Alt significance (#17)

* Update app.py

* fix bugs in new summary layout

* Update plotting.py

account for multiple rule type inputs

app.py

@@ -1,9 +1,6 @@
 # ----- IMPORTS ----- #
 
 
-# ----- IMPORTS ----- #
-
-
 import asyncio
 from datetime import datetime, date, time
 from pathlib import Path
@@ -40,14 +37,6 @@ ui.include_css( Path(__file__).parent.joinpath("www") / "style.css")
 # ----- CREATE OBJECTS ----- #
 
 
-# this text appears in the browser tab
-# 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"
 
@@ -61,15 +50,6 @@ page_header = ui.HTML(
     """
     )
 
-# logo at the top of the sidebar
-page_header = ui.HTML(
-    f"""
-    <div class="header">
-        <span>{HEADER}</span>
-    </div>
-    """
-    )
-
 # logo at the top of the sidebar
 sidebar_logo = ui.HTML(
     f"""
@@ -92,19 +72,6 @@ FOOTER = f"""
 # ----- APP LAYOUT ----- #
 
 
-ui.tags.title(TITLE)
-
-# 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
@@ -113,7 +80,6 @@ page_header
 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."
@@ -124,8 +90,6 @@ with ui.sidebar(open={"desktop": "open", "mobile": "closed"}):
 
     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():
@@ -133,8 +97,7 @@ with ui.layout_column_wrap():
         @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", "Clear Filters", class_="btn-filter", icon=icon_svg("book"))
+        #ui.input_action_button("filter_all", "Clear Filters", class_="btn-filter", icon=icon_svg("book"))
     
     with ui.value_box():
         "Section 3(f)(1) Significant rules *"
@@ -144,8 +107,7 @@ 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", "Filter", class_="btn-filter", icon=icon_svg("book"))
+        #ui.input_action_button("filter_3f1", "Filter", class_="btn-filter", icon=icon_svg("book"))
     
     with ui.value_box():
         "Other Significant rules *"
@@ -155,8 +117,7 @@ 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", "Filter", class_="btn-filter", icon=icon_svg("book"))
+        #ui.input_action_button("filter_other", "Filter", class_="btn-filter", icon=icon_svg("book"))
 
 # documentation note on significance data
 ui.markdown(
@@ -171,7 +132,7 @@ with ui.navset_card_underline(title=""):
     with ui.nav_panel("Rules in detail"):
         @render.data_frame
         def table_rule_detail():
-            df = filtered_sig().copy()
+            df = filtered_df().copy()
             df.loc[:, "date"] = df.loc[:, "publication_date"].apply(lambda x: f"{x.date()}")
             char, limit = " ", 10
             df.loc[:, "title"] = df["title"].apply(lambda x: x if len(x.split(char)) < (limit + 1) else f"{char.join(x.split(char)[:limit])}...")
@@ -203,7 +164,7 @@ with ui.navset_card_underline(title=""):
                         return plot_tf(
                             grouped, 
                             input.frequency(), 
-                            rule_type=filter_value.get(),
+                            rule_types=input.menu_significant(),
                             )
             
             with ui.card(full_screen=True):
@@ -236,16 +197,13 @@ with ui.navset_card_underline(title=""):
             with ui.card(full_screen=True):
                 @render.plot
                 def plot_by_agency():
-                    grouped = grouped_df_agency()                
-                    #if input.switch():
-                    #    pass
-                    #    # placeholder for stacked bar chart
+                    grouped = grouped_df_agency()
                     if len(grouped) < 2:
                         return plot_NA()
                     else:
                         plot = plot_agency(
                                 grouped.head(10), 
-                                rule_type=filter_value.get(), 
+                                rule_types=input.menu_significant(), 
                                 )
                         return plot
 
@@ -346,31 +304,16 @@ def filtered_df():
     return filt_df
 
 
-@reactive.calc
-def filtered_sig():    
-    filt_df = filtered_df()
-    
-    # filter significance
-    if filter_value.get() == "all":
-        pass
-    elif filter_value.get() == "3f1":
-        filt_df = filt_df.loc[filt_df["3f1_significant"] == 1]
-    elif filter_value.get() == "other":
-        filt_df = filt_df.loc[filt_df["other_significant"] == 1]
-    
-    return filt_df
-
-
 @reactive.calc
 def grouped_df_month():
-    filt_df = filtered_sig()
+    filt_df = filtered_df()
     grouped = groupby_date(filt_df, significant=GET_SIGNIFICANT)
     return grouped
 
 
 @reactive.calc
 def grouped_df_day():
-    filt_df = filtered_sig()
+    filt_df = filtered_df()
     date_col = "publication_date"
     grouped = groupby_date(filt_df, group_col=date_col, significant=GET_SIGNIFICANT)
     grouped = pad_missing_dates(
@@ -387,7 +330,7 @@ def grouped_df_day():
 
 @reactive.calc
 def grouped_df_week():
-    filt_df = filtered_sig()
+    filt_df = filtered_df()
     filt_df = add_weeks_to_data(filt_df)
     try:
         grouped = groupby_date(filt_df, group_col=("week_number", "week_of"), significant=GET_SIGNIFICANT)
@@ -407,7 +350,7 @@ def grouped_df_week():
 
 @reactive.calc
 def grouped_df_agency():
-    filt_df = filtered_sig()
+    filt_df = filtered_df()
     grouped = groupby_agency(filt_df, metadata=METADATA, significant=GET_SIGNIFICANT)
     return grouped
 
@@ -423,27 +366,3 @@ def get_grouped_data_over_time():
     else:
         raise ValueError("Only 'daily', 'monthly', or 'weekly' are valid inputs.")
     return grouped
-
-
-# ----- REACTIVE VALUES ----- #
-
-
-filter_value = reactive.value("all")
-
-@reactive.effect
-@reactive.event(input.filter_all)
-def _():
-    filter_value.set("all")
-    filtered_df()
-
-@reactive.effect
-@reactive.event(input.filter_3f1)
-def _():
-    filter_value.set("3f1")
-    filtered_df()
-
-@reactive.effect
-@reactive.event(input.filter_other)
-def _():
-    filter_value.set("other")
-    filtered_df()

modules/plotting.py

@@ -31,20 +31,21 @@ def plot_NA(placeholder_text: str = "Not enough data available to visualize.", p
         )
 
 
-def generate_rule_axis_label(rule_type: str | None = None):
+def generate_rule_axis_label(rule_types: list | None = None):
     """Generate axis label for rules, accounting for rule type ("all", "3f1", or "other")."""
-    y_lab = ""
-    if rule_type is not None:
-        rule_type_options = {
-            "all": "", 
-            "3f1": "3(f)(1) Significant",
-            "other": "Other Significant", 
-        }
-        y_lab = f"Number of {rule_type_options.get(rule_type)} rules".replace("  ", " ")
-    return y_lab
-
-
-def plot_agency(df, group_col = "acronym", value_col = "rules", color="#033C5A", rule_type: str | None = None):
+    categories = ""
+    if (rule_types is None) or ("all" in rule_types):
+        pass
+    elif all(True if cat in rule_types else False for cat in ("3f1-significant", "other-significant")):
+        categories = "significant"
+    elif ("3f1-significant" in rule_types) and ("other-significant" not in rule_types):
+        categories = "Section 3(f)(1) Significant"
+    elif ("3f1-significant" not in rule_types) and ("other-significant" in rule_types):
+        categories = "Other Significant"
+    return f"Number of {categories} rules".replace("  ", " ")
+
+
+def plot_agency(df, group_col = "acronym", value_col = "rules", color="#033C5A", rule_types: list | None = None):
     """Plot rules by agency.
 
     Args:
@@ -57,7 +58,7 @@ def plot_agency(df, group_col = "acronym", value_col = "rules", color="#033C5A",
     """
     order_list = df.loc[:, group_col].to_list()[::-1]
 
-    y_lab = generate_rule_axis_label(rule_type)
+    y_lab = generate_rule_axis_label(rule_types)
     
     plot = (
         ggplot(
@@ -212,7 +213,7 @@ def plot_week(
     return plot
 
 
-def plot_tf(df: DataFrame, frequency: str, rule_type: str | None = None, **kwargs) -> ggplot:
+def plot_tf(df: DataFrame, frequency: str, rule_types: str | None = None, **kwargs) -> ggplot:
     """Plot rules over time by given frequency.
 
     Args:
@@ -234,6 +235,6 @@ def plot_tf(df: DataFrame, frequency: str, rule_type: str | None = None, **kwarg
     if plot_freq is None:
         raise ValueError(f"Frequency must be one of: {', '.join(freq_options.keys())}")
     
-    y_lab = generate_rule_axis_label(rule_type)
+    y_lab = generate_rule_axis_label(rule_types)
 
     return plot_freq(df, y_lab=y_lab, **kwargs)