Much improved matpes explorer.

notebooks/Insert Calculations.ipynb

@@ -2,26 +2,27 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "1671fa59-c236-4401-8548-afaee3e85fcd",
+   "execution_count": null,
+   "id": "0",
    "metadata": {},
    "outputs": [],
    "source": [
     "from __future__ import annotations\n",
+    "\n",
     "import os\n",
-    "from tqdm import tqdm\n",
     "import warnings\n",
     "\n",
     "from monty.serialization import loadfn\n",
     "from pymongo import MongoClient\n",
+    "from tqdm import tqdm\n",
     "\n",
-    "warnings.simplefilter('ignore')"
+    "warnings.simplefilter(\"ignore\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "870765b8-719c-4405-b489-ba3f2733b10f",
+   "execution_count": null,
+   "id": "1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -31,8 +32,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "5e60c4e6-c868-4dc1-ac1e-ed25ceca96ba",
+   "execution_count": null,
+   "id": "2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -45,13 +46,13 @@
     "    Args:\n",
     "        functional (str): The name of the functional. This is used to locate\n",
     "            the appropriate file for loading the data and also defines the MongoDB collection name.\n",
-    "    \n",
+    "\n",
     "    Raises:\n",
     "        FileNotFoundError: If the designated file does not exist or cannot be accessed.\n",
     "\n",
     "    Example:\n",
     "        To create the database and indexes for a given functional 'pbe', run:\n",
-    "        \n",
+    "\n",
     "        ```python\n",
     "        make_db(\"pbe\")\n",
     "        ```\n",
@@ -60,7 +61,7 @@
     "    -------------\n",
     "    1. Load Data:\n",
     "        - Reads data from a gzipped JSON file for the specified `functional`.\n",
-    "    \n",
+    "\n",
     "    2. Extract & Process Fields:\n",
     "        - Each dataset entry is extracted and processed to include information such as:\n",
     "            - `matpesid`: A unique identifier for the material.\n",
@@ -72,12 +73,12 @@
     "            - `composition`: Dictionary depicting the element counts in the structure.\n",
     "            - `formation_energy_per_atom`: Energy per atom (derived from `formation_energy`).\n",
     "            - `structure`: The structure in dictionary format.\n",
-    "    \n",
+    "\n",
     "    3. Store Data in MongoDB:\n",
     "        - Deletes any existing records in the collection corresponding to `functional`.\n",
     "        - Inserts the processed records.\n",
-    "    \n",
-    "    4. Create Indexes: \n",
+    "\n",
+    "    4. Create Indexes:\n",
     "        - Indexes are created on the following fields to optimize searching:\n",
     "            - `natoms`\n",
     "            - `elements`\n",
@@ -99,7 +100,7 @@
     "    - nelements: int\n",
     "        Number of distinct chemical elements.\n",
     "    - chemsys:\n",
-    "        String representation of the elements in the chemical system, \n",
+    "        String representation of the elements in the chemical system,\n",
     "        sorted alphabetically (e.g., 'H-O').\n",
     "    - formula: str\n",
     "        The reduced chemical formula of the material (e.g., 'H2O').\n",
@@ -109,11 +110,11 @@
     "        Formation energy per atom for the material (extracted from `formation_energy`).\n",
     "    - structure: dict\n",
     "        The detailed structure of the material in dictionary format.\n",
-    "    \n",
+    "\n",
     "    Indexes:\n",
     "    --------\n",
     "    The created MongoDB indexes optimize the following fields:\n",
-    "    \n",
+    "\n",
     "    - `natoms`: Number of atoms per structure.\n",
     "    - `elements`: Chemical elements present in the structure.\n",
     "    - `nelements`: Number of distinct elements in the structure.\n",
@@ -127,15 +128,14 @@
     "    - The JSON file path is specific to the user's system configuration.\n",
     "\n",
     "    \"\"\"\n",
-    "    \n",
     "    raw = loadfn(os.path.expanduser(f\"~/Desktop/2024_11_18_MatPES-20240214-{functional}-training-data.json.gz\"))\n",
     "    data = []\n",
-    "    \n",
+    "\n",
     "    for k, v in tqdm(raw.items()):\n",
     "        # Combine IDs and structure information\n",
     "        d = {\"matpesid\": k} | v\n",
     "        comp = d[\"structure\"].composition\n",
-    "        \n",
+    "\n",
     "        # Populate additional fields based on composition\n",
     "        d[\"natoms\"] = len(d[\"structure\"])\n",
     "        d[\"elements\"] = list(comp.chemical_system_set)\n",
@@ -144,37 +144,37 @@
     "        d[\"formula\"] = comp.reduced_formula\n",
     "        d[\"composition\"] = {el.symbol: amt for el, amt in comp.items()}\n",
     "        d[\"structure\"] = d[\"structure\"].as_dict()\n",
-    "        \n",
+    "\n",
     "        # Restructure formation energy data\n",
     "        d[\"formation_energy_per_atom\"] = d[\"formation_energy\"]\n",
     "        del d[\"formation_energy\"]\n",
-    "        \n",
+    "\n",
     "        # Add processed entry to list\n",
     "        data.append(d)\n",
-    "    \n",
+    "\n",
     "    # Get collection from DB and clear old data\n",
     "    collection = db[functional]\n",
     "    collection.delete_many({})\n",
-    "    \n",
+    "\n",
     "    # new data\n",
     "    collection.insert_many(data)\n",
-    "    \n",
+    "\n",
     "    # Create indexes for optimized query performance\n",
     "    for k in [\"natoms\", \"elements\", \"nelements\", \"chemsys\", \"formula\", \"matpesid\"]:\n",
-    "        collection.create_index(k)\n"
+    "        collection.create_index(k)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e7eb2367-d29b-4de2-91bb-489906689761",
+   "id": "3",
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      " 62%|█████████████████████████████████████████████████▎                             | 271161/434712 [01:07<01:16, 2149.16it/s]"
+      "100%|███████████████████████████████████████████████████████████████████████████████| 434712/434712 [03:45<00:00, 1930.57it/s]\n"
      ]
     }
    ],
@@ -185,20 +185,20 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d167bd6c-197a-4e95-9267-c7b10b0ad6ef",
+   "id": "4",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████████████████████████████████████████████| 387897/387897 [02:50<00:00, 2278.72it/s]\n"
+     ]
+    }
+   ],
    "source": [
     "make_db(\"r2SCAN\")"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "89b44d7f-b5e1-4f0f-bff8-f6926a6240c4",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

pyproject.toml

@@ -47,9 +47,16 @@ dependencies = [
     "pymatgen",
     "dash",
     "dash_bootstrap_components",
-    "pymatviz","pymongo"
+    "pymongo"
 ]
-version = "2024.11.13"
+version = "0.0.1"
+
+[tool.setuptools.packages.find]
+where = ["src"]
+include = ["matpes", "matpes.*"]
+
+[project.scripts]
+matpes_explorer = "matpes.ui:main"
 
 [tool.versioningit.vcs]
 method = "git"

app.py → src/matpes/ui.py

@@ -10,11 +10,13 @@
 import dash_bootstrap_components as dbc
 import pandas as pd
 import plotly.express as px
-import pymatviz as pmv
+import plotly.figure_factory as ff
 from dash import Dash, Input, Output, State, callback, dcc, html
 from pymatgen.core import Element
 from pymongo import MongoClient
 
+from matpes.utils import get_pt_heatmap
+
 FUNCTIONALS = ("PBE", "r2SCAN")
 
 # Set up MongoDB client and database
@@ -56,7 +58,7 @@ def get_data(functional, el, chemsys):
 
 # Initialize the Dash app with a Bootstrap theme
 external_stylesheets = [dbc.themes.CERULEAN]
-app = Dash(__name__, external_stylesheets=external_stylesheets)
+app = Dash("MatPES Explorer", external_stylesheets=external_stylesheets)
 
 # Define the app layout
 app.layout = dbc.Container(
@@ -67,7 +69,7 @@ def get_data(functional, el, chemsys):
                 dbc.Col(
                     [
                         html.Label("Functional"),
-                        dcc.RadioItems(
+                        dcc.Dropdown(
                             options=[{"label": f, "value": f} for f in FUNCTIONALS], value="PBE", id="functional"
                         ),
                     ],
@@ -119,6 +121,13 @@ def get_data(functional, el, chemsys):
 )
 
 
+def get_dist_plot(data, label, nbins=100):
+    fig = ff.create_distplot([data], [label], show_rug=False)
+
+    fig.update_layout(xaxis=dict(title=label), showlegend=False)
+    return fig
+
+
 # Define callback to update the heatmap based on selected functional
 @callback(
     [
@@ -135,16 +144,13 @@ def update_graph(functional, el_filter, chemsys_filter):
     df = get_data(functional, el_filter, chemsys_filter)
     el_count = {el.symbol: 0 for el in Element}
     el_count.update(collections.Counter(itertools.chain(*df["elements"])))
-    heatmap_figure = pmv.ptable_heatmap_plotly(el_count)
+    heatmap_figure = get_pt_heatmap(el_count, label="Count", log=True)
     return (
         heatmap_figure,
-        px.histogram(
-            df, x="cohesive_energy_per_atom", labels={"cohesive_energy_per_atom": "Cohesive Energy per Atom (eV/atom)"}
-        ),
-        px.histogram(
-            df,
-            x="formation_energy_per_atom",
-            labels={"formation_energy_per_atom": "Formation Energy per Atom (eV/atom)"},
+        get_dist_plot(df["cohesive_energy_per_atom"], "Cohesive Energy per Atom (eV/atom)"),
+        get_dist_plot(
+            df["formation_energy_per_atom"].dropna(),
+            "Formation Energy per Atom (eV/atom)",
         ),
         px.histogram(df, x="natoms"),
         px.histogram(df, x="nelements"),
@@ -174,5 +180,5 @@ def download(n_clicks, functional, el_filter, chemsys_filter):
 
 
 # Run the app
-if __name__ == "__main__":
+def main():
     app.run(debug=True)