Add percents

evaltools/plotting/boxplot.py

@@ -76,7 +76,7 @@ def boxplot(
                 ax.scatter(
                     boxplot + 1 + jitter_val,
                     score,
-                    color=districtr(plan+1).pop(),
+                    color=proposed_info['colors'][plan],
                     edgecolor='black',
                     s=100,
                     alpha=0.9,

evaltools/plotting/histogram.py

@@ -80,7 +80,7 @@ def histogram(ax,
             # Plot vertical line.
             ax.axvline(
                 s + bin_width / 2 + jitter_val,
-                color=districtr(i+1).pop(),
+                color=proposed_info['colors'][i],
                 lw=2,
                 label=f"{proposed_info['names'][i]}: {round(s,2)}",
             )

evaltools/plotting/sealevel.py

@@ -4,7 +4,7 @@
 import numpy as np
 import random
 
-def sealevel(ax, scores, num_districts, proposed_info, ticksize=12):
+def sealevel(ax, scores, num_districts, proposed_info, rotation=0, ticksize=12):
     """
     Plot a sea level plot: Each plan is a line across our elections on the 
     x-axis, with Democratic vote share on the y-axis. The statewide Dem. vote
@@ -17,6 +17,7 @@ def sealevel(ax, scores, num_districts, proposed_info, ticksize=12):
         proposed_info (dict, optional): Dictionary with keys of `colors`, `names`;
             the \(i\)th color in `color` corresponds to the \(i\)th name in `names`.
         ticksize (float, optional): Font size for tick labels.
+        rotation (float): degrees to rotate xticklabels.
     """
     assert "statewide" in scores
     elections = sort_elections(scores["statewide"].keys())
@@ -43,13 +44,13 @@ def sealevel(ax, scores, num_districts, proposed_info, ticksize=12):
                )
     ax.legend()
     if num_districts <= 20:
-        yticks = np.arange(0, 1 + 1/num_districts, 1/num_districts)
+        yticks = np.arange(0, 1 + 0.5/num_districts, 1/num_districts)
         yticklabels = [f"{i}/{num_districts}" for i in range(num_districts + 1)]
         ax.set_yticks(yticks)
         ax.set_yticklabels(yticklabels)
     ax.axhline(0.5, color=defaultGray, label="50%")
     ax.set_xticks(range(len(elections)))
-    ax.set_xticklabels(elections, fontsize=ticksize)
+    ax.set_xticklabels(elections, fontsize=ticksize, rotation=rotation)
     ax.set_ylim(-0.02, 1)
 
 

evaltools/plotting/violin.py

@@ -82,7 +82,7 @@ def violin(
                 ax.scatter(
                     violin + 1 + jitter_val,
                     score,
-                    color=districtr(plan+1).pop(),
+                    color=proposed_info['colors'][plan],
                     edgecolor='black',
                     s=100,
                     alpha=0.9,

evaltools/scoring/__init__.py

@@ -19,6 +19,7 @@
     "opp_party_districts",
     "party_wins_by_district",
     "seats",
+    "percents",
     "aggregate_seats",
     "responsive_proportionality",
     "stable_proportionality",

evaltools/scoring/partisan.py

@@ -45,6 +45,10 @@ def _seats(part: Partition, election_cols: Iterable[str], party: str, mean: bool
     result = {e: sum([part[e].won(party, d) for d in part.parts.keys() if d != -1]) for e in election_cols}
     return float(np.mean(list(result.values()))) if mean else result
 
+def _percents(part: Partition, election_cols: Iterable[str], party:str, mean: bool = False) -> ScoreValue:
+    result = {e: part[e].percent(party) for e in election_cols}
+    return float(np.mean(list(result.values()))) if mean else result
+
 def _responsive_proportionality(part: Partition, election_cols: Iterable[str], party: str) -> PlanWideScoreValue:
     result = [(part[e].seats(party) / len(part)) - (part[e].percent(party)) for e in election_cols]
     return float(np.mean(result))

evaltools/scoring/scores.py

@@ -12,6 +12,7 @@
     _opp_party_districts,
     _party_wins_by_district,
     _seats,
+    _percents,
     _aggregate_seats,
     _efficiency_gap,
     _simplified_efficiency_gap,
@@ -280,6 +281,26 @@ def seats(election_cols: Iterable[str], party: str, mean: bool = False) -> Score
     prefix = "mean_" if mean else ""
     return Score(f"{prefix}{party}_seats", partial(_seats, election_cols=election_cols, party=party, mean=mean))
 
+def percents(election_cols: Iterable[str], party: str, mean: bool = False) -> Score:
+    """
+    Score representing how the percent of the vote the POV party won in each election (this is
+    constant regardless of districting plan).
+
+    Args:
+        election_cols (Iterable[str]): The names of the election updaters over which to compute
+            results for.
+        party (str): The "point of view" political party.
+        mean (bool): Whether to return the mean of the score over all elections, or a dictionary
+                     of the score for each election.
+
+    Returns:
+        A score object with name `"{party}_percents"` and associated function that takes a partition and
+        returns an ScoreValue for the percent of the statewide vote won by the POV party for each
+        election.
+    """
+    prefix = "mean_" if mean else ""
+    return Score(f"{prefix}{party}_percents", partial(_percents, election_cols=election_cols, party=party, mean=mean))
+
 def aggregate_seats(election_cols: Iterable[str], party: str) -> Score:
     """
     Score representing how many total seats (districts) within a given plan the POV party won across