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