diff --git a/pyzebra/ccl_io.py b/pyzebra/ccl_io.py
index f5e4431..411e448 100644
--- a/pyzebra/ccl_io.py
+++ b/pyzebra/ccl_io.py
@@ -170,7 +170,7 @@ def parse_1D(fileobj, data_type):
             while len(counts) < s["n_points"]:
                 counts.extend(map(float, next(fileobj).split()))
             s["counts"] = np.array(counts)
-            s["counts_err"] = np.sqrt(s["counts"])
+            s["counts_err"] = np.sqrt(np.maximum(s["counts"], 1))
 
             if s["h"].is_integer() and s["k"].is_integer() and s["l"].is_integer():
                 s["h"], s["k"], s["l"] = map(int, (s["h"], s["k"], s["l"]))
@@ -208,7 +208,7 @@ def parse_1D(fileobj, data_type):
         for name in col_names:
             s[name] = np.array(s[name])
 
-        s["counts_err"] = np.sqrt(s["counts"])
+        s["counts_err"] = np.sqrt(np.maximum(s["counts"], 1))
 
         s["scan_motors"] = []
         for motor, step in zip(motors, steps):
diff --git a/pyzebra/ccl_process.py b/pyzebra/ccl_process.py
index 8e94e8d..02595e5 100644
--- a/pyzebra/ccl_process.py
+++ b/pyzebra/ccl_process.py
@@ -220,8 +220,7 @@ def fit_scan(scan, model_dict, fit_from=None, fit_to=None):
         else:
             model += _model
 
-    weights = [1 / y_err if y_err != 0 else 1 for y_err in y_err]
-    scan["fit"] = model.fit(y_fit, x=x_fit, weights=weights)
+    scan["fit"] = model.fit(y_fit, x=x_fit, weights=1 / y_err)
 
 
 def get_area(scan, area_method, lorentz):