From 757be60fad8382cb2620060010f0dfb98eb75f88 Mon Sep 17 00:00:00 2001
From: Jarrod Millman <jarrod.millman@gmail.com>
Date: Fri, 15 Mar 2024 08:38:31 -0700
Subject: [PATCH 1/5] Update theme (v0.14)

---
 themes/scientific-python-hugo-theme | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/themes/scientific-python-hugo-theme b/themes/scientific-python-hugo-theme
index 073d97b947..3968a94ca9 160000
--- a/themes/scientific-python-hugo-theme
+++ b/themes/scientific-python-hugo-theme
@@ -1 +1 @@
-Subproject commit 073d97b947b44c30205c58b58c23dafba894a812
+Subproject commit 3968a94ca95c6fe994d34e9b8107ecbb5a5216f5

From 9538ad94353ace4887ce7a44dd740333fb090936 Mon Sep 17 00:00:00 2001
From: Jarrod Millman <jarrod.millman@gmail.com>
Date: Tue, 19 Mar 2024 06:25:06 -0700
Subject: [PATCH 2/5] Use theme's figure shortcode

---
 content/en/case-studies/blackhole-image.md   | 35 ++++++++++++---
 content/en/case-studies/cricket-analytics.md | 31 +++++++++++--
 content/en/case-studies/deeplabcut-dnn.md    | 47 +++++++++++++++++---
 content/en/case-studies/gw-discov.md         | 34 +++++++++++---
 content/en/user-survey-2020.md               |  6 ++-
 content/ja/case-studies/blackhole-image.md   | 35 ++++++++++++---
 content/ja/case-studies/cricket-analytics.md | 31 +++++++++++--
 content/ja/case-studies/deeplabcut-dnn.md    | 47 +++++++++++++++++---
 content/ja/case-studies/gw-discov.md         | 34 +++++++++++---
 content/ja/user-survey-2020.md               |  6 ++-
 content/pt/case-studies/blackhole-image.md   | 35 ++++++++++++---
 content/pt/case-studies/cricket-analytics.md | 31 +++++++++++--
 content/pt/case-studies/deeplabcut-dnn.md    | 47 +++++++++++++++++---
 content/pt/case-studies/gw-discov.md         | 34 +++++++++++---
 content/pt/user-survey-2020.md               |  6 ++-
 15 files changed, 396 insertions(+), 63 deletions(-)

diff --git a/content/en/case-studies/blackhole-image.md b/content/en/case-studies/blackhole-image.md
index b7c00359fe..969af155dc 100644
--- a/content/en/case-studies/blackhole-image.md
+++ b/content/en/case-studies/blackhole-image.md
@@ -3,7 +3,13 @@ title: "Case Study: First Image of a Black Hole"
 sidebar: false
 ---
 
-{{< figure src="/images/content_images/cs/blackhole.jpg" caption="**Black Hole M87**" alt="black hole image" attr="*(Image Credits: Event Horizon Telescope Collaboration)*" attrlink="https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg" >}}
+{{< 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 >}}
 
 {{< blockquote
     cite="https://www.youtube.com/watch?v=BIvezCVcsYs"
@@ -64,7 +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" class="csfigcaption" caption="**EHT Data Processing Pipeline**" alt="data pipeline" align="middle" attr="(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)" attrlink="https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57" >}}
+{{< 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 >}}
 
 ## NumPy’s Role
 
@@ -80,7 +93,11 @@ 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" class="fig-center" alt="role of numpy" caption="**The role of NumPy in Black Hole imaging**" >}}
+{{< figure >}}
+src = '/images/content_images/cs/bh_numpy_role.png'
+alt = 'role of numpy'
+title = 'The role of NumPy in Black Hole imaging'
+{{< /figure >}}
 
 For example, the [`eht-imaging`][ehtim] Python package provides tools for
 simulating and performing image reconstruction on VLBI data.
@@ -88,7 +105,11 @@ 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" class="fig-center" alt="ehtim dependency map highlighting numpy" caption="**Software dependency chart of ehtim package highlighting NumPy**" >}}
+{{< 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 >}}
 
 [ehtim]: https://github.com/achael/eht-imaging
 
@@ -115,4 +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" class="fig-center" alt="numpy benefits" caption="**Key NumPy Capabilities utilized**" >}}
+{{< figure >}}
+src = '/images/content_images/cs/numpy_bh_benefits.png'
+alt = 'numpy benefits'
+title = 'Key NumPy Capabilities utilized'
+{{< /figure >}}
diff --git a/content/en/case-studies/cricket-analytics.md b/content/en/case-studies/cricket-analytics.md
index 77aef51b6a..926a5624db 100644
--- a/content/en/case-studies/cricket-analytics.md
+++ b/content/en/case-studies/cricket-analytics.md
@@ -3,7 +3,13 @@ title: "Case Study: Cricket Analytics, the game changer!"
 sidebar: false
 ---
 
-{{< figure src="/images/content_images/cs/ipl-stadium.png" caption="**IPLT20, the biggest Cricket Festival in India**" alt="Indian Premier League Cricket cup and stadium" attr="*(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))*" attrlink="https://unsplash.com/@aksh1802" >}}
+{{< 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 >}}
 
 {{< blockquote
     cite="https://www.scoopwhoop.com/sports/ms-dhoni/"
@@ -53,7 +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" class="csfigcaption" caption="**Cricket Pitch, the focal point in the field**" alt="A cricket pitch with bowler and batsmen" align="middle" attr="*(Image credit: Debarghya Das)*" attrlink="http://debarghyadas.com/files/IPLpaper.pdf" >}}
+{{< 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 >}}
 
 ### Key Data Analytics Objectives
 
@@ -68,7 +81,13 @@ metrics for improving match winning chances:
   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" class="fig-center" alt="pose estimator" caption="**Cricket Pose Estimator**" attr="*(Image credit: connect.vin)*" attrlink="https://connect.vin/2019/05/ai-for-cricket-batsman-pose-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 >}}
 
 ### The Challenges
 
@@ -138,4 +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" class="fig-center" alt="Diagram showing benefits of using NumPy for cricket analytics" caption="**Key NumPy Capabilities utilized**" >}}
+{{< 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 >}}
diff --git a/content/en/case-studies/deeplabcut-dnn.md b/content/en/case-studies/deeplabcut-dnn.md
index 2d9e428f5e..9124368629 100644
--- a/content/en/case-studies/deeplabcut-dnn.md
+++ b/content/en/case-studies/deeplabcut-dnn.md
@@ -3,7 +3,13 @@ title: "Case Study: DeepLabCut 3D Pose Estimation"
 sidebar: false
 ---
 
-{{< figure src="/images/content_images/cs/mice-hand.gif" class="fig-center" caption="**Analyzing mice hand-movement using DeepLapCut**" alt="micehandanim" attr="*(Source: www.deeplabcut.org )*" attrlink="http://www.mousemotorlab.org/deeplabcut" >}}
+{{< 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 >}}
 
 {{< blockquote
     cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/"
@@ -18,7 +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" class="fig-center" caption="**Colored dots track the positions of a racehorse’s body part**" alt="horserideranim" attr="*(Source: Mackenzie Mathis)*">}}
+{{< 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 >}}
 
 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.
 
@@ -61,7 +72,14 @@ Recently, the [DeepLabCut model zoo](http://www.mousemotorlab.org/dlc-modelzoo)
   - code for large-scale inference on videos
   - draw inferences using integrated visualization tools
 
-{{< figure src="/images/content_images/cs/deeplabcut-toolkit-steps.png" class="csfigcaption" caption="**Pose estimation steps with DeepLabCut**" alt="dlcsteps" align="middle" attr="(Source: DeepLabCut)" attrlink="https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1" >}}
+{{< 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 >}}
 
 [DLCToolkit]:  https://github.com/DeepLabCut/DeepLabCut
 
@@ -92,7 +110,14 @@ Recently, the [DeepLabCut model zoo](http://www.mousemotorlab.org/dlc-modelzoo)
     arrays corresponding to various images, target tensors and keypoints is
     fairly challenging.
 
-{{< figure src="/images/content_images/cs/pose-estimation.png" class="csfigcaption" caption="**Pose estimation variety and complexity**" alt="challengesfig" align="middle" attr="(Source: Mackenzie Mathis)" attrlink="https://www.biorxiv.org/content/10.1101/476531v1.full.pdf" >}}
+{{< 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 >}}
 
 ## NumPy's Role in meeting Pose Estimation Challenges
 
@@ -130,7 +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" class="fig-center" caption="**DeepLabCut Workflow**" alt="workflow" attr="*(Source: Mackenzie Mathis)*" attrlink="https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962">}}
+{{< 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 >}}
 
 ## Summary
 
@@ -146,4 +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" class="fig-center" alt="numpy benefits" caption="**Key NumPy Capabilities utilized**" >}}
+{{< figure >}}
+src = '/images/content_images/cs/numpy_dlc_benefits.png'
+alt = 'numpy benefits'
+title = 'Key NumPy Capabilities utilized'
+{{< /figure >}}
diff --git a/content/en/case-studies/gw-discov.md b/content/en/case-studies/gw-discov.md
index 01399157ce..ead650f14c 100644
--- a/content/en/case-studies/gw-discov.md
+++ b/content/en/case-studies/gw-discov.md
@@ -3,7 +3,13 @@ title: "Case Study: Discovery of Gravitational Waves"
 sidebar: false
 ---
 
-{{< figure src="/images/content_images/cs/gw_sxs_image.png" class="fig-center" caption="**Gravitational Waves**" alt="binary coalesce black hole generating gravitational waves" attr="*(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)*" attrlink="https://youtu.be/Zt8Z_uzG71o" >}}
+{{< 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 >}}
 
 {{< blockquote
     cite="https://www.youtube.com/watch?v=BIvezCVcsYs"
@@ -88,7 +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" class="fig-center" alt="gravitational waves strain amplitude" caption="**Estimated gravitational-wave strain amplitude from GW150914**" attr="(**Graph Credits:** Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)" attrlink="https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger" >}}
+{{< 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 >}}
 
 ## NumPy’s Role in the Detection of Gravitational Waves
 
@@ -120,11 +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" class="fig-center" alt="gwpy-numpy depgraph" caption="**Dependency graph showing how GwPy package depends on NumPy**" >}}
+{{< 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/PyCBC-numpy-dep-graph.png" class="fig-center" alt="PyCBC-numpy depgraph" caption="**Dependency graph showing how PyCBC 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 >}}
 
 ## Summary
 
@@ -140,4 +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" class="fig-center" alt="numpy benefits" caption="**Key NumPy Capabilities utilized**" >}}
+{{< figure >}}
+src = '/images/content_images/cs/numpy_gw_benefits.png'
+alt = 'numpy benefits'
+title = 'Key NumPy Capabilities utilized'
+{{< /figure >}}
diff --git a/content/en/user-survey-2020.md b/content/en/user-survey-2020.md
index 73b49a40ac..d82c32251d 100644
--- a/content/en/user-survey-2020.md
+++ b/content/en/user-survey-2020.md
@@ -10,7 +10,11 @@ community survey. Over 1,200 users from 75 countries participated to help us
 map out a landscape of the NumPy community and voiced their thoughts about the
 future of the project.
 
-{{< figure src="/surveys/NumPy_usersurvey_2020_report_cover.png" class="fig-left" alt="Cover page of the 2020 NumPy user survey report, titled 'NumPy Community Survey 2020 - results'" width="250" >}}
+{{< figure >}}
+src = '/surveys/NumPy_usersurvey_2020_report_cover.png'
+alt = 'Cover page of the 2020 NumPy user survey report, titled "NumPy Community Survey 2020 - results"'
+width = '250'
+{{< /figure >}}
 
 **[Download the report](/surveys/NumPy_usersurvey_2020_report.pdf)**
 to take a closer look at the survey findings.
diff --git a/content/ja/case-studies/blackhole-image.md b/content/ja/case-studies/blackhole-image.md
index 816687dde5..7d7dfb2505 100644
--- a/content/ja/case-studies/blackhole-image.md
+++ b/content/ja/case-studies/blackhole-image.md
@@ -3,7 +3,13 @@ title: "ケーススタディ：世界初のブラックホール画像"
 sidebar: false
 ---
 
-{{< figure src="/images/content_images/cs/blackhole.jpg" caption="**Black Hole M87**" alt="black hole image" attr="*(Image Credits: Event Horizon Telescope Collaboration)*" attrk="https://www.jpl.nasa.gov/images/universe/90410/blackhole20190410.jpg" >}}
+{{< figure >}}
+src = '/images/content_images/cs/blackhole.jpg'
+title = 'Black Hole M87'
+alt = 'black hole image'
+attribution = '(Image Credits: Event Horizon Telescope Collaboration)'
+attrk = 'https://www.jpl.nasa.gov/images/universe/90410/blackhole20190410.jpg'
+{{< /figure >}}
 
 {{< blockquote
     cite="https://www.youtube.com/watch?v=BIvezCVcsYs"
@@ -38,7 +44,14 @@ M87ブラックホールを画像化することは、見ることのできな
 
     今までに見たことのないものを見るのが研究の目標なら、どうやって科学者はその画像が正しいと確信することができるのでしょうか?
 
-{{< figure src="/images/content_images/cs/dataprocessbh.png" class="csfigcaption" caption="**EHTのデータ処理パイプライン**" alt="data pipeline" align="middle" attr="(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)" attrlink="https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57" >}}
+{{< figure >}}
+src = '/images/content_images/cs/dataprocessbh.png'
+title = 'EHTのデータ処理パイプライン'
+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 >}}
 
 ## NumPyが果たした役割
 
@@ -48,11 +61,19 @@ EHTの共同研究では、最先端の画像再構成技術を使用して、
 
 彼らの研究は、共同のデータ解析を通じて科学を進歩させる、科学的なPythonエコシステムが果たす役割を如実に表しています。
 
-{{< figure src="/images/content_images/cs/bh_numpy_role.png" class="fig-center" alt="role of numpy" caption="**ブラックホール画像化でNumPyが果たした役割**" >}}
+{{< figure >}}
+src = '/images/content_images/cs/bh_numpy_role.png'
+alt = 'role of numpy'
+title = 'ブラックホール画像化でNumPyが果たした役割'
+{{< /figure >}}
 
 例えば、 [`eht-imaging`][ehtim] というPython パッケージは VLBI データで画像の再構築をシミュレートし、実行するためのツールです。 NumPyは、以下のソフトウェア依存関係チャートで示されているように、このパッケージで使用される配列データ処理の中核を担っています。
 
-{{< figure src="/images/content_images/cs/ehtim_numpy.png" class="fig-center" alt="ehtim dependency map highlighting numpy" caption="**NumPyの中心としたehtimのソフトウェア依存図**" >}}
+{{< figure >}}
+src = '/images/content_images/cs/ehtim_numpy.png'
+alt = 'ehtim dependency map highlighting numpy'
+title = 'NumPyの中心としたehtimのソフトウェア依存図'
+{{< /figure >}}
 
 NumPyだけでなく、[SciPy](https://www.scipy.org)や[Pandas](https://pandas.io)などのパッケージもブラックホール画像化におけるデータ処理パイプラインに利用されています。 天文学の標準的なファイル形式や時間/座標変換 は[Astropy][astropy]で実装され、ブラックホールの最終画像の生成を含め、解析パイプライン全体でのデータ可視化には [Matplotlib][mpl]が利用されました。
 
@@ -60,7 +81,11 @@ NumPyだけでなく、[SciPy](https://www.scipy.org)や[Pandas](https://pandas.
 
 NumPyの中心的な機能である、効率的で適用性の高いn次元配列は、研究者が大規模な数値データを操作することを可能にし、世界で初めてのブラックホールの画像化の基礎を築きました。 アインシュタインの理論に素晴らしい視覚的証拠を与えたのは、科学の画期的な瞬間だといえます。 この科学的に偉大な達成には、技術的の飛躍的な進歩だけでなく、200人以上の科学者と世界で 最高の電波観測所の間での国際協力も寄与しました。  革新的なアルゴリズムとデータ処理技術は、既存の天文学モデルを改良し、宇宙の謎を解き明かす助けになったといえます。
 
-{{< figure src="/images/content_images/cs/numpy_bh_benefits.png" class="fig-center" alt="numpy benefits" caption="**利用されたNumPyの主要機能**" >}}
+{{< figure >}}
+src = '/images/content_images/cs/numpy_bh_benefits.png'
+alt = 'numpy benefits'
+title = '利用されたNumPyの主要機能'
+{{< /figure >}}
 
 [resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole
 
diff --git a/content/ja/case-studies/cricket-analytics.md b/content/ja/case-studies/cricket-analytics.md
index baf355a4ea..b76249853f 100644
--- a/content/ja/case-studies/cricket-analytics.md
+++ b/content/ja/case-studies/cricket-analytics.md
@@ -3,7 +3,13 @@ title: "ケーススタディ: クリケット分析、ゲームチェンジャ
 sidebar: false
 ---
 
-{{< figure src="/images/content_images/cs/ipl-stadium.png" caption="** IPLT20、インド最大のクリケットフェスティバル**" alt="Indian Premier League Cricket cup and stadium" attr="*(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))*" attrlink="https://unsplash.com/@aksh1802" >}}
+{{< figure >}}
+src = '/images/content_images/cs/ipl-stadium.png'
+title = ' IPLT20、インド最大のクリケットフェスティバル'
+alt = 'Indian Premier League Cricket cup and stadium'
+attribution = '(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))'
+attributionlink = 'https://unsplash.com/@aksh1802'
+{{< /figure >}}
 
 {{< blockquote
     cite="https://www.scoopwhoop.com/sports/ms-dhoni/"
@@ -27,7 +33,14 @@ sidebar: false
 * プレイヤーの体力や、異なる相手に対するパフォーマンスについての洞察
 * チーム構成に戦略的な決定を下すための、各勝敗へのプレイヤーの貢献
 
-{{< figure src="/images/content_images/cs/cricket-pitch.png" class="csfigcaption" caption="** フィールドのフォーカルポイントとなるクリケットピッチ**" alt="A cricket pitch with bowler and batsmen" align="middle" attr="*(Image credit: Debarghya Das)*" attrlink="http://debarghyadas.com/files/IPLpaper.pdf" >}}
+{{< figure >}}
+src = '/images/content_images/cs/cricket-pitch.png'
+title = ' フィールドのフォーカルポイントとなるクリケットピッチ'
+alt = 'A cricket pitch with bowler and batsmen'
+align = 'center'
+attribution = '(Image credit: Debarghya Das)'
+attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf'
+{{< /figure >}}
 
 ### データ分析の主要な目標
 
@@ -35,7 +48,13 @@ sidebar: false
 * リアルタイムデータ分析は、ゲーム中の洞察を得ることができ、チームや関連ビジネスが経済的利益と成長のために戦術を変更するためも役立ちます。
 * 履歴分析に加えて、予測モデルは可能性のある結果を求めることができますが、かなりの数のナンバークランチングとデータサイエンスのノウハウ、可視化ツール、および分析に新しい観測データを含める機能などが必要になります。
 
-{{< figure src="/images/content_images/cs/player-pose-estimator.png" class="fig-center" alt="pose estimator" caption="**クリケットの姿勢推定**" attr="*(Image credit: connect.vin)*" attrlink="https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/" >}}
+{{< figure >}}
+src = '/images/content_images/cs/player-pose-estimator.png'
+alt = 'pose estimator'
+title = 'クリケットの姿勢推定'
+attribution = '(Image credit: connect.vin)'
+attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/'
+{{< /figure >}}
 
 ### 課題
 
@@ -63,4 +82,8 @@ sidebar: false
 
 スポーツアナリティクスは、プロの試合についてはまさにゲームチェンジャーです。 特に戦略的な意思決定については、最近まで主に「直感」や過去の伝統的な考え方に基づいて行われていたため、大きな影響があります。 NumPyは、データ分析・機械学習・人工知能のアルゴリズムに関連する高レベル関数を提供する沢山のPythonパッケージ群の、堅固な基盤となっています。 これらのパッケージは、ゲームの結果を変えるような意思決定を支援するリアルタイムのインサイトを得るため、クリケットの試合だけでなく関連する推論やビジネスの推進にも広く使用されています。 クリケットの試合結果につながる隠れたパラメータや、パターン、属性を見つけることは、ステークホルダーが数字や統計に隠されているゲームの洞察方法を見つけるのにも役に立つのです。
 
-{{< figure src="/images/content_images/cs/numpy_ca_benefits.png" class="fig-center" alt="クリケット分析にNumPyを使用するメリットを示す図" caption="** 利用されている主なNumPy機能 **" >}}
+{{< figure >}}
+src = '/images/content_images/cs/numpy_ca_benefits.png'
+alt = 'クリケット分析にNumPyを使用するメリットを示す図'
+title = ' 利用されている主なNumPy機能 '
+{{< /figure >}}
diff --git a/content/ja/case-studies/deeplabcut-dnn.md b/content/ja/case-studies/deeplabcut-dnn.md
index f11552283c..006dacadbb 100644
--- a/content/ja/case-studies/deeplabcut-dnn.md
+++ b/content/ja/case-studies/deeplabcut-dnn.md
@@ -3,7 +3,13 @@ title: "ケーススタディ: DeepLabCut 三次元姿勢推定"
 sidebar: false
 ---
 
-{{< figure src="/images/content_images/cs/mice-hand.gif" class="fig-center" caption="**DeepLapCutを用いたマウスの手の動きの解析**" alt="micehandanim" attr="*(Source: www.deeplabcut.org )*" attrlink="http://www.mousemotorlab.org/deeplabcut" >}}
+{{< figure >}}
+src = '/images/content_images/cs/mice-hand.gif'
+title = 'DeepLapCutを用いたマウスの手の動きの解析'
+alt = 'micehandanim'
+attribution = '(Source: www.deeplabcut.org )'
+attributionlink = 'http://www.mousemotorlab.org/deeplabcut'
+{{< /figure >}}
 
 {{< blockquote
     cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/"
@@ -18,7 +24,12 @@ sidebar: false
 
 神経科学、医学、生体力学などのいくつかの研究分野では、動物の動きを追跡したデータを使用しています。 DeepLabCutは、動画に記録された動きを解析することで、人間やその他の動物が何をしているのかを理解することができます。 タグ付けや監視などの、手間のかかる作業を自動化し、深層学習ベースのデータ解析を実施します。 DeepLabCutは、霊長類、マウス、魚、ハエなどの動物を観察する科学研究をより速く正確にしています。
 
-{{< figure src="/images/content_images/cs/race-horse.gif" class="fig-center" caption="**色のついた点は競走馬の体の位置を追跡**" alt="horserideranim" attr="*(Source: Mackenzie Mathis)*">}}
+{{< figure >}}
+src = '/images/content_images/cs/race-horse.gif'
+title = '色のついた点は競走馬の体の位置を追跡'
+alt = 'horserideranim'
+attribution = '(Source: Mackenzie Mathis)'
+{{< /figure >}}
 
 DeepLabCutは、動物の姿勢を抽出することで非侵襲的な行動追跡を行います。 これは、生体力学、遺伝学、倫理学、神経科学などの分野での研究に必要不可欠です。 動的に変化する背景の中で、動物の姿勢をビデオデータから非侵襲的に測定することは、技術的にも、必要な計算リソースやトレーニングデータの点でも、非常に困難な計算処理です。
 
@@ -47,7 +58,14 @@ DeepLabCutでは[転移学習](https://arxiv.org/pdf/1909.11229)という技術
   - 動画における大規模推論のためのコード作成
   - 統合された可視化ツールを使用した推論の描画
 
-{{< figure src="/images/content_images/cs/deeplabcut-toolkit-steps.png" class="csfigcaption" caption="**DeepLabCutによる姿勢推定のステップ**" alt="dlcsteps" align="middle" attr="(Source: DeepLabCut)" attrlink="https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1" >}}
+{{< figure >}}
+src = '/images/content_images/cs/deeplabcut-toolkit-steps.png'
+title = 'DeepLabCutによる姿勢推定のステップ'
+alt = 'dlcsteps'
+align = 'center'
+attribution = '(Source: DeepLabCut)'
+attributionlink = 'https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1'
+{{< /figure >}}
 
 ### 課題
 
@@ -63,7 +81,14 @@ DeepLabCutでは[転移学習](https://arxiv.org/pdf/1909.11229)という技術
 
     最後に、配列の操作もかなり難しい問題です。 様々な画像や、目標のテンソル、キーポイントに対応する大きな配列のスタックを処理しなければならないからです。
 
-{{< figure src="/images/content_images/cs/pose-estimation.png" class="csfigcaption" caption="**姿勢推定の多様性と難しさ**" alt="challengesfig" align="middle" attr="(Source: Mackenzie Mathis)" attrlink="https://www.biorxiv.org/content/10.1101/476531v1.full.pdf" >}}
+{{< figure >}}
+src = '/images/content_images/cs/pose-estimation.png'
+title = '姿勢推定の多様性と難しさ'
+alt = 'challengesfig'
+align = 'center'
+attribution = '(Source: Mackenzie Mathis)'
+attributionlink = 'https://www.biorxiv.org/content/10.1101/476531v1.full.pdf'
+{{< /figure >}}
 
 ## 姿勢推定の課題に対応するためのNumPyの役割
 
@@ -79,13 +104,23 @@ NumPy は DeepLabCutにおける、行動分析の高速化のための数値計
 
 DeepLabCutは、ツールキットが提供するワークフローを通じてNumPyの配列機能を利用しています。 特に、NumPyはヒューマンアノテーションのラベル付けや、アノテーションの書き込み、編集、処理のために、特定のフレームをサンプリングするために使用されています。  TensorFlowを使ったニューラルネットワークは、DeepLabCutの技術によって何千回も訓練され、 フレームから真のアノテーション情報を予測します。 この目的のため、姿勢推定問題を画像-画像変換問題として変換する目標密度(スコアマップ) を作成します。 ニューラルネットワークのロバスト化のため、データの水増しを使用していますが、このためには幾何学・画像的処理を施したスコアマップの計算を行うことが必要になります。 また学習を高速化するため、NumPyのベクトル化機能が利用されています。 推論には、目標のスコアマップから最も可能性の高い予測値を抽出し、効率的に「予測値をリンクさせて個々の動物を組み立てる」ことが必要になります。
 
-{{< figure src="/images/content_images/cs/deeplabcut-workflow.png" class="fig-center" caption="**DeepLabCutのワークフロー**" alt="workflow" attr="*(Source: Mackenzie Mathis)*" attrlink="https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962">}}
+{{< figure >}}
+src = '/images/content_images/cs/deeplabcut-workflow.png'
+title = 'DeepLabCutのワークフロー'
+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 >}}
 
 ## まとめ
 
 行動を観察し、効率的に表現することは、現代倫理学、神経科学、医学、工学の根幹です。 [DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) により、研究者は対象の姿勢を推定し、行動を効率的に定量化できるようになりました。 DeepLabCutというPythonツールボックスを使えば、わずかな学習画像のセットでニューラルネットワークを人間レベルのラベリング精度で学習することができ、実験室での行動分析だけでなく、スポーツ、歩行分析、医学、リハビリテーション研究などへの応用が可能になります。 DeepLabCutアルゴリズムに必要な複雑な組み合わせ処理やデータ処理の問題を、NumPyの配列操作機能が解決しています。
 
-{{< figure src="/images/content_images/cs/numpy_dlc_benefits.png" class="fig-center" alt="numpy benefits" caption="**NumPyの主要機能**" >}}
+{{< figure >}}
+src = '/images/content_images/cs/numpy_dlc_benefits.png'
+alt = 'numpy benefits'
+title = 'NumPyの主要機能'
+{{< /figure >}}
 
 [cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618
 
diff --git a/content/ja/case-studies/gw-discov.md b/content/ja/case-studies/gw-discov.md
index e0bdc846b9..c5275fde58 100644
--- a/content/ja/case-studies/gw-discov.md
+++ b/content/ja/case-studies/gw-discov.md
@@ -3,7 +3,13 @@ title: "ケーススタディ: 重力波の発見"
 sidebar: false
 ---
 
-{{< figure src="/images/content_images/cs/gw_sxs_image.png" class="fig-center" caption="**重力波**" alt="binary coalesce black hole generating gravitational waves" attr="*(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)*" attrlink="https://youtu.be/Zt8Z_uzG71o" >}}
+{{< figure >}}
+src = '/images/content_images/cs/gw_sxs_image.png'
+title = '重力波'
+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 >}}
 
 {{< blockquote
     cite="https://www.youtube.com/watch?v=BIvezCVcsYs"
@@ -40,7 +46,13 @@ sidebar: false
 
     アインシュタイン方程式を元にスーパーコンピュータでデータを解析できるようになったら、次はデータを人間の脳で理解できるようにしなければなりません。 シミュレーションのモデリングや信号の検出には、わかりやすい可視化技術が必要です。  画像処理やシミュレーションによって、解析結果をより多くの人に理解してもらえる状態になる前の段階において、可視化は、数値相対性を十分に重要視していなかった純粋な科学愛好家の目に、数値相対性が、より信頼性の高いものとして映るようにするという役割も果たしています。 複雑な計算と描画を行い、また最新の実験結果と洞察に基づいてシミュレーションと再描画を行う作業は時間のかかるもので、この分野の研究者にとっての課題です。
 
-{{< figure src="/images/content_images/cs/gw_strain_amplitude.png" class="fig-center" alt="gravitational waves strain amplitude" caption="**GW150914から推定される重力波の歪みの振幅**" attr="(**Graph Credits:** Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)" attrlink="https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger" >}}
+{{< figure >}}
+src = '/images/content_images/cs/gw_strain_amplitude.png'
+alt = 'gravitational waves strain amplitude'
+title = 'GW150914から推定される重力波の歪みの振幅'
+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 >}}
 
 ## 重力波の検出におけるNumPyの役割
 
@@ -57,14 +69,26 @@ Python用の標準的な数値解析パッケージNumPyは、LIGOの重力波
 * 相関計算
 * 重力波データ解析のために開発された[ソフトウェア群](https://github.com/lscsoft): [GwPy](https://gwpy.github.io/docs/stable/overview.html)や [PyCBC](https://pycbc.org)は、NumPyやAstroPyを用いて、重力波検出器データを研究するためのユーティリティー・ツール・関数へのオブジェクト指向インターフェースを提供しています。
 
-{{< figure src="/images/content_images/cs/gwpy-numpy-dep-graph.png" class="fig-center" alt="gwpy-numpy depgraph" caption="**GwPyのNumPy依存グラフ**" >}}
+{{< figure >}}
+src = '/images/content_images/cs/gwpy-numpy-dep-graph.png'
+alt = 'gwpy-numpy depgraph'
+title = 'GwPyのNumPy依存グラフ'
+{{< /figure >}}
 
 ----
 
-{{< figure src="/images/content_images/cs/PyCBC-numpy-dep-graph.png" class="fig-center" alt="PyCBC-numpy depgraph" caption="**PyCBCのNumPy依存グラフ**" >}}
+{{< figure >}}
+src = '/images/content_images/cs/PyCBC-numpy-dep-graph.png'
+alt = 'PyCBC-numpy depgraph'
+title = 'PyCBCのNumPy依存グラフ'
+{{< /figure >}}
 
 ## まとめ
 
 一方で、これまで知られてきた深遠な天体物理学の現象に、多くに新たな洞察を提供しました。 数値処理とデータの可視化は、科学者が科学的な観測から収集したデータについての洞察を得て、その結果を理解するのに役立つ重要なステップです。 しかし、その計算は複雑であり、実際の観測データと分析を用いたコンピュータシミュレーションを用いて可視化されない限り、人間が理解することはできませんでした。  NumPyは、matplotlib・pandas・scikit-learnなどのPythonパッケージとともに、研究者が複雑な質問に答え、私たちの宇宙に対するの理解において、新しい地平を発見することを[可能にしています](https://www.gw-openscience.org/events/GW150914/)。
 
-{{< figure src="/images/content_images/cs/numpy_bh_benefits.png" class="fig-center" alt="numpy benefits" caption="**利用されたNumPyの主要機能**" >}}
+{{< figure >}}
+src = '/images/content_images/cs/numpy_bh_benefits.png'
+alt = 'numpy benefits'
+title = '利用されたNumPyの主要機能'
+{{< /figure >}}
diff --git a/content/ja/user-survey-2020.md b/content/ja/user-survey-2020.md
index ce32100800..b79cc13ed8 100644
--- a/content/ja/user-survey-2020.md
+++ b/content/ja/user-survey-2020.md
@@ -5,7 +5,11 @@ sidebar: false
 
 2020年に、NumPyの調査チームは、ミシガン大学とメリーランド大学が共同で開催した、調査方法学の修士コースの学生と教員と共同で、初めて公式のNumPyコミュニティ調査を実施しました。 75カ国から1,200人以上のNumPyユーザーが参加してくれました。NumPyコミュニティの全体像を描き、プロジェクトの未来像についての意見を述べてもらいました。
 
-{{< figure src="/surveys/NumPy_usersurvey_2020_report_cover.png" class="fig-left" alt="Cover page of the 2020 Numpy User survey report, titled 'Numpyコミュニティ調査2020 - 結果'" width="250" >}}
+{{< figure >}}
+src = '/surveys/NumPy_usersurvey_2020_report_cover.png'
+alt = 'Cover page of the 2020 Numpy User survey report, titled "Numpyコミュニティ調査2020 - 結果"'
+width = '250'
+{{< /figure >}}
 
 調査結果を詳細を知りたい場合は、**[こちらのレポート](/surveys/NumPy_usersurvey_2020_report.pdf)** をダウンロードしてください。
 
diff --git a/content/pt/case-studies/blackhole-image.md b/content/pt/case-studies/blackhole-image.md
index b4832fd66f..d8429b35cc 100644
--- a/content/pt/case-studies/blackhole-image.md
+++ b/content/pt/case-studies/blackhole-image.md
@@ -3,7 +3,13 @@ title: "Estudo de Caso: A Primeira Imagem de um Buraco Negro"
 sidebar: false
 ---
 
-{{< figure src="/images/content_images/cs/blackhole.jpg" caption="**Black Hole M87**" alt="black hole image" attr="*(Créditos: Event Horizon Telescope Collaboration)*" attrlink="https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg" >}}
+{{< figure >}}
+src = '/images/content_images/cs/blackhole.jpg'
+title = 'Black Hole M87'
+alt = 'black hole image'
+attribution = '(Créditos: Event Horizon Telescope Collaboration)'
+attributionlink = 'https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg'
+{{< /figure >}}
 
 {{< blockquote
     cite="https://www.youtube.com/watch?v=BIvezCVcsYs"
@@ -38,7 +44,14 @@ O [telescópio Event Horizon (EHT)](https://eventhorizontelescope.org), é um co
 
     Quando o objetivo é algo que nunca foi visto, como os cientistas podem ter confiança de que sua imagem está correta?
 
-{{< figure src="/images/content_images/cs/dataprocessbh.png" class="csfigcaption" caption="**Etapas de Processamento de Dados do EHT**" alt="data pipeline" align="middle" attr="(Créditos do diagrama: The Astrophysical Journal, Event Horizon Telescope Collaboration)" attrlink="https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57" >}}
+{{< figure >}}
+src = '/images/content_images/cs/dataprocessbh.png'
+title = 'Etapas de Processamento de Dados do EHT'
+alt = 'data pipeline'
+align = 'center'
+attribution = '(Créditos do diagrama: The Astrophysical Journal, Event Horizon Telescope Collaboration)'
+attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57'
+{{< /figure >}}
 
 ## O papel do NumPy
 
@@ -48,11 +61,19 @@ A colaboração do EHT venceu esses desafios ao estabelecer equipes independente
 
 O trabalho desse grupo ilustra o papel do ecossistema científico do Python no avanço da ciência através da análise de dados colaborativa.
 
-{{< figure src="/images/content_images/cs/bh_numpy_role.png" class="fig-center" alt="role of numpy" caption="**O papel do NumPy na criação da primeira imagem de um Buraco Negro**" >}}
+{{< figure >}}
+src = '/images/content_images/cs/bh_numpy_role.png'
+alt = 'role of numpy'
+title = 'O papel do NumPy na criação da primeira imagem de um Buraco Negro'
+{{< /figure >}}
 
 Por exemplo, o pacote Python [`eht-imaging`][ehtim] fornece ferramentas para simular e realizar reconstrução de imagem nos dados do VLBI. O NumPy está no coração do processamento de dados vetoriais usado neste pacote, como ilustrado pelo gráfico parcial de dependências de software abaixo.
 
-{{< figure src="/images/content_images/cs/ehtim_numpy.png" class="fig-center" alt="ehtim dependency map highlighting numpy" caption="**Diagrama de dependência de software do pacote ehtim evidenciando o NumPy**" >}}
+{{< figure >}}
+src = '/images/content_images/cs/ehtim_numpy.png'
+alt = 'ehtim dependency map highlighting numpy'
+title = 'Diagrama de dependência de software do pacote ehtim evidenciando o NumPy'
+{{< /figure >}}
 
 Além do NumPy, muitos outros pacotes como [SciPy](https://www.scipy.org) e [Pandas](https://pandas.io) foram usados na *pipeline* de processamento de dados para criar a imagem do buraco negro. Os arquivos astronômicos de formato padrão e transformações de tempo/coordenadas foram tratados pelo [Astropy][astropy] enquanto a [Matplotlib][mpl] foi usada na visualização de dados em todas as etapas de análise, incluindo a geração da imagem final do buraco negro.
 
@@ -60,7 +81,11 @@ Além do NumPy, muitos outros pacotes como [SciPy](https://www.scipy.org) e [Pan
 
 A estrutura de dados n-dimensional que é a funcionalidade central do NumPy permitiu aos pesquisadores manipular grandes conjuntos de dados, fornecendo a base para a primeira imagem de um buraco negro. Esse momento marcante na ciência fornece evidências visuais impressionantes para a teoria de Einstein. Esta conquista abrange não apenas avanços tecnológicos, mas colaboração científica em escala internacional entre mais de 200 cientistas e alguns dos melhores observatórios de rádio do mundo.  Eles usaram algoritmos e técnicas de processamento de dados inovadores, que aperfeiçoaram os modelos astronômicos existentes, para ajudar a descobrir um dos mistérios do universo.
 
-{{< figure src="/images/content_images/cs/numpy_bh_benefits.png" class="fig-center" alt="numpy benefits" caption="**Funcionalidades-chave do NumPy utilizadas**" >}}
+{{< figure >}}
+src = '/images/content_images/cs/numpy_bh_benefits.png'
+alt = 'numpy benefits'
+title = 'Funcionalidades-chave do NumPy utilizadas'
+{{< /figure >}}
 
 [resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole
 
diff --git a/content/pt/case-studies/cricket-analytics.md b/content/pt/case-studies/cricket-analytics.md
index 54da45b73d..8d70c776a6 100644
--- a/content/pt/case-studies/cricket-analytics.md
+++ b/content/pt/case-studies/cricket-analytics.md
@@ -3,7 +3,13 @@ title: "Estudo de Caso: Análise de Críquete, a revolução!"
 sidebar: false
 ---
 
-{{< figure src="/images/content_images/cs/ipl-stadium.png" caption="**IPLT20, o maior festival de Críquete da Índia**" alt="Copa e estádio da Indian Premier League Cricket" attr="*(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))*" attrlink="https://unsplash.com/@aksh1802" >}}
+{{< figure >}}
+src = '/images/content_images/cs/ipl-stadium.png'
+title = 'IPLT20, o maior festival de Críquete da Índia'
+alt = 'Copa e estádio da Indian Premier League Cricket'
+attribution = '(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))'
+attributionlink = 'https://unsplash.com/@aksh1802'
+{{< /figure >}}
 
 {{< blockquote
     cite="https://www.scoopwhoop.com/sports/ms-dhoni/"
@@ -27,7 +33,14 @@ Hoje, existem conjuntos ricos e quase infinitos de estatísticas e informações
 * ganho de informações sobre desempenho e condição física de um determinado jogador contra determinado adversário,
 * contribuições dos jogadores para vitórias e derrotas para a tomada de decisões estratégicas na composição do time
 
-{{< figure src="/images/content_images/cs/cricket-pitch.png" class="csfigcaption" caption="**Pitch de críquete, o ponto focal do campo**" alt="Um pitch de críquete com um boleador e batsmen" align="middle" attr="*(Créditos de imagem: Debarghya Das)*" attrlink="http://debarghyadas.com/files/IPLpaper.pdf" >}}
+{{< figure >}}
+src = '/images/content_images/cs/cricket-pitch.png'
+title = 'Pitch de críquete, o ponto focal do campo'
+alt = 'Um pitch de críquete com um boleador e batsmen'
+align = 'center'
+attribution = '(Créditos de imagem: Debarghya Das)'
+attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf'
+{{< /figure >}}
 
 ### Objetivos Principais da Análise de Dados
 
@@ -35,7 +48,13 @@ Hoje, existem conjuntos ricos e quase infinitos de estatísticas e informações
 * A análise de dados em tempo real pode ajudar a obtenção de informações mesmo durante o jogo para orientar mudanças nas táticas da equipe e dos negócios associados para benefícios e crescimento econômicos.
 * Além da análise histórica, os modelos preditivos explorados para determinar os possíveis resultados das partidas requerem um conhecimento significativo sobre processamento numérico e ciência de dados, ferramentas de visualização e a possibilidade de incluir observações mais recentes na análise.
 
-{{< figure src="/images/content_images/cs/player-pose-estimator.png" class="fig-center" alt="estimador de postura" caption="**Estimador de Postura de Críquete**" attr="*(Créditos de imagem: connect.vin)*" attrlink="https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/" >}}
+{{< figure >}}
+src = '/images/content_images/cs/player-pose-estimator.png'
+alt = 'estimador de postura'
+title = 'Estimador de Postura de Críquete'
+attribution = '(Créditos de imagem: connect.vin)'
+attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/'
+{{< /figure >}}
 
 ### Desafios
 
@@ -63,4 +82,8 @@ A análise de dados esportivos é um campo próspero. Muitos pesquisadores e emp
 
 A análise de dados esportivos é revolucionária quando se trata de como os jogos profissionais são jogados, especialmente se consideramos como acontece a tomada de decisões estratégicas, que até pouco tempo era principalmente feita com base na "intuição" ou adesão a tradições passadas. O NumPy forma uma fundação sólida para um grande conjunto de pacotes Python que fornecem funções de alto nível relacionadas à análise de dados, aprendizagem de máquina e algoritmos de IA. Estes pacotes são amplamente implantados para se obter informações em tempo real que ajudam na tomada de decisão para resultados decisivos, tanto em campo como para se derivar inferências e orientar negócios em torno do jogo de críquete. Encontrar os parâmetros ocultos, padrões, e atributos que levam ao resultado de uma partida de críquete ajuda os envolvidos a tomar nota das percepções do jogo que estariam de outra forma ocultas nos números e estatísticas.
 
-{{< figure src="/images/content_images/cs/numpy_ca_benefits.png" class="fig-center" alt="Diagrama mostrando os benefícios de usar a NumPy para análise de críquete" caption="**Recursos principais da NumPy utilizados**" >}}
+{{< figure >}}
+src = '/images/content_images/cs/numpy_ca_benefits.png'
+alt = 'Diagrama mostrando os benefícios de usar a NumPy para análise de críquete'
+title = 'Recursos principais da NumPy utilizados'
+{{< /figure >}}
diff --git a/content/pt/case-studies/deeplabcut-dnn.md b/content/pt/case-studies/deeplabcut-dnn.md
index a0c37db9c3..557b336ab8 100644
--- a/content/pt/case-studies/deeplabcut-dnn.md
+++ b/content/pt/case-studies/deeplabcut-dnn.md
@@ -3,7 +3,13 @@ title: "Estudo de Caso: Estimativa de Pose 3D com DeepLabCut"
 sidebar: false
 ---
 
-{{< figure src="/images/content_images/cs/mice-hand.gif" class="fig-center" caption="**Análise de movimentos de mãos de camundongos usando DeepLapCut**" alt="micehandanim" attr="*(Fonte: www.deeplabcut.org )*" attrlink="http://www.mousemotorlab.org/deeplabcut" >}}
+{{< figure >}}
+src = '/images/content_images/cs/mice-hand.gif'
+title = 'Análise de movimentos de mãos de camundongos usando DeepLapCut'
+alt = 'micehandanim'
+attribution = '(Fonte: www.deeplabcut.org )'
+attributionlink = 'http://www.mousemotorlab.org/deeplabcut'
+{{< /figure >}}
 
 {{< blockquote
     cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/"
@@ -18,7 +24,12 @@ Software de código aberto está acelerando a Biomedicina. DeepLabCut permite a
 
 Várias áreas de pesquisa, incluindo a neurociência, a medicina e a biomecânica, utilizam dados de rastreamento da movimentação de animais. A DeepLabCut ajuda a compreender o que os seres humanos e outros animais estão fazendo, analisando ações que foram registradas em vídeo. Ao usar automação para tarefas trabalhosas de monitoramento e marcação, junto com análise de dados baseada em redes neurais profundas, a DeepLabCut garante que estudos científicos envolvendo a observação de animais como primatas, camundongos, peixes, moscas etc. sejam mais rápidos e precisos.
 
-{{< figure src="/images/content_images/cs/race-horse.gif" class="fig-center" caption="**Pontos coloridos rastreiam as posições das partes do corpo de um cavalo de corrida**" alt="horserideranim" attr="*(Fonte: Mackenzie Mathis)*">}}
+{{< figure >}}
+src = '/images/content_images/cs/race-horse.gif'
+title = 'Pontos coloridos rastreiam as posições das partes do corpo de um cavalo de corrida'
+alt = 'horserideranim'
+attribution = '(Fonte: Mackenzie Mathis)'
+{{< /figure >}}
 
 O rastreamento não invasivo dos animais pela DeepLabCut através da extração de poses é crucial para pesquisas científicas em domínios como a biomecânica, genética, etologia e neurociência. Medir as poses dos animais de maneira não invasiva através de vídeo - sem marcadores - com fundos dinâmicos é computacionalmente desafiador, tanto tecnicamente quanto em termos de recursos e dados de treinamento necessários.
 
@@ -47,7 +58,14 @@ Recentemente, foi introduzido o [modelo DeepLabCut zoo](http://www.mousemotorlab
   - código para inferência em larga escala em vídeos
   - inferências de desenho usando ferramentas integradas de visualização
 
-{{< figure src="/images/content_images/cs/deeplabcut-toolkit-steps.png" class="csfigcaption" caption="**Passos na estimação de poses com DeepLabCut**" alt="dlcsteps" align="middle" attr="(Fonte: DeepLabCut)" attrlink="https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1" >}}
+{{< figure >}}
+src = '/images/content_images/cs/deeplabcut-toolkit-steps.png'
+title = 'Passos na estimação de poses com DeepLabCut'
+alt = 'dlcsteps'
+align = 'center'
+attribution = '(Fonte: DeepLabCut)'
+attributionlink = 'https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1'
+{{< /figure >}}
 
 ### Desafios
 
@@ -63,7 +81,14 @@ Recentemente, foi introduzido o [modelo DeepLabCut zoo](http://www.mousemotorlab
 
     Por último, mas não menos importante, manipulação de matrizes - processar grandes conjuntos de matrizes correspondentes a várias imagens, tensores alvo e pontos-chave é bastante desafiador.
 
-{{< figure src="/images/content_images/cs/pose-estimation.png" class="csfigcaption" caption="**Estimação de poses e complexidade**" alt="challengesfig" align="middle" attr="(Fonte: Mackenzie Mathis)" attrlink="https://www.biorxiv.org/content/10.1101/476531v1.full.pdf" >}}
+{{< figure >}}
+src = '/images/content_images/cs/pose-estimation.png'
+title = 'Estimação de poses e complexidade'
+alt = 'challengesfig'
+align = 'center'
+attribution = '(Fonte: Mackenzie Mathis)'
+attributionlink = 'https://www.biorxiv.org/content/10.1101/476531v1.full.pdf'
+{{< /figure >}}
 
 ## O papel da NumPy nos desafios da estimação de poses
 
@@ -79,13 +104,23 @@ As seguintes características da NumPy desempenharam um papel fundamental para a
 
 A DeepLabCut utiliza as capacidades de manipulação de arrays da NumPy em todo o fluxo de trabalho oferecido pelo seu conjunto de ferramentas. Em particular, a NumPy é usada para amostragem de quadros distintos para serem rotulados com anotações humanas e para escrita, edição e processamento de dados de anotação.  Dentro da TensorFlow, a rede neural é treinada pela tecnologia DeepLabCut em milhares de iterações para prever as anotações verdadeiras dos quadros. Para este propósito, densidades de alvo (*scoremaps*) são criadas para colocar a estimativa como um problema de tradução de imagem a imagem. Para tornar as redes neurais robustas, o aumento de dados é empregado, o que requer o cálculo de scoremaps alvo sujeitos a várias etapas geométricas e de processamento de imagem. Para tornar o treinamento rápido, os recursos de vectorização da NumPy são utilizados. Para inferência, as previsões mais prováveis de scoremaps alvo precisam ser extraídas e é necessário "vincular previsões para montar animais individuais" de maneira eficiente.
 
-{{< figure src="/images/content_images/cs/deeplabcut-workflow.png" class="fig-center" caption="**Fluxo de dados DeepLabCut**" alt="workflow" attr="*(Fonte: Mackenzie Mathis)*" attrlink="https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962">}}
+{{< figure >}}
+src = '/images/content_images/cs/deeplabcut-workflow.png'
+title = 'Fluxo de dados DeepLabCut'
+alt = 'workflow'
+attribution = '(Fonte: 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 >}}
 
 ## Resumo
 
 Observação e descrição eficiente do comportamento é uma peça fundamental da etologia, neurociência, medicina e tecnologia modernas. [DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) permite que os pesquisadores estimem a pose do sujeito, permitindo efetivamente que o seu comportamento seja quantificado. Com apenas um pequeno conjunto de imagens de treinamento, o conjunto de ferramentas em Python da DeepLabCut permite treinar uma rede neural tão precisa quanto a rotulagem humana, expandindo assim sua aplicação para não só análise de comportamento dentro do laboratório, mas também potencialmente em esportes, análise de locomoção, medicina e estudos sobre reabilitação. Desafios complexos em combinatória e processamento de dados enfrentados pelos algoritmos da DeepLabCut são tratados através do uso de recursos de manipulação de matriz do NumPy.
 
-{{< figure src="/images/content_images/cs/numpy_dlc_benefits.png" class="fig-center" alt="numpy benefits" caption="**Recursos chave do NumPy utilizados**" >}}
+{{< figure >}}
+src = '/images/content_images/cs/numpy_dlc_benefits.png'
+alt = 'numpy benefits'
+title = 'Recursos chave do NumPy utilizados'
+{{< /figure >}}
 
 [cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618
 
diff --git a/content/pt/case-studies/gw-discov.md b/content/pt/case-studies/gw-discov.md
index 4e8bcda54b..cb371914fc 100644
--- a/content/pt/case-studies/gw-discov.md
+++ b/content/pt/case-studies/gw-discov.md
@@ -3,7 +3,13 @@ title: "Estudo de Caso: Descoberta de Ondas Gravitacionais"
 sidebar: false
 ---
 
-{{< figure src="/images/content_images/cs/gw_sxs_image.png" class="fig-center" caption="**Ondas gravitacionais**" alt="binary coalesce black hole generating gravitational waves" attr="*(Créditos de imagem: O projeto Simulating eXtreme Spacetimes (SXS) no LIGO)*" attrlink="https://youtu.be/Zt8Z_uzG71o" >}}
+{{< figure >}}
+src = '/images/content_images/cs/gw_sxs_image.png'
+title = 'Ondas gravitacionais'
+alt = 'binary coalesce black hole generating gravitational waves'
+attribution = '(Créditos de imagem: O projeto Simulating eXtreme Spacetimes (SXS) no LIGO)'
+attributionlink = 'https://youtu.be/Zt8Z_uzG71o'
+{{< /figure >}}
 
 {{< blockquote
     cite="https://www.youtube.com/watch?v=BIvezCVcsYs"
@@ -40,7 +46,13 @@ O [Observatório Interferômetro Laser de Ondas Gravitacionais (LIGO)](https://w
 
     Uma vez que os obstáculos relacionados a compreender as equações de Einstein bem o suficiente para resolvê-las usando supercomputadores foram ultrapassados, o próximo grande desafio era tornar os dados compreensíveis para o cérebro humano. A modelagem de simulações, assim como a detecção de sinais, exigem técnicas de visualização efetiva.  A visualização também desempenha um papel de fornecer mais credibilidade à relatividade numérica aos olhos dos aficionados pela ciência pura, que não dão importância suficiente à relatividade numérica até que a imagem e as simulações tornem mais fácil a compreensão dos resultados para um público maior. A velocidade da computação complexa, e da renderização, re-renderização de imagens e simulações usando as últimas entradas e informações experimentais pode ser uma atividade demorada que desafia pesquisadores neste domínio.
 
-{{< figure src="/images/content_images/cs/gw_strain_amplitude.png" class="fig-center" alt="gravitational waves strain amplitude" caption="**Amplitude estimada da deformação das ondas gravitacionais do evento GW150914**" attr="(**Créditos do gráfico:** Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)" attrlink="https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger" >}}
+{{< figure >}}
+src = '/images/content_images/cs/gw_strain_amplitude.png'
+alt = 'gravitational waves strain amplitude'
+title = 'Amplitude estimada da deformação das ondas gravitacionais do evento GW150914'
+attribution = '(Créditos do gráfico: 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 >}}
 
 ## O papel da NumPy na detecção de ondas gravitacionais
 
@@ -57,14 +69,26 @@ NumPy, o pacote padrão de análise numérica para Python, foi parte do software
 * Cálculo de correlações
 * [Software](https://github.com/lscsoft) fundamental desenvolvido na análise de ondas gravitacionais, como [GwPy](https://gwpy.github.io/docs/stable/overview.html) e [PyCBC](https://pycbc.org) usam NumPy e AstroPy internamente para fornecer interfaces baseadas em objetos para utilidades, ferramentas e métodos para o estudo de dados de detectores de ondas gravitacionais.
 
-{{< figure src="/images/content_images/cs/gwpy-numpy-dep-graph.png" class="fig-center" alt="gwpy-numpy depgraph" caption="**Grafo de dependências mostrando como o pacote GwPy depended da NumPy**" >}}
+{{< figure >}}
+src = '/images/content_images/cs/gwpy-numpy-dep-graph.png'
+alt = 'gwpy-numpy depgraph'
+title = 'Grafo de dependências mostrando como o pacote GwPy depended da NumPy'
+{{< /figure >}}
 
 ----
 
-{{< figure src="/images/content_images/cs/PyCBC-numpy-dep-graph.png" class="fig-center" alt="PyCBC-numpy depgraph" caption="**Grafo de dependências mostrando como o pacote PyCBC depended da NumPy**" >}}
+{{< figure >}}
+src = '/images/content_images/cs/PyCBC-numpy-dep-graph.png'
+alt = 'PyCBC-numpy depgraph'
+title = 'Grafo de dependências mostrando como o pacote PyCBC depended da NumPy'
+{{< /figure >}}
 
 ## Resumo
 
 A detecção de ondas gravitacionais permitiu que pesquisadores descobrissem fenômenos totalmente inesperados ao mesmo tempo em que proporcionaram novas idéias sobre muitos dos fenômenos mais profundos conhecidos na astrofísica. O processamento e a visualização de dados é um passo crucial que ajuda cientistas a obter informações coletadas de observações científicas e a entender os resultados. Os cálculos são complexos e não podem ser compreendidos por humanos a não ser que sejam visualizados usando simulações de computador que são alimentadas com dados e análises reais observados.  A NumPy, junto com outras bibliotecas Python, como matplotlib, pandas, e scikit-learn [permitem que pesquisadores](https://www.gw-openscience.org/events/GW150914/) respondam perguntas complexas e descubram novos horizontes em nossa compreensão do universo.
 
-{{< figure src="/images/content_images/cs/numpy_gw_benefits.png" class="fig-center" alt="numpy benefits" caption="**Recursos chave da NumPy utilizados**" >}}
+{{< figure >}}
+src = '/images/content_images/cs/numpy_gw_benefits.png'
+alt = 'numpy benefits'
+title = 'Recursos chave da NumPy utilizados'
+{{< /figure >}}
diff --git a/content/pt/user-survey-2020.md b/content/pt/user-survey-2020.md
index 45ade422a3..8747efca88 100644
--- a/content/pt/user-survey-2020.md
+++ b/content/pt/user-survey-2020.md
@@ -5,7 +5,11 @@ sidebar: false
 
 Em 2020, o time de pesquisas do NumPy realizou a primeira pesquisa oficial sobre a comunidade NumPy, em parceria com alunos e docentes de um Mestrado em metodologia de pesquisa realizado conjuntamente pela Universidade de Michigan e pela Universidade da Maryland. Mais de 1200 usuários de 75 países participaram para nos ajudar a mapear uma paisagem da comunidade NumPy e expressaram seus pensamentos sobre o futuro do projeto.
 
-{{< figure src="/surveys/NumPy_usersurvey_2020_report_cover.png" class="fig-left" alt="Página de capa do relatório da pesquisa de usuários do NumPy 2020, chamado 'NumPy Community Survey 2020 - results'" width="250" >}}
+{{< figure >}}
+src = '/surveys/NumPy_usersurvey_2020_report_cover.png'
+alt = 'Página de capa do relatório da pesquisa de usuários do NumPy 2020, chamado "NumPy Community Survey 2020 - results"'
+width = '250'
+{{< /figure >}}
 
 **[Faça o download do relatório](/surveys/NumPy_usersurvey_2020_report.pdf)** para ver os detalhes sobre os resultados encontrados.
 

From 957a264fdb8dce4069ad7a1c6040a258b6f5edac Mon Sep 17 00:00:00 2001
From: Jarrod Millman <jarrod.millman@gmail.com>
Date: Tue, 19 Mar 2024 06:39:09 -0700
Subject: [PATCH 3/5] Replace grid1 shotcode with grid

---
 content/en/_index.md      |  4 ++--
 content/en/teams/index.md | 12 ++++++------
 content/ja/_index.md      |  4 ++--
 content/ja/teams/index.md | 12 ++++++------
 content/pt/_index.md      |  4 ++--
 content/pt/teams/index.md | 12 ++++++------
 6 files changed, 24 insertions(+), 24 deletions(-)

diff --git a/content/en/_index.md b/content/en/_index.md
index 8f69427a42..9e9534d428 100644
--- a/content/en/_index.md
+++ b/content/en/_index.md
@@ -2,7 +2,7 @@
 title:
 ---
 
-{{< grid1 columns="1 2 2 3" >}}
+{{< grid columns="1 2 2 3" >}}
 
 [[item]]
 type = 'card'
@@ -46,4 +46,4 @@ body = '''
 NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level.
 '''
 
-{{< /grid1>}}
+{{< /grid>}}
diff --git a/content/en/teams/index.md b/content/en/teams/index.md
index 824152b752..2c5a4c5b8a 100644
--- a/content/en/teams/index.md
+++ b/content/en/teams/index.md
@@ -9,27 +9,27 @@ communities worldwide by building quality, open-source software.
 
 ### Maintainers
 
-{{< grid1 file="maintainers.toml" columns="2 3 4 5" />}}
+{{< grid file="maintainers.toml" columns="2 3 4 5" />}}
 
 ### Docs team
 
-{{< grid1 file="docs-team.toml" columns="2 3 4 5" />}}
+{{< grid file="docs-team.toml" columns="2 3 4 5" />}}
 
 ### Web team
 
-{{< grid1 file="web-team.toml" columns="2 3 4 5" />}}
+{{< grid file="web-team.toml" columns="2 3 4 5" />}}
 
 ### Triage team
 
-{{< grid1 file="triage-team.toml" columns="2 3 4 5" />}}
+{{< grid file="triage-team.toml" columns="2 3 4 5" />}}
 
 ### Survey team
 
-{{< grid1 file="survey-team.toml" columns="2 3 4 5" />}}
+{{< grid file="survey-team.toml" columns="2 3 4 5" />}}
 
 ### Emeritus maintainers
 
-{{< grid1 file="emeritus-maintainers.toml" columns="2 3 4 5" />}}
+{{< grid file="emeritus-maintainers.toml" columns="2 3 4 5" />}}
 
 # Governance
 
diff --git a/content/ja/_index.md b/content/ja/_index.md
index 192989ecf0..1109f91332 100644
--- a/content/ja/_index.md
+++ b/content/ja/_index.md
@@ -2,7 +2,7 @@
 title:
 ---
 
-{{< grid1 columns="1 2 2 3" >}}
+{{< grid columns="1 2 2 3" >}}
 
 [[item]]
 type = 'card'
@@ -49,4 +49,4 @@ NumPyは、寛容な[BSDライセンス](https://github.com/numpy/numpy/blob/mai
 す.
 '''
 
-{{< /grid1 >}}
+{{< /grid >}}
diff --git a/content/ja/teams/index.md b/content/ja/teams/index.md
index bb60e53e19..cb6acb2abf 100644
--- a/content/ja/teams/index.md
+++ b/content/ja/teams/index.md
@@ -7,27 +7,27 @@ sidebar: false
 
 ### Maintainers
 
-{{< grid1 file="maintainers.toml" columns="2 3 4 5" />}}
+{{< grid file="maintainers.toml" columns="2 3 4 5" />}}
 
 ### Docs team
 
-{{< grid1 file="docs-team.toml" columns="2 3 4 5" />}}
+{{< grid file="docs-team.toml" columns="2 3 4 5" />}}
 
 ### Web team
 
-{{< grid1 file="web-team.toml" columns="2 3 4 5" />}}
+{{< grid file="web-team.toml" columns="2 3 4 5" />}}
 
 ### Triage team
 
-{{< grid1 file="triage-team.toml" columns="2 3 4 5" />}}
+{{< grid file="triage-team.toml" columns="2 3 4 5" />}}
 
 ### Survey team
 
-{{< grid1 file="survey-team.toml" columns="2 3 4 5" />}}
+{{< grid file="survey-team.toml" columns="2 3 4 5" />}}
 
 ### Emeritus maintainers
 
-{{< grid1 file="emeritus-maintainers.toml" columns="2 3 4 5" />}}
+{{< grid file="emeritus-maintainers.toml" columns="2 3 4 5" />}}
 
 # ガバナンス
 
diff --git a/content/pt/_index.md b/content/pt/_index.md
index db817f5321..0a39687659 100644
--- a/content/pt/_index.md
+++ b/content/pt/_index.md
@@ -2,7 +2,7 @@
 title:
 ---
 
-{{< grid1 columns="1 2 2 3" >}}
+{{< grid columns="1 2 2 3" >}}
 
 [[item]]
 type = 'card'
@@ -46,4 +46,4 @@ body = '''
 Distribuido com uma [licença BSD](https://github.com/numpy/numpy/blob/main/LICENSE.txt) liberal, o NumPy é desenvolvido e mantido [publicamente no GitHub](https://github.com/numpy/numpy) por uma [comunidade](/pt/community) vibrante, responsiva, e diversa.
 '''
 
-{{< /grid1 >}}
+{{< /grid >}}
diff --git a/content/pt/teams/index.md b/content/pt/teams/index.md
index cc50b7b787..bdd16ffb8c 100644
--- a/content/pt/teams/index.md
+++ b/content/pt/teams/index.md
@@ -7,27 +7,27 @@ Somos uma equipe internacional com a missão de apoiar comunidades científicas
 
 ### Maintainers
 
-{{< grid1 file="maintainers.toml" columns="2 3 4 5" />}}
+{{< grid file="maintainers.toml" columns="2 3 4 5" />}}
 
 ### Docs team
 
-{{< grid1 file="docs-team.toml" columns="2 3 4 5" />}}
+{{< grid file="docs-team.toml" columns="2 3 4 5" />}}
 
 ### Web team
 
-{{< grid1 file="web-team.toml" columns="2 3 4 5" />}}
+{{< grid file="web-team.toml" columns="2 3 4 5" />}}
 
 ### Triage team
 
-{{< grid1 file="triage-team.toml" columns="2 3 4 5" />}}
+{{< grid file="triage-team.toml" columns="2 3 4 5" />}}
 
 ### Survey team
 
-{{< grid1 file="survey-team.toml" columns="2 3 4 5" />}}
+{{< grid file="survey-team.toml" columns="2 3 4 5" />}}
 
 ### Emeritus maintainers
 
-{{< grid1 file="emeritus-maintainers.toml" columns="2 3 4 5" />}}
+{{< grid file="emeritus-maintainers.toml" columns="2 3 4 5" />}}
 
 # Governança
 

From efe003ae73be4c4688c04304243e952acfb090d0 Mon Sep 17 00:00:00 2001
From: Jarrod Millman <jarrod.millman@gmail.com>
Date: Tue, 19 Mar 2024 06:41:18 -0700
Subject: [PATCH 4/5] Update hugo

---
 netlify.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/netlify.toml b/netlify.toml
index dbdd84984d..d10ad0410e 100644
--- a/netlify.toml
+++ b/netlify.toml
@@ -2,7 +2,7 @@
 # unless otherwise overridden by more specific contexts.
 [build.environment]
   PYTHON_VERSION = "3.8" # netlify currently only support 2.7 and 3.8
-  HUGO_VERSION = "0.123.7"
+  HUGO_VERSION = "0.123.8"
   DART_SASS_VERSION = "1.71.1"
   DART_SASS_URL = "https://github.com/sass/dart-sass/releases/download/"
 

From 8226ea8c4f8fd15aa309b4ecbb41f27925431495 Mon Sep 17 00:00:00 2001
From: Jarrod Millman <jarrod.millman@gmail.com>
Date: Wed, 20 Mar 2024 02:58:44 -0700
Subject: [PATCH 5/5] Update theme (v0.15)

---
 themes/scientific-python-hugo-theme | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/themes/scientific-python-hugo-theme b/themes/scientific-python-hugo-theme
index 3968a94ca9..a4459b0e17 160000
--- a/themes/scientific-python-hugo-theme
+++ b/themes/scientific-python-hugo-theme
@@ -1 +1 @@
-Subproject commit 3968a94ca95c6fe994d34e9b8107ecbb5a5216f5
+Subproject commit a4459b0e1721951874e2bcb9bd0c989ec0d2cfc2