#68 for TVR

pynumdiff/finite_difference/_finite_difference.py

@@ -18,7 +18,7 @@ def first_order(x, dt, params=None, options={}, num_iterations=None):
              - **dxdt_hat** -- estimated derivative of x
     """
     if params != None and 'iterate' in options:
-        warn("""`params` and `options` parameters will be removed in a future version. Use `num_iterations` instead.""", DeprecationWarning)
+        warn("`params` and `options` parameters will be removed in a future version. Use `num_iterations` instead.", DeprecationWarning)
         if isinstance(params, list): params = params[0]
         return _iterate_first_order(x, dt, params)
     elif num_iterations:

pynumdiff/linear_model/__init__.py

@@ -5,8 +5,8 @@
     from ._linear_model import lineardiff
 except:
     from warnings import warn
-    warn("""Limited Linear Model Support Detected! CVXPY is not installed.
-        Install CVXPY to use lineardiff derivatives. You can still use other methods.""")
+    warn("Limited Linear Model Support Detected! CVXPY is not installed. " +
+        "Install CVXPY to use lineardiff derivatives. You can still use other methods.")
 
 from ._linear_model import savgoldiff, polydiff, spectraldiff
 

pynumdiff/linear_model/_linear_model.py

@@ -112,8 +112,8 @@ def savgoldiff(x, dt, params=None, options=None, polynomial_order=None, window_s
              - **dxdt_hat** -- estimated derivative of x
     """
     if params != None: # Warning to support old interface for a while. Remove these lines along with params in a future release.
-        warn("""`params` and `options` parameters will be removed in a future version. Use `polynomial_order`,
-            `window_size`, and `smoothing_win` instead.""", DeprecationWarning)
+        warn("`params` and `options` parameters will be removed in a future version. Use `polynomial_order`, " +
+            "`window_size`, and `smoothing_win` instead.", DeprecationWarning)
         polynomial_order, window_size, smoothing_win = params
     elif polynomial_order == None or window_size == None or smoothing_win == None:
         raise ValueError("`polynomial_order`, `window_size`, and `smoothing_win` must be given.")
@@ -192,8 +192,8 @@ def polydiff(x, dt, params=None, options=None, polynomial_order=None, window_siz
              - **dxdt_hat** -- estimated derivative of x
     """
     if params != None:
-        warn("""`params` and `options` parameters will be removed in a future version. Use `polynomial_order`
-            and `window_size` instead.""", DeprecationWarning)
+        warn("`params` and `options` parameters will be removed in a future version. Use `polynomial_order` " +
+            "and `window_size` instead.", DeprecationWarning)
         polynomial_order, window_size = params
         if options != None:
             if 'sliding' in options: sliding = options['sliding']
@@ -426,8 +426,8 @@ def lineardiff(x, dt, params=None, options=None, order=None, gamma=None, window_
              - **dxdt_hat** -- estimated derivative of x
     """
     if params != None:
-        warn("""`params` and `options` parameters will be removed in a future version. Use `order`,
-            `gamma`, and `window_size` instead.""", DeprecationWarning)
+        warn("`params` and `options` parameters will be removed in a future version. Use `order`, " +
+            "`gamma`, and `window_size` instead.", DeprecationWarning)
         order, gamma, window_size = params
         if options != None:
             if 'sliding' in options: sliding = options['sliding']
@@ -483,8 +483,8 @@ def spectraldiff(x, dt, params=None, options=None, high_freq_cutoff=None, even_e
              - **dxdt_hat** -- estimated derivative of x
     """
     if params != None: # Warning to support old interface for a while. Remove these lines along with params in a future release.
-        warn("""`params` and `options` parameters will be removed in a future version. Use `high_freq_cutoff`,
-            `even_extension`, and `pad_to_zero_dxdt` instead.""", DeprecationWarning)
+        warn("`params` and `options` parameters will be removed in a future version. Use `high_freq_cutoff`, " +
+            "`even_extension`, and `pad_to_zero_dxdt` instead.", DeprecationWarning)
         high_freq_cutoff = params[0] if isinstance(params, list) else params
         if options != None:
             if 'even_extension' in options: even_extension = options['even_extension']

pynumdiff/total_variation_regularization/__init__.py

@@ -5,10 +5,10 @@
     from ._total_variation_regularization import velocity, acceleration, jerk, jerk_sliding, smooth_acceleration
 except:
     from warnings import warn
-    warn("""Limited Total Variation Regularization Support Detected! CVXPY is not installed.
-        Many Total Variation Methods require CVXPY including: velocity, acceleration, jerk, jerk_sliding, smooth_acceleration.
-        Please install CVXPY to use these methods. Recommended to also install MOSEK and obtain a MOSEK license.
-        You can still use: total_variation_regularization.iterative_velocity.""") 
+    warn("Limited Total Variation Regularization Support Detected! CVXPY is not installed. " +
+        "Many Total Variation Methods require CVXPY including: velocity, acceleration, jerk, jerk_sliding, smooth_acceleration. " +
+        "Please install CVXPY to use these methods. Recommended to also install MOSEK and obtain a MOSEK license. " +
+        "You can still use: total_variation_regularization.iterative_velocity.") 
 
 from ._total_variation_regularization import iterative_velocity
 

pynumdiff/total_variation_regularization/__chartrand_tvregdiff__.py → pynumdiff/total_variation_regularization/_chartrand_tvregdiff.py

@@ -133,9 +133,9 @@
 #                                                       #
 # (1)                                                   #
 # scipy.sparse.linalg.cg seems to require more          #
-# iterations to reach the same result as                #        
+# iterations to reach the same result as                #
 # MATLAB's pcg. (scipy version 1.1.0)                   #
-# A good guess is the length of the data                #         
+# A good guess is the length of the data                #
 #                                                       #
 # (2)                                                   #
 # Drop last entry of derivative (u) for small scale     #