Revert "Update app.py"

This reverts commit 164c6c162953c6283a1474194d75d0dff27abbed.

app.py

@@ -1,13 +1,10 @@
-# ----- IMPORTS ----- #
-
-
 import asyncio
 from datetime import datetime, date, time
 from pathlib import Path
 
 from faicons import icon_svg
-from pandas import DataFrame
-from numpy import array
+from pandas import DataFrame, to_datetime
+from plotnine import ggplot, labs
 
 from modules import (
     DF, 
@@ -23,33 +20,25 @@ from modules import (
     pad_missing_dates, 
     plot_agency, 
     plot_tf, 
-    plot_NA, 
     )
 
 from shiny import reactive
 from shiny.express import input, render, ui
 
-# load css styles from external file
-ui.include_css( Path(__file__).parent.joinpath("www") / "style.css")
-
-
-# ----- CREATE OBJECTS ----- #
-
-
-# this text appears in the browser tab
 TITLE = "CRA Window Tracker - GW Regulatory Studies Center"
 
-# page header above main content
 HEADER = "Rules in the Congressional Review Act (CRA) Window"
-page_header = ui.HTML(
-    f"""
-    <div class="header">
-        <span>{HEADER}</span>
-    </div>
+
+FOOTER = f"""
+    -----
+    
+    &copy; 2024 [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.
     """
-    )
 
-# logo at the top of the sidebar
+ui.include_css( Path(__file__).parent.joinpath("www") / "style.css")
+
+ui.tags.title(TITLE)
+
 sidebar_logo = ui.HTML(
     f"""
     <div class="header">
@@ -60,48 +49,36 @@ sidebar_logo = ui.HTML(
     """
     )
 
-# footer at the bottom of the page
-FOOTER = f"""
-    -----
-    
-    &copy; 2024 [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.
+page_header = ui.HTML(
+    f"""
+    <div class="header">
+        <span>{HEADER}</span>
+    </div>
     """
-
-
-# ----- APP LAYOUT ----- #
-
-
-ui.tags.title(TITLE)
+    )
 
 page_header
 
-# sidebar settings
 with ui.sidebar(open={"desktop": "open", "mobile": "closed"}):
     sidebar_logo
 
-    #ui.markdown("Estimated CRA window open date: [May 23](https://www.huntonak.com/the-nickel-report/federal-agencies-face-looming-congressional-review-act-deadline)")
-    with ui.tooltip(placement="right", id="window_tooltip"):
-        ui.input_date("start_date", "Select start of window", value=WINDOW_OPEN_DATE, min=START_DATE, max=date.today())
-        "The estimated CRA window open date is May 23. See the notes for more information."
-
+    ui.input_date("start_date", "Select start of window", value=WINDOW_OPEN_DATE, min=START_DATE, max=date.today())
+        
     ui.input_select("menu_agency", "Select agencies", choices=["all"] + AGENCIES, selected="all", multiple=True)
     
-    ui.input_select("frequency", "Select frequency", choices=["daily", "weekly", "monthly"], selected="weekly")
-
-    ui.input_select("menu_significant", "Select rule significance", choices=["all", "3f1-significant", "other-significant"], selected="all", multiple=True)
+    ui.input_select("frequency", "Select frequency", choices=["daily", "monthly", "weekly"], selected="daily")
 
     #ui.input_switch("switch", "Stack significant rules in plots", False)
 
-# value boxes with summary data
 with ui.layout_column_wrap():
-    with ui.value_box():
+    with ui.value_box(showcase=icon_svg("book")):
         "All final rules"
         @render.text
         def count_rules():
             return f"{filtered_df()['document_number'].count()}"
-        ui.input_action_button("filter_all", "Clear Filters", class_="btn-filter", icon=icon_svg("book"))
+        ui.input_action_button("filter_all", "View", ) #class_="btn-success")
     
-    with ui.value_box():
+    with ui.value_box(showcase=icon_svg("book")):
         "Section 3(f)(1) Significant rules *"
         @render.text
         def count_3f1_significant():
@@ -109,9 +86,9 @@ with ui.layout_column_wrap():
             if GET_SIGNIFICANT:
                 output = f"{filtered_df()['3f1_significant'].sum()}"
             return output
-        ui.input_action_button("filter_3f1", "Filter", class_="btn-filter", icon=icon_svg("book"))
+        ui.input_action_button("filter_3f1", "View", ) #class_="btn-success")
     
-    with ui.value_box():
+    with ui.value_box(showcase=icon_svg("book")):
         "Other Significant rules *"
         @render.text
         def count_other_significant():
@@ -119,16 +96,14 @@ with ui.layout_column_wrap():
             if GET_SIGNIFICANT:
                 output = f"{filtered_df()['other_significant'].sum()}"
             return output
-        ui.input_action_button("filter_other", "Filter", class_="btn-filter", icon=icon_svg("book"))
+        ui.input_action_button("filter_other", "View", )
 
-# documentation note on significance data
 ui.markdown(
             f"""
             \* *Executive Order 12866 significance data last updated **{LAST_UPDATED}***.
             """
             )
 
-# main content
 with ui.navset_card_underline(title=""):
     
     with ui.nav_panel("Rules in detail"):
@@ -161,12 +136,14 @@ with ui.navset_card_underline(title=""):
                     count_gte_zero = sum(1 if g > 0 else 0 for g in values)
                     max_val = max(values, default=0)
                     if (max_val < 2) or (count_gte_zero < 2):
-                        return plot_NA()
+                        return (
+                            ggplot()
+                            + labs(title="Not enough data available to visualize.")
+                        )
                     else:
                         return plot_tf(
                             grouped, 
-                            input.frequency(), 
-                            rule_type=filter_value.get(),
+                            input.frequency()
                             )
             
             with ui.card(full_screen=True):
@@ -203,14 +180,10 @@ with ui.navset_card_underline(title=""):
                     #if input.switch():
                     #    pass
                     #    # placeholder for stacked bar chart
-                    if len(grouped) < 2:
-                        return plot_NA()
-                    else:
-                        plot = plot_agency(
-                                grouped.head(10), 
-                                rule_type=filter_value.get(), 
-                                )
-                        return plot
+                    plot = plot_agency(
+                            grouped.head(10),
+                            )
+                    return plot
 
             with ui.card(full_screen=True):
                 @render.data_frame
@@ -225,7 +198,6 @@ with ui.navset_card_underline(title=""):
                         ]
                     return render.DataTable(grouped.loc[:, [c for c in cols if c in grouped.columns]])
 
-# download data
 with ui.accordion(open=False):
     
     with ui.accordion_panel("Download Data"):
@@ -259,7 +231,6 @@ with ui.accordion(open=False):
             await asyncio.sleep(0.25)
             yield filtered_df().loc[:, output_cols].to_csv(index=False)
 
-# notes
 with ui.accordion(open=False):
     
     with ui.accordion_panel("Notes"):
@@ -272,7 +243,6 @@ with ui.accordion(open=False):
             """
             )
 
-# footer citation
 ui.markdown(
     FOOTER
 )
@@ -296,17 +266,6 @@ def filtered_df():
         bool_agency = [True if sum(selected in agency for selected in input.menu_agency()) > 0 else False for agency in filt_df["parent_slug"]]
         filt_df = filt_df.loc[bool_agency]
     
-    print(input.menu_significant())
-    # filter significance
-    bool_ = []
-    if (input.menu_significant() is not None) and ("all" not in input.menu_significant()):
-        if "3f1-significant" in input.menu_significant():
-            bool_.append((filt_df["3f1_significant"] == 1).to_numpy())
-        if "other-significant" in input.menu_significant():
-            bool_.append((filt_df["other_significant"] == 1).to_numpy())
-        filt_df = filt_df.loc[array(bool_).any(axis=0)]
-    
-    # return filtered dataframe
     return filt_df