From f32495764a2b9d8e670d16ba4f4f60fe861d79f8 Mon Sep 17 00:00:00 2001
From: Jonathan Vandermause <jonathan_vandermause@g.harvard.edu>
Date: Thu, 26 Sep 2019 10:29:11 -0400
Subject: [PATCH] some linting

---
 flare/gp.py | 45 +++++++++++++++++++++++++++------------------
 1 file changed, 27 insertions(+), 18 deletions(-)

diff --git a/flare/gp.py b/flare/gp.py
index 89393f362..358f04ff7 100644
--- a/flare/gp.py
+++ b/flare/gp.py
@@ -1,13 +1,13 @@
+"""Gaussian process model of the Born Oppenheimer potential energy surface."""
 import math
+from typing import List, Callable
 import numpy as np
 from scipy.linalg import solve_triangular
 from scipy.optimize import minimize
-from typing import List, Callable
 from flare.env import AtomicEnvironment
 from flare.struc import Structure
-from flare.gp_algebra import get_ky_mat, get_ky_and_hyp, \
-    get_like_from_ky_mat, get_like_grad_from_mats, get_neg_likelihood, \
-    get_neg_like_grad, get_ky_and_hyp_par
+from flare.gp_algebra import get_ky_and_hyp, get_like_grad_from_mats, \
+    get_neg_likelihood, get_neg_like_grad, get_ky_and_hyp_par
 from flare.kernels import str_to_kernel
 from flare.mc_simple import str_to_mc_kernel
 
@@ -112,9 +112,9 @@ def force_list_to_np(forces: list) -> np.ndarray:
         """
         forces_np = []
 
-        for m in range(len(forces)):
-            for n in range(3):
-                forces_np.append(forces[m][n])
+        for force in forces:
+            for force_comp in force:
+                forces_np.append(force_comp)
 
         forces_np = np.array(forces_np)
 
@@ -124,12 +124,9 @@ def train(self, output=None, custom_bounds=None,
               grad_tol: float = 1e-4,
               x_tol: float = 1e-5,
               line_steps: int = 20):
-        """
-        Train Gaussian Process model on training data.
-        Tunes the hyperparameters to maximize the Bayesian likelihood,
-        then computes L and alpha (related to the covariance matrix of the
-        training set).
-        """
+        """Train Gaussian Process model on training data. Tunes the \
+hyperparameters to maximize the likelihood, then computes L and alpha \
+(related to the covariance matrix of the training set)."""
 
         x_0 = self.hyps
 
@@ -183,6 +180,9 @@ def train(self, output=None, custom_bounds=None,
         self.likelihood_gradient = -res.jac
 
     def predict(self, x_t: AtomicEnvironment, d: int) -> [float, float]:
+        """Predict force component of an atomic environment and its \
+uncertainty."""
+
         # Kernel vector allows for evaluation of At. Env.
         k_v = self.get_kernel_vector(x_t, d)
 
@@ -193,7 +193,7 @@ def predict(self, x_t: AtomicEnvironment, d: int) -> [float, float]:
         self_kern = self.kernel(x_t, x_t, d, d, self.hyps,
                                 self.cutoffs)
         pred_var = self_kern - \
-                   np.matmul(np.matmul(k_v, self.ky_mat_inv), k_v)
+            np.matmul(np.matmul(k_v, self.ky_mat_inv), k_v)
 
         return pred_mean, pred_var
 
@@ -212,6 +212,9 @@ def predict_local_energy(self, x_t: AtomicEnvironment) -> float:
         return pred_mean
 
     def predict_local_energy_and_var(self, x_t: AtomicEnvironment):
+        """Predict the local energy of an atomic environment and its \
+uncertainty."""
+
         # get kernel vector
         k_v = self.en_kern_vec(x_t)
 
@@ -252,6 +255,9 @@ def get_kernel_vector(self, x: AtomicEnvironment,
         return k_v
 
     def en_kern_vec(self, x: AtomicEnvironment) -> np.ndarray:
+        """Compute the vector of energy/force kernels between an atomic \
+environment and the environments in the training set."""
+
         ds = [1, 2, 3]
         size = len(self.training_data) * 3
         k_v = np.zeros(size, )
@@ -337,6 +343,7 @@ def update_L_alpha(self):
         self.l_mat_inv = l_mat_inv
 
     def __str__(self):
+        """String representation of the GP model."""
 
         thestr = "GaussianProcess Object\n"
         thestr += 'Kernel: {}\n'.format(self.kernel_name)
@@ -356,6 +363,7 @@ def __str__(self):
         return thestr
 
     def as_dict(self):
+        """Dictionary representation of the GP model."""
 
         out_dict = dict(vars(self))
 
@@ -369,10 +377,11 @@ def as_dict(self):
 
     @staticmethod
     def from_dict(dictionary):
+        """Create GP object from dictionary representation."""
 
         if 'mc' in dictionary['kernel_name']:
-            force_kernel, grad = str_to_mc_kernel(dictionary['kernel_name'],
-                                               include_grad=True)
+            force_kernel, grad = \
+                str_to_mc_kernel(dictionary['kernel_name'], include_grad=True)
         else:
             force_kernel, grad = str_to_kernel(dictionary['kernel_name'],
                                                include_grad=True)
@@ -383,8 +392,8 @@ def from_dict(dictionary):
             energy_kernel = None
 
         if dictionary['energy_force_kernel'] is not None:
-            energy_force_kernel = str_to_kernel(dictionary[
-                                                    'energy_force_kernel'])
+            energy_force_kernel = \
+                str_to_kernel(dictionary['energy_force_kernel'])
         else:
             energy_force_kernel = None