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refactor plot mps and put plotting parties in function

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Hannes Kuchelmeister
2021-05-04 11:35:14 +02:00
parent a2edf05411
commit 2a16648b05
1 changed files with 21 additions and 11 deletions
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32
VotingAnalysis.py
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@@ -172,8 +172,22 @@ def plot_mps(fig, ax, x, y, labels, colors, cmap = plt.cm.RdYlGn):
fig.canvas.mpl_connect("motion_notify_event", hover) fig.canvas.mpl_connect("motion_notify_event", hover)
#plt.show() #plt.show()
def plot_mps(names, xs, ys, party_affiliation):
# converting parties to numeric format
party_index_mapping, party_ids = np.unique(party_affiliation, return_inverse=True)
def plot_parties(xs, ys, party_ids, party_index_mapping): # add random offset to show points that are in the same location
ys_disp = ys + np.random.rand(ys.shape[0])
xs_disp = xs + np.random.rand(xs.shape[0])
parties = party_index_mapping[party_ids]
plot_hoverscatter(xs_disp, ys_disp, data[:,0] + " (" + parties + ")", party_ids)
def plot_parties(xs, ys, party_affiliation):
# converting parties to numeric format
party_index_mapping, party_ids = np.unique(party_affiliation, return_inverse=True)
# calculate average position of party
party_count = np.zeros(party_index_mapping.shape[0]) party_count = np.zeros(party_index_mapping.shape[0])
party_xs = np.zeros(party_index_mapping.shape[0]) party_xs = np.zeros(party_index_mapping.shape[0])
party_ys = np.zeros(party_index_mapping.shape[0]) party_ys = np.zeros(party_index_mapping.shape[0])
@@ -181,9 +195,9 @@ def plot_parties(xs, ys, party_ids, party_index_mapping):
party_xs[party_id] += x party_xs[party_id] += x
party_ys[party_id] += y party_ys[party_id] += y
party_count[party_id] += 1 party_count[party_id] += 1
party_xs /= party_count party_xs /= party_count
party_ys /= party_count party_ys /= party_count
plt.figure() plt.figure()
plt.scatter(party_xs, party_ys) plt.scatter(party_xs, party_ys)
# plotting labels # plotting labels
@@ -227,23 +241,19 @@ plt.axis('off')
plt.colorbar() plt.colorbar()
plt.show() plt.show()
fig,ax = plt.subplots() # predicting mp positions
prediction = sofmnet.predict(X) prediction = sofmnet.predict(X)
print(f'prediction: {prediction}') print(f'prediction: {prediction}')
# converting to x and y coordinates # converting to x and y coordinates
ys, xs = np.unravel_index(np.argmax(X, axis=1), (h, w)) ys, xs = np.unravel_index(np.argmax(X, axis=1), (h, w))
# add random offset to show points that are in the same location # plotting mps
ys_disp = ys + np.random.rand(ys.shape[0]) plot_mps(data[:,0], xs, ys, data[:,1])
xs_disp = xs + np.random.rand(xs.shape[0])
party_index_mapping, party_ids = np.unique(data[:,1], return_inverse=True)
plot_mps(fig, ax, xs_disp, ys_disp, data[:,0] + " (" + data[:,1] + ")", party_ids)
plt.show() plt.show()
# plotting parties # plotting parties
plot_parties(xs, ys, party_ids, party_index_mapping) plot_parties(xs, ys, data[:,1])
plt.show() plt.show()
#Simple SOFM for UK #Simple SOFM for UK