# data collected by PropertyBasedSettingsTest.experimentWith_threshold_p_for_first_filter_separator_character

data = [
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{"value": 0.514000, "errors": 749},
{"value": 0.514500, "errors": 749},
{"value": 0.515000, "errors": 749},
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{"value": 0.521500, "errors": 749},
{"value": 0.522000, "errors": 749},
{"value": 0.522500, "errors": 749},
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{"value": 0.593000, "errors": 747},
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{"value": 0.602000, "errors": 747},
{"value": 0.602500, "errors": 747},
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{"value": 0.604000, "errors": 747},
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{"value": 0.608000, "errors": 747},
{"value": 0.608500, "errors": 747},
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{"value": 0.610000, "errors": 747},
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{"value": 0.619500, "errors": 747},
{"value": 0.620000, "errors": 747},
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{"value": 0.623500, "errors": 747},
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{"value": 0.642500, "errors": 747},
{"value": 0.643000, "errors": 747},
{"value": 0.643500, "errors": 747},
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{"value": 0.673500, "errors": 747},
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{"value": 0.680000, "errors": 747},
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{"value": 0.681000, "errors": 747},
{"value": 0.681500, "errors": 747},
{"value": 0.682000, "errors": 747},
{"value": 0.682500, "errors": 747},
{"value": 0.683000, "errors": 747},
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{"value": 0.685500, "errors": 747},
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{"value": 0.688500, "errors": 747},
{"value": 0.689000, "errors": 747},
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{"value": 0.690000, "errors": 747},
{"value": 0.690500, "errors": 747},
{"value": 0.691000, "errors": 747},
{"value": 0.691500, "errors": 747},
{"value": 0.692000, "errors": 747},
{"value": 0.692500, "errors": 747},
{"value": 0.693000, "errors": 747},
{"value": 0.693500, "errors": 747},
{"value": 0.694000, "errors": 747},
{"value": 0.694500, "errors": 747},
{"value": 0.695000, "errors": 747},
{"value": 0.695500, "errors": 747},
{"value": 0.696000, "errors": 747},
{"value": 0.696500, "errors": 747},
{"value": 0.697000, "errors": 747},
{"value": 0.697500, "errors": 747},
{"value": 0.698000, "errors": 747},
{"value": 0.698500, "errors": 747},
{"value": 0.699000, "errors": 747},
{"value": 0.699500, "errors": 747},
{"value": 0.700000, "errors": 747},
]

x = []
y = []

for value in data:
    x.append(value["value"])
    y.append(value["errors"])

from pandas import *

d = {"x": x, "y": y}
df = DataFrame(d)

import matplotlib.pyplot as plt
from pandas.tools.rplot import *

plt.plot(x, y, 'ro')
plt.ylabel('errors')
plt.xlabel('threshold_p_for_first_filter_separator_character')
plt.title('threshold_p_for_first_filter_separator_character vs errors count')

polynomial = Polynomial(x, y, 4)

new_x = []
new_y = []
current_x = 0.
while current_x < 0.62:
    new_x.append(current_x)
    new_y.append(polynomial.getval(current_x))
    current_x += 0.00005

plt.plot(new_x, new_y, 'ro')
print (polynomial.getval(0.))