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@sjaustirni sjaustirni/ Secret
Created Aug 14, 2017

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from math import sqrt
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy import stats
def clear_nans(no_nans, nans):
Removes elements from both lists at the positions where nans contains a NaN value
:param no_nans: NaNs are ignored here. Gets elements removed on the basis of *nans* param
:param nans: Contains NaNs whose position determine which elements are removed
:return: *no_nans*, *nans*, cleared of elements at positions where *nans* contains a NaN value
assert (len(no_nans) == len(nans)), "Both lists must be the same length!"
# WARNING: Do NOT attempt to change the order of filtering the arrays!
no_nans = np.array([x for i, x in enumerate(no_nans) if not np.isnan(nans[i])])
nans = np.array([x for i, x in enumerate(nans) if not np.isnan(nans[i])])
return no_nans, nans
def plot_segment(slope, intercept, plot_range):
Returns x, y values for plotting a segment with given slope and y-intercept
:param slope: Slope of segment
:param intercept: y-intercept of segment
:param plot_range: Range on which the segment should be plotted
:return: Lists of *x* and *y* values
which when plotted create a segment with given slope and y-intercept within the given range
x_v = np.array(plot_range)
y_v = np.array([(x * slope + intercept) for x in x_v])
return x_v, y_v
def fit_line(x, y):
Returns a slope and y-intercept of a fit line for values from x, y
:param x: List of X values
:param y: List of Y values
x, y = clear_nans(x.values, y.values)
slope, intercept, _, _, _ = stats.linregress(x, y)
return slope, intercept
def plot_subplot(x, y_name, y, plot_range, subplot):
slope, intercept = fit_line(x, y)
x_line, y_line = plot_segment(slope, intercept, plot_range)
subplot.plot(x_line, y_line)
subplot.scatter(x, y, s=2)
def plot(name, filename, poundage, arrows):
fig, axes = plt.subplots(2, 2, figsize=(8, 6))
fig.suptitle(name, fontsize=15)
for arrow in arrows:
name, values, subplot_x, subplot_y = arrow
plot_subplot(poundage, name, values, range(0, 200), axes[subplot_x, subplot_y])
plt.savefig(filename, dpi=300)
def get_dispersion(x, y, slope, intercept):
if np.isnan(y):
return np.nan
return -(slope * x - y + intercept) / sqrt(slope ** 2 + 1)
def compute_dispersion_list(poundage, distance):
result = []
slope, intercept = fit_line(poundage, distance)
for el in zip(poundage, distance):
poundage, distance = el
dispersion = get_dispersion(poundage, distance, slope, intercept)
return result
if __name__ == "__main__":
data = pd.read_csv('data.csv', delimiter=';')
archer = data['Strelec']
bowyer = data['Lukár']
wood = data['Drevo']
event = data['Akcia']
poundage = data['Sila luku']
blbs = data['BLBS']
livery = data['Livery']
fourth = data['A fourth pound']
flight = data['Flight']
plot("Datapoints", "datapoints.png", poundage,
[("BLBS", blbs, 0, 0),
("Livery", livery, 0, 1),
("¼ pound", fourth, 1, 0),
("Flight", flight, 1, 1)])
dispersion_blbs = compute_dispersion_list(poundage, blbs)
dispersion_livery = compute_dispersion_list(poundage, livery)
dispersion_fourth = compute_dispersion_list(poundage, fourth)
dispersion_flight = compute_dispersion_list(poundage, flight)
dispersion = pd.DataFrame(
'Akcia': event,
'Strelec': archer,
'Lukár': bowyer,
'Drevo': wood,
'Sila luku': poundage,
'BLBS': blbs,
'BLBS disperzia': dispersion_blbs,
'Livery': livery,
'Livery disperzia': dispersion_livery,
'¼ pound': fourth,
'¼ pound disperzia': dispersion_fourth,
'Flight': flight,
'Flight disperzia': dispersion_flight
columns=['Akcia', 'Strelec', 'Lukár', 'Drevo', 'Sila luku',
'BLBS', 'BLBS disperzia',
'Livery', 'Livery disperzia',
'¼ pound', '¼ pound disperzia',
'Flight', 'Flight disperzia'])
dispersion.to_csv('dispersion.csv', sep=';', encoding='utf-8')
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