Replace grid1 shortcode with grid (#603)

Author: jarrodmillman

content/_index.md

@@ -2,7 +2,7 @@
 title:
 ---
 
-{{< grid1 columns="1 2 2 3" >}}
+{{< grid columns="1 2 2 3" >}}
 
 [[item]]
 type = 'card'
@@ -53,4 +53,4 @@ Our [community efforts]({{% relref "/community" %}}) focus
 on broadening participation and better coordinating volunteer efforts.
 '''
 
-{{< /grid1 >}}
+{{< /grid >}}

content/about/_index.md

@@ -84,15 +84,15 @@ For more information, see our [governance and decision making process]({{% relre
 
 ### Community Managers
 
-{{< grid1 file="community-managers.toml" columns="2 3 4 5" />}}
+{{< grid file="community-managers.toml" columns="2 3 4 5" />}}
 
 ### Community Leaders
 
-{{< grid1 file="community-leaders.toml" columns="2 3 4 5" />}}
+{{< grid file="community-leaders.toml" columns="2 3 4 5" />}}
 
 ### Emeritus Community Leaders
 
-{{< grid1 file="emeritus-community-leaders.toml" columns="2 3 4 5" />}}
+{{< grid file="emeritus-community-leaders.toml" columns="2 3 4 5" />}}
 
 ### Other Teams
 

content/grants/community_and_communications_infrastructure/_index.md

@@ -17,6 +17,6 @@ status: funded
 pdf_url: https://scientific-python.org/doc/scientific-python-community-and-communications-infrastructure-2022.pdf
 ---
 
-{{< grid1 file="team.toml" columns="2 3 4 5" />}}
+{{< grid file="team.toml" columns="2 3 4 5" />}}
 
 Submitted.

content/grants/planning_next_decade/_index.md

@@ -11,7 +11,7 @@ status: funded
 pdf_url: https://scientific-python.org/doc/scientific-python-planning-grant-2020.pdf
 ---
 
-{{< grid1 file="team.toml" columns="2 3 4 5" />}}
+{{< grid file="team.toml" columns="2 3 4 5" />}}
 
 With an extensive and high-quality ecosystem of libraries, scientific Python
 has emerged as the leading platform for data analysis.

content/grants/sparse_arrays/_index.md

@@ -12,7 +12,7 @@ status: funded
 pdf_url: https://scientific-python.org/doc/sparse-arrays-grant-2022.pdf
 ---
 
-{{< grid1 file="team.toml" columns="2 3 4 5" />}}
+{{< grid file="team.toml" columns="2 3 4 5" />}}
 
 Sparse data refers to datasets where a high percentage of the values are zero
 or empty, which occurs where relationships across dimensions don’t exist or are