Update app.py

app.py

@@ -1,10 +1,13 @@
+# ----- IMPORTS ----- #
+
+
 import asyncio
 from datetime import datetime, date, time
 from pathlib import Path
 
 from faicons import icon_svg
-from pandas import DataFrame, to_datetime
-from plotnine import ggplot, labs
+from pandas import DataFrame
+from numpy import array
 
 from modules import (
     DF, 
@@ -20,65 +23,85 @@ from modules import (
     pad_missing_dates, 
     plot_agency, 
     plot_tf, 
+    plot_NA, 
     )
 
 from shiny import reactive
 from shiny.express import input, render, ui
 
-TITLE = "CRA Window Tracker - GW Regulatory Studies Center"
+# load css styles from external file
+ui.include_css( Path(__file__).parent.joinpath("www") / "style.css")
 
-HEADER = "Rules in the Congressional Review Act (CRA) Window"
 
-FOOTER = f"""
-    -----
-    
-    © 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.
-    """
+# ----- CREATE OBJECTS ----- #
 
-ui.include_css( Path(__file__).parent.joinpath("www") / "style.css")
 
-ui.tags.title(TITLE)
+# this text appears in the browser tab
+TITLE = "CRA Window Tracker - GW Regulatory Studies Center"
 
-sidebar_logo = ui.HTML(
+# page header above main content
+HEADER = "Rules in the Congressional Review Act (CRA) Window"
+page_header = ui.HTML(
     f"""
     <div class="header">
-        <a href="https://go.gwu.edu/regstudies" target="_blank">
-        <img src="logo.png" alt="Regulatory Studies Center logo"/>        
-        </a>
+        <span>{HEADER}</span>
     </div>
     """
     )
 
-page_header = ui.HTML(
+# logo at the top of the sidebar
+sidebar_logo = ui.HTML(
     f"""
     <div class="header">
-        <span>{HEADER}</span>
+        <a href="https://go.gwu.edu/regstudies" target="_blank">
+        <img src="logo.png" alt="Regulatory Studies Center logo"/>        
+        </a>
     </div>
     """
     )
 
+# 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.
+    """
+
+
+# ----- APP LAYOUT ----- #
+
+
+ui.tags.title(TITLE)
+
 page_header
 
+# sidebar settings
 with ui.sidebar(open={"desktop": "open", "mobile": "closed"}):
     sidebar_logo
 
-    ui.input_date("start_date", "Select start of window", value=WINDOW_OPEN_DATE, min=START_DATE, max=date.today())
-        
+    #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_select("menu_agency", "Select agencies", choices=["all"] + AGENCIES, selected="all", multiple=True)
     
-    ui.input_select("frequency", "Select frequency", choices=["daily", "monthly", "weekly"], selected="daily")
+    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_switch("switch", "Stack significant rules in plots", False)
 
+# value boxes with summary data
 with ui.layout_column_wrap():
-    with ui.value_box(showcase=icon_svg("book")):
+    with ui.value_box():
         "All final rules"
         @render.text
         def count_rules():
             return f"{filtered_df()['document_number'].count()}"
-        ui.input_action_button("filter_all", "View", ) #class_="btn-success")
+        ui.input_action_button("filter_all", "Clear Filters", class_="btn-filter", icon=icon_svg("book"))
     
-    with ui.value_box(showcase=icon_svg("book")):
+    with ui.value_box():
         "Section 3(f)(1) Significant rules *"
         @render.text
         def count_3f1_significant():
@@ -86,9 +109,9 @@ with ui.layout_column_wrap():
             if GET_SIGNIFICANT:
                 output = f"{filtered_df()['3f1_significant'].sum()}"
             return output
-        ui.input_action_button("filter_3f1", "View", ) #class_="btn-success")
+        ui.input_action_button("filter_3f1", "Filter", class_="btn-filter", icon=icon_svg("book"))
     
-    with ui.value_box(showcase=icon_svg("book")):
+    with ui.value_box():
         "Other Significant rules *"
         @render.text
         def count_other_significant():
@@ -96,14 +119,16 @@ with ui.layout_column_wrap():
             if GET_SIGNIFICANT:
                 output = f"{filtered_df()['other_significant'].sum()}"
             return output
-        ui.input_action_button("filter_other", "View", )
+        ui.input_action_button("filter_other", "Filter", class_="btn-filter", icon=icon_svg("book"))
 
+# 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"):
@@ -136,14 +161,12 @@ 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 (
-                            ggplot()
-                            + labs(title="Not enough data available to visualize.")
-                        )
+                        return plot_NA()
                     else:
                         return plot_tf(
                             grouped, 
-                            input.frequency()
+                            input.frequency(), 
+                            rule_type=filter_value.get(),
                             )
             
             with ui.card(full_screen=True):
@@ -180,10 +203,14 @@ with ui.navset_card_underline(title=""):
                     #if input.switch():
                     #    pass
                     #    # placeholder for stacked bar chart
-                    plot = plot_agency(
-                            grouped.head(10),
-                            )
-                    return plot
+                    if len(grouped) < 2:
+                        return plot_NA()
+                    else:
+                        plot = plot_agency(
+                                grouped.head(10), 
+                                rule_type=filter_value.get(), 
+                                )
+                        return plot
 
             with ui.card(full_screen=True):
                 @render.data_frame
@@ -198,6 +225,7 @@ 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"):
@@ -231,6 +259,7 @@ 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"):
@@ -243,6 +272,7 @@ with ui.accordion(open=False):
             """
             )
 
+# footer citation
 ui.markdown(
     FOOTER
 )
@@ -266,6 +296,17 @@ 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