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Cherry-pick plot/main.py in 'faeb49eb32e0c5ac30f68d60aedf435f32edb33a' to 'master'

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Quentin Legot 2023-03-31 11:57:12 +02:00
parent b3201ff7a5
commit b78f9fc6b8
1 changed files with 17 additions and 20 deletions
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35
plot/main.py
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@ -8,53 +8,51 @@ nb_files = os.listdir(".." + os.sep + "export")
size = len(nb_files) 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)) averages_mkn = np.empty((size, 2))
nb = 0 nb = 0
for i in nb_files: for nb_users, data in arr:
data = pd.read_csv(".." + os.sep + "export" + os.sep + i, delimiter=';').to_numpy()
rb = data[:, 4] rb = data[:, 4]
total = 0.0 total = 0.0
for x in rb: for x in rb:
total = total + x total = total + x
average = total / len(rb) average = total / len(rb)
nb_users = i.split(".")[0] averages_mkn[nb, 0] = nb_users
averages_mkn[nb, 0] = int(nb_users)
averages_mkn[nb, 1] = average averages_mkn[nb, 1] = average
nb += 1 nb += 1
return averages_mkn return averages_mkn
def rb_available() -> np.ndarray: def rb_available(arr: list[tuple[int, np.ndarray]]) -> np.ndarray:
available = np.zeros((size, 2)) available = np.zeros((size, 2))
nb = 0 nb = 0
for i in nb_files: for nb_users, data in arr:
data = pd.read_csv(".." + os.sep + "export" + os.sep + i, delimiter=';').to_numpy() available[nb, 0] = nb_users
nb_users = i.split(".")[0]
available[nb, 0] = int(nb_users)
available[nb, 1] = (data.shape[0] / (200 * 10000)) * 100 available[nb, 1] = (data.shape[0] / (200 * 10000)) * 100
nb += 1 nb += 1
return available return available
def delay() -> np.ndarray: def delay(arr: list[tuple[int, np.ndarray]]) -> np.ndarray:
delays = np.zeros((size, 2)) delays = np.zeros((size, 2))
nb = 0 nb = 0
for i in nb_files: for nb_users, data in arr:
data = pd.read_csv(".." + os.sep + "export" + os.sep + i, delimiter=';').to_numpy()
nb_users = i.split(".")[0]
d = data[:, 5] d = data[:, 5]
for x in d: for x in d:
delays[nb, 0] = int(nb_users) delays[nb, 0] = nb_users
delays[nb, 1] = float(x) delays[nb, 1] = float(x)
nb += 1 nb += 1
return delays return delays
averages = mean_mkn() np_arr: list[tuple[int, np.ndarray]] = list()
available = rb_available() for i in nb_files:
delays = delay() 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) delays.sort(axis=0)
# Data for plotting # Data for plotting
averages.sort(axis=0) averages.sort(axis=0)
@ -74,7 +72,6 @@ ax.set(xlabel='number of users', ylabel='RB utilisés', title='Pourcentage de RB
ax.grid() ax.grid()
plt.show() plt.show()
fig, ax = plt.subplots() fig, ax = plt.subplots()
ax.scatter(delays[:, 0], delays[:, 1]) ax.scatter(delays[:, 0], delays[:, 1])
ax.set(xlabel='number of users', ylabel='delays(ms)', title='Delay') ax.set(xlabel='number of users', ylabel='delays(ms)', title='Delay')