Merge branch 'cosm' into 'NIFTy_5'

Cosm

See merge request ift/nifty-dev!204

docs/source/conf.py

@@ -29,4 +29,6 @@ add_module_names = False
 html_theme = "sphinx_rtd_theme"
 html_logo = 'nifty_logo_black.png'
 
-exclude_patterns = ['mod/modules.rst', 'mod/*version.rst']
+exclude_patterns = [
+    'mod/modules.rst', 'mod/nifty5.git_version.rst', 'mod/nifty5.logger.rst'
+]

docs/source/installation.rst

@@ -14,12 +14,12 @@ Plotting support is added via::
 
     pip3 install --user matplotlib
 
-FFTW support is added via:
+FFTW support is added via::
 
     sudo apt-get install libfftw3-dev
     pip3 install --user pyfftw
 
-To actually use FFTW in your Nifty calculations, you need to call
+To actually use FFTW in your Nifty calculations, you need to call::
 
     nifty5.fft.enable_fftw()
 

nifty5/minimization/metric_gaussian_kl.py

@@ -57,10 +57,10 @@ class MetricGaussianKL(Energy):
     Notes
     -----
     For further details see: Metric Gaussian Variational Inference
-    (in preparation)
+    (FIXME in preparation)
     """
 
-    def __init__(self, mean, hamiltonian, n_sampels, constants=[],
+    def __init__(self, mean, hamiltonian, n_samples, constants=[],
                  point_estimates=None, mirror_samples=False,
                  _samples=None):
         super(MetricGaussianKL, self).__init__(mean)
@@ -75,7 +75,7 @@ class MetricGaussianKL(Energy):
             met = hamiltonian(Linearization.make_partial_var(
                 mean, point_estimates, True)).metric
             _samples = tuple(met.draw_sample(from_inverse=True)
-                             for _ in range(n_sampels))
+                             for _ in range(n_samples))
             if mirror_samples:
                 _samples += tuple(-s for s in _samples)
         self._samples = _samples