diff --git a/environment.yaml b/environment.yaml
new file mode 100644
index 0000000..73bd326
--- /dev/null
+++ b/environment.yaml
@@ -0,0 +1,9 @@
+channels:
+  - conda-forge
+dependencies:
+  - conda-forge::python=3.7.6
+  - conda-forge::biopython=1.76
+  - conda-forge::matplotlib=3.1.2
+  - conda-forge::numba=0.47.0
+  - conda-forge::numpy=1.17.5
+  - conda-forge::scipy=1.4.1
\ No newline at end of file
diff --git a/inference.py b/inference.py
index cd9009a..e1d60b6 100644
--- a/inference.py
+++ b/inference.py
@@ -90,14 +90,19 @@ def parse_args():
 	parser.add_argument('--tSkip',type=int,default=1)
 	parser.add_argument('--df',type=int,default=150)
 	parser.add_argument('--betaParam',type=float,default=1.0)
+	parser.add_argument('--lik',action='store_true',help='saves likelihood function, used by PALM. **Only compatible when a single selection coefficient is estimated.**')
+	parser.add_argument('--w',type=float,default=0.01,help='width used to estimate likelihood function (--lik)')
 	return parser.parse_args()
 
 
+
 def load_normal_tables():
+    import os
+    dname = os.path.dirname(os.path.abspath(__file__))
     # read in global Phi(z) lookups
-    z_bins = np.genfromtxt('utils/z_bins.txt')
-    z_logcdf = np.genfromtxt('utils/z_logcdf.txt')
-    z_logsf = np.genfromtxt('utils/z_logsf.txt')
+    z_bins = np.genfromtxt(os.path.join(dname, 'utils/z_bins.txt'))
+    z_logcdf = np.genfromtxt(os.path.join(dname, 'utils/z_logcdf.txt'))
+    z_logsf = np.genfromtxt(os.path.join(dname, 'utils/z_logsf.txt'))
     return z_bins,z_logcdf,z_logsf
 
 def load_times(args):
@@ -214,7 +219,6 @@ def likelihood_wrapper(theta,timeBins,N,freqs,z_bins,z_logcdf,z_logsf,ancGLs,anc
         return np.inf
 
     sel = Sprime[np.digitize(epochs,timeBins,right=False)-1]
-
     tShape = times.shape
     if tShape[2] == 0:
     	t = np.zeros((2,0))
@@ -308,6 +312,10 @@ def traj_wrapper(theta,timeBins,N,freqs,z_bins,z_logcdf,z_logsf,ancGLs,ancHapGLs
 	S0 = 0.0 * np.ones(T-1)
 	opts = {'xatol':1e-4}
 
+	if args.lik and T > 2:
+		print('ERROR: Option --lik incompatible with estimating >1 selection coefficient.')
+		raise ValueError
+
 	if T == 2:
 		Simplex = np.reshape(np.array([-0.05,0.05]),(2,1))
 	elif T > 2:
@@ -323,40 +331,56 @@ def traj_wrapper(theta,timeBins,N,freqs,z_bins,z_logcdf,z_logsf,ancGLs,ancHapGLs
 	opts['initial_simplex']=Simplex
 	    
 	#for tup in product(*[[-1,1] for i in range(3)]):
-	logL0 = likelihood_wrapper(S0,timeBins,Ne,freqs,z_bins,z_logcdf,z_logsf,ancientGLs,ancientHapGLs,epochs,noCoals,currFreq,h,sMax,changePts)
+	logL0 = -likelihood_wrapper(S0,timeBins,Ne,freqs,z_bins,z_logcdf,z_logsf,ancientGLs,ancientHapGLs,epochs,noCoals,currFreq,h,sMax,changePts)
 
 	print('Optimizing likelihood surface using Nelder-Mead...')
 	if times.shape[2] > 1:
 		print('\t(Importance sampling with M = %d Relate samples)'%(times.shape[2]))
 		print()
 	minargs = (timeBins,Ne,freqs,z_bins,z_logcdf,z_logsf,ancientGLs,ancientHapGLs,epochs,noCoals,currFreq,h,sMax,changePts)
+	
+
 	res = minimize(likelihood_wrapper,
-	         S0,
-	         args=minargs,
-	         options=opts,
-	         #bounds=bounds,
-	        method='Nelder-Mead')
+		         S0,
+		         args=minargs,
+		         options=opts,
+		         #bounds=bounds,
+		        method='Nelder-Mead')
 
 	S = res.x
 	L = res.fun
-	#Hinv = np.linalg.inv(res.hess)
-	#se = np.sqrt(np.diag(Hinv))
+	
+	if args.lik:
+		print('Fitting likelihood function...')
+		print()
+		Svec = np.linspace(max(S[0]-args.w,-sMax),min(S[0]+args.w,sMax),20)
+		Lvec = []
+		for s in Svec:
+			l = likelihood_wrapper(np.array([s]),timeBins,Ne,freqs,z_bins,z_logcdf,z_logsf,ancientGLs,ancientHapGLs,epochs,noCoals,currFreq,h,sMax,changePts)
+			Lvec.append(l)
+		Lvec = np.array(Lvec)
+		S = np.array([Svec[np.argmax(-Lvec)]])
+		L = np.max(-Lvec)
+		p = np.polyfit(Svec,-Lvec,deg=2)
+		if args.out != None:
+			np.save(args.out+'.quad_fit.npy',p)
+
 
 	print('#'*10)
 	print()
-	print('logLR: %.4f'%(-res.fun+logL0))
+	print('logLR: %.4f'%(L-logL0))
 	print()
 	print('MLE:')
 	print('========')
 	print('epoch\tselection')
 	for s,t,u in zip(S,timeBins[:-1],timeBins[1:]):
-		print('%d-%d\t%.5f'%(t,u,s))
+		print('%d-%d\t%.7f'%(t,u,s))
 
 
 	# infer trajectory @ MLE of selection parameter
 	print(noCoals)
 
-	post = traj_wrapper(res.x,timeBins,Ne,freqs,z_bins,z_logcdf,z_logsf,ancientGLs,ancientHapGLs,epochs,noCoals,currFreq,h,sMax,changePts)
+	post = traj_wrapper(S,timeBins,Ne,freqs,z_bins,z_logcdf,z_logsf,ancientGLs,ancientHapGLs,epochs,noCoals,currFreq,h,sMax,changePts)
 	
 	if args.out != None:
 		out(args,epochs,freqs,post)