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Make file Flake8 compatible

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This commit is contained in:
th0rntwig 2022-08-21 18:34:06 +02:00
parent 6855727f5c
commit 353512899d
1 changed files with 17 additions and 12 deletions
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19
freqtrade/freqai/data_kitchen.py
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@ -601,7 +601,7 @@ class FreqaiDataKitchen:
is an outlier. is an outlier.
""" """
from math import sin, cos from math import cos, sin
if predict: if predict:
train_ft_df = self.data_dictionary['train_features'] train_ft_df = self.data_dictionary['train_features']
@ -622,13 +622,17 @@ class FreqaiDataKitchen:
else: else:
def normalise_distances(distances): def normalise_distances(distances):
normalised_distances = (distances - distances.min()) / (distances.max() - distances.min()) normalised_distances = (distances - distances.min()) / \
(distances.max() - distances.min())
return normalised_distances return normalised_distances
def rotate_point(origin, point, angle): def rotate_point(origin, point, angle):
# rotate a point counterclockwise by a given angle (in radians) around a given origin # rotate a point counterclockwise by a given angle (in radians)
x = origin[0] + cos(angle) * (point[0] - origin[0]) - sin(angle) * (point[1] - origin[1]) # around a given origin
y = origin[1] + sin(angle) * (point[0] - origin[0]) + cos(angle) * (point[1] - origin[1]) x = origin[0] + cos(angle) * (point[0] - origin[0]) - \
sin(angle) * (point[1] - origin[1])
y = origin[1] + sin(angle) * (point[0] - origin[0]) + \
cos(angle) * (point[1] - origin[1])
return (x, y) return (x, y)
MinPts = len(self.data_dictionary['train_features'].columns) * 2 MinPts = len(self.data_dictionary['train_features'].columns) * 2
@ -641,8 +645,9 @@ class FreqaiDataKitchen:
normalised_distances = normalise_distances(distances) normalised_distances = normalise_distances(distances)
x_range = np.linspace(0, 1, len(distances)) x_range = np.linspace(0, 1, len(distances))
line = np.linspace(normalise_distances[0], normalise_distances[-1], len(normalise_distances)) line = np.linspace(normalised_distances[0],
deflection = np.abs(normalise_distances - line) normalised_distances[-1], len(normalised_distances))
deflection = np.abs(normalised_distances - line)
max_deflection_loc = np.where(deflection == deflection.max())[0][0] max_deflection_loc = np.where(deflection == deflection.max())[0][0]
origin = x_range[max_deflection_loc], line[max_deflection_loc] origin = x_range[max_deflection_loc], line[max_deflection_loc]
point = x_range[max_deflection_loc], normalised_distances[max_deflection_loc] point = x_range[max_deflection_loc], normalised_distances[max_deflection_loc]