U /BfZã@szdZddlTddlmZddlZddlZddlZddl Z ddl Z dd„Z dd„Z d d „Zd d „Zd d„ZedkrveƒdS)a†Analyze arbors This file contains functions for analyzing arbors to create the Pareto front. This file requires `pandas` to be installed. This file can also be imported as a module and contains the following functions: * write_front - creates a file containing columns of alpha, wiring cost, and conduction delay * get_pareto_front - returns a list containing wiring_costs and conduction_delays * analyze_arbors - * write_scaling_dists - * main - the main function of the file This file can also be called on its own with arguments `--analyze` to run `analyze_arbors()` and `--scaling` to run `write_scaling_dists()`. é)Ú*)Úread_arbor_fullNc Csb|jd}td||fdƒ<}| d¡t|||ƒD]\}}} | d||| f¡q4W5QRXdS)Nú arbor nameú %s/%s.csvÚwz%alpha, wiring cost, conduction delay z %f, %f, %f )ÚgraphÚopenÚwriteÚzip) ÚGÚoutdirÚalphasÚ wiring_costsÚconduction_delaysÚfnameÚfÚalphaZwiringZdelay©rúl/home/dh_an3skk/arjun-chandrasekhar-teaching.com/tomato/backend/backend_plant_architecture/analyze_arbors.pyÚ write_fronts   rcCsx|t}d||jdf}tj |¡rLtj|dd}t|dƒt|dƒfSt  ||¡\}}t |||||ƒ||fSdS)aéGets the wiring costs and condution delays for the Pareto front. If the file already exists it just reads the file. If it does not, it will use `pareto_front()` from `pareto_functions.py` to calculate the needed information and then write the file using `write_front()` and finally returns the wiring costs and conduction delays. Parameters ---------- G : graph(?) #TODO: maybe figure out what exactly G is? # Could just do type print later for it. The graph information of the arbor (maybe) alphas : alpha numbers (?) Used to create the Pareto front if file doesn't already exist. Returns ------- list a list of wiring costs and conduction delays rrT©Zskipinitialspacez wiring costzconduction delayN) ZPARETO_FRONTS_DIRrÚosÚpathÚexistsÚpdÚread_csvÚlistÚpfZ pareto_frontr)r r Ú pathPrefixr rÚdfrrrrrÚget_pareto_front$s r c Csôt}d|t}tj |¡ }g}|s@tj|dd}t|dƒ}t|dƒ }|rZ|  d¡t  |t ¡D]|}|  d¡sxqh|  d¡|krˆqhtdƒt|ƒt||ƒ}|jd} t|||ƒ\} } t ||| | ¡\} } |  d | | | f¡qhW5QRXdS) Nz%s/arbor_stats.csvTrrÚaz9arbor name, pareto front distance, pareto front location ú.csvz'analyzing arbors from analyze_arbors.pyú %s, %f, %f )ÚDEFAULT_ALPHASÚSTATISTICS_DIRrrrrrrrr ÚlistdirÚRECONSTRUCTIONS_DIRÚendswithÚstripÚprintrrr rZarbor_dist_loc©rr rZ first_timeZ prev_arborsrrZ arbor_fnamer Z arbor_namerrÚdistrrrrÚanalyze_arborsBs,       r-c Csôt}d|t}tj |¡ }g}|s@tj|dd}t|dƒ}t|dƒ }|rZ|  d¡t  |t ¡D]|}|  d¡sxqh|  d¡|krˆqhtdƒt|ƒt||ƒ}|jd} t|||ƒ\} } t ||| | ¡\} } |  d | | | f¡qhW5QRXdS) Nz%s/scaling_distances.csvTrrr!zIarbor name, pareto front scaling distance, pareto front scaling location r"z0writing scaling distances from analyze_arbors.pyr#)r$r%rrrrrrrr r&r'r(r)r*rrr rZarbor_dist_loc_scaler+rrrÚwrite_scaling_distsas,       r.cCsHt ¡}|jddd|jddd| ¡}|jr8tƒ|jrDtƒdS)Nz --analyzeZ store_true)Úactionz --scaling)ÚargparseZArgumentParserZ add_argumentZ parse_argsZanalyzer-Zscalingr.)ZparserÚargsrrrÚmainsr2Ú__main__)Ú__doc__Ú new_constantsÚread_arbor_reconstructionrÚpareto_functionsrZnumpyZnprZpandasrr0rr r-r.r2Ú__name__rrrrÚs