graph part done

mp1/graph_generator.py

+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+
+def parse_log(line: str):
+    pass
+
+
+def generate_records_for_node():
+    """
+           The key metric we will track in this MP is the “transaction processing time”,
+               measured as the time difference between when a transaction is generated at a node,
+               and when it is processed by the last node in the system.
+
+           You need to plot the CDF (cumulative distribution function) of transaction processing time.
+           A datapoint (X,Y) on the CDF indicates that the Y%ile value of the metric is X. Your y-axis must range from 1-99%ile.
+
+           nodeid generated_time processed_time transaction_str
+
+           log example:
+       """
+
+    with open('./node.log', 'r') as file:
+        for line in file:
+            generated_time, processed_time, transaction_str = line.split(" ")
+            pass
+
+
+def draw_graph():
+    step = 0.05
+    indices = np.arange(0, 1 + step, step)
+    time_processed_per_msg = [1, 2, 2, 3, 4]
+    cdf = pd.DataFrame({'dummy': time_processed_per_msg})['dummy'].quantile(indices)
+    plt.plot(cdf, indices, linewidth=9, label='processed time', color='blue')
+    plt.savefig('./graph.png')
+
+
+if __name__ == '__main__':
+    # generate_records_for_node()
+    draw_graph()

requirements.txt

@@ -2,4 +2,5 @@ matplotlib
 numpy
 psutil
 pre-commit
-
+pandas
+pillow