkyCoord(cardinals[i], frame = 'icrs') + table = IrsaDust.get_extinction_table(C,show_progress = False) + curVal[i] = (table['A_SandF'][2]) + a_v[arc_minute] = tuple(curVal) + # bar.next() + #bar.finish() + print('Generating table') + + n = [gal_name] + namesTable = Table([n], names=('n')) + final_name = namesTable.to_pandas() + + av_table = Table(None) + Am = Column(name = 'Arcminute') + North = Column(name = 'North') + East = Column(name = 'East') + South = Column(name = 'South') + West = Column(name = 'West') + av_table.add_columns([Am,North, East, South, West]) + # print(a_v) + for arcminute in range(0,len(a_v)): + av_table.add_row() + av_table[arcminute][0] = arcminute + for i in range(0,4): + av_table[arcminute][i+1] = a_v[arcminute][i] + av_table.add_row() + # print(av_table) + + for i in range(0,5): #this adds a blank line to the table to separate queries + av_table[arcminute+1][i] = None + + final_vals = av_table.to_pandas() + from pandas import ExcelWriter + final_name.to_csv(file_obj, header =False, index = False) + final_vals.to_csv(file_obj, header =True, index = False, sep = ',') + + return(a_v) #returns LIST of a_v values + + def picSaver(directory, ra, dec, galaxy_name): @@ -386,15 +563,19 @@ def graphMaker(directory, a_v, galaxy_name): galaxy_name: name of current galaxy Outputs: None + + CB_color_cycle = ['#377eb8', '#ff7f00', '#4daf4a', + '#f781bf', '#a65628', '#984ea3', + '#999999', '#e41a1c', '#dede00'] """ x = np.arange(len(a_v)) a_v = np.array(a_v) plt.clf() plt.figure(1) - plt.plot(x,a_v[:,0], color = "blue", marker = ".", label = "North") - plt.plot(x,a_v[:,1], color = "red", marker = ".", label = "East") - plt.plot(x,a_v[:,2], color = "green", marker = ".", label = "South") - plt.plot(x,a_v[:,3], color = "black", marker = ".", label = "West") + plt.plot(x,a_v[:,0], color = '#00429d', marker = "+", linestyle='solid', label = "North") + plt.plot(x,a_v[:,1], color = '#73a2c6', marker = "o", linestyle='dotted', label = "East") + plt.plot(x,a_v[:,2], color = '#f4777f', marker = "x", linestyle='dashdot', label = "South") + plt.plot(x,a_v[:,3], color = '#93003a', marker = "s", linestyle='dashed', label = "West") #plt.axvline(x=majAxis[j]) plt.xlabel("Distance from Center of Galaxy [Arcminutes]") plt.ylabel("A$_V$") @@ -404,56 +585,3 @@ def graphMaker(directory, a_v, galaxy_name): plt.savefig(os.path.join(directory,'Graphs',(str(galaxy_name)+"e.png"))) plt.clf() - - -def annulus(distance,ra,dec): - """ - Gets 360 coordinates a specified distance away from the center of the galaxy - Inputs: - distance: distance in arcminutes - ra: right ascension component of the coordinate - dec: declination component of the coordinate - Outputs: - ring_coords: list of coordinates *distance* arcminutes away from the center of the specified galaxy - - #angle is from astropy.coordinates - - """ - - ring_coords = [None]*360 - directions = [i for i in range(360)] -# directions = [0, 90, 180, 270, 360] - coord=SkyCoord(ra+' '+dec) - print(coord) - - for direction in directions: - print(direction) - decli = coord.dec.arcminute+distance*math.cos(direction*2.0*3.14159/360.0) - #print(math.cos(direction*2.0*3.14159/360.0)) - print(decli) - decl = Angle(decli,u.arcminute) - decl = Angle(decl.to_string(unit=u.degree),u.degree) - coord = SkyCoord(ra=coord.ra, dec=decl) - #print(coord) - # converting from arcminutes into right ascension seconds - # 24 h x 60 m/h x 60 s/m = 86400 sec - # 360 deg x 60 arcmin/deg x 60 arsec/arcmin = 1296000 arcsec - # in on arcminute = 60 arcsec x 86400 / 1296000 - ds = distance*4*math.sin(direction*2*3.14159/360.0) - ds/=math.cos(math.radians(coord.dec.degree)) - h = coord.ra.hms.h - m = coord.ra.hms.m - s = coord.ra.hms.s+ds - (s,m,h) = timeFix(s,m,h) #keep time within allowed range - - rad = Angle((h,m,s), unit = u.hour) - rad = Angle(rad.to_string(unit=u.hour),u.hour) - #print(SkyCoord(ra=rad, dec=decl)) - ring_coords[direction] = SkyCoord(ra=rad, dec=decl) - -# print(ring_coords) - - - return ring_coords; #performs transformation of initial coordinate into cardinal coordinates - -