# sample data
target_col = "text"
texts = [
    "Think rich look poor",
    "When you come to a roadblock, take a detour",
    "When it is dark enough, you can see the stars",
    "Never let your memories be greater than your dreams",
    "Victory is sweetest when you’ve known defeat"
    ]
df = pd.DataFrame({target_col: texts})
df.head()

import nlplot
import pandas as pd
import plotly
from plotly.subplots import make_subplots
from plotly.offline import iplot
import matplotlib.pyplot as plt

%matplotlib inline

# target_col as a list type or a string separated by a space.
npt = nlplot.NLPlot(df, target_col='text')

# Stopword calculations can be performed.
stopwords = npt.get_stopword(top_n=30, min_freq=0)

# 1. N-gram bar chart
fig_unigram = npt.bar_ngram(
    title='uni-gram',
    xaxis_label='word_count',
    yaxis_label='word',
    ngram=1,
    top_n=50,
    width=800,
    height=1100,
    color=None,
    horizon=True,
    stopwords=stopwords,
    verbose=False,
    save=False,
)
fig_unigram.show()

fig_bigram = npt.bar_ngram(
    title='bi-gram',
    xaxis_label='word_count',
    yaxis_label='word',
    ngram=2,
    top_n=50,
    width=800,
    height=1100,
    color=None,
    horizon=True,
    stopwords=stopwords,
    verbose=False,
    save=False,
)
fig_bigram.show()


# 2. N-gram tree Map
fig_treemap = npt.treemap(
    title='Tree map',
    ngram=1,
    top_n=50,
    width=1300,
    height=600,
    stopwords=stopwords,
    verbose=False,
    save=False
)
fig_treemap.show()


# 3. Histogram of the word count
fig_histgram = npt.word_distribution(
    title='word distribution',
    xaxis_label='count',
    yaxis_label='',
    width=1000,
    height=500,
    color=None,
    template='plotly',
    bins=None,
    save=False,
)
fig_histgram.show()


# 4. wordcloud
fig_wc = npt.wordcloud(
    width=1000,
    height=600,
    max_words=100,
    max_font_size=100,
    colormap='tab20_r',
    stopwords=stopwords,
    mask_file=None,
    save=False
)
plt.figure(figsize=(15, 25))
plt.imshow(fig_wc, interpolation="bilinear")
plt.axis("off")
plt.show()


# 5. co-occurrence networks
npt.build_graph(stopwords=stopwords, min_edge_frequency=10)
# The number of nodes and edges to which this output is plotted.
# If this number is too large, plotting will take a long time, so adjust the [min_edge_frequency] well.
# >> node_size:70, edge_size:166
fig_co_network = npt.co_network(
    title='Co-occurrence network',
    sizing=100,
    node_size='adjacency_frequency',
    color_palette='hls',
    width=1100,
    height=700,
    save=False
)
iplot(fig_co_network)


# 6. sunburst chart
fig_sunburst = npt.sunburst(
    title='sunburst chart',
    colorscale=True,
    color_continuous_scale='Oryel',
    width=1000,
    height=800,
    save=False
)
fig_sunburst.show()


# other
# The original data frame of the co-occurrence network can also be accessed
display(
    npt.node_df.head(), npt.node_df.shape,
    npt.edge_df.head(), npt.edge_df.shape
)