Spellings spotted by codespell

Doc/Tutorial/chapter_cookbook.tex

@@ -1291,7 +1291,7 @@ We start by creating dictionaries mapping the window-sized sub-sequences to loca
 window = 7
 dict_one = {}
 dict_two = {}
-for (seq, section_dict) in [
+for seq, section_dict in [
     (rec_one.seq.upper(), dict_one),
     (rec_two.seq.upper(), dict_two),
 ]:

Doc/Tutorial/chapter_graphics.tex

@@ -1048,7 +1048,7 @@ entries = [
     ("Chr IV", "CHR_IV/NC_003075.fna"),
     ("Chr V", "CHR_V/NC_003076.fna"),
 ]
-for (name, filename) in entries:
+for name, filename in entries:
     record = SeqIO.read(filename, "fasta")
     print(name, len(record))
 \end{minted}

Doc/Tutorial/chapter_pdb.tex

@@ -1398,7 +1398,7 @@ across multiple protein structures, e.g. Figure~\ref{fig:phepairs}.
 This example superimposes each PHE residue in a chain on its N-C$\alpha$-C$\beta$ atoms,
 and presents all PHEs in the chain in the respective coordinate space as a simple demonstration.
 A more realistic exploration of pairwise sidechain interactions would examine a dataset of
-structures and filter for interaction classes as descussed in the relevant literature.
+structures and filter for interaction classes as discussed in the relevant literature.
 
 \begin{minted}{python}
 # superimpose all phe-phe pairs - quick hack just to demonstrate concept

Doc/biopdb_faq.tex

@@ -842,7 +842,7 @@ Moreover, it is quite easy to build more specialised data structures
 on top of the \texttt{Structure} class (eg. there's a \texttt{Polypeptide}
 class). On the other hand, the \texttt{Structure} object is built
 using a Parser/\-Consumer approach (called \texttt{PDBParser/\-MMCIFParser}
-and \texttt{Structure\-Builder}, respectively). One can easily re-use
+and \texttt{Structure\-Builder}, respectively). One can easily reuse
 the PDB/mmCIF parsers by implementing a specialised \texttt{Structure\-Builder}
 class. It is of course also trivial to add support for new file formats
 by writing new parsers.

Doc/doc.rst

@@ -371,7 +371,7 @@ in the python working space):
    >>> print(dN, dS)
    0.0193877676103 0.0217247139962
 
-If you are using maximum likelihood methdo to estimate dN and dS, you
+If you are using maximum likelihood method to estimate dN and dS, you
 are also able to specify equilibrium codon frequency to ``cfreq``
 argument. Available options include ``F1x4``, ``F3x4`` and ``F61``.
 

Doc/doc.tex

@@ -381,7 +381,7 @@ ATGATGATTATCAGCATGTGGATGAGCATATCGCGAGGATTGTGGGACAGCAGCTCC...GTG gi|24657088|ref|
 0.0193877676103 0.0217247139962
 \end{minted}
 
-If you are using maximum likelihood methdo to estimate dN and dS, you
+If you are using maximum likelihood method to estimate dN and dS, you
 are also able to specify equilibrium codon frequency to \texttt{cfreq}
 argument. Available options include \texttt{F1x4}, \texttt{F3x4} and
 \texttt{F61}.