diff --git a/Exercise_02/molecular_dynamics.py b/Exercise_02/molecular_dynamics.py
index 5e516a8..3fa4a39 100644
--- a/Exercise_02/molecular_dynamics.py
+++ b/Exercise_02/molecular_dynamics.py
@@ -119,7 +119,7 @@ def kinetic(velocity):
     """
     Computes the kenetic energy given a systems `velocity` state.
     """
-    return (mass / 2.0) * jnp.linalg.norm(velocity, axis = 1).sum()
+    return (mass / 2.0) * (velocity**2).sum()
 
 @jit
 def step(position, velocity, acceleration, box_size, step_size):
diff --git a/Exercise_02/task02.py b/Exercise_02/task02.py
index ea0dd18..5235f19 100644
--- a/Exercise_02/task02.py
+++ b/Exercise_02/task02.py
@@ -99,15 +99,3 @@ if config.verbose:
     print( "Final Mean Forces:       {:.4e} {:.4e} {:.4e}".format(*final_mean_forces))
     print(f"Final Mean ||Forces||:   {final_mean_fnorm:.4e}")
     print(f"Done: saved inital state to '{config.output}'")
-
-# # if a plot path is provided
-# if not config.plot is None:
-#     from mpl_toolkits import mplot3d
-#     import matplotlib.pyplot as plt
-#     # new plot with 3D axis
-#     plt.figure()
-#     ax = plt.axes(projection = "3d")
-#     # create 3D position scatter plot
-#     ax.scatter(position[:, 0], position[:, 1], position[:, 2])
-#     # and save to file
-#     plt.savefig(config.plot)
diff --git a/Exercise_02/task04.py b/Exercise_02/task04.py
index 793e489..1399c31 100644
--- a/Exercise_02/task04.py
+++ b/Exercise_02/task04.py
@@ -10,6 +10,15 @@ arg_parser = OptionParser(usage = usage)
 arg_parser.add_option("-i", "--input", action = "store", type = str,
     dest = "input", default = None,
     help = "input file path (required!)")
+arg_parser.add_option("-A", "--outputA", action = "store", type = str,
+    dest = "outputA", default = "task04A.txt",
+    help = "first output file path (default: 'task04A.txt')")
+arg_parser.add_option("-B", "--outputB", action = "store", type = str,
+    dest = "outputB", default = "task04B.txt",
+    help = "second output file path (default: 'task04B.txt')")
+arg_parser.add_option("-s", "--cdf_sample_count", action = "store", type = int,
+    dest = "cdf_sample_count", default = 5,
+    help = "Radius interval sample count for CDF estimate (default: 5)")
 arg_parser.add_option("-a", "--animate", action = "store_true",
     dest = "animate", default = False,
     help = "creates an trajectory animation of the states in the input file")
@@ -31,36 +40,109 @@ else:
         exit(-1)
 
 ################################################################################
-###                      task 3 / trajectory generation                      ###
+###                         task 4 / post processing                         ###
 ################################################################################
 # note, load module _after_ processing script parameters (no need to load all
 # of the heavy numeric modules if only usage or alike is needed)
 import numpy as np
-import matplotlib.pyplot as plt
-from mpl_toolkits import mplot3d
 from molecular_dynamics import iter_load, energy, kinetic
 
-if config.animate:
-    # new plot with 3D axis
-    fig = plt.figure()
-    ax = fig.add_subplot(111, projection = "3d")
 
-count = 0
-for (position, velocity), box_size in iter_load(config.input):
-    count += 1
-    e_pot = energy(position, box_size)
-    e_kin = kinetic(velocity)
-    print(f"{count: >3} - {e_pot:10.5e} {e_kin:10.5e} {(e_pot + e_kin):10.5e}")
+# counte the number of states (system snapshots) in the input file
+nr_states = 0
+# first output file and collect some info used in the next analysis!
+with open(config.outputA, "w") as output:
+    # iterate over all state snapshots stored in the `config.input` file
+    for (position, velocity), box_size in iter_load(config.input):
+        nr_states += 1
+        # compute potneital and kinetic energy for current snapshot
+        e_pot = energy(position, box_size)
+        e_kin = kinetic(velocity)
+        # energy results
+        print(e_pot, e_kin, file = output)
+# report file path
+print(f"Saved energy change to '{config.outputA}'")
 
-    if config.animate:
-        ax.cla()
-        plt.title(f"time step {count}")
-        plt.xlim([0, box_size])
-        plt.ylim([0, box_size])
-        ax.set_zlim([0, box_size])
-        # create 3D position scatter plot
-        position = np.mod(position, box_size)
-        ax.scatter(position[:, 0], position[:, 1], position[:, 2])
-        # # and save to file
-        # plt.savefig(config.plot)
-        plt.pause(0.01)
+# CDF radius sample points (`box_size` known from last loop)
+radii = np.linspace(0, box_size / 2.0, config.cdf_sample_count)[np.newaxis, :]
+# radius Commulative Distribution Function estimate
+CDF = np.zeros((config.cdf_sample_count, ))
+
+# extract only one of two distance combinations of non-equal particles
+lower_tri = np.tril_indices(position.shape[0], k = -1)
+# maximum distance two particles (in folded space) can be apart
+max_dist = box_size / 2.0
+
+# second iteration to estimate pairwise distance CDF
+for i, ((position, velocity), box_size) in enumerate(iter_load(config.input)):
+    # ignore the first 25% (just keep it simple)
+    if 4 * i < nr_states:
+        continue
+    # magic for all particle pair distances
+    diff = position[:, np.newaxis, :] - position
+    diff = diff[lower_tri[0], lower_tri[1], :]
+    # fold space in all directions. The `max_dist` is the maximum distance
+    # in the folded space untill the distance through the folded space
+    # border is smaller than inside the box. 3-axis parallel fold
+    diff = np.mod(diff + max_dist, box_size) - max_dist
+    # count nr particles pairs with distance smaller than r
+    dist = np.linalg.norm(diff, axis = 1)
+    # accumulate per timepoint partial mean estimates of the CDF
+    CDF += np.mean(dist[:, np.newaxis] < radii, axis = 0)
+# final division by timesteps as mean over pairs and time.
+CDF /= nr_states
+
+# store the CDF estimate to the second file
+with open(config.outputB, "w") as output:
+    for r, cdf in zip(radii.ravel(), CDF.ravel()):
+        print(r, cdf, file = output)
+# report file path
+print(f"Saved radius CDF estimate to '{config.outputB}'")
+
+
+# import matplotlib.pyplot as plt
+# from mpl_toolkits import mplot3d
+
+# if config.animate:
+#     # new plot with 3D axis
+#     count = 0
+#     fig = plt.figure()
+#     ax = fig.add_subplot(111, projection = "3d")
+
+# for (position, velocity), box_size in iter_load(config.input):
+#     e_pot = energy(position, box_size)
+#     e_kin = kinetic(velocity)
+#     print(f"{e_pot:10.5e} {e_kin:10.5e} {(e_pot + e_kin):10.5e}")
+
+#     if config.animate:
+#         count += 1
+#         ax.cla()
+#         plt.title(f"time step {count}")
+#         plt.xlim([0, box_size])
+#         plt.ylim([0, box_size])
+#         ax.set_zlim([0, box_size])
+#         # create 3D position scatter plot
+#         position = np.mod(position, box_size)
+#         ax.scatter(position[:, 0], position[:, 1], position[:, 2])
+#         # # and save to file
+#         # plt.savefig(config.plot)
+#         plt.pause(0.01)
+
+
+
+# import numpy as np
+# import matplotlib.pyplot as plt
+# from mpl_toolkits import mplot3d
+# from molecular_dynamics import load, energy, kinetic
+
+# (position, velocity), box_size = load("task02.xyz"):
+
+# fig = plt.figure()
+# ax = fig.add_subplot(111, projection = "3d")
+# ax.cla()
+# plt.xlim([0, box_size])
+# plt.ylim([0, box_size])
+# ax.set_zlim([0, box_size])
+# # create 3D position scatter plot
+# position = np.mod(position, box_size)
+# ax.scatter(position[:, 0], position[:, 1], position[:, 2])
diff --git a/Exercise_02_Assignment.pdf b/Exercise_02_Assignment.pdf
new file mode 100644
index 0000000..3664e46
Binary files /dev/null and b/Exercise_02_Assignment.pdf differ