#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Felix Muzny
11/8/2022
DS 2000
Lecture 18 - sentiment analysis,
            sets,
            moving averages,
            jupyter notebooks  


Logistics:
    - Homework 8 is out
        - this is your last HW for DS 2000
        - there is a significant amount of extra credit available
        (so start early if this is something that you need)
        - due 11/18
        - yes, you are doing sentiment analysis for this homework
    
    - checking your grades:
        - most accurate is calculate based on Gradescope grades
            - HW is 90%
            - Quizzes are 10% (drop your lowest quiz)
        - you can look in Canvas—know that this lags behind Gradescope

    - No quiz this week
    
    - No lecture on Friday (Veteran's day!)
        - I'll see you all next week!
        
    - remote attendance (https://bit.ly/remote-ds2000-muzny)
    
    
 Three ways to participate (please do one of these!)
 1) via the PollEverywhere website: https://pollev.com/muzny
 2) via text: text "muzny" to the number 22333 to join the session
 3) via Poll Everywhere app (available for iOS or Android)

"""

"""
HW 7 reflection
---

Look at the sample speeds.pdf figure. 
(Or the one that you produced)

How do we feel about it?

A. looks good
B. I have questions
C. looks bad
D. That line at 0 makes me queasy
E. This graph is wrong?

"""


"""
Sentiment
----

Are the following reviews:
A. positive
B. negative    
C. neutral


"""



"""
Sets
---
"""


"""
Moving Averages
---
"""

# from dataproc.py (Prof. Rachlin's version of
# data_utils.py)

def avg(L):
    """ Compute the numerical average of a list of numbers.
    If list is empty, return 0.0 """
    
    if len(L) > 0:
        return sum(L) / len(L)
    else:
        return 0.0

def get_window(L, idx, window_size=1):
    """ Extract a window of values of specified size
    centered on the specified index
    L: List of values
    idx: Center index
    window_size: window size
    """
    
    minrange = max(idx - window_size // 2, 0)
    maxrange = idx + window_size // 2 + (window_size % 2)
    return L[minrange:maxrange]
    
def moving_average(L, window_size=1):
    """ Compute a moving average over the list L
    using the specified window size
    L: List of values
    window_size - The window size (default=1)
    return - A new list with smoothed values
    """
    mavg = []
    for i in range(len(L)):
        window = get_window(L, i, window_size)
        mavg.append(avg(window))

    return mavg


"""
Next time:
    - Jupyter Notebooks
    - Classes and objects
"""