diff --git a/cscripts/obsutils.py b/cscripts/obsutils.py
index d87cfb5..d6b2052 100755
--- a/cscripts/obsutils.py
+++ b/cscripts/obsutils.py
@@ -690,78 +690,78 @@ def set_obs_patterns(pattern, ra=83.6331, dec=22.0145, offset=1.5):
 # Set model for TS fitting #
 # ======================== #
 def set_ts_model(models, srcname, ra=None, dec=None, fitspat=False, fitspec=False):
-        """
-        Set model for TS fitting
-
-        Parameters
-        ----------
-        models : `~gammalib.GModels`
-            Input model container
-        srcname : str
-            Test source name
-        ra : float, optional
-            Right Ascension of test source (deg)
-        dec : float, optional
-            Declination of test source (deg)
-        fitspat : bool, optional
-            Fit spatial parameter?
-        fitspec : bool, optional
-            Fit spectral parameters?
-
-        Returns
-        -------
-        model : `~gammalib.GModels`
-            Model container for TS fitting
-        """
-        # Create a clone of the input model
-        outmodels = models.copy()
-
-        # Disable TS computation for all model components (will enable the
-        # test source later)
-        for model in outmodels:
-            model.tscalc(False)
-
-        # Get source model and enable TS computation
-        model = outmodels[srcname]
-        model.tscalc(True)
-
-        # If source model has no "Prefactor" parameter then raise an exception
-        if not model.has_par('Prefactor'):
-            msg = ('Model "%s" has no parameter "Prefactor". Only spectral '
-                   'models with a "Prefactor" parameter are supported.' %
-                   srcname)
-            raise RuntimeError(msg)
-
-        # Set position of test source
-        if ra != None and dec != None:
-            if model.has_par('RA') and model.has_par('DEC'):
-                model['RA'].value(ra)
-                model['DEC'].value(dec)
-
-        # Set possible spatial and spectral parameters
-        spatial  = ['RA', 'DEC', 'Sigma', 'Radius', 'Width', 'PA',
-                    'MinorRadius', 'MajorRadius']
-        spectral = ['Index', 'Index1', 'Index2', 'BreakEnergy', 'CutoffEnergy',
-                    'InverseCutoffEnergy']
-
-        # Fit or fix spatial parameters
-        for par in spatial:
-            if model.has_par(par):
-                if fitspat:
-                    model[par].free()
-                else:
-                    model[par].fix()
-
-        # Fit or fix spectral parameters
-        for par in spectral:
-            if model.has_par(par):
-                if fitspec:
-                    model[par].free()
-                else:
-                    model[par].fix()
-
-        # Return model container
-        return outmodels
+    """
+    Set model for TS fitting
+
+    Parameters
+    ----------
+    models : `~gammalib.GModels`
+        Input model container
+    srcname : str
+        Test source name
+    ra : float, optional
+        Right Ascension of test source (deg)
+    dec : float, optional
+        Declination of test source (deg)
+    fitspat : bool, optional
+        Fit spatial parameter?
+    fitspec : bool, optional
+        Fit spectral parameters?
+
+    Returns
+    -------
+    model : `~gammalib.GModels`
+        Model container for TS fitting
+    """
+    # Create a clone of the input model
+    outmodels = models.copy()
+
+    # Disable TS computation for all model components (will enable the
+    # test source later)
+    for model in outmodels:
+        model.tscalc(False)
+
+    # Get source model and enable TS computation
+    model = outmodels[srcname]
+    model.tscalc(True)
+
+    # If source model has no "Prefactor" parameter then raise an exception
+    if not model.has_par('Prefactor'):
+        msg = ('Model "%s" has no parameter "Prefactor". Only spectral '
+               'models with a "Prefactor" parameter are supported.' %
+               srcname)
+        raise RuntimeError(msg)
+
+    # Set position of test source
+    if ra != None and dec != None:
+        if model.has_par('RA') and model.has_par('DEC'):
+            model['RA'].value(ra)
+            model['DEC'].value(dec)
+
+    # Set possible spatial and spectral parameters
+    spatial  = ['RA', 'DEC', 'Sigma', 'Radius', 'Width', 'PA',
+                'MinorRadius', 'MajorRadius']
+    spectral = ['Index', 'Index1', 'Index2', 'BreakEnergy', 'CutoffEnergy',
+                'InverseCutoffEnergy']
+
+    # Fit or fix spatial parameters
+    for par in spatial:
+        if model.has_par(par):
+            if fitspat:
+                model[par].free()
+            else:
+                model[par].fix()
+
+    # Fit or fix spectral parameters
+    for par in spectral:
+        if model.has_par(par):
+            if fitspec:
+                model[par].free()
+            else:
+                model[par].fix()
+
+    # Return model container
+    return outmodels
 
 
 # ==================== #