Update scaling files

plot_calls.py

@@ -1,3 +1,4 @@
+import math
 from datetime import datetime
 
 import matplotlib.pyplot as plt
@@ -14,14 +15,44 @@ log_files = [
 for log_file in log_files:
     path_ = f"./data/{log_file}"
     df = pd.read_csv(filepath_or_buffer=path_, sep=";")
-    df["elapsed"] = df["finished"].apply(lambda x: datetime.strptime(x, "%Y-%m-%d %H:%M:%S.%f")) - df["started"].apply(
-        lambda x: datetime.strptime(x, "%Y-%m-%d %H:%M:%S.%f"))
-    df["elapsed"] = df["elapsed"].apply(lambda x: x.total_seconds())
+    df["finished_ts"] = df["finished"].apply(
+        lambda x: datetime.strptime(x, "%Y-%m-%d %H:%M:%S.%f").timestamp())
+    df["started_ts"] = df["started"].apply(
+        lambda x: datetime.strptime(x, "%Y-%m-%d %H:%M:%S.%f").timestamp())
+    df["elapsed"] = df["finished_ts"] - df["started_ts"]
+
     df["success"] = df["outputs"].apply(lambda x: 0 if "Time-out" in x else 1)
-    df.to_csv(f"./data/processed_{log_file}", index=False, sep=";")
 
     student_numbers = sorted(df['active_students'].unique())
 
+    bins_dict = dict()  # bins size for each group
+    min_finished_dict = dict()  # zero time for each group
+
+    for student_number in student_numbers:
+        # for each student group calculates bins size and zero time
+        min_finished = df["finished_ts"][df["active_students"] == student_number].min()
+        max_finished = df["finished_ts"][df["active_students"] == student_number].max()
+        bins = math.ceil(max_finished - min_finished)
+        bins_dict.update({student_number: bins})
+        min_finished_dict.update({student_number: min_finished})
+        print(f"student number: {student_number}")
+        print(f"min finished: {min_finished}")
+        print(f"max finished: {max_finished}")
+        print(f"bins finished seconds: {bins}, minutes: {bins / 60}")
+
+    df["time_line"] = None
+    for student_number in student_numbers:
+        # calculates time-line for each student group
+        df["time_line"] = df.apply(
+            lambda x: x["finished_ts"] - min_finished_dict[student_number]
+            if x["active_students"] == student_number
+            else x["time_line"],
+            axis=1
+        )
+
+    # creates a '.csv' from the dataframe
+    df.to_csv(f"./data/processed_{log_file}", index=False, sep=";")
+
     result = df.groupby(['active_students', 'success']) \
         .agg({
         'elapsed': ['mean', 'median', 'min', 'max'],
@@ -38,6 +69,7 @@ for log_file in log_files:
         title = "API result for 'text2int' endpoint"
 
     for student_number in student_numbers:
+        # Prints percentage of the successful and failed calls
         try:
             failed_calls = result.loc[(student_number, 0), 'success'][0]
         except:
@@ -48,25 +80,17 @@ for log_file in log_files:
 
     rows = len(student_numbers)
 
-    # plt.figure(figsize=(16, 10))
-    # for index, student_number in enumerate(student_numbers, 1):
-    #     data = df[df["active_students"] == student_number]
-    #     fig = plt.subplot(rows, 2, 2 * index - 1)
-    #     plt.title("y=seconds, x=active students", x=0.75, y=0.75)
-    #     plt.boxplot(x=data["elapsed"], labels=[student_number])
-    #     plt.subplot(rows, 2, 2 * index)
-    #     plt.title("y=count of seconds, x=seconds", x=0.75, y=0.75)
-    #     plt.hist(x=data["elapsed"], bins=25, edgecolor='white')
-
     fig, axs = plt.subplots(rows, 2)  # (rows, columns)
 
     for index, student_number in enumerate(student_numbers):
+        # creates a boxplot for each test group
         data = df[df["active_students"] == student_number]
         axs[index][0].boxplot(x=data["elapsed"])  # axs[row][column]
         # axs[index][0].set_title(f'Boxplot for {student_number} students')
         axs[index][0].set_xlabel(f'student number {student_number}')
         axs[index][0].set_ylabel('Elapsed time (s)')
 
+        # creates a histogram for each test group
         axs[index][1].hist(x=data["elapsed"], bins=25)  # axs[row][column]
         # axs[index][1].set_title(f'Histogram for {student_number} students')
         axs[index][1].set_xlabel('seconds')
@@ -74,4 +98,19 @@ for log_file in log_files:
 
     fig.suptitle(title, fontsize=16)
 
+    fig, axs = plt.subplots(rows, 1)  # (rows, columns)
+
+    for index, student_number in enumerate(student_numbers):
+        # creates a histogram and shows API calls on a timeline for each test group
+        data = df[df["active_students"] == student_number]
+
+        print(data["time_line"].head(10))
+
+        axs[index].hist(x=data["time_line"], bins=bins_dict[student_number])  # axs[row][column]
+        # axs[index][1].set_title(f'Histogram for {student_number} students')
+        axs[index].set_xlabel('seconds')
+        axs[index].set_ylabel('Count of API calls')
+
+    fig.suptitle(title, fontsize=16)
+
 plt.show()

requirements.txt

@@ -6,3 +6,4 @@ python-dotenv
 transformers
 torch
 httpx
+matplotlib