Add helper functions

streamlit_helpers.py

@@ -0,0 +1,150 @@
+from collections import Counter
+from typing import List
+import numpy as np
+import streamlit as st  # pylint: disable=import-error
+import pandas as pd
+
+
+class Collapsable:
+    """
+    Creates a collapsable text composed of a preamble (clickable section of text)
+    and epilogue (collapsable text).
+    """
+
+    def __init__(self, preamble="", epilogue=""):
+        self.preamble = preamble
+        self.epilogue = epilogue
+        self.small_font = 18
+        self.large_font = 18
+        self.sections = []
+
+    def add_section(self, heading, text):
+        # Convert text to bullet points if it is a list
+        if isinstance(text, list):
+            text = (
+                "<ul>"
+                + "".join(
+                    [
+                        f'<li style="font-size:{self.small_font}px;" align="justify">{x}</li>'
+                        for x in text
+                    ]
+                )
+                + "</ul>"
+            )
+
+        # Append section
+        self.sections.append((heading, text))
+
+    def deploy(self):
+
+        secs = "".join(
+            [
+                (
+                    "<details>"
+                    f"<summary style='font-size:{self.large_font}px;'>{heading}</summary>"
+                    f"<blockquote style='font-size:{self.small_font}px;max-width: 80%;'"
+                    f"align='justify'>{text}</details>"
+                )
+                for heading, text in self.sections
+            ]
+        )
+        collapsable_sec = f"""
+        <ol>
+        {self.preamble}
+        {secs}
+        {self.epilogue}
+        </ol>
+        """
+        st.markdown(collapsable_sec, unsafe_allow_html=True)
+
+
+def add_filter(
+    data_frame_list: List[pd.DataFrame],
+    name: str,
+    label: str,
+    options: List[str] = None,
+    num_cols: int = 1,
+    last_is_others: bool = True,
+):
+    """
+    Creates a filter on the side bar using checkboxes
+    """
+
+    # Get list of all options and return if no options are available
+    all_options = set(data_frame_list[-1][label])
+    if "-" in all_options:
+        all_options.remove("-")
+    if len(all_options) == 0:
+        return data_frame_list
+
+    st.markdown(f"#### {name}")
+
+    # Create list of options if selectable options are not provided
+    if options is None:
+        options_dict = Counter(data_frame_list[-1][label])
+        sorted_options = sorted(options_dict, key=options_dict.get, reverse=True)
+        if "-" in sorted_options:
+            sorted_options.remove("-")
+        if len(sorted_options) > 8:
+            options = list(sorted_options[:7]) + ["others"]
+            last_is_others = True
+        else:
+            options = list(sorted_options)
+            last_is_others = False
+
+    cols = st.columns(num_cols)
+    instantiated_checkbox = []
+    for idx in range(len(options)):
+        with cols[idx % num_cols]:
+            instantiated_checkbox.append(
+                st.checkbox(options[idx], False, key=f"{label}_{options[idx]}")
+            )
+
+    selected_options = [
+        options[idx] for idx, checked in enumerate(instantiated_checkbox) if checked
+    ]
+
+    # The last checkbox will always correspond to "other"
+    if instantiated_checkbox[-1] and last_is_others:
+        selected_options = selected_options[:-1]
+        other_options = [x for x in all_options if x not in options]
+        selected_options = set(selected_options + other_options)
+
+    if len(selected_options) > 0:
+        for idx, _ in enumerate(data_frame_list):
+            data_frame_list[idx] = data_frame_list[idx][
+                [
+                    any([x == model_entry for x in selected_options])
+                    for model_entry in data_frame_list[idx][label]
+                ]
+            ]
+    return data_frame_list
+
+
+def slider_filter(
+    data_frame_list: List[pd.DataFrame],
+    title: str,
+    filter_by: str,
+    max_val: int = 1000,
+):
+    """
+    Creates slider to filter dataframes according to a given label.
+    label must be numeric. Values are in millions.
+    """
+
+    start_val, end_val = st.select_slider(
+        title,
+        options=[str(x) for x in np.arange(0, max_val + 1, 10, dtype=int)],
+        value=("0", str(max_val)),
+    )
+
+    for idx in range(len(data_frame_list)):
+        data_frame_list[idx] = data_frame_list[idx][
+            [
+                int(model_entry) >= int(start_val) * 1000000
+                and int(model_entry) <= int(end_val) * 1000000
+                for model_entry in data_frame_list[idx][filter_by]
+            ]
+        ]
+
+    return data_frame_list