diff --git a/coursera_compneuro/w4quiz/pop_coding_3.4.pickle b/coursera_compneuro/w4quiz/pop_coding_3.4.pickle
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diff --git a/coursera_compneuro/w4quiz/pop_coding_3.4.pickle b/coursera_compneuro/w4quiz/pop_coding_3.4.pickle
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diff --git a/coursera_compneuro/w4quiz/quiz4.py b/coursera_compneuro/w4quiz/quiz4.py
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+# %%
+import numpy as np
+import pickle
+
+with open('tuning_3.4.pickle', 'rb') as f:
+    data = pickle.load(f)
+
+#%%
+data['neuron1'].shape
+
+import matplotlib.pyplot as plt
+
+
+# plt.plot(data['stim'],data['neuron1'].mean(axis=0))
+# plt.show()
+
+
+# plt.plot(data['stim'],data['neuron2'].mean(axis=0))
+# plt.show()
+
+
+# plt.plot(data['stim'],data['neuron3'].mean(axis=0))
+# plt.show()
+
+
+# plt.plot(data['stim'],data['neuron4'].mean(axis=0))
+# plt.show()
+
+import numpy as np
+
+def fano(neuron):
+  d = data[neuron]
+  f = (100/10) * d.std(axis=0) / d.mean(axis=0)
+  return f
+
+# calc r_max per neuron
+r_max={}
+for i in range(1,5):
+  n = f'neuron{i}'
+  print(f'fano of {n}:', np.nanmean(fano(n)))
+  # r_max_index = data[n].mean(axis=0).argmax()
+  # r_max[n] = data['stim'][r_max_index]
+  r_max[n] = data[n].mean(axis=0).max()
+
+print('r_max: ', r_max)
+
+#%%
+
+# part 2 (last question)
+with open('pop_coding_3.4.pickle', 'rb') as f:
+    pdata = pickle.load(f)
+
+r0 = 0
+v_total = [0,0]
+for n in range(1,5):
+  v = pdata[f'c{n}'] * np.mean(pdata[f'r{n}']) / r_max[f'neuron{n}']
+  v_total += v
+
+# map to the expected coord (clockwise, y-axis is zero degree)
+v_deg = np.rad2deg(np.arctan(v_total)[1])
+np.round(-(v_deg - 90) % 360)

diff --git a/coursera_compneuro/w4quiz/pop_coding_3.4.pickle b/coursera_compneuro/w4quiz/pop_coding_3.4.pickle
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+++ b/coursera_compneuro/w4quiz/pop_coding_3.4.pickle
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diff --git a/coursera_compneuro/w4quiz/quiz4.py b/coursera_compneuro/w4quiz/quiz4.py
new file mode 100644
index 0000000..97f96b7
--- /dev/null
+++ b/coursera_compneuro/w4quiz/quiz4.py
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+# %%
+import numpy as np
+import pickle
+
+with open('tuning_3.4.pickle', 'rb') as f:
+    data = pickle.load(f)
+
+#%%
+data['neuron1'].shape
+
+import matplotlib.pyplot as plt
+
+
+# plt.plot(data['stim'],data['neuron1'].mean(axis=0))
+# plt.show()
+
+
+# plt.plot(data['stim'],data['neuron2'].mean(axis=0))
+# plt.show()
+
+
+# plt.plot(data['stim'],data['neuron3'].mean(axis=0))
+# plt.show()
+
+
+# plt.plot(data['stim'],data['neuron4'].mean(axis=0))
+# plt.show()
+
+import numpy as np
+
+def fano(neuron):
+  d = data[neuron]
+  f = (100/10) * d.std(axis=0) / d.mean(axis=0)
+  return f
+
+# calc r_max per neuron
+r_max={}
+for i in range(1,5):
+  n = f'neuron{i}'
+  print(f'fano of {n}:', np.nanmean(fano(n)))
+  # r_max_index = data[n].mean(axis=0).argmax()
+  # r_max[n] = data['stim'][r_max_index]
+  r_max[n] = data[n].mean(axis=0).max()
+
+print('r_max: ', r_max)
+
+#%%
+
+# part 2 (last question)
+with open('pop_coding_3.4.pickle', 'rb') as f:
+    pdata = pickle.load(f)
+
+r0 = 0
+v_total = [0,0]
+for n in range(1,5):
+  v = pdata[f'c{n}'] * np.mean(pdata[f'r{n}']) / r_max[f'neuron{n}']
+  v_total += v
+
+# map to the expected coord (clockwise, y-axis is zero degree)
+v_deg = np.rad2deg(np.arctan(v_total)[1])
+np.round(-(v_deg - 90) % 360)
diff --git a/coursera_compneuro/w4quiz/tuning_3.4.pickle b/coursera_compneuro/w4quiz/tuning_3.4.pickle
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