# Modified Stylesheet for notebook.
from IPython.core.display import HTML
def css_styling():
    styles = open("custom.css", "r").read()
    return HTML(styles)

css_styling()

import matplotlib.pyplot as plt
import numpy as np

from collections import defaultdict
np.random.seed(seed=111111)
np.set_printoptions(precision=3, suppress=True)

import sys; sys.path.append('../src/')
from change_detector import ChangeDetector, OnlineSimulator

sig1 = np.ones(150)
sig1[:100] *= 50
sig1[100:] *= 40

plt.figure(figsize=(15, 5))
plt.plot(sig1, 'b.')
plt.plot(sig1, '-', alpha=0.2)
plt.ylim(0,100)
plt.title("sig1 : A trivial signal")

class MeanDetector(ChangeDetector):
    """
    Static Mean Detector
    
    Residuals: 
        mean_: the mean of signal values seen so far
        diff_: the difference between new value and mean_
    
    Stopping Rule:
        Stop if diff_ exceeds some threshold percentage value. 
        Default is 5%.     
    """
    
    def __init__(self, threshold=0.05): 
        super( MeanDetector, self ).__init__()

        # Save hyper-parameter(s)
        self.threshold = threshold
        
        # Required Attributes
        self.total_val = 0  # Used for calculating mean
        
        # new residuals(s)
        self.diff_ = np.nan 
    
    def update_residuals(self, new_signal_value):
        self._update_base_residuals(new_signal_value)

        # Update attributes
        self.total_val += new_signal_value
        
        #Update residuals 
        self.mean_ = self.total_val / self.signal_size
        self.diff_ = np.absolute(self.mean_ - new_signal_value)
    
    def check_stopping_rules(self, new_signal_value): 
        #check if new value is more than % different from mean
        threshold_level = self.mean_ * self.threshold
        
        if self.diff_ > threshold_level:
            self.rules_triggered = True
        

# Create detector
detector = MeanDetector(threshold=0.05)

OnlineSimulator(detector, sig1).run()

# MeanDetector easily accomodates a scaling of sig1 
#  as the threshold is given as a percentage.

detector = MeanDetector(threshold=0.05)
OnlineSimulator(detector, sig1 * 1000).run()

# Size of change in our test signal
jump_size = sig1[0] - sig1[-1]

# We'll add a small amount (0.02 x jump_size) of Gaussian noise to the signal. 
noise = np.random.normal(
    size=sig1.shape,
    scale=jump_size * 0.02)

detector = MeanDetector(threshold=0.05)

OnlineSimulator(detector, sig1 + noise).run(signal_name='Sig1 with 2% noise')



# 10% noise sometimes causes trouble
noise = np.random.normal(
    size=sig1.shape,
    scale=jump_size * 0.10)

detector = MeanDetector(threshold=0.05)
OnlineSimulator(detector, sig1 + noise).run(signal_name='Sig1 with 10% noise')

# 20% noise pretty much always causes problems
noise = np.random.normal(
    size=sig1.shape,
    scale=jump_size * 0.20)

detector = MeanDetector(threshold=0.05)

OnlineSimulator(detector, sig1 + noise).run(signal_name='Sig1 with 20% noise')

Note that values for diff_, our primary residual, are all over the place. That's a good indication that our change detector is not well matched for the target signal type.
# Create a seasonal signal
# I imagined a metric that rises from 0 to 5 each calendar month

sig2 = np.linspace(0, 5, num=30)
sig2 = np.concatenate([sig2 for x in xrange(12)])

# Add a jump
jump_size = 5
sig2[250:] = sig2[250:] + jump_size

# Noise
noise = np.random.normal(
    size=sig2.shape,
    scale=jump_size * 0.02)

plt.figure(figsize=(15,5))
plt.plot(sig2 + noise, 'b.', linestyle='')
plt.plot(sig2 + noise, 'b-', alpha=0.15)
plt.ylim(0,15)
plt.xlim(0,365)
plt.title("Imaginary Seasonal signal")