Speed up parsing distances matrix

[leonlan]

Closes #57. In particular:

• Computing the pairwise Euclidean distance matrix is much faster.
• Computing the full distances matrix from the lower row matrix is 2x faster.

[leonlan]

Much faster now!

In [3]: %timeit vrplib.read_instance(f"tmp/X-n1001-k43.txt")
27.2 ms ± 754 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

[codecov]

Codecov Report

Base: 96.61% // Head: 96.53% // Decreases project coverage by -0.08% ⚠️

Coverage data is based on head (18b0f27) compared to base (f8aacbf).
Patch coverage: 100.00% of modified lines in pull request are covered.

❗ Current head 18b0f27 differs from pull request most recent head 75abca3. Consider uploading reports for the commit 75abca3 to get more accurate results

Additional details and impacted files

@@            Coverage Diff             @@
##           master      #68      +/-   ##
==========================================
- Coverage   96.61%   96.53%   -0.08%     
==========================================
  Files          16       16              
  Lines         236      231       -5     
==========================================
- Hits          228      223       -5     
  Misses          8        8              

Impacted Files	Coverage Δ
vrplib/parse/parse_distances.py	90.00% <100.00%> (-0.91%)	⬇️

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[leonlan]

%timeit vrplib.read_instance(f"tmp/Loggi-n1001-k31.txt")
121 ms ± 2.31 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

Also slightly faster. There is still room for speeding up stuff (e.g. infer_type for parsing the edge weight section is quite redundant), but these running times are quite reasonable.