diff --git a/to_mongo.py b/to_mongo.py
index ec86643c2fb03ed7ebd261a86c0e89d46e075516..dcf98b0b89109b5ad45f09d20f51c7e546fbba2c 100644
--- a/to_mongo.py
+++ b/to_mongo.py
@@ -62,16 +62,16 @@ for year in years:
     tmaxs = data.tmax.data 
 
 
-    if is_leap_year(year): # extra day in leap year screws everything up
+    # if is_leap_year(year): # extra day in leap year screws everything up
 
-        tmin_1 = tmins[:59]
-        tmin_2 = tmins[60:]
+    #     tmin_1 = tmins[:59]
+    #     tmin_2 = tmins[60:]
 
-        tmax_1 = tmaxs[:59]
-        tmin_2 = tmaxs[60:]
+    #     tmax_1 = tmaxs[:59]
+    #     tmin_2 = tmaxs[60:]
 
-        tmins = np.concatenate([tmin_1, tmin_2], axis=0)
-        tmaxs = np.concatenate([tmax_1, tmin_2], axis=0)
+    #     tmins = np.concatenate([tmin_1, tmin_2], axis=0)
+    #     tmaxs = np.concatenate([tmax_1, tmin_2], axis=0)
 
 
     locs = []
@@ -127,30 +127,52 @@ gdd = db["normals"]
 resp = gdd.create_index([ ("location", "2dsphere") ])
 resp = gdd.create_index([ ("year", 1) ])
 
-single_year_min = np.zeros((365, 621, 1405))
-single_year_max = np.zeros((365, 621, 1405))
+single_year_min = np.zeros((366, 621, 1405))
+single_year_max = np.zeros((366, 621, 1405))
+
+ld_min = np.zeros((single_year_min.shape[-2], single_year_min.shape[-1]))
+ld_max = np.zeros((single_year_min.shape[-2], single_year_min.shape[-1]) )
+ld_count = 0 
 
 for year in range(1981, 2010+1):
     print (year)
     data = xr.open_dataset("data/temps_%s.nc" % year)
 
     tmins = data.tmin.data
-    tmaxs = data.tmax.data  
+    tmaxs = data.tmax.data 
+
+    ## idx 59 is leap day
+
+    if not is_leap_year(year): # extra day in leap year screws everything up
 
-    if is_leap_year(year): # extra day in leap year screws everything up
+        insert = np.zeros((1, tmins.shape[-2], tmins.shape[-1])) 
+        insert[:] = np.nan
 
         tmin_1 = tmins[:59]
-        tmin_2 = tmins[60:]
+        tmin_2 = tmins[59:]
 
         tmax_1 = tmaxs[:59]
-        tmin_2 = tmaxs[60:]
+        tmin_2 = tmaxs[59:]
+
+        tmins = np.concatenate([tmin_1, insert, tmin_2], axis=0)
+        tmaxs = np.concatenate([tmax_1, insert, tmin_2], axis=0)
+    
+    else:
+
+        ld_min += tmins[59]
+        ld_max += tmaxs[59]
+        ld_count += 1
 
-        tmins = np.concatenate([tmin_1, tmin_2], axis=0)
-        tmaxs = np.concatenate([tmax_1, tmin_2], axis=0)
 
     single_year_max += tmaxs/30
     single_year_min += tmins/30
 
+ld_min = ld_min/ld_count
+ld_max = ld_max/ld_count
+
+single_year_min[59] = ld_min 
+single_year_max[59] = ld_max
+
 x = np.where(~np.isnan(np.nanmean(single_year_max, axis=0)))
 # x = [(a, b) for a, b in zip(x[0], x[1])]