From 7c7a1baa674eef288dd1bf74a8e1ac078b877741 Mon Sep 17 00:00:00 2001
From: David Preiss <davepreiss@gmail.com>
Date: Thu, 2 Mar 2023 22:46:20 +0000
Subject: [PATCH] Update week3/random_walker.py, week3/README.MD

---
 week3/README.MD        | 2 +-
 week3/random_walker.py | 3 +--
 2 files changed, 2 insertions(+), 3 deletions(-)

diff --git a/week3/README.MD b/week3/README.MD
index 353d386..2751a4d 100644
--- a/week3/README.MD
+++ b/week3/README.MD
@@ -4,4 +4,4 @@
 
 Code is [here](random_walker.py).
 
-![](img/random_walker.jpg)
+![](random_walker.jpg)
diff --git a/week3/random_walker.py b/week3/random_walker.py
index 24e797b..c2f38fd 100644
--- a/week3/random_walker.py
+++ b/week3/random_walker.py
@@ -22,7 +22,6 @@ def build_LFSR(seed, bits_needed=1000):
 walker_states = np.zeros((10, 1000))
 
 # create our seeds by grabbing a 16 bit number from the LSB of the current time in us
-# cheating and just adding a fixed delay to get different seeds
 seeds = []
 for i in range(10):
     delay = .1
@@ -41,7 +40,7 @@ timestep = np.array(range(1000))
 import matplotlib.pyplot as plt
 plt.plot(walker_states.T) # transpose it so we can plot it
 
-# for the 3 sigma lines, we will plot the sqrt of the timestep and divide the result by 1.5 to plot above and below the mean (0)
+# for the 3 sigma lines, we will multiply the sqrt of the timestep and divide the result by 1.5 to plot above and below the mean (0)
 plt.plot(timestep, 1.5 * np.sqrt(timestep), color='black', alpha = 0.25)
 plt.plot(timestep, -1.5 * np.sqrt(timestep), color='black', alpha = 0.25)
 plt.show()
-- 
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