# -*- coding: utf-8 -*-
"""
@author: John Rachlin
@date  : September 13, 2021
@file  : cherry.py

Analysis of the full-flowering day-of-year for cherry blossums 
in Kyoto Japan.

Why is this significant?   Scientists are interested in understanding
periodic biological phenomena (flowering, breeding, migration) 
especially as it relates to climatic conditions.  (The scientific
study of these types of phenomena is known as PHENOLOGY.)

Data source: https://www.ncei.noaa.gov/pub/data/paleo/historical/phenology/japan/LatestVersion/KyotoFullFlower7.xls

Data for 2020 and 2021 added to data set.

2020: March 30 - based on Japan Meterological Corporation (JMC) 2020 forecast. 
2021: March 26 - based on reported actual (Osaka University)

"""



import csv
import matplotlib.pyplot as plt



def read_cherry_data(filename):
    
    """ Read cherry blossum full-flowering day-of-year data.
    filename - name of data file
    return   - column lists: years, blossom-day-of-years]
    Return a list mapping year to day-of-year (of full blossums) """
       
    years = []  # measurement year
    doys  = []  # day of the year (kyoto full blossoming)
    
    with open(filename, 'r') as infile:
        infile.readline() # skip header
        for line in infile.readlines():
            
            # extract values
            vals = line.strip().split(",")
            year = int(vals[0])
            doy = vals[1]
            
            # Missing data converted to zero
            if doy == '':
                doy = 0
            else:
                doy = int(doy)
                
            # Store values
            years.append(year)
            doys.append(doy)

    return years, doys




def impute_values(L, missing_value = 0):
    """ Fill in missing values with the previous value in the list
    L - list of numeric values
    missing_value - code for a missing value
    return new list with imputed values
    """
    
    imputed = L[:] # copy the original list
    for i in range(len(imputed)):
        if imputed[i] == missing_value and i != 0:
            imputed[i] = imputed[i-1]
    return imputed



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)
        avg = sum(window) / len(window)
        mavg.append(avg)

    return mavg



def plot_blossum_day_history(years, doys):
    """ Plot the day-of-year full blossuming by year
    for all available years """
    
    
    # fill in missing values
    imputed = impute_values(doys)
    moving = moving_average(imputed, window_size = 30)

 
    
    plt.figure(figsize=(10,6), dpi=200)
    plt.title("Cherry Blossoms: Full Blossom Date (Kyoto, Japan)")
    plt.xlabel("Year")
    plt.ylabel("Date")

    plt.scatter(years, doys, marker='.', color='#FF66B2', label='Full blossom date') # The color of cherry blossums!
    plt.plot(years, moving, color='b', label='30 year moving avg')

    plt.yticks([80,90,100,110,120], ['Mar 21', 'Mar 31', 'Apr 10', 'Apr 20', 'Apr 30'])

    
    # Highlight 2021 (Record)
    plt.text(1940, 84.5, '2021')
    
    # Outliers are interesting!
    # There was a global temperature reduction at that time.
    # See global_temps.png
    
    plt.text(1333,124, '1323 (May 4th)')
    plt.text(1416, 86, '1409') # Until 2021, this was the previous record year
    
    # Annotate figure with "The little ice-age" - a period of cooling
    # occurring between 1400-1800, though, according to wikipedia,
    # some experts prefer the alternative timespan of 1300-1850
    
    plt.text(1300,84, '<- - - - - -------- The "Little Ice Age" ---------- - ->')

    # Include a legend
    plt.legend(facecolor='lightgrey', edgecolor='black')
    
    plt.ylim(80,130)
    plt.grid()
    plt.savefig('cherry.png')
    plt.show()
    
    



def main():
    
    # Read the data into a dictionary (year -> doy)
    years, doys = read_cherry_data('cherry.csv')
    


    # Visualize full-blossum day-of-year (Kyoto, Japan)
    plot_blossum_day_history(years, doys)
        
    
    
if __name__ == '__main__':
    main()