chagen import alias

mu_map/polar_map/eval_perfusion.py

 from typing import List, Optional
 
-import matplotlib as mlp
+import matplotlib as mpl
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
@@ -40,13 +40,13 @@ def absolute_percent_error(prediction: np.ndarray, target: np.ndarray) -> float:
     return 100.0 * diff / mean_t
 
 
-def same_limits(axes: List[mlp.axes.Axes]):
+def same_limits(axes: List[mpl.axes.Axes]):
     """
     Adjust the limits of a all axes to be the same.
 
     Parameters
     ----------
-    axes: List[mlp.axes.Axes]
+    axes: List[mpl.axes.Axes]
         axes whose limits are adjusted
     """
     x_min = min(map(lambda ax: ax.get_xlim()[0], axes))
@@ -61,7 +61,7 @@ def same_limits(axes: List[mlp.axes.Axes]):
 def bland_altman(
     data1: np.ndarray,
     data2: np.ndarray,
-    ax: Optional[mlp.axes.Axes] = None,
+    ax: Optional[mpl.axes.Axes] = None,
     color=color_lists["printer_friendly"][0],
     color_mean=color_lists["printer_friendly"][5],
     color_std="black",
@@ -77,7 +77,7 @@ def bland_altman(
         first data array
     data2: np.ndarray
         second data array
-    ax: mlp.axes.Axes, optional
+    ax: mpl.axes.Axes, optional
         axes to plot on, a new one is created if not given
     color: str
         color of the dots
@@ -112,7 +112,7 @@ def bland_altman(
 def plot_corellation(
     data1: np.ndarray,
     data2: np.ndarray,
-    ax: Optional[mlp.axes.Axes] = None,
+    ax: Optional[mpl.axes.Axes] = None,
     color: str = color_lists["printer_friendly"][0],
     color_line: str = color_lists["printer_friendly"][5],
     v_min: Optional[float] = None,
@@ -127,7 +127,7 @@ def plot_corellation(
         first data array
     data2: np.ndarray
         second data array
-    ax: mlp.axes.Axes, optional
+    ax: mpl.axes.Axes, optional
         axes to plot on, a new one is created if not given
     color: str
         color of the dots