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]
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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)...
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