Linear Examples (#27), closes #15

Linear Examples

src/mud_examples/__init__.py

@@ -1,5 +1,5 @@
 # -*- coding: utf-8 -*-
-from pkg_resources import get_distribution, DistributionNotFound
+from pkg_resources import DistributionNotFound, get_distribution
 
 try:
     # Change here if project is renamed and does not equal the package name

src/mud_examples/helpers.py

@@ -1,21 +1,21 @@
 # -*- coding: utf-8 -*-
 #!/usr/env/bin python
 
-import importlib
 import argparse
+import importlib
 import logging
 import os
 import sys
 import types
 
 import numpy as np
-from mud.funs import mud_sol, map_sol
+from mud import __version__ as __mud_version__
+from mud.funs import map_sol, mud_sol
+from mud_examples import __version__
 
 __author__ = "Mathematical Michael"
 __copyright__ = "Mathematical Michael"
 __license__ = "mit"
-from mud_examples import __version__
-from mud import __version__ as __mud_version__
 
 _logger = logging.getLogger(__name__)
 
@@ -277,7 +277,7 @@ def compare_linear_sols(transform, lam_ref, A, b,
     if cov is None:
         cov = np.eye(A.shape[1])
 
-    print("alpha = {}".format(alpha_list))
+    _logger.info("alpha = {}".format(alpha_list))
     if isinstance(A, list):  # svd approach returns list
         dim_output = A[0].shape[0]
     else:

src/mud_examples/inv.py

@@ -1,5 +1,6 @@
 # -*- coding: utf-8 -*-
 #!/usr/env/bin python
+
 import argparse
 import logging
 import os
@@ -67,7 +68,7 @@ def main(args):
         plt.rcParams['font.family'] = 'STIXGeneral'
 
     N, mu, sigma = int(1E3), 0.25, 0.1 # number of samples from initial and observed mean (mu) and st. dev (sigma)
-    lam = np.random.uniform(low=-1,high=1,size=N) # sample set of the initial
+    lam = np.random.uniform(low=-1, high=1, size=N) # sample set of the initial
 
     # Evaluate the QoI map on this initial sample set to form a predicted data set
     qvals_predict = QoI(lam,5) # Evaluate lam^5 samples