| import asyncio
|
| from datetime import datetime, date, time
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|
|
| from faicons import icon_svg
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|
|
| from modules.get_rules_in_window import (
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| DF,
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| LAST_UPDATED,
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| START_DATE,
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| GET_SIGNIFICANT,
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| METADATA,
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| groupby_agency,
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| groupby_ym,
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| plot_agency,
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| plot_month,
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| )
|
|
|
| from shiny import reactive
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| from shiny.express import input, render, ui
|
|
|
| FOOTER = f"""
|
| -----
|
|
|
| Developed by the [GW Regulatory Studies Center](https://go.gwu.edu/regstudies). See our page on the [Congressional Review Act](https://regulatorystudies.columbian.gwu.edu/congressional-review-act) for more information.
|
| """
|
|
|
| ui.page_opts(
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| title="Rules in the Congressional Review Act (CRA) Window",
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| )
|
|
|
| with ui.sidebar(title="Settings"):
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| ui.input_date("start_date", "Start of window", value=START_DATE, min=START_DATE, max=date.today())
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|
|
| ui.input_switch("switch", "Show significant rules in plots", False)
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|
|
|
|
|
|
|
|
|
|
|
|
| with ui.layout_column_wrap():
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| with ui.value_box(showcase=icon_svg("book")):
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| "All final rules"
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|
|
| @render.text
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| def count_rules():
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| return f"{filtered_df()['document_number'].count()}"
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|
|
| with ui.value_box(showcase=icon_svg("book")):
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| "Other Significant rules"
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|
|
| @render.text
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| def count_other_significant():
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| output = "Not available"
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| if GET_SIGNIFICANT:
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| output = f"{filtered_df()['other_significant'].sum()}"
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| return output
|
|
|
| with ui.value_box(showcase=icon_svg("book")):
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| "Section 3(f)(1) Significant rules"
|
|
|
| @render.text
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| def count_3f1_significant():
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| output = "Not available"
|
| if GET_SIGNIFICANT:
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| output = f"{filtered_df()['3f1_significant'].sum()}"
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| return output
|
|
|
| with ui.navset_card_underline(title=""):
|
|
|
| with ui.nav_panel("Rules in detail"):
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| @render.data_frame
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| def table_rule_detail():
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| df = filtered_df()
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|
|
|
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| df.loc[:, "date"] = df.apply(lambda x: f"{x['publication_date'].date()}", axis=1)
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| char = " "
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| df.loc[:, "title"] = df["title"].apply(lambda x: f"{char.join(x.split(char)[:9])}...")
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| df.loc[:, "agencies"] = df["parent_slug"].apply(lambda x: "; ".join(x))
|
| cols = [
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| "date",
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| "title",
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| "agencies",
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| "3f1_significant",
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| "other_significant",
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| ]
|
| return render.DataTable(df.loc[:, [c for c in cols if c in df.columns]])
|
|
|
| with ui.nav_panel("By month"):
|
|
|
| with ui.layout_columns():
|
|
|
| @render.plot
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| def plot_by_month():
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| grouped = grouped_df_month()
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| return plot_month(
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| grouped
|
| )
|
|
|
| @render.data_frame
|
| def table_by_month():
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| grouped = grouped_df_month()
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| cols = [
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| "publication_year",
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| "publication_month",
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| "rules",
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| "3f1_significant",
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| "other_significant",
|
| ]
|
| return render.DataTable(grouped.loc[:, [c for c in cols if c in grouped.columns]])
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|
|
| with ui.nav_panel("By agency"):
|
|
|
| with ui.layout_columns():
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|
|
| @render.plot
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| def plot_by_agency():
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| grouped = grouped_df_agency()
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| return plot_agency(
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| grouped.head(10),
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| )
|
|
|
| @render.data_frame
|
| def table_by_agency():
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| grouped = grouped_df_agency()
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| cols = [
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| "agency",
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| "acronym",
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| "rules",
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| "3f1_significant",
|
| "other_significant",
|
| ]
|
| return render.DataTable(grouped.loc[:, [c for c in cols if c in grouped.columns]])
|
|
|
| with ui.accordion(open=False):
|
|
|
| with ui.accordion_panel("Download Data"):
|
|
|
| @render.download(
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| label="Download data as CSV",
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| filename=f"rules_in_cra_window_accessed_{date.today()}.csv",
|
| )
|
| async def download():
|
| await asyncio.sleep(0.25)
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| yield filtered_df().to_csv(index=False)
|
|
|
| with ui.accordion(open=False):
|
|
|
| with ui.accordion_panel("Notes"):
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|
|
| ui.markdown(
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| f"""
|
| Rule data retrieved from the [Federal Register API](https://www.federalregister.gov/developers/documentation/api/v1).
|
|
|
| Executive Order 12866 significance data last updated **{LAST_UPDATED}**.
|
| """
|
| )
|
|
|
| ui.markdown(
|
| FOOTER
|
| )
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| @reactive.calc
|
| def filtered_df():
|
| filt_df = DF
|
|
|
| try:
|
| filt_df = filt_df.loc[filt_df["publication_date"] >= input.start_date()]
|
| except TypeError:
|
| filt_df = filt_df.loc[filt_df["publication_date"] >= datetime.combine(input.start_date(), time(0, 0))]
|
| return filt_df
|
|
|
| @reactive.calc
|
| def grouped_df_month():
|
| filt_df = filtered_df()
|
| grouped = groupby_ym(filt_df, significant=GET_SIGNIFICANT)
|
| return grouped
|
|
|
| @reactive.calc
|
| def grouped_df_agency():
|
| filt_df = filtered_df()
|
| grouped = groupby_agency(filt_df, metadata=METADATA, significant=GET_SIGNIFICANT)
|
| return grouped
|
|
|
