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trial.py
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trial.py
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import tkinter as tk
from tkinter import ttk, messagebox
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
from sklearn.preprocessing import MinMaxScaler
from sklearn.linear_model import LinearRegression
import numpy as np
from sklearn.metrics import mean_squared_error
import datetime
import fractions
class DataLoader:
@staticmethod
def load_data(file_path='excel.xlsx', sheet_name='main'):
return pd.read_excel(file_path, sheet_name=sheet_name)
class Plotter:
def __init__(self, root):
self.root = root
def create_scrolled_window(self, parent, width=1400, height=600):
container = ttk.Frame(parent)
canvas = tk.Canvas(container, width=width, height=height)
scrollbar = ttk.Scrollbar(container, orient="vertical", command=canvas.yview)
scrollable_frame = ttk.Frame(canvas)
scrollable_frame.bind(
"<Configure>",
lambda e: canvas.configure(
scrollregion=canvas.bbox("all")
)
)
canvas.create_window((0, 0), window=scrollable_frame, anchor="nw")
canvas.configure(yscrollcommand=scrollbar.set)
container.pack(fill=tk.BOTH, expand=True)
canvas.pack(side="left", fill=tk.BOTH, expand=True)
scrollbar.pack(side="right", fill="y")
return scrollable_frame
def plot_data(self, data, selected_category, normalized=False, smoothed=False):
categories = ['aircraft', 'helicopter', 'tank', 'APC', 'field artillery', 'MRL', 'drone', 'naval ship',
'anti-aircraft warfare']
time_series_data = {category: data[['date', category]].set_index('date') for category in categories}
if normalized or smoothed:
scaler = MinMaxScaler()
time_series_data = {category: pd.DataFrame(scaler.fit_transform(time_series_data[category]),
index=time_series_data[category].index,
columns=[category]) for category in categories}
if smoothed:
window_size = 7 # Moving average window size
time_series_data = {category: time_series_data[category].rolling(window=window_size, min_periods=1).mean()
for category in categories}
plot_window = tk.Toplevel(self.root)
plot_window.title("Data Chart")
scrollable_frame = self.create_scrolled_window(plot_window)
if selected_category == "All categories":
fig, axes = plt.subplots(len(categories), 1, figsize=(14, 40))
for i, category in enumerate(categories):
axes[i].plot(time_series_data[category], label=category, marker='o', linestyle='-')
title_suffix = " (Normalized)" if normalized else ""
title_suffix += " (Smoothed)" if smoothed else ""
axes[i].set_title(f'{category.capitalize()}{title_suffix}')
axes[i].set_xlabel('Date')
axes[i].set_ylabel('Count' if not normalized else 'Normalized Count')
axes[i].grid(True)
fig.tight_layout()
else:
fig, ax = plt.subplots(figsize=(14, 6))
ax.plot(time_series_data[selected_category], label=selected_category, marker='o', linestyle='-')
title_suffix = " (Normalized)" if normalized else ""
title_suffix += " (Smoothed)" if smoothed else ""
ax.set_title(f'{selected_category.capitalize()}{title_suffix}')
ax.set_xlabel('Date')
ax.set_ylabel('Count' if not normalized else 'Normalized Count')
ax.grid(True)
fig.tight_layout()
canvas = FigureCanvasTkAgg(fig, master=scrollable_frame)
canvas.draw()
canvas.get_tk_widget().pack(fill=tk.BOTH, expand=True)
def plot_trend_lines(self, data, selected_category):
categories = ['aircraft', 'helicopter', 'tank', 'APC', 'field artillery', 'MRL', 'drone', 'naval ship',
'anti-aircraft warfare']
time_series_data = {category: data[['date', category]].set_index('date') for category in categories}
scaler = MinMaxScaler()
normalized_data = {category: pd.DataFrame(scaler.fit_transform(time_series_data[category]),
index=time_series_data[category].index,
columns=[category])
for category in categories}
window_size = 7 # Moving average window size
smoothed_data = {category: normalized_data[category].rolling(window=window_size, min_periods=1).mean()
for category in categories}
models = {}
for category in categories:
model = LinearRegression()
X = smoothed_data[category].dropna().index.map(pd.Timestamp.toordinal).values.reshape(-1, 1)
y = smoothed_data[category].dropna().values
model.fit(X, y)
models[category] = model
plot_window = tk.Toplevel(self.root)
plot_window.title("Trend Lines")
scrollable_frame = self.create_scrolled_window(plot_window)
if selected_category == "All categories":
fig, axes = plt.subplots(len(categories), 1, figsize=(14, 40))
for i, category in enumerate(categories):
axes[i].plot(smoothed_data[category], label=f'{category} (Smoothed)', marker='o', linestyle='-')
X = smoothed_data[category].dropna().index.map(pd.Timestamp.toordinal).values.reshape(-1, 1)
axes[i].plot(smoothed_data[category].dropna().index, models[category].predict(X), color='red',
label='Trend Line')
axes[i].set_title(f'{category.capitalize()} (Smoothed and Trend)')
axes[i].set_xlabel('Date')
axes[i].set_ylabel('Normalized Count')
axes[i].legend()
axes[i].grid(True)
fig.tight_layout()
else:
fig, ax = plt.subplots(figsize=(14, 6))
ax.plot(smoothed_data[selected_category], label=f'{selected_category} (Smoothed)', marker='o',
linestyle='-')
X = smoothed_data[selected_category].dropna().index.map(pd.Timestamp.toordinal).values.reshape(-1, 1)
ax.plot(smoothed_data[selected_category].dropna().index, models[selected_category].predict(X), color='red',
label='Trend Line')
ax.set_title(f'{selected_category.capitalize()} (Smoothed and Trend)')
ax.set_xlabel('Date')
ax.set_ylabel('Normalized Count')
ax.legend()
ax.grid(True)
fig.tight_layout()
canvas = FigureCanvasTkAgg(fig, master=scrollable_frame)
canvas.draw()
canvas.get_tk_widget().pack(fill=tk.BOTH, expand=True)
def plot_derivatives(self, data, selected_category):
categories = ['aircraft', 'helicopter', 'tank', 'APC', 'field artillery', 'MRL', 'drone', 'naval ship',
'anti-aircraft warfare']
time_series_data = {category: data[['date', category]].set_index('date') for category in categories}
scaler = MinMaxScaler()
normalized_data = {category: pd.DataFrame(scaler.fit_transform(time_series_data[category]),
index=time_series_data[category].index,
columns=[category])
for category in categories}
window_size = 7 # Moving average window size
smoothed_data = {category: normalized_data[category].rolling(window=window_size, min_periods=1).mean()
for category in categories}
models = {}
rmse_scores = {}
for category in categories:
model = LinearRegression()
X = smoothed_data[category].dropna().index.map(pd.Timestamp.toordinal).values.reshape(-1, 1)
y = smoothed_data[category].dropna().values
model.fit(X, y)
models[category] = model
predictions = model.predict(X)
rmse_scores[category] = np.sqrt(mean_squared_error(y, predictions))
derivatives = {}
second_derivatives = {}
for category in categories:
X = smoothed_data[category].dropna().index.map(pd.Timestamp.toordinal).values
y = smoothed_data[category].dropna().values.flatten()
first_derivative = np.gradient(y, X)
derivatives[category] = pd.DataFrame(first_derivative, index=smoothed_data[category].dropna().index,
columns=[category + '_first_derivative'])
second_derivative = np.gradient(first_derivative, X)
second_derivatives[category] = pd.DataFrame(second_derivative, index=smoothed_data[category].dropna().index,
columns=[category + '_second_derivative'])
plot_window = tk.Toplevel(self.root)
plot_window.title("Derivatives")
scrollable_frame = self.create_scrolled_window(plot_window)
if selected_category == "All categories":
fig, axes = plt.subplots(len(categories), 3, figsize=(14, 52)) # Adjusted figure size
for i, category in enumerate(categories):
axes[i, 0].plot(smoothed_data[category], label=f'{category} (Smoothed)', marker='o', linestyle='-')
X = smoothed_data[category].dropna().index.map(pd.Timestamp.toordinal).values.reshape(-1, 1)
axes[i, 0].plot(smoothed_data[category].dropna().index, models[category].predict(X), color='red',
label='Trend Line')
axes[i, 0].set_title(
f'{category.capitalize()} (Smoothed and Trend)\nRMSE = {rmse_scores[category]:.2f}')
axes[i, 0].set_xlabel('Date')
axes[i, 0].set_ylabel('Normalized Count')
axes[i, 0].legend()
axes[i, 0].grid(True)
# Adjust date ticks
axes[i, 0].xaxis.set_major_locator(plt.MaxNLocator(3))
axes[i, 1].plot(derivatives[category], label=f'{category} First Derivative', color='blue', marker='x')
axes[i, 1].set_title(f'{category.capitalize()} I Derivative (Trend Velocity)')
axes[i, 1].set_xlabel('Date')
axes[i, 1].set_ylabel('First Derivative')
axes[i, 1].legend()
axes[i, 1].grid(True)
# Adjust date ticks
axes[i, 1].xaxis.set_major_locator(plt.MaxNLocator(3))
axes[i, 2].plot(second_derivatives[category], label=f'{category} Second Derivative', color='green',
marker='s')
axes[i, 2].set_title(f'{category.capitalize()} II Derivative (Trend Acceleration)')
axes[i, 2].set_xlabel('Date')
axes[i, 2].set_ylabel('Second Derivative')
axes[i, 2].legend()
axes[i, 2].grid(True)
# Adjust date ticks
axes[i, 2].xaxis.set_major_locator(plt.MaxNLocator(3))
fig.tight_layout()
else:
fig, axes = plt.subplots(1, 3, figsize=(14, 6)) # Adjusted figure size
axes[0].plot(smoothed_data[selected_category], label=f'{selected_category} (Smoothed)', marker='o',
linestyle='-')
X = smoothed_data[selected_category].dropna().index.map(pd.Timestamp.toordinal).values.reshape(-1, 1)
axes[0].plot(smoothed_data[selected_category].dropna().index, models[selected_category].predict(X),
color='red', label='Trend Line')
axes[0].set_title(
f'{selected_category.capitalize()} (Smoothed and Trend)\nRMSE = {rmse_scores[selected_category]:.2f}')
axes[0].set_xlabel('Date')
axes[0].set_ylabel('Normalized Count')
axes[0].legend()
axes[0].grid(True)
# Adjust date ticks
axes[0].xaxis.set_major_locator(plt.MaxNLocator(3))
axes[1].plot(derivatives[selected_category], label=f'{selected_category} First Derivative', color='blue',
marker='x')
axes[1].set_title(f'{selected_category.capitalize()} I Derivative (Trend Velocity)')
axes[1].set_xlabel('Date')
axes[1].set_ylabel('First Derivative')
axes[1].legend()
axes[1].grid(True)
# Adjust date ticks
axes[1].xaxis.set_major_locator(plt.MaxNLocator(3))
axes[2].plot(second_derivatives[selected_category], label=f'{selected_category} Second Derivative',
color='green', marker='s')
axes[2].set_title(f'{selected_category.capitalize()} II Derivative (Trend Acceleration)')
axes[2].set_xlabel('Date')
axes[2].set_ylabel('Second Derivative')
axes[2].legend()
axes[2].grid(True)
# Adjust date ticks
axes[2].xaxis.set_major_locator(plt.MaxNLocator(3))
fig.tight_layout()
canvas = FigureCanvasTkAgg(fig, master=scrollable_frame)
canvas.draw()
canvas.get_tk_widget().pack(fill=tk.BOTH, expand=True)
def plot_extrapolated_trends(self, data, selected_category):
categories = ['aircraft', 'helicopter', 'tank', 'APC', 'field artillery', 'MRL', 'drone', 'naval ship',
'anti-aircraft warfare']
time_series_data = {category: data[['date', category]].set_index('date') for category in categories}
scaler = MinMaxScaler()
normalized_data = {category: pd.DataFrame(scaler.fit_transform(time_series_data[category]),
index=time_series_data[category].index,
columns=[category])
for category in categories}
window_size = 7 # Moving average window size
smoothed_data = {category: normalized_data[category].rolling(window=window_size, min_periods=1).mean()
for category in categories}
models = {}
for category in categories:
model = LinearRegression()
X = smoothed_data[category].dropna().index.map(pd.Timestamp.toordinal).values.reshape(-1, 1)
y = smoothed_data[category].dropna().values
model.fit(X, y)
models[category] = model
future_days = 100 # Number of days to predict
last_date = max(data['date'])
future_dates = [last_date + datetime.timedelta(days=i) for i in range(1, future_days + 1)]
future_dates_ordinal = np.array([pd.Timestamp(date).toordinal() for date in future_dates]).reshape(-1, 1)
plot_window = tk.Toplevel(self.root)
plot_window.title("Forecasted Trends")
scrollable_frame = self.create_scrolled_window(plot_window)
if selected_category == "All categories":
fig, axes = plt.subplots(len(categories), 1, figsize=(14, 40))
for i, category in enumerate(categories):
axes[i].plot(smoothed_data[category], label=f'{category} (Smoothed Data)', marker='o', linestyle='-')
X = smoothed_data[category].dropna().index.map(pd.Timestamp.toordinal).values.reshape(-1, 1)
axes[i].plot(smoothed_data[category].dropna().index, models[category].predict(X), color='red',
label='Trend Line')
future_predictions = models[category].predict(future_dates_ordinal)
future_dates_index = pd.to_datetime(
[pd.Timestamp.fromordinal(int(date)) for date in future_dates_ordinal])
axes[i].plot(future_dates_index, future_predictions, color='orange', linestyle='--', label='Forecast')
axes[i].set_title(f'{category.capitalize()} (Smoothed Data, Trend, and Forecast)')
axes[i].set_xlabel('Date')
axes[i].set_ylabel('Normalized Count')
axes[i].legend()
axes[i].grid(True)
fig.tight_layout()
else:
fig, ax = plt.subplots(figsize=(14, 6))
ax.plot(smoothed_data[selected_category], label=f'{selected_category} (Smoothed Data)', marker='o',
linestyle='-')
X = smoothed_data[selected_category].dropna().index.map(pd.Timestamp.toordinal).values.reshape(-1, 1)
ax.plot(smoothed_data[selected_category].dropna().index, models[selected_category].predict(X), color='red',
label='Trend Line')
future_predictions = models[selected_category].predict(future_dates_ordinal)
future_dates_index = pd.to_datetime([pd.Timestamp.fromordinal(int(date)) for date in future_dates_ordinal])
ax.plot(future_dates_index, future_predictions, color='orange', linestyle='--', label='Forecast')
ax.set_title(f'{selected_category.capitalize()} (Smoothed Data, Trend, and Forecast)')
ax.set_xlabel('Date')
ax.set_ylabel('Normalized Count')
ax.legend()
ax.grid(True)
fig.tight_layout()
canvas = FigureCanvasTkAgg(fig, master=scrollable_frame)
canvas.draw()
canvas.get_tk_widget().pack(fill=tk.BOTH, expand=True)
class Application:
def __init__(self, root):
self.root = root
self.plotter = Plotter(root)
self.setup_main_window()
def setup_main_window(self):
self.root.title("System for Multi-Factor Assessment of Critical Situations")
self.root.geometry("500x300")
self.root.configure(bg="#f0f4f8")
style = ttk.Style()
style.configure("TButton", font=("Helvetica", 12), padding=10, background="#1976d2", foreground="black")
style.map("TButton", background=[('active', '#1565c0')], foreground=[('active', 'black')])
style.configure("TLabel", font=("Helvetica", 14), background="#f0f4f8", foreground="#0d47a1")
tk.Label(self.root, text="\nSystem for Multi-Factor Assessment\n of Critical Situations",
font=("Helvetica", 18, "bold"), bg="#f0f4f8", fg="#0d47a1").pack(pady=20)
ttk.Button(self.root, text="Start", command=self.open_second_window, style="TButton").pack(expand=True)
def open_second_window(self):
second_window = tk.Toplevel(self.root)
second_window.title("System for Multi-Factor Assessment of Critical Situations")
second_window.geometry("500x500")
second_window.configure(bg="#e3f2fd")
buttons_texts = [
("Segment data in time series format", lambda: self.open_category_selection(False, False)),
("Normalize Data", lambda: self.open_category_selection(True, False)),
("Smooth Data", lambda: self.open_category_selection(False, True)),
("Trend Lines", self.open_category_selection_trend_lines),
("Derivatives - Trend`s Velocity and Acceleration", self.open_category_selection_derivatives),
("Extrapolate Trends for Future Values", self.open_category_selection_extrapolated_trends),
("Calculate Weighted Integrated Indicator", self.open_weight_input_window)
]
for text, command in buttons_texts:
btn = ttk.Button(second_window, text=text, style="TButton", command=command)
btn.pack(pady=10, padx=20, fill='x')
def open_category_selection(self, normalized=False, smoothed=False):
selection_window = tk.Toplevel(self.root)
selection_window.title("Category Selection")
selection_window.geometry("400x300")
selection_window.configure(bg="#e3f2fd")
tk.Label(selection_window, text="Select a category:", font=("Helvetica", 14), bg="#e3f2fd", fg="black").pack(
pady=10)
categories = ['All categories', 'aircraft', 'helicopter', 'tank', 'APC', 'field artillery', 'MRL', 'drone',
'naval ship', 'anti-aircraft warfare']
selected_category = tk.StringVar(value="All categories")
category_menu = ttk.Combobox(selection_window, textvariable=selected_category, values=categories,
font=("Helvetica", 12))
category_menu.pack(pady=10)
ttk.Button(selection_window, text="Show Plot", style="TButton",
command=lambda: self.plotter.plot_data(DataLoader.load_data(), selected_category.get(), normalized,
smoothed)).pack(pady=20)
def open_category_selection_trend_lines(self):
selection_window = tk.Toplevel(self.root)
selection_window.title("Category Selection")
selection_window.geometry("400x300")
selection_window.configure(bg="#e3f2fd")
tk.Label(selection_window, text="Select a category:", font=("Helvetica", 14), bg="#e3f2fd", fg="black").pack(
pady=10)
categories = ['All categories', 'aircraft', 'helicopter', 'tank', 'APC', 'field artillery', 'MRL', 'drone',
'naval ship', 'anti-aircraft warfare']
selected_category = tk.StringVar(value="All categories")
category_menu = ttk.Combobox(selection_window, textvariable=selected_category, values=categories,
font=("Helvetica", 12))
category_menu.pack(pady=10)
ttk.Button(selection_window, text="Show Plot", style="TButton",
command=lambda: self.plotter.plot_trend_lines(DataLoader.load_data(), selected_category.get())).pack(
pady=20)
def open_category_selection_derivatives(self):
selection_window = tk.Toplevel(self.root)
selection_window.title("Category Selection")
selection_window.geometry("400x300")
selection_window.configure(bg="#e3f2fd")
tk.Label(selection_window, text="Select a category:", font=("Helvetica", 14), bg="#e3f2fd", fg="black").pack(
pady=10)
categories = ['All categories', 'aircraft', 'helicopter', 'tank', 'APC', 'field artillery', 'MRL', 'drone',
'naval ship', 'anti-aircraft warfare']
selected_category = tk.StringVar(value="All categories")
category_menu = ttk.Combobox(selection_window, textvariable=selected_category, values=categories,
font=("Helvetica", 12))
category_menu.pack(pady=10)
ttk.Button(selection_window, text="Show Plot", style="TButton",
command=lambda: self.plotter.plot_derivatives(DataLoader.load_data(), selected_category.get())).pack(
pady=20)
def open_category_selection_extrapolated_trends(self):
selection_window = tk.Toplevel(self.root)
selection_window.title("Category Selection")
selection_window.geometry("400x300")
selection_window.configure(bg="#e3f2fd")
tk.Label(selection_window, text="Select a category:", font=("Helvetica", 14), bg="#e3f2fd", fg="black").pack(
pady=10)
categories = ['All categories', 'aircraft', 'helicopter', 'tank', 'APC', 'field artillery', 'MRL', 'drone',
'naval ship', 'anti-aircraft warfare']
selected_category = tk.StringVar(value="All categories")
category_menu = ttk.Combobox(selection_window, textvariable=selected_category, values=categories,
font=("Helvetica", 12))
category_menu.pack(pady=10)
ttk.Button(selection_window, text="Show Plot", style="TButton",
command=lambda: self.plotter.plot_extrapolated_trends(DataLoader.load_data(),
selected_category.get())).pack(pady=20)
def open_weight_input_window(self):
weight_window = tk.Toplevel(self.root)
weight_window.title("Enter Weights")
weight_window.geometry("400x600")
weight_window.configure(bg="#e3f2fd")
tk.Label(weight_window, text="Enter weights for each category:", font=("Helvetica", 14), bg="#e3f2fd",
fg="black").grid(row=0, column=0, columnspan=2, pady=10)
categories = ['aircraft', 'helicopter', 'tank', 'APC', 'field artillery', 'MRL', 'drone', 'naval ship',
'anti-aircraft warfare']
entries = {}
for i, category in enumerate(categories):
tk.Label(weight_window, text=f"{category.capitalize()}: ", font=("Helvetica", 12), bg="#e3f2fd",
fg="black").grid(row=i + 1, column=0, padx=10, pady=5, sticky="e")
entry = tk.Entry(weight_window, font=("Helvetica", 12), width=20)
entry.grid(row=i + 1, column=1, padx=10, pady=5, sticky="w")
entries[category] = entry
ttk.Button(weight_window, text="Calculate Indicator", style="TButton",
command=lambda: self.calculate_integrated_score(entries)).grid(row=len(categories) + 1, column=0,
columnspan=2, pady=20)
def validate_weight(self, value):
try:
float_value = float(value.replace(',', '.'))
return True
except ValueError:
try:
fractions.Fraction(value)
return True
except ValueError:
return False
def calculate_integrated_score(self, entries):
weights = {}
for category, entry in entries.items():
value = entry.get()
if self.validate_weight(value):
try:
weights[category] = float(value.replace(',', '.'))
except ValueError:
weights[category] = float(fractions.Fraction(value))
else:
messagebox.showerror("Invalid Input",
f"Invalid weight value for {category}: {value}. Please enter a valid number.")
return
data = DataLoader.load_data()
categories = list(weights.keys())
# Normalize data
scaler = MinMaxScaler()
data[categories] = scaler.fit_transform(data[categories])
# Convert weights to numpy array
weights_array = np.array([weights[category] for category in categories])
# Apply smoothing
window_size = 7
smoothed_data = {category: data[[category]].rolling(window=window_size, min_periods=1).mean() for category in
categories}
# Calculate integrated score
integrated_score = np.dot(data[categories], weights_array)
data['Integrated Score'] = integrated_score
# Display results
plot_window = tk.Toplevel(self.root)
plot_window.title("Integrated Score")
plot_window.geometry("800x600")
fig, ax = plt.subplots(figsize=(14, 6))
ax.plot(data['date'], data['Integrated Score'], marker='o', linestyle='-', label='Integrated Score')
ax.set_title('Integrated Score Over Time')
ax.set_xlabel('Date')
ax.set_ylabel('Score')
ax.legend()
ax.grid(True)
fig.tight_layout()
canvas = FigureCanvasTkAgg(fig, master=plot_window)
canvas.draw()
canvas.get_tk_widget().pack(fill=tk.BOTH, expand=True)
if __name__ == "__main__":
root = tk.Tk()
app = Application(root)
root.mainloop()