| # import streamlit as st | |
| # import pandas as pd | |
| # from io import BytesIO | |
| # from itertools import product | |
| # from statsmodels.tsa.statespace.sarimax import SARIMAX | |
| # import plotly.express as px | |
| # st.set_page_config(layout="wide") | |
| # # Function to run the SARIMAX Model | |
| # def run_sarimax(city_data, order, seasonal_order): | |
| # try: | |
| # # Check if the data is non-empty and in the correct format | |
| # if city_data.empty: | |
| # st.error(f"No data available for modeling.") | |
| # return None, None | |
| # model = SARIMAX(city_data, order=order, seasonal_order=seasonal_order, enforce_stationarity=False, enforce_invertibility=False) | |
| # model_fit = model.fit(disp=False) | |
| # forecast = model_fit.forecast(steps=6) | |
| # # Check if the forecast is valid | |
| # if forecast is None or forecast.empty: | |
| # st.error(f"Forecast failed, the model returned an empty forecast.") | |
| # return None, None | |
| # return forecast, model_fit.aic | |
| # except Exception as e: | |
| # st.error(f"An error occurred during model fitting: {e}") | |
| # return None, None | |
| # def create_data(): | |
| # # Assuming you have a CSV file named 'accident_count.csv' with 'City' and 'Accident Count' columns | |
| # data = pd.read_csv('accident_count.csv', parse_dates=True, index_col=0) | |
| # data.index = pd.to_datetime(data.index, format='%Y%m') | |
| # data = data.groupby('City').resample('M').sum().reset_index() | |
| # data.index = data['Accident Month Bracket'] | |
| # data = data.drop(['Accident Month Bracket'], axis=1) | |
| # data.index = data.index.strftime('%Y-%m') | |
| # return data | |
| # def to_excel(df): | |
| # output = BytesIO() | |
| # writer = pd.ExcelWriter(output, engine='xlsxwriter') | |
| # df.to_excel(writer, sheet_name='Sheet1') | |
| # writer.save() | |
| # processed_data = output.getvalue() | |
| # return processed_data | |
| # # Initialize session state for best parameters | |
| # if 'best_params' not in st.session_state: | |
| # st.session_state.best_params = {'order': (1, 1, 1), 'seasonal_order': (1, 1, 1, 12)} | |
| # st.title("SARIMAX Forecasting") | |
| # # Data preparation | |
| # data = create_data() | |
| # unique_cities = data['City'].unique() | |
| # # Creating tabs for each city | |
| # tabs = st.tabs([city for city in unique_cities]) | |
| # for tab, city in zip(tabs, unique_cities): | |
| # with tab: | |
| # # SARIMAX specific sliders | |
| # p = st.slider('AR Order (p)', 0, 5, value=st.session_state.best_params['order'][0], key=city+'p') | |
| # d = st.slider('Differencing Order (d)', 0, 2, value=st.session_state.best_params['order'][1], key=city+'d') | |
| # q = st.slider('MA Order (q)', 0, 5, value=st.session_state.best_params['order'][2], key=city+'q') | |
| # P = st.slider('Seasonal AR Order (P)', 0, 5, value=st.session_state.best_params['seasonal_order'][0], key=city+'P') | |
| # D = st.slider('Seasonal Differencing Order (D)', 0, 2, value=st.session_state.best_params['seasonal_order'][1], key=city+'D') | |
| # Q = st.slider('Seasonal MA Order (Q)', 0, 5, value=st.session_state.best_params['seasonal_order'][2], key=city+'Q') | |
| # S = st.slider('Seasonal Period (S)', 1, 24, value=st.session_state.best_params['seasonal_order'][3], key=city+'S') | |
| # city_data = data[data['City'] == city]['Accident Count'] | |
| # forecast, aic = run_sarimax(city_data, (p, d, q), (P, D, Q, S)) | |
| # if forecast is not None: | |
| # st.write(f"Best Parameters with AIC: {aic}") | |
| # st.write(f"Non-Seasonal Order: {(p, d, q)}, Seasonal Order: {(P, D, Q, S)}") | |
| # forecast_index = pd.date_range(start=city_data.index[-1], periods=7, freq='M')[1:] | |
| # forecast_index = forecast_index.to_period('M') # Convert to period index with monthly frequency | |
| # forecast_df = pd.DataFrame(forecast, columns=['predicted_mean']) | |
| # forecast_df = forecast_df.round(0) | |
| # st.table(forecast_df) | |
| # fig = px.line(forecast_df, x=forecast_df.index, y="predicted_mean") | |
| # st.plotly_chart(fig) | |
| # # Grid search button | |
| # if st.button(f'Run Grid Search for {city}'): | |
| # best_aic = float('inf') | |
| # best_params = None | |
| # # Define the range for each parameter | |
| # p_range = d_range = q_range = range(3) | |
| # P_range = D_range = Q_range = range(3) | |
| # S = 12 # Assuming a fixed seasonal period, adjust as needed | |
| # # Perform the grid search | |
| # for params in product(p_range, d_range, q_range, P_range, D_range, Q_range): | |
| # order = params[:3] | |
| # seasonal_order = params[3:] + (S,) | |
| # try: | |
| # _, temp_aic = run_sarimax(city_data, order, seasonal_order) | |
| # if temp_aic < best_aic: | |
| # best_aic = temp_aic | |
| # best_params = (order, seasonal_order) | |
| # except Exception as e: | |
| # st.error(f"An error occurred for parameters {params}: {e}") | |
| # # Update the session state with the best parameters | |
| # if best_params is not None: | |
| # st.session_state.best_params = { | |
| # 'order': best_params[0], | |
| # 'seasonal_order': best_params[1] | |
| # } | |
| # st.write(f"Best Parameters for {city}: {best_params} with AIC: {best_aic}") | |
| # # Export to Excel button | |
| # if st.button(f'Export {city} to Excel'): | |
| # df_to_export = forecast_df | |
| # excel_data = to_excel(df_to_export) | |
| # st.download_button(label='📥 Download Excel', data=excel_data, file_name=f'{city}_forecast.xlsx', mime='application/vnd.ms-excel') | |
| import streamlit as st | |
| def deletefile(filename, dfname): | |
| # Your delete file logic here | |
| pass | |
| def spawnbutton(filename, dfname): | |
| # Check if the button has already been clicked | |
| if st.session_state.get(f"{filename}_clicked", False): | |
| # Button logic after being clicked (if any) | |
| pass | |
| else: | |
| # Show the button if it hasn't been clicked yet | |
| if st.button(f"Delete file ({st.session_state[filename].name})", use_container_width=True, key=f'{filename}_deleter'): | |
| deletefile(filename, dfname) | |
| statecheck(dfname) | |
| statecheck(filename) | |
| # Set the state to indicate the button has been clicked | |
| st.session_state[f"{filename}_clicked"] = True | |
| # Example usage | |
| if 'example_file' not in st.session_state: | |
| st.session_state['example_file'] = "file.txt" | |
| if 'example_df' not in st.session_state: | |
| st.session_state['example_df'] = "dataframe" | |
| spawnbutton('example_file', 'example_df') | |
| # import streamlit as st | |
| # import numpy as np | |
| # import matplotlib.pyplot as plt | |
| # # Sample data for multiple plots | |
| # data = [(np.linspace(0, 10, 10), np.sin(np.linspace(0, 10, 10))) for _ in range(3)] | |
| # # Initialize session state | |
| # if 'current_plot_index' not in st.session_state: | |
| # st.session_state['current_plot_index'] = 0 | |
| # def update_plot(index, x, y): | |
| # plt.figure() | |
| # plt.plot(x, y, marker='o') | |
| # plt.title(f"Plot {index+1}") | |
| # st.pyplot(plt) | |
| # def next_plot(): | |
| # st.session_state['current_plot_index'] = (st.session_state['current_plot_index'] + 1) % len(data) | |
| # # Display the plot | |
| # index = st.session_state['current_plot_index'] | |
| # x, y = data[index] | |
| # update_plot(index, x, y) | |
| # # Select and update point | |
| # point_index = st.number_input('Point Index', min_value=0, max_value=len(x)-1, step=1) | |
| # new_value = st.number_input('New Y Value', value=y[point_index]) | |
| # if st.button('Update Point'): | |
| # y[point_index] = new_value | |
| # update_plot(index, x, y) | |
| # # Next plot button | |
| # if st.button('Next Plot'): | |
| # next_plot() | |
| # import plotly.express as px | |
| # import streamlit as st | |
| # from streamlit_plotly_events import plotly_events | |
| # # Sample data | |
| # df = px.data.gapminder().query("country=='Canada'") | |
| # fig = px.line(df, x="year", y="lifeExp", title='Life Expectancy in Canada Over Years') | |
| # # Capture the selected points | |
| # selected_points = plotly_events(fig, click_event=True) | |
| # # Handle the click event | |
| # if selected_points: | |
| # st.write("You clicked on:", selected_points) | |
| # point_index = selected_points[0]['pointIndex'] | |
| # new_value = st.number_input('Enter new value for life expectancy', value=df.iloc[point_index]['lifeExp']) | |
| # if st.button('Update Data'): | |
| # df.at[point_index, 'lifeExp'] = new_value | |
| # fig = px.line(df, x="year", y="lifeExp", title='Life Expectancy in Canada Over Years') | |
| # st.plotly_chart(fig) | |
| # else: | |
| # st.plotly_chart(fig) | |