minimal linting here

examples/demo_lhc_rigid_waist_shift.py

@@ -36,8 +36,8 @@
     builds, but you can use any branch you want.
 """
 # sphinx_gallery_thumbnail_number = 3
-from collections import namedtuple
 from multiprocessing import cpu_count
+from typing import NamedTuple
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -324,9 +324,9 @@
 # We can use the above to determine these values for different knob settings.
 # First, let's define some structures and functions.
 
-Waist = namedtuple("Waist", ["x", "y"])
-BetasIP = namedtuple("Betas", ["x", "y"])
-Result = namedtuple("Result", ["waists", "betas"])
+Waist = NamedTuple("Waist", ["x", "y"])
+BetasIP = NamedTuple("Betas", ["x", "y"])
+Result = NamedTuple("Result", ["waists", "betas"])
 
 
 def find_waists(current_twiss: tfs.TfsDataFrame, initial_twiss: tfs.TfsDataFrame) -> Waist:

examples/demo_stats_fitting.py

@@ -237,11 +237,11 @@ def chi_dist(num: int, meas_used: int) -> np.ndarray:
     label="Generated Chi Distribution",
     figsize=(20, 12),
 )
-dataYLim = ac.get_ylim()
+data_ylim = ac.get_ylim()
 
 # Find best fit candidate
 best_fit_func, best_fit_params = fitting.best_fit_distribution(chi_data, 200, ac)
-ac.set_ylim(dataYLim)
+ac.set_ylim(data_ylim)
 ac.set_title("All Fitted Distributions")
 ac.set_ylabel("Normed Hist Counts")
 plt.legend()