Pep8 changes

financepy/__init__.py

@@ -1,7 +1,7 @@
 cr = "\n"
 
 s = "####################################################################" + cr
-s += "#  FINANCEPY BETA Version " + str('0.34') + " - This build:  19 Feb 2024 at 21:02 #" + cr
+s += "#  FINANCEPY BETA Version " + str('0.350') + " - This build: 22 Feb 2024 at 19:25 #" + cr
 s += "#     This software is distributed FREE AND WITHOUT ANY WARRANTY   #" + cr
 s += "#  Report bugs as issues at https://github.com/domokane/FinancePy  #" + cr
 s += "####################################################################"

financepy/__init__.template

@@ -1,7 +1,7 @@
 cr = "\n"
 
 s = "####################################################################" + cr
-s += "#  FINANCEPY BETA Version " + str(__version__) + " - This build:  __dateandtime__ #" + cr
+s += "#  FINANCEPY BETA Version " + str(__version__) + " - This build: __dateandtime__ #" + cr
 s += "#     This software is distributed FREE AND WITHOUT ANY WARRANTY   #" + cr
 s += "#  Report bugs as issues at https://github.com/domokane/FinancePy  #" + cr
 s += "####################################################################"

financepy/market/curves/discount_curve.py

@@ -1,6 +1,6 @@
-##############################################################################
+###############################################################################
 # Copyright (C) 2018, 2019, 2020 Dominic O'Kane
-##############################################################################
+###############################################################################
 
 
 import numpy as np
@@ -9,7 +9,7 @@
 
 from ...utils.date import Date
 from ...utils.error import FinError
-from ...utils.global_vars import gDaysInYear, gSmall
+from ...utils.global_vars import gDaysInYear, g_small
 from ...utils.frequency import annual_frequency, FrequencyTypes
 from ...utils.day_count import DayCount, DayCountTypes
 from ...utils.math import test_monotonicity
@@ -89,7 +89,7 @@ def value_dt(self):
     ###########################################################################
 
     def _zero_to_df(self,
-                    value_dt: Date,  # TODO: why is value_date not used ?
+                    value_dt: Date,  # TODO: why is value_dt not used ?
                     rates: (float, np.ndarray),
                     times: (float, np.ndarray),
                     freq_type: FrequencyTypes,
@@ -101,7 +101,7 @@ def _zero_to_df(self,
         if isinstance(times, float):
             times = np.array([times])
 
-        t = np.maximum(times, gSmall)
+        t = np.maximum(times, g_small)
 
         f = annual_frequency(freq_type)
 
@@ -147,17 +147,17 @@ def _df_to_zero(self,
         if len(date_list) != len(df_list):
             raise FinError("Date list and df list do not have same length")
 
-        num_dts = len(date_list)
+        num_dates = len(date_list)
         zero_rates = []
 
         times = times_from_dates(
             date_list, self._value_dt, dc_type)
 
-        for i in range(0, num_dts):
+        for i in range(0, num_dates):
 
             df = df_list[i]
 
-            t = max(times[i], gSmall)
+            t = max(times[i], g_small)
 
             if freq_type == FrequencyTypes.CONTINUOUS:
                 r = -np.log(df) / t
@@ -269,7 +269,7 @@ def swap_rate(self,
                 pv01 += alpha * df
                 prev_dt = next_dt
 
-            if abs(pv01) < gSmall:
+            if abs(pv01) < g_small:
                 par_rate = 0.0
             else:
                 df_start = self.df(effective_dt)
@@ -424,9 +424,9 @@ def fwd_rate(self,
 
         day_count = DayCount(dc_type)
 
-        num_dts = len(start_dts)
+        num_dates = len(start_dts)
         fwd_rates = []
-        for i in range(0, num_dts):
+        for i in range(0, num_dates):
             dt1 = start_dts[i]
 
             if isinstance(date_or_tenor, str):

financepy/market/curves/discount_curve_ns.py

@@ -6,7 +6,7 @@
 
 from ...utils.date import Date
 from ...utils.frequency import FrequencyTypes
-from ...utils.global_vars import gSmall
+from ...utils.global_vars import g_small
 from ...utils.error import FinError
 from ...market.curves.discount_curve import DiscountCurve
 from ...utils.helpers import check_argument_types
@@ -101,7 +101,7 @@ def _zero_rate(self,
         """ Zero rate for Nelson-Siegel curve parametrisation. This means that
         the t vector must use the curve day count."""
 
-        t = np.maximum(times, gSmall)
+        t = np.maximum(times, g_small)
 
         theta = t / self._tau
         e = np.exp(-theta)

financepy/market/curves/discount_curve_nss.py

@@ -6,7 +6,7 @@
 
 from ...utils.date import Date
 from ...utils.frequency import FrequencyTypes
-from ...utils.global_vars import gSmall
+from ...utils.global_vars import g_small
 from ...utils.helpers import label_to_string
 from ...utils.error import FinError
 from ...market.curves.discount_curve import DiscountCurve
@@ -112,7 +112,7 @@ def _zero_rate(self,
         times. This function can return a single zero rate or a vector of zero
         rates. The compounding frequency must be provided. """
 
-        t = np.maximum(times, gSmall)
+        t = np.maximum(times, g_small)
 
         theta_1 = t / self._tau_1
         theta_2 = t / self._tau_2

financepy/market/curves/discount_curve_poly.py

@@ -6,7 +6,7 @@
 
 from ...utils.date import Date
 from ...utils.error import FinError
-from ...utils.global_vars import gSmall
+from ...utils.global_vars import g_small
 from ...utils.helpers import label_to_string
 from ...market.curves.discount_curve import DiscountCurve
 from ...utils.helpers import check_argument_types
@@ -89,7 +89,7 @@ def _zero_rate(self,
         use. The compounding frequency defaults to that specified in the
         constructor of the curve object. Which may be annual to continuous. """
 
-        t = np.maximum(times, gSmall)
+        t = np.maximum(times, g_small)
 
         zero_rate = 0.0
         for n in range(0, len(self._coefficients)):

financepy/market/curves/discount_curve_pwf.py

@@ -6,7 +6,7 @@
 
 from ...utils.date import Date
 from ...utils.error import FinError
-from ...utils.global_vars import gSmall
+from ...utils.global_vars import g_small
 from ...utils.math import test_monotonicity
 from ...utils.frequency import FrequencyTypes
 from ...utils.helpers import label_to_string
@@ -69,7 +69,7 @@ def _zero_rate(self,
         if np.any(times < 0.0):
             raise FinError("All times must be positive")
 
-        times = np.maximum(times, gSmall)
+        times = np.maximum(times, g_small)
 
         zero_rates = []
 

financepy/market/curves/interpolator.py

@@ -8,13 +8,12 @@
 from scipy.interpolate import PchipInterpolator
 from scipy.interpolate import CubicSpline
 from ...utils.error import FinError
-from ...utils.global_vars import gSmall
+from ...utils.global_vars import g_small
 
 ###############################################################################
 
 
 class InterpTypes(Enum):
-    ''' Types of interpolation '''
     FLAT_FWD_RATES = 1
     LINEAR_FWD_RATES = 2
     LINEAR_ZERO_RATES = 4
@@ -223,7 +222,7 @@ def fit(self,
 
         elif self._interp_type == InterpTypes.PCHIP_ZERO_RATES:
 
-            g_small_vector = np.ones(len(self._times)) * gSmall
+            g_small_vector = np.ones(len(self._times)) * g_small
             zero_rates = -np.log(self._dfs) / (self._times + g_small_vector)
 
             if self._times[0] == 0.0:
@@ -243,7 +242,7 @@ def fit(self,
 
             """ Second derivatives at left is zero and first derivative at
             right is clamped to zero. """
-            g_small_vector = np.ones(len(self._times)) * gSmall
+            g_small_vector = np.ones(len(self._times)) * g_small
             zero_rates = -np.log(self._dfs) / (self._times + g_small_vector)
 
             if self._times[0] == 0.0:
@@ -262,7 +261,7 @@ def fit(self,
         elif self._interp_type == InterpTypes.NATCUBIC_ZERO_RATES:
 
             """ Second derivatives are clamped to zero at end points """
-            g_small_vector = np.ones(len(self._times)) * gSmall
+            g_small_vector = np.ones(len(self._times)) * g_small
             zero_rates = -np.log(self._dfs) / (self._times + g_small_vector)
 
             if self._times[0] == 0.0:
@@ -294,7 +293,7 @@ def interpolate(self,
                 print(t)
                 raise FinError("Interpolate times must all be >= 0")
 
-            if np.abs(t) < gSmall:
+            if np.abs(t) < g_small:
                 return 1.0
 
             tvec = np.array([t])