Categories missing with highly partitioned dask dataframes in PointsModel #1009

Closed
#1010
Closed
Categories missing with highly partitioned dask dataframes in PointsModel#1009
#1010

Description

@jonas2612
jonas2612
opened on Oct 30, 2025

  1. Reproduce using the blobs dataset

    importnumpyasnpimportpandasaspdimportdask.dataframeasddfromspatialdata.datasetsimportblobss=blobs()
tbl=next(iter(s.tables.values()))
df=tbl.obs.copy()
n_cats=15cats=pd.Index([f"G{i:04d}"foriinrange(n_cats)], dtype="string")
rng=np.random.default_rng(0)
n=len(df)
k_front=min(10_000, n)
front=rng.choice(cats[:20], size=k_front)
back=rng.choice(cats, size=n-k_front)
df["gene"] =pd.Index(np.concatenate([front, back]), dtype="string")
ddf_many=dd.from_pandas(df, npartitions=217)
c1=ddf_many["gene"].astype(str).astype("category").head(1).cat.categoriesprint("many partitions, categories seen via head(1):", len(c1)) # typically ~20ddf_as_known=ddf_many["gene"].astype("category").cat.as_known()
print("with .cat.as_known(), categories:", len(ddf_as_known._meta.cat.categories))

Describe the bug
The setting of the categories in L888 of scr/spatialdata/models/models.py is not taking into account all categories. If the categories are set per partition, not all categories will be properly registered, leading to an inadvertent filtering of the points dataframe and feature loss.

To Reproduce
See example above on blobs dataset or datasets of the Allen Brain Atlas (https://knowledge.brain-map.org/abcatlas#AQEBSzlKTjIzUDI0S1FDR0s5VTc1QQACSFNZWlBaVzE2NjlVODIxQldZUAADAAQBAAKEUL8fg4IJfwOFLj12hMQ92QQyTlFUSUU3VEFNUDhQUUFITzRQAAWBr6ZKgemsDoGggUeAktXoBgAHAAAFAAYBAQJGUzAwRFhWMFQ5UjFYOUZKNFFFAAN%2BAAAABAAACFZGT0ZZUEZRR1JLVURRVVozRkYACUxWREJKQVc4Qkk1WVNTMVFVQkcACgALAVRMT0tXQ0w5NVJVMDNEOVBFVEcAAjczR1ZURFhERUdFMjdNMlhKTVQAAwEEAQACIzAwMDAwMAADyAEABQEBAiMwMDAwMDAAA8gBAAAAAgEA). As far as we know the error is occuring on all datasets there.

According to https://docs.dask.org/en/stable/dataframe-categoricals.html a solution could be to use

data[c] =data[c].cat.as_known()

to make the categories visible and then for registration

data[c] =data[c].cat.set_categories(data[c]._meta.cat.categories)

Although, the last step could probably be skipped. This implementation is a bit slower than the previous one.

I'll open a pull request with these change later.

Expected behavior
Registration of all categories within points-dataframe.

  • OS: Linux Ubuntu
  • Version 0.5.0

Additional context
spatialdata_io.readers.merscope with datasets from the Allen Brain Atlas result in transcript-dataframe with ~20-30 genes instead of ~500 genes.

Metadata

Metadata

Assignees

No one assigned

    Labels

    No labels
    No labels

    Type

    No type

    Projects

    No projects

      Milestone

      No milestone

      Relationships

      None yet

      Development

      No branches or pull requests

      Issue actions

      , '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
      Categories missing with highly partitioned dask dataframes in PointsModel #1009
      Closed
      #1010
      Closed
      Categories missing with highly partitioned dask dataframes in PointsModel#1009
      #1010
      Description
      @jonas2612
      jonas2612
      opened on Oct 30, 2025
      1. Reproduce using the blobs dataset
        importnumpyasnpimportpandasaspdimportdask.dataframeasddfromspatialdata.datasetsimportblobss=blobs()
tbl=next(iter(s.tables.values()))
df=tbl.obs.copy()
n_cats=15cats=pd.Index([f"G{i:04d}"foriinrange(n_cats)], dtype="string")
rng=np.random.default_rng(0)
n=len(df)
k_front=min(10_000, n)
front=rng.choice(cats[:20], size=k_front)
back=rng.choice(cats, size=n-k_front)
df["gene"] =pd.Index(np.concatenate([front, back]), dtype="string")
ddf_many=dd.from_pandas(df, npartitions=217)
c1=ddf_many["gene"].astype(str).astype("category").head(1).cat.categoriesprint("many partitions, categories seen via head(1):", len(c1)) # typically ~20ddf_as_known=ddf_many["gene"].astype("category").cat.as_known()
print("with .cat.as_known(), categories:", len(ddf_as_known._meta.cat.categories))
      Describe the bug
      
The setting of the categories in L888 of scr/spatialdata/models/models.py is not taking into account all categories. If the categories are set per partition, not all categories will be properly registered, leading to an inadvertent filtering of the points dataframe and feature loss.
      To Reproduce
      
See example above on blobs dataset or datasets of the Allen Brain Atlas (https://knowledge.brain-map.org/abcatlas#AQEBSzlKTjIzUDI0S1FDR0s5VTc1QQACSFNZWlBaVzE2NjlVODIxQldZUAADAAQBAAKEUL8fg4IJfwOFLj12hMQ92QQyTlFUSUU3VEFNUDhQUUFITzRQAAWBr6ZKgemsDoGggUeAktXoBgAHAAAFAAYBAQJGUzAwRFhWMFQ5UjFYOUZKNFFFAAN%2BAAAABAAACFZGT0ZZUEZRR1JLVURRVVozRkYACUxWREJKQVc4Qkk1WVNTMVFVQkcACgALAVRMT0tXQ0w5NVJVMDNEOVBFVEcAAjczR1ZURFhERUdFMjdNMlhKTVQAAwEEAQACIzAwMDAwMAADyAEABQEBAiMwMDAwMDAAA8gBAAAAAgEA). As far as we know the error is occuring on all datasets there.
      According to https://docs.dask.org/en/stable/dataframe-categoricals.html a solution could be to use
      data[c] =data[c].cat.as_known()
      to make the categories visible and then for registration
      data[c] =data[c].cat.set_categories(data[c]._meta.cat.categories)
      Although, the last step could probably be skipped. This implementation is a bit slower than the previous one.
      I'll open a pull request with these change later.
      Expected behavior
      
Registration of all categories within points-dataframe.
      • OS: Linux Ubuntu
      • Version 0.5.0
      Additional context
      
spatialdata_io.readers.merscope with datasets from the Allen Brain Atlas result in transcript-dataframe with ~20-30 genes instead of ~500 genes.
      Metadata
      Metadata
      Assignees
      No one assigned
        Labels
        No labels
        No labels
        Type
        No type
        Projects
        No projects
          Milestone
          No milestone
          Relationships
          None yet
          Development
          No branches or pull requests
          Issue actions
          
, '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");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

          Skip to content
          Categories missing with highly partitioned dask dataframes in PointsModel #1009
          Closed
          #1010
          Closed
          Categories missing with highly partitioned dask dataframes in PointsModel#1009
          #1010
          Description
          @jonas2612
          jonas2612
          opened on Oct 30, 2025
          1. Reproduce using the blobs dataset
            importnumpyasnpimportpandasaspdimportdask.dataframeasddfromspatialdata.datasetsimportblobss=blobs()
tbl=next(iter(s.tables.values()))
df=tbl.obs.copy()
n_cats=15cats=pd.Index([f"G{i:04d}"foriinrange(n_cats)], dtype="string")
rng=np.random.default_rng(0)
n=len(df)
k_front=min(10_000, n)
front=rng.choice(cats[:20], size=k_front)
back=rng.choice(cats, size=n-k_front)
df["gene"] =pd.Index(np.concatenate([front, back]), dtype="string")
ddf_many=dd.from_pandas(df, npartitions=217)
c1=ddf_many["gene"].astype(str).astype("category").head(1).cat.categoriesprint("many partitions, categories seen via head(1):", len(c1)) # typically ~20ddf_as_known=ddf_many["gene"].astype("category").cat.as_known()
print("with .cat.as_known(), categories:", len(ddf_as_known._meta.cat.categories))
          Describe the bug
          
The setting of the categories in L888 of scr/spatialdata/models/models.py is not taking into account all categories. If the categories are set per partition, not all categories will be properly registered, leading to an inadvertent filtering of the points dataframe and feature loss.
          To Reproduce
          
See example above on blobs dataset or datasets of the Allen Brain Atlas (https://knowledge.brain-map.org/abcatlas#AQEBSzlKTjIzUDI0S1FDR0s5VTc1QQACSFNZWlBaVzE2NjlVODIxQldZUAADAAQBAAKEUL8fg4IJfwOFLj12hMQ92QQyTlFUSUU3VEFNUDhQUUFITzRQAAWBr6ZKgemsDoGggUeAktXoBgAHAAAFAAYBAQJGUzAwRFhWMFQ5UjFYOUZKNFFFAAN%2BAAAABAAACFZGT0ZZUEZRR1JLVURRVVozRkYACUxWREJKQVc4Qkk1WVNTMVFVQkcACgALAVRMT0tXQ0w5NVJVMDNEOVBFVEcAAjczR1ZURFhERUdFMjdNMlhKTVQAAwEEAQACIzAwMDAwMAADyAEABQEBAiMwMDAwMDAAA8gBAAAAAgEA). As far as we know the error is occuring on all datasets there.
          According to https://docs.dask.org/en/stable/dataframe-categoricals.html a solution could be to use
          data[c] =data[c].cat.as_known()
          to make the categories visible and then for registration
          data[c] =data[c].cat.set_categories(data[c]._meta.cat.categories)
          Although, the last step could probably be skipped. This implementation is a bit slower than the previous one.
          I'll open a pull request with these change later.
          Expected behavior
          
Registration of all categories within points-dataframe.
          • OS: Linux Ubuntu
          • Version 0.5.0
          Additional context
          
spatialdata_io.readers.merscope with datasets from the Allen Brain Atlas result in transcript-dataframe with ~20-30 genes instead of ~500 genes.
          Metadata
          Metadata
          Assignees
          No one assigned
            Labels
            No labels
            No labels
            Type
            No type
            Projects
            No projects
              Milestone
              No milestone
              Relationships
              None yet
              Development
              No branches or pull requests
              Issue actions
              
, '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
              Categories missing with highly partitioned dask dataframes in PointsModel #1009
              Closed
              #1010
              Closed
              Categories missing with highly partitioned dask dataframes in PointsModel#1009
              #1010
              Description
              @jonas2612
              jonas2612
              opened on Oct 30, 2025
              1. Reproduce using the blobs dataset
                importnumpyasnpimportpandasaspdimportdask.dataframeasddfromspatialdata.datasetsimportblobss=blobs()
tbl=next(iter(s.tables.values()))
df=tbl.obs.copy()
n_cats=15cats=pd.Index([f"G{i:04d}"foriinrange(n_cats)], dtype="string")
rng=np.random.default_rng(0)
n=len(df)
k_front=min(10_000, n)
front=rng.choice(cats[:20], size=k_front)
back=rng.choice(cats, size=n-k_front)
df["gene"] =pd.Index(np.concatenate([front, back]), dtype="string")
ddf_many=dd.from_pandas(df, npartitions=217)
c1=ddf_many["gene"].astype(str).astype("category").head(1).cat.categoriesprint("many partitions, categories seen via head(1):", len(c1)) # typically ~20ddf_as_known=ddf_many["gene"].astype("category").cat.as_known()
print("with .cat.as_known(), categories:", len(ddf_as_known._meta.cat.categories))
              Describe the bug
              
The setting of the categories in L888 of scr/spatialdata/models/models.py is not taking into account all categories. If the categories are set per partition, not all categories will be properly registered, leading to an inadvertent filtering of the points dataframe and feature loss.
              To Reproduce
              
See example above on blobs dataset or datasets of the Allen Brain Atlas (https://knowledge.brain-map.org/abcatlas#AQEBSzlKTjIzUDI0S1FDR0s5VTc1QQACSFNZWlBaVzE2NjlVODIxQldZUAADAAQBAAKEUL8fg4IJfwOFLj12hMQ92QQyTlFUSUU3VEFNUDhQUUFITzRQAAWBr6ZKgemsDoGggUeAktXoBgAHAAAFAAYBAQJGUzAwRFhWMFQ5UjFYOUZKNFFFAAN%2BAAAABAAACFZGT0ZZUEZRR1JLVURRVVozRkYACUxWREJKQVc4Qkk1WVNTMVFVQkcACgALAVRMT0tXQ0w5NVJVMDNEOVBFVEcAAjczR1ZURFhERUdFMjdNMlhKTVQAAwEEAQACIzAwMDAwMAADyAEABQEBAiMwMDAwMDAAA8gBAAAAAgEA). As far as we know the error is occuring on all datasets there.
              According to https://docs.dask.org/en/stable/dataframe-categoricals.html a solution could be to use
              data[c] =data[c].cat.as_known()
              to make the categories visible and then for registration
              data[c] =data[c].cat.set_categories(data[c]._meta.cat.categories)
              Although, the last step could probably be skipped. This implementation is a bit slower than the previous one.
              I'll open a pull request with these change later.
              Expected behavior
              
Registration of all categories within points-dataframe.
              • OS: Linux Ubuntu
              • Version 0.5.0
              Additional context
              
spatialdata_io.readers.merscope with datasets from the Allen Brain Atlas result in transcript-dataframe with ~20-30 genes instead of ~500 genes.
              Metadata
              Metadata
              Assignees
              No one assigned
                Labels
                No labels
                No labels
                Type
                No type
                Projects
                No projects
                  Milestone
                  No milestone
                  Relationships
                  None yet
                  Development
                  No branches or pull requests
                  Issue actions
                  
, '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
                  Categories missing with highly partitioned dask dataframes in PointsModel #1009
                  Closed
                  #1010
                  Closed
                  Categories missing with highly partitioned dask dataframes in PointsModel#1009
                  #1010
                  Description
                  @jonas2612
                  jonas2612
                  opened on Oct 30, 2025
                  1. Reproduce using the blobs dataset
                    importnumpyasnpimportpandasaspdimportdask.dataframeasddfromspatialdata.datasetsimportblobss=blobs()
tbl=next(iter(s.tables.values()))
df=tbl.obs.copy()
n_cats=15cats=pd.Index([f"G{i:04d}"foriinrange(n_cats)], dtype="string")
rng=np.random.default_rng(0)
n=len(df)
k_front=min(10_000, n)
front=rng.choice(cats[:20], size=k_front)
back=rng.choice(cats, size=n-k_front)
df["gene"] =pd.Index(np.concatenate([front, back]), dtype="string")
ddf_many=dd.from_pandas(df, npartitions=217)
c1=ddf_many["gene"].astype(str).astype("category").head(1).cat.categoriesprint("many partitions, categories seen via head(1):", len(c1)) # typically ~20ddf_as_known=ddf_many["gene"].astype("category").cat.as_known()
print("with .cat.as_known(), categories:", len(ddf_as_known._meta.cat.categories))
                  Describe the bug
                  
The setting of the categories in L888 of scr/spatialdata/models/models.py is not taking into account all categories. If the categories are set per partition, not all categories will be properly registered, leading to an inadvertent filtering of the points dataframe and feature loss.
                  To Reproduce
                  
See example above on blobs dataset or datasets of the Allen Brain Atlas (https://knowledge.brain-map.org/abcatlas#AQEBSzlKTjIzUDI0S1FDR0s5VTc1QQACSFNZWlBaVzE2NjlVODIxQldZUAADAAQBAAKEUL8fg4IJfwOFLj12hMQ92QQyTlFUSUU3VEFNUDhQUUFITzRQAAWBr6ZKgemsDoGggUeAktXoBgAHAAAFAAYBAQJGUzAwRFhWMFQ5UjFYOUZKNFFFAAN%2BAAAABAAACFZGT0ZZUEZRR1JLVURRVVozRkYACUxWREJKQVc4Qkk1WVNTMVFVQkcACgALAVRMT0tXQ0w5NVJVMDNEOVBFVEcAAjczR1ZURFhERUdFMjdNMlhKTVQAAwEEAQACIzAwMDAwMAADyAEABQEBAiMwMDAwMDAAA8gBAAAAAgEA). As far as we know the error is occuring on all datasets there.
                  According to https://docs.dask.org/en/stable/dataframe-categoricals.html a solution could be to use
                  data[c] =data[c].cat.as_known()
                  to make the categories visible and then for registration
                  data[c] =data[c].cat.set_categories(data[c]._meta.cat.categories)
                  Although, the last step could probably be skipped. This implementation is a bit slower than the previous one.
                  I'll open a pull request with these change later.
                  Expected behavior
                  
Registration of all categories within points-dataframe.
                  • OS: Linux Ubuntu
                  • Version 0.5.0
                  Additional context
                  
spatialdata_io.readers.merscope with datasets from the Allen Brain Atlas result in transcript-dataframe with ~20-30 genes instead of ~500 genes.
                  Metadata
                  Metadata
                  Assignees
                  No one assigned
                    Labels
                    No labels
                    No labels
                    Type
                    No type
                    Projects
                    No projects
                      Milestone
                      No milestone
                      Relationships
                      None yet
                      Development
                      No branches or pull requests
                      Issue actions
                      
, '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
                      Categories missing with highly partitioned dask dataframes in PointsModel #1009
                      Closed
                      #1010
                      Closed
                      Categories missing with highly partitioned dask dataframes in PointsModel#1009
                      #1010
                      Description
                      @jonas2612
                      jonas2612
                      opened on Oct 30, 2025
                      1. Reproduce using the blobs dataset
                        importnumpyasnpimportpandasaspdimportdask.dataframeasddfromspatialdata.datasetsimportblobss=blobs()
tbl=next(iter(s.tables.values()))
df=tbl.obs.copy()
n_cats=15cats=pd.Index([f"G{i:04d}"foriinrange(n_cats)], dtype="string")
rng=np.random.default_rng(0)
n=len(df)
k_front=min(10_000, n)
front=rng.choice(cats[:20], size=k_front)
back=rng.choice(cats, size=n-k_front)
df["gene"] =pd.Index(np.concatenate([front, back]), dtype="string")
ddf_many=dd.from_pandas(df, npartitions=217)
c1=ddf_many["gene"].astype(str).astype("category").head(1).cat.categoriesprint("many partitions, categories seen via head(1):", len(c1)) # typically ~20ddf_as_known=ddf_many["gene"].astype("category").cat.as_known()
print("with .cat.as_known(), categories:", len(ddf_as_known._meta.cat.categories))
                      Describe the bug
                      
The setting of the categories in L888 of scr/spatialdata/models/models.py is not taking into account all categories. If the categories are set per partition, not all categories will be properly registered, leading to an inadvertent filtering of the points dataframe and feature loss.
                      To Reproduce
                      
See example above on blobs dataset or datasets of the Allen Brain Atlas (https://knowledge.brain-map.org/abcatlas#AQEBSzlKTjIzUDI0S1FDR0s5VTc1QQACSFNZWlBaVzE2NjlVODIxQldZUAADAAQBAAKEUL8fg4IJfwOFLj12hMQ92QQyTlFUSUU3VEFNUDhQUUFITzRQAAWBr6ZKgemsDoGggUeAktXoBgAHAAAFAAYBAQJGUzAwRFhWMFQ5UjFYOUZKNFFFAAN%2BAAAABAAACFZGT0ZZUEZRR1JLVURRVVozRkYACUxWREJKQVc4Qkk1WVNTMVFVQkcACgALAVRMT0tXQ0w5NVJVMDNEOVBFVEcAAjczR1ZURFhERUdFMjdNMlhKTVQAAwEEAQACIzAwMDAwMAADyAEABQEBAiMwMDAwMDAAA8gBAAAAAgEA). As far as we know the error is occuring on all datasets there.
                      According to https://docs.dask.org/en/stable/dataframe-categoricals.html a solution could be to use
                      data[c] =data[c].cat.as_known()
                      to make the categories visible and then for registration
                      data[c] =data[c].cat.set_categories(data[c]._meta.cat.categories)
                      Although, the last step could probably be skipped. This implementation is a bit slower than the previous one.
                      I'll open a pull request with these change later.
                      Expected behavior
                      
Registration of all categories within points-dataframe.
                      • OS: Linux Ubuntu
                      • Version 0.5.0
                      Additional context
                      
spatialdata_io.readers.merscope with datasets from the Allen Brain Atlas result in transcript-dataframe with ~20-30 genes instead of ~500 genes.
                      Metadata
                      Metadata
                      Assignees
                      No one assigned
                        Labels
                        No labels
                        No labels
                        Type
                        No type
                        Projects
                        No projects
                          Milestone
                          No milestone
                          Relationships
                          None yet
                          Development
                          No branches or pull requests
                          Issue actions
                          
, '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 = "*";
var __re = new RegExp('^' + ".*" + '

                          Skip to content
                          Categories missing with highly partitioned dask dataframes in PointsModel #1009
                          Closed
                          #1010
                          Closed
                          Categories missing with highly partitioned dask dataframes in PointsModel#1009
                          #1010
                          Description
                          @jonas2612
                          jonas2612
                          opened on Oct 30, 2025
                          1. Reproduce using the blobs dataset
                            importnumpyasnpimportpandasaspdimportdask.dataframeasddfromspatialdata.datasetsimportblobss=blobs()
tbl=next(iter(s.tables.values()))
df=tbl.obs.copy()
n_cats=15cats=pd.Index([f"G{i:04d}"foriinrange(n_cats)], dtype="string")
rng=np.random.default_rng(0)
n=len(df)
k_front=min(10_000, n)
front=rng.choice(cats[:20], size=k_front)
back=rng.choice(cats, size=n-k_front)
df["gene"] =pd.Index(np.concatenate([front, back]), dtype="string")
ddf_many=dd.from_pandas(df, npartitions=217)
c1=ddf_many["gene"].astype(str).astype("category").head(1).cat.categoriesprint("many partitions, categories seen via head(1):", len(c1)) # typically ~20ddf_as_known=ddf_many["gene"].astype("category").cat.as_known()
print("with .cat.as_known(), categories:", len(ddf_as_known._meta.cat.categories))
                          Describe the bug
                          
The setting of the categories in L888 of scr/spatialdata/models/models.py is not taking into account all categories. If the categories are set per partition, not all categories will be properly registered, leading to an inadvertent filtering of the points dataframe and feature loss.
                          To Reproduce
                          
See example above on blobs dataset or datasets of the Allen Brain Atlas (https://knowledge.brain-map.org/abcatlas#AQEBSzlKTjIzUDI0S1FDR0s5VTc1QQACSFNZWlBaVzE2NjlVODIxQldZUAADAAQBAAKEUL8fg4IJfwOFLj12hMQ92QQyTlFUSUU3VEFNUDhQUUFITzRQAAWBr6ZKgemsDoGggUeAktXoBgAHAAAFAAYBAQJGUzAwRFhWMFQ5UjFYOUZKNFFFAAN%2BAAAABAAACFZGT0ZZUEZRR1JLVURRVVozRkYACUxWREJKQVc4Qkk1WVNTMVFVQkcACgALAVRMT0tXQ0w5NVJVMDNEOVBFVEcAAjczR1ZURFhERUdFMjdNMlhKTVQAAwEEAQACIzAwMDAwMAADyAEABQEBAiMwMDAwMDAAA8gBAAAAAgEA). As far as we know the error is occuring on all datasets there.
                          According to https://docs.dask.org/en/stable/dataframe-categoricals.html a solution could be to use
                          data[c] =data[c].cat.as_known()
                          to make the categories visible and then for registration
                          data[c] =data[c].cat.set_categories(data[c]._meta.cat.categories)
                          Although, the last step could probably be skipped. This implementation is a bit slower than the previous one.
                          I'll open a pull request with these change later.
                          Expected behavior
                          
Registration of all categories within points-dataframe.
                          • OS: Linux Ubuntu
                          • Version 0.5.0
                          Additional context
                          
spatialdata_io.readers.merscope with datasets from the Allen Brain Atlas result in transcript-dataframe with ~20-30 genes instead of ~500 genes.
                          Metadata
                          Metadata
                          Assignees
                          No one assigned
                            Labels
                            No labels
                            No labels
                            Type
                            No type
                            Projects
                            No projects
                              Milestone
                              No milestone
                              Relationships
                              None yet
                              Development
                              No branches or pull requests
                              Issue actions
                              
, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

                              Skip to content
                              Categories missing with highly partitioned dask dataframes in PointsModel #1009
                              Closed
                              #1010
                              Closed
                              Categories missing with highly partitioned dask dataframes in PointsModel#1009
                              #1010
                              Description
                              @jonas2612
                              jonas2612
                              opened on Oct 30, 2025
                              1. Reproduce using the blobs dataset
                                importnumpyasnpimportpandasaspdimportdask.dataframeasddfromspatialdata.datasetsimportblobss=blobs()
tbl=next(iter(s.tables.values()))
df=tbl.obs.copy()
n_cats=15cats=pd.Index([f"G{i:04d}"foriinrange(n_cats)], dtype="string")
rng=np.random.default_rng(0)
n=len(df)
k_front=min(10_000, n)
front=rng.choice(cats[:20], size=k_front)
back=rng.choice(cats, size=n-k_front)
df["gene"] =pd.Index(np.concatenate([front, back]), dtype="string")
ddf_many=dd.from_pandas(df, npartitions=217)
c1=ddf_many["gene"].astype(str).astype("category").head(1).cat.categoriesprint("many partitions, categories seen via head(1):", len(c1)) # typically ~20ddf_as_known=ddf_many["gene"].astype("category").cat.as_known()
print("with .cat.as_known(), categories:", len(ddf_as_known._meta.cat.categories))
                              Describe the bug
                              
The setting of the categories in L888 of scr/spatialdata/models/models.py is not taking into account all categories. If the categories are set per partition, not all categories will be properly registered, leading to an inadvertent filtering of the points dataframe and feature loss.
                              To Reproduce
                              
See example above on blobs dataset or datasets of the Allen Brain Atlas (https://knowledge.brain-map.org/abcatlas#AQEBSzlKTjIzUDI0S1FDR0s5VTc1QQACSFNZWlBaVzE2NjlVODIxQldZUAADAAQBAAKEUL8fg4IJfwOFLj12hMQ92QQyTlFUSUU3VEFNUDhQUUFITzRQAAWBr6ZKgemsDoGggUeAktXoBgAHAAAFAAYBAQJGUzAwRFhWMFQ5UjFYOUZKNFFFAAN%2BAAAABAAACFZGT0ZZUEZRR1JLVURRVVozRkYACUxWREJKQVc4Qkk1WVNTMVFVQkcACgALAVRMT0tXQ0w5NVJVMDNEOVBFVEcAAjczR1ZURFhERUdFMjdNMlhKTVQAAwEEAQACIzAwMDAwMAADyAEABQEBAiMwMDAwMDAAA8gBAAAAAgEA). As far as we know the error is occuring on all datasets there.
                              According to https://docs.dask.org/en/stable/dataframe-categoricals.html a solution could be to use
                              data[c] =data[c].cat.as_known()
                              to make the categories visible and then for registration
                              data[c] =data[c].cat.set_categories(data[c]._meta.cat.categories)
                              Although, the last step could probably be skipped. This implementation is a bit slower than the previous one.
                              I'll open a pull request with these change later.
                              Expected behavior
                              
Registration of all categories within points-dataframe.
                              • OS: Linux Ubuntu
                              • Version 0.5.0
                              Additional context
                              
spatialdata_io.readers.merscope with datasets from the Allen Brain Atlas result in transcript-dataframe with ~20-30 genes instead of ~500 genes.
                              Metadata
                              Metadata
                              Assignees
                              No one assigned
                                Labels
                                No labels
                                No labels
                                Type
                                No type
                                Projects
                                No projects
                                  Milestone
                                  No milestone
                                  Relationships
                                  None yet
                                  Development
                                  No branches or pull requests
                                  Issue actions