Fix formatting

py_wave_runup/datasets.py

@@ -19,28 +19,28 @@
 
 def load_power18():
  """
-  Loads wave runup data included with Power et al (2018)
-
-  This function loads the supplementary data from:
-
-  Power, H.E., Gharabaghi, B., Bonakdari, H., Robertson, B., Atkinson, A.L.,
-  Baldock, T.E., 2018. Prediction of wave runup on beaches using
-  Gene-Expression Programming and empirical relationships. Coastal Engineering.
-  https://doi.org/10.1016/j.coastaleng.2018.10.006
-
-  Examples:
-  >>> from py_wave_runup import datasets
-  >>> df = datasets.load_power18()
-  >>> df.describe()
-  hs tp beta roughness r2
-  count 1390.000000 1390.000000 1390.000000 1390.000000 1390.000000
-  mean 1.893131 9.227035 0.120612 0.024779 2.318814
-  std 1.309243 3.589004 0.062236 0.043617 1.776918
-  min 0.018576 0.805805 0.009000 0.000003 0.027336
-  25% 0.895942 7.517556 0.088228 0.001000 1.103500
-  50% 1.848050 9.963089 0.108422 0.003750 1.923500
-  75% 2.391756 10.995500 0.129220 0.007500 3.406660
-  max 7.174100 23.680333 0.286551 0.125000 12.669592
+ Loads wave runup data included with Power et al (2018)
+
+ This function loads the supplementary data from:
+
+ Power, H.E., Gharabaghi, B., Bonakdari, H., Robertson, B., Atkinson, A.L.,
+ Baldock, T.E., 2018. Prediction of wave runup on beaches using
+ Gene-Expression Programming and empirical relationships. Coastal Engineering.
+ https://doi.org/10.1016/j.coastaleng.2018.10.006
+
+ Examples:
+ >>> from py_wave_runup import datasets
+ >>> df = datasets.load_power18()
+ >>> df.describe()
+ hs tp beta roughness r2
+ count 1390.000000 1390.000000 1390.000000 1390.000000 1390.000000
+ mean 1.893131 9.227035 0.120612 0.024779 2.318814
+ std 1.309243 3.589004 0.062236 0.043617 1.776918
+ min 0.018576 0.805805 0.009000 0.000003 0.027336
+ 25% 0.895942 7.517556 0.088228 0.001000 1.103500
+ 50% 1.848050 9.963089 0.108422 0.003750 1.923500
+ 75% 2.391756 10.995500 0.129220 0.007500 3.406660
+ max 7.174100 23.680333 0.286551 0.125000 12.669592
 
  """
 

py_wave_runup/models.py

@@ -32,7 +32,7 @@ def __init__(self, Hs=None, Tp=None, beta=None, Lp=None, h=None, r=None):
  Must be defined if ``Lp`` is not defined.
  Lp (:obj:`float` or :obj:`list`): Peak wave length
  Must be definied if ``Tp`` is not defined.
- h (:obj:`float` or :obj:`list`): Depth of wave measurement(s). If not 
+ h (:obj:`float` or :obj:`list`): Depth of wave measurement(s). If not
  given deep-water conditions are assumed.
  r (:obj:`float` or :obj:`list`): Hydraulic roughness length. Can be
  approximated by :math:`r=2.5D_{50}`.
@@ -61,14 +61,17 @@ def __init__(self, Hs=None, Tp=None, beta=None, Lp=None, h=None, r=None):
  if self.h:
  k = []
  for T in self.Tp:
- k.append(_newtRaph(T,self.h))
- self.Lp = (2*np.pi)/k
+ k.append(_newtRaph(T, self.h))
+ self.Lp = (2 * np.pi) / k
  else:
  self.Lp = 9.81 * (self.Tp ** 2) / 2 / np.pi
  else:
  self.Lp = np.atleast_1d(Lp)
  if self.h:
- self.Tp = np.sqrt( (2*np.pi*self.Lp)/(9.81*np.tanh((2*np.pi*self.h)/self.Lp)) )
+ self.Tp = np.sqrt(
+ (2 * np.pi * self.Lp)
+ / (9.81 * np.tanh((2 * np.pi * self.h) / self.Lp))
+ )
  else:
  self.Tp = np.sqrt(2 * np.pi * self.Lp / 9.81)
 
@@ -87,28 +90,31 @@ def _return_one_or_array(self, val):
  return val.item()
  else:
  return val
- 
- def _newtRaph(self,T,h):
+
+ def _newtRaph(self, T, h):
 
  # Function to determine k from dispersion relation given period (T) and depth (h) using
  # the Newton-Raphson method.
 
  if not np.isnan(T):
- L_not = (9.81*(T**2))/(2*np.pi)
- k1 = (2*np.pi)/L_not
+ L_not = (9.81 * (T ** 2)) / (2 * np.pi)
+ k1 = (2 * np.pi) / L_not
 
  def fk(k):
- return (((2*np.pi)/T)**2)-(9.81*k*np.tanh(k*h))
+ return (((2 * np.pi) / T) ** 2) - (9.81 * k * np.tanh(k * h))
+
  def f_prime_k(k):
- return (-9.81*np.tanh(k*h))-(9.81*k*(1-(np.tanh(k*h)**2)))
+ return (-9.81 * np.tanh(k * h)) - (
+ 9.81 * k * (1 - (np.tanh(k * h) ** 2))
+ )
 
  k2 = 100
  i = 0
- while abs((k2-k1))/k1 > 0.01:
-  i+=1
-  if i!=1:
-  k1=k2
-  k2 = k1-(fk(k1)/f_prime_k(k1))
+ while abs((k2 - k1)) / k1 > 0.01:
+ i += 1
+ if i != 1:
+ k1 = k2
+ k2 = k1 - (fk(k1) / f_prime_k(k1))
  else:
  k2 = np.nan