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How to use setup2 method in Sure

Best Python code snippet using sure_python

31_official_.py

Source:31_official_.py

...44    e = (3*v)45    v += 146    x += 147    48def setup2():49    global e, x, v, o50    scores[x] = total51    e = (3*v)52    v += 153    o += 154    x += 155    5657def drawcard1(): #add 3 of a kind58    #if newcard doesn't match suit with any card:59    global upcardsuit, upcardnum, total60    if (deck[-o - 1][1]) != deck[e][1] and (deck[-o - 1][1]) != deck[e+1][1] and (deck[-o - 1][1]) != deck[e+2][1]:61        total = max(deck[e][0], deck[e+1][0], deck[e+2][0], (deck[-o - 1][0]))62        upcardnum = min(deck[e][0], deck[e+1][0], deck[e+2][0], (deck[-o - 1][0]))63        if upcardnum == deck[e][0]:64            upcardsuit = deck[e][1]65            setup2()66        elif upcardnum == deck[e+1][0]:67            upcardsuit = deck[e+1][1]68            setup2()69        elif upcardnum == deck[e+2][0]:70            upcardsuit = deck[e+2][1]71            setup2()72        else:73            upcardsuit = deck[-o - 1][1]74            setup2()7576    #if newcard matches suit with card 1:77    elif (deck[-o - 1][1]) == deck[e][1]:78        #if newcard and card 1 combined aren't max or even:79        if ((deck[-o - 1][0]) + deck[e][0]) <= max(deck[e+1][0], deck[e+2][0]):80            total = max(deck[e+1][0], deck[e+2][0])81            upcardnum = min(deck[e+1][0], deck[e+2][0],(deck[-o - 1][0]), deck[e][0])82            if upcardnum == deck[e][0]:83                upcardsuit = deck[e][1]84                setup2()85            elif upcardnum == deck[e+1][0]:86                upcardsuit = deck[e+1][1]87                setup2()88            elif upcardnum == deck[e+2][0]:89                upcardsuit = deck[e+2][1]90                setup2()91            else:92                upcardsuit = deck[-o-1][1]93                setup2()94            95        #if newcard and card 1 combined are max:96        else:             97            total = (deck[-o - 1][0]) + (deck[e][0]) 98            upcardnum = min(deck[e+1][0], deck[e+2][0])99            if upcardnum == deck[e+1][0]:100                upcardsuit = deck[e+1][1]101                setup2()102            else:103                upcardsuit = deck[e+2][1]104                setup2()105106    #if newcard matches suit with card 2: 107    elif (deck[-o - 1][1]) == deck[e+1][1]:108        #if newcard and card 2 combined aren't max or even:109        if ((deck[-o - 1][0]) + deck[e+1][0]) <= max(deck[e][0], deck[e+2][0]):110            total = max(deck[e][0], deck[e+2][0])111            upcardnum = min(deck[e+1][0], deck[e+2][0],(deck[-o - 1][0]), deck[e][0])112            if upcardnum == deck[e][0]:113                upcardsuit = deck[e][1]114                setup2()115            elif upcardnum == deck[e+1][0]:116                upcardsuit = deck[e+1][1]117                setup2()118            elif upcardnum == deck[e+2][0]:119                upcardsuit = deck[e+2][1]120                setup2()121            else:122                upcardsuit = deck[-o-1][1]123                setup2()124        #if newcard and card 2 combined are max:125        else:             126            total = (deck[-o - 1][0]) + (deck[e+1][0]) 127            upcardnum = min(deck[e][0], deck[e+2][0])128            if upcardnum == deck[e][0]:129                upcardsuit = deck[e][1]130                setup2()131            else:132                upcardsuit = deck[e+2][1]133                setup2()134135    #if newcard matches suit with card 3: 136    elif (deck[-o - 1][1]) == deck[e+2][1]:137        #if newcard and card 3 combined aren't max or even:138        if ((deck[-o - 1][0]) + deck[e+2][0]) <= max(deck[e+1][0], deck[e][0]):139            total = max(deck[e+1][0], deck[e][0])140            upcardnum = min(deck[e+1][0], deck[e+2][0],(deck[-o - 1][0]), deck[e][0])141            if upcardnum == deck[e][0]:142                upcardsuit = deck[e][1]143                setup2()144            elif upcardnum == deck[e+1][0]:145                upcardsuit = deck[e+1][1]146                setup2()147            elif upcardnum == deck[e+2][0]:148                upcardsuit = deck[e+2][1]149                setup2()150            else:151                upcardsuit = deck[-o-1][1]152                setup2()153        #if newcard and card 3 combined are max:154        else:             155            total = (deck[-o - 1][0]) + (deck[e+2][0]) 156            upcardnum = min(deck[e+1][0], deck[e][0])157            if upcardnum == deck[e+1][0]:158                upcardsuit = deck[e+1][1]159                setup2()160            else:161                upcardsuit = deck[e][1]162                setup2()163def drawcard2():164    global upcardsuit, upcardnum, total165    #if newcard shares no suits:166    if deck[-o - 1][1] != deck[e][1] and deck[-o - 1][1] != deck[e+2][1]:167        total = max(deck[-o - 1][0], deck[e][0] + deck[e+1][0], deck[e+2][0])168        if deck[-o - 1][0] ==  min(deck[-o - 1][0], deck[e][0] + deck[e+1][0], deck[e+2][0]):169            upcardnum = deck[-o - 1][0]170            upcardsuit = deck[-o - 1][1]171            setup2()172        elif deck[e+2][0] == min(deck[-o - 1][0], deck[e][0] + deck[e+1][0], deck[e+2][0]):173            upcardnum = deck[e+2][0]174            upcardsuit = deck[e+2][1]175            setup2()176        else: 177            if deck[e][0] < deck[e+1][0]:178                upcardnum = deck[e][0]179                upcardsuit = deck[e][1]180                setup2()181            else:182                upcardnum = deck[e+1][0]183                upcardsuit = deck[e+1][1]184                setup2()185186    #if newcard shares a suit with the 3rd card:187    elif deck[-o - 1][1] == deck[e+2][1]:188        total = max(deck[-o - 1][0] + deck[e+2][0], deck[e][0] + deck[e+1][0])189        if deck[-o - 1][0] + deck[e+2][0] > deck[e][0] + deck[e+1][0]:190            upcardnum = min(deck[e][0], deck[e+1][0])191            if upcardnum == deck[e][0]:192                upcardsuit = deck[e][1]193                setup2()194            else:195                upcardsuit = deck[e+1][1]196                setup2()197        elif deck[-o - 1][0] + deck[e+2][0] < deck[e][0] + deck[e+1][0]:198            upcardnum = min(deck[-o - 1][0], deck[e+2][0])199            if upcardnum == deck[e+2][0]:200                upcardsuit = deck[e+2][1]201                setup2()202            else:203                upcardsuit = deck[-o - 1][1]204                setup2()205        else:206            upcardnum = min(deck[-o - 1][0], deck[e+2][0], deck[e][0], deck[e+1][0])207            if upcardnum == deck[e][0]:208                upcardsuit = deck[e][1]209                setup2()210            elif upcardnum == deck[e+1][0]:211                upcardsuit = deck[e+1][1]212                setup2()213            elif upcardnum == deck[e+2][0]:214                upcardsuit = deck[e+2][1]215                setup2()216            else:217                upcardsuit = deck[-o - 1][1]218                setup2()                 219220    #if newcard shares a suit with cards 1 & 2:221    else:222        total = max(deck[-o - 1][0] + deck[e][0] + deck[e+1][0], deck[e+2][0])223        if deck[-o - 1][0] + deck[e][0] + deck[e+1][0] > deck[e+2][0]:224            upcardnum = deck[e+2][0]225            upcardsuit = deck[e+2][1]226            setup2()227        else:228            upcardnum = min(deck[-o - 1][0], deck[e][0], deck[e+1][0])229            if upcardnum == deck[-o - 1][0]:230                upcardsuit = deck[-o - 1][1]231                setup2()232            elif upcardnum == deck[e][0]:233                upcardsuit == deck[e][1]234                setup2()235            else:236                upcardsuit = deck[e+1][1]237                setup2()238239def drawcard3():240    global upcardsuit, upcardnum, total241    #if newcard shares no suits:242    if deck[-o - 1][1] != deck[e+1][1] and deck[-o - 1][1] != deck[e+2][1]:243        total = max(deck[-o - 1][0], deck[e][0] + deck[e+2][0], deck[e+1][0])244        if deck[-o - 1][0] ==  min(deck[-o - 1][0], deck[e+2][0] + deck[e][0], deck[e+1][0]):245            upcardnum = deck[-o - 1][0]246            upcardsuit = deck[-o - 1][1]247            setup2()248        elif deck[e+1][0] == min(deck[-o - 1][0], deck[e][0] + deck[e+2][0], deck[e+1][0]):249            upcardnum = deck[e+1][0]250            upcardsuit = deck[e+1][1]251            setup2()252        else: 253            if deck[e][0] < deck[e+2][0]:254                upcardnum = deck[e][0]255                upcardsuit = deck[e][1]256                setup2()257            else:258                upcardnum = deck[e+2][0]259                upcardsuit = deck[e+2][1]260                setup2()261262    #if newcard shares a suit with the 2nd card:263    elif deck[-o - 1][1] == deck[e+1][1]:264        total = max(deck[-o - 1][0] + deck[e+1][0], deck[e][0] + deck[e+2][0])265        if deck[-o - 1][0] + deck[e+1][0] > deck[e][0] + deck[e+2][0]:266            upcardnum = min(deck[e][0], deck[e+2][0])267            if upcardnum == deck[e][0]:268                upcardsuit = deck[e][1]269                setup2()270            else:271                upcardsuit = deck[e+2][1]272                setup2()273        elif deck[-o - 1][0] + deck[e+1][0] < deck[e][0] + deck[e+2][0]:274            upcardnum = min(deck[-o - 1][0], deck[e+1][0])275            if upcardnum == deck[e+1][0]:276                upcardsuit = deck[e+1][1]277                setup2()278            else:279                upcardsuit = deck[-o - 1][1]280                setup2()281        else:282            upcardnum = min(deck[-o - 1][0], deck[e+1][0], deck[e][0], deck[e+2][0])283            if upcardnum == deck[e][0]:284                upcardsuit = deck[e][1]285                setup2()286            elif upcardnum == deck[e+1][0]:287                upcardsuit = deck[e+1][1]288                setup2()289            elif upcardnum == deck[e+2][0]:290                upcardsuit = deck[e+2][1]291                setup2()292            else:293                upcardsuit = deck[-o - 1][1]294                setup2()                 295296    #if newcard shares a suit with cards 1 & 3:297    else:298        total = max(deck[-o - 1][0] + deck[e][0] + deck[e+2][0], deck[e+1][0])299        if deck[-o - 1][0] + deck[e][0] + deck[e+2][0] > deck[e+1][0]:300            upcardnum = deck[e+1][0]301            upcardsuit = deck[e+1][1]302            setup2()303        else:304            upcardnum = min(deck[-o - 1][0], deck[e][0], deck[e+2][0])305            if upcardnum == deck[-o - 1][0]:306                upcardsuit = deck[-o - 1][1]307                setup2()308            elif upcardnum == deck[e][0]:309                upcardsuit == deck[e][1]310                setup2()311            else:312                upcardsuit = deck[e+2][1]313                setup2()314315def drawcard4():316    global upcardsuit, upcardnum, total317    #if newcard shares no suits:318    if deck[-o - 1][1] != deck[e][1] and deck[-o - 1][1] != deck[e+2][1]:319        total = max(deck[-o - 1][0], deck[e+1][0] + deck[e+2][0], deck[e][0])320        if deck[-o - 1][0] ==  min(deck[-o - 1][0], deck[e+2][0] + deck[e+1][0], deck[e][0]):321            upcardnum = deck[-o - 1][0]322            upcardsuit = deck[-o - 1][1]323            setup2()324        elif deck[e][0] == min(deck[-o - 1][0], deck[e+1][0] + deck[e+2][0], deck[e][0]):325            upcardnum = deck[e][0]326            upcardsuit = deck[e][1]327            setup2()328        else: 329            if deck[e+1][0] < deck[e+2][0]:330                upcardnum = deck[e+1][0]331                upcardsuit = deck[e+1][1]332                setup2()333            else:334                upcardnum = deck[e+2][0]335                upcardsuit = deck[e+2][1]336                setup2()337338    #if newcard shares a suit with the 1st card:339    elif deck[-o - 1][1] == deck[e][1]:340        total = max(deck[-o - 1][0] + deck[e][0], deck[e+1][0] + deck[e+2][0])341        if deck[-o - 1][0] + deck[e][0] > deck[e+1][0] + deck[e+2][0]:342            upcardnum = min(deck[e+1][0], deck[e+2][0])343            if upcardnum == deck[e+1][0]:344                upcardsuit = deck[e+1][1]345                setup2()346            else:347                upcardsuit = deck[e+2][1]348                setup2()349        elif deck[-o - 1][0] + deck[e][0] < deck[e+1][0] + deck[e+2][0]:350            upcardnum = min(deck[-o - 1][0], deck[e][0])351            if upcardnum == deck[e][0]:352                upcardsuit = deck[e][1]353                setup2()354            else:355                upcardsuit = deck[-o - 1][1]356                setup2()357        else:358            upcardnum = min(deck[-o - 1][0], deck[e+1][0], deck[e][0], deck[e+2][0])359            if upcardnum == deck[e][0]:360                upcardsuit = deck[e][1]361                setup2()362            elif upcardnum == deck[e+1][0]:363                upcardsuit = deck[e+1][1]364                setup2()365            elif upcardnum == deck[e+2][0]:366                upcardsuit = deck[e+2][1]367                setup2()368            else:369                upcardsuit = deck[-o - 1][1]370                setup2()                 371372    #if newcard shares a suit with cards 2 & 3:373    else:374        total = max(deck[-o - 1][0] + deck[e+1][0] + deck[e+2][0], deck[e][0])375        if deck[-o - 1][0] + deck[e+1][0] + deck[e+2][0] > deck[e][0]:376            upcardnum = deck[e][0]377            upcardsuit = deck[e][1]378            setup2()379        else:380            upcardnum = min(deck[-o - 1][0], deck[e+1][0], deck[e+2][0])381            if upcardnum == deck[-o - 1][0]:382                upcardsuit = deck[-o - 1][1]383                setup2()384            elif upcardnum == deck[e+1][0]:385                upcardsuit == deck[e+1][1]386                setup2()387            else:388                upcardsuit = deck[e+2][1]389                setup2()390391392393num = int(input('Enter number of players: '))394count = 0395iterations = int(input('Enter number of iterations: '))396all_total = 0397total_scores = []398while count < iterations:399    # 1 =  number, 2 = 10, 3 = jack, 4 = queen, 5 = king, 6 = ace400    deck = [(5, 'Club', 1), (10, 'Club', 2), (11, 'Diamond', 6), (10, 'Heart', 2),(6, 'Spade', 1), (3, 'Spade', 1), (10, 'Club', 3), (10, 'Spade', 2), (2, 'Spade', 1),(8, 'Spade', 1), (7, 'Club', 1), (3, 'Heart', 1), (4, 'Spade', 1), (11, 'Club', 6),(5, 'Diamond', 1), (10, 'Heart', 3), (10, 'Diamond', 2), (9, 'Spade', 1), (9, 'Club', 1),(6, 'Heart', 1), (4, 'Heart', 1), (10, 'Diamond', 3), (3, 'Diamond', 1), (6, 'Club', 1),(11, 'Heart', 6), (10, 'Spade', 3), (10, 'Diamond', 4), (10, 'Spade', 4), (5, 'Spade', 1),(7, 'Heart', 1), (6, 'Diamond', 1), (7, 'Diamond', 1), (4, 'Club', 1), (8, 'Heart', 1),(11, 'Spade', 6), (2, 'Club', 1), (3, 'Club', 1), (10, 'Heart', 4), (10, 'Club', 4),(8, 'Club', 1), (2, 'Heart', 1), (9, 'Diamond', 1), (7, 'Spade', 1), (2, 'Diamond', 1),(10, 'Spade', 5), (4, 'Diamond', 1), (10, 'Heart', 5), (9, 'Heart', 1), (8, 'Diamond', 1),(10, 'Club', 5), (5, 'Heart', 1), (10, 'Diamond', 5)]401    deck = random.sample(deck, k=len(deck))402    scores = []403    404#first draw405    e = 0406    v = 1407408    while v <= num:409        total = 0410        #no matching suit:411        if deck[e+1][1] != deck[e+2][1] and deck[e][1] != deck[e+1][1] and deck[e][1] != deck[e+2][1]:412            total = max(deck[e][0], deck[e+1][0], deck[e+2][0])413            414        #all matching suit:415        elif deck[e+1][1] == deck[e+2][1] and deck[e][1] == deck[e+1][1] and deck[e+1][1] == deck[e+2][1]:416            total = deck[e][0] + deck[e+1][0] + deck[e+2][0]417418        #at least 1 matching suit:419        else:420            if deck[e][1] == deck[e+1][1]:421                total = max(deck[e][0] + deck[e+1][0], deck[e+2][0])422            elif deck[e][1] == deck[e+2][1]:423                total = max(deck[e][0] + deck[e+2][0], deck[e+1][0])424            else:    425                total = max(deck[e+1][0] + deck[e+2][0], deck[e][0])    426        427        scores.append(total)428        total_scores.append(total)429        #print(scores)430        #print(deck[e][0],deck[e][1],deck[e+1][0],deck[e+1][1], deck[e+2][0],deck[e+2][1])431        e = (3*v)432        v += 1433        434435    #2nd draw, 1st player knocks:436437    e = 3438    v = 2439    o = 1440    x = 1441    upcardnum = deck[-o][0]442    upcardsuit = deck[-o][1]443    while v <= (num):444        total = 0445                446#-------------------------------------------------------------------------------------------------------------------------------------------------------------------        447        #all  3 same suit:448        if deck[e+1][1] == deck[e+2][1] and deck[e][1] == deck[e+1][1] and deck[e+1][1] == deck[e+2][1]:449450        #if upcard doesn't share the same suit:451            if upcardsuit != deck[e][1]: 452                #if upcard is greater than or equal to the sum of all 3 cards:453                if upcardnum >= (deck[e][0] + deck[e+1][0] + deck[e+2][0]):454                    total = upcardnum455                    upcardnum = min(deck[e][0], deck[e+1][0], deck[e+2][0])456                    if upcardnum == deck[e][0]:457                        upcardsuit = deck[e][1]458                        setup1()459                        continue460                        461                    elif upcardnum ==  deck[e+1][0]:462                        upcardsuit = deck[e+1][1]463                        setup1()464                        continue465                        466                    else:467                        upcardsuit = deck[e+2][1]468                        setup1()469                        continue470                                            471                #if upcard is not greater than or equal to the sum of all 3 cards:472                else:473                    #if newcard shares a suit:474                    if deck[-o - 1][1] == deck[e][1]:475                        total = (deck[-o-1][0] + deck[e][0] + deck[e+1][0] + deck[e+2][0]) - min(deck[-o-1][0], deck[e][0], deck[e+1][0], deck[e+2][0])476                        upcardnum = min(deck[-o-1][0], deck[e][0], deck[e+1][0], deck[e+2][0])477                        upcardsuit = deck[e][1]478                        setup2()479                        continue480                        481                    #if newcard doesn't share a suit:482                    else:483                        #if newcard is greater than or equal to the sum of the three cards:484                        if deck[-o - 1][0] >= (deck[e][0] + deck[e+1][0] + deck[e+2][0]):485                            total = deck[-o - 1][0]486                            upcardnum = min(deck[e][0], deck[e+1][0], deck[e+2][0]) 487                            upcardsuit = deck[e][1]488                            setup2()489                            continue490                            491                        #if newcard is not greater than or equal to the sum of the three cards:492                        else:493                            total = deck[e][0] + deck[e+1][0] + deck[e+2][0]494                            upcardnum = deck[-o - 1][0]495                            upcardsuit = deck[-o - 1][1]496                            setup2()497                            continue498                        499            #if upcard is also same suit:500            elif upcardsuit == deck[e][1]:501                #if upcard is greater than at least 1 of the cards[otherwise newcard]:502                if upcardnum > deck[e][0] or upcardnum > deck[e+1][0] or upcardnum > deck[e+2][0]:503                    total = (total + deck[e][0] + deck[e+1][0] + deck[e+2][0] + upcardnum) - min(deck[e][0], deck[e+1][0], deck[e+2][0])504                    upcardnum = min(deck[e][0], deck[e+1][0], deck[e+2][0])505506                    if upcardnum == deck[e][0]:507                        upcardsuit = deck[e][1]508                        setup1()509                        continue510                        511                    elif upcardnum == deck[e+1][0]:512                        upcardsuit = deck[e+1][1]513                        setup1()514                        continue515                        516                    else:517                        upcardsuit = deck[e+2][1]518                        setup1()519                        continue520                        521                #if upcard is not greater than any of the cards:522                else:523                    #if newcard shares a suit:524                    if deck[-o - 1][1] == deck[e][1]:525                        total = (deck[-o-1][0] + deck[e][0] + deck[e+1][0] + deck[e+2][0]) - min(deck[-o-1][0], deck[e][0], deck[e+1][0], deck[e+2][0])526                        upcardnum = min(deck[-o-1][0], deck[e][0], deck[e+1][0], deck[e+2][0])527                        upcardsuit = deck[e][1]528                        setup2()529                        continue530                        531                    #if newcard doesn't share a suit:532                    else:533                        #if newcard is greater than or equal to the sum of the three cards:534                        if deck[-o - 1][0] >= (deck[e][0] + deck[e+1][0] + deck[e+2][0]):535                            total = deck[-o - 1][0]536                            upcardnum = min(deck[e][0], deck[e+1][0], deck[e+2][0]) 537                            upcardsuit = deck[e][1]538                            setup2()539                            continue540                            541                        #if newcard is not greater than or equal to the sum of the three cards:542                        else:543                            total = deck[e][0] + deck[e+1][0] + deck[e+2][0]544                            upcardnum = deck[-o - 1][0]545                            upcardsuit = deck[-o - 1][1]546                            setup2()547                            continue548                            549#-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------                        550        #no same suit:551        if deck[e][1] != deck[e+2][1] and deck[e][1] != deck[e+1][1] and deck[e+1][1] != deck[e+2][1]:552            #if cards are three of a kind:553            #if the cards are below 10:554            if deck[e][0] < 10:555                #if all 3 card values are the same:556                if deck[e][0] == deck[e+1][0] and deck[e][0] == deck[e][0] == deck[e+2][0]:557                    total = 30.5558                    upcardnum = deck[-o - 1][0]559                    upcardsuit = deck[-o - 1][1]560                    setup2()561                    continue 562            #if the cards are 10 or face cards:563            if deck[e][0] == 10 or deck[e][0] == 11:564                #if all 3 card values are the same:565                if deck[e][2] == deck[e+1][2]  and deck[e][2] == deck[e+2][2]:566                    total = 30.5567                    upcardnum = deck[-o - 1][0]568                    upcardsuit = deck[-o - 1][1]569                    setup2()570                    continue  571572            #if upcard doesn't share a suit:573            if upcardsuit != deck[e][1] and upcardsuit != deck[e+1][1] and upcardsuit != deck[e+2][1]:574                #If upcard is the highest [COULD FLESH OUT MORE: perhaps if upcard is lower than 5 then you choose from the deck, not just the upcard]:575                if upcardnum > deck[e][0] and upcardnum > deck[e+1][0] and upcardnum > deck[e+2][0]:576                    total = upcardnum577                    upcardnum = min(deck[e][0], deck[e+1][0], deck[e+2][0])578                    if upcardnum == deck[e][0]:579                        upcardsuit = deck[e][1]580                        setup1()581                        continue582                    elif upcardnum == deck[e+1][0]:583                         upcardsuit = deck[e+1][1]
...

smartplots2_setup.py

Source:smartplots2_setup.py

1import matplotlib2import sys3import pandas as pd4import matplotlib.pyplot as plt5import numpy as np6import matplotlib.patches as mpatches7import matplotlib.lines as mlines8import os9plt.style.use('seaborn-colorblind')10# plt.style.use('ggplot')11plt.rcParams['axes.edgecolor'] = 'black'12plt.rcParams['axes.facecolor'] = 'white'13plt.rcParams['savefig.facecolor'] = 'white'14plt.rcParams['savefig.edgecolor'] = 'black'15colors = {'blue': '#377eb8', 'green': '#227222', 'orange': '#C66200', 'purple': '#470467', 'red': '#B30C0C',16          'yellow': '#C6A600', 'light.green': '#C0E0C0', 'magenta': '#D0339D', 'dark.blue': '#23128F',17          'brown': '#542D06', 'grey': '#8A8A8A', 'dark.grey': '#2D2D2D', 'light.yellow': '#FFE664',18          'light.purple': '#9C50C0', 'light.orange': '#FFB164', 'black': '#000000'}19mode_colors = {'RH': colors['red'],20               'Car': colors['grey'],21               'Walk': colors['green'],22               'Transit': colors['blue'],23               'RHT': colors['light.purple'],24               'RHP': 'mediumorchid',25               'CAV': colors['light.yellow'],26               'Bike': colors['light.orange'],27               'NM': colors['light.orange'],28               'electricity': colors['blue'],29               'gas': colors['purple'],30               'diesel': colors['yellow']}31def getDfForPlt(_plt_setup2, _output_folder):32    if not os.path.exists('{}/makeplots'.format(_output_folder)):33        os.makedirs('{}/makeplots'.format(_output_folder))34    top_labels = _plt_setup2['top_labels']35    years = _plt_setup2['scenarios_year']36    ids = _plt_setup2['scenarios_id']37    iterations = _plt_setup2['scenarios_itr']38    top_labels_xpos = [1]39    bottom_labels_xpos = [1]40    for i in range(1, len(top_labels)):41        top_labels_xpos.append(top_labels_xpos[i-1] + 1 + i % 2)42        if i % 2 == 0:43            bottom_labels_xpos.append((top_labels_xpos[i] + top_labels_xpos[i-1])/2)44    df = pd.DataFrame()45    for i in range(len(ids)):46        iteration = iterations[i]47        year = years[i]48        id = ids[i]49        metrics_file = "{}/{}.{}.metrics-final.csv".format(_output_folder, year, iteration)50        df_temp = pd.read_csv(metrics_file).fillna(0)51        df = pd.concat([df, df_temp[df_temp['Rank'] == id]])52    return (df.sort_values(by=['Rank']), top_labels_xpos, bottom_labels_xpos)53# plots54def pltModeSplitByTrips(_plt_setup2, _output_folder):55    plot_size = _plt_setup2['plot_size']56    top_labels = _plt_setup2['top_labels']57    bottom_labels = _plt_setup2['bottom_labels']58    nb_scenarios = len(_plt_setup2['scenarios_id'])59    angle = 1260    (df, top_labels_xpos, bottom_labels_xpos) = getDfForPlt(_plt_setup2, _output_folder)61    output_png = '{}/makeplots/{}.modesplit_trips.png'.format(_output_folder, _plt_setup2['name'])62    output_csv = '{}/makeplots/{}.modesplit_trips.csv'.format(_output_folder, _plt_setup2['name'])63    createColumnIfNotExist(df, 'cav_counts', 0)64    data = pd.DataFrame(65        {'transit': (df['drive_transit_counts'].values + df['ride_hail_transit_counts'].values + df['walk_transit_counts'].values),66         'car': df['car_counts'].values,67         'cav': df['cav_counts'].values,68         'rh': df['ride_hail_counts'].values,69         'rhp': df['ride_hail_pooled_counts'].values,70         'walk': df['walk_counts'].values,71         'bike': df['bike_counts'].values72         })73    data = data.div(data.sum(axis=1), axis=0)74    data['scenario'] = df['Scenario'].values.copy()75    data['technology'] = df['Technology'].values.copy()76    data.to_csv(output_csv)77    plt.figure(figsize=plot_size)78    plt_transit = plt.bar(x=top_labels_xpos, height=data['transit'], color=mode_colors['Transit'])79    plt_car = plt.bar(x=top_labels_xpos, height=data['car'], bottom=data['transit'], color=mode_colors['Car'])80    plt_cav = plt.bar(x=top_labels_xpos, height=data['cav'], bottom=data[['transit', 'car']].sum(axis=1), color=mode_colors['CAV'])81    plt_rh = plt.bar(x=top_labels_xpos, height=data['rh'], bottom=data[['transit', 'car', 'cav']].sum(axis=1), color=mode_colors['RH'])82    plt_rhp = plt.bar(x=top_labels_xpos, height=data['rhp'], bottom=data[['transit', 'car', 'cav', 'rh']].sum(axis=1), color=mode_colors['RHP'])83    plt_bike = plt.bar(x=top_labels_xpos, height=data['bike'], bottom=data[['transit', 'car', 'cav', 'rh', 'rhp']].sum(axis=1), color=mode_colors['Bike'])84    plt_walk = plt.bar(x=top_labels_xpos, height=data['walk'], bottom=data[['transit', 'car', 'cav', 'rh', 'rhp', 'bike']].sum(axis=1), color=mode_colors['Walk'])85    plt.xticks(bottom_labels_xpos, bottom_labels, rotation=angle)86    plt.legend((plt_transit, plt_car, plt_cav, plt_rh, plt_rhp, plt_bike, plt_walk),87               ('Transit', 'Car', 'CAV', 'Ridehail', 'Ridehail Pool', 'Bike', 'Walk'),88               labelspacing=-2.5, bbox_to_anchor=(1.05, 0.5), frameon=False)89    ax = plt.gca()90    ax.grid(False)91    for ind in range(nb_scenarios):92        plt.text(top_labels_xpos[ind], 1.02, top_labels[ind], ha='center')93    ax.set_ylim((0, 1.0))94    plt.ylabel('Portion of Trips')95    plt.savefig(output_png, transparent=True, bbox_inches='tight', dpi=200, facecolor='white')96    plt.clf()97    plt.close()98def createColumnIfNotExist(df, name, value):99    if name not in df.columns:100        df[name] = value101    else:102        df[name].fillna(0, inplace=True)103def tableSummary(_plt_setup2, _output_folder):104    factor = _plt_setup2['expansion_factor']105    (df, top_labels_xpos, bottom_labels_xpos) = getDfForPlt(_plt_setup2, _output_folder)106    output_csv = '{}/makeplots/{}.summary.csv'.format(_output_folder, _plt_setup2['name'])107    createColumnIfNotExist(df, 'VMT_car_CAV', 0)108    createColumnIfNotExist(df, 'VMT_car_RH_CAV', 0)109    tot_vmt_transit = (df['VMT_bus'].values+df['VMT_cable_car'].values+df['VMT_ferry'].values+df['VMT_rail'].values +110                       df['VMT_subway'].values+df['VMT_tram'].values)111    tot_vmt_non_transit = (df['VMT_car'].values+df['VMT_car_CAV'].values+df['VMT_car_RH'].values +112                           df['VMT_car_RH_CAV'].values + df['VMT_walk'].values+df['VMT_bike'].values)113    114    tot_ldv_vmt = (df['VMT_car'].values+df['VMT_car_CAV'].values+df['VMT_car_RH'].values + df['VMT_car_RH_CAV'].values)115    createColumnIfNotExist(df, 'personTravelTime_cav', 0)116    tot_pht = (df['personTravelTime_bike'].values+df['personTravelTime_car'].values+df['personTravelTime_cav'].values +117               df['personTravelTime_drive_transit'].values+df['personTravelTime_mixed_mode'].values +118               df['personTravelTime_onDemandRide'].values+df['personTravelTime_onDemandRide_pooled'].values +119               df['personTravelTime_onDemandRide_transit'].values+df['personTravelTime_walk'].values +120               df['personTravelTime_walk_transit'].values) * factor121    tot_energy = (df['totalEnergy_Biodiesel'].values+df['totalEnergy_Diesel'].values +122                  df['totalEnergy_Electricity'].values+df['totalEnergy_Gasoline'].values) * factor123    data = pd.DataFrame(124        {'VMT Total (10^6)': (tot_vmt_transit + tot_vmt_non_transit * factor) / 1000000,125         'VMT per Capita': (tot_vmt_transit+tot_vmt_non_transit)/df['population'],126         'VMT Light Duty Total (10^6)': tot_ldv_vmt * factor / 1000000,127         'VMT Light Duty per Capita': tot_ldv_vmt/df['population'],128         'Driving Speed [miles/h]': tot_ldv_vmt/df['total_vehicleHoursTravelled_LightDutyVehicles'],129         'Person Hours (10^6)': tot_pht / 60 / 1000000,130         'PEV (%)': 0,131         'Vehicle Energy (GJ)': tot_energy / 1000000000,132         'MEP': 0133         })134    data['Scenario'] = df['Scenario'].values.copy()135    data['Technology'] = df['Technology'].values.copy()136    data['year'] = _plt_setup2['scenarios_year']137    data.to_csv(output_csv)138def pltLdvRhOccupancy(_plt_setup2, _output_folder):139    plot_size = _plt_setup2['plot_size']140    top_labels = _plt_setup2['top_labels']141    bottom_labels = _plt_setup2['bottom_labels']142    nb_scenarios = len(_plt_setup2['scenarios_id'])143    factor = _plt_setup2['expansion_factor']144    angle = 12145    (df, top_labels_xpos, bottom_labels_xpos) = getDfForPlt(_plt_setup2, _output_folder)146    output_png = '{}/makeplots/{}.ldv_rh_occupancy.png'.format(_output_folder, _plt_setup2['name'])147    output_csv = '{}/makeplots/{}.ldv_rh_occupancy.csv'.format(_output_folder, _plt_setup2['name'])148    createColumnIfNotExist(df, 'PMT_car_CAV', 0)149    createColumnIfNotExist(df, 'PMT_car_RH_CAV', 0)150    createColumnIfNotExist(df, 'PMT_car_RH_CAV_shared', 0)151    createColumnIfNotExist(df, 'PMT_car_RH_CAV_shared_2p', 0)152    createColumnIfNotExist(df, 'PMT_car_RH_CAV_shared_3p', 0)153    createColumnIfNotExist(df, 'PMT_car_RH_CAV_shared_4p', 0)154    createColumnIfNotExist(df, 'VMT_car_CAV', 0)155    createColumnIfNotExist(df, 'VMT_car_RH_CAV', 0)156    data = pd.DataFrame(157        {158            'non_rh_ldv': df[['PMT_car', 'PMT_car_CAV']].sum(axis=1),159            'rh_1p': df[['PMT_car_RH', 'PMT_car_RH_CAV']].sum(axis=1)-df[['PMT_car_RH_shared', 'PMT_car_RH_CAV_shared']].sum(axis=1),160            'rh_2p': df[['PMT_car_RH_shared_2p', 'PMT_car_RH_CAV_shared_2p']].sum(axis=1),161            'rh_3p': df[['PMT_car_RH_shared_3p', 'PMT_car_RH_CAV_shared_3p']].sum(axis=1),162            'rh_4p': df[['PMT_car_RH_shared_4p', 'PMT_car_RH_CAV_shared_4p']].sum(axis=1)163        })164    data = data.div(df[['VMT_car', 'VMT_car_CAV', 'VMT_car_RH', 'VMT_car_RH_CAV']].sum(axis=1), axis=0)165    height_all = data.sum(axis=1)166    data['scenario'] = df['Scenario'].values.copy()167    data['technology'] = df['Technology'].values.copy()168    data.to_csv(output_csv)169    plt.figure(figsize=plot_size)170    plt_non_rh_ldv = plt.bar(x=top_labels_xpos, height=data['non_rh_ldv'], color=colors['grey'])171    plt_rh_1p = plt.bar(x=top_labels_xpos, height=data['rh_1p'], bottom=data['non_rh_ldv'], color=mode_colors['RH'])172    plt_rh_shared = plt.bar(x=top_labels_xpos, height=data[['rh_2p', 'rh_3p', 'rh_4p']].sum(axis=1), bottom=data[['non_rh_ldv', 'rh_1p']].sum(axis=1), color=mode_colors['RHP'])173    plt.bar(x=top_labels_xpos, height=data['rh_2p'], bottom=data[['non_rh_ldv', 'rh_1p']].sum(axis=1), hatch='xxx', fill=False, linewidth=0)174    plt.bar(x=top_labels_xpos, height=data['rh_3p'], bottom=data[['non_rh_ldv', 'rh_1p', 'rh_2p']].sum(axis=1), hatch='|||', fill=False, linewidth=0)175    plt.bar(x=top_labels_xpos, height=data['rh_4p'], bottom=data[['non_rh_ldv', 'rh_1p', 'rh_2p', 'rh_3p']].sum(axis=1), hatch='....', fill=False, linewidth=0)176    shared_2p = mpatches.Patch(facecolor='white', label='The white data', hatch='xxx')177    shared_3p = mpatches.Patch(facecolor='white', label='The white data', hatch='|||')178    shared_4p = mpatches.Patch(facecolor='white', label='The white data', hatch='....')179    plt.xticks(bottom_labels_xpos, bottom_labels, rotation=angle)180    plt.legend((plt_non_rh_ldv, plt_rh_1p, plt_rh_shared, shared_2p, shared_3p, shared_4p),181               ('non-Ridehail LDV', 'Ridehail', 'Ridehail Pool', '2 passengers', '3 passengers', '4+ passengers'),182               labelspacing=-2.5, bbox_to_anchor=(1.05, 0.5), frameon=False)183    plt.axhline(y=1.0, color='black', linestyle='dashed', lw=0.5, alpha=0.2)184    ax = plt.gca()185    ax.grid(False)186    max_value = max(height_all)187    ax.set_ylim((0, max_value))188    for ind in range(nb_scenarios):189        plt.text(top_labels_xpos[ind], max_value + 0.02*max_value,  top_labels[ind], ha='center')190    plt.ylabel('Distance Based Occupancy')191    plt.savefig(output_png, transparent=True, bbox_inches='tight', dpi=200, facecolor='white')192    plt.clf()193    plt.close()194def pltLdvRhOccupancyByVMT(_plt_setup2, _output_folder):195    plot_size = _plt_setup2['plot_size']196    top_labels = _plt_setup2['top_labels']197    bottom_labels = _plt_setup2['bottom_labels']198    nb_scenarios = len(_plt_setup2['scenarios_id'])199    factor = _plt_setup2['expansion_factor']200    scale = 1 / 1000000201    angle = 12202    (df, top_labels_xpos, bottom_labels_xpos) = getDfForPlt(_plt_setup2, _output_folder)203    output_png = '{}/makeplots/{}.ldv_rh_occupancy_vmt.png'.format(_output_folder, _plt_setup2['name'])204    output_csv = '{}/makeplots/{}.ldv_rh_occupancy_vmt.csv'.format(_output_folder, _plt_setup2['name'])205    createColumnIfNotExist(df, 'VMT_car_CAV', 0)206    createColumnIfNotExist(df, 'VMT_car_RH_CAV', 0)207    createColumnIfNotExist(df, 'VMT_car_RH_CAV_shared', 0)208    createColumnIfNotExist(df, 'VMT_car_CAV_empty', 0)209    createColumnIfNotExist(df, 'VMT_car_CAV_shared', 0)210    createColumnIfNotExist(df, 'VMT_car_RH_CAV_empty', 0)211    createColumnIfNotExist(df, 'VMT_car_RH_CAV_shared_2p', 0)212    createColumnIfNotExist(df, 'VMT_car_RH_CAV_shared_3p', 0)213    createColumnIfNotExist(df, 'VMT_car_RH_CAV_shared_4p', 0)214    data = pd.DataFrame(215        {216            'car': (df[['VMT_car', 'VMT_car_CAV']].sum(axis=1)-df[['VMT_car_shared', 'VMT_car_CAV_shared']].sum(axis=1)) * factor * scale,217            'car_shared': df[['VMT_car_shared', 'VMT_car_CAV_shared']].sum(axis=1) * factor * scale,218            'rh': (df[['VMT_car_RH', 'VMT_car_RH_CAV']].sum(axis=1)-df[['VMT_car_RH_shared', 'VMT_car_RH_CAV_shared']].sum(axis=1)) * factor * scale,219            'rh_2p': df[['VMT_car_RH_shared_2p', 'VMT_car_RH_CAV_shared_2p']].sum(axis=1) * factor * scale,220            'rh_3p': df[['VMT_car_RH_shared_3p', 'VMT_car_RH_CAV_shared_3p']].sum(axis=1) * factor * scale,221            'rh_4p': df[['VMT_car_RH_shared_4p', 'VMT_car_RH_CAV_shared_4p']].sum(axis=1) * factor * scale222        })223    height_all = data.sum(axis=1)224    data['car_empty'] = df[['VMT_car_empty', 'VMT_car_CAV_empty']].sum(axis=1) * factor * scale225    data['rh_empty'] = df[['VMT_car_RH_empty', 'VMT_car_RH_CAV_empty']].sum(axis=1) * factor * scale226    data['scenario'] = df['Scenario'].values.copy()227    data['technology'] = df['Technology'].values.copy()228    data.to_csv(output_csv)229    plt.figure(figsize=plot_size)230    plt_car = plt.bar(x=top_labels_xpos, height=data['car'], color=mode_colors['Car'])231    plt.bar(x=top_labels_xpos, height=-data['car_empty'], bottom=data['car'], hatch='///', fill=False, linewidth=0)232    plt_car_shared = plt.bar(x=top_labels_xpos, height=data['car_shared'], bottom=data['car'], color=mode_colors['CAV'])233    plt_rh = plt.bar(x=top_labels_xpos, height=data['rh'], bottom=data[['car', 'car_shared']].sum(axis=1), color=mode_colors['RH'])234    plt.bar(x=top_labels_xpos, height=-data['rh_empty'], bottom=data[['car', 'car_shared', 'rh']].sum(axis=1), hatch='///', fill=False, linewidth=0)235    plt_rh_shared = plt.bar(x=top_labels_xpos, height=data[['rh_2p', 'rh_3p', 'rh_4p']].sum(axis=1), bottom=data[['car', 'car_shared', 'rh']].sum(axis=1), color=mode_colors['RHP'])236    plt.bar(x=top_labels_xpos, height=data['rh_2p'], bottom=data[['car', 'car_shared', 'rh']].sum(axis=1), hatch='xxx', fill=False, linewidth=0)237    plt.bar(x=top_labels_xpos, height=data['rh_3p'], bottom=data[['car', 'car_shared', 'rh', 'rh_2p']].sum(axis=1), hatch='|||', fill=False, linewidth=0)238    plt.bar(x=top_labels_xpos, height=data['rh_4p'], bottom=data[['car', 'car_shared', 'rh', 'rh_2p', 'rh_3p']].sum(axis=1), hatch='....', fill=False, linewidth=0)239    empty = mpatches.Patch(facecolor='white', label='The white data', hatch='///')240    shared_2p = mpatches.Patch(facecolor='white', label='The white data', hatch='xxx')241    shared_3p = mpatches.Patch(facecolor='white', label='The white data', hatch='|||')242    shared_4p = mpatches.Patch(facecolor='white', label='The white data', hatch='....')243    plt.xticks(bottom_labels_xpos, bottom_labels, rotation=angle)244    plt.legend((plt_car, plt_car_shared, plt_rh, plt_rh_shared, shared_2p, shared_3p, shared_4p, empty),245               ('Car/CAV', 'CAV Shared', 'Ridehail', 'Ridehail Pool', '2 passengers', '3 passengers', '4+ passengers', 'Deadheading'),246               labelspacing=-2.5, bbox_to_anchor=(1.05, 0.5), frameon=False)247    plt.axhline(y=1.0, color='black', linestyle='dashed', lw=0.5, alpha=0.2)248    ax = plt.gca()249    ax.grid(False)250    max_value = max(height_all)251    ax.set_ylim((0, max_value))252    for ind in range(nb_scenarios):253        plt.text(top_labels_xpos[ind], max_value + 0.02*max_value,  top_labels[ind], ha='center')254    plt.ylabel('Light Duty Vehicle Miles Traveled (millions)')255    plt.savefig(output_png, transparent=True, bbox_inches='tight', dpi=200, facecolor='white')256    plt.clf()257    plt.close()258def pltLdvPersonHourTraveled(_plt_setup2, _output_folder):259    plot_size = _plt_setup2['plot_size']260    top_labels = _plt_setup2['top_labels']261    bottom_labels = _plt_setup2['bottom_labels']262    nb_scenarios = len(_plt_setup2['scenarios_id'])263    factor = _plt_setup2['expansion_factor']264    scale = 1 / 1000000 / 60265    angle = 12266    (df, top_labels_xpos, bottom_labels_xpos) = getDfForPlt(_plt_setup2, _output_folder)267    output_png = '{}/makeplots/{}.ldv_person_hours_traveled.png'.format(_output_folder, _plt_setup2['name'])268    output_csv = '{}/makeplots/{}.ldv_person_hours_traveled.csv'.format(_output_folder, _plt_setup2['name'])269    createColumnIfNotExist(df, 'personTravelTime_cav', 0)270    data = pd.DataFrame(271        {'car': df['personTravelTime_car'].values * factor * scale,272         'cav': df['personTravelTime_cav'].values * factor * scale,273         'rh': df['personTravelTime_onDemandRide'].values * factor * scale,274         'rhp': df['personTravelTime_onDemandRide_pooled'].values * factor * scale275         })276    height_all = data.sum(axis=1)277    data['scenario'] = df['Scenario'].values.copy()278    data['technology'] = df['Technology'].values.copy()279    data.to_csv(output_csv)280    plt.figure(figsize=plot_size)281    plt_car = plt.bar(x=top_labels_xpos, height=data['car'], color=mode_colors['Car'])282    plt_cav = plt.bar(x=top_labels_xpos, height=data['cav'], bottom=data['car'], color=mode_colors['CAV'])283    plt_rh = plt.bar(x=top_labels_xpos, height=data['rh'], bottom=data[['car', 'cav']].sum(axis=1), color=mode_colors['RH'])284    plt_rhp = plt.bar(x=top_labels_xpos, height=data['rhp'], bottom=data[['car', 'cav', 'rh']].sum(axis=1), color=mode_colors['RHP'])285    plt.xticks(bottom_labels_xpos, bottom_labels, rotation=angle)286    plt.legend((plt_car, plt_cav, plt_rh, plt_rhp),287               ('Car', 'CAV', 'Ridehail', 'Ridehail Pool'),288               labelspacing=-2.5, bbox_to_anchor=(1.05, 0.5), frameon=False)289    ax = plt.gca()290    ax.grid(False)291    max_value = max(height_all)292    ax.set_ylim((0, max_value))293    for ind in range(nb_scenarios):294        plt.text(top_labels_xpos[ind], max_value + 0.02*max_value, top_labels[ind], ha='center')295    plt.ylabel('Person Hours Traveled in LDV (millions)')296    plt.savefig(output_png, transparent=True, bbox_inches='tight', dpi=200, facecolor='white')297    plt.clf()298    plt.close()299 300    301def pltModeSplitInPMT(_plt_setup2, _output_folder):302    pltModeSplitInPMT_internal(_plt_setup2, _output_folder,_plt_setup2['expansion_factor'],'modesplit_pmt',1 / 1000000,'Person Miles Traveled (millions)')303    304def pltModeSplitInPMTPerCapita(_plt_setup2, _output_folder):305    (df, top_labels_xpos, bottom_labels_xpos) = getDfForPlt(_plt_setup2, _output_folder)306    pltModeSplitInPMT_internal(_plt_setup2, _output_folder,1/df['population'].values,'modesplit_pmt_per_capita',1,'Person Miles Traveled')307def pltModeSplitInPMT_internal(_plt_setup2, _output_folder,factor,fileNameLabel,scale,ylabel):308    plot_size = _plt_setup2['plot_size']309    top_labels = _plt_setup2['top_labels']310    bottom_labels = _plt_setup2['bottom_labels']311    nb_scenarios = len(_plt_setup2['scenarios_id'])312    #factor = _plt_setup2['expansion_factor']313    #scale = 1 / 1000000314    angle = 12315    (df, top_labels_xpos, bottom_labels_xpos) = getDfForPlt(_plt_setup2, _output_folder)316    output_png = '{}/makeplots/{}.{}.png'.format(_output_folder, _plt_setup2['name'],fileNameLabel)317    output_csv = '{}/makeplots/{}.{}.csv'.format(_output_folder, _plt_setup2['name'],fileNameLabel)318    createColumnIfNotExist(df, 'PMT_car_CAV', 0)319    createColumnIfNotExist(df, 'PMT_car_RH_CAV', 0)320    data = pd.DataFrame(321        {'transit': (df['PMT_bus'].values+df['PMT_ferry'].values+df['PMT_rail'].values+df['PMT_subway'].values+322                     df['PMT_tram'].values+df['PMT_cable_car'].values) * factor * scale,323         'car': df['PMT_car'].values * factor * scale,324         'cav': df['PMT_car_CAV'].values * factor * scale,325         'rh': (df['PMT_car_RH'].values+df['PMT_car_RH_CAV'].values) * factor * scale,326         'walk': df['PMT_walk'].values * factor * scale,327         'bike': df['PMT_bike'].values * factor * scale328         })329    height_all = data.sum(axis=1)330    data['scenario'] = df['Scenario'].values.copy()331    data['technology'] = df['Technology'].values.copy()332    data.to_csv(output_csv)333    plt.figure(figsize=plot_size)334    plt_transit = plt.bar(x=top_labels_xpos, height=data['transit'], color=mode_colors['Transit'])335    plt_car = plt.bar(x=top_labels_xpos, height=data['car'], bottom=data['transit'], color=mode_colors['Car'])336    plt_cav = plt.bar(x=top_labels_xpos, height=data['cav'], bottom=data[['transit', 'car']].sum(axis=1), color=mode_colors['CAV'])337    plt_rh = plt.bar(x=top_labels_xpos, height=data['rh'], bottom=data[['transit', 'car', 'cav']].sum(axis=1), color=mode_colors['RH'])338    plt_bike = plt.bar(x=top_labels_xpos, height=data['bike'], bottom=data[['transit', 'car', 'cav', 'rh']].sum(axis=1), color=mode_colors['Bike'])339    plt_walk = plt.bar(x=top_labels_xpos, height=data['walk'], bottom=data[['transit', 'car', 'cav', 'rh', 'bike']].sum(axis=1), color=mode_colors['Walk'])340    plt.xticks(bottom_labels_xpos, bottom_labels, rotation=angle)341    plt.legend((plt_transit, plt_car, plt_cav, plt_rh, plt_bike, plt_walk),342               ('Transit', 'Car', 'CAV', 'Ridehail', 'Bike', 'Walk'),343               labelspacing=-2.5, bbox_to_anchor=(1.05, 0.5), frameon=False)344    ax = plt.gca()345    ax.grid(False)346    max_value = max(height_all)347    ax.set_ylim((0, max_value))348    for ind in range(nb_scenarios):349        plt.text(top_labels_xpos[ind], max_value + 0.02*max_value, top_labels[ind], ha='center')350    plt.ylabel(ylabel)351    plt.savefig(output_png, transparent=True, bbox_inches='tight', dpi=200, facecolor='white')352    plt.clf()353    plt.close()354def pltModeSplitInVMT(_plt_setup2, _output_folder):355    pltModeSplitInVMT_internal(_plt_setup2, _output_folder,_plt_setup2['expansion_factor'],'modesplit_vmt',1 / 1000000,'Vehicle Miles Traveled (millions)',1)356    357def pltModeSplitInVMTPerCapita(_plt_setup2, _output_folder):358    (df, top_labels_xpos, bottom_labels_xpos) = getDfForPlt(_plt_setup2, _output_folder)359    pltModeSplitInVMT_internal(_plt_setup2, _output_folder,1/df['population'].values,'modesplit_vmt_per_capita',1,'Vehicle Miles Traveled',1/_plt_setup2['expansion_factor']/df['population'].values)360    361def pltModeSplitInVMT_internal(_plt_setup2, _output_folder,factor,fileNameLabel,scale,ylabel,transitFactor):362    plot_size = _plt_setup2['plot_size']363    top_labels = _plt_setup2['top_labels']364    bottom_labels = _plt_setup2['bottom_labels']365    nb_scenarios = len(_plt_setup2['scenarios_id'])366    #factor = _plt_setup2['expansion_factor']367    #scale = 1 / 1000000368    angle = 12369    (df, top_labels_xpos, bottom_labels_xpos) = getDfForPlt(_plt_setup2, _output_folder)370    output_png = '{}/makeplots/{}.{}.png'.format(_output_folder, _plt_setup2['name'],fileNameLabel)371    output_csv = '{}/makeplots/{}.{}.csv'.format(_output_folder, _plt_setup2['name'],fileNameLabel)372    createColumnIfNotExist(df, 'VMT_car_CAV', 0)373    createColumnIfNotExist(df, 'VMT_car_RH_CAV', 0)374    createColumnIfNotExist(df, 'VMT_car_RH_CAV_shared', 0)375    createColumnIfNotExist(df, 'VMT_car_CAV_empty', 0)376    createColumnIfNotExist(df, 'VMT_car_CAV_shared', 0)377    createColumnIfNotExist(df, 'VMT_car_RH_CAV_empty', 0)378    data = pd.DataFrame(379    {'transit': (df['VMT_bus'].values+df['VMT_ferry'].values+df['VMT_rail'].values+df['VMT_subway'].values+380                 df['VMT_tram'].values+df['VMT_cable_car'].values) * scale * transitFactor,381     'car': df['VMT_car'].values * factor * scale,382     'cav': df['VMT_car_CAV'].values * factor * scale,383     'rh': (df['VMT_car_RH'].values+df['VMT_car_RH_CAV'].values-df['VMT_car_RH_shared'].values-df['VMT_car_RH_CAV_shared'].values) * factor * scale,384     'rhp':(df['VMT_car_RH_shared'].values + df['VMT_car_RH_CAV_shared'].values) * factor * scale,385     'nm': (df['VMT_walk'].values+df['VMT_bike'].values) * factor * scale386     })387    height_all = data.sum(axis=1)388    data['cav_empty'] = df['VMT_car_CAV_empty'].values * factor * scale389    data['cav_shared'] = df['VMT_car_CAV_shared'].values * factor * scale390    data['rh_empty'] = (df['VMT_car_RH_empty'].values + df['VMT_car_RH_CAV_empty'].values) * factor * scale391    data['scenario'] = df['Scenario'].values.copy()392    data['technology'] = df['Technology'].values.copy()393    data.to_csv(output_csv)394    plt.figure(figsize=plot_size)395    plt_transit = plt.bar(x=top_labels_xpos, height=data['transit'], color=mode_colors['Transit'])396    plt_car = plt.bar(x=top_labels_xpos, height=data['car'], bottom=data['transit'], color=mode_colors['Car'])397    plt_cav = plt.bar(x=top_labels_xpos, height=data['cav'], bottom=data[['transit', 'car']].sum(axis=1), color=mode_colors['CAV'])398    plt_rh = plt.bar(x=top_labels_xpos, height=data['rh'], bottom=data[['transit', 'car', 'cav']].sum(axis=1), color=mode_colors['RH'])399    plt_rhp = plt.bar(x=top_labels_xpos, height=data['rhp'], bottom=data[['transit', 'car', 'cav', 'rh']].sum(axis=1), color=mode_colors['RHP'])400    plt_nm = plt.bar(x=top_labels_xpos, height=data['nm'], bottom=data[['transit', 'car', 'cav', 'rh', 'rhp']].sum(axis=1), color=mode_colors['NM'])401    empty = mpatches.Patch(facecolor='white', label='The white data', hatch='///')402    plt.bar(x=top_labels_xpos, height=-data['cav_empty'], bottom=data[['transit', 'car', 'cav']].sum(axis=1), hatch='///', fill=False, linewidth=0)403    plt.bar(x=top_labels_xpos, height=-data['rh_empty'], bottom=data[['transit', 'car', 'cav', 'rh']].sum(axis=1), hatch='///', fill=False, linewidth=0)404    plt.xticks(bottom_labels_xpos, bottom_labels, rotation=angle)405    plt.legend((plt_transit, plt_car, plt_cav, plt_rh, plt_rhp, plt_nm, empty),406               ('Transit', 'Car', 'CAV', 'Ridehail', 'Ridehail Pool', 'NonMotorized', 'Deadheading'),407               labelspacing=-2.5, bbox_to_anchor=(1.05, 0.5), frameon=False)408    ax = plt.gca()409    ax.grid(False)410    max_value = max(height_all)411    ax.set_ylim((0, max_value))412    for ind in range(nb_scenarios):413        plt.text(top_labels_xpos[ind], max_value + 0.02*max_value, top_labels[ind], ha='center')414    plt.ylabel(ylabel)415    plt.savefig(output_png, transparent=True, bbox_inches='tight', dpi=200, facecolor='white')416    plt.clf()417    plt.close()418def pltLdvTechnologySplitInVMT(_plt_setup2, _output_folder):419    plot_size = _plt_setup2['plot_size']420    top_labels = _plt_setup2['top_labels']421    bottom_labels = _plt_setup2['bottom_labels']422    nb_scenarios = len(_plt_setup2['scenarios_id'])423    factor = _plt_setup2['expansion_factor']424    scale = 1 / 1000000425    angle = 12426    (df, top_labels_xpos, bottom_labels_xpos) = getDfForPlt(_plt_setup2, _output_folder)427    output_png = '{}/makeplots/{}.ldv_technologysplit_vmt.png'.format(_output_folder, _plt_setup2['name'])428    output_csv = '{}/makeplots/{}.ldv_technologysplit_vmt.csv'.format(_output_folder, _plt_setup2['name'])429    data = pd.DataFrame(430        {'L1': df['VMT_L1'].values * factor * scale,431         'L3': df['VMT_L3'].values * factor * scale,432         'L5': df['VMT_L5'].values * factor * scale433         })434    height_all = data.sum(axis=1)435    data['scenario'] = df['Scenario'].values.copy()436    data['technology'] = df['Technology'].values.copy()437    data.to_csv(output_csv)438    plt.figure(figsize=plot_size)439    plt_Low = plt.bar(x=top_labels_xpos, height=data['L1'])440    plt_High = plt.bar(x=top_labels_xpos, height=data['L3'], bottom=data['L1'])441    plt_CAV = plt.bar(x=top_labels_xpos, height=data['L5'], bottom=data[['L1', 'L3']].sum(axis=1))442    plt.xticks(bottom_labels_xpos, bottom_labels, rotation=angle)443    plt.legend((plt_Low, plt_High, plt_CAV),444               ('No Automation', 'Partial Automation', 'Full Automation'),445               labelspacing=-2.5, bbox_to_anchor=(1.05, 0.5), frameon=False)446    ax = plt.gca()447    ax.grid(False)448    max_value = max(height_all)449    ax.set_ylim((0, max_value))450    for ind in range(nb_scenarios):451        plt.text(top_labels_xpos[ind], max_value + 0.02*max_value, top_labels[ind], ha='center')452    plt.ylabel('Vehicle Miles Traveled (millions)')453    plt.savefig(output_png, transparent=True, bbox_inches='tight', dpi=200, facecolor='white')454    plt.clf()455    plt.close()456def pltRHWaitTime(_plt_setup2, _output_folder):457    plot_size = _plt_setup2['plot_size']458    top_labels = _plt_setup2['top_labels']459    bottom_labels = _plt_setup2['bottom_labels']460    nb_scenarios = len(_plt_setup2['scenarios_id'])461    angle = 12462    (df, top_labels_xpos, bottom_labels_xpos) = getDfForPlt(_plt_setup2, _output_folder)463    output_png = '{}/makeplots/{}.rh_wait_time.png'.format(_output_folder, _plt_setup2['name'])464    output_csv = '{}/makeplots/{}.rh_wait_time.csv'.format(_output_folder, _plt_setup2['name'])465    data = pd.DataFrame(466        {'rh_wait_time': df['averageOnDemandRideWaitTimeInMin'].values.copy()467         })468    height_all = data.sum(axis=1)469    data['scenario'] = df['Scenario'].values.copy()470    data['technology'] = df['Technology'].values.copy()471    data.to_csv(output_csv)472    plt.figure(figsize=plot_size)473    plt.bar(x=top_labels_xpos, height=data['rh_wait_time'], color=mode_colors['RH'])474    plt.xticks(bottom_labels_xpos, bottom_labels, rotation=angle)475    ax = plt.gca()476    ax.grid(False)477    max_value = max(height_all)478    ax.set_ylim((0, max_value))479    for ind in range(nb_scenarios):480        plt.text(top_labels_xpos[ind], max_value + 0.02*max_value, top_labels[ind], ha='center')481    plt.ylabel('Average Ride Hail Wait (min)')482    plt.savefig(output_png, transparent=True, bbox_inches='tight', dpi=200, facecolor='white')483    plt.clf()484    plt.close()485def pltRHEmptyPooled(_plt_setup2, _output_folder):486    plot_size = _plt_setup2['plot_size']487    top_labels = _plt_setup2['top_labels']488    bottom_labels = _plt_setup2['bottom_labels']489    nb_scenarios = len(_plt_setup2['scenarios_id'])490    factor = _plt_setup2['expansion_factor']491    scale = 1 / 1000000492    angle = 12493    (df, top_labels_xpos, bottom_labels_xpos) = getDfForPlt(_plt_setup2, _output_folder)494    output_png = '{}/makeplots/{}.rh_empty_shared.png'.format(_output_folder, _plt_setup2['name'])495    output_csv = '{}/makeplots/{}.rh_empty_shared.csv'.format(_output_folder, _plt_setup2['name'])496    createColumnIfNotExist(df, 'VMT_car_CAV', 0)497    createColumnIfNotExist(df, 'VMT_car_RH_CAV', 0)498    createColumnIfNotExist(df, 'VMT_car_RH_CAV_shared', 0)499    createColumnIfNotExist(df, 'VMT_car_CAV_empty', 0)500    createColumnIfNotExist(df, 'VMT_car_CAV_shared', 0)501    createColumnIfNotExist(df, 'VMT_car_RH_CAV_empty', 0)502    data = pd.DataFrame(503        {'rh': (df['VMT_car_RH'].values+df['VMT_car_RH_CAV'].values-df['VMT_car_RH_shared'].values-df['VMT_car_RH_CAV_shared'].values) * factor * scale,504         'rhp': (df['VMT_car_RH_shared'].values+df['VMT_car_RH_CAV_shared'].values) * factor * scale505         })506    507    #print(df['VMT_car_RH_CAV_shared'])508    #print(data)509    510    height_all = data.sum(axis=1)511    data['rh_empty'] = (df['VMT_car_RH_empty'].values+df['VMT_car_RH_CAV_empty'].values) * factor * scale512    data['scenario'] = df['Scenario'].values.copy()513    data['technology'] = df['Technology'].values.copy()514    data.to_csv(output_csv)515    plt.figure(figsize=plot_size)516    plt_rh = plt.bar(x=top_labels_xpos, height=data['rh'], color=mode_colors['RH'])517    plt_rhp = plt.bar(x=top_labels_xpos, height=data['rhp'], bottom=data['rh'], color=mode_colors['RHP'])518    plt.bar(x=top_labels_xpos, height=-data['rh_empty'], bottom=data['rh'], hatch='///', fill=False, lw=0)519    plt.xticks(bottom_labels_xpos, bottom_labels, rotation=angle)520    empty = mpatches.Patch(facecolor='white', label='The white data', hatch='///')521    ax = plt.gca()522    ax.grid(False)523    max_value = max(height_all)524    ax.set_ylim((0, max_value))525    for ind in range(nb_scenarios):526        plt.text(top_labels_xpos[ind], max_value + 0.02*max_value, top_labels[ind], ha='center')527    plt.ylabel('Ridehail Vehicle Miles Traveled (millions)')528    plt.legend((plt_rh, plt_rhp, empty),529               ('Ridehail', 'Ridehail Pool', 'Deadheading'),530               bbox_to_anchor=(1.05, 0.5), frameon=False)531    plt.savefig(output_png, transparent=True, bbox_inches='tight', dpi=200, facecolor='white')532    plt.clf()533    plt.close()534def pltEnergyPerCapita(_plt_setup2, _output_folder):535    plot_size = _plt_setup2['plot_size']536    top_labels = _plt_setup2['top_labels']537    bottom_labels = _plt_setup2['bottom_labels']538    nb_scenarios = len(_plt_setup2['scenarios_id'])539    scale = 1 / 1000000000540    angle = 12541    (df, top_labels_xpos, bottom_labels_xpos) = getDfForPlt(_plt_setup2, _output_folder)542    output_png = '{}/makeplots/{}.energy_source_percapita.png'.format(_output_folder, _plt_setup2['name'])543    output_csv = '{}/makeplots/{}.energy_source_percapita.csv'.format(_output_folder, _plt_setup2['name'])544    data = pd.DataFrame(545        {'gas': (df['totalEnergy_Gasoline'].values / df['population'].values) * scale,546         'diesel': (df['totalEnergy_Diesel'].values / df['population'].values) * scale,547         'electricity': (df['totalEnergy_Electricity'].values / df['population'].values) * scale548         })549    height_all = data.sum(axis=1)550    data['scenario'] = df['Scenario'].values.copy()551    data['technology'] = df['Technology'].values.copy()552    data.to_csv(output_csv)553    plt.figure(figsize=plot_size)554    plt_Gas = plt.bar(x=top_labels_xpos, height=data['gas'], color=mode_colors['gas'])555    plt_Diesel = plt.bar(x=top_labels_xpos, height=data['diesel'], bottom=data['gas'], color=mode_colors['diesel'])556    plt_Electricity = plt.bar(x=top_labels_xpos, height=data['electricity'], bottom=data[['gas', 'diesel']].sum(axis=1), color=mode_colors['electricity'])557    plt.xticks(bottom_labels_xpos, bottom_labels, rotation=angle)558    ax = plt.gca()559    ax.grid(False)560    max_value = max(height_all)561    ax.set_ylim((0, max_value))562    for ind in range(nb_scenarios):563        plt.text(top_labels_xpos[ind], max_value + 0.02*max_value,  top_labels[ind], ha='center')564    plt.ylabel('Light Duty Vehicle Energy per Capita (GJ)')565    plt.legend((plt_Electricity, plt_Diesel, plt_Gas),566               ('Electricity', 'Diesel', 'Gasoline'), bbox_to_anchor=(1.05, 0.5), frameon=False)567    plt.savefig(output_png, transparent=True, bbox_inches='tight', dpi=200, facecolor='white')568    plt.clf()...

bench.py

Source:bench.py

1import timeit, codecs2reqs = """3python setup.py install4pip install simplejson5pip install ujson6pip install python-rapidjson7pip install mrpacker8"""9setup = u'''10import codecs, json, mrpacker11import ZZZ12f = codecs.open( "bench/json/YYY", "rb", encoding="utf-8")13s = f.read()14f.close()15obj = json.loads(s)16packed = mrpacker.pack(obj)17'''18mods = ["json", "simplejson", "rapidjson", "ujson", "mrjson"]19tsts = ["str128.json", "canada.json", "twit.json", "twitter.json", "citm_catalog.json"]20for tst in tsts:21  print ("Test",tst,"loads")22  23  for mod in mods:24    setup2 = setup.replace("ZZZ",mod).replace("YYY",tst)25    print ("  ",(min(timeit.Timer(mod+'.loads(s)', setup=setup2).repeat(10, 1))), "  \t", mod)26  setup2 = setup.replace("ZZZ","mrpacker").replace("YYY",tst)27  print ("  ",(min(timeit.Timer('mrpacker.unpack(packed)', setup=setup2).repeat(10, 1))), "  \t", "mrpacker")28for tst in tsts:29  print ("Test",tst,"dumps")30  for mod in mods:31    setup2 = setup.replace("ZZZ",mod).replace("YYY",tst)32    print ("  ",(min(timeit.Timer(mod+'.dumps(obj)', setup=setup2).repeat(10, 1))), "  \t", mod)33  setup2 = setup.replace("ZZZ","mrpacker").replace("YYY",tst)...

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.