In [ ]:
# Creates network based on queries on SF Muni data
In [1]:
%load_ext autoreload
%autoreload 2
In [2]:
import pandas as pd
import matplotlib.pyplot as plt
pd.options.display.max_columns = 150
pd.options.display.float_format='{:,.2f}'.format
import sys
sys.path.append('C:/CASA/Workspace/sfdata_wrangler/sfdata_wrangler')
from SFMuniDataHelper import SFMuniDataHelper
from DataFrameViewer import DataFrameViewer
sfmuniHelper = SFMuniDataHelper()
viewer = DataFrameViewer()
In [3]:
# eventually convert filenames to arguments
hdffile = "D:/sfbusdatastore/sfmuni_aggregate.h5"
store = pd.HDFStore(hdffile)
store
Out[3]:
<class 'pandas.io.pytables.HDFStore'>
File path: D:/sfbusdatastore/sfmuni_aggregate.h5
/average frame_table (typ->appendable,nrows->28692042,ncols->85,indexers->[index],dc->[MONTH,DOW,ROUTE,PATTCODE,DIR,TRIP,SEQ,NUMDAYS,OBSTRIPS,ROUTEA,VEHNO,SCHOOL,LASTTRIP,NEXTTRIP,HEADWAY,HEADWAY_STD,TOD,QSTOP,STOPNAME,TIMEPOINT,EOL,LAT,LAT_STD,LON,LON_STD,NS,EW,MAXVEL,MAXVEL_STD,MILES,MILES_STD,GODOM,GODOM_STD,VEHMILES,VEHMILES_STD,ON,ON_STD,OFF,OFF_STD,LOAD_ARR,LOAD_ARR_STD,LOAD_DEP,LOAD_DEP_STD,PASSMILES,PASSMILES_STD,PASSHOURS,PASSHOURS_STD,RDBRDNGS,RDBRDNGS_STD,LOADCODE,LOADCODE_STD,CAPACITY,CAPACITY_STD,DOORCYCLES,DOORCYCLES_STD,WHEELCHAIR,WHEELCHAIR_STD,BIKERACK,BIKERACK_STD,TIMESTOP,TIMESTOP_S,TIMESTOP_DEV,TIMESTOP_DEV_STD,DOORCLOSE,DOORCLOSE_S,DOORCLOSE_DEV,DOORCLOSE_DEV_STD,DWELL,DWELL_STD,DWELL_S,PULLOUT,PULLDWELL,PULLDWELL_STD,RUNTIME,RUNTIME_STD,RUNTIME_S,RECOVERY,RECOVERY_STD,RECOVERY_S,DLPMIN,DLPMIN_STD,ONTIME2,ONTIME2_STD,ONTIME10,ONTIME10_STD])
/daily_route_stops frame_table (typ->appendable,nrows->987424,ncols->47,indexers->[index],dc->[MONTH,DOW,ROUTE,PATTCODE,DIR,SEQ,NUMDAYS,DAILYTRIPS,TOTTRIPS,OBSTRIPS,ROUTEA,HEADWAY,QSTOP,STOPNAME,TIMEPOINT,EOL,LAT,LON,NS,EW,MAXVEL,MILES,GODOM,VEHMILES,ON,OFF,LOAD_ARR,LOAD_DEP,PASSMILES,PASSHOURS,RDBRDNGS,CAPACITY,DOORCYCLES,WHEELCHAIR,BIKERACK,TIMESTOP_DEV,DOORCLOSE_DEV,DWELL,DWELL_S,PULLDWELL,RUNTIME,RUNTIME_S,RECOVERY,RECOVERY_S,DLPMIN,ONTIME2,ONTIME10])
/daily_routes frame_table (typ->appendable,nrows->12784,ncols->32,indexers->[index],dc->[MONTH,DOW,ROUTE,DIR,NUMDAYS,DAILYTRIPS,TOTTRIPS,OBSTRIPS,ROUTEA,HEADWAY,VEHMILES,ON,OFF,PASSMILES,PASSHOURS,RDBRDNGS,CAPACITY,DOORCYCLES,WHEELCHAIR,BIKERACK,TIMESTOP_DEV,DOORCLOSE_DEV,DWELL,DWELL_S,PULLDWELL,RUNTIME,RUNTIME_S,RECOVERY,RECOVERY_S,DLPMIN,ONTIME2,ONTIME10])
/daily_stops frame_table (typ->appendable,nrows->381941,ncols->25,indexers->[index],dc->[MONTH,DOW,NUMDAYS,DAILYTRIPS,TOTTRIPS,OBSTRIPS,QSTOP,STOPNAME,TIMEPOINT,EOL,LAT,LON,ON,OFF,RDBRDNGS,DOORCYCLES,WHEELCHAIR,BIKERACK,TIMESTOP_DEV,DOORCLOSE_DEV,DWELL,DWELL_S,PULLDWELL,ONTIME2,ONTIME10])
/daily_system frame_table (typ->appendable,nrows->121,ncols->28,indexers->[index],dc->[MONTH,DOW,NUMDAYS,DAILYTRIPS,TOTTRIPS,OBSTRIPS,VEHMILES,ON,OFF,PASSMILES,PASSHOURS,RDBRDNGS,CAPACITY,DOORCYCLES,WHEELCHAIR,BIKERACK,TIMESTOP_DEV,DOORCLOSE_DEV,DWELL,DWELL_S,PULLDWELL,RUNTIME,RUNTIME_S,RECOVERY,RECOVERY_S,DLPMIN,ONTIME2,ONTIME10])
/tod_route_stops frame_table (typ->appendable,nrows->3810220,ncols->48,indexers->[index],dc->[TOD,MONTH,DOW,ROUTE,PATTCODE,DIR,SEQ,NUMDAYS,DAILYTRIPS,TOTTRIPS,OBSTRIPS,ROUTEA,HEADWAY,QSTOP,STOPNAME,TIMEPOINT,EOL,LAT,LON,NS,EW,MAXVEL,MILES,GODOM,VEHMILES,ON,OFF,LOAD_ARR,LOAD_DEP,PASSMILES,PASSHOURS,RDBRDNGS,CAPACITY,DOORCYCLES,WHEELCHAIR,BIKERACK,TIMESTOP_DEV,DOORCLOSE_DEV,DWELL,DWELL_S,PULLDWELL,RUNTIME,RUNTIME_S,RECOVERY,RECOVERY_S,DLPMIN,ONTIME2,ONTIME10])
/tod_routes frame_table (typ->appendable,nrows->67767,ncols->33,indexers->[index],dc->[TOD,MONTH,DOW,ROUTE,DIR,NUMDAYS,DAILYTRIPS,TOTTRIPS,OBSTRIPS,ROUTEA,HEADWAY,VEHMILES,ON,OFF,PASSMILES,PASSHOURS,RDBRDNGS,CAPACITY,DOORCYCLES,WHEELCHAIR,BIKERACK,TIMESTOP_DEV,DOORCLOSE_DEV,DWELL,DWELL_S,PULLDWELL,RUNTIME,RUNTIME_S,RECOVERY,RECOVERY_S,DLPMIN,ONTIME2,ONTIME10])
/tod_stops frame_table (typ->appendable,nrows->2367349,ncols->26,indexers->[index],dc->[TOD,MONTH,DOW,NUMDAYS,DAILYTRIPS,TOTTRIPS,OBSTRIPS,QSTOP,STOPNAME,TIMEPOINT,EOL,LAT,LON,ON,OFF,RDBRDNGS,DOORCYCLES,WHEELCHAIR,BIKERACK,TIMESTOP_DEV,DOORCLOSE_DEV,DWELL,DWELL_S,PULLDWELL,ONTIME2,ONTIME10])
/tod_system frame_table (typ->appendable,nrows->926,ncols->29,indexers->[index],dc->[TOD,MONTH,DOW,NUMDAYS,DAILYTRIPS,TOTTRIPS,OBSTRIPS,VEHMILES,ON,OFF,PASSMILES,PASSHOURS,RDBRDNGS,CAPACITY,DOORCYCLES,WHEELCHAIR,BIKERACK,TIMESTOP_DEV,DOORCLOSE_DEV,DWELL,DWELL_S,PULLDWELL,RUNTIME,RUNTIME_S,RECOVERY,RECOVERY_S,DLPMIN,ONTIME2,ONTIME10])
In [4]:
#checks number of unique stops
all_routes = store.select('tod_route_stops',where=['DOW==1','TOD==900','MONTH==20100301'])
qs = all_routes[['QSTOP']]
qs_check = qs.groupby(['QSTOP']).count()
qs_check.head()
Out[4]:
QSTOP
QSTOP
390
3
392
1
660
2
913
2
3003
1
5 rows × 1 columns
In [5]:
import csv
c = csv.writer(open("network-test.csv", "wb"))
all_routes = store.select('tod_route_stops',where=['DOW==1','TOD==900','MONTH==20100301'])
tod_route_stops_subset = all_routes[['QSTOP','ROUTE','SEQ','LOAD_ARR','PATTCODE']] #unique stop id, route id, route sequence, arrival load, trip i
df= tod_route_stops_subset
#no initial load on arrival at first stop in sequence
current_trip=0
current_load=0
current_route=0
current_qstop=0
current_seq=0
c.writerow(['W1','W2','VALUE'])
for row in df.iterrows():
count = 0
previous_trip = current_trip
previous_load = current_load
previous_route = current_route
previous_qstop = current_qstop
previous_seq = current_seq
for data in row[1]:
count=count+1
if count == 1:
current_qstop = data
if count == 2:
current_route = data
if count == 3:
current_seq = data
if count == 4:
current_load= data
if count == 5:
current_trip = data
#checks that previous row is part of the same route, trip and was the previous (-1) in sequence - then writes the pair
if (current_qstop != previous_qstop and current_route == previous_route and current_trip == previous_trip and current_seq == previous_seq+1):
c.writerow([int(previous_qstop),int(current_qstop),float(current_load)])
In [6]:
from pandas import read_csv
network_all = read_csv("network-test.csv")
network_sum = network_all.groupby(['W1','W2']).agg({'VALUE': np.sum}).reset_index()
network_sum.head()
Out[6]:
W1
W2
VALUE
0
390
6,950.00
44.00
1
392
5,950.00
726.17
2
660
5,582.00
365.17
3
913
7,248.00
12.50
4
3003
3,009.00
160.33
5 rows × 3 columns
In [22]:
import networkx as nx
G.clear()
G=nx.Graph()
W1 = 0;
W2 = 0;
VALUE = 0;
for row in network_sum.iterrows():
count = 0;
for data in row[1]:
count=count+1
if count == 1:
W1 = data
if count == 2:
W2 = data
if count == 3:
VALUE = data
G.add_edge(int(W1),int(W2),weight=VALUE)
print int(W1),int(W2),VALUE
390 6950 44.0
392 5950 726.166666667
660 5582 365.166666667
913 7248 12.5
3003 3009 160.333333333
3004 3015 89.0
3008 3010 108.666666667
3009 7496 158.333333333
3010 3004 100.833333333
3011 3008 248.833333333
3013 3011 243.833333333
3015 6692 86.0
3018 3685 170.166666667
3019 5932 216.166666667
3020 5029 206.5
3021 3688 178.333333333
3023 3705 40.0
3024 5331 111.0
3025 3030 241.333333333
3030 6328 240.833333333
3032 3034 305.333333333
3033 6709 425.0
3034 3036 306.333333333
3035 3033 395.666666667
3036 3038 305.333333333
3037 3035 374.0
3038 3040 291.0
3039 3037 371.666666667
3040 3070 283.333333333
3041 3039 367.333333333
3043 3045 247.333333333
3044 3071 358.333333333
3045 3047 236.0
3046 3044 340.666666667
3047 3049 227.0
3048 3046 315.333333333
3049 3051 220.333333333
3050 3048 311.333333333
3051 3053 209.666666667
3052 3050 305.0
3053 3055 189.666666667
3054 3052 295.666666667
3055 3057 181.0
3056 3054 285.0
3057 3060 167.333333333
3058 3056 262.0
3059 3058 257.0
3059 3553 194.666666667
3060 3061 286.0
3061 3063 263.666666667
3062 3059 421.666666667
3063 3065 244.833333333
3064 3062 386.166666667
3065 3571 227.666666667
3066 3064 342.5
3067 3032 312.666666667
3070 3043 270.666666667
3071 3041 365.333333333
3072 3077 126.0
3074 3076 73.5
3075 3072 132.0
3076 5173 80.0
3077 3074 129.0
3082 3084 255.833333333
3084 7005 253.0
3085 6492 376.75
3086 4605 407.416666667
3087 3082 233.833333333
3093 5992 69.6666666667
3096 3693 11.0
3114 3144 698.333333333
3124 3128 1060.25
3127 5049 175.0
3129 3135 76.6666666667
3134 3127 158.0
3135 3143 83.5
3136 3137 2502.75
3137 4821 2601.5
3141 3134 154.666666667
3141 5490 211.083333333
3143 6249 101.0
3144 3124 868.333333333
3156 3163 142.5
3158 7291 774.75
3159 3158 998.916666667
3160 3159 1191.66666667
3161 3160 1261.41666667
3163 3174 177.5
3166 4969 188.5
3168 4969 243.5
3169 7214 280.0
3170 3172 317.0
3171 3168 258.5
3172 3611 305.0
3173 3171 264.5
3174 3166 185.5
3177 3184 518.833333333
3178 7538 508.333333333
3181 3185 343.5
3184 4043 321.333333333
3185 3178 493.833333333
3188 5253 1158.66666667
3189 4852 1198.5
3190 3826 135.166666667
3191 6999 293.666666667
3192 3194 370.833333333
3193 3192 361.166666667
3194 3195 375.666666667
3195 3196 360.333333333
3196 7423 263.333333333
3197 3191 284.166666667
3201 7539 532.833333333
3202 4772 487.166666667
3203 3210 292.0
3204 3203 301.0
3205 3206 349.666666667
3206 3204 341.0
3207 3205 379.333333333
3208 3207 390.833333333
3209 3208 384.833333333
3211 3220 533.0
3213 3215 446.0
3214 3211 514.833333333
3214 5192 779.916666667
3215 3216 1267.75
3216 3218 1286.5
3217 3214 1321.5
3218 3222 171.416666667
3218 4853 1106.0
3218 4856 41.0
3219 3217 170.5
3220 4771 560.833333333
3221 3213 476.0
3222 3224 148.5
3223 3219 159.0
3224 3226 130.5
3225 3223 133.583333333
3226 5827 119.166666667
3229 3674 373.0
3230 5826 90.8333333333
3231 3230 82.5
3232 3234 97.8333333333
3233 3231 75.9166666667
3234 6168 87.0
3235 3238 973.0
3236 3240 1385.5
3237 3239 1186.5
3238 3236 1332.5
3238 4665 159.166666667
3239 3729 312.5
3239 3755 186.5
3239 4436 752.0
3240 3242 992.5
3240 5545 422.0
3241 3237 1217.5
3242 3244 936.0
3243 3241 1241.0
3244 5652 1138.0
3245 3243 743.0
3247 6171 58.0833333333
3248 6468 43.5
3249 3252 41.0
3250 3248 46.5
3251 3250 46.0
3252 6737 41.0
3253 3820 19.0
3254 3260 19.0
3256 3253 19.0
3257 3259 3.0
3258 3256 19.0
3259 3261 3.0
3260 3258 19.0
3261 3255 5.0
3262 3263 40.0
3263 3264 38.0
3264 3265 37.0
3265 7573 37.0
3270 5141 62.0
3272 4861 37.0
3273 7231 42.0
3275 3277 42.0
3276 3272 37.0
3277 5739 41.0
3278 3276 38.0
3279 3278 37.0
3280 3279 37.0
3281 3288 1081.16666667
3282 3296 881.666666667
3283 7073 841.0
3284 3283 864.75
3285 3287 836.916666667
3286 3284 844.166666667
3287 3301 810.25
3288 7289 1142.16666667
3289 3282 1131.16666667
3290 5226 396.583333333
3291 3300 789.416666667
3292 3299 358.5
3293 3299 796.5
3295 3281 853.583333333
3296 3297 456.833333333
3296 6029 241.5
3297 3290 446.666666667
3299 3289 1170.5
3300 3286 840.583333333
3301 3293 786.666666667
3302 3295 417.166666667
3304 3306 38.5
3305 7572 21.0
3307 3310 7.0
3309 3307 8.0
3310 4090 7.0
3312 3317 372.25
3312 4350 113.666666667
3313 3320 316.416666667
3314 4185 49.0
3317 3302 395.916666667
3319 3312 384.333333333
3320 4124 327.25
3321 3410 164.416666667
3322 3344 399.5
3323 3331 351.0
3324 4123 250.75
3325 3332 375.0
3326 3341 319.5
3327 3339 363.0
3328 3336 278.0
3329 3326 286.5
3330 3322 399.5
3331 3334 347.0
3332 3335 375.5
3333 3330 385.5
3334 3349 353.5
3335 3327 373.0
3336 3329 281.0
3337 3321 189.583333333
3338 3348 341.0
3339 4079 362.0
3340 3325 384.0
3341 3345 324.5
3342 3346 201.583333333
3343 3337 188.083333333
3344 3340 392.0
3345 3323 327.0
3346 3324 211.916666667
3347 3343 201.25
3348 3333 368.0
3349 5553 331.0
3351 390 371.0
3352 3408 19.3333333333
3354 7448 685.25
3355 3378 703.333333333
3356 4207 33.0
3359 3365 691.916666667
3360 392 707.833333333
3362 3351 371.0
3363 5136 385.0
3364 3360 735.25
3365 3367 753.25
3366 3364 774.583333333
3367 3369 750.5
3368 3366 773.833333333
3369 3372 749.583333333
3370 6984 388.5
3371 3359 654.166666667
3372 3375 752.083333333
3373 3368 763.916666667
3374 3373 766.0
3375 3380 771.666666667
3377 3354 731.583333333
3378 3391 736.333333333
3379 3374 777.916666667
3380 3382 777.916666667
3381 3379 780.083333333
3382 3384 757.75
3383 3381 779.583333333
3384 3389 785.916666667
3388 3383 799.75
3389 3393 783.166666667
3390 3377 763.416666667
3391 3401 762.583333333
3392 3388 813.25
3393 3395 780.833333333
3394 3392 814.416666667
3395 3397 774.916666667
3396 3394 775.75
3397 3399 777.916666667
3398 3396 778.25
3399 3400 781.416666667
3400 3390 780.0
3401 3398 768.25
3402 3355 662.416666667
3404 4545 65.0
3405 7211 58.0
3406 3404 71.0
3408 3406 70.0
3408 6952 69.6666666667
3409 3797 55.0
3410 6657 178.916666667
3411 3432 35.5
3412 3652 32.6666666667
3413 4127 82.6666666667
3414 3415 47.0
3415 4538 43.0
3424 4444 206.5
3425 3429 428.5
3426 3425 470.0
3427 3426 480.5
3428 3427 482.5
3429 5311 431.0
3430 3428 476.5
3431 3430 458.0
3432 3436 30.5
3435 7523 52.0
3436 3435 37.5
3437 7473 40.0
3439 7126 58.5
3441 3442 463.666666667
3442 3443 473.333333333
3443 3444 468.5
3444 3446 464.0
3445 3441 401.333333333
3446 3447 455.166666667
3447 5741 418.333333333
3448 4370 33.0
3449 4541 51.0
3450 3452 224.666666667
3452 4356 223.0
3452 6198 209.666666667
3453 3480 234.5
3453 6039 32.1666666667
3454 3456 217.333333333
3455 3453 200.333333333
3456 3450 223.666666667
3457 6966 81.3333333333
3458 3473 398.0
3459 6038 298.5
3460 3481 239.5
3461 3467 304.5
3462 3464 214.0
3463 7263 283.0
3464 3468 209.0
3465 3463 233.5
3466 3460 255.0
3467 3472 308.5
3468 3475 196.5
3469 3483 621.5
3470 3474 365.5
3471 3466 258.0
3472 3486 310.5
3473 3469 436.0
3474 3459 340.0
3476 3485 258.0
3476 6877 23.3333333333
3476 7532 89.0
3477 3484 362.0
3478 3462 232.0
3480 3458 312.5
3481 3478 240.5
3482 3461 281.5
3483 3476 614.0
3484 3470 366.5
3485 3471 272.5
3486 3477 283.0
3489 3499 290.5
3490 3501 304.0
3491 3495 97.5
3492 3503 64.8333333333
3495 3500 141.666666667
3496 3492 111.0
3497 5263 53.5
3498 3502 50.5
3499 5309 217.0
3501 3496 148.0
3503 3497 60.1666666667
3504 3512 126.833333333
3504 7525 75.0
3505 4130 155.166666667
3505 4345 85.0
3507 3504 107.0
3511 4964 61.3333333333
3512 3504 79.5
3512 3524 149.333333333
3513 3505 232.5
3514 4329 465.416666667
3516 3522 343.666666667
3517 6962 374.166666667
3519 3523 44.0
3520 3514 467.333333333
3521 5732 318.416666667
3522 5229 343.666666667
3523 3932 111.0
3524 3516 339.0
3525 6735 10.0
3526 6153 22.0
3527 3528 17.0
3528 3530 16.0
3529 3526 23.0
3530 3532 11.0
3531 3529 24.0
3532 3534 7.0
3533 3531 24.0
3534 6835 7.0
3535 3543 444.833333333
3536 3538 290.25
3537 3535 462.583333333
3537 3966 47.0
3538 3541 303.666666667
3538 5571 33.5
3540 3537 503.0
3541 4157 377.916666667
3542 5737 437.0
3543 3542 449.0
3544 3536 305.666666667
3545 3635 61.0
3546 3844 312.5
3547 3549 188.0
3548 3546 304.5
3551 3554 245.0
3552 3559 189.0
3553 3552 193.166666667
3554 3060 118.833333333
3554 3545 48.5
3555 3548 259.0
3555 4069 49.1666666667
3556 3558 49.1666666667
3558 3551 110.5
3559 3555 60.0
3559 4275 116.0
3560 3551 145.5
3561 5322 414.666666667
3562 6528 345.5
3563 6162 21.0
3564 3563 21.0
3565 6245 16.0
3566 5511 133.666666667
3567 3568 170.75
3568 4285 169.75
3570 3066 329.833333333
3571 3573 219.666666667
3572 3570 297.833333333
3573 3924 211.0
3574 3576 152.166666667
3575 3582 165.166666667
3576 3578 169.5
3577 3575 167.333333333
3578 3580 173.833333333
3579 3577 164.166666667
3580 3584 174.666666667
3581 3579 160.833333333
3582 5324 168.333333333
3583 3574 149.333333333
3584 3587 171.166666667
3585 3581 160.166666667
3586 3585 155.333333333
3587 3588 191.333333333
3588 3590 191.666666667
3589 3586 146.333333333
3590 3592 198.0
3591 3589 142.166666667
3592 3594 196.5
3593 3591 141.666666667
3593 6165 11.0
3594 3596 194.166666667
3595 3593 135.666666667
3596 3598 192.166666667
3597 3595 133.166666667
3598 3600 176.333333333
3599 3597 133.166666667
3600 3602 177.833333333
3601 3599 107.833333333
3602 6836 173.666666667
3603 3601 106.166666667
3604 3603 104.833333333
3605 5762 64.5
3606 7208 169.166666667
3608 4286 110.0
3611 7215 333.0
3612 3173 263.0
3613 3614 9.0
3614 3616 7.0
3615 3618 15.0
3616 3617 10.0
3617 4360 12.0
3618 4579 11.0
3619 4576 9.0
3620 3624 47.0
3621 6484 125.333333333
3622 3631 132.666666667
3624 4567 44.0
3628 3622 109.5
3630 3621 141.666666667
3631 6324 134.166666667
3632 6430 154.166666667
3635 3559 69.0
3636 3639 16.0
3637 3638 19.0
3638 4916 19.0
3639 3637 18.0
3640 3636 13.0
3642 4224 173.5
3643 6293 48.5
3644 3645 76.5
3644 7250 176.333333333
3645 3649 108.5
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In [14]:
import matplotlib.pyplot as plt
nx.draw(G)
plt.show()
In [23]:
G.nodes()
Out[23]:
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In [24]:
G.edges()
Out[24]:
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In [29]:
##apends lat / lon to current nodes
df1 = store.select('tod_route_stops',where=['DOW==1','TOD==900','MONTH==20100301'])
df2 = all_routes[['QSTOP','LAT','LON']]
df3 = df2.groupby(['QSTOP']).agg({'LAT':np.mean,'LON':np.mean}).reset_index()
for row in df3.iterrows():
count = 0
for data in row[1]:
count=count+1
if count == 1:
qstop_id = data
if count == 2:
qstop_lat = data
if count == 3:
qstop_lon = data
#G.add_node(int(qstop_id), lat=qstop_lat, lon=qstop_lon)
iqstop_id = int(qstop_id)
try:
G.node[iqstop_id]['latitude'] = qstop_lat.astype(double)
G.node[iqstop_id]['longitude'] = qstop_lon.astype(double)
print int(qstop_id),qstop_lat,qstop_lon
except KeyError:
print iqstop_id
390 37.7215736871 -122.475005366
392 37.765148625 -122.477195333
660 37.7243794737 -122.435230088
913 37.719486 -122.42640975
3003 37.78179 -122.392036296
3004 37.781925625 -122.392218958
3008 37.7854097348 -122.396580246
3009 37.7843437037 -122.395165185
3010 37.7841854167 -122.3950575
3011 37.7865601326 -122.398016799
3013 37.7887761553 -122.400680445
3015 37.7806520833 -122.390905625
3018 37.7277569231 -122.403244359
3019 37.727697381 -122.40325869
3020 37.7265797436 -122.407359359
3021 37.7265404762 -122.407661667
3023 37.7902015625 -122.482336667
3024 37.7975361 -122.4458363
3025 37.7983248011 -122.401041619
3030 37.796834233 -122.400745104
3031
3032 37.7773868627 -122.461789804
3033 37.7772980952 -122.462032976
3034 37.7772945098 -122.464287843
3035 37.7772117857 -122.463779405
3036 37.7771784314 -122.466060196
3037 37.7771057143 -122.466413333
3038 37.7770872549 -122.468146667
3039 37.7770192857 -122.468484167
3040 37.7769876471 -122.470325686
3041 37.776927619 -122.470515595
3043 37.7767390196 -122.475823333
3044 37.7766677381 -122.476044762
3045 37.7766607843 -122.477862941
3046 37.7765533333 -122.478178929
3047 37.7765527451 -122.480038627
3048 37.7764788095 -122.480358571
3049 37.7764556863 -122.482139216
3050 37.7763779762 -122.482403095
3051 37.7763643137 -122.484724902
3052 37.7762714286 -122.484299881
3053 37.7762305882 -122.487478235
3054 37.7761355952 -122.487744643
3055 37.7760919608 -122.490047843
3056 37.7760379762 -122.489863452
3057 37.776004902 -122.49190902
3058 37.7759492857 -122.492029048
3059 37.775878642 -122.494375741
3060 37.775896939 -122.494537805
3061 37.7757422222 -122.497191133
3062 37.7757002998 -122.497336164
3063 37.7756105283 -122.500360991
3064 37.775547231 -122.500647575
3065 37.775468219 -122.503624907
3066 37.7754127249 -122.503813607
3067 37.7774688235 -122.458862549
3070 37.776925098 -122.472361373
3071 37.7768155952 -122.4724925
3072 37.8020807143 -122.444954286
3074 37.803607619 -122.445276071
3075 37.8012425 -122.444775476
3076 37.8044127381 -122.445088214
3077 37.8029071429 -122.445109048
3082 37.7979364962 -122.406280994
3084 37.7981818939 -122.40404625
3085 37.7942197126 -122.436244195
3086 37.7941709409 -122.436259301
3087 37.79762375 -122.40836017
3088
3089
3093 37.8063532091 -122.423111522
3094
3096 37.737001 -122.431282
3114 37.7793482857 -122.393337621
3124 37.7840915238 -122.399221886
3127 37.740133 -122.388856333
3128 37.7849166333 -122.400266264
3129 37.7393273077 -122.389188462
3134 37.7373577381 -122.38973881
3135 37.7378344872 -122.389745641
3136 37.7869614818 -122.402970972
3137 37.7876721274 -122.403358127
3141 37.7344132273 -122.390720061
3143 37.7346870513 -122.390829487
3144 37.7822065188 -122.396852612
3156 37.7782558 -122.3965596
3158 37.7818105582 -122.400957727
3159 37.783341467 -122.402486564
3160 37.7843459716 -122.403712116
3161 37.7855167154 -122.405443306
3163 37.7773560897 -122.395139968
3165
3166 37.7765854 -122.398523
3168 37.78037375 -122.403637188
3169 37.77903625 -122.401681875
3170 37.7814776667 -122.404856
3171 37.78121375 -122.40470625
3172 37.7830093333 -122.406670667
3173 37.782825625 -122.406736875
3174 37.7758616 -122.397054
3177 37.777110873 -122.46398627
3178 37.776977619 -122.464076746
3181 37.7830820635 -122.464487222
3183 37.7738975 -122.4637225
3184 37.7808320635 -122.464238016
3185 37.780694127 -122.464264444
3188 37.7566048994 -122.463706707
3189 37.7567699802 -122.463723284
3190 37.7834477778 -122.465368016
3191 37.7733087179 -122.403545128
3192 37.7766738462 -122.407656667
3193 37.7757411538 -122.406452051
3194 37.7779037179 -122.409215256
3195 37.7790879487 -122.410817051
3196 37.7803003846 -122.412411154
3197 37.7720151282 -122.402301154
3201 37.7736904762 -122.46590881
3202 37.7735448413 -122.465906984
3203 37.771552 -122.405867667
3204 37.7725832292 -122.407228854
3205 37.7745925 -122.410181875
3206 37.7732908333 -122.408518438
3207 37.7761298958 -122.412022917
3208 37.7771080208 -122.413218333
3209 37.7781704167 -122.4146325
3210 37.7701814444 -122.404103889
3211 37.7642561111 -122.466333175
3213 37.763800873 -122.46636627
3214 37.7619798177 -122.466147751
3215 37.7618754683 -122.466189641
3216 37.7604334902 -122.466092741
3217 37.7601740286 -122.466029038
3218 37.7586053203 -122.465961934
3219 37.7583000397 -122.465894722
3220 37.765748254 -122.466425476
3221 37.765695873 -122.46648119
3222 37.7567418182 -122.465866098
3223 37.756453254 -122.465763214
3224 37.75482875 -122.465729242
3225 37.7545934524 -122.465627262
3226 37.7530378788 -122.465699091
3229 37.8249912963 -122.370208519
3230 37.7526884127 -122.46635873
3231 37.7508534127 -122.466460079
3232 37.7507501515 -122.466508712
3233 37.749465119 -122.466431349
3234 37.7492143939 -122.466525379
3235 37.7699701786 -122.41135
3236 37.7720789107 -122.414207818
3237 37.7716484964 -122.413808214
3238 37.7708597613 -122.412672172
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6577
6578 37.7121710417 -122.417668542
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7188
7191 37.7133734433 -122.4020735
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7230 37.7132144872 -122.456074872
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7250 37.7833252381 -122.459405833
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7300
7301 37.7859106813 -122.412246754
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7537 37.7648345833 -122.401655208
7538 37.7753650794 -122.464863889
7539 37.7752645238 -122.465081587
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7547 37.7806425926 -122.390945926
7548 37.7866903704 -122.398087407
7549 37.7884961111 -122.400338241
7550 37.79047 -122.400801296
7551 37.748092963 -122.418552593
7552 37.7486153704 -122.413702037
7553 37.7927268519 -122.432727593
7554 37.70651075 -122.459513
7555 37.8199394444 -122.366895741
7556 37.8223009259 -122.367961481
7557 37.8251677778 -122.369871667
7558 37.8283336111 -122.372019815
7559 37.8255368519 -122.376314815
7560 37.8241548148 -122.372497222
7561 37.7732006522 -122.452389348
7563 37.7811703833 -122.4123025
7568 37.8020478571 -122.443327619
7572 37.74865 -122.47391
7573 37.7487775 -122.47379375
In [34]:
#export to gml for test
nx.write_gml(G,"test.gml")
# bug, see below for fix nx.write_graphml(G, "test.graphml")
In [35]:
#patched write to graphml script
#source: https://networkx.lanl.gov/trac/attachment/ticket/696/graphml.py
"""
*******
GraphML
*******
Read and write graphs in GraphML format.
This implementation does not support mixed graphs (directed and unidirected
edges together), hyperedges, nested graphs, or ports.
"GraphML is a comprehensive and easy-to-use file format for graphs. It
consists of a language core to describe the structural properties of a
graph and a flexible extension mechanism to add application-specific
data. Its main features include support of
* directed, undirected, and mixed graphs,
* hypergraphs,
* hierarchical graphs,
* graphical representations,
* references to external data,
* application-specific attribute data, and
* light-weight parsers.
Unlike many other file formats for graphs, GraphML does not use a
custom syntax. Instead, it is based on XML and hence ideally suited as
a common denominator for all kinds of services generating, archiving,
or processing graphs."
http://graphml.graphdrawing.org/
Format
------
GraphML is an XML format. See
http://graphml.graphdrawing.org/specification.html for the specification and
http://graphml.graphdrawing.org/primer/graphml-primer.html
for examples.
"""
__author__ = """\n""".join(['Salim Fadhley',
'Aric Hagberg (hagberg@lanl.gov)'
])
__all__ = ['write_graphml', 'read_graphml', 'generate_graphml',
'GraphMLWriter', 'GraphMLReader']
import networkx as nx
from networkx.utils import open_file, make_str
import warnings
import numpy as np
try:
from xml.etree.cElementTree import Element, ElementTree, tostring
except ImportError:
try:
from xml.etree.ElementTree import Element, ElementTree, tostring
except ImportError:
pass
@open_file(1,mode='wb')
def write_graphml(G, path, encoding='utf-8',prettyprint=True):
"""Write G in GraphML XML format to path
Parameters
----------
G : graph
A networkx graph
path : file or string
File or filename to write.
Filenames ending in .gz or .bz2 will be compressed.
encoding : string (optional)
Encoding for text data.
prettyprint : bool (optional)
If True use line breaks and indenting in output XML.
Examples
--------
>>> G=nx.path_graph(4)
>>> nx.write_graphml(G, "test.graphml")
Notes
-----
This implementation does not support mixed graphs (directed and unidirected
edges together) hyperedges, nested graphs, or ports.
"""
writer = GraphMLWriter(encoding=encoding,prettyprint=prettyprint)
writer.add_graph_element(G)
writer.dump(path)
def generate_graphml(G, encoding='utf-8',prettyprint=True):
"""Generate GraphML lines for G
Parameters
----------
G : graph
A networkx graph
encoding : string (optional)
Encoding for text data.
prettyprint : bool (optional)
If True use line breaks and indenting in output XML.
Examples
--------
>>> G=nx.path_graph(4)
>>> linefeed=chr(10) # linefeed=\n
>>> s=linefeed.join(nx.generate_graphml(G)) # doctest: +SKIP
>>> for line in nx.generate_graphml(G): # doctest: +SKIP
... print(line)
Notes
-----
This implementation does not support mixed graphs (directed and unidirected
edges together) hyperedges, nested graphs, or ports.
"""
writer = GraphMLWriter(encoding=encoding,prettyprint=prettyprint)
writer.add_graph_element(G)
for line in str(writer).splitlines():
yield line
@open_file(0,mode='rb')
def read_graphml(path,node_type=str):
"""Read graph in GraphML format from path.
Parameters
----------
path : file or string
File or filename to write.
Filenames ending in .gz or .bz2 will be compressed.
node_type: Python type (default: str)
Convert node ids to this type
Returns
-------
graph: NetworkX graph
If no parallel edges are found a Graph or DiGraph is returned.
Otherwise a MultiGraph or MultiDiGraph is returned.
Notes
-----
This implementation does not support mixed graphs (directed and unidirected
edges together), hypergraphs, nested graphs, or ports.
For multigraphs the GraphML edge "id" will be used as the edge
key. If not specified then they "key" attribute will be used. If
there is no "key" attribute a default NetworkX multigraph edge key
will be provided.
Files with the yEd "yfiles" extension will can be read but the graphics
information is discarded.
yEd compressed files ("file.graphmlz" extension) can be read by renaming
the file to "file.graphml.gz".
"""
reader = GraphMLReader(node_type=node_type)
# need to check for multiple graphs
glist=list(reader(path))
return glist[0]
class GraphML(object):
NS_GRAPHML = "http://graphml.graphdrawing.org/xmlns"
NS_XSI = "http://www.w3.org/2001/XMLSchema-instance"
#xmlns:y="http://www.yworks.com/xml/graphml"
NS_Y = "http://www.yworks.com/xml/graphml"
SCHEMALOCATION = \
' '.join(['http://graphml.graphdrawing.org/xmlns',
'http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd'])
try:
chr(12345) # Fails on Py!=3.
unicode = str # Py3k's str is our unicode type
long = int # Py3K's int is our long type
except ValueError:
# Python 2.x
pass
types=[(int,"integer"), # for Gephi GraphML bug
(str,"yfiles"),(str,"string"), (unicode,"string"),
(int,"int"), (long,"long"),
(float,"float"), (float,"double"),
(bool, "boolean"), (np.float64,"float"),
(np.float32,"float"), (np.float16,"float"),
(np.float,"float"), (np.int32, "integer"),
(np.int64, "integer"), (np.int8, "integer"),
(np.int16, "integer"), (np.uint32, "integer"),
(np.uint64, "integer"), (np.uint8, "integer"),
(np.uint16, "integer")]
xml_type = dict(types)
python_type = dict(reversed(a) for a in types)
convert_bool={'true':True,'false':False,
'True': True, 'False': False}
class GraphMLWriter(GraphML):
def __init__(self, graph=None, encoding="utf-8",prettyprint=True):
try:
import xml.etree.ElementTree
except ImportError:
raise ImportError('GraphML writer requires '
'xml.elementtree.ElementTree')
self.prettyprint=prettyprint
self.encoding = encoding
self.xml = Element("graphml",
{'xmlns':self.NS_GRAPHML,
'xmlns:xsi':self.NS_XSI,
'xsi:schemaLocation':self.SCHEMALOCATION}
)
self.keys={}
if graph is not None:
self.add_graph_element(graph)
def __str__(self):
if self.prettyprint:
self.indent(self.xml)
s=tostring(self.xml).decode(self.encoding)
return s
def get_key(self, name, attr_type, scope, default):
keys_key = (name, attr_type, scope)
try:
return self.keys[keys_key]
except KeyError:
new_id = "d%i" % len(list(self.keys))
self.keys[keys_key] = new_id
key_kwargs = {"id":new_id,
"for":scope,
"attr.name":name,
"attr.type":attr_type}
key_element=Element("key",**key_kwargs)
# add subelement for data default value if present
if default is not None:
default_element=Element("default")
default_element.text=make_str(default)
key_element.append(default_element)
self.xml.insert(0,key_element)
return new_id
def add_data(self, name, element_type, value,
scope="all",
default=None):
"""
Make a data element for an edge or a node. Keep a log of the
type in the keys table.
"""
if element_type not in self.xml_type:
print name, element_type, value
raise nx.NetworkXError('GraphML writer does not support '
'%s types as data values.' % str(element_type))
key_id = self.get_key(name, self.xml_type[element_type], scope, default)
data_element = Element("data", key=key_id)
data_element.text = make_str(value)
return data_element
def add_attributes(self, scope, xml_obj, data, default):
"""Appends attributes to edges or nodes.
"""
for k,v in data.items():
default_value=default.get(k)
obj=self.add_data(make_str(k), type(v), make_str(v),
scope=scope, default=default_value)
xml_obj.append(obj)
def add_nodes(self, G, graph_element):
for node,data in G.nodes_iter(data=True):
node_element = Element("node", id = make_str(node))
default=G.graph.get('node_default',{})
self.add_attributes("node", node_element, data, default)
graph_element.append(node_element)
def add_edges(self, G, graph_element):
if G.is_multigraph():
for u,v,key,data in G.edges_iter(data=True,keys=True):
edge_element = Element("edge",source=make_str(u),
target=make_str(v))
default=G.graph.get('edge_default',{})
self.add_attributes("edge", edge_element, data, default)
self.add_attributes("edge", edge_element,
{'key':key}, default)
graph_element.append(edge_element)
else:
for u,v,data in G.edges_iter(data=True):
edge_element = Element("edge",source=make_str(u),
target=make_str(v))
default=G.graph.get('edge_default',{})
self.add_attributes("edge", edge_element, data, default)
graph_element.append(edge_element)
def add_graph_element(self, G):
"""
Serialize graph G in GraphML to the stream.
"""
if G.is_directed():
default_edge_type='directed'
else:
default_edge_type='undirected'
graphid=G.graph.pop('id',None)
if graphid is None:
graph_element = Element("graph",
edgedefault = default_edge_type)
else:
graph_element = Element("graph",
edgedefault = default_edge_type,
id=graphid)
default={}
data=dict((k,v) for (k,v) in G.graph.items()
if k not in ['node_default','edge_default'])
self.add_attributes("graph", graph_element, data, default)
self.add_nodes(G,graph_element)
self.add_edges(G,graph_element)
self.xml.append(graph_element)
def add_graphs(self, graph_list):
"""
Add many graphs to this GraphML document.
"""
for G in graph_list:
self.add_graph_element(G)
def dump(self, stream):
if self.prettyprint:
self.indent(self.xml)
document = ElementTree(self.xml)
header='<?xml version="1.0" encoding="%s"?>'%self.encoding
stream.write(header.encode(self.encoding))
document.write(stream, encoding=self.encoding)
def indent(self, elem, level=0):
# in-place prettyprint formatter
i = "\n" + level*" "
if len(elem):
if not elem.text or not elem.text.strip():
elem.text = i + " "
if not elem.tail or not elem.tail.strip():
elem.tail = i
for elem in elem:
self.indent(elem, level+1)
if not elem.tail or not elem.tail.strip():
elem.tail = i
else:
if level and (not elem.tail or not elem.tail.strip()):
elem.tail = i
class GraphMLReader(GraphML):
"""Read a GraphML document. Produces NetworkX graph objects.
"""
def __init__(self, node_type=str):
try:
import xml.etree.ElementTree
except ImportError:
raise ImportError('GraphML reader requires '
'xml.elementtree.ElementTree')
self.node_type=node_type
self.multigraph=False # assume multigraph and test for parallel edges
def __call__(self, stream):
self.xml = ElementTree(file=stream)
(keys,defaults) = self.find_graphml_keys(self.xml)
for g in self.xml.findall("{%s}graph" % self.NS_GRAPHML):
yield self.make_graph(g, keys, defaults)
def make_graph(self, graph_xml, graphml_keys, defaults):
# set default graph type
edgedefault = graph_xml.get("edgedefault", None)
if edgedefault=='directed':
G=nx.MultiDiGraph()
else:
G=nx.MultiGraph()
# set defaults for graph attributes
for key_id,value in defaults.items():
key_for=graphml_keys[key_id]['for']
name=graphml_keys[key_id]['name']
python_type=graphml_keys[key_id]['type']
if key_for=='node':
G.graph['node_default']={name:python_type(value)}
if key_for=='edge':
G.graph['edge_default']={name:python_type(value)}
# hyperedges are not supported
hyperedge=graph_xml.find("{%s}hyperedge" % self.NS_GRAPHML)
if hyperedge is not None:
raise nx.NetworkXError("GraphML reader does not support hyperedges")
# add nodes
for node_xml in graph_xml.findall("{%s}node" % self.NS_GRAPHML):
self.add_node(G, node_xml, graphml_keys)
# add edges
for edge_xml in graph_xml.findall("{%s}edge" % self.NS_GRAPHML):
self.add_edge(G, edge_xml, graphml_keys)
# add graph data
data = self.decode_data_elements(graphml_keys, graph_xml)
G.graph.update(data)
# switch to Graph or DiGraph if no parallel edges were found.
if not self.multigraph:
if G.is_directed():
return nx.DiGraph(G)
else:
return nx.Graph(G)
else:
return G
def add_node(self, G, node_xml, graphml_keys):
"""Add a node to the graph.
"""
# warn on finding unsupported ports tag
ports=node_xml.find("{%s}port" % self.NS_GRAPHML)
if ports is not None:
warnings.warn("GraphML port tag not supported.")
# find the node by id and cast it to the appropriate type
node_id = self.node_type(node_xml.get("id"))
# get data/attributes for node
data = self.decode_data_elements(graphml_keys, node_xml)
G.add_node(node_id, data)
def add_edge(self, G, edge_element, graphml_keys):
"""Add an edge to the graph.
"""
# warn on finding unsupported ports tag
ports=edge_element.find("{%s}port" % self.NS_GRAPHML)
if ports is not None:
warnings.warn("GraphML port tag not supported.")
# raise error if we find mixed directed and undirected edges
directed = edge_element.get("directed")
if G.is_directed() and directed=='false':
raise nx.NetworkXError(\
"directed=false edge found in directed graph.")
if (not G.is_directed()) and directed=='true':
raise nx.NetworkXError(\
"directed=true edge found in undirected graph.")
source = self.node_type(edge_element.get("source"))
target = self.node_type(edge_element.get("target"))
data = self.decode_data_elements(graphml_keys, edge_element)
# GraphML stores edge ids as an attribute
# NetworkX uses them as keys in multigraphs too if no key
# attribute is specified
edge_id = edge_element.get("id")
if edge_id:
data["id"] = edge_id
if G.has_edge(source,target):
# mark this as a multigraph
self.multigraph=True
if edge_id is None:
# no id specified, try using 'key' attribute as id
edge_id=data.pop('key',None)
G.add_edge(source, target, key=edge_id, **data)
def decode_data_elements(self, graphml_keys, obj_xml):
"""Use the key information to decode the data XML if present."""
data = {}
for data_element in obj_xml.findall("{%s}data" % self.NS_GRAPHML):
key = data_element.get("key")
try:
data_name=graphml_keys[key]['name']
data_type=graphml_keys[key]['type']
except KeyError:
raise nx.NetworkXError("Bad GraphML data: no key %s"%key)
text=data_element.text
# assume anything with subelements is a yfiles extension
if text is not None and len(list(data_element))==0:
if data_type==bool:
data[data_name] = self.convert_bool[text]
else:
data[data_name] = data_type(text)
elif len(list(data_element)) > 0:
# Assume yfiles as subelements, try to extract node_label
node_label = None
for node_type in ['ShapeNode', 'SVGNode', 'ImageNode']:
if node_label is None:
node_label = data_element.find("{%s}%s/{%s}NodeLabel" %
(self.NS_Y, node_type, self.NS_Y))
if node_label is not None:
data['label'] = node_label.text
edge_label = data_element.find("{%s}PolyLineEdge/{%s}EdgeLabel"%
(self.NS_Y, (self.NS_Y)))
if edge_label is not None:
data['label'] = edge_label.text
return data
def find_graphml_keys(self, graph_element):
"""Extracts all the keys and key defaults from the xml.
"""
graphml_keys = {}
graphml_key_defaults = {}
for k in graph_element.findall("{%s}key" % self.NS_GRAPHML):
attr_id = k.get("id")
attr_type=k.get('attr.type')
attr_name=k.get("attr.name")
if attr_type is None:
attr_name=k.get('yfiles.type')
attr_type='yfiles'
if attr_name is None:
raise nx.NetworkXError("Unknown key type in file.")
graphml_keys[attr_id] = {
"name":attr_name,
"type":self.python_type[attr_type],
"for":k.get("for")}
# check for "default" subelement of key element
default=k.find("{%s}default" % self.NS_GRAPHML)
if default is not None:
graphml_key_defaults[attr_id]=default.text
return graphml_keys,graphml_key_defaults
# fixture for nose tests
def setup_module(module):
from nose import SkipTest
try:
import xml.etree.ElementTree
except:
raise SkipTest("xml.etree.ElementTree not available")
# fixture for nose tests
def teardown_module(module):
import os
try:
os.unlink('test.graphml')
except:
pass
In [36]:
write_graphml(G, 'graph.graphml', encoding='utf-8',prettyprint=True)
Content source: UCL/sfdata_wrangler
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