from collections import defaultdict
import copy
import random
from PIL import Image
from numpy.core.fromnumeric import sort

with open("data.csv") as f:
    raw_data = [line.replace("\n","").split(",") for line in f.readlines()]
    columns=copy.deepcopy(raw_data[0])
    raw_data=raw_data[1:]

def transform_csv(columns, raw_data):
    count_data={}
    for i,column in enumerate(columns):
        values_count=defaultdict(lambda: 0)
        values_percent={}
        total=0
        for row in raw_data:
            values_count[row[i]]+=1
            total+=1
        for value in values_count.keys():
            values_percent[value] = values_count[value]/total
        count_data[column] = values_percent
    return count_data
import timeit
time_result = timeit.timeit(lambda: transform_csv(columns,raw_data), number=1)
print(time_result*1000)
def generate_dataset(transformed_csv):
    # print(transformed_csv)
    legend = list(transformed_csv.keys())
    series = [[entry[1] for entry in sorted(list(values.items()), key= lambda x: x[0])] for values in transformed_csv.values()]
    return (legend,series)

def fig2img(fig):
    import io
    buf = io.BytesIO()
    fig.savefig(buf, format="svg")
    buf.seek(0)
    plt.close(fig)
    return buf
# importing package
import matplotlib.pyplot as plt
import numpy as np
def benchmark_linear_to_csv(results):
    csv = ""
    for k, v in results.items():
        csv += f"{k}, {v}\n"
    return csv

def benchmark_linear(start,end,function):
    out = {}
    for i in range(start,end):
        out[i] = timeit.timeit(function, number=i)*1000
    return out
# create data
def createLinePlot():
    x = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
    y = [150, 230, 224, 218, 135, 147, 260]
    fig,ax1 = plt.subplots()
    ax1.plot(x,y)
    return fig

def createBarPlot():
    np.random.seed(19680801)
    plt.rcdefaults()
    fig, ax = plt.subplots()
    # Example data
    people = ('Tom', 'Dick', 'Harry', 'Slim', 'Jim')
    y_pos = np.arange(len(people))
    performance = [1,2,3,4,5]

    ax.barh(y_pos, performance, align='center')
    ax.set_yticks(y_pos, labels=people)
    ax.invert_yaxis()  
    ax.set_xlabel('Performance')
    ax.set_title('How fast do you want to go today?')
    return fig



def createPlotFromStratch():

    x = ['1', '2', '3', '4', '5']
    dataset = generate_dataset(transform_csv(columns,raw_data))

    set_already = []

    dataset = (dataset[0][0], dataset[1][0])

    # print("dataset")
    # print(dataset)
    labels = dataset[0]
    fig,ax = plt.subplots()
    already = [0]
    for bar_series in zip(x,dataset[1]):
        # print(already)
        # print(bar_series)
        ax.barh(labels, bar_series[1], 0.1, label=bar_series[0], left=sum(already))
        already=already+[bar_series[1]]
    ax.set_ylabel("Wyniki")
    ax.legend()
    return fig
# print("Czas tworzenia i zapisywania 100 wykresów:")
# time_result = timeit.timeit(lambda: fig2img(createPlotFromStratch()), number=100)
# print(time_result*1000)

# print("Czas zapisywania 100 wykresów:")
# figure = createPlotFromStratch()
# time_result = timeit.timeit(lambda: fig2img(figure), number=100)
# print(time_result*1000)
with open("barPlotPython.svg", 'wb+') as f:
    f.write(fig2img(createBarPlot()).read())
input()
print("Liniowy benchmark create from scratch")
def generate_and_close():
    fig = createPlotFromStratch()
    plt.close('all')
    plt.close()
    plt.cla()
    plt.clf()
    
# print(benchmark_linear_to_csv(benchmark_linear(1,5,lambda:plt.close(createPlotFromStratch()) )))

print(benchmark_linear_to_csv(benchmark_linear(1,20,lambda:plt.close(createBarPlot()) )))

# print("Liniowy benchmark create from scratch  + save")
# print(benchmark_linear_to_csv(benchmark_linear(1,20,lambda: fig2img(createPlotFromStratch()) )))

# print("Liniowy benchmark zapisywania do bufora")
# print(benchmark_linear_to_csv(benchmark_linear(1,25,lambda: fig2img(figure) )))