13 SS13 Results
| Pos | Car | Code | Time (s) | Gap | Diff |
|---|---|---|---|---|---|
| 1 | 6 | SOR | 1045.0 | 0.0 | 0.0 |
| 2 | 33 | EVA | 1050.1 | 5.1 | 5.1 |
| 3 | 16 | FOU | 1051.0 | 6.0 | 0.9 |
| 4 | 69 | ROV | 1062.0 | 17.0 | 11.0 |
| 5 | 1 | OGI | 1069.6 | 24.6 | 7.6 |
| 6 | 11 | NEU | 1080.1 | 35.1 | 10.5 |
| 7 | 44 | GRE | 1080.7 | 35.7 | 0.6 |
| 8 | 18 | KAT | 1102.5 | 57.5 | 21.8 |
| 9 | 8 | TÄN | 1171.8 | 126.8 | 43.2 |
13.1 Stage SS13 Splits Analysis
Reports on splits
13.1.1 Split Locations
Length of each split section in km:
| full_split_distances | stage_dist | |
|---|---|---|
| split_1 | 5.26 | 5.26 |
| split_2 | 4.85 | 10.11 |
| split_3 | 5.29 | 15.40 |
| split_4 | 5.17 | 20.57 |
| split_5 | 4.73 | 25.30 |
| split_6 | 3.04 | 28.34 |
| split_N | 2.70 | 31.04 |
13.1.2 Stage Split Times & Result
Summary of stage split times and overall stage result:
| code | split_1 | split_2 | split_3 | split_4 | split_5 | split_6 | split_N | diffFirstS | position |
|---|---|---|---|---|---|---|---|---|---|
| SOR | 174.8 | 338.4 | 483.4 | 628.4 | 787.7 | 919.1 | 1045.0 | 0.0 | 1 |
| EVA | 174.8 | 334.1 | 478.9 | 626.8 | 789.5 | 922.4 | 1050.1 | 5.1 | 2 |
| FOU | 176.4 | 338.3 | 483.6 | 634.9 | 798.6 | 925.5 | 1051.0 | 6.0 | 3 |
| ROV | 177.1 | 339.0 | 485.8 | 632.3 | 795.6 | 931.9 | 1062.0 | 17.0 | 4 |
| OGI | 172.0 | 332.2 | 481.3 | 648.3 | 817.3 | 945.5 | 1069.6 | 24.6 | 5 |
| NEU | 176.1 | 356.5 | 513.5 | 670.5 | 832.1 | 957.4 | 1080.1 | 35.1 | 6 |
| GRE | 174.0 | 334.1 | 479.9 | 647.8 | 820.9 | 952.0 | 1080.7 | 35.7 | 7 |
| KAT | 176.4 | 350.9 | 513.0 | 678.3 | 844.9 | 976.4 | 1102.5 | 57.5 | 8 |
| TÄN | 172.9 | 346.5 | 536.3 | 741.4 | 914.0 | 1047.4 | 1171.8 | 126.8 | 10 |
13.1.3 Split Time Rankings
Rank of each split time:
| code | split_1 | split_2 | split_3 | split_4 | split_5 | split_6 | split_N |
|---|---|---|---|---|---|---|---|
| SOR | 4 | 4 | 4 | 2 | 1 | 1 | 1 |
| EVA | 4 | 2 | 1 | 1 | 2 | 2 | 2 |
| FOU | 6 | 3 | 5 | 4 | 4 | 3 | 3 |
| ROV | 7 | 5 | 6 | 3 | 3 | 4 | 4 |
| OGI | 1 | 1 | 3 | 6 | 5 | 5 | 5 |
| NEU | 5 | 8 | 8 | 7 | 7 | 7 | 6 |
| GRE | 3 | 2 | 2 | 5 | 6 | 6 | 7 |
| KAT | 6 | 7 | 7 | 8 | 8 | 8 | 8 |
| TÄN | 2 | 6 | 9 | 9 | 9 | 9 | 9 |
Rank on each split section:
| code | split_1 | split_2 | split_3 | split_4 | split_5 | split_6 | split_N |
|---|---|---|---|---|---|---|---|
| SOR | 4 | 5 | 2 | 1 | 1 | 5 | 5 |
| EVA | 4 | 1 | 1 | 3 | 3 | 7 | 7 |
| FOU | 6 | 4 | 3 | 4 | 5 | 2 | 4 |
| ROV | 7 | 4 | 5 | 2 | 4 | 9 | 9 |
| OGI | 1 | 3 | 6 | 7 | 7 | 3 | 2 |
| NEU | 5 | 8 | 7 | 5 | 2 | 1 | 1 |
| GRE | 3 | 2 | 4 | 8 | 9 | 4 | 8 |
| KAT | 6 | 7 | 8 | 6 | 6 | 6 | 6 |
| TÄN | 2 | 6 | 9 | 9 | 8 | 8 | 3 |
13.1.4 Split Section Pace
Pace is measured in seconds per kilometer, which is to say, how long it takes to complete each kilometer (lower is better):
| code | split_1 | split_2 | split_3 | split_4 | split_5 | split_6 | split_N |
|---|---|---|---|---|---|---|---|
| SOR | 33.23 | 33.73 | 27.41 | 28.05 | 33.68 | 43.22 | 46.63 |
| EVA | 33.23 | 32.85 | 27.37 | 28.61 | 34.40 | 43.72 | 47.30 |
| FOU | 33.54 | 33.38 | 27.47 | 29.26 | 34.61 | 41.74 | 46.48 |
| ROV | 33.67 | 33.38 | 27.75 | 28.34 | 34.52 | 44.84 | 48.19 |
| OGI | 32.70 | 33.03 | 28.19 | 32.30 | 35.73 | 42.17 | 45.96 |
| NEU | 33.48 | 37.20 | 29.68 | 30.37 | 34.16 | 41.22 | 45.44 |
| GRE | 33.08 | 33.01 | 27.56 | 32.48 | 36.60 | 43.13 | 47.67 |
| KAT | 33.54 | 35.98 | 30.64 | 31.97 | 35.22 | 43.26 | 46.70 |
| TÄN | 32.87 | 35.79 | 35.88 | 39.67 | 36.49 | 43.88 | 46.07 |
13.1.5 Pace Chart
Pace relative to the stage winner.
13.1.6 Ultimate Stage Comparisons
Split time comparisons relative to various ultimate stage time calculations.
These views provide various ways of comparing the performance of each driver to various ghost drivers:
- best recorded split time at each split (ultimate actuals): gives an estimate of how well the driver’s split times compare with each first ranked split time (time into stage at each split);
- fastest time going from one split to the next (ultimate sections): gives an estimate of how quickly each driver completed each split section relative to the quickest time recorded completing that split section (time taken to go from one split point to the next);
- accumulated ultimate sections times: gives and estimate of how quickly the driver is going relative to the fastest possible completion of the stage based on accumulated ultimate split section times.
<IP_ADDRESS> Ultimate Actuals
Driver times compared to the first ranked recorded split time at each split:
| code | split_1 | split_2 | split_3 | split_4 | split_5 | split_6 | split_N |
|---|---|---|---|---|---|---|---|
| SOR | 2.8 | 6.2 | 4.5 | 1.6 | 0.0 | 0.0 | 0.0 |
| EVA | 2.8 | 1.9 | 0.0 | 0.0 | 1.8 | 3.3 | 5.1 |
| FOU | 4.4 | 6.1 | 4.7 | 8.1 | 10.9 | 6.4 | 6.0 |
| ROV | 5.1 | 6.8 | 6.9 | 5.5 | 7.9 | 12.8 | 17.0 |
| OGI | 0.0 | 0.0 | 2.4 | 21.5 | 29.6 | 26.4 | 24.6 |
| NEU | 4.1 | 24.3 | 34.6 | 43.7 | 44.4 | 38.3 | 35.1 |
| GRE | 2.0 | 1.9 | 1.0 | 21.0 | 33.2 | 32.9 | 35.7 |
| KAT | 4.4 | 18.7 | 34.1 | 51.5 | 57.2 | 57.3 | 57.5 |
| TÄN | 0.9 | 14.3 | 57.4 | 114.6 | 126.3 | 128.3 | 126.8 |
| ultimate | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
<IP_ADDRESS> Ultimate Sections
Split durations rebased relative to the fastest transit of each split section:
| code | split_1 | split_2 | split_3 | split_4 | split_5 | split_6 | split_N |
|---|---|---|---|---|---|---|---|
| SOR | 2.8 | 4.3 | 0.2 | 0.0 | 0.0 | 6.1 | 3.2 |
| EVA | 2.8 | 0.0 | 0.0 | 2.9 | 3.4 | 7.6 | 5.0 |
| FOU | 4.4 | 2.6 | 0.5 | 6.3 | 4.4 | 1.6 | 2.8 |
| ROV | 5.1 | 2.6 | 2.0 | 1.5 | 4.0 | 11.0 | 7.4 |
| OGI | 0.0 | 0.9 | 4.3 | 22.0 | 9.7 | 2.9 | 1.4 |
| NEU | 4.1 | 21.1 | 12.2 | 12.0 | 2.3 | 0.0 | 0.0 |
| GRE | 2.0 | 0.8 | 1.0 | 22.9 | 13.8 | 5.8 | 6.0 |
| KAT | 4.4 | 15.2 | 17.3 | 20.3 | 7.3 | 6.2 | 3.4 |
| TÄN | 0.9 | 14.3 | 45.0 | 60.1 | 13.3 | 8.1 | 1.7 |
| ultimate | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
<IP_ADDRESS> Ultimate Accumulated Sections
Actual split times relative to the accumulated ultimate split section times:
| code | split_1 | split_2 | split_3 | split_4 | split_5 | split_6 | split_N |
|---|---|---|---|---|---|---|---|
| SOR | 2.8 | 7.1 | 7.3 | 7.3 | 7.3 | 13.4 | 16.6 |
| EVA | 2.8 | 2.8 | 2.8 | 5.7 | 9.1 | 16.7 | 21.7 |
| FOU | 4.4 | 7.0 | 7.5 | 13.8 | 18.2 | 19.8 | 22.6 |
| ROV | 5.1 | 7.7 | 9.7 | 11.2 | 15.2 | 26.2 | 33.6 |
| OGI | 0.0 | 0.9 | 5.2 | 27.2 | 36.9 | 39.8 | 41.2 |
| NEU | 4.1 | 25.2 | 37.4 | 49.4 | 51.7 | 51.7 | 51.7 |
| GRE | 2.0 | 2.8 | 3.8 | 26.7 | 40.5 | 46.3 | 52.3 |
| KAT | 4.4 | 19.6 | 36.9 | 57.2 | 64.5 | 70.7 | 74.1 |
| TÄN | 0.9 | 15.2 | 60.2 | 120.3 | 133.6 | 141.7 | 143.4 |
| ultimate | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
13.1.7 Split Section Pace Distribution
Distribution of pace values for each split section:
13.2 Text report
| code | identifier | entryId | stage | overall_pos | prev_position | retained_lead | lost_lead | gained_lead | overall_position_delta | overall_chase_code | overall_lead_code | overall_gap | overall_diff | overall_chase | overall_stage_pos | stage_position | stage_win | position | TimeInS | gap | diff | chase | chase_code | lead_code |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SOR | 6 | 22829 | SS13 | 9 | 9 | FALSE | FALSE | FALSE | 0 | SOL | EVA | 3018.6 | 10.4 | NA | 1 | 1 | TRUE | 1 | 1045.0 | 0.0 | 0.0 | 5.1 | EVA | NA |
| EVA | 33 | 22912 | SS13 | 8 | 8 | FALSE | FALSE | FALSE | 0 | SOR | ROV | 3008.2 | 2343.9 | 10.4 | 2 | 2 | FALSE | 2 | 1050.1 | 5.1 | 5.1 | 0.9 | FOU | SOR |
| FOU | 16 | 22831 | SS13 | 6 | 6 | FALSE | FALSE | FALSE | 0 | ROV | GRE | 171.4 | 12.0 | 492.9 | 3 | 3 | FALSE | 3 | 1051.0 | 6.0 | 0.9 | 11.0 | ROV | EVA |
| ROV | 69 | 22827 | SS13 | 7 | 7 | FALSE | FALSE | FALSE | 0 | EVA | FOU | 664.3 | 492.9 | 2343.9 | 4 | 4 | FALSE | 4 | 1062.0 | 17.0 | 11.0 | 7.6 | OGI | FOU |
| OGI | 1 | 22911 | SS13 | 3 | 4 | FALSE | FALSE | FALSE | 1 | TÄN | KAT | 75.5 | 18.1 | 65.7 | 5 | 5 | FALSE | 5 | 1069.6 | 24.6 | 7.6 | 10.5 | NEU | ROV |
| NEU | 11 | 22825 | SS13 | 1 | 1 | TRUE | FALSE | FALSE | 0 | KAT | NA | 0.0 | NA | 57.4 | 6 | 6 | FALSE | 6 | 1080.1 | 35.1 | 10.5 | 0.6 | GRE | OGI |
| GRE | 44 | 22830 | SS13 | 5 | 5 | FALSE | FALSE | FALSE | 0 | FOU | TÄN | 159.4 | 18.2 | 12.0 | 7 | 7 | FALSE | 7 | 1080.7 | 35.7 | 0.6 | 21.8 | KAT | NEU |
| KAT | 18 | 22828 | SS13 | 2 | 2 | FALSE | FALSE | FALSE | 0 | OGI | NEU | 57.4 | 57.4 | 18.1 | 8 | 8 | FALSE | 8 | 1102.5 | 57.5 | 21.8 | 69.3 | TÄN | GRE |
| TÄN | 8 | 22826 | SS13 | 4 | 3 | FALSE | FALSE | FALSE | -1 | GRE | OGI | 141.2 | 65.7 | 18.2 | 10 | 9 | FALSE | 9 | 1171.8 | 126.8 | 43.2 | NA | NA | KAT |
Stage report for SS13:
SOR recorded the stage win and taking a time of 1045.0s.
EVA took second on stage, 5.1s behind stage winner SOR in a time of 1050.1s .
FOU took third place on stage, 6.0s behind SOR , only 0.9s behind EVA.
ROV was in fourth on stage, with a time of 1062.0s .
OGI was in fifth place on stage, with a time of 1069.6s and moved up one place to third overall.
NEU took sixth position on stage, 35.1s behind stage winner SOR in a time of 1080.1s and retained overall lead.
GRE took seventh place on stage, with a time of 1080.7s, just 0.6s off NEU.
KAT was in eighth on stage, 57.5s behind stage winner SOR .
TÄN was in tenth on stage, 126.8s behind SOR dropped one place to fourth overall.
13.3 Driver Report, SS13 - OGI
OGI was in fifth place on stage, with a time of 1069.6s and moved up one place to third overall.
Stage report for OGI.
| Pos | Car | Code | Time (s) | Gap | Diff |
|---|---|---|---|---|---|
| 1 | 6 | SOR | -24.6 | -24.6 | 0.0 |
| 2 | 33 | EVA | -19.5 | -19.5 | 5.1 |
| 3 | 16 | FOU | -18.6 | -18.6 | 0.9 |
| 4 | 69 | ROV | -7.6 | -7.6 | 11.0 |
| 5 | 1 | OGI | 0.0 | 0.0 | 7.6 |
| 6 | 11 | NEU | 10.5 | 10.5 | 10.5 |
| 7 | 44 | GRE | 11.1 | 11.1 | 0.6 |
| 8 | 18 | KAT | 32.9 | 32.9 | 21.8 |
| 10 | 8 | TÄN | 102.2 | 102.2 | 43.2 |
13.3.1 Rebased Split Times
OGI’s delta at each split compared to other drivers.
| code | split_1 | split_2 | split_3 | split_4 | split_5 | split_6 | split_N |
|---|---|---|---|---|---|---|---|
| SOR | 2.8 | 6.2 | 2.1 | -19.9 | -29.6 | -26.4 | -24.6 |
| EVA | 2.8 | 1.9 | -2.4 | -21.5 | -27.8 | -23.1 | -19.5 |
| FOU | 4.4 | 6.1 | 2.3 | -13.4 | -18.7 | -20.0 | -18.6 |
| ROV | 5.1 | 6.8 | 4.5 | -16.0 | -21.7 | -13.6 | -7.6 |
| OGI | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| NEU | 4.1 | 24.3 | 32.2 | 22.2 | 14.8 | 11.9 | 10.5 |
| GRE | 2.0 | 1.9 | -1.4 | -0.5 | 3.6 | 6.5 | 11.1 |
| KAT | 4.4 | 18.7 | 31.7 | 30.0 | 27.6 | 30.9 | 32.9 |
| TÄN | 0.9 | 14.3 | 55.0 | 93.1 | 96.7 | 101.9 | 102.2 |
13.3.2 Off-the-Pace Chart
Accumulated stage time deltas at each split:
13.3.3 Within-Split Duration Deltas
OGI’s delta in time to complete each split section compared to other drivers:
| code | split_1 | split_2 | split_3 | split_4 | split_5 | split_6 | split_N |
|---|---|---|---|---|---|---|---|
| SOR | 2.8 | 3.4 | -4.1 | -22.0 | -9.7 | 3.2 | 1.8 |
| EVA | 2.8 | -0.9 | -4.3 | -19.1 | -6.3 | 4.7 | 3.6 |
| FOU | 4.4 | 1.7 | -3.8 | -15.7 | -5.3 | -1.3 | 1.4 |
| ROV | 5.1 | 1.7 | -2.3 | -20.5 | -5.7 | 8.1 | 6.0 |
| OGI | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| NEU | 4.1 | 20.2 | 7.9 | -10.0 | -7.4 | -2.9 | -1.4 |
| GRE | 2.0 | -0.1 | -3.3 | 0.9 | 4.1 | 2.9 | 4.6 |
| KAT | 4.4 | 14.3 | 13.0 | -1.7 | -2.4 | 3.3 | 2.0 |
| TÄN | 0.9 | 13.4 | 40.7 | 38.1 | 3.6 | 5.2 | 0.3 |
13.3.4 Within-Split Pace Deltas
| code | split_1 | split_2 | split_3 | split_4 | split_5 | split_6 | split_N |
|---|---|---|---|---|---|---|---|
| SOR | 0.53 | 0.70 | -0.78 | -4.26 | -2.05 | 1.05 | 0.67 |
| EVA | 0.53 | -0.19 | -0.81 | -3.69 | -1.33 | 1.55 | 1.33 |
| FOU | 0.84 | 0.35 | -0.72 | -3.04 | -1.12 | -0.43 | 0.52 |
| ROV | 0.97 | 0.35 | -0.43 | -3.97 | -1.21 | 2.66 | 2.22 |
| OGI | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| NEU | 0.78 | 4.16 | 1.49 | -1.93 | -1.56 | -0.95 | -0.52 |
| GRE | 0.38 | -0.02 | -0.62 | 0.17 | 0.87 | 0.95 | 1.70 |
| KAT | 0.84 | 2.95 | 2.46 | -0.33 | -0.51 | 1.09 | 0.74 |
| TÄN | 0.17 | 2.76 | 7.69 | 7.37 | 0.76 | 1.71 | 0.11 |
13.3.5 Within-Split Pace Map
13.4 Driver Report, SS13 - EVA
EVA took second on stage, 5.1s behind stage winner SOR in a time of 1050.1s .
Stage report for EVA.
| Pos | Car | Code | Time (s) | Gap | Diff |
|---|---|---|---|---|---|
| 1 | 6 | SOR | -5.1 | -5.1 | 0.0 |
| 2 | 33 | EVA | 0.0 | 0.0 | 5.1 |
| 3 | 16 | FOU | 0.9 | 0.9 | 0.9 |
| 4 | 69 | ROV | 11.9 | 11.9 | 11.0 |
| 5 | 1 | OGI | 19.5 | 19.5 | 7.6 |
| 6 | 11 | NEU | 30.0 | 30.0 | 10.5 |
| 7 | 44 | GRE | 30.6 | 30.6 | 0.6 |
| 8 | 18 | KAT | 52.4 | 52.4 | 21.8 |
| 10 | 8 | TÄN | 121.7 | 121.7 | 43.2 |
13.4.1 Rebased Split Times
EVA’s delta at each split compared to other drivers.
| code | split_1 | split_2 | split_3 | split_4 | split_5 | split_6 | split_N |
|---|---|---|---|---|---|---|---|
| SOR | 0.0 | 4.3 | 4.5 | 1.6 | -1.8 | -3.3 | -5.1 |
| EVA | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| FOU | 1.6 | 4.2 | 4.7 | 8.1 | 9.1 | 3.1 | 0.9 |
| ROV | 2.3 | 4.9 | 6.9 | 5.5 | 6.1 | 9.5 | 11.9 |
| OGI | -2.8 | -1.9 | 2.4 | 21.5 | 27.8 | 23.1 | 19.5 |
| NEU | 1.3 | 22.4 | 34.6 | 43.7 | 42.6 | 35.0 | 30.0 |
| GRE | -0.8 | 0.0 | 1.0 | 21.0 | 31.4 | 29.6 | 30.6 |
| KAT | 1.6 | 16.8 | 34.1 | 51.5 | 55.4 | 54.0 | 52.4 |
| TÄN | -1.9 | 12.4 | 57.4 | 114.6 | 124.5 | 125.0 | 121.7 |
13.4.2 Off-the-Pace Chart
Accumulated stage time deltas at each split:
13.4.3 Within-Split Duration Deltas
EVA’s delta in time to complete each split section compared to other drivers:
| code | split_1 | split_2 | split_3 | split_4 | split_5 | split_6 | split_N |
|---|---|---|---|---|---|---|---|
| SOR | 0.0 | 4.3 | 0.2 | -2.9 | -3.4 | -1.5 | -1.8 |
| EVA | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| FOU | 1.6 | 2.6 | 0.5 | 3.4 | 1.0 | -6.0 | -2.2 |
| ROV | 2.3 | 2.6 | 2.0 | -1.4 | 0.6 | 3.4 | 2.4 |
| OGI | -2.8 | 0.9 | 4.3 | 19.1 | 6.3 | -4.7 | -3.6 |
| NEU | 1.3 | 21.1 | 12.2 | 9.1 | -1.1 | -7.6 | -5.0 |
| GRE | -0.8 | 0.8 | 1.0 | 20.0 | 10.4 | -1.8 | 1.0 |
| KAT | 1.6 | 15.2 | 17.3 | 17.4 | 3.9 | -1.4 | -1.6 |
| TÄN | -1.9 | 14.3 | 45.0 | 57.2 | 9.9 | 0.5 | -3.3 |
13.4.4 Within-Split Pace Deltas
| code | split_1 | split_2 | split_3 | split_4 | split_5 | split_6 | split_N |
|---|---|---|---|---|---|---|---|
| SOR | 0.00 | 0.89 | 0.04 | -0.56 | -0.72 | -0.49 | -0.67 |
| EVA | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| FOU | 0.30 | 0.54 | 0.09 | 0.66 | 0.21 | -1.97 | -0.81 |
| ROV | 0.44 | 0.54 | 0.38 | -0.27 | 0.13 | 1.12 | 0.89 |
| OGI | -0.53 | 0.19 | 0.81 | 3.69 | 1.33 | -1.55 | -1.33 |
| NEU | 0.25 | 4.35 | 2.31 | 1.76 | -0.23 | -2.50 | -1.85 |
| GRE | -0.15 | 0.16 | 0.19 | 3.87 | 2.20 | -0.59 | 0.37 |
| KAT | 0.30 | 3.13 | 3.27 | 3.37 | 0.82 | -0.46 | -0.59 |
| TÄN | -0.36 | 2.95 | 8.51 | 11.06 | 2.09 | 0.16 | -1.22 |
13.4.5 Within-Split Pace Map
13.5 Driver Report, SS13 - NEU
NEU took sixth position on stage, 35.1s behind stage winner SOR in a time of 1080.1s and retained overall lead.
Stage report for NEU.
| Pos | Car | Code | Time (s) | Gap | Diff |
|---|---|---|---|---|---|
| 1 | 6 | SOR | -35.1 | -35.1 | 0.0 |
| 2 | 33 | EVA | -30.0 | -30.0 | 5.1 |
| 3 | 16 | FOU | -29.1 | -29.1 | 0.9 |
| 4 | 69 | ROV | -18.1 | -18.1 | 11.0 |
| 5 | 1 | OGI | -10.5 | -10.5 | 7.6 |
| 6 | 11 | NEU | 0.0 | 0.0 | 10.5 |
| 7 | 44 | GRE | 0.6 | 0.6 | 0.6 |
| 8 | 18 | KAT | 22.4 | 22.4 | 21.8 |
| 10 | 8 | TÄN | 91.7 | 91.7 | 43.2 |
13.5.1 Rebased Split Times
NEU’s delta at each split compared to other drivers.
| code | split_1 | split_2 | split_3 | split_4 | split_5 | split_6 | split_N |
|---|---|---|---|---|---|---|---|
| SOR | -1.3 | -18.1 | -30.1 | -42.1 | -44.4 | -38.3 | -35.1 |
| EVA | -1.3 | -22.4 | -34.6 | -43.7 | -42.6 | -35.0 | -30.0 |
| FOU | 0.3 | -18.2 | -29.9 | -35.6 | -33.5 | -31.9 | -29.1 |
| ROV | 1.0 | -17.5 | -27.7 | -38.2 | -36.5 | -25.5 | -18.1 |
| OGI | -4.1 | -24.3 | -32.2 | -22.2 | -14.8 | -11.9 | -10.5 |
| NEU | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| GRE | -2.1 | -22.4 | -33.6 | -22.7 | -11.2 | -5.4 | 0.6 |
| KAT | 0.3 | -5.6 | -0.5 | 7.8 | 12.8 | 19.0 | 22.4 |
| TÄN | -3.2 | -10.0 | 22.8 | 70.9 | 81.9 | 90.0 | 91.7 |
13.5.2 Off-the-Pace Chart
Accumulated stage time deltas at each split:
13.5.3 Within-Split Duration Deltas
NEU’s delta in time to complete each split section compared to other drivers:
| code | split_1 | split_2 | split_3 | split_4 | split_5 | split_6 | split_N |
|---|---|---|---|---|---|---|---|
| SOR | -1.3 | -16.8 | -12.0 | -12.0 | -2.3 | 6.1 | 3.2 |
| EVA | -1.3 | -21.1 | -12.2 | -9.1 | 1.1 | 7.6 | 5.0 |
| FOU | 0.3 | -18.5 | -11.7 | -5.7 | 2.1 | 1.6 | 2.8 |
| ROV | 1.0 | -18.5 | -10.2 | -10.5 | 1.7 | 11.0 | 7.4 |
| OGI | -4.1 | -20.2 | -7.9 | 10.0 | 7.4 | 2.9 | 1.4 |
| NEU | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| GRE | -2.1 | -20.3 | -11.2 | 10.9 | 11.5 | 5.8 | 6.0 |
| KAT | 0.3 | -5.9 | 5.1 | 8.3 | 5.0 | 6.2 | 3.4 |
| TÄN | -3.2 | -6.8 | 32.8 | 48.1 | 11.0 | 8.1 | 1.7 |
13.5.4 Within-Split Pace Deltas
| code | split_1 | split_2 | split_3 | split_4 | split_5 | split_6 | split_N |
|---|---|---|---|---|---|---|---|
| SOR | -0.25 | -3.46 | -2.27 | -2.32 | -0.49 | 2.01 | 1.19 |
| EVA | -0.25 | -4.35 | -2.31 | -1.76 | 0.23 | 2.50 | 1.85 |
| FOU | 0.06 | -3.81 | -2.21 | -1.10 | 0.44 | 0.53 | 1.04 |
| ROV | 0.19 | -3.81 | -1.93 | -2.03 | 0.36 | 3.62 | 2.74 |
| OGI | -0.78 | -4.16 | -1.49 | 1.93 | 1.56 | 0.95 | 0.52 |
| NEU | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| GRE | -0.40 | -4.19 | -2.12 | 2.11 | 2.43 | 1.91 | 2.22 |
| KAT | 0.06 | -1.22 | 0.96 | 1.61 | 1.06 | 2.04 | 1.26 |
| TÄN | -0.61 | -1.40 | 6.20 | 9.30 | 2.33 | 2.66 | 0.63 |
13.5.5 Within-Split Pace Map
13.6 Driver Report, SS13 - TÄN
TÄN was in tenth on stage, 126.8s behind SOR dropped one place to fourth overall.
Stage report for TÄN.
| Pos | Car | Code | Time (s) | Gap | Diff |
|---|---|---|---|---|---|
| 1 | 6 | SOR | -126.8 | -126.8 | 0.0 |
| 2 | 33 | EVA | -121.7 | -121.7 | 5.1 |
| 3 | 16 | FOU | -120.8 | -120.8 | 0.9 |
| 4 | 69 | ROV | -109.8 | -109.8 | 11.0 |
| 5 | 1 | OGI | -102.2 | -102.2 | 7.6 |
| 6 | 11 | NEU | -91.7 | -91.7 | 10.5 |
| 7 | 44 | GRE | -91.1 | -91.1 | 0.6 |
| 8 | 18 | KAT | -69.3 | -69.3 | 21.8 |
| 10 | 8 | TÄN | 0.0 | 0.0 | 43.2 |
13.6.1 Rebased Split Times
TÄN’s delta at each split compared to other drivers.
| code | split_1 | split_2 | split_3 | split_4 | split_5 | split_6 | split_N |
|---|---|---|---|---|---|---|---|
| SOR | 1.9 | -8.1 | -52.9 | -113.0 | -126.3 | -128.3 | -126.8 |
| EVA | 1.9 | -12.4 | -57.4 | -114.6 | -124.5 | -125.0 | -121.7 |
| FOU | 3.5 | -8.2 | -52.7 | -106.5 | -115.4 | -121.9 | -120.8 |
| ROV | 4.2 | -7.5 | -50.5 | -109.1 | -118.4 | -115.5 | -109.8 |
| OGI | -0.9 | -14.3 | -55.0 | -93.1 | -96.7 | -101.9 | -102.2 |
| NEU | 3.2 | 10.0 | -22.8 | -70.9 | -81.9 | -90.0 | -91.7 |
| GRE | 1.1 | -12.4 | -56.4 | -93.6 | -93.1 | -95.4 | -91.1 |
| KAT | 3.5 | 4.4 | -23.3 | -63.1 | -69.1 | -71.0 | -69.3 |
| TÄN | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
13.6.2 Off-the-Pace Chart
Accumulated stage time deltas at each split:
13.6.3 Within-Split Duration Deltas
TÄN’s delta in time to complete each split section compared to other drivers:
| code | split_1 | split_2 | split_3 | split_4 | split_5 | split_6 | split_N |
|---|---|---|---|---|---|---|---|
| SOR | 1.9 | -10.0 | -44.8 | -60.1 | -13.3 | -2.0 | 1.5 |
| EVA | 1.9 | -14.3 | -45.0 | -57.2 | -9.9 | -0.5 | 3.3 |
| FOU | 3.5 | -11.7 | -44.5 | -53.8 | -8.9 | -6.5 | 1.1 |
| ROV | 4.2 | -11.7 | -43.0 | -58.6 | -9.3 | 2.9 | 5.7 |
| OGI | -0.9 | -13.4 | -40.7 | -38.1 | -3.6 | -5.2 | -0.3 |
| NEU | 3.2 | 6.8 | -32.8 | -48.1 | -11.0 | -8.1 | -1.7 |
| GRE | 1.1 | -13.5 | -44.0 | -37.2 | 0.5 | -2.3 | 4.3 |
| KAT | 3.5 | 0.9 | -27.7 | -39.8 | -6.0 | -1.9 | 1.7 |
| TÄN | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
13.6.4 Within-Split Pace Deltas
| code | split_1 | split_2 | split_3 | split_4 | split_5 | split_6 | split_N |
|---|---|---|---|---|---|---|---|
| SOR | 0.36 | -2.06 | -8.47 | -11.62 | -2.81 | -0.66 | 0.56 |
| EVA | 0.36 | -2.95 | -8.51 | -11.06 | -2.09 | -0.16 | 1.22 |
| FOU | 0.67 | -2.41 | -8.41 | -10.41 | -1.88 | -2.14 | 0.41 |
| ROV | 0.80 | -2.41 | -8.13 | -11.33 | -1.97 | 0.95 | 2.11 |
| OGI | -0.17 | -2.76 | -7.69 | -7.37 | -0.76 | -1.71 | -0.11 |
| NEU | 0.61 | 1.40 | -6.20 | -9.30 | -2.33 | -2.66 | -0.63 |
| GRE | 0.21 | -2.78 | -8.32 | -7.20 | 0.11 | -0.76 | 1.59 |
| KAT | 0.67 | 0.19 | -5.24 | -7.70 | -1.27 | -0.63 | 0.63 |
| TÄN | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
13.6.5 Within-Split Pace Map
13.7 Driver Report, SS13 - ROV
ROV was in fourth on stage, with a time of 1062.0s .
Stage report for ROV.
| Pos | Car | Code | Time (s) | Gap | Diff |
|---|---|---|---|---|---|
| 1 | 6 | SOR | -17.0 | -17.0 | 0.0 |
| 2 | 33 | EVA | -11.9 | -11.9 | 5.1 |
| 3 | 16 | FOU | -11.0 | -11.0 | 0.9 |
| 4 | 69 | ROV | 0.0 | 0.0 | 11.0 |
| 5 | 1 | OGI | 7.6 | 7.6 | 7.6 |
| 6 | 11 | NEU | 18.1 | 18.1 | 10.5 |
| 7 | 44 | GRE | 18.7 | 18.7 | 0.6 |
| 8 | 18 | KAT | 40.5 | 40.5 | 21.8 |
| 10 | 8 | TÄN | 109.8 | 109.8 | 43.2 |
13.7.1 Rebased Split Times
ROV’s delta at each split compared to other drivers.
| code | split_1 | split_2 | split_3 | split_4 | split_5 | split_6 | split_N |
|---|---|---|---|---|---|---|---|
| SOR | -2.3 | -0.6 | -2.4 | -3.9 | -7.9 | -12.8 | -17.0 |
| EVA | -2.3 | -4.9 | -6.9 | -5.5 | -6.1 | -9.5 | -11.9 |
| FOU | -0.7 | -0.7 | -2.2 | 2.6 | 3.0 | -6.4 | -11.0 |
| ROV | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| OGI | -5.1 | -6.8 | -4.5 | 16.0 | 21.7 | 13.6 | 7.6 |
| NEU | -1.0 | 17.5 | 27.7 | 38.2 | 36.5 | 25.5 | 18.1 |
| GRE | -3.1 | -4.9 | -5.9 | 15.5 | 25.3 | 20.1 | 18.7 |
| KAT | -0.7 | 11.9 | 27.2 | 46.0 | 49.3 | 44.5 | 40.5 |
| TÄN | -4.2 | 7.5 | 50.5 | 109.1 | 118.4 | 115.5 | 109.8 |
13.7.2 Off-the-Pace Chart
Accumulated stage time deltas at each split:
13.7.3 Within-Split Duration Deltas
ROV’s delta in time to complete each split section compared to other drivers:
| code | split_1 | split_2 | split_3 | split_4 | split_5 | split_6 | split_N |
|---|---|---|---|---|---|---|---|
| SOR | -2.3 | 1.7 | -1.8 | -1.5 | -4.0 | -4.9 | -4.2 |
| EVA | -2.3 | -2.6 | -2.0 | 1.4 | -0.6 | -3.4 | -2.4 |
| FOU | -0.7 | 0.0 | -1.5 | 4.8 | 0.4 | -9.4 | -4.6 |
| ROV | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| OGI | -5.1 | -1.7 | 2.3 | 20.5 | 5.7 | -8.1 | -6.0 |
| NEU | -1.0 | 18.5 | 10.2 | 10.5 | -1.7 | -11.0 | -7.4 |
| GRE | -3.1 | -1.8 | -1.0 | 21.4 | 9.8 | -5.2 | -1.4 |
| KAT | -0.7 | 12.6 | 15.3 | 18.8 | 3.3 | -4.8 | -4.0 |
| TÄN | -4.2 | 11.7 | 43.0 | 58.6 | 9.3 | -2.9 | -5.7 |
13.7.4 Within-Split Pace Deltas
| code | split_1 | split_2 | split_3 | split_4 | split_5 | split_6 | split_N |
|---|---|---|---|---|---|---|---|
| SOR | -0.44 | 0.35 | -0.34 | -0.29 | -0.85 | -1.61 | -1.56 |
| EVA | -0.44 | -0.54 | -0.38 | 0.27 | -0.13 | -1.12 | -0.89 |
| FOU | -0.13 | 0.00 | -0.28 | 0.93 | 0.08 | -3.09 | -1.70 |
| ROV | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| OGI | -0.97 | -0.35 | 0.43 | 3.97 | 1.21 | -2.66 | -2.22 |
| NEU | -0.19 | 3.81 | 1.93 | 2.03 | -0.36 | -3.62 | -2.74 |
| GRE | -0.59 | -0.37 | -0.19 | 4.14 | 2.07 | -1.71 | -0.52 |
| KAT | -0.13 | 2.60 | 2.89 | 3.64 | 0.70 | -1.58 | -1.48 |
| TÄN | -0.80 | 2.41 | 8.13 | 11.33 | 1.97 | -0.95 | -2.11 |
13.7.5 Within-Split Pace Map
13.8 Driver Report, SS13 - KAT
KAT was in eighth on stage, 57.5s behind stage winner SOR .
Stage report for KAT.
| Pos | Car | Code | Time (s) | Gap | Diff |
|---|---|---|---|---|---|
| 1 | 6 | SOR | -57.5 | -57.5 | 0.0 |
| 2 | 33 | EVA | -52.4 | -52.4 | 5.1 |
| 3 | 16 | FOU | -51.5 | -51.5 | 0.9 |
| 4 | 69 | ROV | -40.5 | -40.5 | 11.0 |
| 5 | 1 | OGI | -32.9 | -32.9 | 7.6 |
| 6 | 11 | NEU | -22.4 | -22.4 | 10.5 |
| 7 | 44 | GRE | -21.8 | -21.8 | 0.6 |
| 8 | 18 | KAT | 0.0 | 0.0 | 21.8 |
| 10 | 8 | TÄN | 69.3 | 69.3 | 43.2 |
13.8.1 Rebased Split Times
KAT’s delta at each split compared to other drivers.
| code | split_1 | split_2 | split_3 | split_4 | split_5 | split_6 | split_N |
|---|---|---|---|---|---|---|---|
| SOR | -1.6 | -12.5 | -29.6 | -49.9 | -57.2 | -57.3 | -57.5 |
| EVA | -1.6 | -16.8 | -34.1 | -51.5 | -55.4 | -54.0 | -52.4 |
| FOU | 0.0 | -12.6 | -29.4 | -43.4 | -46.3 | -50.9 | -51.5 |
| ROV | 0.7 | -11.9 | -27.2 | -46.0 | -49.3 | -44.5 | -40.5 |
| OGI | -4.4 | -18.7 | -31.7 | -30.0 | -27.6 | -30.9 | -32.9 |
| NEU | -0.3 | 5.6 | 0.5 | -7.8 | -12.8 | -19.0 | -22.4 |
| GRE | -2.4 | -16.8 | -33.1 | -30.5 | -24.0 | -24.4 | -21.8 |
| KAT | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| TÄN | -3.5 | -4.4 | 23.3 | 63.1 | 69.1 | 71.0 | 69.3 |
13.8.2 Off-the-Pace Chart
Accumulated stage time deltas at each split:
13.8.3 Within-Split Duration Deltas
KAT’s delta in time to complete each split section compared to other drivers:
| code | split_1 | split_2 | split_3 | split_4 | split_5 | split_6 | split_N |
|---|---|---|---|---|---|---|---|
| SOR | -1.6 | -10.9 | -17.1 | -20.3 | -7.3 | -0.1 | -0.2 |
| EVA | -1.6 | -15.2 | -17.3 | -17.4 | -3.9 | 1.4 | 1.6 |
| FOU | 0.0 | -12.6 | -16.8 | -14.0 | -2.9 | -4.6 | -0.6 |
| ROV | 0.7 | -12.6 | -15.3 | -18.8 | -3.3 | 4.8 | 4.0 |
| OGI | -4.4 | -14.3 | -13.0 | 1.7 | 2.4 | -3.3 | -2.0 |
| NEU | -0.3 | 5.9 | -5.1 | -8.3 | -5.0 | -6.2 | -3.4 |
| GRE | -2.4 | -14.4 | -16.3 | 2.6 | 6.5 | -0.4 | 2.6 |
| KAT | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| TÄN | -3.5 | -0.9 | 27.7 | 39.8 | 6.0 | 1.9 | -1.7 |
13.8.4 Within-Split Pace Deltas
| code | split_1 | split_2 | split_3 | split_4 | split_5 | split_6 | split_N |
|---|---|---|---|---|---|---|---|
| SOR | -0.30 | -2.25 | -3.23 | -3.93 | -1.54 | -0.03 | -0.07 |
| EVA | -0.30 | -3.13 | -3.27 | -3.37 | -0.82 | 0.46 | 0.59 |
| FOU | 0.00 | -2.60 | -3.18 | -2.71 | -0.61 | -1.51 | -0.22 |
| ROV | 0.13 | -2.60 | -2.89 | -3.64 | -0.70 | 1.58 | 1.48 |
| OGI | -0.84 | -2.95 | -2.46 | 0.33 | 0.51 | -1.09 | -0.74 |
| NEU | -0.06 | 1.22 | -0.96 | -1.61 | -1.06 | -2.04 | -1.26 |
| GRE | -0.46 | -2.97 | -3.08 | 0.50 | 1.37 | -0.13 | 0.96 |
| KAT | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| TÄN | -0.67 | -0.19 | 5.24 | 7.70 | 1.27 | 0.63 | -0.63 |
13.8.5 Within-Split Pace Map
13.9 Driver Report, SS13 - SOR
SOR recorded the stage win and taking a time of 1045.0s.
Stage report for SOR.
| Pos | Car | Code | Time (s) | Gap | Diff |
|---|---|---|---|---|---|
| 1 | 6 | SOR | 0.0 | 0.0 | 0.0 |
| 2 | 33 | EVA | 5.1 | 5.1 | 5.1 |
| 3 | 16 | FOU | 6.0 | 6.0 | 0.9 |
| 4 | 69 | ROV | 17.0 | 17.0 | 11.0 |
| 5 | 1 | OGI | 24.6 | 24.6 | 7.6 |
| 6 | 11 | NEU | 35.1 | 35.1 | 10.5 |
| 7 | 44 | GRE | 35.7 | 35.7 | 0.6 |
| 8 | 18 | KAT | 57.5 | 57.5 | 21.8 |
| 10 | 8 | TÄN | 126.8 | 126.8 | 43.2 |
13.9.1 Rebased Split Times
SOR’s delta at each split compared to other drivers.
| code | split_1 | split_2 | split_3 | split_4 | split_5 | split_6 | split_N |
|---|---|---|---|---|---|---|---|
| SOR | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| EVA | 0.0 | -4.3 | -4.5 | -1.6 | 1.8 | 3.3 | 5.1 |
| FOU | 1.6 | -0.1 | 0.2 | 6.5 | 10.9 | 6.4 | 6.0 |
| ROV | 2.3 | 0.6 | 2.4 | 3.9 | 7.9 | 12.8 | 17.0 |
| OGI | -2.8 | -6.2 | -2.1 | 19.9 | 29.6 | 26.4 | 24.6 |
| NEU | 1.3 | 18.1 | 30.1 | 42.1 | 44.4 | 38.3 | 35.1 |
| GRE | -0.8 | -4.3 | -3.5 | 19.4 | 33.2 | 32.9 | 35.7 |
| KAT | 1.6 | 12.5 | 29.6 | 49.9 | 57.2 | 57.3 | 57.5 |
| TÄN | -1.9 | 8.1 | 52.9 | 113.0 | 126.3 | 128.3 | 126.8 |
13.9.2 Off-the-Pace Chart
Accumulated stage time deltas at each split:
13.9.3 Within-Split Duration Deltas
SOR’s delta in time to complete each split section compared to other drivers:
| code | split_1 | split_2 | split_3 | split_4 | split_5 | split_6 | split_N |
|---|---|---|---|---|---|---|---|
| SOR | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| EVA | 0.0 | -4.3 | -0.2 | 2.9 | 3.4 | 1.5 | 1.8 |
| FOU | 1.6 | -1.7 | 0.3 | 6.3 | 4.4 | -4.5 | -0.4 |
| ROV | 2.3 | -1.7 | 1.8 | 1.5 | 4.0 | 4.9 | 4.2 |
| OGI | -2.8 | -3.4 | 4.1 | 22.0 | 9.7 | -3.2 | -1.8 |
| NEU | 1.3 | 16.8 | 12.0 | 12.0 | 2.3 | -6.1 | -3.2 |
| GRE | -0.8 | -3.5 | 0.8 | 22.9 | 13.8 | -0.3 | 2.8 |
| KAT | 1.6 | 10.9 | 17.1 | 20.3 | 7.3 | 0.1 | 0.2 |
| TÄN | -1.9 | 10.0 | 44.8 | 60.1 | 13.3 | 2.0 | -1.5 |
13.9.4 Within-Split Pace Deltas
| code | split_1 | split_2 | split_3 | split_4 | split_5 | split_6 | split_N |
|---|---|---|---|---|---|---|---|
| SOR | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| EVA | 0.00 | -0.89 | -0.04 | 0.56 | 0.72 | 0.49 | 0.67 |
| FOU | 0.30 | -0.35 | 0.06 | 1.22 | 0.93 | -1.48 | -0.15 |
| ROV | 0.44 | -0.35 | 0.34 | 0.29 | 0.85 | 1.61 | 1.56 |
| OGI | -0.53 | -0.70 | 0.78 | 4.26 | 2.05 | -1.05 | -0.67 |
| NEU | 0.25 | 3.46 | 2.27 | 2.32 | 0.49 | -2.01 | -1.19 |
| GRE | -0.15 | -0.72 | 0.15 | 4.43 | 2.92 | -0.10 | 1.04 |
| KAT | 0.30 | 2.25 | 3.23 | 3.93 | 1.54 | 0.03 | 0.07 |
| TÄN | -0.36 | 2.06 | 8.47 | 11.62 | 2.81 | 0.66 | -0.56 |
13.9.5 Within-Split Pace Map
13.10 Driver Report, SS13 - GRE
GRE took seventh place on stage, with a time of 1080.7s, just 0.6s off NEU.
Stage report for GRE.
| Pos | Car | Code | Time (s) | Gap | Diff |
|---|---|---|---|---|---|
| 1 | 6 | SOR | -35.7 | -35.7 | 0.0 |
| 2 | 33 | EVA | -30.6 | -30.6 | 5.1 |
| 3 | 16 | FOU | -29.7 | -29.7 | 0.9 |
| 4 | 69 | ROV | -18.7 | -18.7 | 11.0 |
| 5 | 1 | OGI | -11.1 | -11.1 | 7.6 |
| 6 | 11 | NEU | -0.6 | -0.6 | 10.5 |
| 7 | 44 | GRE | 0.0 | 0.0 | 0.6 |
| 8 | 18 | KAT | 21.8 | 21.8 | 21.8 |
| 10 | 8 | TÄN | 91.1 | 91.1 | 43.2 |
13.10.1 Rebased Split Times
GRE’s delta at each split compared to other drivers.
| code | split_1 | split_2 | split_3 | split_4 | split_5 | split_6 | split_N |
|---|---|---|---|---|---|---|---|
| SOR | 0.8 | 4.3 | 3.5 | -19.4 | -33.2 | -32.9 | -35.7 |
| EVA | 0.8 | 0.0 | -1.0 | -21.0 | -31.4 | -29.6 | -30.6 |
| FOU | 2.4 | 4.2 | 3.7 | -12.9 | -22.3 | -26.5 | -29.7 |
| ROV | 3.1 | 4.9 | 5.9 | -15.5 | -25.3 | -20.1 | -18.7 |
| OGI | -2.0 | -1.9 | 1.4 | 0.5 | -3.6 | -6.5 | -11.1 |
| NEU | 2.1 | 22.4 | 33.6 | 22.7 | 11.2 | 5.4 | -0.6 |
| GRE | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| KAT | 2.4 | 16.8 | 33.1 | 30.5 | 24.0 | 24.4 | 21.8 |
| TÄN | -1.1 | 12.4 | 56.4 | 93.6 | 93.1 | 95.4 | 91.1 |
13.10.2 Off-the-Pace Chart
Accumulated stage time deltas at each split:
13.10.3 Within-Split Duration Deltas
GRE’s delta in time to complete each split section compared to other drivers:
| code | split_1 | split_2 | split_3 | split_4 | split_5 | split_6 | split_N |
|---|---|---|---|---|---|---|---|
| SOR | 0.8 | 3.5 | -0.8 | -22.9 | -13.8 | 0.3 | -2.8 |
| EVA | 0.8 | -0.8 | -1.0 | -20.0 | -10.4 | 1.8 | -1.0 |
| FOU | 2.4 | 1.8 | -0.5 | -16.6 | -9.4 | -4.2 | -3.2 |
| ROV | 3.1 | 1.8 | 1.0 | -21.4 | -9.8 | 5.2 | 1.4 |
| OGI | -2.0 | 0.1 | 3.3 | -0.9 | -4.1 | -2.9 | -4.6 |
| NEU | 2.1 | 20.3 | 11.2 | -10.9 | -11.5 | -5.8 | -6.0 |
| GRE | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| KAT | 2.4 | 14.4 | 16.3 | -2.6 | -6.5 | 0.4 | -2.6 |
| TÄN | -1.1 | 13.5 | 44.0 | 37.2 | -0.5 | 2.3 | -4.3 |
13.10.4 Within-Split Pace Deltas
| code | split_1 | split_2 | split_3 | split_4 | split_5 | split_6 | split_N |
|---|---|---|---|---|---|---|---|
| SOR | 0.15 | 0.72 | -0.15 | -4.43 | -2.92 | 0.10 | -1.04 |
| EVA | 0.15 | -0.16 | -0.19 | -3.87 | -2.20 | 0.59 | -0.37 |
| FOU | 0.46 | 0.37 | -0.09 | -3.21 | -1.99 | -1.38 | -1.19 |
| ROV | 0.59 | 0.37 | 0.19 | -4.14 | -2.07 | 1.71 | 0.52 |
| OGI | -0.38 | 0.02 | 0.62 | -0.17 | -0.87 | -0.95 | -1.70 |
| NEU | 0.40 | 4.19 | 2.12 | -2.11 | -2.43 | -1.91 | -2.22 |
| GRE | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| KAT | 0.46 | 2.97 | 3.08 | -0.50 | -1.37 | 0.13 | -0.96 |
| TÄN | -0.21 | 2.78 | 8.32 | 7.20 | -0.11 | 0.76 | -1.59 |
13.10.5 Within-Split Pace Map
13.11 Driver Report, SS13 - FOU
FOU took third place on stage, 6.0s behind SOR , only 0.9s behind EVA.
Stage report for FOU.
| Pos | Car | Code | Time (s) | Gap | Diff |
|---|---|---|---|---|---|
| 1 | 6 | SOR | -6.0 | -6.0 | 0.0 |
| 2 | 33 | EVA | -0.9 | -0.9 | 5.1 |
| 3 | 16 | FOU | 0.0 | 0.0 | 0.9 |
| 4 | 69 | ROV | 11.0 | 11.0 | 11.0 |
| 5 | 1 | OGI | 18.6 | 18.6 | 7.6 |
| 6 | 11 | NEU | 29.1 | 29.1 | 10.5 |
| 7 | 44 | GRE | 29.7 | 29.7 | 0.6 |
| 8 | 18 | KAT | 51.5 | 51.5 | 21.8 |
| 10 | 8 | TÄN | 120.8 | 120.8 | 43.2 |
13.11.1 Rebased Split Times
FOU’s delta at each split compared to other drivers.
| code | split_1 | split_2 | split_3 | split_4 | split_5 | split_6 | split_N |
|---|---|---|---|---|---|---|---|
| SOR | -1.6 | 0.1 | -0.2 | -6.5 | -10.9 | -6.4 | -6.0 |
| EVA | -1.6 | -4.2 | -4.7 | -8.1 | -9.1 | -3.1 | -0.9 |
| FOU | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| ROV | 0.7 | 0.7 | 2.2 | -2.6 | -3.0 | 6.4 | 11.0 |
| OGI | -4.4 | -6.1 | -2.3 | 13.4 | 18.7 | 20.0 | 18.6 |
| NEU | -0.3 | 18.2 | 29.9 | 35.6 | 33.5 | 31.9 | 29.1 |
| GRE | -2.4 | -4.2 | -3.7 | 12.9 | 22.3 | 26.5 | 29.7 |
| KAT | 0.0 | 12.6 | 29.4 | 43.4 | 46.3 | 50.9 | 51.5 |
| TÄN | -3.5 | 8.2 | 52.7 | 106.5 | 115.4 | 121.9 | 120.8 |
13.11.2 Off-the-Pace Chart
Accumulated stage time deltas at each split:
13.11.3 Within-Split Duration Deltas
FOU’s delta in time to complete each split section compared to other drivers:
| code | split_1 | split_2 | split_3 | split_4 | split_5 | split_6 | split_N |
|---|---|---|---|---|---|---|---|
| SOR | -1.6 | 1.7 | -0.3 | -6.3 | -4.4 | 4.5 | 0.4 |
| EVA | -1.6 | -2.6 | -0.5 | -3.4 | -1.0 | 6.0 | 2.2 |
| FOU | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| ROV | 0.7 | 0.0 | 1.5 | -4.8 | -0.4 | 9.4 | 4.6 |
| OGI | -4.4 | -1.7 | 3.8 | 15.7 | 5.3 | 1.3 | -1.4 |
| NEU | -0.3 | 18.5 | 11.7 | 5.7 | -2.1 | -1.6 | -2.8 |
| GRE | -2.4 | -1.8 | 0.5 | 16.6 | 9.4 | 4.2 | 3.2 |
| KAT | 0.0 | 12.6 | 16.8 | 14.0 | 2.9 | 4.6 | 0.6 |
| TÄN | -3.5 | 11.7 | 44.5 | 53.8 | 8.9 | 6.5 | -1.1 |
13.11.4 Within-Split Pace Deltas
| code | split_1 | split_2 | split_3 | split_4 | split_5 | split_6 | split_N |
|---|---|---|---|---|---|---|---|
| SOR | -0.30 | 0.35 | -0.06 | -1.22 | -0.93 | 1.48 | 0.15 |
| EVA | -0.30 | -0.54 | -0.09 | -0.66 | -0.21 | 1.97 | 0.81 |
| FOU | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| ROV | 0.13 | 0.00 | 0.28 | -0.93 | -0.08 | 3.09 | 1.70 |
| OGI | -0.84 | -0.35 | 0.72 | 3.04 | 1.12 | 0.43 | -0.52 |
| NEU | -0.06 | 3.81 | 2.21 | 1.10 | -0.44 | -0.53 | -1.04 |
| GRE | -0.46 | -0.37 | 0.09 | 3.21 | 1.99 | 1.38 | 1.19 |
| KAT | 0.00 | 2.60 | 3.18 | 2.71 | 0.61 | 1.51 | 0.22 |
| TÄN | -0.67 | 2.41 | 8.41 | 10.41 | 1.88 | 2.14 | -0.41 |
13.11.5 Within-Split Pace Map
13.12 Driver Report, SS13 - SOL
No stage report for SOL.


P680K shabu seized from HVT
by Bernard GalangANGELES CITY – Some P680,000 worth of shabu was confiscated from a high value target during a buy-bust operation in Barangay Balibago here the other day.
Col. Joyce Patrick Sangalang, city police director here, said that the illegal drugs were seized from Basco Kamal, 31, listed as level 1 high value target in the city police’s list of suspected drug personalities.
The suspect, a resident of Quezon City, was arrested in broad daylight along MacArthur Highway by operatives from the Police Station 4, City Drug Enforcement Unit and City Mobile Force Company.
Armed with coordination document from the Philippine Drug Enforcement Agency, the policemen used a poseur-buyer who bought a medium-sized sachet of shabu from the suspect with a marked peso bill mixed with boodle money.
Some 100 grams of shabu with street value of P680,000 were confiscated from the suspect.
Meanwhile, policemen arrested recently an American national in Barangay Malabanias here for alleged rape.


Rules of Hashi:
Connect the circles (“islands”) with straight bridges such that all the circles form one connected component, and
- At most two bridges connect each pair of islands;
- All bridges are horizontal or vertical;
- No two bridges intersect each other.
Furthermore, the number in each circle indicates how many bridges it is connected to.
Each puzzle has a unique solution.


Gordon Sondland told Trump Ukrainian president ‘loves your ass’
by Jon LevineUkrainian president Volodymyr Zelensky “loves your ass” and “will do anything you ask of him,” an EU ambassador told the president, according to a witness.
The alleged crass remark from European Union ambassador Gordon Sondland came to light in a closed-door deposition in front of House lawmakers by David Holmes, a consular official at the US embassy in Ukraine.
“So, he’s going to do the investigation?” Trump apparently replied, to which Sondland answered, “He’s going to do it … President Zelensky will do anything you ask of him,” Holmes said of the exchange.
Holmes’ Friday statement, uploaded in full by CNN, was part of the ongoing impeachment inquiry into the president’s dealings with Ukraine.
The president is accused of attempting to use hundreds of millions of dollars in military aid as leverage in an attempt to get the country to investigate Hunter Biden’s involvement with the energy company Burisma Holdings. Biden’s father, former vice-president Joe Biden, is currently a leading candidate for the 2020 Democratic presidential nomination.


You may be a person who is enthusiastic about internet and not keen on internet aware of dangers in the cyber world and who takes precautions to protect privacy and sensitive data. However do you know anything in detail about the new friends you made in the cyber world, anything about the groups or communities you joined or about a person in the cyber world who you think as a good man?. Will you inform your parents/wife/husband/friend that you are going to meet a person who is a friend of you in the cyber world? Think about it. Do not share your private information with anyone in the cyber world Chances are there that a cordial relationship of today will become enmity tomorrow. The photos, mails, chats, and videos etc you shared with a person while you were in friendship may be used against you after your break up. The information you share in the cyber world may be used against you by your jealous friend, enemy or by a criminal. Do not share your password with anyone. Do not share your password and other sensitive information with your friends, is blind belief knowingly or unknowingly your friend may use your credentials against you. Unplug or Close webcam when it is not in use Softwares that can spy on you by recording your video are available now. These softwares can start themselves and can record your video without your permission and knowledge. So always unplug or close webcams when not in use.
Do not allow others to download pirated materials by using your computerYou may be well aware of the consequence of downloading pirated materials in your computer or mobile phone. If you share your computer or mobile with others, chances are there that they may access it in future. Do not allow others to download pirated materials by using your computer You may be well aware of the consequence of downloading pirated materials in your computer or mobile phone. If you share your computer or mobile with others, chances are there that they may access it in future without your permission. They may access your emails and notes etc. So always secure your computer or mobile phones etc by using passwords. Use antivirus / Security softwares. Use updated antivirus or Security softwares. Do not show laziness in installing or updating antivirus/ security softwares. Do the antivirus software updation periodically .Use antivirus / security softwares after ensuring credibility and authenticity.
Internet Tips


A bar graph of sales by quarter. Each series is a salesperson.
| Quarter 1 | Quarter 2 | Quarter 3 | Quarter 4 |
|---|---|---|---|
| 5 | 4 | 3 | 7 |
| 3 | 2 | 9 | 5 |
| 1 | 5 | 8 | 4 |
| 2 | 3 | 4 | 6 |
| 4 | 1 | 2 | 1 |
A bar graph of sales by quarter. Each series is a salesperson.
| Quarter 1 | Quarter 2 | Quarter 3 | Quarter 4 |
|---|---|---|---|
| 5 | 4 | 3 | 7 |
| 3 | 2 | 9 | 5 |
| 1 | 5 | 8 | 4 |
| 2 | 3 | 4 | 6 |
| 4 | 1 | 2 | 1 |
A bar graph of sales by quarter. Each series is a salesperson.


curl_multi_remove_handle - remove an easy handle from a
multi session
SYNOPSIS
#include <curl/curl.h>
CURLMcode curl_multi_remove_handle(CURLM *multi_handle,
CURL *easy_handle);
DESCRIPTION
Removes a given easy_handle from the multi_handle. This
will make the specified easy handle be removed from this
multi handle's control.
When the easy handle has been removed from a multi stack,
it is again perfectly legal to invoke curl_easy_perform()
on this easy handle.
RETURN VALUE
CURLMcode type, general libcurl multi interface error
code.
SEE ALSO
curl_multi_cleanup(3),curl_multi_init(3)
Man(1) output converted with
man2html


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Botnet C&C
Control Panel
File Browser
Task Managment
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Bot Information
{STATS}
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Anti-Virus
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Keylogger
Reverce HTTP
Files Folder
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Screenshots Folder
Installed Software
IP Config
System Information
WiFi Information


VOLO
Apr 23, 2018 in Portfolio
Read PostApr 23, 2018 in Portfolio
Read PostApr 23, 2018 in Portfolio
Read PostJan 4, 2017 in Work-in-Progress
Read PostDec 16, 2016 in Manifestos
Read PostDec 14, 2016 in Work-in-Progress
Read PostDec 9, 2016 in Work-in-Progress
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Porsche 928 Resurrected In Modern Rendering
Does a modernized 928 look better than the original?
by Chris OkulaWhen the Porsche 928 debuted in 1977 the futuristic grand tourer was set to usher in a new age for Porsche with the goal of replacing the aging 911. Fortunately, the 928 was able to coexist with the 911 thanks to its radically different water-cooled front-engine V8 layout. Porsche ended 928 production in 1995 leaving us to wonder, what would a modern 928 look like?
Luckily photoshop master TheSketchMoneky is hard at work in his latest video creating a modern rendition of the Porsche 928. The V8 powered 928 was a very polarizing design when it debuted but can a modern rendition broaden its appeal?
Gallery: Porsche 928 Resurrected In Modern Rendering
In 1977 a pre-production 928 set the record for the fastest naturally aspirated production car. At the Bonneville salt flats, IMSA racer Al Holbert took a pre-production 928 S4 to a top speed of 171 mph. The 928’s powerful V8 and aerodynamic design made it the perfect autobahn cruiser and capable of this impressive top speed.
The 1977 928 featured a 4.5-liter V8 which produced 219 horsepower and 268 lb-ft of torque. As time went on, displacement rose and electronic fuel injection was introduced to provide more power. By 1985 a 5.0-liter V8 was standard in the American 928 and power rose to 288 horsepower to compete with more powerful American competition. Most 928’s were sold with a Mercedes derived automatic transmission although a dogleg 5-speed manual transmission was available.
Top Porsche news
The crown jewel of the 928 came in 1993 with the debut of the Porsche 928 GTS. This top-level trim used a 5.4-liter V8 resulting in 345 horsepower and could sprint from 0-60 in under 6 seconds. This rare trim level marked the end of the 928’s production run with only 406 examples reaching American dealerships.
Would you buy a modernized Porsche 928?
Source: TheSketchMonkey


The integer solutions to the Pythagorean Theorem, a^2 + b^2 = c^2 are called Pythagorean Triples which contains three positive integers a, b, and c.
Enter exactly one number
a : b : c : Solve Reset


Module textredux.core.style
The style module lets you define and use custom, non-lexer-based styles.
The default styles
Textredux piggybacks on the default lexer styles defined by a user's theme, and makes them available for your Textredux interface. The big benefit of this is that by using those styles, or by basing your custom styles on them, your interface stands a much higher chance of blending in well with the color scheme used. As an example, your custom style with cyan foreground text might look great with your own dark theme, but may be pretty near invisible for some user with a light blue background.
You can read more about the default lexer styles in the
Textadept lexer documentation.
You access a default style (or any style for that matter), by indexing the
style module, like so: style.<name>
. For reference, the default styles
available are these:
- style.nothing
- style.whitespace
- style.comment
- style.string
- style.number
- style.keyword
- style.identifier
- style.operator
- style.error
- style.preproc
- style.constant
- style.variable
- style.function
- style.class
- style.type
- style.default
- style.line_number
- style.bracelight
- style.bracebad
- style.controlchar
- style.indentguide
- style.calltip
What's a style?
Textredux styling has been made to resemble the lexer based style creation.
A style is thus just a table with certain properties, almost exactly the same
as for style created for a lexer or theme. Please see the documentation for
lexer.style
for information about the fields. The one exception compared to lexer styles
is that colors are specified using the standard '#rrggbb'
notation instead of
the lexer styles' bgr
notation. This is what you use to create custom styles
(see below), and also what you get when accessing any already existing styles.
Defining styles
You define a new style by assigning the style to the style module, like so:
style.foo_header = { italic = true, fore = '#680000' }
As has been previously said, it's often a good idea to base your custom styles on an existing default style. Similarily to defining a lexer style in Textadept you can achieve this by concatenating styles:
style.foo_header = style.string .. { underline = true }
NB: Watch out for the mistake of not assigning the style to the style module:
local header = style.string .. { underline = true }
This will not work, as the style is not correctly defined with the style module.
In order to avoid name clashes, it's suggested that you name any custom styles
by prefixing their name with the name of your module. E.g. if your module is
named awesome
, then name your style something like style.awesome_style
.
Using styles
You typically use a style by inserting text through
textredux.core.buffer's text insertion methods, specifying the style.
Please see the example in examples/buffer_styling.lua
for usage of this.
Info:
- Copyright: 2011-2012
- License: MIT (see LICENSE)
- Author: Nils Nordman
Functions
| apply (style, buffer, start_pos, length) | Applies the specified style for the given text range and buffer. |
| define_styles () | Defines the currently used custom styles for the current buffer. |
Functions
- apply (style, buffer, start_pos, length)
-
Applies the specified style for the given text range and buffer.
While you could use this directly, you'd typically use the text insertion
methods in textredux.core.buffer to style content.
Parameters:
- style The defined style
- buffer The buffer to apply the style for
- start_pos The starting position of the style
- length The number of positions to style
- define_styles ()
- Defines the currently used custom styles for the current buffer. This must be called whenever a buffer with custom styles is switched to. This is automatically done by the textredux.core.buffer class, and thus not something you typically have to worry about.


EXTROVERTED | SENSING | FEELING | JUDGING
As Sailor Jupiter, The Provider you are warmhearted, practical and the sociable type. When it comes to responsibilities in your life, you take them very seriously. You are the cheerleader, enjoy setting the tone of your environment and also are sensitive which pairs well with your other traits of being loyal, reliable and true.
You are not devoted because you have to be, but instead truly enjoy hearing about what’s going on with friends and people you care about, being deeply invested in small details and a plentiful amount of warmth and sensitivity when it may be required. You are also talented in the art of noticing tension, doing your best to restore harmony when it may not be present.
Being The Provider means a lot of your energy gets channeled into things like establishing order, you thrive in areas of organization, open activities and surprise get togethers where your social heart is free to interact with everyone.
One of the main traits of The Provider is their sensitivity, both being an asset and a challenge when certain things hit their heart in a heavy way. The great thing is, that while others may disagree sometimes, that doesn’t mean that you can’t continue to do your best. This is in the form of being a role model, taking care of what is important to you and enjoying life because there are large amounts of people who appreciate The Provider not only for what they do, but who they are and the efforts they make.


Design by Bagus Sandyartha 2016
The true sign of intelligence is not knowledge but imagination. Albert Einstein


WATCH: Trailer for new Irish-language Famine film "Arracht"
by IrishCentral Staff“Arracht” will debut at the Tallinn Black Nights Film Festival this month
“Arracht,” a new Irish-language film about Ireland’s Famine, will premiere later this month at the Tallinn Black Nights Film Festival in Estonia.
Read More: Irish Famine movie "Black 47" now available on Netflix in Ireland
The filmmakers said in a statement: “We are delighted to share that Arracht will have its world premiere at the Tallinn Black Nights Film Festival on November 29, 2019. We’re also excited that the film has been selected as part of the festival’s main competition, the Grand Prix.
“As an A-list festival, the Tallinn Black Nights Film Festival, aka PÖFF, is one of the largest and most distinctive film events in Northern Europe, screening over 250 feature films, having an attendance of over 80,000 and hosting over 1,200 film professionals and journalists from around the world every year. It is particularly impressive for Tom Sullivan’s film to be included in the main competition, The Grand Prix.
Of his first feature film, director and Dublin native Tom Sullivan said: “I am honored that our film will have its world premiere in the main competition at Tallinn.
“It has been a life-changing experience making Arracht. From witnessing the commitment of the cast, particularly Dónall Ó Héalaí, who’s total dedication and immersion in his process inspired us all. The crew, who were fantastic, had to battle three Atlantic storms to bring Arracht home. We are all so excited to launch the film in the beautiful city of Tallinn along with so many talented filmmakers.”
Producer Cúán Mac Conghail of Macalla added: “I am incredibly proud of Tom and this film. Arracht was hard work but a joy for all of us to work on and I am delighted that it has been selected in competition for the Grand Prix.”
"Arracht" is produced by Macalla Teoranta with funding from TG4, Screen Ireland, Broadcasting Authority of Ireland (BAI) and Section 481.
Read More: "Black 47" finally breaks Hollywood's silence on the Irish Famine
A synopsis of “Arracht” reveals that the film is set in 1845 Ireland on the eve of The Great Hunger. Colmán Sharkey, a fisherman, a father, a husband, takes in a stranger at the behest of a local priest. Patsy, a former soldier in the Napoleonic wars arrives just ahead of ‘the blight,’ a disease that eventually wipes out the country’s potato crop, contributing to the death and displacement of millions.
As the crops rot in the fields, Colmán, his brother and Patsy travel to the English Landlord’s house to request a stay on rent increases that Colmán predicts will destroy his community. His request falls on deaf ears and Patsy’s subsequent actions set Colmán on a path that will take him to the edge of survival, and sanity.
It is only upon encountering an abandoned young girl that Colmán’s resolve is lifted. Just in time for the darkness of his past to pay another visit.”
The featured cast includes Dónall Ó Healaí, Michael McElhatton, Siobhán O’Kelly, Dara Devaney, Peter Coonan, Seán T. Ó Meallaigh, Michelle Beamish and Eoin Ó Dubhghaill.
Read More: Irish Famine studies in the US should be mandatory, like the Holocaust
You can watch the debut trailer for “Arracht” here:


Set the minimum number of keys that will be stored on any single
Btree page.
This value is used to determine which keys will be stored on overflow
pages, i.e. if a key or data item is larger than the pagesize divided
by the bt_minkey value, it will be stored on overflow pages
instead of in the page itself.
The bt_minkey value specified must be at least 2; if bt_minkey
is not explicitly set, a value of 2 is used.


A downside about open source software is the fact that application program interfaces (APIs) do not remain stable very long. If an author creates a research paper using a software stack to produce the results, then doesn't maintain the scripts as the software dependencies are updated, and then comes back 1 year later it seems highly likely that the software will not run with updated dependencies. One year isn't that long of a time in a research world. This isn't good for reproducibility. There are different proposed solutions, like shipping a virtual machine image with a paper that freezes the entire stack. It is interesting to note that Matlab code that is 10+ years old tends to run just fine with new version, leading to the belief that Mathworks takes this API breakage much more seriously. Software developers are constantly faced with the difficulties of maintain backwards compatibility and how easier it is to introduce new features to software if the backwards compatibility shackles are not present.
This project would work to characterize:
- how quickly changes in downstream dependencies break scientific software
- the ranking of stability in API for core software packages, i.e. can we label all software packages on their backwards incompatibility quality in a quantifiable way?
- can we determine how broadly semantic versioning is actually used and abided by?
- comparing the API stability culture among languages, e.g. Python and R
- how deep in the stack do you have to go to get stable APIs (for example the Linux kernel API is probably rock solid stable)
Hypothesis: On average a given script or software package that relies on a high level scientific computing software stack will break within a year due to unstable dependency APIs.
Possible Methods
Here is an idea for a method to do this:
- Download a package or script at the top of (or near top of) the stack and log its release date
- Install the dependencies specified at the time of release and ensure the software runs
- Increment the dependency versions in chronological order and test if the script/package still runs at every increment. You can detect whether is runs or not and also whether deprecation warnings are emitted. If a single dependency fails, you can then fix it at the last working version and then continue to increment the other until you get to the script's release date or all dependencies fail.
- Record the dates that your software gets deprecation warnings and fails.
Another method:
Track a code bases through git commits and somehow measure the frequency and time of depredations and removals.
We will have to find a reliable way to get old dependencies installed. This is often quite a painful process to simply get things installed as they were from some point in the past.
Another thought:
We could check how many tests of a prior version raise errors or deprecation warnings.
How to Apply
Send an email to<EMAIL_ADDRESS>with the title of the project in the subject line. Include an approximately half-page motivation letter explaining why you want to work in the Bicycle Lab on this project along with your current resume or C.V.


--- layout: page title: The Interview description: The art of the interview background: '/img/t-interview.jpg' ---
The art of conversation lends itself beautifully to engrossing storytelling, so it’s no wonder that many popular podcasts are based on interviews. In this genre, you can organize your podcast around a recorded talk with another person.
Public Intellectual with Jessa Crispin
Come up with an idea and make a plan.
Do some research about your interview subject. Learn about their life and what interests them.
Come up with questions for your interview subject. In the biz, this is called your “Q Line.” You can share these questions with them before the recorded interview--or not.
Set a time and place for the interview.
Meet your interview subject and record your interview. Before you record, make sure to confirm good sound levels. Don’t be afraid to throw out your Q Line if it’s not working or if you improvisationally come across better conversation. To be recording is to be getting your “Tape.”
Finish recording interviews, share a release form with your interview subjects and have them sign it.
Transcribe all your Tape. O Transcribe is a useful tool for this.
Consulting your transcription, pick your “Tracks.” Your Tracks are the interesting parts that you’ll use in your podcast. You also need to write your “Acts.” Your Acts are what you’ll use to set up or deal with your Tracks.
Record yourself reading your Acts.
Determine if you need music and sound design.
Create, license, or find Creative Commons music and sounds.
Put everything together with Audacity--your Acts, Tracks, music, and sounds.
Share the draft of your podcast and get feedback. Share it with your interview subject--or not.
Consider, reject, or incorporate feedback. Reflect on your process and the meaning of your podcast. Repeat previous steps.
In a spreadsheet, enter the metadata for all the files you created or used.
Finish your complete transcript.  It will be a combination of your Acts, Tracks, and descriptions of any of the music and sounds you used.
Decide, as a whole, whether or not your podcast should have a Creative Commons designation.
If you are going to put your podcasts online, do an audit of your online safety and security. Do you have safe passwords? Consider making passwords using the “diceware” method and using LastPass. If you Google yourself, what information do data brokers have about you? Consider contacting data brokers and requesting they remove your information.
No! The steps above are a guide--not the rule. You might do things in a different order. For example, you might write some of your Acts before conducting your interview and writing your transcript. Or, early on, you might find music that you want to use before you even decide who you want to interview. You might find that some of these steps don’t even apply. For instance, journalists don’t always have interview subjects sign release forms because they operate under the assumption that their subjects have agreed to be on the record.


The diesel versions of the Chevrolet Silverado and GMC Sierra may very soon get a very welcomed upgrade in their tow ratings. Initially, General Motors was focused on the trucks’ fuel economy and the results were impressive. The major tradeoff, however, was the lowered tow ratings for both Duramax-powered pickups.
According to John Barta, assistant chief engineer of diesel engine controls at General Motors, who spoke to MuscleCarsAndTrucks recently, important improvements are on their way for the half-ton trucks. As a result, the tow ratings of the two models could be increased.
Tested:
“We’re actually looking at upgrading some of the materials around [the engine bay] to see if we can maybe raise our tow rating,” Barta told MC&T during the interview. “We do know that [95 percent] of our light-duty customers don’t tow over 9,000 pounds. There was a conscious decision to maybe not go after the towing but go after the fuel economy. We realized that our tow rating is hitting 95 percent of the customer we have,” he added.
Gallery: 2020 Chevrolet Silverado 1500 Diesel: First Drive
Currently, the segment leaders in terms of towing ratings are the Ford F-150 Power Stroke and the Ram 1500 EcoDiesel with 11,400 lbs and 12,560 lbs, respectively. The highest number for a Duramax-powered truck is just 9,300 lbs.
The MPG King:
“Even though it’s nice to brag about big numbers, in reality, light-duty customers are not towing that large with their trucks,” Barta explained. “We think we hit the right balance with delivering class-leading fuel economy and still be able to tow what they tow as a light-duty customer.”
Indeed, the 2020 Chevrolet Silverado 1500 Duramax and 2020 GMC Sierra 1500 Duramax are segment kings in terms of fuel economy with 30 miles per gallon on the highway or better. According to Barta, much of that impressive result can be contributed to the inline-six diesel, which has fewer rotating components than a comparable V6 does.


Package that contains all the SDK classes.
data class Entitlement
An entitlement represents features or content that a user is "entitled" to. Entitlements are unlocked by having an active subscription or making a one-time purchase. Many different products can unlock. Most subscription apps only have one entitlement, unlocking all premium features. However, if you had two tiers of content such as premium and premium_plus, you would have 2 entitlements. A common and simple setup example is one entitlement with identifier pro, one offering monthly, with one product. See this link for more info |
|
data class Offering
Most well monetized subscription apps provide many different offerings to purchase an entitlement. These are usually associated with different durations i.e. an annual plan and a monthly plan. See this link for more info |
|
class PurchaserInfo
Class containing all information regarding the purchaser |
|
class Purchases : PurchasesUpdatedListener, ActivityLifecycleCallbacks
Entry point for Purchases. It should be instantiated as soon as your app has a unique user id for your user. This can be when a user logs in if you have accounts or on launch if you can generate a random user identifier. Make sure you follow the quickstart guide to setup your RevenueCat account. |


HDF5/netCDF-4
This section details a set of additional conventions that are specific to the HDF5 file format.
Note that this section also applies to the netCDF-4 format, since netCDF-4 actually uses HDF5 underneath.
The following table shows the mapping between HARP data types and HDF5 data types. The HDF5 backend uses this mapping for writing only.
HARP data type |
HDF5 data type |
|---|---|
int8 |
H5T_NATIVE_SCHAR |
int16 |
H5T_NATIVE_SHORT |
int32 |
H5T_NATIVE_INT |
float |
H5T_NATIVE_FLOAT |
double |
H5T_NATIVE_DOUBLE |
string |
H5T_C_S1 |
The mapping used by the HDF5 backend for reading is shown below. The HDF5 data type interface (H5T) is used to introspect the data type of the variable to be read.
H5T_get_class() |
H5T_get_size() |
H5T_get_sign() |
H5Tget_native_type() |
HARP data type |
|---|---|---|---|---|
H5T_INTEGER |
1 |
H5T_SGN_2 |
int8 |
|
H5T_INTEGER |
2 |
H5T_SGN_2 |
int16 |
|
H5T_INTEGER |
4 |
H5T_SGN_2 |
int32 |
|
H5T_FLOAT |
H5T_NATIVE_FLOAT |
float |
||
H5T_FLOAT |
H5T_NATIVE_DOUBLE |
double |
||
H5T_STRING |
string |
HDF5 data types not covered in this table are not supported by HARP.
In the HDF5 data model there is no concept of shared dimensions (unlike netCDF). The shape of an HDF5 dataset is specified as a list of dimension lengths. However, the netCDF-4 library uses HDF5 as its storage backend. It represents shared dimensions using HDF5 dimension scales.
Dimension scales were introduced in HDF5 version 1.8.0. A dimension scale is a special dataset that can be attached to one or more dimensions of other datasets. Multiple dimension scales can be attached to a single dimension, and the length of the dimension scale does not have to be the same as the length of the dimension it is attached to. There are no limitations on the shape or dimensionality of a dimension scale, since it is just a dataset with particular attributes attached.
To represent shared dimensions, netCDF-4 creates dimension scales for each shared dimension and attaches these dimension
scales to the corresponding dimensions of all variables. If a product contains a one-dimensional variable with the same
name as a shared dimension where the single dimension of the variable also matches that shared dimension then the dataset
containing the values of the variable will be used as the dimension scale. Such a variable is called a
coordinate variable in netCDF-4. If a variable has the same name as the dimension but the variable is not
one-dimensional or the single dimension of the variable does not match the dimension then the variable will be stored
with a _nc4_non_coord_
name prefix in the HDF5 file (this is identical to how netCDF-4 deals with this case).
For shared dimensions for which a variable with the same name does not exist, a stub dataset containing fill values is
created and used as the dimension scale. The optional NAME
attribute of the dimension scale is set to
This is a netCDF dimension but not a netCDF variable.
, which causes the netCDF-4 library to hide the stub dataset
from the user. For more information about the netCDF-4 format, see the NetCDF User’s Guide.
The HDF5 file format conventions used by HARP are designed to be compatible with netCDF-4. Like netCDF-4, HARP uses
dimension scales to represent shared dimensions. For independent dimensions, the same approach is used as for the
netCDF-3 backend. For each unique dimension length L
, a dimension scale named independent_L
is created.
To summarize, HARP dimensions types are mapped to HDF5 dimension scales as follows:
HARP dimension type |
HDF5 dimension scale |
|---|---|
time |
time |
latitude |
latitude |
longitude |
longitude |
vertical |
vertical |
spectral |
spectral |
independent |
independent_<length> |
The _nc3_strict
attribute is attached to the root group of the HDF5 file such that it will be interpreted using the
netCDF classic data model by the netCDF-4 library. Enhanced features of netCDF-4 beyond the classic data model, such as
groups and user-defined types, are not supported by HARP.
HDF5 can represent strings in several ways. Both fixed and variable length strings are supported. The HDF5 backend stores a HARP variable of type string as an HDF5 dataset of fixed length strings. The fixed string length equals the length of the longest string, or 1 if the length of the longest string is zero. Shorter strings are padded with null- termination characters.
HARP uses empty strings to represent the unit of dimensionless quantities (to distinguish them from non-quantities,
which will lack a unit attribute). However, HDF5 cannot store string attributes with length zero. For this reason
an empty unit string will be written as a units
attribute with value "1"
when writing data to HDF5.
When reading from HDF5 a unit string value "1"
will be converted back again to an empty unit string.
Note that even though the time
dimension is conceptually considered appendable, this dimension is not stored as an
actual appendable dimension in HDF5. Products are read/written from/to files in full and are only modified in memory.
The appendable aspect is only relevant for tools such as plotting routines that combine the data from a series of HARP
products in order to provide plots/statistics for a whole dataset (and thus, where data from different files will have
to be concatenated).


| R.oo-package | Package R.oo |
| charToInt | Converts a vector of ASCII characters into a vector of integers |
| charToInt.default | Converts a vector of ASCII characters into a vector of integers |
| Class | The Class class describes an Object class |
| dimension | Gets the dimension of the object |
| dimension.default | Gets the dimension of the object |
| equals | Compares an object with another |
| equals.default | Compares an object with another |
| Exception | The Exception class to be thrown and caught |
| extend | Extends a object |
| extend.default | Extends a object |
| getConstructorS3 | Get a constructor method |
| getConstructorS3.default | Get a constructor method |
| getName.environment | Gets the name of an environment |
| hashCode | Gets an integer hashcoded for R objects |
| hashCode.default | Gets an integer hashcoded for R objects |
| InternalErrorException | InternalErrorException represents internal errors |
| intToChar | Converts a vector of integers into a vector of ASCII characters |
| intToChar.default | Converts a vector of integers into a vector of ASCII characters |
| ll | Generates a list of informative properties of all members of an environment |
| ll.default | Generates a list of informative properties of all members of an environment |
| Object | The root class that every class must inherit from |
| objectSize | Gets the size of the object in bytes |
| objectSize.default | Gets the size of the object in bytes |
| objectSize.environment | Gets the size of an environment in bytes |
| Package | The Package class provides methods for accessing package information |
| R.oo | Package R.oo |
| RccViolationException | An RccViolationException indicates a violation of the R Coding Conventions (RCC) |
| Rdoc | Class for converting Rdoc comments to Rd files |
| RdocException | RdocException are thrown by the Rdoc compiler |
| setConstructorS3 | Defines a class in S3/UseMethod style |
| setConstructorS3.default | Defines a class in S3/UseMethod style |
| throw | Throws an Exception |
| throw.default | Throws an Exception |
| throw.error | Throws (rethrows) an object of class 'error' |
| trim | Converts to a string and removes leading and trailing whitespace |
| trim.default | Converts to a string and removes leading and trailing whitespace |
| typeOfClass | Gets the type of a class (S3 or S4) |
| typeOfClass.default | Gets the type of a class (S3 or S4) |


| Company |
{{ lead.company.company_name }}
|
| Volume (mt) - Price |
{{ lead.pivot.volume | number }}mt - {{ lead.pivot.deal_currency_id }} {{ lead.pivot.deal_price | number }} || {{ lead.pivot.base_currency_id }} {{ lead.pivot.base_price | number }}
|
|
| Input Date |
{{ lead.order_date | date:'mediumDate' }} |
| Validity Period |
|
| Laycan |
FROM
TO
|
| Shipping Term |
{{ lead.pivot.trading_term }} |
| Payment Term |
{{ lead.pivot.payment_term }} |
| Location |
{{ lead.city }}
-Contact {{ lead.trader.name }}-
|
| Remarks |
|


ruleCode: BEFORE-LOAN-RULE

callBackUrl: dfafsafsaf

sysCode: PC

sceneCode: apply

orderNo: dfafsafsaf

sysCode: PV


Review: Stir of Echoes
Starring: Kevin Bacon, Kathryn Erbe, Illeana Douglas, Zachary David Cope
Directed by: David Koepp
Review by: Joy Wyse, the SILVER Screen Critic
It is astonishing that major motion picture studios often release movies with similar plots within weeks of each other. Last year there was Deep Impact and Armageddon. So, there is no question that Stir of Echoes will be compared to the summer's surprise hit, Sixth Sense. Both have similar storylines involving children who see ghosts. In Stir of Echoes the child is Jake Witzky, played quite well by Zachary David Cope. He is the only one of the family who is aware of the presence of a young female ghost who resides in the house with them, that is, until one night when his father, Tom, (Bacon) is hypnotized at a party by Lisa (Douglas). Her post-hypnotic suggestion gives Tom powers to see and hear unusual glimpses of things that surround him. But he can't seem to figure out what is happening. And, his wife, Maggie, (Erbe) certainly isn't very understanding. Even at a cemetery, when she starts to notice that her son has some weird abilities, she refuses to accept them.
When Jake reminds his mother to take her purse with her, you remember that she has a knife in it, and surely she will use it. She does. The major problem here is that we are aware what's happening before the cast is.
Oh, yes, there is a mystery here. Who is the ghost? Why is she there? Well, it doesn't take long to know who she is and she's there because she's probably been murdered. But who did it? There are some scary moments and if that's what you like, you'll be entertained. As for the acting, Kevin Bacon's portrayal is uneven to say the least. But now we have another group of actors who can be included in `Six Degrees of Kevin Bacon'.
It's not a movie for young children to watch, but most teens will like it. As for me, I might watch this again when it comes to free TV. I give it a C.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


This example searches for places to eat (i.e. within the
eat-drink
category) close to the
Chinatown district of
San Francisco (37.7942°N,-122.4070°W). The response contains places links and those links are rendered on the map as markers. All Places API's Search & Discovery entrypoints return place links with a function for easy access to place details. On place link click, user triggers this function and returns place details inside a panel. For more information about place details responses visit
Places API Documentation.


Same App, Different Way
Same App, Different Way is a concept for tutorials that allows us to build the same solution in different frameworks, technologies and/or languages. We did this so that folks who don’t have much development experience can see how a relatively simple app is done in a myriad of different ways. This allows them to also learn, examine and possibly choose a new framework or technology based on what it takes to create the application.
Since all applications are designed to solve a problem, let’s take a look at the problem this application is trying to solve.
The Problem
Podcasting is taking over the world. Well, some of the world. Everyday more and more podcasters get their start, but recently iTunes took it to a whole new level. They placed the podcasts app as one of their default applications on the phone. This means a whole new world of subscribers just got their first introduction into this dynamic and fascinating world of internet broadcasting. Now for the subscribers this is great, for the podcasters it created a problem.
See, iTunes uses an RSS feed to put items in the podcast store for users of iPhones to subscribe to. All a podcaster can do is create a feed and submit to iTunes for acceptance. However, most poscasters are not web designers or developers and most of them don’t even know what an RSS feed is. This means there is a need to have a web application that a podcaster can easily use that allows them to enter in the URL’s to their podcasts and some superfluous information and have it dynamically generate their RSS feed for iTunes.
Our Application Requirements
The application will allow a user to login (preferably using facebook or twitter). Once logged in the user will be presented with all of the podcasts they have submitted organized by the date. From here they can add/remove/update all of the media for each podcast they run. When they are done, the RSS feed for that specific podcast will reflect the changes.
Some important points:
- The application will only save the URL’s to media that is stored somewhere else. We won’t be taking the uploads themselves (dropbox, box, icloud etc).
- The application will allow a single user to maintain multiple podcasts.
- The application will need to have a different feed for each podcast.
- The application will allow for all of the itunes specific elements to be maintained for each podcasts and episode.
- The target user of the application is someone who isn’t a developer.
The CastR application will allow a podcaster to maintain their podcasts as well as provide an RSS feed for iTunes.
The domain looks like:
The Tutorials
Each tutorial will build this app differently, however the core use of the application will remain the same. If you have suggestions on specific frameworks and/or technologies you would like to see, leave them in the comments!


Buttons (using button.css)
Primary Button/Info
Secondary/Success
Tertiary/Warning
Error
Default
Primary Button/Info
Secondary/Success
Tertiary/Warning
Error
Gray
Cards (using card.css)
Topic : Global positioning system and its application
Subtopic : Limits of artificial intelligence
Data Structure and Algorithm - Assignment 1 Paperback
Academy of European Law (ERA)
100%


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Name:
Company:
Phone:
Fax:
Email:
Contact By:
Objective:
Event Type:
Theme:
Estimated Budget:
Event Date:
Additional Information:


After gaining some experience in the YouTube Graphic Design community I began collaborating with some larger organizations. Denial E-Sports is a competitive E-Sports organization with several teams across several games. I was apart of their graphic design team for several months and this is one of my better designs. I created this YouTube Banner for Denial Twizz, a professionl Call of Duty player. He used this on his YouTube Channel until he left Denial.


Start quizzing!
Create new quiz
Future quizzes
Ongoing quizzes
Ended quizzes
Future quizzes:
(Click a quiz to join)
Quizname
Start time
Theme
Questions
Players
Ongoing quizzes:
Quizname
Started
View scoreboard!
Ended quizzes:
Quizname
Ended
View scoreboard


A suite of pre-built Dojo widgets, ready to use in your web application. These widgets are built using Dojo’s widget authoring system [(@dojo/framework/core)](https://github.com/dojo/framework/blob/master/src/core/README.md).
To use @dojo/widgets
in your project, you will need to install the package:
npm install @dojo/widgets
This package contains all of the widgets in this repo.
All of the widgets are on the same release schedule, that is to say, that we release all widgets at the same time. Minor releases may include new widgets and/or features, whereas patch releases may contain fixes to more than 1 widget.
To use a widget in your application, you will need to import each widget individually:
import Button from '@dojo/widgets/button';
Each widget module has a default export of the widget itself, as well as named exports for things such as properties specific to the widget:
import Button, { ButtonProperties } from '@dojo/widgets/button';
Because each widget is a separate module, when you create a release build of your
application, you will only include the widgets that you have explicitly imported. This
allows our dojo cli
build tooling
to make sure that the production build of your application only includes the widgets you
use and is as small as possible.
All widgets are supported in all evergreen browsers (Chrome, Edge, Firefox, IE11+, and Safari) as well as popular mobile browsers (Mobile Safari, Chrome on Android).
All widgets are designed to be accessible. If custom ARIA semantics are
required, widgets have an aria
property that may be passed an
object with custom aria-*
attributes.
All widgets are fully themeable. Example themes are available in the [@dojo/themes](https://github.com/dojo/themes) repository.
All widgets support internationalization (i18n
)


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Discussion 1,Value of Visualization
Discussion 2,Data and Image Models
Discussion 3,Visualization Design


This course covers a wide range of topics, focuses on OOP, and has plenty graded practical exercises. It is one of the best Java courses available and it is free.
I've been using the tutorial on their office website. Very thorough and everything is explained fantastic. I'll get ya a link in a second.
oIt depends on your preference entirely , do you like visual , do you like more hands on do it on your own or do you want to learn at your own speed through a book, I learned Java in college and we had only one book to refer Java complete reference,


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