fix errors in eda_wbm.py

GitHub repo language stats ignore generated svelte figs nested under site/figs/**/*
add n_sites col to 2023-01-10-mp-energies.csv.gz and rename formula_pretty col to formula

Author: janosh

.gitattributes

@@ -1,3 +1,3 @@
 # exclude generated plot files when calculating repo language statistics on GitHub
-**/figs/* linguist-generated
+**/figs/**/* linguist-generated
 data/**/*.svelte linguist-generated

data/mp/2023-01-10-mp-energies.csv.gz

@@ -36,13 +36,8 @@
     f"{data_page}/mp-element-counts-by-occurrence.json", typ="series"
 )
 df_mp = pd.read_csv(DataFiles.mp_energies.path, na_filter=False)
-# TODO get the real formula from the Composition rather than rename.
-df_mp = df_mp.rename(columns={"formula_pretty": Key.formula, "nsites": Key.n_sites})
-df_mp.loc[
-    df_mp[Key.mat_id].isin(["mp-1080032", "mp-1179882", "mp-1009221"]), Key.formula
-] = "NaN"
-assert len(df_mp[df_mp[Key.formula].isna() | (df_mp[Key.formula] == "")]) == 0
 df_mp = df_mp.set_index(Key.mat_id)
+assert sum(df_mp[Key.formula].isna() | (df_mp[Key.formula] == "")) == 0
 
 
 # %% --- load preprocessed MPtrj summary data if available ---

data/mp/eda_mp_trj.py

@@ -78,7 +78,7 @@
 assert (spg_nums.sort_index() == df_spg["number"].sort_index()).all()
 
 df_mp.to_csv(DataFiles.mp_energies.path)
-# df = pd.read_csv(DataFiles.mp_energies.path, na_filter=False).set_index(Key.mat_id)
+# df_mp = pd.read_csv(DataFiles.mp_energies.path, na_filter=False).set_index(Key.mat_id)
 
 
 # %% reproduce fig. 1b from https://arxiv.org/abs/2001.10591 (as data consistency check)

data/mp/get_mp_energies.py

@@ -41,7 +41,13 @@
 # %%
 ids_path = f"{module_dir}/2023-03-15-mp-task-ids.csv.bz2"
 fields = (
-    f"{Key.task_id} formula_pretty run_type nsites task_type tags completed_at".split()
+    Key.task_id,
+    "formula_pretty",
+    "run_type",
+    "nsites",
+    Key.task_type,
+    "tags",
+    "completed_at",
 )
 
 if os.path.isfile(ids_path):

data/mp/get_mp_traj.py

@@ -12,14 +12,14 @@
 from matplotlib.colors import SymLogNorm
 from pymatgen.core import Composition, Structure
 from pymatviz.enums import Key
-from pymatviz.utils import PLOTLY, si_fmt, si_fmt_int
+from pymatviz.utils import PLOTLY, si_fmt_int
 
 from matbench_discovery import PDF_FIGS, ROOT, SITE_FIGS, STABILITY_THRESHOLD
 from matbench_discovery import plots as plots
 from matbench_discovery.data import DataFiles, df_wbm
 from matbench_discovery.energy import mp_elem_ref_entries
 from matbench_discovery.enums import MbdKey
-from matbench_discovery.preds import Model, df_each_err
+from matbench_discovery.preds import df_each_err
 
 __author__ = "Janosh Riebesell"
 __date__ = "2023-03-30"
@@ -29,21 +29,21 @@
 
 
 # %% load MP training set
-df_mp = pd.read_csv(DataFiles.mp_energies.path, na_filter=False, na_values=[])
-
-df_mp[df_mp[Key.formula].isna()]
+df_mp = pd.read_csv(DataFiles.mp_energies.path, na_filter=False)
+df_mp = df_mp.set_index(Key.mat_id)
 
 
 # %%
-wbm_occu_counts = pmv.count_elements(
-    df_wbm[Key.formula], count_mode="occurrence"
-).astype(int)
+wbm_occu_counts = pmv.count_elements(df_wbm[Key.formula], count_mode="occurrence")
+wbm_occu_counts = wbm_occu_counts.dropna().astype(int)
 wbm_comp_counts = pmv.count_elements(df_wbm[Key.formula], count_mode="composition")
+wbm_comp_counts = wbm_comp_counts.dropna().astype(int)
+
+mp_occu_counts = pmv.count_elements(df_mp[Key.formula], count_mode="occurrence")
+mp_occu_counts = mp_occu_counts.dropna().astype(int)
 
-mp_occu_counts = pmv.count_elements(df_mp[Key.formula], count_mode="occurrence").astype(
-    int
-)
 mp_comp_counts = pmv.count_elements(df_mp[Key.formula], count_mode="composition")
+mp_comp_counts = mp_comp_counts.dropna().astype(int)
 
 all_counts = (
     ("wbm", "occurrence", wbm_occu_counts),
@@ -74,23 +74,20 @@
     elem_counts.to_json(f"{data_page}/{filename}.json")
 
     title = f"Number of {dataset.upper()} structures containing each element"
-    fig = pmv.ptable_heatmap_plotly(elem_counts, font_size=10)
+    fig = pmv.ptable_heatmap_plotly(elem_counts, font_size=10, fmt=",.0f")
     fig.layout.title.update(text=title, x=0.4, y=0.9)
     fig.show()
 
-    ax_mp_cnt = pmv.ptable_heatmap(  # matplotlib version looks better for SI
+    # saving matplotlib heatmap to PDF mostly for historical reasons, could also use
+    # pmv.ptable_heatmap_plotly
+    ax_elem_counts = pmv.ptable_heatmap(
         elem_counts,
-        fmt=lambda x, _: si_fmt(x, ".0f"),
-        cbar_fmt=lambda x, _: si_fmt(x, ".0f"),
-        zero_color="#efefef",
-        label_font_size=17,
-        value_font_size=14,
         cbar_title=f"{dataset.upper()} Element Count",
         log=(log := SymLogNorm(linthresh=100)),
     )
     if log:
         filename += "-symlog" if isinstance(log, SymLogNorm) else "-log"
-    pmv.save_fig(ax_mp_cnt, f"{PDF_FIGS}/{filename}.pdf")
+    pmv.save_fig(ax_elem_counts, f"{PDF_FIGS}/{filename}.pdf")
 
 
 # %% ratio of WBM to MP counts
@@ -264,31 +261,20 @@
 df_2d_tsne = df_2d_tsne.set_index(Key.mat_id)
 
 df_3d_tsne = pd.read_csv(f"{module_dir}/tsne/one-hot-112-composition-3d.csv.gz")
-model = Model.wrenformer.label
-df_3d_tsne = pd.read_csv(
-    f"{module_dir}/tsne/one-hot-112-composition+{model}-each-err-3d-metric=eucl.csv.gz"
-)
-df_3d_tsne = df_3d_tsne.set_index(Key.mat_id)
 
 df_wbm[list(df_2d_tsne)] = df_2d_tsne
 df_wbm[list(df_3d_tsne)] = df_3d_tsne
 df_wbm[list(df_each_err.add_suffix(" abs EACH error"))] = df_each_err.abs()
 
 
-# %%
-color_col = f"{model} abs EACH error"
-clr_range_max = df_wbm[color_col].mean() + df_wbm[color_col].std()
-
-
 # %%
 fig = px.scatter(
     df_wbm,
     x="2d t-SNE 1",
     y="2d t-SNE 2",
-    color=color_col,
+    color="step",
     hover_name=Key.mat_id,
     hover_data=(Key.formula, MbdKey.each_true),
-    range_color=(0, clr_range_max),
 )
 fig.show()
 
@@ -299,23 +285,22 @@
     x="3d t-SNE 1",
     y="3d t-SNE 2",
     z="3d t-SNE 3",
-    color=color_col,
-    custom_data=[Key.mat_id, Key.formula, MbdKey.each_true, color_col],
-    range_color=(0, clr_range_max),
+    color="step",
+    custom_data=[Key.mat_id, Key.formula, MbdKey.each_true],
 )
 fig.data[0].hovertemplate = (
     "<b>material_id: %{customdata[0]}</b><br><br>"
     "t-SNE: (%{x:.2f}, %{y:.2f}, %{z:.2f})<br>"
     "Formula: %{customdata[1]}<br>"
     "E<sub>above hull</sub>: %{customdata[2]:.2f}<br>"
-    f"{color_col}: %{{customdata[3]:.2f}}<br>"
+    "WBM step: %{customdata[3]:.2f}<br>"
 )
 fig.show()
 
 
 # %%
 df_wbm[Key.spg_num] = df_wbm[MbdKey.init_wyckoff].str.split("_").str[2].astype(int)
-df_mp[Key.spg_num] = df_mp[Key.wyckoff].str.split("_").str[2].astype(int)
+df_mp[Key.spg_num] = df_mp[f"{Key.wyckoff}_spglib"].str.split("_").str[2].astype(int)
 
 
 # %%
@@ -371,16 +356,15 @@
 
 # %% find large structures that changed symmetry during relaxation
 df_sym_change = (
-    df_wbm.query(f"{MbdKey.init_wyckoff} != {Key.wyckoff}")
+    df_wbm.query(f"{MbdKey.init_wyckoff} != {Key.wyckoff}_spglib")
     .filter(regex="wyckoff|sites")
     .nlargest(10, Key.n_sites)
 )
 
 
 # %%
-df_wbm_structs = pd.read_json(DataFiles.wbm_cses_plus_init_structs.path).set_index(
-    Key.mat_id
-)
+df_wbm_structs = pd.read_json(DataFiles.wbm_cses_plus_init_structs.path)
+df_wbm_structs = df_wbm_structs.set_index(Key.mat_id)
 
 
 # %%

data/wbm/eda_wbm.py

@@ -287,7 +287,7 @@ class DataFiles(Files):
     )
     mp_energies = (
         "mp/2023-01-10-mp-energies.csv.gz",
-        "https://figshare.com/ndownloader/files/41296875",
+        "https://figshare.com/ndownloader/files/49083124",
     )
     mp_patched_phase_diagram = (
         "mp/2023-02-07-ppd-mp.pkl.gz",

matbench_discovery/data.py

@@ -13,7 +13,7 @@
       "2023-02-07-mp-elemental-reference-entries.json.gz"
     ],
     "mp_energies": [
-      "https://figshare.com/ndownloader/files/41296875",
+      "https://figshare.com/ndownloader/files/49083124",
       "2023-01-10-mp-energies.csv.gz"
     ],
     "mp_patched_phase_diagram": [

matbench_discovery/figshare/1.0.0.json

@@ -1 +1 @@
-{"H":82651.0,"He":8.0,"Li":68094.0,"Be":3368.0,"B":24452.0,"C":32142.0,"N":38570.0,"O":875788.0,"F":69127.0,"Ne":1.0,"Na":43175.0,"Mg":94342.0,"Al":27435.0,"Si":42222.0,"P":55151.0,"S":67290.0,"Cl":27129.0,"Ar":2.0,"K":16561.0,"Ca":18724.0,"Sc":5730.0,"Ti":19468.0,"V":23426.0,"Cr":14274.0,"Mn":37674.0,"Fe":40513.0,"Co":31098.0,"Ni":24960.0,"Cu":23482.0,"Zn":16243.0,"Ga":13866.0,"Ge":15316.0,"As":8986.0,"Se":25339.0,"Br":12274.0,"Kr":26.0,"Rb":9262.0,"Sr":16720.0,"Y":9753.0,"Zr":9121.0,"Nb":13893.0,"Mo":14207.0,"Tc":1118.0,"Ru":5050.0,"Rh":6318.0,"Pd":7198.0,"Ag":8507.0,"Cd":6869.0,"In":9629.0,"Sn":13562.0,"Sb":12381.0,"Te":14257.0,"I":11851.0,"Xe":302.0,"Cs":7352.0,"Ba":18855.0,"La":13513.0,"Ce":5921.0,"Pr":6202.0,"Nd":6977.0,"Pm":823.0,"Sm":5912.0,"Eu":3683.0,"Gd":2770.0,"Tb":4604.0,"Dy":4643.0,"Ho":4535.0,"Er":4768.0,"Tm":3449.0,"Yb":4446.0,"Lu":3131.0,"Hf":5471.0,"Ta":8541.0,"W":10159.0,"Re":3042.0,"Os":2191.0,"Ir":4105.0,"Pt":5177.0,"Au":5088.0,"Hg":5547.0,"Tl":5665.0,"Pb":6927.0,"Bi":12069.0,"Po":0.0,"At":0.0,"Rn":0.0,"Fr":0.0,"Ra":0.0,"Ac":495.0,"Th":1920.0,"Pa":374.0,"U":4524.0,"Np":574.0,"Pu":735.0,"Am":0.0,"Cm":0.0,"Bk":0.0,"Cf":0.0,"Es":0.0,"Fm":0.0,"Md":0.0,"No":0.0,"Lr":0.0,"Rf":0.0,"Db":0.0,"Sg":0.0,"Bh":0.0,"Hs":0.0,"Mt":0.0,"Ds":0.0,"Rg":0.0,"Cn":0.0,"Nh":0.0,"Fl":0.0,"Mc":0.0,"Lv":0.0,"Ts":0.0,"Og":0.0}
+{"H":82651,"He":8,"Li":68094,"Be":3368,"B":24452,"C":32142,"N":38573,"O":875788,"F":69127,"Ne":1,"Na":43178,"Mg":94342,"Al":27435,"Si":42222,"P":55151,"S":67290,"Cl":27129,"Ar":2,"K":16561,"Ca":18724,"Sc":5730,"Ti":19468,"V":23426,"Cr":14274,"Mn":37674,"Fe":40513,"Co":31098,"Ni":24960,"Cu":23482,"Zn":16243,"Ga":13866,"Ge":15316,"As":8986,"Se":25339,"Br":12274,"Kr":26,"Rb":9262,"Sr":16720,"Y":9753,"Zr":9121,"Nb":13893,"Mo":14207,"Tc":1118,"Ru":5050,"Rh":6318,"Pd":7198,"Ag":8507,"Cd":6869,"In":9629,"Sn":13562,"Sb":12381,"Te":14257,"I":11851,"Xe":302,"Cs":7352,"Ba":18855,"La":13513,"Ce":5921,"Pr":6202,"Nd":6977,"Pm":823,"Sm":5912,"Eu":3683,"Gd":2770,"Tb":4604,"Dy":4643,"Ho":4535,"Er":4768,"Tm":3449,"Yb":4446,"Lu":3131,"Hf":5471,"Ta":8541,"W":10159,"Re":3042,"Os":2191,"Ir":4105,"Pt":5177,"Au":5088,"Hg":5547,"Tl":5665,"Pb":6927,"Bi":12069,"Ac":495,"Th":1920,"Pa":374,"U":4524,"Np":574,"Pu":735}

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

@@ -1 +1 @@
-{"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":0,"At":0,"Rn":0,"Fr":0,"Ra":0,"Ac":304,"Th":1059,"Pa":286,"U":2439,"Np":410,"Pu":463,"Am":0,"Cm":0,"Bk":0,"Cf":0,"Es":0,"Fm":0,"Md":0,"No":0,"Lr":0,"Rf":0,"Db":0,"Sg":0,"Bh":0,"Hs":0,"Mt":0,"Ds":0,"Rg":0,"Cn":0,"Nh":0,"Fl":0,"Mc":0,"Lv":0,"Ts":0,"Og":0}
+{"H":10409,"He":8,"Li":21576,"Be":1186,"B":6363,"C":9069,"N":11422,"O":81887,"F":12067,"Ne":1,"Na":12798,"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,"Ac":304,"Th":1059,"Pa":286,"U":2439,"Np":410,"Pu":463}

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

@@ -1 +1 @@
-{"H":1351.0,"He":0.0,"Li":958.0,"Be":644.0,"B":721.0,"C":560.0,"N":993.0,"O":4814.0,"F":2256.0,"Ne":0.0,"Na":853.0,"Mg":3185.0,"Al":5531.0,"Si":2521.0,"P":2446.0,"S":5420.0,"Cl":4182.0,"Ar":0.0,"K":914.0,"Ca":2045.0,"Sc":2398.0,"Ti":2602.0,"V":2141.0,"Cr":2490.0,"Mn":4959.0,"Fe":6678.0,"Co":5108.0,"Ni":6567.0,"Cu":5663.0,"Zn":4877.0,"Ga":3539.0,"Ge":2917.0,"As":2472.0,"Se":5192.0,"Br":4391.0,"Kr":0.0,"Rb":718.0,"Sr":2006.0,"Y":2266.0,"Zr":2761.0,"Nb":2176.0,"Mo":574.0,"Tc":323.0,"Ru":3693.0,"Rh":5358.0,"Pd":5347.0,"Ag":2163.0,"Cd":2566.0,"In":4690.0,"Sn":4101.0,"Sb":2098.0,"Te":4279.0,"I":4254.0,"Xe":2.0,"Cs":615.0,"Ba":1462.0,"La":2035.0,"Ce":1884.0,"Pr":1742.0,"Nd":1750.0,"Pm":203.0,"Sm":1793.0,"Eu":1068.0,"Gd":974.0,"Tb":2343.0,"Dy":2102.0,"Ho":2097.0,"Er":2085.0,"Tm":2272.0,"Yb":2021.0,"Lu":1702.0,"Hf":2419.0,"Ta":1676.0,"W":780.0,"Re":819.0,"Os":1955.0,"Ir":4169.0,"Pt":5427.0,"Au":5429.0,"Hg":1903.0,"Tl":1643.0,"Pb":2532.0,"Bi":1684.0,"Po":0.0,"At":0.0,"Rn":0.0,"Fr":0.0,"Ra":0.0,"Ac":302.0,"Th":1793.0,"Pa":433.0,"U":1863.0,"Np":1239.0,"Pu":1407.0,"Am":0.0,"Cm":0.0,"Bk":0.0,"Cf":0.0,"Es":0.0,"Fm":0.0,"Md":0.0,"No":0.0,"Lr":0.0,"Rf":0.0,"Db":0.0,"Sg":0.0,"Bh":0.0,"Hs":0.0,"Mt":0.0,"Ds":0.0,"Rg":0.0,"Cn":0.0,"Nh":0.0,"Fl":0.0,"Mc":0.0,"Lv":0.0,"Ts":0.0,"Og":0.0}
+{"H":1351.0,"He":null,"Li":958.0,"Be":644.0,"B":721.0,"C":560.0,"N":993.0,"O":4814.0,"F":2256.0,"Ne":null,"Na":853.0,"Mg":3185.0,"Al":5531.0,"Si":2521.0,"P":2446.0,"S":5420.0,"Cl":4182.0,"Ar":null,"K":914.0,"Ca":2045.0,"Sc":2398.0,"Ti":2602.0,"V":2141.0,"Cr":2490.0,"Mn":4959.0,"Fe":6678.0,"Co":5108.0,"Ni":6567.0,"Cu":5663.0,"Zn":4877.0,"Ga":3539.0,"Ge":2917.0,"As":2472.0,"Se":5192.0,"Br":4391.0,"Kr":null,"Rb":718.0,"Sr":2006.0,"Y":2266.0,"Zr":2761.0,"Nb":2176.0,"Mo":574.0,"Tc":323.0,"Ru":3693.0,"Rh":5358.0,"Pd":5347.0,"Ag":2163.0,"Cd":2566.0,"In":4690.0,"Sn":4101.0,"Sb":2098.0,"Te":4279.0,"I":4254.0,"Xe":2.0,"Cs":615.0,"Ba":1462.0,"La":2035.0,"Ce":1884.0,"Pr":1742.0,"Nd":1750.0,"Pm":203.0,"Sm":1793.0,"Eu":1068.0,"Gd":974.0,"Tb":2343.0,"Dy":2102.0,"Ho":2097.0,"Er":2085.0,"Tm":2272.0,"Yb":2021.0,"Lu":1702.0,"Hf":2419.0,"Ta":1676.0,"W":780.0,"Re":819.0,"Os":1955.0,"Ir":4169.0,"Pt":5427.0,"Au":5429.0,"Hg":1903.0,"Tl":1643.0,"Pb":2532.0,"Bi":1684.0,"Po":null,"At":null,"Rn":null,"Fr":null,"Ra":null,"Ac":302.0,"Th":1793.0,"Pa":433.0,"U":1863.0,"Np":1239.0,"Pu":1407.0,"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/wbm-element-counts-arity=2.json

@@ -1 +1 @@
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