Bump SPHT to v0.22 + adopt new syntax for applicable shortcodes

* Update shortcodes to new syntax

* Bump theme to v22

* Bump Hugo to v0.152.2

* Bump Dart-Sass to v1.93.2

* Fix teams grid shortcode closing tag

* Fix image shortcodes inside cards

-

Author: agriyakhetarpal

content/en/case-studies/blackhole-image.md

@@ -3,13 +3,13 @@ title: "Case Study: First Image of a Black Hole"
 sidebar: false
 ---
 
-{{< figure >}}
-src = '/images/content_images/cs/blackhole.jpg'
-title = 'Black Hole M87'
-alt = 'black hole image'
-attribution = '(Image Credits: Event Horizon Telescope Collaboration)'
-attributionlink = 'https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg'
-{{< /figure >}}
+{{< figure
+    src="/images/content_images/cs/blackhole.jpg"
+    title="Black Hole M87"
+    alt="black hole image"
+    attribution="(Image Credits: Event Horizon Telescope Collaboration)"
+    attributionlink="https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg"
+>}}
 
 {{< blockquote
     cite="https://www.youtube.com/watch?v=BIvezCVcsYs"
@@ -70,14 +70,14 @@ from a sidewalk café in Paris!
     When the goal is to see something never before seen, how can scientists be
     confident the image is correct?
 
-{{< figure >}}
-src = '/images/content_images/cs/dataprocessbh.png'
-title = 'EHT Data Processing Pipeline'
-alt = 'data pipeline'
-align = 'center'
-attribution = '(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)'
-attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57'
-{{< /figure >}}
+{{< figure
+    src="/images/content_images/cs/dataprocessbh.png"
+    title="EHT Data Processing Pipeline"
+    alt="data pipeline"
+    align="center"
+    attribution="(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)"
+    attributionlink="https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57"
+>}}
 
 ## NumPy’s Role
 
@@ -93,23 +93,23 @@ first-of-a-kind image of the black hole.
 Their work illustrates the role the scientific Python ecosystem plays in
 advancing science through collaborative data analysis.
 
-{{< figure >}}
-src = '/images/content_images/cs/bh_numpy_role.png'
-alt = 'role of numpy'
-title = 'The role of NumPy in Black Hole imaging'
-{{< /figure >}}
+{{< figure
+    src="/images/content_images/cs/bh_numpy_role.png"
+    alt="role of numpy"
+    title="The role of NumPy in Black Hole imaging"
+>}}
 
 For example, the [`eht-imaging`][ehtim] Python package provides tools for
 simulating and performing image reconstruction on VLBI data.
 NumPy is at the core of array data processing used
 in this package, as illustrated by the partial software
 dependency chart below.
 
-{{< figure >}}
-src = '/images/content_images/cs/ehtim_numpy.png'
-alt = 'ehtim dependency map highlighting numpy'
-title = 'Software dependency chart of ehtim package highlighting NumPy'
-{{< /figure >}}
+{{< figure
+    src="/images/content_images/cs/ehtim_numpy.png"
+    alt="ehtim dependency map highlighting numpy"
+    title="Software dependency chart of ehtim package highlighting NumPy"
+>}}
 
 [ehtim]: https://github.com/achael/eht-imaging
 
@@ -136,8 +136,8 @@ best radio observatories.  Innovative algorithms and data processing
 techniques, improving upon existing astronomical models, helped unfold a
 mystery of the universe.
 
-{{< figure >}}
-src = '/images/content_images/cs/numpy_bh_benefits.png'
-alt = 'numpy benefits'
-title = 'Key NumPy Capabilities utilized'
-{{< /figure >}}
+{{< figure
+    src="/images/content_images/cs/numpy_bh_benefits.png"
+    alt="numpy benefits"
+    title="Key NumPy Capabilities utilized"
+>}}

content/en/case-studies/cricket-analytics.md

@@ -3,13 +3,13 @@ title: "Case Study: Cricket Analytics, the game changer!"
 sidebar: false
 ---
 
-{{< figure >}}
-src = '/images/content_images/cs/ipl-stadium.png'
-title = 'IPLT20, the biggest Cricket Festival in India'
-alt = 'Indian Premier League Cricket cup and stadium'
-attribution = '(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))'
-attributionlink = 'https://unsplash.com/@aksh1802'
-{{< /figure >}}
+{{< figure
+    src="/images/content_images/cs/ipl-stadium.png"
+    title="IPLT20, the biggest Cricket Festival in India"
+    alt="Indian Premier League Cricket cup and stadium"
+    attribution="(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))" 
+    attributionlink="https://unsplash.com/@aksh1802"
+>}}
 
 {{< blockquote
     cite="https://www.scoopwhoop.com/sports/ms-dhoni/"
@@ -59,14 +59,14 @@ metrics for improving match winning chances:
 * gaining insights into fitness and performance of a player against different opposition,
 * player contribution to wins and losses for making strategic decisions on team composition
 
-{{< figure >}}
-src = '/images/content_images/cs/cricket-pitch.png'
-title = 'Cricket Pitch, the focal point in the field'
-alt = 'A cricket pitch with bowler and batsmen'
-align = 'center'
-attribution = '(Image credit: Debarghya Das)'
-attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf'
-{{< /figure >}}
+{{< figure
+    src="/images/content_images/cs/cricket-pitch.png"
+    title="Cricket Pitch, the focal point in the field"
+    alt="A cricket pitch with bowler and batsmen"
+    align="center"
+    attribution="(Image credit: Debarghya Das)"
+    attributionlink="http://debarghyadas.com/files/IPLpaper.pdf"
+>}}
 
 ### Key Data Analytics Objectives
 
@@ -81,13 +81,13 @@ attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf'
   number crunching and data science know-how, visualization tools and capability
   to include newer observations in the analysis.
 
-{{< figure >}}
-src = '/images/content_images/cs/player-pose-estimator.png'
-alt = 'pose estimator'
-title = 'Cricket Pose Estimator'
-attribution = '(Image credit: connect.vin)'
-attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/'
-{{< /figure >}}
+{{< figure
+    src="/images/content_images/cs/player-pose-estimator.png" 
+    alt="pose estimator"
+    title="Cricket Pose Estimator"
+    attribution="(Image credit: connect.vin)"
+    attributionlink="https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/"
+>}}
 
 ### The Challenges
 
@@ -157,8 +157,8 @@ hidden parameters, patterns, and attributes that lead to the outcome of a
 cricket match helps the stakeholders to take notice of game insights that are
 otherwise hidden in numbers and statistics.
 
-{{< figure >}}
-src = '/images/content_images/cs/numpy_ca_benefits.png'
-alt = 'Diagram showing benefits of using NumPy for cricket analytics'
-title = 'Key NumPy Capabilities utilized'
-{{< /figure >}}
+{{< figure
+    src="/images/content_images/cs/numpy_ca_benefits.png"
+    alt="Diagram showing benefits of using NumPy for cricket analytics"
+    title="Key NumPy Capabilities utilized"
+>}}

content/en/case-studies/deeplabcut-dnn.md

@@ -3,13 +3,13 @@ title: "Case Study: DeepLabCut 3D Pose Estimation"
 sidebar: false
 ---
 
-{{< figure >}}
-src = '/images/content_images/cs/mice-hand.gif'
-title = 'Analyzing mice hand-movement using DeepLapCut'
-alt = 'micehandanim'
-attribution = '(Source: www.deeplabcut.org )'
-attributionlink = 'http://www.mousemotorlab.org/deeplabcut'
-{{< /figure >}}
+{{< figure
+    src="/images/content_images/cs/mice-hand.gif"
+    title="Analyzing mice hand-movement using DeepLapCut"
+    alt="micehandanim"
+    attribution="(Source: www.deeplabcut.org )"
+    attributionlink="http://www.mousemotorlab.org/deeplabcut"
+>}}
 
 {{< blockquote
     cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/"
@@ -24,12 +24,12 @@ Open Source Software is accelerating Biomedicine. DeepLabCut enables automated v
 
 Several areas of research, including neuroscience, medicine, and biomechanics, use data from tracking animal movement. DeepLabCut helps in understanding what humans and other animals are doing by parsing actions that have been recorded on film. Using automation for laborious tasks of tagging and monitoring, along with deep neural network based data analysis, DeepLabCut makes scientific studies involving observing animals, such as primates, mice, fish, flies etc., much faster and more accurate.
 
-{{< figure >}}
-src = '/images/content_images/cs/race-horse.gif'
-title = 'Colored dots track the positions of a racehorse’s body part'
-alt = 'horserideranim'
-attribution = '(Source: Mackenzie Mathis)'
-{{< /figure >}}
+{{< figure
+    src="/images/content_images/cs/race-horse.gif"
+    title="Colored dots track the positions of a racehorse’s body part"
+    alt="horserideranim"
+    attribution="(Source: Mackenzie Mathis)"
+>}}
 
 DeepLabCut's non-invasive behavioral tracking of animals by extracting the poses of animals is crucial for scientific pursuits in domains such as biomechanics, genetics, ethology & neuroscience. Measuring animal poses non-invasively from video - without markers - in dynamically changing backgrounds is computationally challenging, both technically as well as in terms of resource needs and training data required.
 
@@ -72,14 +72,14 @@ Recently, the [DeepLabCut model zoo](https://deeplabcut.github.io/DeepLabCut/doc
   - code for large-scale inference on videos
   - draw inferences using integrated visualization tools
 
-{{< figure >}}
-src = '/images/content_images/cs/deeplabcut-toolkit-steps.png'
-title = 'Pose estimation steps with DeepLabCut'
-alt = 'dlcsteps'
-align = 'center'
-attribution = '(Source: DeepLabCut)'
-attributionlink = 'https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1'
-{{< /figure >}}
+{{< figure
+    src="/images/content_images/cs/deeplabcut-toolkit-steps.png"
+    title="Pose estimation steps with DeepLabCut"
+    alt="dlcsteps"
+    align="center"
+    attribution="(Source: DeepLabCut)"
+    attributionlink="https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1"
+>}}
 
 [DLCToolkit]:  https://github.com/DeepLabCut/DeepLabCut
 
@@ -110,14 +110,14 @@ attributionlink = 'https://twitter.com/DeepLabCut/status/1198046918284210176/pho
     arrays corresponding to various images, target tensors and keypoints is
     fairly challenging.
 
-{{< figure >}}
-src = '/images/content_images/cs/pose-estimation.png'
-title = 'Pose estimation variety and complexity'
-alt = 'challengesfig'
-align = 'center'
-attribution = '(Source: Mackenzie Mathis)'
-attributionlink = 'https://www.biorxiv.org/content/10.1101/476531v1.full.pdf'
-{{< /figure >}}
+{{< figure
+    src="/images/content_images/cs/pose-estimation.png"
+    title="Pose estimation variety and complexity"
+    alt="challengesfig"
+    align="center"
+    attribution="(Source: Mackenzie Mathis)"
+    attributionlink="https://www.biorxiv.org/content/10.1101/476531v1.full.pdf"
+>}}
 
 ## NumPy's Role in meeting Pose Estimation Challenges
 
@@ -155,13 +155,13 @@ training fast, NumPy’s vectorization capabilities are leveraged. For inference
 the most likely predictions from target scoremaps need to extracted and one
 needs to efficiently “link predictions to assemble individual animals”.
 
-{{< figure >}}
-src = '/images/content_images/cs/deeplabcut-workflow.png'
-title = 'DeepLabCut Workflow'
-alt = 'workflow'
-attribution = '(Source: Mackenzie Mathis)'
-attributionlink = 'https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962'
-{{< /figure >}}
+{{< figure
+    src="/images/content_images/cs/deeplabcut-workflow.png"
+    title="DeepLabCut Workflow"
+    alt="workflow"
+    attribution="(Source: Mackenzie Mathis)"
+    attributionlink="https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962"
+>}}
 
 ## Summary
 
@@ -177,8 +177,8 @@ medicine and rehabilitation studies. Complex combinatorics, data processing
 challenges faced by DeepLabCut algorithms are addressed through the use of
 NumPy's array manipulation capabilities.
 
-{{< figure >}}
-src = '/images/content_images/cs/numpy_dlc_benefits.png'
-alt = 'numpy benefits'
-title = 'Key NumPy Capabilities utilized'
-{{< /figure >}}
+{{< figure
+    src="/images/content_images/cs/numpy_dlc_benefits.png"
+    alt="numpy benefits"
+    title="Key NumPy Capabilities utilized"
+>}}

content/en/case-studies/gw-discov.md

@@ -3,13 +3,13 @@ title: "Case Study: Discovery of Gravitational Waves"
 sidebar: false
 ---
 
-{{< figure >}}
-src = '/images/content_images/cs/gw_sxs_image.png'
-title = 'Gravitational Waves'
-alt = 'binary coalesce black hole generating gravitational waves'
-attribution = '(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)'
-attributionlink = 'https://youtu.be/Zt8Z_uzG71o'
-{{< /figure >}}
+{{< figure
+    src="/images/content_images/cs/gw_sxs_image.png"
+    title="Gravitational Waves"
+    alt="binary coalesce black hole generating gravitational waves"
+    attribution="(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)"
+    attributionlink="https://youtu.be/Zt8Z_uzG71o"
+>}}
 
 {{< blockquote
     cite="https://www.youtube.com/watch?v=BIvezCVcsYs"
@@ -94,13 +94,13 @@ made from warped spacetime.
     simulations using latest experimental inputs and insights can be a time
     consuming activity that challenges researchers in this domain.
 
-{{< figure >}}
-src = '/images/content_images/cs/gw_strain_amplitude.png'
-alt = 'gravitational waves strain amplitude'
-title = 'Estimated gravitational-wave strain amplitude from GW150914'
-attribution = '(Graph Credits: Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)'
-attributionlink = 'https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger'
-{{< /figure >}}
+{{< figure
+    src="/images/content_images/cs/gw_strain_amplitude.png"
+    alt="gravitational waves strain amplitude"
+    title="Estimated gravitational-wave strain amplitude from GW150914"
+    attribution="(Graph Credits: Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)"
+    attributionlink="https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger"
+>}}
 
 ## NumPy’s Role in the Detection of Gravitational Waves
 
@@ -132,19 +132,19 @@ speed.  Here are some examples:
   providing object based interfaces to utilities, tools, and methods for
   studying data from gravitational-wave detectors.
 
-{{< figure >}}
-src = '/images/content_images/cs/gwpy-numpy-dep-graph.png'
-alt = 'gwpy-numpy depgraph'
-title = 'Dependency graph showing how GwPy package depends on NumPy'
-{{< /figure >}}
+{{< figure
+    src="/images/content_images/cs/gwpy-numpy-dep-graph.png"
+    alt="gwpy-numpy depgraph"
+    title="Dependency graph showing how GwPy package depends on NumPy"
+>}}
 
 ----
 
-{{< figure >}}
-src = '/images/content_images/cs/PyCBC-numpy-dep-graph.png'
-alt = 'PyCBC-numpy depgraph'
-title = 'Dependency graph showing how PyCBC package depends on NumPy'
-{{< /figure >}}
+{{< figure
+    src="/images/content_images/cs/PyCBC-numpy-dep-graph.png"
+    alt="PyCBC-numpy depgraph"
+    title="Dependency graph showing how PyCBC package depends on NumPy"
+>}}
 
 ## Summary
 
@@ -160,8 +160,8 @@ is [enabling researchers](https://www.gw-openscience.org/events/GW150914/) to
 answer complex questions and discover new horizons in our understanding of the
 universe.
 
-{{< figure >}}
-src = '/images/content_images/cs/numpy_gw_benefits.png'
-alt = 'numpy benefits'
-title = 'Key NumPy Capabilities utilized'
-{{< /figure >}}
+{{< figure
+    src="/images/content_images/cs/numpy_gw_benefits.png"
+    alt="numpy benefits"
+    title="Key NumPy Capabilities utilized"
+>}}