analysis pkg v 1.0.0.10

analysis.py v 1.1.13.008
superscript.py v 0.0.5.001

.devcontainer/Dockerfile

@@ -0,0 +1,2 @@
+FROM python
+WORKDIR ~/

.devcontainer/devcontainer.json

@@ -0,0 +1,26 @@
+{
+    "name": "TRA Analysis Development Environment",
+    "build": {
+		"dockerfile": "Dockerfile",
+    },
+    "settings": { 
+		"terminal.integrated.shell.linux": "/bin/bash",
+		"python.pythonPath": "/usr/local/bin/python",
+		"python.linting.enabled": true,
+		"python.linting.pylintEnabled": true,
+		"python.formatting.autopep8Path": "/usr/local/py-utils/bin/autopep8",
+		"python.formatting.blackPath": "/usr/local/py-utils/bin/black",
+		"python.formatting.yapfPath": "/usr/local/py-utils/bin/yapf",
+		"python.linting.banditPath": "/usr/local/py-utils/bin/bandit",
+		"python.linting.flake8Path": "/usr/local/py-utils/bin/flake8",
+		"python.linting.mypyPath": "/usr/local/py-utils/bin/mypy",
+		"python.linting.pycodestylePath": "/usr/local/py-utils/bin/pycodestyle",
+		"python.linting.pydocstylePath": "/usr/local/py-utils/bin/pydocstyle",
+		"python.linting.pylintPath": "/usr/local/py-utils/bin/pylint",
+		"python.testing.pytestPath": "/usr/local/py-utils/bin/pytest"
+	},
+	"extensions": [
+		"mhutchie.git-graph",
+	],
+	"postCreateCommand": "pip install -r analysis-master/analysis-amd64/requirements.txt"
+}

.gitignore

@@ -19,4 +19,6 @@ data analysis/keys.txt
 data analysis/check_for_new_matches.ipynb
 data analysis/test.ipynb
 data analysis/visualize_pit.ipynb
-data analysis/config/keys.config
+data analysis/config/keys.config
+analysis-master/analysis/__pycache__/
+data analysis/__pycache__/

analysis-master/analysis-amd64/analysis.egg-info/PKG-INFO

@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: analysis
-Version: 1.0.0.8
+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

analysis-master/analysis-amd64/analysis.egg-info/SOURCES.txt

@@ -1,6 +1,7 @@
 setup.py
 analysis/__init__.py
 analysis/analysis.py
+analysis/glicko2.py
 analysis/regression.py
 analysis/titanlearn.py
 analysis/trueskill.py

analysis-master/analysis-amd64/analysis/analysis.py

@@ -7,10 +7,14 @@
 #    current benchmark of optimization: 1.33 times faster
 # setup:
 
-__version__ = "1.1.13.006"
+__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:
         - cleaned up imports
     1.1.13.005:
@@ -269,7 +273,6 @@ __all__ = [
     'SVM',
     'random_forest_classifier',
     'random_forest_regressor',
-    'Glicko2',
     # all statistics functions left out due to integration in other functions
 ]
 
@@ -278,6 +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
@@ -450,7 +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)
 
@@ -461,13 +465,13 @@ def trueskill(teams_data, observations): # teams_data is array of array of tuple
     team_ratings = []
 
     for team in teams_data:
-        team_temp = []
+        team_temp = ()
         for player in team:
             player = Trueskill.Rating(player[0], player[1])
-            team_temp.append(player)
+            team_temp = team_temp + (player,)
         team_ratings.append(team_temp)
 
-    return Trueskill.rate(teams_data, observations)
+    return Trueskill.rate(team_ratings, ranks=observations)
 
 class RegressionMetrics():
 
@@ -688,103 +692,4 @@ def random_forest_regressor(data, outputs, test_size, n_estimators="warn", crite
     kernel.fit(data_train, outputs_train)
     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()
+    return kernel, RegressionMetrics(predictions, outputs_test)

analysis-master/analysis-amd64/analysis/glicko2.py

@@ -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()

analysis-master/analysis-amd64/build.sh

@@ -0,0 +1 @@
+python setup.py sdist bdist_wheel || python3 setup.py sdist bdist_wheel

analysis-master/analysis-amd64/build/lib/analysis/analysis.py

@@ -7,10 +7,14 @@
 #    current benchmark of optimization: 1.33 times faster
 # setup:
 
-__version__ = "1.1.13.006"
+__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:
         - cleaned up imports
     1.1.13.005:
@@ -269,7 +273,6 @@ __all__ = [
     'SVM',
     'random_forest_classifier',
     'random_forest_regressor',
-    'Glicko2',
     # all statistics functions left out due to integration in other functions
 ]
 
@@ -278,6 +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
@@ -450,7 +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)
 
@@ -461,13 +465,13 @@ def trueskill(teams_data, observations): # teams_data is array of array of tuple
     team_ratings = []
 
     for team in teams_data:
-        team_temp = []
+        team_temp = ()
         for player in team:
             player = Trueskill.Rating(player[0], player[1])
-            team_temp.append(player)
+            team_temp = team_temp + (player,)
         team_ratings.append(team_temp)
 
-    return Trueskill.rate(teams_data, observations)
+    return Trueskill.rate(team_ratings, ranks=observations)
 
 class RegressionMetrics():
 
@@ -688,103 +692,4 @@ def random_forest_regressor(data, outputs, test_size, n_estimators="warn", crite
     kernel.fit(data_train, outputs_train)
     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()
+    return kernel, RegressionMetrics(predictions, outputs_test)

analysis-master/analysis-amd64/build/lib/analysis/glicko2.py

@@ -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()

analysis-master/analysis-amd64/docker/Dockerfile

@@ -0,0 +1,5 @@
+FROM python
+WORKDIR ~/
+COPY ./ ./
+RUN pip install -r requirements.txt
+CMD ["bash"]

analysis-master/analysis-amd64/docker/start-docker.sh

@@ -0,0 +1,3 @@
+cd ..
+docker build -t tra-analysis-amd64-dev -f docker/Dockerfile .
+docker run -it tra-analysis-amd64-dev

analysis-master/analysis-amd64/requirements.txt

@@ -0,0 +1,6 @@
+numba
+numpy
+scipy
+scikit-learn
+six
+matplotlib

analysis-master/analysis-amd64/setup.py

@@ -1,8 +1,14 @@
 import setuptools
 
+requirements = []
+
+with open("requirements.txt", 'r') as file:
+    for line in file:
+        requirements.append(line)
+
 setuptools.setup(
-    name="analysis", # Replace with your own username
-    version="1.0.0.008",
+    name="analysis",
+    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",
@@ -10,14 +16,7 @@ setuptools.setup(
     long_description_content_type="text/markdown",
     url="https://github.com/titanscout2022/tr2022-strategy",
     packages=setuptools.find_packages(),
-    install_requires=[
-        "numba",
-        "numpy",
-        "scipy",
-        "scikit-learn",
-        "six",
-        "matplotlib"
-    ],
+    install_requires=requirements,
     license = "GNU General Public License v3.0",
     classifiers=[
         "Programming Language :: Python :: 3",

analysis-master/analysis-arm64/docker/start-docker.sh

@@ -0,0 +1,3 @@
+cd ..
+docker build -t tra-analysis-amd64-dev -f docker/Dockerfile .
+docker run -it tra-analysis-amd64-dev

analysis-master/build.sh

@@ -1 +0,0 @@
-python3 setup.py sdist bdist_wheel

data analysis/requirements.txt

@@ -0,0 +1,4 @@
+requests
+pymongo
+pandas
+dnspython

data analysis/superscript.py

@@ -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