diff --git a/singlepulse.py b/singlepulse.py
index 68cc155..3df4840 100644
--- a/singlepulse.py
+++ b/singlepulse.py
@@ -3,6 +3,8 @@
 """
 @author: Shawaiz Tabassum
 This script uses code from YOUR.
+
+Modified by Abyss Halley.
 """
 import h5py
 import numpy as np
@@ -16,15 +18,20 @@ import pandas as pd
 from scipy.signal import find_peaks
 import smplotlib #plot style, not needed
 
-# from pygdsm import GlobalSkyModel
-# from astropy.coordinates import SkyCoord
-# from astropy.coordinates import ICRS, Galactic, FK4, FK5  
-# from astropy.coordinates import Angle, Latitude, Longitude
-# import astropy.units as u
+from pygdsm import GlobalSkyModel
+from astropy.coordinates import SkyCoord
+from astropy.coordinates import ICRS, Galactic, FK4, FK5  
+from astropy.coordinates import Angle, Latitude, Longitude
+import astropy.units as u
 
-pulsar_dir = "/home/st3105/greenburst/J1944+0907/data_2025-02-27_17-41-33_composite/"
-pulsar_period = 5.185201908798642 #period of the pulsar.
+#AH: Modifying this to support multiple directories
+main_dir = os.path.join("ldata","trunk")
+pulsar_dir_list = [os.path.join(main_dir, name, "") for name in 
+    ["data_2025-04-24_07-36-04", "data_2025-04-29_07-50-16", "data_2025-04-30_07-53-07", "data_2025-04-30_08-18-17", "data_2025-05-01_07-47-34"]]
+#AH: old variable: pulsar_dir
+pulsar_period = 1.34789947290 * 1000 #period of the pulsar. AH: in ms
 
+#AH: Leaving these as default for now.
 flux_threshold = 35 #minimum flux in mJy
 distance_peaks = 6 #Number of samples/bins each pulse needs to be away from another pulse to be considered a detection.
 time_threshold = 0.1 #error interval for matching pulses only at pulsar spin period apart. Set to 1 if don't want to apply this filter
@@ -32,21 +39,22 @@ time_threshold = 0.1 #error interval for matching pulses only at pulsar spin per
 #Set options
 detrend_ft = True
 mad_filter = True
-save_path = "/home/st3105/greenburst/J1944+0907/data_2025-02-27_17-41-33_composite/calib-pngs/take3/"
+save_path_list = [os.path.join(name, "calib-pngs", "") for name in pulsar_dir_list]
+#AH: old variable: save_path
 #%%
 
 #Need to get radio sky temparature at the location of the pulsar. Get it using pygdsm.
 
 t_sys = 18 #Fig 3 of the GBT document: https://greenbankobservatory.org/wp-content/uploads/2017/07/GBTpg.pdf)
-# freq_mhz = 1400
-# sky_coord = SkyCoord(47.160175 * u.deg, -7.357055 * u.deg, frame="galactic") #For J1944+0907
-# gsm = GlobalSkyModel()
-# t_sky = gsm.get_sky_temperature(sky_coord, freq_mhz)
-t_sky = 4.9805965
+freq_mhz = 1400
+sky_coord = SkyCoord(339.492 * u.deg, 0.406 * u.deg, frame="galactic") #AH: For J1643-4522
+gsm = GlobalSkyModel()
+t_sky = gsm.get_sky_temperature(sky_coord, freq_mhz)
+#t_sky = 4.9805965
 t = t_sys + t_sky
 gain = 2.0 #same doc as above
 n_p = 2.0 #number of polarizations
-bw = 800*10**6 #Hz
+bw = 960*10**6 #Hz
 
 def radiometer(T, G, np, bandwidth, W, sigma):
     """
@@ -317,36 +325,37 @@ def compute_snr_from_h5(h5_file, detrend_ft=True, mad_filter=True, save=False, s
     
 
 #%%
-h5_dir = pulsar_dir + "h5/"
-h5_files = sorted(glob.glob(f"{pulsar_dir}*.h5"))
+for i, pulsar_dir in enumerate(pulsar_dir_list):
+    save_path = save_path_list[i] #AH: link directory and save path in a bit of a messy way oops
+    h5_dir = os.join(pulsar_dir,"cands","")
+    h5_files = sorted(glob.glob(f"{pulsar_dir}*.h5"))
 
-#%%
-results = []
+    results = []
 
-for filepath in h5_files:
-    try:
-        fluxes_mjy, flux_stds, widths_t, peak_mjds, time_peaks, time_pulse, dm = compute_snr_from_h5(filepath, 
-                                                                                                 detrend_ft=True, 
-                                                                                                 mad_filter=True, 
-                                                                                                 save=True, 
-                                                                                                 show=False, 
-                                                                                                 save_path=save_path)
-  
-        for i in range(0,(len(fluxes_mjy))):     
-            results.append({
-                "mjd": peak_mjds[i],
-                "flux_std": flux_stds[i],
-                "flux_mJy": fluxes_mjy[i],
-                "width_s": widths_t[i],
-                "Pulse time (ms)": time_peaks[i],
-                "tcand": time_pulse,
-                "dm": dm,
-                "filename": os.path.basename(filepath)
-            })                
-    except Exception as e:
-        print(f"Error processing {filepath}: {e}")
+    for filepath in h5_files:
+        try:
+            fluxes_mjy, flux_stds, widths_t, peak_mjds, time_peaks, time_pulse, dm = compute_snr_from_h5(filepath, 
+                                                                                                    detrend_ft=True, 
+                                                                                                    mad_filter=True, 
+                                                                                                    save=True, 
+                                                                                                    show=False, 
+                                                                                                    save_path=save_path)
+    
+            for i in range(0,(len(fluxes_mjy))):     
+                results.append({
+                    "mjd": peak_mjds[i],
+                    "flux_std": flux_stds[i],
+                    "flux_mJy": fluxes_mjy[i],
+                    "width_s": widths_t[i],
+                    "Pulse time (ms)": time_peaks[i],
+                    "tcand": time_pulse,
+                    "dm": dm,
+                    "filename": os.path.basename(filepath)
+                })                
+        except Exception as e:
+            print(f"Error processing {filepath}: {e}")
 
-df = pd.DataFrame(results)
-#%%
-df['pulse_energy_jyus'] = (df['flux_mJy']/1000) * (df['width_s']*10**6)
-df.to_csv(save_path+"flux.csv", index=False)
+    df = pd.DataFrame(results)
+    #%%
+    df['pulse_energy_jyus'] = (df['flux_mJy']/1000) * (df['width_s']*10**6)
+    df.to_csv(save_path+"flux.csv", index=False)