[MNT] Switch to 1 decimal place for all plots (#94)

* set global # decimal places

* add param for # decimals in all plotting funcs

* specify global # decimals when calling plotting funcs

* update type hint

climate_emotions_map/app.py

@@ -23,6 +23,7 @@
 from .utility import (  # IMPACT_COLORMAP,; OPINION_COLORMAP,
     ALL_STATES_LABEL,
     DEFAULT_QUESTION,
+    NUM_DECIMALS,
     SECTION_TITLES,
 )
 
@@ -141,6 +142,7 @@ def update_sample_descriptive_plot(state):
     """Update the sample descriptive plot based on the selected state."""
     return make_descriptive_plots(
         state=state,
+        decimals=NUM_DECIMALS,
     )
 
 
@@ -212,6 +214,7 @@ def update_map(question_value, state, impact):
         impact=impact,
         colormap_range_padding=MAP_LAYOUT["colormap_range_padding"],
         margins=MAP_LAYOUT["margin"],
+        decimals=NUM_DECIMALS,
         # opinion_colormap=OPINION_COLORMAP,
         # impact_colormap=IMPACT_COLORMAP,
     )
@@ -247,6 +250,7 @@ def update_selected_question_bar_plot(
         state=state,
         stratify=is_party_stratify_checked,
         threshold=threshold,
+        decimals=NUM_DECIMALS,
         fig_kw=SINGLE_SUBQUESTION_FIG_KW,
     )
     return figure
@@ -319,6 +323,7 @@ def update_stacked_bar_plots(
             state=state,
             stratify=is_party_stratify_checked,
             threshold=threshold,
+            decimals=NUM_DECIMALS,
         )
         figures.append(figure)
 

climate_emotions_map/layout.py

@@ -12,6 +12,7 @@
 from .utility import (  # IMPACT_COLORMAP,; OPINION_COLORMAP,
     ALL_STATES_LABEL,
     DEFAULT_QUESTION,
+    NUM_DECIMALS,
     SECTION_TITLES,
 )
 
@@ -140,6 +141,7 @@ def create_sample_descriptive_plot():
         id="sample-descriptive-plot",
         figure=make_descriptive_plots(
             state=None,
+            decimals=NUM_DECIMALS,
         ),
         config=DCC_GRAPH_CONFIG,
         # TODO: Revisit
@@ -368,6 +370,7 @@ def create_map_plot():
                 outcome=DEFAULT_QUESTION["outcome"],
                 colormap_range_padding=MAP_LAYOUT["colormap_range_padding"],
                 margins=MAP_LAYOUT["margin"],
+                decimals=NUM_DECIMALS,
                 # opinion_colormap=OPINION_COLORMAP,
             ),
             # vh = % of viewport height
@@ -443,6 +446,7 @@ def create_bar_plots_for_question(question_id: str, subquestion_id: str):
                 state=None,
                 stratify=False,
                 threshold=DEFAULT_QUESTION["outcome"],
+                decimals=NUM_DECIMALS,
             ),
             config=DCC_GRAPH_CONFIG,
         ),
@@ -470,6 +474,7 @@ def create_selected_question_bar_plot():
                 state=None,
                 stratify=False,
                 threshold=DEFAULT_QUESTION["outcome"],
+                decimals=NUM_DECIMALS,
                 fig_kw=SINGLE_SUBQUESTION_FIG_KW,
             ),
             config=DCC_GRAPH_CONFIG,

climate_emotions_map/make_descriptive_plots.py

@@ -1,7 +1,6 @@
 #!/usr/bin/env python
 from functools import partial
 from textwrap import wrap
-from typing import Optional
 
 import numpy as np
 import pandas as pd
@@ -138,8 +137,6 @@
     Q2_LABEL: (11, 1),
 }
 
-DECIMALS = 0
-
 
 def get_categories_dict(df: pd.DataFrame) -> dict:
     """
@@ -179,8 +176,9 @@ def get_category_to_display(category: str, demographic_variable: str):
 def make_descriptive_plot_traces(
     df: pd.DataFrame,
     demographic_variable: str,
-    marker_color=None,
-    reverse=True,
+    marker_color: str | None = None,
+    reverse: bool = True,
+    decimals: int = 1,
 ) -> go.Bar:
     """
     Make a single plot for a descriptive demographic variable.
@@ -192,6 +190,8 @@ def make_descriptive_plot_traces(
         and "percentage".
     demographic_variable : str
         Demographic variable to plot.
+    decimals : int, optional
+        Number of decimal places to display, by default 1
     """
     # subset the data
     df: pd.DataFrame = df.loc[
@@ -233,7 +233,7 @@ def make_descriptive_plot_traces(
             #     for n, percentage in zip(df[COL_N], df[COL_PERCENTAGE])
             # ],
             hovertemplate=(
-                f"<b>%{{customdata[1]}}</b>: %{{x:.{DECIMALS}f}}% (%{{customdata[0]}})"
+                f"<b>%{{customdata[1]}}</b>: %{{x:.{decimals}f}}% (%{{customdata[0]}})"
                 "<extra></extra>"
             ),
             marker_color=marker_color,
@@ -259,7 +259,7 @@ def wrap_text_label(text: str, width: int) -> pd.DataFrame:
 
 
 def make_impact_plot_traces(
-    df: pd.DataFrame, marker_color=None, text_wrap_width=10
+    df: pd.DataFrame, marker_color=None, text_wrap_width=10, decimals=1
 ):
     data_impact = df.loc[
         df[COL_DEMOGRAPHIC_VARIABLE].isin(IMPACT_VARIABLES)
@@ -290,7 +290,7 @@ def make_impact_plot_traces(
                 customdata=list(zip(x, data_category[COL_N])),
                 hovertemplate=(
                     "<b>%{customdata[0]}</b>"
-                    f": %{{y:.{DECIMALS}f}}% (%{{customdata[1]}})"
+                    f": %{{y:.{decimals}f}}% (%{{customdata[1]}})"
                     "<extra></extra>"
                 ),
                 marker_color=marker_color,
@@ -303,9 +303,10 @@ def make_impact_plot_traces(
 
 def make_descriptive_plots(
     state: str | None = None,
-    margins: Optional[dict] = None,
-    text_wrap_width=14,
-    colors: list[str] = None,
+    margins: dict | None = None,
+    text_wrap_width: int = 14,
+    colors: list[str] | None = None,
+    decimals: int = 1,
 ) -> go.Figure:
     """Make the sample descriptive plots
 
@@ -319,6 +320,8 @@ def make_descriptive_plots(
         Maximum width for wrapping some text labels, by default 14
     colors : list[str], optional
         List of colors for the bar plots, by default None (uses the turbo colorscale)
+    decimals : int, optional
+        Number of decimal places to display, by default 1
 
     Returns
     -------
@@ -363,11 +366,18 @@ def make_descriptive_plots(
 
         if demographic_variable == IMPACTS_LABEL:
             traces = make_impact_plot_traces(
-                data, text_wrap_width=text_wrap_width, marker_color=color
+                data,
+                text_wrap_width=text_wrap_width,
+                marker_color=color,
+                decimals=decimals,
             )
         else:
             traces = make_descriptive_plot_traces(
-                data, demographic_variable, reverse=True, marker_color=color
+                data,
+                demographic_variable,
+                reverse=True,
+                marker_color=color,
+                decimals=decimals,
             )
 
         for trace in traces:

climate_emotions_map/make_map.py

@@ -64,7 +64,7 @@ def make_map(
     impact_marker_size_scale: float = 1.0,
     colormap_range_padding: int = 10,
     margins: dict = None,
-    decimals: int = 0,
+    decimals: int = 1,
 ) -> go.Figure:
     """Generate choropleth map showing opinion and/or impact data.
 
@@ -97,7 +97,7 @@ def make_map(
     margins : dict | None, optional
         Margins for the Plotly figure, by default 30 everywhere
     decimals : int, optional
-        Number of decimals to show in the hoverbox, by default 0
+        Number of decimals to show in the hoverbox, by default 1
 
     Returns
     -------

climate_emotions_map/make_stacked_bar_plots.py

@@ -135,7 +135,7 @@ def plot_bars(
     y="question",
     color="outcome",
     title=None,  # TODO: remove this argument?
-    round_to=2,  # NOTE: This is the number of decimal places to round the data to, BEFORE multiplying by 100.
+    decimals=1,
     sort_order="descending",
     facet_order=None,
     palette=None,
@@ -145,11 +145,7 @@ def plot_bars(
     """Make a stacked bar plot of the opinions of the whole sample, split by state and party."""
     facet_var = "sub_question"
 
-    # Determine the appropriate number of decimal places to use after converting data
-    # to percentages based on the applied rounding, to use in hover text
-    decimals = max(0, round_to - 2)
-
-    plot_df[x] = plot_df[x].round(round_to) * 100
+    plot_df[x] = plot_df[x] * 100
 
     # sort by subquestion
 
@@ -331,6 +327,7 @@ def make_stacked_bar(
     state: str | None = None,
     stratify: bool = False,
     threshold: str | None = None,
+    decimals: int = 1,
     palettes: dict = None,
     fig_kw: dict = None,
 ) -> px.bar:
@@ -350,6 +347,8 @@ def make_stacked_bar(
         Whether to stratify the data by party. The default is False.
     threshold : str, optional
         The outcome ID for the Likert endorsement level to threshold at (e.g. "3+"). The default is None.
+    decimals : int, optional
+        The number of decimal places to display for the percentage values. The default is 1.
     palettes : dict, optional
         A dictionary of color palettes for different numbers of outcomes. The default is None.
     fig_kw : dict, optional
@@ -449,6 +448,7 @@ def make_stacked_bar(
         y=y,
         facet_order=facet_order,
         sort_order=sort_order,
+        decimals=decimals,
         palette=palette,
         fig_kw=fig_kw,
     )

climate_emotions_map/utility.py

@@ -17,6 +17,8 @@
     "all_questions": "Select a survey domain to view responses for",
     "demographics": "Sample Characteristics",
 }
+# Number of decimal places to round to in all plots
+NUM_DECIMALS = 1
 
 # We have not yet decided on the best colormaps to use
 # OPINION_COLORMAP = "OrRd"