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feat: updating data and code

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Saeed Ranjbar 2024-07-10 09:40:57 -04:00
parent 6f4c89a756
commit 911c61c00a
11 changed files with 654 additions and 48 deletions
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7/8/2024 8:54,7/8/2024 4:54,63.66987,64.360722,70.751102,70.751102
7/8/2024 8:59,7/8/2024 4:59,63.583514,64.317544,70.664746,70.837459
7/8/2024 9:04,7/8/2024 5:04,63.799405,64.015296,70.707924,70.707924
7/8/2024 9:09,7/8/2024 5:09,63.756227,63.972118,70.707924,70.794281
7/8/2024 9:14,7/8/2024 5:14,63.66987,64.058474,70.664746,70.794281
7/8/2024 9:19,7/8/2024 5:19,63.583514,63.92894,70.707924,70.535211
7/8/2024 9:24,7/8/2024 5:24,63.713048,64.317544,70.707924,70.664746
7/8/2024 9:29,7/8/2024 5:29,63.583514,64.188009,70.664746,70.751102
7/8/2024 9:34,7/8/2024 5:34,63.713048,64.144831,70.664746,70.794281
7/8/2024 9:39,7/8/2024 5:39,63.799405,64.058474,70.837459,70.794281
7/8/2024 9:44,7/8/2024 5:44,63.540335,63.972118,70.794281,70.794281
7/8/2024 9:49,7/8/2024 5:49,63.583514,63.842583,70.621568,70.57839
7/8/2024 9:54,7/8/2024 5:54,63.756227,63.885761,70.664746,70.794281
7/8/2024 9:59,7/8/2024 5:59,63.756227,63.92894,70.57839,70.448855
7/8/2024 10:04,7/8/2024 6:04,63.799405,64.360722,70.707924,70.837459
7/8/2024 10:09,7/8/2024 6:09,63.92894,64.058474,70.664746,70.707924
7/8/2024 10:14,7/8/2024 6:14,63.885761,64.188009,70.621568,70.535211
7/8/2024 10:19,7/8/2024 6:19,63.92894,64.188009,70.621568,70.751102
7/8/2024 10:24,7/8/2024 6:24,64.058474,64.835683,70.707924,70.621568
7/8/2024 10:29,7/8/2024 6:29,63.92894,64.447079,70.707924,70.621568
7/8/2024 10:34,7/8/2024 6:34,63.92894,64.4039,70.621568,70.707924
7/8/2024 10:39,7/8/2024 6:39,63.885761,64.490257,70.57839,70.621568
7/8/2024 10:44,7/8/2024 6:44,63.799405,63.799405,70.621568,70.707924
7/8/2024 10:49,7/8/2024 6:49,63.799405,64.360722,70.751102,70.794281
7/8/2024 10:54,7/8/2024 6:54,64.015296,64.274366,70.535211,70.621568
7/8/2024 10:59,7/8/2024 6:59,64.058474,64.188009,70.621568,70.621568
7/8/2024 11:04,7/8/2024 7:04,64.015296,64.274366,70.621568,70.621568
7/8/2024 11:09,7/8/2024 7:09,63.92894,64.66297,70.57839,70.621568
7/8/2024 11:14,7/8/2024 7:14,63.972118,64.144831,70.621568,70.621568
7/8/2024 11:19,7/8/2024 7:19,63.885761,64.144831,70.57839,70.535211
7/8/2024 11:24,7/8/2024 7:24,64.101653,64.144831,70.707924,70.751102
7/8/2024 11:29,7/8/2024 7:29,63.972118,64.490257,70.664746,70.751102
7/8/2024 11:34,7/8/2024 7:34,63.972118,64.66297,70.707924,70.664746
7/8/2024 11:39,7/8/2024 7:39,63.972118,64.188009,70.57839,70.492033
7/8/2024 11:44,7/8/2024 7:44,64.317544,64.66297,70.535211,70.535211
7/8/2024 11:49,7/8/2024 7:49,64.188009,64.749326,70.57839,70.707924
7/8/2024 11:54,7/8/2024 7:54,64.101653,64.533435,70.535211,70.621568
7/8/2024 11:59,7/8/2024 7:59,64.188009,64.576613,70.57839,70.448855
7/8/2024 12:04,7/8/2024 8:04,64.274366,64.792505,70.57839,70.57839
7/8/2024 12:09,7/8/2024 8:09,64.231187,64.66297,70.492033,70.492033
7/8/2024 12:14,7/8/2024 8:14,64.274366,64.706148,70.405677,70.57839
7/8/2024 12:19,7/8/2024 8:19,64.360722,64.749326,70.535211,70.362498
7/8/2024 12:24,7/8/2024 8:24,64.317544,64.835683,70.492033,70.621568
7/8/2024 12:29,7/8/2024 8:29,64.490257,64.66297,70.492033,70.621568
7/8/2024 12:34,7/8/2024 8:34,64.447079,64.533435,70.448855,70.621568
7/8/2024 12:39,7/8/2024 8:39,64.447079,64.66297,70.57839,70.57839
7/8/2024 12:44,7/8/2024 8:44,64.317544,64.66297,70.57839,70.57839
7/8/2024 12:49,7/8/2024 8:49,64.533435,64.447079,70.448855,70.57839
7/8/2024 12:54,7/8/2024 8:54,64.447079,64.66297,70.405677,70.448855
7/8/2024 12:59,7/8/2024 8:59,64.447079,64.706148,70.535211,70.362498
7/8/2024 13:04,7/8/2024 9:04,64.447079,64.749326,70.751102,70.751102

1 Date/Time (UTC) Date/Time (--4:0:0) Sewer Water In Sewer Water Out Return Air Supply Air
3434 7/7/2024 11:42 7/7/2024 7:42 63.66987 63.713048 70.103429 70.103429
3435 7/7/2024 11:47 7/7/2024 7:47 63.626692 64.015296 69.973894 70.31932
3436 7/7/2024 11:52 7/7/2024 7:52 63.713048 64.360722 70.103429 69.973894
3437 7/7/2024 11:54 7/7/2024 7:54 63.410801 64.490257 70.017072 69.930716
3438 7/7/2024 11:59 7/7/2024 7:59 63.540335 64.058474 70.146607 70.017072
3439 7/7/2024 12:04 7/7/2024 8:04 63.453979 64.274366 70.189785 69.973894
3440 7/7/2024 12:09 7/7/2024 8:09 63.799405 64.619792 69.930716 70.017072
3441 7/7/2024 12:14 7/7/2024 8:14 63.626692 64.188009 70.146607 69.973894
3442 7/7/2024 12:19 7/7/2024 8:19 64.015296 64.274366 70.103429 70.017072
3443 7/7/2024 12:24 7/7/2024 8:24 63.842583 64.4039 70.017072 70.146607
3444 7/7/2024 12:29 7/7/2024 8:29 64.058474 64.447079 69.973894 70.103429
3445 7/7/2024 12:34 7/7/2024 8:34 64.101653 65.051574 70.017072 69.930716
3446 7/7/2024 12:39 7/7/2024 8:39 64.360722 64.749326 69.973894 69.887538
3447 7/7/2024 12:44 7/7/2024 8:44 64.231187 64.619792 69.930716 69.844359
3448 7/7/2024 12:49 7/7/2024 8:49 64.231187 64.835683 69.973894 69.844359
3449 7/7/2024 12:54 7/7/2024 8:54 64.4039 64.965218 69.887538 69.844359
3450 7/7/2024 12:59 7/7/2024 8:59 64.619792 64.922039 70.060251 70.146607
3451 7/7/2024 13:04 7/7/2024 9:04 64.792505 64.965218 70.146607 70.146607
3452 7/7/2024 13:09 7/7/2024 9:09 64.878861 65.181109 70.017072 70.017072
3453 7/7/2024 13:14 7/7/2024 9:14 65.008396 65.224287 69.930716 70.146607
3454 7/7/2024 13:19 7/7/2024 9:19 64.835683 65.310643 69.930716 70.017072
3455 7/7/2024 13:24 7/7/2024 9:24 65.181109 64.835683 69.973894 69.844359
3456 7/7/2024 13:29 7/7/2024 9:29 65.051574 65.181109 70.146607 70.017072
3457 7/7/2024 13:34 7/7/2024 9:34 65.051574 65.526535 70.146607 70.189785
3458 7/7/2024 13:39 7/7/2024 9:39 64.965218 65.397 70.060251 70.189785
3459 7/7/2024 13:44 7/7/2024 9:44 65.008396 65.353822 69.973894 70.103429
3460 7/7/2024 13:49 7/7/2024 9:49 64.835683 65.181109 70.017072 70.103429
3461 7/7/2024 13:54 7/7/2024 9:54 64.878861 65.353822 70.060251 70.103429
3462 7/7/2024 13:59 7/7/2024 9:59 64.749326 64.835683 70.31932 70.448855
3463 7/7/2024 14:04 7/7/2024 10:04 64.835683 64.878861 70.060251 70.232964
3464 7/7/2024 14:09 7/7/2024 10:09 65.051574 65.051574 70.405677 70.448855
3465 7/7/2024 14:14 7/7/2024 10:14 65.051574 64.965218 70.492033 70.448855
3466 7/7/2024 14:19 7/7/2024 10:19 64.922039 65.051574 70.276142 70.448855
3467 7/7/2024 14:24 7/7/2024 10:24 64.922039 64.922039 70.448855 70.535211
3468 7/7/2024 14:29 7/7/2024 10:29 64.965218 64.922039 70.492033 70.362498
3469 7/7/2024 14:34 7/7/2024 10:34 65.008396 64.835683 70.31932 70.362498
3470 7/7/2024 14:39 7/7/2024 10:39 65.051574 65.181109 70.492033 70.448855
3471 7/7/2024 14:44 7/7/2024 10:44 64.706148 65.526535 70.535211 70.448855
3472 7/7/2024 14:49 7/7/2024 10:49 64.922039 65.181109 70.189785 70.492033
3473 7/7/2024 14:54 7/7/2024 10:54 65.008396 65.051574 70.448855 70.664746
3474 7/7/2024 14:59 7/7/2024 10:59 65.008396 65.137931 70.492033 70.621568
3475 7/7/2024 15:04 7/7/2024 11:04 64.965218 65.224287 70.535211 70.621568
3476 7/7/2024 15:09 7/7/2024 11:09 65.008396 65.397 70.492033 70.837459
3477 7/7/2024 15:14 7/7/2024 11:14 65.137931 65.828782 70.448855 70.492033
3478 7/7/2024 15:19 7/7/2024 11:19 65.094752 65.483356 70.362498 70.621568
3479 7/7/2024 15:24 7/7/2024 11:24 65.137931 65.267465 70.405677 70.535211
3480 7/7/2024 15:29 7/7/2024 11:29 64.965218 65.310643 70.448855 70.57839
3481 7/7/2024 15:34 7/7/2024 11:34 65.051574 65.094752 70.405677 70.621568
3482 7/7/2024 15:39 7/7/2024 11:39 64.922039 65.656069 70.31932 70.448855
3483 7/7/2024 15:44 7/7/2024 11:44 65.051574 65.224287 70.405677 70.621568
3484 7/7/2024 15:49 7/7/2024 11:49 65.094752 65.699248 70.362498 70.664746
3485 7/7/2024 15:54 7/7/2024 11:54 65.094752 65.397 70.448855 70.57839
3486 7/7/2024 15:59 7/7/2024 11:59 64.965218 65.051574 70.405677 70.535211
3487 7/7/2024 16:04 7/7/2024 12:04 65.008396 65.353822 70.492033 70.535211
3488 7/7/2024 16:09 7/7/2024 12:09 64.965218 65.871961 70.448855 70.621568
3489 7/7/2024 16:14 7/7/2024 12:14 64.922039 65.440178 70.448855 70.621568
3490 7/7/2024 16:19 7/7/2024 12:19 64.878861 65.267465 70.492033 70.535211
3491 7/7/2024 16:24 7/7/2024 12:24 64.878861 65.181109 70.405677 70.57839
3492 7/7/2024 16:29 7/7/2024 12:29 65.094752 65.181109 70.448855 70.751102
3493 7/7/2024 16:34 7/7/2024 12:34 65.094752 65.310643 70.405677 70.57839
3494 7/7/2024 16:39 7/7/2024 12:39 65.137931 65.569713 70.405677 70.664746
3495 7/7/2024 16:44 7/7/2024 12:44 65.267465 65.569713 70.57839 70.492033
3496 7/7/2024 16:49 7/7/2024 12:49 65.483356 65.915139 70.621568 70.621568
3497 7/7/2024 16:54 7/7/2024 12:54 65.526535 65.785604 70.621568 70.794281
3498 7/7/2024 16:59 7/7/2024 12:59 65.656069 65.483356 70.621568 70.966994
3499 7/7/2024 17:04 7/7/2024 13:04 65.440178 65.612891 70.535211 70.664746
3500 7/7/2024 17:09 7/7/2024 13:09 65.353822 65.526535 70.621568 70.837459
3501 7/7/2024 17:14 7/7/2024 13:14 65.397 65.828782 70.535211 70.664746
3502 7/7/2024 17:19 7/7/2024 13:19 65.353822 65.612891 70.535211 70.751102
3503 7/7/2024 17:24 7/7/2024 13:24 65.181109 65.440178 70.621568 70.837459
3504 7/7/2024 17:29 7/7/2024 13:29 65.137931 65.008396 70.751102 70.837459
3505 7/7/2024 17:34 7/7/2024 13:34 65.008396 65.612891 70.621568 70.880637
3506 7/7/2024 17:39 7/7/2024 13:39 65.094752 65.483356 70.57839 70.880637
3507 7/7/2024 17:44 7/7/2024 13:44 65.137931 65.181109 70.880637 71.010172
3508 7/7/2024 17:49 7/7/2024 13:49 65.008396 65.353822 70.535211 70.880637
3509 7/7/2024 17:54 7/7/2024 13:54 65.051574 65.267465 70.751102 70.837459
3510 7/7/2024 17:59 7/7/2024 13:59 64.792505 65.397 70.837459 71.010172
3511 7/7/2024 18:04 7/7/2024 14:04 64.965218 65.569713 70.621568 70.880637
3512 7/7/2024 18:09 7/7/2024 14:09 65.051574 64.792505 70.794281 71.182885
3513 7/7/2024 18:14 7/7/2024 14:14 64.619792 65.181109 70.707924 70.880637
3514 7/7/2024 18:19 7/7/2024 14:19 64.922039 65.051574 70.794281 71.096528
3515 7/7/2024 18:24 7/7/2024 14:24 64.66297 65.224287 70.880637 71.182885
3516 7/7/2024 18:29 7/7/2024 14:29 64.619792 65.267465 70.751102 70.966994
3517 7/7/2024 18:34 7/7/2024 14:34 64.706148 65.008396 70.837459 70.837459
3518 7/7/2024 18:39 7/7/2024 14:39 64.706148 64.878861 70.751102 70.880637
3519 7/7/2024 18:44 7/7/2024 14:44 64.533435 64.922039 70.794281 70.707924
3520 7/7/2024 18:49 7/7/2024 14:49 64.533435 65.094752 70.880637 70.880637
3521 7/7/2024 18:54 7/7/2024 14:54 64.490257 64.878861 70.664746 70.966994
3522 7/7/2024 18:59 7/7/2024 14:59 64.490257 65.051574 70.923815 70.880637
3523 7/7/2024 19:04 7/7/2024 15:04 64.533435 65.267465 70.880637 70.923815
3524 7/7/2024 19:09 7/7/2024 15:09 64.447079 64.965218 70.837459 71.010172
3525 7/7/2024 19:14 7/7/2024 15:14 64.490257 64.66297 70.880637 71.010172
3526 7/7/2024 19:19 7/7/2024 15:19 64.533435 64.66297 70.751102 71.096528
3527 7/7/2024 19:24 7/7/2024 15:24 64.4039 65.008396 71.010172 71.31242
3528 7/7/2024 19:29 7/7/2024 15:29 64.576613 64.706148 70.966994 71.096528
3529 7/7/2024 19:34 7/7/2024 15:34 64.447079 65.267465 70.923815 71.182885
3530 7/7/2024 19:39 7/7/2024 15:39 64.360722 65.397 71.096528 71.096528
3531 7/7/2024 19:44 7/7/2024 15:44 64.360722 64.965218 70.923815 71.139707
3532 7/7/2024 19:49 7/7/2024 15:49 64.360722 64.792505 70.880637 71.05335
3533 7/7/2024 19:54 7/7/2024 15:54 64.490257 64.619792 70.880637 71.010172
3534 7/7/2024 19:59 7/7/2024 15:59 64.447079 65.137931 70.923815 71.096528
3535 7/7/2024 20:04 7/7/2024 16:04 64.490257 65.008396 70.751102 71.182885
3536 7/7/2024 20:09 7/7/2024 16:09 64.4039 64.4039 70.837459 71.096528
3537 7/7/2024 20:14 7/7/2024 16:14 64.360722 64.706148 71.096528 71.269241
3538 7/7/2024 20:19 7/7/2024 16:19 64.490257 64.965218 70.966994 71.139707
3539 7/7/2024 20:24 7/7/2024 16:24 64.447079 64.619792 70.966994 71.269241
3540 7/7/2024 20:29 7/7/2024 16:29 64.360722 64.965218 71.05335 71.139707
3541 7/7/2024 20:34 7/7/2024 16:34 64.360722 64.447079 70.880637 71.226063
3542 7/7/2024 20:39 7/7/2024 16:39 64.360722 64.749326 71.010172 71.05335
3543 7/7/2024 20:44 7/7/2024 16:44 64.490257 64.878861 70.923815 71.182885
3544 7/7/2024 20:49 7/7/2024 16:49 64.231187 64.965218 71.010172 71.139707
3545 7/7/2024 20:54 7/7/2024 16:54 64.144831 64.792505 71.010172 71.31242
3546 7/7/2024 20:59 7/7/2024 16:59 64.447079 64.490257 70.837459 71.182885
3547 7/7/2024 21:04 7/7/2024 17:04 64.490257 65.094752 71.010172 71.096528
3548 7/7/2024 21:09 7/7/2024 17:09 64.4039 65.008396 71.010172 71.139707
3549 7/7/2024 21:14 7/7/2024 17:14 64.66297 64.835683 71.05335 71.441954
3550 7/7/2024 21:19 7/7/2024 17:19 64.490257 65.094752 71.139707 71.269241
3551 7/7/2024 21:24 7/7/2024 17:24 64.274366 64.965218 70.966994 71.226063
3552 7/7/2024 21:29 7/7/2024 17:29 64.490257 64.533435 71.010172 71.139707
3553 7/7/2024 21:34 7/7/2024 17:34 64.447079 65.094752 71.010172 71.269241
3554 7/7/2024 21:39 7/7/2024 17:39 64.4039 65.181109 70.923815 71.182885
3555 7/7/2024 21:44 7/7/2024 17:44 64.533435 65.224287 71.182885 71.226063
3556 7/7/2024 21:49 7/7/2024 17:49 64.66297 65.094752 70.880637 71.139707
3557 7/7/2024 21:54 7/7/2024 17:54 64.490257 64.878861 71.05335 71.269241
3558 7/7/2024 21:59 7/7/2024 17:59 64.66297 65.051574 70.966994 71.182885
3559 7/7/2024 22:04 7/7/2024 18:04 64.706148 65.137931 70.923815 71.096528
3560 7/7/2024 22:09 7/7/2024 18:09 64.576613 64.792505 70.837459 71.096528
3561 7/7/2024 22:14 7/7/2024 18:14 64.66297 65.483356 70.880637 71.139707
3562 7/7/2024 22:19 7/7/2024 18:19 64.533435 65.008396 71.31242 71.355598
3563 7/7/2024 22:24 7/7/2024 18:24 64.533435 65.094752 71.096528 71.355598
3564 7/7/2024 22:29 7/7/2024 18:29 64.490257 65.181109 70.880637 71.096528
3565 7/7/2024 22:34 7/7/2024 18:34 64.447079 65.051574 70.837459 71.139707
3566 7/7/2024 22:39 7/7/2024 18:39 64.533435 65.094752 70.923815 71.226063
3567 7/7/2024 22:44 7/7/2024 18:44 64.533435 64.965218 70.966994 71.182885
3568 7/7/2024 22:49 7/7/2024 18:49 64.4039 64.792505 71.05335 71.139707
3569 7/7/2024 22:54 7/7/2024 18:54 64.835683 64.749326 70.966994 71.226063
3570 7/7/2024 22:59 7/7/2024 18:59 64.360722 64.965218 71.096528 71.657846
3571 7/7/2024 23:04 7/7/2024 19:04 64.360722 64.922039 71.182885 71.614667
3572 7/7/2024 23:09 7/7/2024 19:09 64.4039 65.267465 71.096528 71.05335
3573 7/7/2024 23:14 7/7/2024 19:14 64.576613 64.965218 71.010172 71.139707
3574 7/7/2024 23:19 7/7/2024 19:19 64.490257 64.922039 70.923815 71.139707
3575 7/7/2024 23:24 7/7/2024 19:24 64.576613 65.094752 71.010172 71.182885
3576 7/7/2024 23:29 7/7/2024 19:29 64.792505 65.051574 70.923815 71.269241
3577 7/7/2024 23:34 7/7/2024 19:34 65.008396 65.008396 70.794281 71.139707
3578 7/7/2024 23:39 7/7/2024 19:39 64.835683 65.181109 71.010172 71.139707
3579 7/7/2024 23:44 7/7/2024 19:44 64.965218 65.137931 71.226063 71.441954
3580 7/7/2024 23:49 7/7/2024 19:49 65.008396 65.224287 70.966994 71.05335
3581 7/7/2024 23:54 7/7/2024 19:54 64.835683 65.094752 71.096528 71.31242
3582 7/7/2024 23:59 7/7/2024 19:59 65.094752 65.569713 71.05335 71.355598
3583 7/8/2024 0:04 7/7/2024 20:04 65.051574 65.353822 71.05335 71.269241
3584 7/8/2024 0:09 7/7/2024 20:09 65.267465 65.569713 71.05335 71.441954
3585 7/8/2024 0:14 7/7/2024 20:14 65.008396 65.267465 71.139707 71.096528
3586 7/8/2024 0:19 7/7/2024 20:19 65.051574 65.526535 71.182885 71.269241
3587 7/8/2024 0:24 7/7/2024 20:24 65.137931 65.267465 71.139707 71.139707
3588 7/8/2024 0:29 7/7/2024 20:29 65.137931 65.656069 71.139707 71.441954
3589 7/8/2024 0:34 7/7/2024 20:34 65.224287 65.699248 71.05335 71.226063
3590 7/8/2024 0:39 7/7/2024 20:39 65.267465 65.569713 71.010172 71.269241
3591 7/8/2024 0:44 7/7/2024 20:44 65.008396 65.526535 71.010172 71.096528
3592 7/8/2024 0:49 7/7/2024 20:49 65.224287 65.742426 70.923815 71.226063
3593 7/8/2024 0:54 7/7/2024 20:54 65.267465 65.397 70.880637 71.096528
3594 7/8/2024 0:59 7/7/2024 20:59 65.224287 65.526535 71.139707 71.05335
3595 7/8/2024 1:04 7/7/2024 21:04 65.440178 65.656069 71.05335 71.05335
3596 7/8/2024 1:09 7/7/2024 21:09 65.612891 65.612891 70.923815 71.05335
3597 7/8/2024 1:14 7/7/2024 21:14 65.008396 65.569713 71.010172 71.05335
3598 7/8/2024 1:19 7/7/2024 21:19 65.569713 65.483356 70.966994 71.05335
3599 7/8/2024 1:24 7/7/2024 21:24 65.397 65.742426 70.880637 71.139707
3600 7/8/2024 1:29 7/7/2024 21:29 65.310643 65.526535 70.966994 71.182885
3601 7/8/2024 1:34 7/7/2024 21:34 65.310643 65.526535 70.794281 70.966994
3602 7/8/2024 1:39 7/7/2024 21:39 65.310643 65.267465 71.05335 71.05335
3603 7/8/2024 1:44 7/7/2024 21:44 65.267465 64.965218 70.966994 71.269241
3604 7/8/2024 1:49 7/7/2024 21:49 65.224287 65.267465 71.05335 71.096528
3605 7/8/2024 1:54 7/7/2024 21:54 65.310643 65.785604 70.966994 71.096528
3606 7/8/2024 1:59 7/7/2024 21:59 65.224287 65.699248 71.355598 71.355598
3607 7/8/2024 2:04 7/7/2024 22:04 65.353822 65.699248 70.966994 71.139707
3608 7/8/2024 2:09 7/7/2024 22:09 65.224287 65.440178 71.096528 71.182885
3609 7/8/2024 2:14 7/7/2024 22:14 65.051574 65.871961 70.923815 71.182885
3610 7/8/2024 2:19 7/7/2024 22:19 65.310643 65.915139 70.966994 71.355598
3611 7/8/2024 2:24 7/7/2024 22:24 65.440178 65.526535 71.096528 71.269241
3612 7/8/2024 2:29 7/7/2024 22:29 65.094752 65.871961 70.966994 71.139707
3613 7/8/2024 2:34 7/7/2024 22:34 65.224287 65.310643 71.05335 71.269241
3614 7/8/2024 2:39 7/7/2024 22:39 65.008396 65.440178 71.139707 71.441954
3615 7/8/2024 2:44 7/7/2024 22:44 65.310643 65.785604 71.139707 71.528311
3616 7/8/2024 2:49 7/7/2024 22:49 65.224287 65.483356 71.010172 71.441954
3617 7/8/2024 2:54 7/7/2024 22:54 65.353822 65.483356 71.096528 71.571489
3618 7/8/2024 2:59 7/7/2024 22:59 65.094752 65.310643 71.269241 71.485133
3619 7/8/2024 3:04 7/7/2024 23:04 65.137931 65.181109 71.182885 71.096528
3620 7/8/2024 3:09 7/7/2024 23:09 65.008396 65.181109 71.355598 71.139707
3621 7/8/2024 3:14 7/7/2024 23:14 64.835683 65.353822 71.182885 71.226063
3622 7/8/2024 3:19 7/7/2024 23:19 64.835683 65.267465 71.05335 71.05335
3623 7/8/2024 3:24 7/7/2024 23:24 65.224287 65.310643 71.226063 71.355598
3624 7/8/2024 3:29 7/7/2024 23:29 65.008396 65.181109 71.05335 71.139707
3625 7/8/2024 3:34 7/7/2024 23:34 64.706148 65.137931 71.182885 71.226063
3626 7/8/2024 3:39 7/7/2024 23:39 64.922039 64.965218 71.182885 71.139707
3627 7/8/2024 3:44 7/7/2024 23:44 64.922039 65.397 71.182885 71.226063
3628 7/8/2024 3:49 7/7/2024 23:49 64.965218 65.526535 71.226063 71.226063
3629 7/8/2024 3:54 7/7/2024 23:54 65.008396 65.224287 71.226063 71.226063
3630 7/8/2024 3:59 7/7/2024 23:59 64.749326 65.310643 71.139707 71.441954
3631 7/8/2024 4:04 7/8/2024 0:04 64.835683 64.922039 71.182885 71.31242
3632 7/8/2024 4:09 7/8/2024 0:09 64.706148 64.965218 71.269241 71.139707
3633 7/8/2024 4:14 7/8/2024 0:14 64.706148 64.922039 71.096528 71.096528
3634 7/8/2024 4:19 7/8/2024 0:19 64.490257 64.965218 71.010172 71.05335
3635 7/8/2024 4:24 7/8/2024 0:24 64.447079 64.835683 71.010172 71.096528
3636 7/8/2024 4:29 7/8/2024 0:29 64.66297 64.878861 71.096528 71.096528
3637 7/8/2024 4:34 7/8/2024 0:34 64.360722 64.878861 71.096528 71.182885
3638 7/8/2024 4:39 7/8/2024 0:39 64.447079 64.922039 71.05335 71.226063
3639 7/8/2024 4:44 7/8/2024 0:44 64.360722 64.922039 71.096528 71.182885
3640 7/8/2024 4:49 7/8/2024 0:49 64.533435 64.706148 71.096528 71.139707
3641 7/8/2024 4:54 7/8/2024 0:54 64.490257 64.835683 70.966994 71.139707
3642 7/8/2024 4:59 7/8/2024 0:59 64.706148 64.965218 71.010172 71.139707
3643 7/8/2024 5:04 7/8/2024 1:04 64.706148 64.965218 71.226063 71.05335
3644 7/8/2024 5:09 7/8/2024 1:09 64.317544 64.706148 71.096528 71.05335
3645 7/8/2024 5:14 7/8/2024 1:14 64.274366 64.533435 71.05335 71.182885
3646 7/8/2024 5:19 7/8/2024 1:19 64.447079 64.576613 71.139707 71.182885
3647 7/8/2024 5:24 7/8/2024 1:24 64.619792 64.749326 71.226063 71.269241
3648 7/8/2024 5:29 7/8/2024 1:29 64.533435 64.749326 71.139707 71.139707
3649 7/8/2024 5:34 7/8/2024 1:34 64.447079 64.619792 70.966994 70.794281
3650 7/8/2024 5:39 7/8/2024 1:39 63.885761 64.576613 70.966994 70.966994
3651 7/8/2024 5:44 7/8/2024 1:44 63.92894 64.533435 71.139707 71.139707
3652 7/8/2024 5:49 7/8/2024 1:49 64.188009 64.576613 70.794281 70.794281
3653 7/8/2024 5:54 7/8/2024 1:54 64.015296 64.835683 70.880637 70.966994
3654 7/8/2024 5:59 7/8/2024 1:59 64.274366 64.576613 70.707924 70.923815
3655 7/8/2024 6:04 7/8/2024 2:04 63.92894 64.533435 70.923815 70.923815
3656 7/8/2024 6:09 7/8/2024 2:09 64.015296 64.66297 71.05335 71.010172
3657 7/8/2024 6:14 7/8/2024 2:14 63.972118 64.144831 70.966994 70.966994
3658 7/8/2024 6:19 7/8/2024 2:19 63.92894 64.274366 70.923815 71.05335
3659 7/8/2024 6:24 7/8/2024 2:24 63.972118 63.92894 71.05335 71.05335
3660 7/8/2024 6:29 7/8/2024 2:29 64.058474 64.4039 71.05335 71.010172
3661 7/8/2024 6:34 7/8/2024 2:34 63.972118 64.101653 71.010172 71.010172
3662 7/8/2024 6:39 7/8/2024 2:39 63.92894 64.66297 71.010172 71.139707
3663 7/8/2024 6:44 7/8/2024 2:44 63.842583 64.188009 71.010172 70.794281
3664 7/8/2024 6:49 7/8/2024 2:49 63.842583 64.015296 71.05335 71.05335
3665 7/8/2024 6:54 7/8/2024 2:54 64.015296 63.92894 70.664746 70.707924
3666 7/8/2024 6:59 7/8/2024 2:59 63.842583 63.92894 70.664746 70.664746
3667 7/8/2024 7:04 7/8/2024 3:04 63.92894 64.274366 70.966994 70.923815
3668 7/8/2024 7:09 7/8/2024 3:09 63.92894 64.360722 71.010172 71.010172
3669 7/8/2024 7:14 7/8/2024 3:14 63.92894 64.231187 70.923815 70.57839
3670 7/8/2024 7:19 7/8/2024 3:19 63.972118 64.274366 70.664746 70.751102
3671 7/8/2024 7:24 7/8/2024 3:24 63.972118 64.274366 70.57839 70.664746
3672 7/8/2024 7:29 7/8/2024 3:29 63.972118 64.447079 70.751102 70.621568
3673 7/8/2024 7:34 7/8/2024 3:34 63.885761 64.274366 70.751102 70.664746
3674 7/8/2024 7:39 7/8/2024 3:39 63.92894 64.447079 70.664746 70.664746
3675 7/8/2024 7:44 7/8/2024 3:44 64.015296 64.360722 70.664746 70.751102
3676 7/8/2024 7:49 7/8/2024 3:49 63.842583 63.972118 70.664746 70.794281
3677 7/8/2024 7:54 7/8/2024 3:54 63.583514 64.360722 70.751102 70.664746
3678 7/8/2024 7:59 7/8/2024 3:59 64.101653 63.972118 70.621568 70.751102
3679 7/8/2024 8:04 7/8/2024 4:04 64.058474 64.274366 70.751102 70.707924
3680 7/8/2024 8:09 7/8/2024 4:09 63.713048 64.015296 70.751102 70.837459
3681 7/8/2024 8:14 7/8/2024 4:14 63.756227 64.274366 70.794281 70.707924
3682 7/8/2024 8:19 7/8/2024 4:19 63.799405 64.317544 70.664746 70.751102
3683 7/8/2024 8:24 7/8/2024 4:24 63.540335 64.188009 70.621568 70.794281
3684 7/8/2024 8:29 7/8/2024 4:29 63.713048 64.144831 70.923815 70.664746
3685 7/8/2024 8:34 7/8/2024 4:34 63.626692 64.360722 70.751102 70.664746
3686 7/8/2024 8:39 7/8/2024 4:39 63.66987 64.144831 70.664746 70.880637
3687 7/8/2024 8:44 7/8/2024 4:44 63.713048 63.92894 70.751102 70.794281
3688 7/8/2024 8:49 7/8/2024 4:49 63.583514 63.92894 70.751102 70.794281
3689 7/8/2024 8:54 7/8/2024 4:54 63.66987 64.360722 70.751102 70.751102
3690 7/8/2024 8:59 7/8/2024 4:59 63.583514 64.317544 70.664746 70.837459
3691 7/8/2024 9:04 7/8/2024 5:04 63.799405 64.015296 70.707924 70.707924
3692 7/8/2024 9:09 7/8/2024 5:09 63.756227 63.972118 70.707924 70.794281
3693 7/8/2024 9:14 7/8/2024 5:14 63.66987 64.058474 70.664746 70.794281
3694 7/8/2024 9:19 7/8/2024 5:19 63.583514 63.92894 70.707924 70.535211
3695 7/8/2024 9:24 7/8/2024 5:24 63.713048 64.317544 70.707924 70.664746
3696 7/8/2024 9:29 7/8/2024 5:29 63.583514 64.188009 70.664746 70.751102
3697 7/8/2024 9:34 7/8/2024 5:34 63.713048 64.144831 70.664746 70.794281
3698 7/8/2024 9:39 7/8/2024 5:39 63.799405 64.058474 70.837459 70.794281
3699 7/8/2024 9:44 7/8/2024 5:44 63.540335 63.972118 70.794281 70.794281
3700 7/8/2024 9:49 7/8/2024 5:49 63.583514 63.842583 70.621568 70.57839
3701 7/8/2024 9:54 7/8/2024 5:54 63.756227 63.885761 70.664746 70.794281
3702 7/8/2024 9:59 7/8/2024 5:59 63.756227 63.92894 70.57839 70.448855
3703 7/8/2024 10:04 7/8/2024 6:04 63.799405 64.360722 70.707924 70.837459
3704 7/8/2024 10:09 7/8/2024 6:09 63.92894 64.058474 70.664746 70.707924
3705 7/8/2024 10:14 7/8/2024 6:14 63.885761 64.188009 70.621568 70.535211
3706 7/8/2024 10:19 7/8/2024 6:19 63.92894 64.188009 70.621568 70.751102
3707 7/8/2024 10:24 7/8/2024 6:24 64.058474 64.835683 70.707924 70.621568
3708 7/8/2024 10:29 7/8/2024 6:29 63.92894 64.447079 70.707924 70.621568
3709 7/8/2024 10:34 7/8/2024 6:34 63.92894 64.4039 70.621568 70.707924
3710 7/8/2024 10:39 7/8/2024 6:39 63.885761 64.490257 70.57839 70.621568
3711 7/8/2024 10:44 7/8/2024 6:44 63.799405 63.799405 70.621568 70.707924
3712 7/8/2024 10:49 7/8/2024 6:49 63.799405 64.360722 70.751102 70.794281
3713 7/8/2024 10:54 7/8/2024 6:54 64.015296 64.274366 70.535211 70.621568
3714 7/8/2024 10:59 7/8/2024 6:59 64.058474 64.188009 70.621568 70.621568
3715 7/8/2024 11:04 7/8/2024 7:04 64.015296 64.274366 70.621568 70.621568
3716 7/8/2024 11:09 7/8/2024 7:09 63.92894 64.66297 70.57839 70.621568
3717 7/8/2024 11:14 7/8/2024 7:14 63.972118 64.144831 70.621568 70.621568
3718 7/8/2024 11:19 7/8/2024 7:19 63.885761 64.144831 70.57839 70.535211
3719 7/8/2024 11:24 7/8/2024 7:24 64.101653 64.144831 70.707924 70.751102
3720 7/8/2024 11:29 7/8/2024 7:29 63.972118 64.490257 70.664746 70.751102
3721 7/8/2024 11:34 7/8/2024 7:34 63.972118 64.66297 70.707924 70.664746
3722 7/8/2024 11:39 7/8/2024 7:39 63.972118 64.188009 70.57839 70.492033
3723 7/8/2024 11:44 7/8/2024 7:44 64.317544 64.66297 70.535211 70.535211
3724 7/8/2024 11:49 7/8/2024 7:49 64.188009 64.749326 70.57839 70.707924
3725 7/8/2024 11:54 7/8/2024 7:54 64.101653 64.533435 70.535211 70.621568
3726 7/8/2024 11:59 7/8/2024 7:59 64.188009 64.576613 70.57839 70.448855
3727 7/8/2024 12:04 7/8/2024 8:04 64.274366 64.792505 70.57839 70.57839
3728 7/8/2024 12:09 7/8/2024 8:09 64.231187 64.66297 70.492033 70.492033
3729 7/8/2024 12:14 7/8/2024 8:14 64.274366 64.706148 70.405677 70.57839
3730 7/8/2024 12:19 7/8/2024 8:19 64.360722 64.749326 70.535211 70.362498
3731 7/8/2024 12:24 7/8/2024 8:24 64.317544 64.835683 70.492033 70.621568
3732 7/8/2024 12:29 7/8/2024 8:29 64.490257 64.66297 70.492033 70.621568
3733 7/8/2024 12:34 7/8/2024 8:34 64.447079 64.533435 70.448855 70.621568
3734 7/8/2024 12:39 7/8/2024 8:39 64.447079 64.66297 70.57839 70.57839
3735 7/8/2024 12:44 7/8/2024 8:44 64.317544 64.66297 70.57839 70.57839
3736 7/8/2024 12:49 7/8/2024 8:49 64.533435 64.447079 70.448855 70.57839
3737 7/8/2024 12:54 7/8/2024 8:54 64.447079 64.66297 70.405677 70.448855
3738 7/8/2024 12:59 7/8/2024 8:59 64.447079 64.706148 70.535211 70.362498
3739 7/8/2024 13:04 7/8/2024 9:04 64.447079 64.749326 70.751102 70.751102

10
daily_plots.py
View File

@ -29,13 +29,13 @@ for day in pd.Index(data.index.date).unique():
fig = go.Figure() fig = go.Figure()
fig.add_trace(go.Scatter(x=day_data.index, y=day_data['Sewer Water In Smooth'], mode='lines', name='Sewer Water In', fig.add_trace(go.Scatter(x=day_data.index, y=day_data['Sewer Water In Smooth'], mode='lines', name='Sewer Water In',
line=dict(shape='spline', color='blue', width=4, dash='solid'))) line=dict(shape='spline', color='blue', width=2, dash='solid')))
fig.add_trace(go.Scatter(x=day_data.index, y=day_data['Sewer Water Out Smooth'], mode='lines', name='Sewer Water Out', fig.add_trace(go.Scatter(x=day_data.index, y=day_data['Sewer Water Out Smooth'], mode='lines', name='Sewer Water Out',
line=dict(shape='spline', color='red', width=4, dash='solid'))) line=dict(shape='spline', color='red', width=2, dash='solid')))
fig.add_trace(go.Scatter(x=day_data.index, y=day_data['Return Air Smooth'], mode='lines', name='Return Air', fig.add_trace(go.Scatter(x=day_data.index, y=day_data['Return Air Smooth'], mode='lines', name='Return Air',
line=dict(shape='spline', color='orange', width=4, dash='solid'))) line=dict(shape='spline', color='orange', width=2, dash='solid')))
fig.add_trace(go.Scatter(x=day_data.index, y=day_data['Supply Air Smooth'], mode='lines', name='Supply Air', fig.add_trace(go.Scatter(x=day_data.index, y=day_data['Supply Air Smooth'], mode='lines', name='Supply Air',
line=dict(shape='spline', color='green', width=4, dash='solid'))) line=dict(shape='spline', color='green', width=2, dash='solid')))
fig.update_layout( fig.update_layout(
title=dict( title=dict(
@ -60,6 +60,6 @@ for day in pd.Index(data.index.date).unique():
) )
# Save the plot (requires kaleido package) # Save the plot (requires kaleido package)
fig.write_image(f'plotly_plot_{day}.png') fig.write_image(f'temperature_variations_without_Q_{day}.png')

54
double_y.py
View File

@ -1,44 +1,64 @@
import pandas as pd import pandas as pd
import plotly.graph_objects as go
from plotly.subplots import make_subplots
# Load the data (assuming you've already loaded and processed it as shown) # Load the data
data = pd.read_csv('06-25_07-07.csv') data = pd.read_csv('06-25_07-07.csv')
# Calculation of Q (BTU / lb)
data['Q (BTU/lb)'] = data['Sewer Water Out'] - data['Sewer Water In']
# Convert 'Date/Time (--4:0:0)' column to datetime # Convert 'Date/Time (--4:0:0)' column to datetime
data['Date/Time (--4:0:0)'] = pd.to_datetime(data['Date/Time (--4:0:0)']) data['Date/Time (--4:0:0)'] = pd.to_datetime(data['Date/Time (--4:0:0)'])
# Calculate the time difference between consecutive points in minutes
data['Time Diff (min)'] = data['Date/Time (--4:0:0)'].diff().dt.total_seconds() / 60
# Set flow rate and specific heat capacity
flow_rate_gpm = 10
density_water = 62.4 # lbm/ft^3
flow_rate_lbm_min = flow_rate_gpm * 0.133681 * density_water # Convert GPM to lbm/min
# Calculate Q (BTU)
cp_water = 1 # BTU/lbm°F
data['Delta T (F)'] = data['Sewer Water Out'] - data['Sewer Water In']
data['Q (BTU)'] = flow_rate_lbm_min * cp_water * data['Delta T (F)'] * data['Time Diff (min)']
# Apply a smoothing function (moving average) # Apply a smoothing function (moving average)
window_size = 10 window_size = 10
data['Sewer Water In Smooth'] = data['Sewer Water In'].rolling(window=window_size, center=True).mean() data['Sewer Water In Smooth'] = data['Sewer Water In'].rolling(window=window_size, center=True).mean()
data['Sewer Water Out Smooth'] = data['Sewer Water Out'].rolling(window=window_size, center=True).mean() data['Sewer Water Out Smooth'] = data['Sewer Water Out'].rolling(window=window_size, center=True).mean()
data['Return Air Smooth'] = data['Return Air'].rolling(window=window_size, center=True).mean() data['Return Air Smooth'] = data['Return Air'].rolling(window=window_size, center=True).mean()
data['Supply Air Smooth'] = data['Supply Air'].rolling(window=window_size, center=True).mean() data['Supply Air Smooth'] = data['Supply Air'].rolling(window=window_size, center=True).mean()
data['Q Smooth'] = data['Q (BTU/lb)'].rolling(window=window_size, center=True).mean() data['Q Smooth'] = data['Q (BTU)'].rolling(window=window_size, center=True).mean()
# Plotting the data
import plotly.graph_objects as go
from plotly.subplots import make_subplots
fig = make_subplots(rows=1, cols=1, shared_xaxes=True, vertical_spacing=0.1, fig = make_subplots(rows=1, cols=1, shared_xaxes=True, vertical_spacing=0.1,
subplot_titles=(f'Temperature Variations and Transferred Heat from 25/06/2024-07/07/2024',), subplot_titles=('Temperature Variations and Transferred Heat from 25/06/2024-08/07/2024',),
specs=[[{"secondary_y": True}]]) specs=[[{"secondary_y": True}]])
fig.add_trace(go.Scatter(x=data['Date/Time (--4:0:0)'], y=data['Sewer Water In Smooth'], mode='lines', name='Sewer Water In', fig.add_trace(go.Scatter(x=data['Date/Time (--4:0:0)'], y=data['Sewer Water In Smooth'], mode='lines', name='Sewer Water In',
line=dict(shape='spline', color='blue', width=4, dash='solid')), secondary_y=False) line=dict(shape='spline', color='blue', width=2, dash='solid')), secondary_y=False)
fig.add_trace(go.Scatter(x=data['Date/Time (--4:0:0)'], y=data['Sewer Water Out Smooth'], mode='lines', name='Sewer Water Out', fig.add_trace(go.Scatter(x=data['Date/Time (--4:0:0)'], y=data['Sewer Water Out Smooth'], mode='lines', name='Sewer Water Out',
line=dict(shape='spline', color='red', width=4, dash='solid')), secondary_y=False) line=dict(shape='spline', color='red', width=2, dash='solid')), secondary_y=False)
fig.add_trace(go.Scatter(x=data['Date/Time (--4:0:0)'], y=data['Return Air Smooth'], mode='lines', name='Return Air', fig.add_trace(go.Scatter(x=data['Date/Time (--4:0:0)'], y=data['Return Air Smooth'], mode='lines', name='Return Air',
line=dict(shape='spline', color='orange', width=4, dash='solid')), secondary_y=False) line=dict(shape='spline', color='orange', width=2, dash='solid')), secondary_y=False)
fig.add_trace(go.Scatter(x=data['Date/Time (--4:0:0)'], y=data['Supply Air Smooth'], mode='lines', name='Supply Air', fig.add_trace(go.Scatter(x=data['Date/Time (--4:0:0)'], y=data['Supply Air Smooth'], mode='lines', name='Supply Air',
line=dict(shape='spline', color='green', width=4, dash='solid')), secondary_y=False) line=dict(shape='spline', color='green', width=2, dash='solid')), secondary_y=False)
fig.add_trace(go.Scatter(x=data['Date/Time (--4:0:0)'], y=data['Q Smooth'], mode='lines', name='Transferred Heat (BTU/lb)', fig.add_trace(go.Scatter(x=data['Date/Time (--4:0:0)'], y=data['Q Smooth'], mode='lines', name='Transferred Heat (BTU)',
line=dict(shape='spline', color='purple', width=4, dash='solid')), secondary_y=True) line=dict(shape='spline', color='purple', width=2, dash='solid')), secondary_y=True)
# Update layout
# Update layout with secondary y-axis title
fig.update_layout( fig.update_layout(
xaxis_title='Date/Time', xaxis_title='Date/Time',
yaxis_title='Temperature (F)', yaxis_title='Temperature (F)',
yaxis2_title='Transferred Heat (BTU)',
legend_title_text='Legend', legend_title_text='Legend',
font=dict(size=14), font=dict(size=14),
width=1400, width=1400,
height=800 height=800
) )
# Save the plot (requires kaleido package) # Save the plot (requires kaleido package)
fig.write_image('plotly_plot_with_q.png') fig.write_image('temperature_variations_with_q.png')
# Display the plot
fig.show()

33
double_y_daily.py
View File

@ -2,22 +2,32 @@ import pandas as pd
import plotly.graph_objects as go import plotly.graph_objects as go
from plotly.subplots import make_subplots from plotly.subplots import make_subplots
# Load the data (assuming you've already loaded and processed it as shown) # Load the data
data = pd.read_csv('06-25_07-07.csv') data = pd.read_csv('06-25_07-07.csv')
# Calculation of Q (BTU / lb)
data['Q (BTU/lb)'] = data['Sewer Water Out'] - data['Sewer Water In']
# Convert 'Date/Time (--4:0:0)' column to datetime # Convert 'Date/Time (--4:0:0)' column to datetime
data['Date/Time (--4:0:0)'] = pd.to_datetime(data['Date/Time (--4:0:0)']) data['Date/Time (--4:0:0)'] = pd.to_datetime(data['Date/Time (--4:0:0)'])
# Calculate the time difference between consecutive points in minutes
data['Time Diff (min)'] = data['Date/Time (--4:0:0)'].diff().dt.total_seconds() / 60
# Set flow rate and specific heat capacity
flow_rate_gpm = 10
density_water = 62.4 # lbm/ft^3
flow_rate_lbm_min = flow_rate_gpm * 0.133681 * density_water # Convert GPM to lbm/min
# Calculate Q (BTU)
cp_water = 1 # BTU/lbm°F
data['Delta T (F)'] = data['Sewer Water Out'] - data['Sewer Water In']
data['Q (BTU)'] = flow_rate_lbm_min * cp_water * data['Delta T (F)'] * data['Time Diff (min)']
# Apply a smoothing function (moving average) # Apply a smoothing function (moving average)
window_size = 10 window_size = 10
data['Sewer Water In Smooth'] = data['Sewer Water In'].rolling(window=window_size, center=True).mean() data['Sewer Water In Smooth'] = data['Sewer Water In'].rolling(window=window_size, center=True).mean()
data['Sewer Water Out Smooth'] = data['Sewer Water Out'].rolling(window=window_size, center=True).mean() data['Sewer Water Out Smooth'] = data['Sewer Water Out'].rolling(window=window_size, center=True).mean()
data['Return Air Smooth'] = data['Return Air'].rolling(window=window_size, center=True).mean() data['Return Air Smooth'] = data['Return Air'].rolling(window=window_size, center=True).mean()
data['Supply Air Smooth'] = data['Supply Air'].rolling(window=window_size, center=True).mean() data['Supply Air Smooth'] = data['Supply Air'].rolling(window=window_size, center=True).mean()
data['Q Smooth'] = data['Q (BTU/lb)'].rolling(window=window_size, center=True).mean() data['Q Smooth'] = data['Q (BTU)'].rolling(window=window_size, center=True).mean()
# Iterate through each calendar day and create separate plots # Iterate through each calendar day and create separate plots
for day in pd.Index(data['Date/Time (--4:0:0)'].dt.date).unique(): for day in pd.Index(data['Date/Time (--4:0:0)'].dt.date).unique():
@ -31,20 +41,21 @@ for day in pd.Index(data['Date/Time (--4:0:0)'].dt.date).unique():
# Add traces for each line with spline smoothing and increased visibility # Add traces for each line with spline smoothing and increased visibility
fig.add_trace(go.Scatter(x=day_data['Date/Time (--4:0:0)'], y=day_data['Sewer Water In Smooth'], mode='lines', name='Sewer Water In', fig.add_trace(go.Scatter(x=day_data['Date/Time (--4:0:0)'], y=day_data['Sewer Water In Smooth'], mode='lines', name='Sewer Water In',
line=dict(shape='spline', color='blue', width=4, dash='solid')), secondary_y=False) line=dict(shape='spline', color='blue', width=2, dash='solid')), secondary_y=False)
fig.add_trace(go.Scatter(x=day_data['Date/Time (--4:0:0)'], y=day_data['Sewer Water Out Smooth'], mode='lines', name='Sewer Water Out', fig.add_trace(go.Scatter(x=day_data['Date/Time (--4:0:0)'], y=day_data['Sewer Water Out Smooth'], mode='lines', name='Sewer Water Out',
line=dict(shape='spline', color='red', width=4, dash='solid')), secondary_y=False) line=dict(shape='spline', color='red', width=2, dash='solid')), secondary_y=False)
fig.add_trace(go.Scatter(x=day_data['Date/Time (--4:0:0)'], y=day_data['Return Air Smooth'], mode='lines', name='Return Air', fig.add_trace(go.Scatter(x=day_data['Date/Time (--4:0:0)'], y=day_data['Return Air Smooth'], mode='lines', name='Return Air',
line=dict(shape='spline', color='orange', width=4, dash='solid')), secondary_y=False) line=dict(shape='spline', color='orange', width=2, dash='solid')), secondary_y=False)
fig.add_trace(go.Scatter(x=day_data['Date/Time (--4:0:0)'], y=day_data['Supply Air Smooth'], mode='lines', name='Supply Air', fig.add_trace(go.Scatter(x=day_data['Date/Time (--4:0:0)'], y=day_data['Supply Air Smooth'], mode='lines', name='Supply Air',
line=dict(shape='spline', color='green', width=4, dash='solid')), secondary_y=False) line=dict(shape='spline', color='green', width=2, dash='solid')), secondary_y=False)
fig.add_trace(go.Scatter(x=day_data['Date/Time (--4:0:0)'], y=day_data['Q Smooth'], mode='lines', name='Transferred Heat (BTU/lb)', fig.add_trace(go.Scatter(x=day_data['Date/Time (--4:0:0)'], y=day_data['Q Smooth'], mode='lines', name='Transferred Heat (BTU/lb)',
line=dict(shape='spline', color='purple', width=4, dash='solid')), secondary_y=True) line=dict(shape='spline', color='purple', width=2, dash='solid')), secondary_y=True)
# Update layout # Update layout
fig.update_layout( fig.update_layout(
xaxis_title='Date/Time', xaxis_title='Date/Time',
yaxis_title='Temperature (F)', yaxis_title='Temperature (F)',
yaxis2_title='Transferred Heat (BTU/lb)',
legend_title_text='Legend', legend_title_text='Legend',
font=dict(size=14), font=dict(size=14),
width=1400, width=1400,
@ -52,4 +63,4 @@ for day in pd.Index(data['Date/Time (--4:0:0)'].dt.date).unique():
) )
# Save the plot (requires kaleido package) # Save the plot (requires kaleido package)
fig.write_image(f'plotly_plot_{day}.png') fig.write_image(f'temperature_variations_with_q_{day}.png')

12
main.py
View File

@ -21,13 +21,13 @@ fig = go.Figure()
# Add traces for each line with spline smoothing and increased visibility # Add traces for each line with spline smoothing and increased visibility
fig.add_trace(go.Scatter(x=data.index, y=data['Sewer Water In Smooth'], mode='lines', name='Sewer Water In', fig.add_trace(go.Scatter(x=data.index, y=data['Sewer Water In Smooth'], mode='lines', name='Sewer Water In',
line=dict(shape='spline', color='blue', width=4, dash='solid'))) line=dict(shape='spline', color='blue', width=2, dash='solid')))
fig.add_trace(go.Scatter(x=data.index, y=data['Sewer Water Out Smooth'], mode='lines', name='Sewer Water Out', fig.add_trace(go.Scatter(x=data.index, y=data['Sewer Water Out Smooth'], mode='lines', name='Sewer Water Out',
line=dict(shape='spline', color='red', width=4, dash='solid'))) line=dict(shape='spline', color='red', width=2, dash='solid')))
fig.add_trace(go.Scatter(x=data.index, y=data['Return Air Smooth'], mode='lines', name='Return Air', fig.add_trace(go.Scatter(x=data.index, y=data['Return Air Smooth'], mode='lines', name='Return Air',
line=dict(shape='spline', color='orange', width=4, dash='solid'))) line=dict(shape='spline', color='orange', width=2, dash='solid')))
fig.add_trace(go.Scatter(x=data.index, y=data['Supply Air Smooth'], mode='lines', name='Supply Air', fig.add_trace(go.Scatter(x=data.index, y=data['Supply Air Smooth'], mode='lines', name='Supply Air',
line=dict(shape='spline', color='green', width=4, dash='solid'))) line=dict(shape='spline', color='green', width=2, dash='solid')))
# Add shading to separate days # Add shading to separate days
for i in range((data.index[-1] - data.index[0]).days + 1): for i in range((data.index[-1] - data.index[0]).days + 1):
@ -43,7 +43,7 @@ for i in range((data.index[-1] - data.index[0]).days + 1):
# Update layout for better styling and to set figure size # Update layout for better styling and to set figure size
fig.update_layout( fig.update_layout(
title=dict( title=dict(
text='Temperature Variations from 25/06/2024-07/07/2024', text='Temperature Variations from 25/06/2024-08/07/2024',
font=dict(size=20, family='Arial', color='black', weight='bold') font=dict(size=20, family='Arial', color='black', weight='bold')
), ),
xaxis_title=dict( xaxis_title=dict(
@ -64,7 +64,7 @@ fig.update_layout(
) )
# Save the plot (requires kaleido package) # Save the plot (requires kaleido package)
fig.write_image('plotly_plot.png') fig.write_image('temperatures without Q.png')
# Display the plot # Display the plot
fig.show() fig.show()

27
q_calculation.py
View File

@ -1,29 +1,38 @@
import pandas as pd import pandas as pd
import plotly.graph_objects as go import plotly.graph_objects as go
# Load the data (assuming you've already loaded and processed it as shown) # Load the data
data = pd.read_csv('06-25_07-07.csv') data = pd.read_csv('06-25_07-07.csv')
# Calculation of Q (BTU / lb)
t_sewer_in = data['Sewer Water In'].to_list()
t_sewer_out = data['Sewer Water Out'].to_list() # corrected column name
q = [t_sewer_out[i] - t_sewer_in[i] for i in range(len(t_sewer_out))]
data['Q (BTU/lb)'] = q
# Convert 'Date/Time (--4:0:0)' column to datetime # Convert 'Date/Time (--4:0:0)' column to datetime
data['Date/Time (--4:0:0)'] = pd.to_datetime(data['Date/Time (--4:0:0)']) data['Date/Time (--4:0:0)'] = pd.to_datetime(data['Date/Time (--4:0:0)'])
# Calculate the time difference between consecutive points in minutes
data['Time Diff (min)'] = data['Date/Time (--4:0:0)'].diff().dt.total_seconds() / 60
# Set flow rate and specific heat capacity
flow_rate_gpm = 10
density_water = 62.4 # lbm/ft^3
flow_rate_lbm_min = flow_rate_gpm * 0.133681 * density_water # Convert GPM to lbm/min
# Calculate Q (BTU)
cp_water = 1 # BTU/lbm°F
data['Delta T (F)'] = data['Sewer Water Out'] - data['Sewer Water In']
data['Q (BTU)'] = flow_rate_lbm_min * cp_water * data['Delta T (F)'] * data['Time Diff (min)']
# Set the datetime column as the index # Set the datetime column as the index
data.set_index('Date/Time (--4:0:0)', inplace=True) data.set_index('Date/Time (--4:0:0)', inplace=True)
# Apply a smoothing function (moving average) # Apply a smoothing function (moving average)
window_size = 10 window_size = 10
data['Q Smooth'] = data['Q (BTU/lb)'].rolling(window=window_size, center=True).mean() data['Q Smooth'] = data['Q (BTU)'].rolling(window=window_size, center=True).mean()
# Ensure index is a datetime index for ease of filtering # Ensure index is a datetime index for ease of filtering
data.index = pd.to_datetime(data.index) data.index = pd.to_datetime(data.index)
# Create the plot # Create the plot
fig = go.Figure() fig = go.Figure()
# Add traces for each line with spline smoothing and increased visibility # Add traces for each line with spline smoothing and increased visibility
fig.add_trace(go.Scatter(x=data.index, y=data['Q Smooth'], mode='lines', name='Transferred Heat (BTU/lb)', fig.add_trace(go.Scatter(x=data.index, y=data['Q Smooth'], mode='lines', name='Transferred Heat (BTU/lb)',
line=dict(shape='spline', color='blue', width=4, dash='solid'))) line=dict(shape='spline', color='blue', width=2, dash='solid')))
# Add shading to separate days # Add shading to separate days
for i in range((data.index[-1] - data.index[0]).days + 1): for i in range((data.index[-1] - data.index[0]).days + 1):
@ -47,7 +56,7 @@ fig.update_layout(
font=dict(size=18, family='Arial', color='black', weight='bold') font=dict(size=18, family='Arial', color='black', weight='bold')
), ),
yaxis_title=dict( yaxis_title=dict(
text='Energy (BTU/lb)', text='Energy (BTU)',
font=dict(size=18, family='Arial', color='black', weight='bold') font=dict(size=18, family='Arial', color='black', weight='bold')
), ),
legend=dict( legend=dict(