Deploying to main from @ numpy/numpy.org@c566df3 🚀

Author: InessaPawson

case-studies/gw-discov/index.html

@@ -34,12 +34,12 @@
 detect gravitational waves from some of the most violent and energetic
 processes in the Universe, the data LIGO collects may have far-reaching
 effects on many areas of physics including gravitation, relativity,
-astrophysics, cosmology, particle physics, and nuclear physics.</li><li>Crunch observed data via numerical relativity computations that involves
+astrophysics, cosmology, particle physics, and nuclear physics.</li><li>Crunch observed data via numerical relativity computations that involve
 complex maths in order to discern signal from noise, filter out relevant
-signal and statistically estimate significance of observed data.</li><li>Data visualization so that the binary / numerical results can be
+signal and statistically estimate the significance of observed data.</li><li>Data visualization so that the binary / numerical results can be
 comprehended.</li></ul><h3 id=the-challenges>The Challenges<a class=headerlink href=#the-challenges title="Link to this heading">#</a></h3><ul><li><p><strong>Computation</strong></p><p>Gravitational Waves are hard to detect as they produce a very small effect
 and have tiny interaction with matter. Processing and analyzing all of
-LIGO&rsquo;s data requires a vast computing infrastructure.After taking care of
+LIGO&rsquo;s data requires a vast computing infrastructure. After taking care of
 noise, which is billions of times of the signal, there is still very
 complex relativity equations and huge amounts of data which present a
 computational challenge:
@@ -76,8 +76,8 @@
 providing object based interfaces to utilities, tools, and methods for
 studying data from gravitational-wave detectors.</li></ul><figure class=align-default id=id003><img src=/images/content_images/cs/gwpy-numpy-dep-graph.png alt="gwpy-numpy depgraph" class=align-center><figcaption><strong class=caption-title>Dependency graph showing how GwPy package depends on NumPy</strong><a class=headerlink href=#id003 title="Link to this image">#</a><br><p><span class=caption-text></span></figcaption></figure><hr><figure class=align-default id=id004><img src=/images/content_images/cs/PyCBC-numpy-dep-graph.png alt="PyCBC-numpy depgraph" class=align-center><figcaption><strong class=caption-title>Dependency graph showing how PyCBC package depends on NumPy</strong><a class=headerlink href=#id004 title="Link to this image">#</a><br><p><span class=caption-text></span></figcaption></figure><h2 id=summary>Summary<a class=headerlink href=#summary title="Link to this heading">#</a></h2><p>GW detection has enabled researchers to discover entirely unexpected phenomena
 while providing new insight into many of the most profound astrophysical
-phenomena known. Number crunching and data visualization is a crucial step
-that helps scientists gain insights into data gathered from the scientific
+phenomena known. Number crunching and data visualization are crucial steps
+that help scientists gain insights into data gathered from scientific
 observations and understand the results. The computations are complex and
 cannot be comprehended by humans unless it is visualized using computer
 simulations that are fed with the real observed data and analysis. NumPy