Using xarray to make transformations less ambiguous #47

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Closed
Using xarray to make transformations less ambiguous#47
Labels
element: points ⊙Points in spacemethod: transforms 🔃

Description

@LucaMarconato
LucaMarconato
opened on Nov 2, 2022

Cost/benefit analysis of using xarray to annotate the dimensions of vectors (see code examples below).
Pro:

  • would make transformations less ambiguous
  • possibility to apply transformations to points of any shapes, for instance (n, 2) and (2, n).

Cons:

  • extra verbosity for the use

Current approach: initially I am using numpy arrays for the vectors describing points and performing a shape check (e.g. 2D translations can't be applied to a set of points stored as a numpy array of shape (2, n), but only with a shape (n, 2) (excluding edge cases like n=2).
Later we can discuss the preferred approach.

Code examples from @ivirshup of using xarray.DataArray to annotate the dimensions of vectors.

import xarray as xr
da = xr.DataArray([1,2,3], coords={"dim": ["x", "y", "z"]})
da.sel({"dim": "x"})
<xarray.DataArray ()>
array(1)
Coordinates:
dim <U1 'x'

import xarray as xr, numpy as np
N_POINTS = 10
points = xr.DataArray(
np.random.random((N_POINTS, 3)),
coords={
"points": range(N_POINTS),
"dim": ["x", "y", "z"],
},
)
points
<xarray.DataArray (points: 10, dim: 3)>
array([[0.97379558, 0.29238395, 0.37392129],
[0.62459118, 0.13214354, 0.0514482 ],
[0.41742495, 0.59828447, 0.79762354],
[0.87583248, 0.71631053, 0.30032732],
[0.93012066, 0.73074886, 0.46370001],
[0.74505332, 0.84503157, 0.52606046],
[0.85789939, 0.44050558, 0.23522551],
[0.35954281, 0.11675383, 0.23321456],
[0.99866969, 0.83124205, 0.55161158],
[0.25951685, 0.09205387, 0.00736066]])
Coordinates:
* points (points) int64 0 1 2 3 4 5 6 7 8 9
* dim (dim) <U1 'x' 'y' 'z'

translation = xr.DataArray([0, 1, 0], coords={"dim": ["x", "y", "z"]})
points + translation

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      , 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
       blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

      Skip to content
      Using xarray to make transformations less ambiguous #47
      Closed
      Closed
      Using xarray to make transformations less ambiguous#47
      Labels
      element: points ⊙Points in spacemethod: transforms 🔃
      Description
      @LucaMarconato
      LucaMarconato
      opened on Nov 2, 2022
      Cost/benefit analysis of using xarray to annotate the dimensions of vectors (see code examples below).
      
Pro:
      • would make transformations less ambiguous
      • possibility to apply transformations to points of any shapes, for instance (n, 2) and (2, n).
      Cons:
      • extra verbosity for the use
      Current approach: initially I am using numpy arrays for the vectors describing points and performing a shape check (e.g. 2D translations can't be applied to a set of points stored as a numpy array of shape (2, n), but only with a shape (n, 2) (excluding edge cases like n=2).
      
Later we can discuss the preferred approach.
      Code examples from @ivirshup of using xarray.DataArray to annotate the dimensions of vectors.
      import xarray as xr
da = xr.DataArray([1,2,3], coords={"dim": ["x", "y", "z"]})
da.sel({"dim": "x"})
<xarray.DataArray ()>
array(1)
Coordinates:
dim <U1 'x'

      import xarray as xr, numpy as np
N_POINTS = 10
points = xr.DataArray(
np.random.random((N_POINTS, 3)),
coords={
"points": range(N_POINTS),
"dim": ["x", "y", "z"],
},
)
points
<xarray.DataArray (points: 10, dim: 3)>
array([[0.97379558, 0.29238395, 0.37392129],
[0.62459118, 0.13214354, 0.0514482 ],
[0.41742495, 0.59828447, 0.79762354],
[0.87583248, 0.71631053, 0.30032732],
[0.93012066, 0.73074886, 0.46370001],
[0.74505332, 0.84503157, 0.52606046],
[0.85789939, 0.44050558, 0.23522551],
[0.35954281, 0.11675383, 0.23321456],
[0.99866969, 0.83124205, 0.55161158],
[0.25951685, 0.09205387, 0.00736066]])
Coordinates:
* points (points) int64 0 1 2 3 4 5 6 7 8 9
* dim (dim) <U1 'x' 'y' 'z'

      translation = xr.DataArray([0, 1, 0], coords={"dim": ["x", "y", "z"]})
points + translation

      Activity
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

          Skip to content
          Using xarray to make transformations less ambiguous #47
          Closed
          Closed
          Using xarray to make transformations less ambiguous#47
          Labels
          element: points ⊙Points in spacemethod: transforms 🔃
          Description
          @LucaMarconato
          LucaMarconato
          opened on Nov 2, 2022
          Cost/benefit analysis of using xarray to annotate the dimensions of vectors (see code examples below).
          
Pro:
          • would make transformations less ambiguous
          • possibility to apply transformations to points of any shapes, for instance (n, 2) and (2, n).
          Cons:
          • extra verbosity for the use
          Current approach: initially I am using numpy arrays for the vectors describing points and performing a shape check (e.g. 2D translations can't be applied to a set of points stored as a numpy array of shape (2, n), but only with a shape (n, 2) (excluding edge cases like n=2).
          
Later we can discuss the preferred approach.
          Code examples from @ivirshup of using xarray.DataArray to annotate the dimensions of vectors.
          import xarray as xr
da = xr.DataArray([1,2,3], coords={"dim": ["x", "y", "z"]})
da.sel({"dim": "x"})
<xarray.DataArray ()>
array(1)
Coordinates:
dim <U1 'x'

          import xarray as xr, numpy as np
N_POINTS = 10
points = xr.DataArray(
np.random.random((N_POINTS, 3)),
coords={
"points": range(N_POINTS),
"dim": ["x", "y", "z"],
},
)
points
<xarray.DataArray (points: 10, dim: 3)>
array([[0.97379558, 0.29238395, 0.37392129],
[0.62459118, 0.13214354, 0.0514482 ],
[0.41742495, 0.59828447, 0.79762354],
[0.87583248, 0.71631053, 0.30032732],
[0.93012066, 0.73074886, 0.46370001],
[0.74505332, 0.84503157, 0.52606046],
[0.85789939, 0.44050558, 0.23522551],
[0.35954281, 0.11675383, 0.23321456],
[0.99866969, 0.83124205, 0.55161158],
[0.25951685, 0.09205387, 0.00736066]])
Coordinates:
* points (points) int64 0 1 2 3 4 5 6 7 8 9
* dim (dim) <U1 'x' 'y' 'z'

          translation = xr.DataArray([0, 1, 0], coords={"dim": ["x", "y", "z"]})
points + translation

          Activity
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            element: points ⊙Points in spacemethod: transforms 🔃
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

              Skip to content
              Using xarray to make transformations less ambiguous #47
              Closed
              Closed
              Using xarray to make transformations less ambiguous#47
              Labels
              element: points ⊙Points in spacemethod: transforms 🔃
              Description
              @LucaMarconato
              LucaMarconato
              opened on Nov 2, 2022
              Cost/benefit analysis of using xarray to annotate the dimensions of vectors (see code examples below).
              
Pro:
              • would make transformations less ambiguous
              • possibility to apply transformations to points of any shapes, for instance (n, 2) and (2, n).
              Cons:
              • extra verbosity for the use
              Current approach: initially I am using numpy arrays for the vectors describing points and performing a shape check (e.g. 2D translations can't be applied to a set of points stored as a numpy array of shape (2, n), but only with a shape (n, 2) (excluding edge cases like n=2).
              
Later we can discuss the preferred approach.
              Code examples from @ivirshup of using xarray.DataArray to annotate the dimensions of vectors.
              import xarray as xr
da = xr.DataArray([1,2,3], coords={"dim": ["x", "y", "z"]})
da.sel({"dim": "x"})
<xarray.DataArray ()>
array(1)
Coordinates:
dim <U1 'x'

              import xarray as xr, numpy as np
N_POINTS = 10
points = xr.DataArray(
np.random.random((N_POINTS, 3)),
coords={
"points": range(N_POINTS),
"dim": ["x", "y", "z"],
},
)
points
<xarray.DataArray (points: 10, dim: 3)>
array([[0.97379558, 0.29238395, 0.37392129],
[0.62459118, 0.13214354, 0.0514482 ],
[0.41742495, 0.59828447, 0.79762354],
[0.87583248, 0.71631053, 0.30032732],
[0.93012066, 0.73074886, 0.46370001],
[0.74505332, 0.84503157, 0.52606046],
[0.85789939, 0.44050558, 0.23522551],
[0.35954281, 0.11675383, 0.23321456],
[0.99866969, 0.83124205, 0.55161158],
[0.25951685, 0.09205387, 0.00736066]])
Coordinates:
* points (points) int64 0 1 2 3 4 5 6 7 8 9
* dim (dim) <U1 'x' 'y' 'z'

              translation = xr.DataArray([0, 1, 0], coords={"dim": ["x", "y", "z"]})
points + translation

              Activity
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                element: points ⊙Points in spacemethod: transforms 🔃
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

                  Skip to content
                  Using xarray to make transformations less ambiguous #47
                  Closed
                  Closed
                  Using xarray to make transformations less ambiguous#47
                  Labels
                  element: points ⊙Points in spacemethod: transforms 🔃
                  Description
                  @LucaMarconato
                  LucaMarconato
                  opened on Nov 2, 2022
                  Cost/benefit analysis of using xarray to annotate the dimensions of vectors (see code examples below).
                  
Pro:
                  • would make transformations less ambiguous
                  • possibility to apply transformations to points of any shapes, for instance (n, 2) and (2, n).
                  Cons:
                  • extra verbosity for the use
                  Current approach: initially I am using numpy arrays for the vectors describing points and performing a shape check (e.g. 2D translations can't be applied to a set of points stored as a numpy array of shape (2, n), but only with a shape (n, 2) (excluding edge cases like n=2).
                  
Later we can discuss the preferred approach.
                  Code examples from @ivirshup of using xarray.DataArray to annotate the dimensions of vectors.
                  import xarray as xr
da = xr.DataArray([1,2,3], coords={"dim": ["x", "y", "z"]})
da.sel({"dim": "x"})
<xarray.DataArray ()>
array(1)
Coordinates:
dim <U1 'x'

                  import xarray as xr, numpy as np
N_POINTS = 10
points = xr.DataArray(
np.random.random((N_POINTS, 3)),
coords={
"points": range(N_POINTS),
"dim": ["x", "y", "z"],
},
)
points
<xarray.DataArray (points: 10, dim: 3)>
array([[0.97379558, 0.29238395, 0.37392129],
[0.62459118, 0.13214354, 0.0514482 ],
[0.41742495, 0.59828447, 0.79762354],
[0.87583248, 0.71631053, 0.30032732],
[0.93012066, 0.73074886, 0.46370001],
[0.74505332, 0.84503157, 0.52606046],
[0.85789939, 0.44050558, 0.23522551],
[0.35954281, 0.11675383, 0.23321456],
[0.99866969, 0.83124205, 0.55161158],
[0.25951685, 0.09205387, 0.00736066]])
Coordinates:
* points (points) int64 0 1 2 3 4 5 6 7 8 9
* dim (dim) <U1 'x' 'y' 'z'

                  translation = xr.DataArray([0, 1, 0], coords={"dim": ["x", "y", "z"]})
points + translation

                  Activity
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                    element: points ⊙Points in spacemethod: transforms 🔃
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

                      Skip to content
                      Using xarray to make transformations less ambiguous #47
                      Closed
                      Closed
                      Using xarray to make transformations less ambiguous#47
                      Labels
                      element: points ⊙Points in spacemethod: transforms 🔃
                      Description
                      @LucaMarconato
                      LucaMarconato
                      opened on Nov 2, 2022
                      Cost/benefit analysis of using xarray to annotate the dimensions of vectors (see code examples below).
                      
Pro:
                      • would make transformations less ambiguous
                      • possibility to apply transformations to points of any shapes, for instance (n, 2) and (2, n).
                      Cons:
                      • extra verbosity for the use
                      Current approach: initially I am using numpy arrays for the vectors describing points and performing a shape check (e.g. 2D translations can't be applied to a set of points stored as a numpy array of shape (2, n), but only with a shape (n, 2) (excluding edge cases like n=2).
                      
Later we can discuss the preferred approach.
                      Code examples from @ivirshup of using xarray.DataArray to annotate the dimensions of vectors.
                      import xarray as xr
da = xr.DataArray([1,2,3], coords={"dim": ["x", "y", "z"]})
da.sel({"dim": "x"})
<xarray.DataArray ()>
array(1)
Coordinates:
dim <U1 'x'

                      import xarray as xr, numpy as np
N_POINTS = 10
points = xr.DataArray(
np.random.random((N_POINTS, 3)),
coords={
"points": range(N_POINTS),
"dim": ["x", "y", "z"],
},
)
points
<xarray.DataArray (points: 10, dim: 3)>
array([[0.97379558, 0.29238395, 0.37392129],
[0.62459118, 0.13214354, 0.0514482 ],
[0.41742495, 0.59828447, 0.79762354],
[0.87583248, 0.71631053, 0.30032732],
[0.93012066, 0.73074886, 0.46370001],
[0.74505332, 0.84503157, 0.52606046],
[0.85789939, 0.44050558, 0.23522551],
[0.35954281, 0.11675383, 0.23321456],
[0.99866969, 0.83124205, 0.55161158],
[0.25951685, 0.09205387, 0.00736066]])
Coordinates:
* points (points) int64 0 1 2 3 4 5 6 7 8 9
* dim (dim) <U1 'x' 'y' 'z'

                      translation = xr.DataArray([0, 1, 0], coords={"dim": ["x", "y", "z"]})
points + translation

                      Activity
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                      Assignees
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                        Labels
                        element: points ⊙Points in spacemethod: transforms 🔃
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
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                          Using xarray to make transformations less ambiguous #47
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                          Using xarray to make transformations less ambiguous#47
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                          element: points ⊙Points in spacemethod: transforms 🔃
                          Description
                          @LucaMarconato
                          LucaMarconato
                          opened on Nov 2, 2022
                          Cost/benefit analysis of using xarray to annotate the dimensions of vectors (see code examples below).
                          
Pro:
                          • would make transformations less ambiguous
                          • possibility to apply transformations to points of any shapes, for instance (n, 2) and (2, n).
                          Cons:
                          • extra verbosity for the use
                          Current approach: initially I am using numpy arrays for the vectors describing points and performing a shape check (e.g. 2D translations can't be applied to a set of points stored as a numpy array of shape (2, n), but only with a shape (n, 2) (excluding edge cases like n=2).
                          
Later we can discuss the preferred approach.
                          Code examples from @ivirshup of using xarray.DataArray to annotate the dimensions of vectors.
                          import xarray as xr
da = xr.DataArray([1,2,3], coords={"dim": ["x", "y", "z"]})
da.sel({"dim": "x"})
<xarray.DataArray ()>
array(1)
Coordinates:
dim <U1 'x'

                          import xarray as xr, numpy as np
N_POINTS = 10
points = xr.DataArray(
np.random.random((N_POINTS, 3)),
coords={
"points": range(N_POINTS),
"dim": ["x", "y", "z"],
},
)
points
<xarray.DataArray (points: 10, dim: 3)>
array([[0.97379558, 0.29238395, 0.37392129],
[0.62459118, 0.13214354, 0.0514482 ],
[0.41742495, 0.59828447, 0.79762354],
[0.87583248, 0.71631053, 0.30032732],
[0.93012066, 0.73074886, 0.46370001],
[0.74505332, 0.84503157, 0.52606046],
[0.85789939, 0.44050558, 0.23522551],
[0.35954281, 0.11675383, 0.23321456],
[0.99866969, 0.83124205, 0.55161158],
[0.25951685, 0.09205387, 0.00736066]])
Coordinates:
* points (points) int64 0 1 2 3 4 5 6 7 8 9
* dim (dim) <U1 'x' 'y' 'z'

                          translation = xr.DataArray([0, 1, 0], coords={"dim": ["x", "y", "z"]})
points + translation

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                              Skip to content
                              Using xarray to make transformations less ambiguous #47
                              Closed
                              Closed
                              Using xarray to make transformations less ambiguous#47
                              Labels
                              element: points ⊙Points in spacemethod: transforms 🔃
                              Description
                              @LucaMarconato
                              LucaMarconato
                              opened on Nov 2, 2022
                              Cost/benefit analysis of using xarray to annotate the dimensions of vectors (see code examples below).
                              
Pro:
                              • would make transformations less ambiguous
                              • possibility to apply transformations to points of any shapes, for instance (n, 2) and (2, n).
                              Cons:
                              • extra verbosity for the use
                              Current approach: initially I am using numpy arrays for the vectors describing points and performing a shape check (e.g. 2D translations can't be applied to a set of points stored as a numpy array of shape (2, n), but only with a shape (n, 2) (excluding edge cases like n=2).
                              
Later we can discuss the preferred approach.
                              Code examples from @ivirshup of using xarray.DataArray to annotate the dimensions of vectors.
                              import xarray as xr
da = xr.DataArray([1,2,3], coords={"dim": ["x", "y", "z"]})
da.sel({"dim": "x"})
<xarray.DataArray ()>
array(1)
Coordinates:
dim <U1 'x'

                              import xarray as xr, numpy as np
N_POINTS = 10
points = xr.DataArray(
np.random.random((N_POINTS, 3)),
coords={
"points": range(N_POINTS),
"dim": ["x", "y", "z"],
},
)
points
<xarray.DataArray (points: 10, dim: 3)>
array([[0.97379558, 0.29238395, 0.37392129],
[0.62459118, 0.13214354, 0.0514482 ],
[0.41742495, 0.59828447, 0.79762354],
[0.87583248, 0.71631053, 0.30032732],
[0.93012066, 0.73074886, 0.46370001],
[0.74505332, 0.84503157, 0.52606046],
[0.85789939, 0.44050558, 0.23522551],
[0.35954281, 0.11675383, 0.23321456],
[0.99866969, 0.83124205, 0.55161158],
[0.25951685, 0.09205387, 0.00736066]])
Coordinates:
* points (points) int64 0 1 2 3 4 5 6 7 8 9
* dim (dim) <U1 'x' 'y' 'z'

                              translation = xr.DataArray([0, 1, 0], coords={"dim": ["x", "y", "z"]})
points + translation

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                              Metadata
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                                Labels
                                element: points ⊙Points in spacemethod: transforms 🔃
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