FIles amended to pylint

financepy/market/curves/discount_curve.py

@@ -85,7 +85,7 @@ def __init__(self,
     ###########################################################################
 
     def _zero_to_df(self,
-                    value_dt: Date,
+                    value_dt: Date,       #TODO: why is value_date not used ?
                     rates: (float, np.ndarray),
                     times: (float, np.ndarray),
                     freq_type: FrequencyTypes,
@@ -342,15 +342,15 @@ def fwd(self,
         one date. """
 
         if isinstance(dts, Date):
-            dtsPlusOneDays = [dts.add_days(1)]
+            dts_plus_one_days = [dts.add_days(1)]
         else:
-            dtsPlusOneDays = []
+            dts_plus_one_days = []
             for dt in dts:
-                dtsPlusOneDay = dt.add_days(1)
-                dtsPlusOneDays.append(dtsPlusOneDay)
+                dts_plus_one_day = dt.add_days(1)
+                dts_plus_one_days.append(dts_plus_one_day)
 
         df1 = self.df(dts)
-        df2 = self.df(dtsPlusOneDays)
+        df2 = self.df(dts_plus_one_days)
         dt = 1.0 / gDaysInYear
         fwd = np.log(df1 / df2) / (1.0 * dt)
 
@@ -392,12 +392,12 @@ def bump(self,
             t = times[i]
             values[i] = values[i] * np.exp(-bump_size * t)
 
-        discCurve = DiscountCurve(self._value_dt,
-                                  times,
-                                  values,
-                                  self._interp_type)
+        disc_curve = DiscountCurve(self._value_dt,
+                                   times,
+                                   values,
+                                   self._interp_type)
 
-        return discCurve
+        return disc_curve
 
     ###########################################################################
 

financepy/market/curves/discount_curve_flat.py

@@ -54,11 +54,11 @@ def __init__(self,
 
         # Need to set up a grid of times and discount factors
         years = np.linspace(0.0, 10.0, 41)
-        dates = self._value_dt.add_years(years)
+        dts = self._value_dt.add_years(years)
 
         # Set up a grid of times and discount factors for functions
-        self._dfs = self.df(dates)
-        self._times = times_from_dates(dates, self._value_dt, dc_type)
+        self._dfs = self.df(dts)
+        self._times = times_from_dates(dts, self._value_dt, dc_type)
 
 ###############################################################################
 
@@ -67,25 +67,25 @@ def bump(self,
         """ Creates a new FinDiscountCurveFlat object with the entire curve
         bumped up by the bumpsize. All other parameters are preserved."""
 
-        rBumped = self._flat_rate + bump_size
-        discCurve = DiscountCurveFlat(self._value_dt,
-                                      rBumped,
-                                      freq_type=self._freq_type,
-                                      dc_type=self._dc_type)
-        return discCurve
+        rate_bumped = self._flat_rate + bump_size
+        disc_curve = DiscountCurveFlat(self._value_dt,
+                                       rate_bumped,
+                                       freq_type=self._freq_type,
+                                       dc_type=self._dc_type)
+        return disc_curve
 
 ###############################################################################
 
     def df(self,
-           dates: (Date, list)):
-        """ Return discount factors given a single or vector of dates. The
+           dts: (Date, list)):
+        """ Return discount factors given a single or vector of dts. The
         discount factor depends on the rate and this in turn depends on its
         compounding frequency and it defaults to continuous compounding. It
         also depends on the day count convention. This was set in the
         construction of the curve to be ACT_ACT_ISDA. """
 
         # Get day count times to use with curve day count convention
-        dc_times = times_from_dates(dates,
+        dc_times = times_from_dates(dts,
                                     self._value_dt,
                                     self._dc_type)
 
@@ -95,7 +95,7 @@ def df(self,
                                self._freq_type,
                                self._dc_type)
 
-        if isinstance(dates, Date):
+        if isinstance(dts, Date):
             return dfs[0]
         else:
             return np.array(dfs)

financepy/market/curves/discount_curve_zeros.py

@@ -99,11 +99,11 @@ def __init__(self,
 #             t = times[i]
 #             discount_factors[i] = discount_factors[i] * np.exp(-bump_size*t)
 
-#         discCurve = FinDiscountCurve(self._value_dt, times,
+#         disc_curve = FinDiscountCurve(self._value_dt, times,
 #                                      discount_factors,
 #                                      self._interp_type)
 
-#         return discCurve
+#         return disc_curve
 
 ###############################################################################
 

financepy/market/curves/interpolator.py

@@ -219,8 +219,8 @@ def fit(self,
 
         if self._interp_type == InterpTypes.PCHIP_LOG_DISCOUNT:
 
-            logDfs = np.log(self._dfs)
-            self._interp_fn = PchipInterpolator(self._times, logDfs)
+            log_dfs = np.log(self._dfs)
+            self._interp_fn = PchipInterpolator(self._times, log_dfs)
 
         elif self._interp_type == InterpTypes.PCHIP_ZERO_RATES:
 
@@ -236,8 +236,8 @@ def fit(self,
 
         #     """ Second derivatives at left is zero and first derivative at
         #     right is clamped to zero. """
-        #     logDfs = np.log(self._dfs)
-        #     self._interp_fn = CubicSpline(self._times, logDfs,
+        #     log_dfs = np.log(self._dfs)
+        #     self._interp_fn = CubicSpline(self._times, log_dfs,
         #                                  bc_type=((2, 0.0), (1, 0.0)))
 
         elif self._interp_type == InterpTypes.FINCUBIC_ZERO_RATES:
@@ -256,8 +256,8 @@ def fit(self,
         elif self._interp_type == InterpTypes.NATCUBIC_LOG_DISCOUNT:
 
             """ Second derivatives are clamped to zero at end points """
-            logDfs = np.log(self._dfs)
-            self._interp_fn = CubicSpline(self._times, logDfs,
+            log_dfs = np.log(self._dfs)
+            self._interp_fn = CubicSpline(self._times, log_dfs,
                                           bc_type='natural')
 
         elif self._interp_type == InterpTypes.NATCUBIC_ZERO_RATES:
@@ -274,8 +274,8 @@ def fit(self,
 
     #        elif self._interp_type  == InterpTypes.LINEAR_LOG_DISCOUNT:
     #
-    #            logDfs = np.log(self._dfs)
-    #            self._interp_fn = interp1d(self._times, logDfs,
+    #            log_dfs = np.log(self._dfs)
+    #            self._interp_fn = interp1d(self._times, log_dfs,
     #                                      fill_value="extrapolate")
 
     ###########################################################################

financepy/market/volatility/equity_vol_surface.py

@@ -185,9 +185,9 @@ def _delta_fit(k, *args):
     v = vol_function(vol_type_value, params, f, k, t)
     delta_out = bs_delta(s, t, k, r, q, v, option_type_value)
     inverse_delta_out = norminvcdf(np.abs(delta_out))
-    invObjFn = inverse_delta_target - inverse_delta_out
+    inv_obj_fn = inverse_delta_target - inverse_delta_out
 
-    return invObjFn
+    return inv_obj_fn
 
 ###############################################################################
 # Unable to cache this function due to dynamic globals warning. Revisit.
@@ -709,10 +709,10 @@ def implied_dbns(self, lowS, highS, numIntervals):
             dS = (highS - lowS) / numIntervals
 
             disDF = self._discount_curve._df(t)
-            divDF = self._dividend_curve._df(t)
+            div_df = self._dividend_curve._df(t)
 
             r = -np.log(disDF) / t
-            q = -np.log(divDF) / t
+            q = -np.log(div_df) / t
 
             Ks = []
             vols = []
@@ -747,9 +747,9 @@ def plot_vol_curves(self):
         lowK = self._strikes[0] * 0.9
         highK = self._strikes[-1] * 1.1
 
-        for tenorIndex in range(0, self._numExpiryDates):
+        for tenor_index in range(0, self._numExpiryDates):
 
-            expiry_dt = self._expiry_dts[tenorIndex]
+            expiry_dt = self._expiry_dts[tenor_index]
             plt.figure()
 
             ks = []
@@ -766,12 +766,12 @@ def plot_vol_curves(self):
                 fitted_vols.append(fittedVol)
                 K = K + dK
 
-            labelStr = "FITTED AT " + str(self._expiry_dts[tenorIndex])
-            plt.plot(ks, fitted_vols, label=labelStr)
+            label_str = "FITTED AT " + str(self._expiry_dts[tenor_index])
+            plt.plot(ks, fitted_vols, label=label_str)
 
-            labelStr = "MARKET AT " + str(self._expiry_dts[tenorIndex])
-            mkt_vols = self._volatility_grid[tenorIndex] * 100.0
-            plt.plot(self._strikes, mkt_vols, 'o', label=labelStr)
+            label_str = "MARKET AT " + str(self._expiry_dts[tenor_index])
+            mkt_vols = self._volatility_grid[tenor_index] * 100.0
+            plt.plot(self._strikes, mkt_vols, 'o', label=label_str)
 
             plt.xlabel("Strike")
             plt.ylabel("Volatility")