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LSYS · Dec 16, 2023 · 5a5bd3e · 5a5bd3e
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commit 5a5bd3e
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Showing 2 changed files with 17 additions and 11 deletions.
diff --git a/forestplot/mplot_graph_utils.py b/forestplot/mplot_graph_utils.py
@@ -142,7 +142,7 @@ def mdraw_est_markers(
     mcolor : Union[Sequence[str], None], optional
         A sequence of colors for each model group, defaults to ["0", "0.4", ".8", "0.2"].
     **kwargs : Any
-        Additional keyword arguments. Supported customizations include 'markersize' (default 40) 
+        Additional keyword arguments. Supported customizations include 'markersize' (default 40)
         and 'offset' for the spacing between markers of different model groups.
 
     Returns
@@ -159,7 +159,7 @@ def mdraw_est_markers(
         _y = base_y_vector + (ix * offset)
         ax.scatter(y=_y, x=_df[estimate], marker=msymbols[ix], color=mcolor[ix], s=markersize)
     return ax
- 
+
 
 def mdraw_ci(
     dataframe: pd.core.frame.DataFrame,

diff --git a/tests/test_mplot_graph_utils.py b/tests/test_mplot_graph_utils.py
@@ -2,11 +2,11 @@
 import pandas as pd
 from matplotlib.pyplot import Axes
 
-from forestplot.mplot_graph_utils import mdraw_ref_xline, mdraw_yticklabels, mdraw_est_markers
+from forestplot.mplot_graph_utils import mdraw_est_markers, mdraw_ref_xline, mdraw_yticklabels
 
 x, y = [0, 1, 2], [0, 1, 2]
 str_vector = ["a", "b", "c"]
-models_vector =["m1", "m1", "m2"]
+models_vector = ["m1", "m1", "m2"]
 input_df = pd.DataFrame(
     {
         "yticklabel": str_vector,
@@ -55,11 +55,17 @@ def test_mdraw_yticklabels():
 
 
 def test_mdraw_est_markers():
-	_, ax = plt.subplots()
-	ax = mdraw_est_markers(input_df, estimate='estimate', model_col='model', models=list(set(models_vector)), ax=ax)
-	assert (all(isinstance(tick, int)) for tick in ax.get_yticks())
+    _, ax = plt.subplots()
+    ax = mdraw_est_markers(
+        input_df,
+        estimate="estimate",
+        model_col="model",
+        models=list(set(models_vector)),
+        ax=ax,
+    )
+    assert (all(isinstance(tick, int)) for tick in ax.get_yticks())
 
-	xmin, xmax = ax.get_xlim()
-	assert xmin <= input_df["estimate"].min()
-	assert xmax >= input_df["estimate"].max()
-	assert len(ax.collections) == len(set(models_vector))
+    xmin, xmax = ax.get_xlim()
+    assert xmin <= input_df["estimate"].min()
+    assert xmax >= input_df["estimate"].max()
+    assert len(ax.collections) == len(set(models_vector))