%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('seaborn')

import pandas as pd
import numpy as np

from sklearn.decomposition import PCA
from sklearn.mixture import GaussianMixture

"""
Explanation: Unsupervised Analysis of Days of Week
Using daily crossings to learn about relationships between various days.
End of explanation
"""


from jupyterworkflow.data import get_fremont_data
df = get_fremont_data()

pivoted = df.pivot_table('Total', index=df.index.time, columns=df.index.date)
pivoted.plot(legend=False, alpha=0.02);

X = pivoted.fillna(0).T.values
X.shape

"""
Explanation: Get Data
End of explanation
"""


X2 = PCA(2, svd_solver='full').fit_transform(X)
X2.shape

plt.scatter(X2[:,0], X2[:,1])

gmm = GaussianMixture(2).fit(X)
labels = gmm.predict(X)

plt.scatter(X2[:,0], X2[:,1], c=labels, cmap='rainbow')

fig, ax = plt.subplots(1, 2, figsize=(14,6))

pivoted.T[labels==0].T.plot(legend=False, alpha=0.1, ax=ax[0])
pivoted.T[labels==1].T.plot(legend=False, alpha=0.1, ax=ax[1])

ax[0].set_title('Purple Cluster')
ax[1].set_title('Red Cluster');

dayofweek = pd.DatetimeIndex(pivoted.columns).dayofweek

plt.scatter(X2[:,0], X2[:,1], c=dayofweek, cmap='rainbow')
plt.colorbar()

"""
Explanation: Unsupervised Clustering
End of explanation
"""


dates = pd.DatetimeIndex(pivoted.columns)

dates[(labels==1) & (dayofweek < 5)]

"""
Explanation: Analyzing Outliers
The following points are weekdays with a holiday-like pattern
End of explanation
"""


