docstring corrections.

README.rst

@@ -67,7 +67,7 @@ apply to fields.
 
 * (and more)
 
-*Parts of this summary are taken* [1]_ *without marking them explicitly as
+*Parts of this summary are taken from* [1]_ *without marking them explicitly as
 quotations.*
 
 Installation

nifty_cmaps.py

@@ -33,7 +33,7 @@
 
     The visualization of fields is useful for obvious reasons, and therefore
     some nice color maps are here to be found. Those are segmented color maps
-    that can be used in many settings, including the native ploting method for
+    that can be used in many settings, including the native plotting method for
     fields. (Some of the color maps offered here are results from IFT
     publications, cf. references below.)
 
@@ -237,4 +237,4 @@ class ncmap(object):
 
         return cm("Plus Minus", segmentdata, N=int(ncolors), gamma=1.0)
 
-##-----------------------------------------------------------------------------
\ No newline at end of file
+##-----------------------------------------------------------------------------

nifty_power.py

@@ -36,11 +36,11 @@
     homogeneity and isotropy. Fields which are only statistically homogeneous
     can also be created using the diagonal operator routine.
 
-    In the moment, NIFTy offers one additional routine for power spectrum
+    At the moment, NIFTy offers one additional routine for power spectrum
     manipulation, the smooth_power function to smooth a power spectrum with a
     Gaussian convolution kernel. This can be necessary in cases where power
-    spectra are reconstructed and reused in an interative algorithm, where
-    too much statistical variation might effect severely the results.
+    spectra are reconstructed and reused in an iterative algorithm, where
+    too much statistical variation might severely effect the results.
 
 """