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| import pandas as pd | |
| import streamlit as st | |
| import time | |
| import keyboard | |
| import os | |
| import psutil | |
| from plot_data import * | |
| from data_analysis import * | |
| def main(): | |
| st.markdown("<h2 style='text-align: left; color: black; font-family:Arial;font-size:2.25rem;'>CSV Datenanalyse</h2>", unsafe_allow_html=True) | |
| uploaded_file = st.file_uploader("CSV hochladen", type=["csv"]) | |
| if uploaded_file is not None: | |
| try: | |
| df = pd.read_csv(uploaded_file) | |
| except pd.errors.EmptyDataError: | |
| st.error("Fehler: Leere CSV Datei") | |
| except Exception as e: | |
| st.error(f"Error: {str(e)}") | |
| tab1, tab2, tab3 = st.tabs(["Import", "Analyse", "Beenden"]) | |
| with tab1: | |
| if uploaded_file is not None: | |
| time_column = st.selectbox("Zeit auswählen", df.columns, index=0) | |
| data_column = st.selectbox("Daten auswählen", df.columns) | |
| try: | |
| fig = plot_data_matplotlib(df, time_column, data_column) | |
| st.pyplot(fig) | |
| except: | |
| pass | |
| with tab2: | |
| if uploaded_file is not None: | |
| scatter = st.checkbox("Scatterplot", key="scatter") | |
| fig = plot_data_zeitfaktor(df, time_column, data_column, scatter) | |
| st.pyplot(fig) | |
| st.subheader("Zu analysierenden Zeitbereich auswählen") | |
| t0 = df[time_column].min() | |
| tn = df[time_column].max() | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| t1 = st.slider("Linke Grenze", min_value=t0, max_value=tn, value=t0, step=0.1) | |
| with col2: | |
| t2 = st.slider("Rechte Grenze", min_value=t0, max_value=tn, value=tn, step=0.1) | |
| if t1 > t2: | |
| st.error("Fehler: Linke Grenze muss kleiner als rechte Grenze sein") | |
| st.stop() | |
| col12, col22 = st.columns([0.3, 0.7]) | |
| with col12: | |
| st.checkbox("Mehrfach-Analyse", key="mehrfach") | |
| with col22: | |
| data_column_2 = st.selectbox("Zeiten Datensatz auswählen", df.columns, disabled=not(st.session_state.mehrfach)) | |
| try: | |
| if not st.session_state.mehrfach: | |
| result = aktualisierungsrate(df[time_column], df[data_column], t1, t2) | |
| fig2 = plot_filtered(df, time_column, data_column, t1, t2, scatter) | |
| st.pyplot(fig2) | |
| st.subheader("Aktualisierungsrate") | |
| st.write("Durchschnitt: ", result[0]) | |
| st.write("Minimal: ", result[1]) | |
| st.write("Maximal: ", result[2]) | |
| else: | |
| result1 = aktualisierungsrate(df[time_column], df[data_column], t1, t2) | |
| result2 = aktualisierungsrate(df[time_column], df[data_column_2], t1, t2) | |
| fig2 = plot_filtered_mehrfach(df, time_column, data_column, data_column_2, t1, t2) | |
| st.pyplot(fig2) | |
| st.subheader("Aktualisierungsrate") | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.write(data_column) | |
| st.write("Durchschnitt: ", result1[0]) | |
| st.write("Minimal: ", result1[1]) | |
| st.write("Maximal: ", result1[2]) | |
| with col2: | |
| st.write(data_column_2) | |
| st.write("Durchschnitt: ", result2[0]) | |
| st.write("Minimal: ", result2[1]) | |
| st.write("Maximal: ", result2[2]) | |
| st.subheader("Latenz") | |
| latenzen, smaller_dataset = latenz(df[time_column], df[data_column], df[data_column_2], t1, t2) | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.write("Durchschnitt: ", latenzen.mean()) | |
| st.write("Minimal: ", latenzen.min()) | |
| st.write("Maximal: ", latenzen.max()) | |
| with col2: | |
| st.write("Latenz berechnet anhand der Datenpunkte:") | |
| st.dataframe(smaller_dataset, hide_index=True) | |
| except: | |
| pass | |
| with tab3: | |
| exit_app = st.button("App beenden") | |
| if exit_app: | |
| try: | |
| time.sleep(3) | |
| keyboard.press_and_release('ctrl+w') | |
| pid = os.getpid() | |
| p = psutil.Process(pid) | |
| p.terminate() | |
| except: | |
| st.error("Beenden nicht möglich") | |
| if __name__ == "__main__": | |
| main() | |