Closed yiyange closed 6 years ago
I ran similar process using UrbanAccess. Here is part of the comparison if you are interested
gtfs id | UrbanAccess | PT with trimming |
---|---|---|
f-9q9-bart | 4.0s, 97 edges, 50 stops | 3.8s, 97 edges, 50 stops |
f-9q9-actransit | Failed | 59.8s, 5670 edges, 5050 stops |
f-9q8y-sfmta | 97.4s, 3806 edges, 3409 stops | 66.7s, 3806 edges, 3409 stops |
f-9qb-goldengatetransit | Failed | 7.5s, 519 edges, 473 stops |
f-9qc-fairfield~ca~us | 4.1s, 297 unique edges, 250 stops | 3.1s, 280 edges, 245 stops |
f-9qc0-soltrans~ca~us | 3.2s, 473 unique edges, 407 stops | 4.3s, 475 edges, 408 stops |
f-9qc-westcat~ca~us | 2.6s, 263 edges, 220 stops | 3.6s, 263 edges, 220 stops |
f-9-amtrak*** | Failed | 12.0s, 306 edges, 260 stops |
Merging #93 into master will decrease coverage by
0.28%
. The diff coverage is85.71%
.
@@ Coverage Diff @@
## master #93 +/- ##
==========================================
- Coverage 91.91% 91.62% -0.29%
==========================================
Files 12 12
Lines 866 872 +6
==========================================
+ Hits 796 799 +3
- Misses 70 73 +3
Impacted Files | Coverage Δ | |
---|---|---|
peartree/summarizer.py | 91.44% <85.71%> (+0.35%) |
:arrow_up: |
peartree/parallel.py | 96.4% <0%> (-2.16%) |
:arrow_down: |
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Again, thank you so much! Amazing.
Hmm, I wonder why codecov dropped...
Stop time interpolation works on the entire
stop_times
df, majority of which will be tossed later based on given requested time range.Trimming down stop_times before passing it to the stop time interpolation step increases performance by a huge amount.
Here are some evidence of the performance gain on the
pt.paths.generate_summary_graph_elements
step: