<jupyter_start><jupyter_text>range (-1,2) to 1200 bins<jupyter_code>plt.hist(list(combs.T[0,:][:100000]),range=(-1,1),bins=60)
sample=np.random.choice(np.arange(len(combs)),25,replace=False)
n=1
plt.figure(figsize=(15,15))
plt.ylim=(-0.5,0.5)
for s in sample:
    plt.subplot(5,5,n)
    axes = plt.gca()
#     axes.set_xlim([xmin,xmax])
    axes.set_ylim([-0.5,0.5])
    
    plt.plot(combs[s,:])
    n+=1
plt.show()
sample=np.random.choice(np.arange(len(combs)),250,replace=False)
sample_combs=combs[sample,:]
for i in range(len(sample_combs)):
    sample_combs[i,:]
# from mpl_toolkits.mplot3d import Axes3D
# fig = plt.figure()
# ax = fig.add_subplot(111, projection='3d')
# ax.scatter(xs=combs[sample,0], ys=combs[sample,1], zs=combs[sample,2])
# combs[sample,0]
import plotly
plotly.tools.set_credentials_file(username='yp653', api_key='R50kMp9GpijMsUK59JDa')
import plotly.plotly as py
import plotly.graph_objs as go

import numpy as np

trace1 = go.Scatter3d(
    x=combs[sample,0], y=combs[sample,1], z=combs[sample,2],
    mode='markers',
    marker=dict(
        size=12,
        line=dict(
            color='rgba(217, 217, 217, 0.14)',
            width=0.5
        ),
        opacity=0.8
    )
)


data = [trace1]
layout = go.Layout(
    margin=dict(
        l=0,
        r=0,
        b=0,
        t=0
    )
)
fig = go.Figure(data=data, layout=layout)
py.iplot(fig, filename='simple-3d-scatter')<jupyter_output>High five! You successfully sent some data to your account on plotly. View your plot in your browser at https://plot.ly/~yp653/0 or inside your plot.ly account where it is named 'simple-3d-scatter'
{'fraction_non_alphanumeric': 0.16718075262183837, 'fraction_numerical': 0.055521283158544106, 'mean_word_length': 3.8855421686746987, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 4, 'https://': 1, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '280681', 'license': 'MIT', 'n_tokens_mistral': 716, 'n_tokens_neox': 670, 'n_words': 115, 'path': 'analysis/Discretize.ipynb', 'repo_name': 'YichengPu/web_traffic', 'size': '1621'}