fix /data page MP/WBM element count ptable heatmaps

Author: janosh

data/wbm/readme.md

@@ -120,17 +120,23 @@ The number of stable materials (according to the MP convex hull which is spanned
 
 The WBM test set and even more so the MP training set are heavily oxide dominated. The WBM test set is about 75% larger than the MP training set and also more chemically diverse, containing a higher fraction of transition metals, post-transition metals and metalloids. Our goal in picking such a large diverse test set is future-proofing. Ideally, this data will provide a challenging materials discovery test bed even for large foundational ML models in the future.
 
+### WBM
+
+The WBM test set consists of 256,963 WBM `ComputedStructureEntries`
+
 <slot name="wbm-elements-heatmap">
   <img src="./figs/wbm-elements.svg" alt="Periodic table log heatmap of WBM elements">
 </slot>
 
-The WBM test set consists of 256,963 WBM `ComputedStructureEntries`
+### MP
+
+The MP training set consists of 154,719 `ComputedStructureEntries`
 
 <slot name="mp-elements-heatmap">
   <img src="./figs/mp-elements.svg" alt="Periodic table log heatmap of MP elements">
 </slot>
 
-The MP training set consists of 154,719 `ComputedStructureEntries`
+### MPtrj
 
 <slot name="mp-trj-elements-heatmap" />
 

matbench_discovery/preds.py

@@ -11,7 +11,7 @@
 
 from matbench_discovery import ROOT, STABILITY_THRESHOLD
 from matbench_discovery.data import Files, df_wbm, glob_to_df
-from matbench_discovery.enums import MbdKey
+from matbench_discovery.enums import MbdKey, TestSubset
 from matbench_discovery.metrics import stable_metrics
 from matbench_discovery.plots import plotly_colors, plotly_line_styles, plotly_markers
 
@@ -92,7 +92,7 @@ def load_df_wbm_with_preds(
     models: Sequence[str] = (),
     pbar: bool = True,
     id_col: str = Key.mat_id,
-    subset: pd.Index | Sequence[str] | Literal["uniq_protos"] | None = None,
+    subset: pd.Index | Sequence[str] | Literal[TestSubset.uniq_protos] | None = None,
     max_error_threshold: float | None = 5.0,
     **kwargs: Any,
 ) -> pd.DataFrame:
@@ -177,7 +177,7 @@ def load_df_wbm_with_preds(
         exc.add_note(f"Failed to load {model_name=}")
         raise
 
-    if subset == "uniq_protos":
+    if subset == TestSubset.uniq_protos:
         df_out = df_out.query(Key.uniq_proto)
     elif subset is not None:
         df_out = df_out.loc[subset]

site/src/lib/PtableHeatmap.svelte

@@ -32,7 +32,6 @@
       label_side="top"
       {color_scale}
       tick_labels={5}
-      precision={3}
       range={[0, Math.max(...Object.values(heatmap_values))]}
       style="width: 85%; margin: 0 2em 2em;"
       {...color_bar_props}

site/src/routes/data/+page.svelte

@@ -66,29 +66,24 @@
       <code>composition</code>
       mode maps it to {`{Fe: 2, O: 3}`}.
     </p>
-    <h3>WBM</h3>
     <PtableHeatmap
       heatmap_values={wbm_elem_counts}
       color_scale={color_scale[0]}
-      color_bar_props={{ label: `WBM Element Occurrences` }}
+      color_bar_props={{ label: `WBM element counts by ${count_mode}` }}
       {log}
-      {count_mode}
     />
   </svelte:fragment>
 
   <svelte:fragment slot="mp-elements-heatmap">
-    <h3>MP</h3>
     <PtableHeatmap
       heatmap_values={mp_elem_counts}
       color_scale={color_scale[0]}
-      color_bar_props={{ label: `MP Element Occurrences` }}
+      color_bar_props={{ label: `MP element counts by ${count_mode}` }}
       {log}
-      {count_mode}
     />
   </svelte:fragment>
 
   <svelte:fragment slot="mp-trj-elements-heatmap">
-    <h3>MPtrj</h3>
     <MPtrjElemCountsPtable {count_mode} {log} color_scale={color_scale[0]} />
   </svelte:fragment>
 

site/src/routes/data/mp-element-counts-by-composition.json

@@ -1 +1 @@
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site/src/routes/data/mp-element-counts-by-occurrence.json

@@ -1 +1 @@
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+{"H":10409,"He":8,"Li":21576,"Be":1186,"B":6363,"C":9069,"N":11419,"O":81887,"F":12067,"Ne":1,"Na":12795,"Mg":19007,"Al":7803,"Si":12756,"P":16814,"S":15341,"Cl":6385,"Ar":2,"K":8034,"Ca":8422,"Sc":2686,"Ti":7721,"V":9703,"Cr":6774,"Mn":13966,"Fe":12889,"Co":11045,"Ni":8293,"Cu":9903,"Zn":6905,"Ga":5076,"Ge":5497,"As":3894,"Se":6509,"Br":3011,"Kr":15,"Rb":4599,"Sr":7297,"Y":5012,"Zr":3847,"Nb":5460,"Mo":5411,"Tc":696,"Ru":2453,"Rh":2512,"Pd":2985,"Ag":4109,"Cd":3396,"In":4400,"Sn":6157,"Sb":5784,"Te":4980,"I":3004,"Xe":157,"Cs":4071,"Ba":8338,"La":5599,"Ce":3271,"Pr":2894,"Nd":3234,"Pm":537,"Sm":2902,"Eu":2431,"Gd":1659,"Tb":2225,"Dy":2318,"Ho":2164,"Er":2208,"Tm":1752,"Yb":2121,"Lu":1634,"Hf":2729,"Ta":3407,"W":5121,"Re":1476,"Os":1229,"Ir":2053,"Pt":2444,"Au":2515,"Hg":2341,"Tl":2998,"Pb":3021,"Bi":4936,"Po":null,"At":null,"Rn":null,"Fr":null,"Ra":null,"Ac":304,"Th":1059,"Pa":286,"U":2439,"Np":410,"Pu":463,"Am":null,"Cm":null,"Bk":null,"Cf":null,"Es":null,"Fm":null,"Md":null,"No":null,"Lr":null,"Rf":null,"Db":null,"Sg":null,"Bh":null,"Hs":null,"Mt":null,"Ds":null,"Rg":null,"Cn":null,"Nh":null,"Fl":null,"Mc":null,"Lv":null,"Ts":null,"Og":null}

site/src/routes/data/mptrj/MPtrjElemCountsPtable.svelte

@@ -22,7 +22,6 @@
 <PtableHeatmap
   heatmap_values={mp_trj_elem_counts}
   {log}
-  {count_mode}
-  {...$$props}
-  color_bar_props={{ label: `MPtrj Element Occurrences` }}
+  {...$$restProps}
+  color_bar_props={{ label: `MPtrj element counts by ${count_mode}` }}
 />

site/src/routes/data/wbm-element-counts-by-composition.json

@@ -1 +1 @@
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site/src/routes/data/wbm-element-counts-by-occurrence.json

@@ -1 +1 @@
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