From c1a72b706d25a16e8a8ca6e8dc1e81b19a4ba861 Mon Sep 17 00:00:00 2001
From: Sandro Zimmermann <sandro.zimmermann@outlook.com>
Date: Sat, 29 Nov 2025 16:16:16 +0100
Subject: [PATCH] added argv support with class Arguments and Enum

---
 __pycache__/arguments.cpython-313.pyc         |   Bin 0 -> 3232 bytes
 absolut.py                                    |    13 +-
 arguments.py                                  |    52 +
 data/shots_dev.csv                            |   955 -
 data/shots_dev.xlsx                           |   Bin 20306 -> 0 bytes
 data/synthetic_data.csv                       |  1001 -
 data/synthetic_shots.csv                      | 25001 ++++++++++++++++
 ...d_values.py => generate_synthetic_shots.py |    54 +-
 main.py                                       |    23 +-
 9 files changed, 25098 insertions(+), 2001 deletions(-)
 create mode 100644 __pycache__/arguments.cpython-313.pyc
 create mode 100644 arguments.py
 delete mode 100644 data/shots_dev.csv
 delete mode 100644 data/shots_dev.xlsx
 delete mode 100644 data/synthetic_data.csv
 create mode 100644 data/synthetic_shots.csv
 rename rule_based_values.py => generate_synthetic_shots.py (63%)

diff --git a/__pycache__/arguments.cpython-313.pyc b/__pycache__/arguments.cpython-313.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..323885f023aaaab14ce6a596dcf02cc2d32f90e7
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diff --git a/absolut.py b/absolut.py
index cdb8af8..06edbea 100644
--- a/absolut.py
+++ b/absolut.py
@@ -15,7 +15,7 @@ FEATURES = ["points", "x", "y"]
 def load_dataframe():
     try:
         colum_list = FEATURES
-        df = pd.read_csv("data/shots.csv", usecols = colum_list).dropna()
+        df = pd.read_csv("data/synthetic_data.csv", usecols = colum_list).dropna()
         return df.abs()
     except FileNotFoundError as error:
         print(error)
@@ -33,14 +33,11 @@ def calc_f1_score(y_true, y_pred):
     tn = np.sum(np.multiply([i==False for i in y_pred], [not(j) for j in y_true]))
     fp = np.sum(np.multiply([i==True for i in y_pred], [not(j) for j in y_true]))
     fn = np.sum(np.multiply([i==False for i in y_pred], y_true))
-    
-    '''print(tp)
-    print(fp)
 
     precision = calc_precision(tp, fp)
-    recall = calc_recall(tp, fn)'''
+    recall = calc_recall(tp, fn)
 
-    if tp != 0 and fp != 0:
+    '''if tp != 0 and fp != 0:
         precision = calc_precision(tp, fp)
     else:
         precision = 0
@@ -48,7 +45,7 @@ def calc_f1_score(y_true, y_pred):
     if tp != 0 and fn != 0:
         recall = calc_recall(tp, fn)
     else:
-        recall = 0
+        recall = 0'''
     
     if precision != 0 and recall != 0:
         f1 = (2 * precision * recall) / (precision + recall)
@@ -77,7 +74,6 @@ def main():
     print(df.head())
     print(df.head().info())
     
-
     sns.countplot(x = df["points"])
     plt.show()
 
@@ -86,7 +82,6 @@ def main():
 
     sns.scatterplot(x=df['x'], y=df['y'], hue=df['points'])
     plt.show()
-    quit()
 
     features = ["x", "y"]
     X = df[features]
diff --git a/arguments.py b/arguments.py
new file mode 100644
index 0000000..ce15969
--- /dev/null
+++ b/arguments.py
@@ -0,0 +1,52 @@
+from enum import Enum
+
+class Mode(Enum):
+    V = "v"
+    A = "a"
+    C = "c"
+
+class Information(Enum):
+    DISABLED = False
+    ENABLED = True
+
+class Graph(Enum):
+    DISABLED = False
+    ENABLED = True
+
+class Arguments:
+
+    def __init__(self, file_path):
+        self.file_path = file_path
+        self.mode = None
+        self.information = None
+        self.graph = None
+
+    def get_file_path(self):
+        return self.file_path
+
+    def get_mode(self):
+        return self.mode
+
+    def set_mode(self, value):
+        try:
+            self.mode = Mode(value)
+        except ValueError:
+            raise ValueError(f"Invalid mode '{value}'. Allowed values: {[m.value for m in Mode]}")
+    
+    def get_information(self):
+        return self.information
+
+    def set_information(self, value):
+        try:
+            self.information = Information(value)
+        except ValueError:
+            raise ValueError(f"Invalid information '{value}'. Allowed values: {[m.value for m in Information]}")
+        
+    def get_graph(self):
+        return self.graph
+
+    def set_graph(self, value):
+        try:
+            self.graph = Graph(value)
+        except ValueError:
+            raise ValueError(f"Invalid graph '{value}'. Allowed values: {[m.value for m in Graph]}")
diff --git a/data/shots_dev.csv b/data/shots_dev.csv
deleted file mode 100644
index edb6562..0000000
--- a/data/shots_dev.csv
+++ /dev/null
@@ -1,955 +0,0 @@
-points,x,y
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diff --git a/data/synthetic_data.csv b/data/synthetic_data.csv
deleted file mode 100644
index 69a4138..0000000
--- a/data/synthetic_data.csv
+++ /dev/null
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diff --git a/data/synthetic_shots.csv b/data/synthetic_shots.csv
new file mode 100644
index 0000000..324b2e7
--- /dev/null
+++ b/data/synthetic_shots.csv
@@ -0,0 +1,25001 @@
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diff --git a/rule_based_values.py b/generate_synthetic_shots.py
similarity index 63%
rename from rule_based_values.py
rename to generate_synthetic_shots.py
index c362b03..f93e76e 100644
--- a/rule_based_values.py
+++ b/generate_synthetic_shots.py
@@ -1,23 +1,26 @@
-# generate points and x, y coordinates based on this rules
-# 1. points possible: 0 to 10
-# 2. possible points for x, y range
-# 2a. 10 (0,0) (1.5, 1.5)
-# 2.b 9 (1.5,0) (1.5, 1.5)
-# 2.c 8 (0,0) (1.5, 1.5)
-# 2.d 7 (0,0) (1.5, 1.5)
-# 2.e 6 (0,0) (1.5, 1.5)
-# 2.f 5 (0,0) (1.5, 1.5)
-# 2.g 4 (0,0) (1.5, 1.5)
-# 2.h 3 (0,0) (1.5, 1.5)
-# 2.i 2 (0,0) (1.5, 1.5)
-# 2.j 1 (0,0) (1.5, 1.5)
-# 2.j 0 (0,0) (1.5, 1.5)
+'''--------------------------------------------------
+Script randomly generates coordinates from (-10, -10)
+to (10, 10), with step 0.5. 
+Then applies points from 0 to 10 to the coordinates.
 
+Points value is determined by circle area.
+
+Export the dataset to "data/synthetic_shots.csv"
+
+---------------------------------------------------'''
+
+import sys
 import pandas as pd
 import numpy as np
 import csv
 
-FEATURES = ["points", "x", "y"]
+args = sys.argv[1:]
+
+if not args:
+    print("Usage: python3 generate_synthetic_shots.py <number of generated shots>")
+    sys.exit(1)
+
+n = int(args[0])
 
 # Area circle
 A10 = 1.5 ** 2 * np.pi
@@ -31,21 +34,9 @@ A3 = 8.5 ** 2 * np.pi
 A2 = 9.5 ** 2 * np.pi
 A1 = 10.5 ** 2 * np.pi
 
-'''print(A10)
-print(A9)
-print(A8)
-print(A7)
-print(A6)
-print(A5)
-print(A4)
-print(A3)
-print(A2)
-print(A1)
-print("")'''
+possible_values = np.linspace(-10, 10, 41) # fromn -10 to 10 with step 0.5
 
-possible_values = np.linspace(-10, 10, 41) # frin -10 to 10 with step 0.5
-
-xy = [(np.random.choice(possible_values), np.random.choice(possible_values)) for _ in range(1000)]
+xy = [(np.random.choice(possible_values), np.random.choice(possible_values)) for _ in range(n)]
 
 dataset = []
 
@@ -77,9 +68,8 @@ for i in xy:
     elif A > A1:
         dataset.append([0, i[0], i[1]])
     
-#print(dataset)
-
-with open('data/synthetic_data.csv', 'w', newline='') as csvfile:
+with open('data/synthetic_shots.csv', 'w', newline='') as csvfile:
     fieldnames = ['points', 'x', 'y']
     writer = csv.writer(csvfile)
+    writer.writerow(fieldnames)
     writer.writerows(dataset)
diff --git a/main.py b/main.py
index fccea88..88b7412 100644
--- a/main.py
+++ b/main.py
@@ -1,3 +1,5 @@
+import sys
+from arguments import Arguments
 import pandas as pd
 import numpy as np
 import seaborn as sns
@@ -9,14 +11,27 @@ from sklearn.neighbors import KNeighborsClassifier
 from sklearn.tree import DecisionTreeClassifier
 from sklearn.preprocessing import LabelEncoder
 
+if not sys.argv[1:]:
+    print("Usage: python3 main.py <path to csv> <mode vector [v] (default) or absolut [a] or cartesian [c]> <optional information [true]> <optional graphs [true]>")
+    sys.exit(1)
+
+args = Arguments(sys.argv[1])
+args.set_mode(sys.argv[2])
+
+try:
+    args.set_information(sys.argv[3])
+    args.set_graph(sys.argv[4])
+except IndexError:
+    args.set_information(False)
+    args.set_graph(False)
+
 FEATURES = ["points", "x", "y"]
 
 # create dataframe from csv and drop any row with null values
-def load_dataframe():
+def load_dataframe(file_path):
     try:
         colum_list = FEATURES
-        #df = pd.read_csv("data/shots.csv", usecols = colum_list).dropna()
-        df = pd.read_csv("data/synthetic_data.csv", usecols = colum_list).dropna()
+        df = pd.read_csv(file_path, usecols = colum_list).dropna()
         return df
     except FileNotFoundError as error:
         print(error)
@@ -73,7 +88,7 @@ def get_score_from_cli():
         return None
 
 def main():
-    df = load_dataframe()
+    df = load_dataframe(args.get_file_path())
     print(df.describe())
     #print(df.head())
     #print(df.head().info())