scipy
for data scientists, python developers
Why this scored 7
every term, weightedIts fastest-moving item, measured against the pace of its own source
Whether that velocity is itself speeding up, as a per-hour rate
How many independent communities its own items come from
Decays to zero over 14 days, counted from when we first saw it
Subtracted once something is big and old — sized by its biggest item, aged from when we first saw it
Weights are hand-tuned, not learned — we're calibrating them against realized trends as history accumulates. On an entity's first sighting there's no previous reading to compare against, so acceleration starts from a neutral prior rather than a measurement, and velocity falls back to engagement over its whole lifetime until a second reading exists. Full methodology
Signal history
7-day window (free)The evidence
The live items this entity's score aggregates — every community independently talking about it right now. This is the corroboration, shown, not claimed.
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curve_fit() with multiple independent variables in integral function
Using scipy.curve_fit with multi-variable integrals in Python scientific code. · for data scientists, python developers
stackoverflowSteady 2 · pythondata41d ago - 217
SciPy spsolve / SuperLU SUPERLU_MALLOC failure despite available RAM
SuperLU memory bug hinders large sparse matrix solves, needing workarounds. · for scientific programmers, data engineers
stackoverflowSteadydata60d ago - 315
Understanding a certain condition for overlap-add method implemented in Scipy
Scipy convolution method explained · for data scientists
stackoverflowSteadydevtools75d ago - 414
Is there a way to make scipy.optimize.minimize(method='trust-constr') accurate?
Improving trust-constr optimizer accuracy benefits reliability of scientific computations. · for data scientists, research engineers
stackoverflowSteadydata48d ago - 54
How to properly deal with nan values when using scipy.stats.pearsonr?
Handle nan values · for data scientists
stackoverflowSteadydata101d ago