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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:
 Structure of the lines is
Step1:  How many hymns do we have in total?
Step2:  How are their lengths distributed?
Step3:  Most seem to have around 25 lines, consisting of 8 MPs (metrical position).
Let us extract n-grams from the lines and find out what plain clustering does.
Step4:  Let us turn this data into a corpus
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Python Code:
hymns = {}
for line in open('dimeter-mp.csv', 'r'):
    comps = line.strip().split(',')
    line, pada, meter = comps[0:3]
    mps = comps[3:]
    hymn_id = '-'.join(line.split('.')[:2])
    if hymn_id not in hymns:
        hymns[hymn_id] = []
    hymns[hymn_id].append(mps)
Explanation: Structure of the lines is:
Line,Pada,Meter,MP1,MP2,MP3,MP4,MP5,MP6,MP7,MP8
End of explanation
print(len(hymns))
Explanation: How many hymns do we have in total?
End of explanation
from seaborn.distributions import distplot
lens = [len(hymns[h]) for h in hymns]
distplot(lens)
Explanation: How are their lengths distributed?
End of explanation
ids, texts = [], []
for h in hymns:
    # add book label:
    ids.append(h)
    # stringify mps:
    t = ''
    for line in hymns[h]:
        t += (''.join(line[:4])+'%'+''.join(line[4:])+'$')
    texts.append(t)
Explanation: Most seem to have around 25 lines, consisting of 8 MPs (metrical position).
Let us extract n-grams from the lines and find out what plain clustering does.
End of explanation
from pystyl.corpus import Corpus
corpus = Corpus(language='other')
corpus.add_texts(texts=texts,
                 titles=ids, #['b'+str(i) for i in range(len(texts))],
                 target_names=ids)
print(corpus)
mfi = corpus.vectorize(mfi=1000000,
                       ngram_type='char',
                       ngram_size=4,
                       vector_space='tf_std',
                       min_df=0.0)
print(len(mfi))
print(len(corpus))
from pystyl.analysis import pca
pca_coor, pca_loadings = pca(corpus, nb_dimensions=2)
from pystyl.visualization import scatterplot
scatterplot(corpus, coor=pca_coor,
            nb_clusters=3, loadings=pca_loadings,
            save=True, outputfile="/Users/mike/Desktop/pca.pdf")
from pystyl.analysis import pca
pca_coor, pca_loadings = pca(corpus, nb_dimensions=2)
from pystyl.visualization import scatterplot
scatterplot(corpus, coor=pca_coor,
            nb_clusters=3, loadings=pca_loadings,
            save=True, outputfile="/Users/mike/Desktop/pca.pdf")
from pystyl.analysis import distance_matrix
dm = distance_matrix(corpus, 'minmax')
from pystyl.analysis import hierarchical_clustering
cluster_tree = hierarchical_clustering(dm, linkage='ward')
from pystyl.visualization import scipy_dendrogram
scipy_dendrogram(corpus=corpus, tree=cluster_tree,
                 outputfile='~/Desktop/scipy_dendrogram.pdf',
                 fontsize=3, save=True, return_svg=False)
from sklearn.cluster import AgglomerativeClustering
clustering = AgglomerativeClustering(linkage='ward',
                                     affinity='euclidean',
                                     n_clusters=2)
clustering.fit(corpus.vectorizer.X.toarray())
for title, label in zip(corpus.titles, clustering.labels_):
    if label == 1:
        print(title)
# add slice names:
#for x, y, name, cluster_label in zip(x1, x2, labels, clustering.labels_):
Explanation: Let us turn this data into a corpus:
End of explanation
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