More cleanup.

README.md

@@ -1,13 +1,7 @@
 [![GitHub license](https://img.shields.io/github/license/materialsvirtuallab/python_template)](https://github.com/materialsvirtuallab/python_template/blob/main/LICENSE)
 [![Linting](https://github.com/materialsvirtuallab/python_template/workflows/Linting/badge.svg)](https://github.com/materialsvirtuallab/python_template/workflows/Linting/badge.svg)
 [![Testing](https://github.com/materialsvirtuallab/python_template/workflows/Testing/badge.svg)](https://github.com/materialsvirtuallab/python_template/workflows/Testing/badge.svg)
-<!--
-[![Downloads](https://pepy.tech/badge/python_template)](https://pepy.tech/project/python_template)
-[![codecov](https://codecov.io/gh/materialsvirtuallab/python_template/branch/main/graph/badge.svg?token=3V3O79GODQ)]
-(https://codecov.io/gh/materialsvirtuallab/python_template)
--->
 
 # Introduction
 
-This is a template for setting up Python packages in the Materials Virtual Lab. It comes with the standard Github
-workflows, pyproject and linting.
+Tools for working with MatPES.

src/matpes/db.py

@@ -19,6 +19,14 @@ def __init__(self, dbname="matpes"):
         client = MongoClient()
         self.db = client[dbname]
 
+    def get_json(self, functional: str, criteria: dict) -> list:
+        """
+        Args:
+            functional (str): The name of the functional to query.
+            criteria (dict): The criteria to query.
+        """
+        return list(self.db[functional].find(criteria))
+
     def get_df(self, functional: str) -> pd.DataFrame:
         """
         Retrieve data for the given functional from the MongoDB database.
@@ -41,6 +49,7 @@ def get_df(self, functional: str) -> pd.DataFrame:
                     "formation_energy_per_atom",
                     "natoms",
                     "nelements",
+                    "bandgap",
                 ],
             )
         )

src/matpes/ui.py

@@ -3,6 +3,7 @@
 from __future__ import annotations
 
 import collections
+import functools
 import itertools
 import json
 from typing import TYPE_CHECKING
@@ -24,24 +25,34 @@
 DB = MatPESDB()
 
 
-def get_data(functional: str, element_filter: list, chemsys: str) -> pd.DataFrame:
+@functools.lru_cache
+def get_full_data(functional: str) -> pd.DataFrame:
+    """Cache data for each functional for more responsive UI."""
+    return DB.get_df(functional)
+
+
+def get_data(functional: str, element_filter: list, chemsys_filter: str, bandgap_filter) -> pd.DataFrame:
     """
     Filter data based on the selected functional, element, and chemical system.
 
     Args:
         functional (str): Functional to filter data for.
         element_filter (list | None): Elements to filter (if any).
-        chemsys (str | None): Chemical system to filter (if any).
+        chemsys_filter (str | None): Chemical system to filter (if any).
+        bandgap_filter (str | None): Bandgap to filter (not used at the moment).
 
     Returns:
         pd.DataFrame: Filtered data.
     """
-    df = DB.get_df(functional)
+    df = get_full_data(functional)
     if element_filter:
         df = df[df["elements"].apply(lambda x: set(x).issuperset(element_filter))]
-    if chemsys:
-        sorted_chemsys = "-".join(sorted(chemsys.split("-")))
+    if chemsys_filter:
+        sorted_chemsys = "-".join(sorted(chemsys_filter.split("-")))
         df = df[df["chemsys"] == sorted_chemsys]
+
+    # df = df[bandgap_filter[0] <= df["bandgap"]]
+    # df = df[df["bandgap"] <= bandgap_filter[1]]
     return df
 
 
@@ -53,11 +64,16 @@ def get_data(functional: str, element_filter: list, chemsys: str) -> pd.DataFram
         Output("natoms_hist", "figure"),
         Output("nelements_hist", "figure"),
     ],
-    [Input("functional", "value"), Input("el_filter", "value"), Input("chemsys_filter", "value")],
+    [
+        Input("functional", "value"),
+        Input("el_filter", "value"),
+        Input("chemsys_filter", "value"),
+        # Input("bandgap_filter", "value")
+    ],
 )
-def update_graph(functional, el_filter, chemsys_filter):
+def update_graph(functional, el_filter, chemsys_filter, bandgap_filter):
     """Update graphs based on user inputs."""
-    df = get_data(functional, el_filter, chemsys_filter)
+    df = get_data(functional, el_filter, chemsys_filter, bandgap_filter)
     element_counts = collections.Counter(itertools.chain(*df["elements"]))
     heatmap_figure = pt_heatmap(element_counts, label="Count", log=True)
     return (
@@ -90,13 +106,12 @@ def update_graph(functional, el_filter, chemsys_filter):
 )
 def download_data(n_clicks, functional, el_filter, chemsys_filter):
     """Handle data download requests."""
-    collection = DB[functional]
     criteria = {}
     if el_filter:
         criteria["elements"] = el_filter
     if chemsys_filter:
         criteria["chemsys"] = "-".join(sorted(chemsys_filter.split("-")))
-    data = list(collection.find(criteria))
+    data = DB.get_json(functional, criteria)
     for entry in data:
         entry.pop("_id", None)  # Remove MongoDB's internal ID
     return dict(
@@ -120,6 +135,9 @@ def main():
                                 src="https://github.com/materialsvirtuallab/matpes/blob"
                                 "/2b7f8de716289de8089504a63c6431c456268172/assets/logo.png?raw=true",
                                 width="50%",
+                                style={
+                                    "padding": "12px",
+                                },
                             )
                         ],
                         width={"size": 8, "order": 1, "offset": 4},
@@ -158,14 +176,23 @@ def main():
                     ),
                     dbc.Col(
                         [
-                            html.Label("Filter by Chemsys"),
+                            html.Div("Filter by Chemsys"),
                             dcc.Input(
                                 id="chemsys_filter",
                                 placeholder="Li-Fe-O",
                             ),
                         ],
                         width=2,
                     ),
+                    # dbc.Col(
+                    #     [
+                    #         html.Div("Bandgap", className="text-center"),
+                    #         dcc.RangeSlider(0, 10, 0.1,
+                    #         marks={i: str(i) for i in range(0, 10)},
+                    #         value=[0, 10], id='bandgap_filter'),
+                    #     ],
+                    #     width=2,
+                    # ),
                     dbc.Col(
                         [
                             html.Label("Data Tools"),
@@ -179,7 +206,7 @@ def main():
             dbc.Row(
                 dbc.Col(
                     dcc.Graph(id="ptheatmap", style={"marginLeft": "auto", "marginRight": "auto"}),
-                    width={"size": 8, "offset": 2},
+                    width={"size": 16},
                 )
             ),
             dbc.Row(