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--- courses:be5b33prg:homeworks:protein_folding [2015/11/23 09:52]
xposik
+++ courses:be5b33prg:homeworks:protein_folding [2015/11/23 09:57]
xposik [Suggestions for elaboration]
@@ Line 4: / Line 4: @@
 <WRAP round download>
-Download {{:courses:be5b33prg:homeworks:protein_folding.zip|''protein_folding.zip''}} archive, containing both
+Download ''{{:courses:be5b33prg:homeworks:protein_folding.zip|protein_folding.zip}}'' archive, containing both
-  * the {{:courses:be5b33prg:homeworks:folding.py|''folding.py''}} helper library, and
+  * ''{{:courses:be5b33prg:homeworks:folding.py|folding.py}}'' helper library, and
-  * the {{:courses:be5b33prg:homeworks:testsuite.txt|''testsuite.txt''}} file containing the amono acid chains to be folded.
+  * ''{{:courses:be5b33prg:homeworks:testsuite.txt|testsuite.txt}}'' file containing the amono acid chains to be folded.
 </WRAP>
@@ Line 127: / Line 127: @@
 ===== Data =====
-We provide a {{courses:a4b99rph:cviceni:testsuite.txt|dataset with various amino acids sequences}} to be folded into minimal energy configurations. This dataset will also be used for the task evaluation.
+We provide a {{:courses:be5b33prg:homeworks:testsuite.txt|dataset with various amino acids sequences}} to be folded into minimal energy configurations. This dataset will also be used for the task evaluation.
@@ Line 143: / Line 143: @@
 First and utmost, read the specs carefully and make sure you understand them.
-  * Decide what formalism you will use. Implement a vector class for representation of amino acid sequences and configurations, which also allows to compute the cumulative sum of the vector (to compute the positions based on configuration), the elementwise multiplication (for folding), and computation of the free energy. You can use the formalism chosen for module ''folding'', or you can choose a different one. Module ''folding'' is implemented in pure Python; maybe using ''numpy'' library would be better.
+  * Decide what formalism you will use. Implement a vector class for representation of amino acid sequences and configurations, which also allows to compute the cumulative sum of the vector (to compute the positions based on configuration), the elementwise multiplication (for folding), and computation of the free energy. You can use the formalism chosen for module ''folding'', or you can choose a different one. Module ''folding'' is implemented in pure Python; maybe using ''[[http://www.numpy.org/|numpy]]'' library would be better (not part of Python standard library, must be installed separately).
-  * Your solver (maybe a function named ''solve(a)'') shall take a sequence of amino acids as its input, and shall provide configuration ''c'' (sequence of length ''len(a)-1'') representing the final configuration of protein. You should make sure that the resulting configuration is self-avoiding, i.e. when positions of amino acids are computed based on the configuration, no position is attended twice or more times.
+  * It is highly desirable to create a visualization tool which would display your protein configurations graphically. It may be e.g. a function ''visualize(a,c)'' taking the amino acid sequence and the configuration as arguments. Consider using e.g. the ''[[http://matplotlib.org|matplotlib]]'' plotting library (not part of Python standard library, must be installed separately).
-  * It is highly desirable to create a visualization tool which would display your protein configurations graphically. It may be e.g. a function ''visualize(a,c)'' taking the amino acid sequence and the configuration as arguments.
+  * Your solver (maybe a function named ''solve(a)'') shall take a sequence of amino acids as its input, and shall provide configuration ''c'' (sequence of length ''len(a)-1'') representing the final configuration of protein. You should make sure that the resulting configuration is self-avoiding, i.e. when positions of amino acids are computed based on the configuration, no position is attended twice or more times.