mirror of
https://github.com/titanscouting/tra-analysis.git
synced 2024-11-10 06:54:44 +00:00
analysis pkg v 1.0.0.10
analysis.py v 1.1.13.008 superscript.py v 0.0.5.001
This commit is contained in:
ltcptgeneral
parent
57350f7ad4
commit
d635fed0ae
@ -1,6 +1,6 @@
|
||||
Metadata-Version: 2.1
|
||||
Name: analysis
|
||||
Version: 1.0.0.9
|
||||
Version: 1.0.0.10
|
||||
Summary: analysis package developed by Titan Scouting for The Red Alliance
|
||||
Home-page: https://github.com/titanscout2022/tr2022-strategy
|
||||
Author: The Titan Scouting Team
|
||||
|
||||
@ -1,6 +1,7 @@
|
||||
setup.py
|
||||
analysis/__init__.py
|
||||
analysis/analysis.py
|
||||
analysis/glicko2.py
|
||||
analysis/regression.py
|
||||
analysis/titanlearn.py
|
||||
analysis/trueskill.py
|
||||
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
@ -7,10 +7,12 @@
|
||||
# current benchmark of optimization: 1.33 times faster
|
||||
# setup:
|
||||
|
||||
__version__ = "1.1.13.007"
|
||||
__version__ = "1.1.13.008"
|
||||
|
||||
# changelog should be viewed using print(analysis.__changelog__)
|
||||
__changelog__ = """changelog:
|
||||
1.1.13.008:
|
||||
- moved Glicko2 to a seperate package
|
||||
1.1.13.007:
|
||||
- fixed bug with trueskill
|
||||
1.1.13.006:
|
||||
@ -271,7 +273,6 @@ __all__ = [
|
||||
'SVM',
|
||||
'random_forest_classifier',
|
||||
'random_forest_regressor',
|
||||
'Glicko2',
|
||||
# all statistics functions left out due to integration in other functions
|
||||
]
|
||||
|
||||
@ -280,6 +281,7 @@ __all__ = [
|
||||
# imports (now in alphabetical order! v 1.0.3.006):
|
||||
|
||||
import csv
|
||||
from analysis import glicko2 as Glicko2
|
||||
import numba
|
||||
from numba import jit
|
||||
import numpy as np
|
||||
@ -452,7 +454,7 @@ def elo(starting_score, opposing_score, observed, N, K):
|
||||
|
||||
def glicko2(starting_score, starting_rd, starting_vol, opposing_score, opposing_rd, observations):
|
||||
|
||||
player = Glicko2(rating = starting_score, rd = starting_rd, vol = starting_vol)
|
||||
player = Glicko2.Glicko2(rating = starting_score, rd = starting_rd, vol = starting_vol)
|
||||
|
||||
player.update_player([x for x in opposing_score], [x for x in opposing_rd], observations)
|
||||
|
||||
@ -691,102 +693,3 @@ def random_forest_regressor(data, outputs, test_size, n_estimators="warn", crite
|
||||
predictions = kernel.predict(data_test)
|
||||
|
||||
return kernel, RegressionMetrics(predictions, outputs_test)
|
||||
|
||||
class Glicko2:
|
||||
|
||||
_tau = 0.5
|
||||
|
||||
def getRating(self):
|
||||
return (self.__rating * 173.7178) + 1500
|
||||
|
||||
def setRating(self, rating):
|
||||
self.__rating = (rating - 1500) / 173.7178
|
||||
|
||||
rating = property(getRating, setRating)
|
||||
|
||||
def getRd(self):
|
||||
return self.__rd * 173.7178
|
||||
|
||||
def setRd(self, rd):
|
||||
self.__rd = rd / 173.7178
|
||||
|
||||
rd = property(getRd, setRd)
|
||||
|
||||
def __init__(self, rating = 1500, rd = 350, vol = 0.06):
|
||||
|
||||
self.setRating(rating)
|
||||
self.setRd(rd)
|
||||
self.vol = vol
|
||||
|
||||
def _preRatingRD(self):
|
||||
|
||||
self.__rd = math.sqrt(math.pow(self.__rd, 2) + math.pow(self.vol, 2))
|
||||
|
||||
def update_player(self, rating_list, RD_list, outcome_list):
|
||||
|
||||
rating_list = [(x - 1500) / 173.7178 for x in rating_list]
|
||||
RD_list = [x / 173.7178 for x in RD_list]
|
||||
|
||||
v = self._v(rating_list, RD_list)
|
||||
self.vol = self._newVol(rating_list, RD_list, outcome_list, v)
|
||||
self._preRatingRD()
|
||||
|
||||
self.__rd = 1 / math.sqrt((1 / math.pow(self.__rd, 2)) + (1 / v))
|
||||
|
||||
tempSum = 0
|
||||
for i in range(len(rating_list)):
|
||||
tempSum += self._g(RD_list[i]) * \
|
||||
(outcome_list[i] - self._E(rating_list[i], RD_list[i]))
|
||||
self.__rating += math.pow(self.__rd, 2) * tempSum
|
||||
|
||||
|
||||
def _newVol(self, rating_list, RD_list, outcome_list, v):
|
||||
|
||||
i = 0
|
||||
delta = self._delta(rating_list, RD_list, outcome_list, v)
|
||||
a = math.log(math.pow(self.vol, 2))
|
||||
tau = self._tau
|
||||
x0 = a
|
||||
x1 = 0
|
||||
|
||||
while x0 != x1:
|
||||
# New iteration, so x(i) becomes x(i-1)
|
||||
x0 = x1
|
||||
d = math.pow(self.__rating, 2) + v + math.exp(x0)
|
||||
h1 = -(x0 - a) / math.pow(tau, 2) - 0.5 * math.exp(x0) \
|
||||
/ d + 0.5 * math.exp(x0) * math.pow(delta / d, 2)
|
||||
h2 = -1 / math.pow(tau, 2) - 0.5 * math.exp(x0) * \
|
||||
(math.pow(self.__rating, 2) + v) \
|
||||
/ math.pow(d, 2) + 0.5 * math.pow(delta, 2) * math.exp(x0) \
|
||||
* (math.pow(self.__rating, 2) + v - math.exp(x0)) / math.pow(d, 3)
|
||||
x1 = x0 - (h1 / h2)
|
||||
|
||||
return math.exp(x1 / 2)
|
||||
|
||||
def _delta(self, rating_list, RD_list, outcome_list, v):
|
||||
|
||||
tempSum = 0
|
||||
for i in range(len(rating_list)):
|
||||
tempSum += self._g(RD_list[i]) * (outcome_list[i] - self._E(rating_list[i], RD_list[i]))
|
||||
return v * tempSum
|
||||
|
||||
def _v(self, rating_list, RD_list):
|
||||
|
||||
tempSum = 0
|
||||
for i in range(len(rating_list)):
|
||||
tempE = self._E(rating_list[i], RD_list[i])
|
||||
tempSum += math.pow(self._g(RD_list[i]), 2) * tempE * (1 - tempE)
|
||||
return 1 / tempSum
|
||||
|
||||
def _E(self, p2rating, p2RD):
|
||||
|
||||
return 1 / (1 + math.exp(-1 * self._g(p2RD) * \
|
||||
(self.__rating - p2rating)))
|
||||
|
||||
def _g(self, RD):
|
||||
|
||||
return 1 / math.sqrt(1 + 3 * math.pow(RD, 2) / math.pow(math.pi, 2))
|
||||
|
||||
def did_not_compete(self):
|
||||
|
||||
self._preRatingRD()
|
||||
|
||||
99
analysis-master/analysis-amd64/analysis/glicko2.py
Normal file
99
analysis-master/analysis-amd64/analysis/glicko2.py
Normal file
@ -0,0 +1,99 @@
|
||||
import math
|
||||
|
||||
class Glicko2:
|
||||
_tau = 0.5
|
||||
|
||||
def getRating(self):
|
||||
return (self.__rating * 173.7178) + 1500
|
||||
|
||||
def setRating(self, rating):
|
||||
self.__rating = (rating - 1500) / 173.7178
|
||||
|
||||
rating = property(getRating, setRating)
|
||||
|
||||
def getRd(self):
|
||||
return self.__rd * 173.7178
|
||||
|
||||
def setRd(self, rd):
|
||||
self.__rd = rd / 173.7178
|
||||
|
||||
rd = property(getRd, setRd)
|
||||
|
||||
def __init__(self, rating = 1500, rd = 350, vol = 0.06):
|
||||
|
||||
self.setRating(rating)
|
||||
self.setRd(rd)
|
||||
self.vol = vol
|
||||
|
||||
def _preRatingRD(self):
|
||||
|
||||
self.__rd = math.sqrt(math.pow(self.__rd, 2) + math.pow(self.vol, 2))
|
||||
|
||||
def update_player(self, rating_list, RD_list, outcome_list):
|
||||
|
||||
rating_list = [(x - 1500) / 173.7178 for x in rating_list]
|
||||
RD_list = [x / 173.7178 for x in RD_list]
|
||||
|
||||
v = self._v(rating_list, RD_list)
|
||||
self.vol = self._newVol(rating_list, RD_list, outcome_list, v)
|
||||
self._preRatingRD()
|
||||
|
||||
self.__rd = 1 / math.sqrt((1 / math.pow(self.__rd, 2)) + (1 / v))
|
||||
|
||||
tempSum = 0
|
||||
for i in range(len(rating_list)):
|
||||
tempSum += self._g(RD_list[i]) * \
|
||||
(outcome_list[i] - self._E(rating_list[i], RD_list[i]))
|
||||
self.__rating += math.pow(self.__rd, 2) * tempSum
|
||||
|
||||
|
||||
def _newVol(self, rating_list, RD_list, outcome_list, v):
|
||||
|
||||
i = 0
|
||||
delta = self._delta(rating_list, RD_list, outcome_list, v)
|
||||
a = math.log(math.pow(self.vol, 2))
|
||||
tau = self._tau
|
||||
x0 = a
|
||||
x1 = 0
|
||||
|
||||
while x0 != x1:
|
||||
# New iteration, so x(i) becomes x(i-1)
|
||||
x0 = x1
|
||||
d = math.pow(self.__rating, 2) + v + math.exp(x0)
|
||||
h1 = -(x0 - a) / math.pow(tau, 2) - 0.5 * math.exp(x0) \
|
||||
/ d + 0.5 * math.exp(x0) * math.pow(delta / d, 2)
|
||||
h2 = -1 / math.pow(tau, 2) - 0.5 * math.exp(x0) * \
|
||||
(math.pow(self.__rating, 2) + v) \
|
||||
/ math.pow(d, 2) + 0.5 * math.pow(delta, 2) * math.exp(x0) \
|
||||
* (math.pow(self.__rating, 2) + v - math.exp(x0)) / math.pow(d, 3)
|
||||
x1 = x0 - (h1 / h2)
|
||||
|
||||
return math.exp(x1 / 2)
|
||||
|
||||
def _delta(self, rating_list, RD_list, outcome_list, v):
|
||||
|
||||
tempSum = 0
|
||||
for i in range(len(rating_list)):
|
||||
tempSum += self._g(RD_list[i]) * (outcome_list[i] - self._E(rating_list[i], RD_list[i]))
|
||||
return v * tempSum
|
||||
|
||||
def _v(self, rating_list, RD_list):
|
||||
|
||||
tempSum = 0
|
||||
for i in range(len(rating_list)):
|
||||
tempE = self._E(rating_list[i], RD_list[i])
|
||||
tempSum += math.pow(self._g(RD_list[i]), 2) * tempE * (1 - tempE)
|
||||
return 1 / tempSum
|
||||
|
||||
def _E(self, p2rating, p2RD):
|
||||
|
||||
return 1 / (1 + math.exp(-1 * self._g(p2RD) * \
|
||||
(self.__rating - p2rating)))
|
||||
|
||||
def _g(self, RD):
|
||||
|
||||
return 1 / math.sqrt(1 + 3 * math.pow(RD, 2) / math.pow(math.pi, 2))
|
||||
|
||||
def did_not_compete(self):
|
||||
|
||||
self._preRatingRD()
|
||||
@ -7,10 +7,12 @@
|
||||
# current benchmark of optimization: 1.33 times faster
|
||||
# setup:
|
||||
|
||||
__version__ = "1.1.13.007"
|
||||
__version__ = "1.1.13.008"
|
||||
|
||||
# changelog should be viewed using print(analysis.__changelog__)
|
||||
__changelog__ = """changelog:
|
||||
1.1.13.008:
|
||||
- moved Glicko2 to a seperate package
|
||||
1.1.13.007:
|
||||
- fixed bug with trueskill
|
||||
1.1.13.006:
|
||||
@ -271,7 +273,6 @@ __all__ = [
|
||||
'SVM',
|
||||
'random_forest_classifier',
|
||||
'random_forest_regressor',
|
||||
'Glicko2',
|
||||
# all statistics functions left out due to integration in other functions
|
||||
]
|
||||
|
||||
@ -280,6 +281,7 @@ __all__ = [
|
||||
# imports (now in alphabetical order! v 1.0.3.006):
|
||||
|
||||
import csv
|
||||
from analysis import glicko2 as Glicko2
|
||||
import numba
|
||||
from numba import jit
|
||||
import numpy as np
|
||||
@ -452,7 +454,7 @@ def elo(starting_score, opposing_score, observed, N, K):
|
||||
|
||||
def glicko2(starting_score, starting_rd, starting_vol, opposing_score, opposing_rd, observations):
|
||||
|
||||
player = Glicko2(rating = starting_score, rd = starting_rd, vol = starting_vol)
|
||||
player = Glicko2.Glicko2(rating = starting_score, rd = starting_rd, vol = starting_vol)
|
||||
|
||||
player.update_player([x for x in opposing_score], [x for x in opposing_rd], observations)
|
||||
|
||||
@ -691,102 +693,3 @@ def random_forest_regressor(data, outputs, test_size, n_estimators="warn", crite
|
||||
predictions = kernel.predict(data_test)
|
||||
|
||||
return kernel, RegressionMetrics(predictions, outputs_test)
|
||||
|
||||
class Glicko2:
|
||||
|
||||
_tau = 0.5
|
||||
|
||||
def getRating(self):
|
||||
return (self.__rating * 173.7178) + 1500
|
||||
|
||||
def setRating(self, rating):
|
||||
self.__rating = (rating - 1500) / 173.7178
|
||||
|
||||
rating = property(getRating, setRating)
|
||||
|
||||
def getRd(self):
|
||||
return self.__rd * 173.7178
|
||||
|
||||
def setRd(self, rd):
|
||||
self.__rd = rd / 173.7178
|
||||
|
||||
rd = property(getRd, setRd)
|
||||
|
||||
def __init__(self, rating = 1500, rd = 350, vol = 0.06):
|
||||
|
||||
self.setRating(rating)
|
||||
self.setRd(rd)
|
||||
self.vol = vol
|
||||
|
||||
def _preRatingRD(self):
|
||||
|
||||
self.__rd = math.sqrt(math.pow(self.__rd, 2) + math.pow(self.vol, 2))
|
||||
|
||||
def update_player(self, rating_list, RD_list, outcome_list):
|
||||
|
||||
rating_list = [(x - 1500) / 173.7178 for x in rating_list]
|
||||
RD_list = [x / 173.7178 for x in RD_list]
|
||||
|
||||
v = self._v(rating_list, RD_list)
|
||||
self.vol = self._newVol(rating_list, RD_list, outcome_list, v)
|
||||
self._preRatingRD()
|
||||
|
||||
self.__rd = 1 / math.sqrt((1 / math.pow(self.__rd, 2)) + (1 / v))
|
||||
|
||||
tempSum = 0
|
||||
for i in range(len(rating_list)):
|
||||
tempSum += self._g(RD_list[i]) * \
|
||||
(outcome_list[i] - self._E(rating_list[i], RD_list[i]))
|
||||
self.__rating += math.pow(self.__rd, 2) * tempSum
|
||||
|
||||
|
||||
def _newVol(self, rating_list, RD_list, outcome_list, v):
|
||||
|
||||
i = 0
|
||||
delta = self._delta(rating_list, RD_list, outcome_list, v)
|
||||
a = math.log(math.pow(self.vol, 2))
|
||||
tau = self._tau
|
||||
x0 = a
|
||||
x1 = 0
|
||||
|
||||
while x0 != x1:
|
||||
# New iteration, so x(i) becomes x(i-1)
|
||||
x0 = x1
|
||||
d = math.pow(self.__rating, 2) + v + math.exp(x0)
|
||||
h1 = -(x0 - a) / math.pow(tau, 2) - 0.5 * math.exp(x0) \
|
||||
/ d + 0.5 * math.exp(x0) * math.pow(delta / d, 2)
|
||||
h2 = -1 / math.pow(tau, 2) - 0.5 * math.exp(x0) * \
|
||||
(math.pow(self.__rating, 2) + v) \
|
||||
/ math.pow(d, 2) + 0.5 * math.pow(delta, 2) * math.exp(x0) \
|
||||
* (math.pow(self.__rating, 2) + v - math.exp(x0)) / math.pow(d, 3)
|
||||
x1 = x0 - (h1 / h2)
|
||||
|
||||
return math.exp(x1 / 2)
|
||||
|
||||
def _delta(self, rating_list, RD_list, outcome_list, v):
|
||||
|
||||
tempSum = 0
|
||||
for i in range(len(rating_list)):
|
||||
tempSum += self._g(RD_list[i]) * (outcome_list[i] - self._E(rating_list[i], RD_list[i]))
|
||||
return v * tempSum
|
||||
|
||||
def _v(self, rating_list, RD_list):
|
||||
|
||||
tempSum = 0
|
||||
for i in range(len(rating_list)):
|
||||
tempE = self._E(rating_list[i], RD_list[i])
|
||||
tempSum += math.pow(self._g(RD_list[i]), 2) * tempE * (1 - tempE)
|
||||
return 1 / tempSum
|
||||
|
||||
def _E(self, p2rating, p2RD):
|
||||
|
||||
return 1 / (1 + math.exp(-1 * self._g(p2RD) * \
|
||||
(self.__rating - p2rating)))
|
||||
|
||||
def _g(self, RD):
|
||||
|
||||
return 1 / math.sqrt(1 + 3 * math.pow(RD, 2) / math.pow(math.pi, 2))
|
||||
|
||||
def did_not_compete(self):
|
||||
|
||||
self._preRatingRD()
|
||||
|
||||
99
analysis-master/analysis-amd64/build/lib/analysis/glicko2.py
Normal file
99
analysis-master/analysis-amd64/build/lib/analysis/glicko2.py
Normal file
@ -0,0 +1,99 @@
|
||||
import math
|
||||
|
||||
class Glicko2:
|
||||
_tau = 0.5
|
||||
|
||||
def getRating(self):
|
||||
return (self.__rating * 173.7178) + 1500
|
||||
|
||||
def setRating(self, rating):
|
||||
self.__rating = (rating - 1500) / 173.7178
|
||||
|
||||
rating = property(getRating, setRating)
|
||||
|
||||
def getRd(self):
|
||||
return self.__rd * 173.7178
|
||||
|
||||
def setRd(self, rd):
|
||||
self.__rd = rd / 173.7178
|
||||
|
||||
rd = property(getRd, setRd)
|
||||
|
||||
def __init__(self, rating = 1500, rd = 350, vol = 0.06):
|
||||
|
||||
self.setRating(rating)
|
||||
self.setRd(rd)
|
||||
self.vol = vol
|
||||
|
||||
def _preRatingRD(self):
|
||||
|
||||
self.__rd = math.sqrt(math.pow(self.__rd, 2) + math.pow(self.vol, 2))
|
||||
|
||||
def update_player(self, rating_list, RD_list, outcome_list):
|
||||
|
||||
rating_list = [(x - 1500) / 173.7178 for x in rating_list]
|
||||
RD_list = [x / 173.7178 for x in RD_list]
|
||||
|
||||
v = self._v(rating_list, RD_list)
|
||||
self.vol = self._newVol(rating_list, RD_list, outcome_list, v)
|
||||
self._preRatingRD()
|
||||
|
||||
self.__rd = 1 / math.sqrt((1 / math.pow(self.__rd, 2)) + (1 / v))
|
||||
|
||||
tempSum = 0
|
||||
for i in range(len(rating_list)):
|
||||
tempSum += self._g(RD_list[i]) * \
|
||||
(outcome_list[i] - self._E(rating_list[i], RD_list[i]))
|
||||
self.__rating += math.pow(self.__rd, 2) * tempSum
|
||||
|
||||
|
||||
def _newVol(self, rating_list, RD_list, outcome_list, v):
|
||||
|
||||
i = 0
|
||||
delta = self._delta(rating_list, RD_list, outcome_list, v)
|
||||
a = math.log(math.pow(self.vol, 2))
|
||||
tau = self._tau
|
||||
x0 = a
|
||||
x1 = 0
|
||||
|
||||
while x0 != x1:
|
||||
# New iteration, so x(i) becomes x(i-1)
|
||||
x0 = x1
|
||||
d = math.pow(self.__rating, 2) + v + math.exp(x0)
|
||||
h1 = -(x0 - a) / math.pow(tau, 2) - 0.5 * math.exp(x0) \
|
||||
/ d + 0.5 * math.exp(x0) * math.pow(delta / d, 2)
|
||||
h2 = -1 / math.pow(tau, 2) - 0.5 * math.exp(x0) * \
|
||||
(math.pow(self.__rating, 2) + v) \
|
||||
/ math.pow(d, 2) + 0.5 * math.pow(delta, 2) * math.exp(x0) \
|
||||
* (math.pow(self.__rating, 2) + v - math.exp(x0)) / math.pow(d, 3)
|
||||
x1 = x0 - (h1 / h2)
|
||||
|
||||
return math.exp(x1 / 2)
|
||||
|
||||
def _delta(self, rating_list, RD_list, outcome_list, v):
|
||||
|
||||
tempSum = 0
|
||||
for i in range(len(rating_list)):
|
||||
tempSum += self._g(RD_list[i]) * (outcome_list[i] - self._E(rating_list[i], RD_list[i]))
|
||||
return v * tempSum
|
||||
|
||||
def _v(self, rating_list, RD_list):
|
||||
|
||||
tempSum = 0
|
||||
for i in range(len(rating_list)):
|
||||
tempE = self._E(rating_list[i], RD_list[i])
|
||||
tempSum += math.pow(self._g(RD_list[i]), 2) * tempE * (1 - tempE)
|
||||
return 1 / tempSum
|
||||
|
||||
def _E(self, p2rating, p2RD):
|
||||
|
||||
return 1 / (1 + math.exp(-1 * self._g(p2RD) * \
|
||||
(self.__rating - p2rating)))
|
||||
|
||||
def _g(self, RD):
|
||||
|
||||
return 1 / math.sqrt(1 + 3 * math.pow(RD, 2) / math.pow(math.pi, 2))
|
||||
|
||||
def did_not_compete(self):
|
||||
|
||||
self._preRatingRD()
|
||||
BIN
analysis-master/analysis-amd64/dist/analysis-1.0.0.10-py3-none-any.whl
vendored
Normal file
BIN
analysis-master/analysis-amd64/dist/analysis-1.0.0.10-py3-none-any.whl
vendored
Normal file
Binary file not shown.
BIN
analysis-master/analysis-amd64/dist/analysis-1.0.0.10.tar.gz
vendored
Normal file
BIN
analysis-master/analysis-amd64/dist/analysis-1.0.0.10.tar.gz
vendored
Normal file
Binary file not shown.
@ -8,7 +8,7 @@ with open("requirements.txt", 'r') as file:
|
||||
|
||||
setuptools.setup(
|
||||
name="analysis",
|
||||
version="1.0.0.009",
|
||||
version="1.0.0.010",
|
||||
author="The Titan Scouting Team",
|
||||
author_email="titanscout2022@gmail.com",
|
||||
description="analysis package developed by Titan Scouting for The Red Alliance",
|
||||
|
||||
4
data analysis/requirements.txt
Normal file
4
data analysis/requirements.txt
Normal file
@ -0,0 +1,4 @@
|
||||
requests
|
||||
pymongo
|
||||
pandas
|
||||
dnspython
|
||||
@ -3,12 +3,15 @@
|
||||
# Notes:
|
||||
# setup:
|
||||
|
||||
__version__ = "0.0.5.000"
|
||||
__version__ = "0.0.5.001"
|
||||
|
||||
# changelog should be viewed using print(analysis.__changelog__)
|
||||
__changelog__ = """changelog:
|
||||
0.0.5.001:
|
||||
- text fixes
|
||||
- removed matplotlib requirement
|
||||
0.0.5.000:
|
||||
improved user interface
|
||||
- improved user interface
|
||||
0.0.4.002:
|
||||
- removed unessasary code
|
||||
0.0.4.001:
|
||||
@ -84,7 +87,6 @@ __all__ = [
|
||||
from analysis import analysis as an
|
||||
import data as d
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from os import system, name
|
||||
from pathlib import Path
|
||||
import time
|
||||
|
||||
Loading…
Reference in New Issue
Block a user