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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 The filter that we used seems to take off data across singal strength without much of a bias. However, the distribution after the filter is applied became skewed to the left. This suggests that more data with weaker signal are taken out than that with stronger signal. We will try to go back to modify the metric we used to define each synapse on the raw image of the synapsin channels. But for now let us proceed to take out other irrelevant synapse entries based on other features.
Step1:  We then proceeded to try to separate the resulting filtered synapses into two groups through a naive kMean method, after tranforming the synapse vector to a lower dimension.
Step2:  The above example uses all the features; let's remove dapi, tubuli, synapsins, and all the exhibitory/inhibitory synapse markers and repeat the clustering procedure.
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Python Code:
markers = ['Synap','Synap','VGlut1','VGlut1','VGlut2','Vglut3',
              'psd','glur2','nmdar1','nr2b','gad','VGAT',
              'PV','Gephyr','GABAR1','GABABR','CR1','5HT1A',
              'NOS','TH','VACht','Synapo','tubuli','DAPI']
synapType = ['synap','synap','ex.pre','ex.pre','ex.pre','in.pre',
                  'ex.post','ex.post','ex.post','ex.post','in.pre','in.pre',
                  'in.pre','in.post','in.post','in.post','other','other',
                  'other','other','other','other','none','none']
# filter away synapses so those with high tubulin and dapi expression will  be pruned away
sub_integratedData = integratedData[(synapsin1_sample != 0) & (synapsin2_sample != 0)]
avginttub = np.mean(sub_integratedData[:,22])
intfilteredtub = [synapse for synapse, value in enumerate(sub_integratedData[:,22]) if value < avginttub]
avgintdap = np.mean(sub_integratedData[:,23])
intfiltereddap = [synapse for synapse, value in enumerate(sub_integratedData[:,23]) if value < avgintdap]
FilteredInd = np.array(list(set(intfilteredtub).intersection(intfiltereddap)))
filteredInt = integratedData[FilteredInd]
Explanation: The filter that we used seems to take off data across singal strength without much of a bias. However, the distribution after the filter is applied became skewed to the left. This suggests that more data with weaker signal are taken out than that with stronger signal. We will try to go back to modify the metric we used to define each synapse on the raw image of the synapsin channels. But for now let us proceed to take out other irrelevant synapse entries based on other features.
End of explanation
from sklearn import decomposition
pca = decomposition.PCA(n_components=3)
pca.fit(filteredInt)
filtered_trans = pca.transform(filteredInt)
from sklearn.cluster import KMeans
KM = KMeans(init='k-means++', n_clusters=2)
KM.fit(filtered_trans)
k_means_labels = KM.labels_
k_means_cluster_centers = KM.cluster_centers_
fig = plt.figure(figsize=(8, 3))
fig.subplots_adjust(left=0.02, right=0.98, bottom=0.05, top=0.9)
colors = ['#FF0000', '#6600FF']
ax = fig.add_subplot(1, 1, 1)
for k, col in zip(range(2), colors):
    my_members = k_means_labels == k
    cluster_center = k_means_cluster_centers[k]
    ax.plot(filtered_trans[my_members, 0], filtered_trans[my_members, 1], 'w',
            markerfacecolor=col, marker='.')
    
ax.set_title('KMeans')
Explanation: We then proceeded to try to separate the resulting filtered synapses into two groups through a naive kMean method, after tranforming the synapse vector to a lower dimension.
End of explanation
other = filteredInt[:, 15:21]
pca = decomposition.PCA(n_components=3)
pca.fit(other)
filtered_trans = pca.transform(other)
from sklearn.cluster import KMeans
KM = KMeans(init='k-means++', n_clusters=2)
KM.fit(other)
k_means_labels = KM.labels_
k_means_cluster_centers = KM.cluster_centers_
fig = plt.figure(figsize=(8, 3))
fig.subplots_adjust(left=0.02, right=0.98, bottom=0.05, top=0.9)
colors = ['#FF0000', '#6600FF']
ax = fig.add_subplot(1, 1, 1)
for k, col in zip(range(2), colors):
    my_members = k_means_labels == k
    cluster_center = k_means_cluster_centers[k]
    ax.plot(other[my_members, 0], other[my_members, 1], 'w',
            markerfacecolor=col, marker='.')
    
ax.set_title('Clustering based on the other synapses markers')
Explanation: The above example uses all the features; let's remove dapi, tubuli, synapsins, and all the exhibitory/inhibitory synapse markers and repeat the clustering procedure.
End of explanation
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