From b78f9fc6b868f66fe4edb79738812e88553b7d14 Mon Sep 17 00:00:00 2001
From: Quentin Legot <legotquentin@gmail.com>
Date: Fri, 31 Mar 2023 11:57:12 +0200
Subject: [PATCH] Cherry-pick plot/main.py in
 'faeb49eb32e0c5ac30f68d60aedf435f32edb33a' to 'master'

---
 plot/main.py | 37 +++++++++++++++++--------------------
 1 file changed, 17 insertions(+), 20 deletions(-)

diff --git a/plot/main.py b/plot/main.py
index 5f91b25..a666898 100644
--- a/plot/main.py
+++ b/plot/main.py
@@ -8,53 +8,51 @@ nb_files = os.listdir(".." + os.sep + "export")
 size = len(nb_files)
 
 
-def mean_mkn() -> np.ndarray:
+def mean_mkn(arr: list[tuple[int, np.ndarray]]) -> np.ndarray:
     averages_mkn = np.empty((size, 2))
     nb = 0
-    for i in nb_files:
-        data = pd.read_csv(".." + os.sep + "export" + os.sep + i, delimiter=';').to_numpy()
+    for nb_users, data in arr:
         rb = data[:, 4]
 
         total = 0.0
         for x in rb:
             total = total + x
         average = total / len(rb)
-        nb_users = i.split(".")[0]
-        averages_mkn[nb, 0] = int(nb_users)
+        averages_mkn[nb, 0] = nb_users
         averages_mkn[nb, 1] = average
         nb += 1
     return averages_mkn
 
 
-def rb_available() -> np.ndarray:
+def rb_available(arr: list[tuple[int, np.ndarray]]) -> np.ndarray:
     available = np.zeros((size, 2))
     nb = 0
-    for i in nb_files:
-        data = pd.read_csv(".." + os.sep + "export" + os.sep + i, delimiter=';').to_numpy()
-        nb_users = i.split(".")[0]
-        available[nb, 0] = int(nb_users)
+    for nb_users, data in arr:
+        available[nb, 0] = nb_users
         available[nb, 1] = (data.shape[0] / (200 * 10000)) * 100
         nb += 1
     return available
 
 
-def delay() -> np.ndarray:
+def delay(arr: list[tuple[int, np.ndarray]]) -> np.ndarray:
     delays = np.zeros((size, 2))
     nb = 0
-    for i in nb_files:
-        data = pd.read_csv(".." + os.sep + "export" + os.sep + i, delimiter=';').to_numpy()
-        nb_users = i.split(".")[0]
+    for nb_users, data in arr:
         d = data[:, 5]
         for x in d:
-            delays[nb, 0] = int(nb_users)
+            delays[nb, 0] = nb_users
             delays[nb, 1] = float(x)
         nb += 1
     return delays
 
 
-averages = mean_mkn()
-available = rb_available()
-delays = delay()
+np_arr: list[tuple[int, np.ndarray]] = list()
+for i in nb_files:
+    np_arr.append((int(i.split(".")[0]), pd.read_csv(".." + os.sep + "export" + os.sep + i, delimiter=';').to_numpy()))
+
+averages = mean_mkn(np_arr)
+available = rb_available(np_arr)
+delays = delay(np_arr)
 delays.sort(axis=0)
 # Data for plotting
 averages.sort(axis=0)
@@ -74,9 +72,8 @@ ax.set(xlabel='number of users', ylabel='RB utilisés', title='Pourcentage de RB
 ax.grid()
 plt.show()
 
-
 fig, ax = plt.subplots()
 ax.scatter(delays[:, 0], delays[:, 1])
 ax.set(xlabel='number of users', ylabel='delays(ms)', title='Delay')
 ax.grid()
-plt.show()
\ No newline at end of file
+plt.show()