132 lines
3.9 KiB
Python
132 lines
3.9 KiB
Python
from collections import defaultdict
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import copy
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import random
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from PIL import Image
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from numpy.core.fromnumeric import sort
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with open("data.csv") as f:
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raw_data = [line.replace("\n","").split(",") for line in f.readlines()]
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columns=copy.deepcopy(raw_data[0])
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raw_data=raw_data[1:]
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def transform_csv(columns, raw_data):
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count_data={}
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for i,column in enumerate(columns):
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values_count=defaultdict(lambda: 0)
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values_percent={}
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total=0
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for row in raw_data:
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values_count[row[i]]+=1
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total+=1
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for value in values_count.keys():
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values_percent[value] = values_count[value]/total
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count_data[column] = values_percent
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return count_data
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import timeit
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time_result = timeit.timeit(lambda: transform_csv(columns,raw_data), number=1)
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print(time_result*1000)
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def generate_dataset(transformed_csv):
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# print(transformed_csv)
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legend = list(transformed_csv.keys())
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series = [[entry[1] for entry in sorted(list(values.items()), key= lambda x: x[0])] for values in transformed_csv.values()]
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return (legend,series)
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def fig2img(fig):
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import io
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buf = io.BytesIO()
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fig.savefig(buf, format="svg")
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buf.seek(0)
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plt.close(fig)
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return buf
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# importing package
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import matplotlib.pyplot as plt
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import numpy as np
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def benchmark_linear_to_csv(results):
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csv = ""
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for k, v in results.items():
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csv += f"{k}, {v}\n"
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return csv
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def benchmark_linear(start,end,function):
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out = {}
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for i in range(start,end):
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out[i] = timeit.timeit(function, number=i)*1000
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return out
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# create data
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def createLinePlot():
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x = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
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y = [150, 230, 224, 218, 135, 147, 260]
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fig,ax1 = plt.subplots()
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ax1.plot(x,y)
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return fig
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def createBarPlot():
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np.random.seed(19680801)
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plt.rcdefaults()
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fig, ax = plt.subplots()
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# Example data
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people = ('Tom', 'Dick', 'Harry', 'Slim', 'Jim')
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y_pos = np.arange(len(people))
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performance = [1,2,3,4,5]
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ax.barh(y_pos, performance, align='center')
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ax.set_yticks(y_pos, labels=people)
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ax.invert_yaxis()
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ax.set_xlabel('Performance')
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ax.set_title('How fast do you want to go today?')
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return fig
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def createPlotFromStratch():
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x = ['1', '2', '3', '4', '5']
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dataset = generate_dataset(transform_csv(columns,raw_data))
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set_already = []
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dataset = (dataset[0][0], dataset[1][0])
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# print("dataset")
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# print(dataset)
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labels = dataset[0]
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fig,ax = plt.subplots()
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already = [0]
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for bar_series in zip(x,dataset[1]):
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# print(already)
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# print(bar_series)
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ax.barh(labels, bar_series[1], 0.1, label=bar_series[0], left=sum(already))
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already=already+[bar_series[1]]
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ax.set_ylabel("Wyniki")
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ax.legend()
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return fig
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# print("Czas tworzenia i zapisywania 100 wykresów:")
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# time_result = timeit.timeit(lambda: fig2img(createPlotFromStratch()), number=100)
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# print(time_result*1000)
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# print("Czas zapisywania 100 wykresów:")
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# figure = createPlotFromStratch()
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# time_result = timeit.timeit(lambda: fig2img(figure), number=100)
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# print(time_result*1000)
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with open("barPlotPython.svg", 'wb+') as f:
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f.write(fig2img(createBarPlot()).read())
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input()
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print("Liniowy benchmark create from scratch")
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def generate_and_close():
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fig = createPlotFromStratch()
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plt.close('all')
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plt.close()
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plt.cla()
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plt.clf()
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# print(benchmark_linear_to_csv(benchmark_linear(1,5,lambda:plt.close(createPlotFromStratch()) )))
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print(benchmark_linear_to_csv(benchmark_linear(1,20,lambda:plt.close(createBarPlot()) )))
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# print("Liniowy benchmark create from scratch + save")
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# print(benchmark_linear_to_csv(benchmark_linear(1,20,lambda: fig2img(createPlotFromStratch()) )))
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# print("Liniowy benchmark zapisywania do bufora")
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# print(benchmark_linear_to_csv(benchmark_linear(1,25,lambda: fig2img(figure) )))
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