Update 7_🗺️ Dashboard.py

Author: Devanik21

pages/7_🗺️ Dashboard.py

@@ -14,7 +14,6 @@
 import time
 import matplotlib.pyplot as plt  # Needed for shap.summary_plot
 from streamlit_autorefresh import st_autorefresh
-from streamlit_lottie import st_lottie # For new animations
 
 # Constants
 NASA_CTA_URL = "https://ssd-api.jpl.nasa.gov/cad.api"  # Corrected URL
@@ -173,106 +172,6 @@ def create_parameter_matrix(body_type):
     }
     return params.get(body_type, ['unknown_body_param'])
 
-# --- New Advanced Feature Functions ---
-
-@st.cache_data(ttl=3600)
-def analyze_exoplanet_atmosphere(planet_name):
-    """Generates mock atmospheric composition for a given exoplanet."""
-    np.random.seed(hash(planet_name) % (2**32 - 1)) # Seed based on planet name for consistency
-    gases = ['N2', 'O2', 'CO2', 'CH4', 'Ar', 'H2O Vapor', 'He']
-    composition = np.random.dirichlet(np.ones(len(gases)) * np.random.rand() * 10, size=1).flatten() * 100
-    return pd.DataFrame({'Gas': gases, 'Percentage': composition})
-
-@st.cache_data(ttl=3600)
-def plan_interstellar_trajectory(target_system):
-    """Generates mock waypoints for an interstellar trajectory."""
-    np.random.seed(hash(target_system) % (2**32 - 1))
-    num_waypoints = np.random.randint(5, 15)
-    waypoints = pd.DataFrame({
-        'x': np.cumsum(np.random.normal(0, 10, num_waypoints)),
-        'y': np.cumsum(np.random.normal(0, 10, num_waypoints)),
-        'z': np.cumsum(np.random.normal(0, 5, num_waypoints)),
-        'waypoint': [f"WP-{i}" for i in range(num_waypoints)]
-    })
-    return waypoints
-
-@st.cache_data(ttl=60) # More frequent refresh for something like signal detection
-def detect_alien_signal():
-    """Generates mock signal data with a chance of a 'detection'."""
-    time_points = np.linspace(0, 100, 500)
-    noise = np.random.normal(0, 1, 500)
-    signal_strength = 5 * np.sin(time_points / 10) + noise
-    detected = False
-    if np.random.rand() < 0.1: # 10% chance of detection
-        idx = np.random.randint(100, 400)
-        signal_strength[idx:idx+10] += np.random.uniform(5, 10) # Spike for detection
-        detected = True
-    return pd.DataFrame({'Time': time_points, 'SignalStrength': signal_strength}), detected
-
-@st.cache_data(ttl=3600)
-def monitor_terraforming_progress(planet_name):
-    """Generates mock terraforming metrics."""
-    np.random.seed(hash(planet_name) % (2**32 - 1))
-    metrics = {
-        'Oxygen Level (%)': np.random.uniform(0, 21),
-        'Surface Temp (°C)': np.random.uniform(-50, 30),
-        'Liquid Water Coverage (%)': np.random.uniform(0, 70),
-        'Atmospheric Pressure (kPa)': np.random.uniform(0, 101)
-    }
-    return metrics
-
-@st.cache_data(ttl=1800)
-def calculate_dyson_swarm_energy(completion_percentage):
-    """Generates mock energy output for a Dyson swarm."""
-    # Assume max output of a star like Sol is ~3.8e26 Watts
-    # Let's scale this down for a more relatable number in Petawatts (1e15 W)
-    max_petawatts = 3.8e11 
-    current_output = max_petawatts * (completion_percentage / 100)**2 # Non-linear increase
-    time_series = pd.DataFrame({
-        'Year': np.arange(2000, 2000 + int(completion_percentage) + 1),
-        'EnergyOutput_PW': max_petawatts * (np.linspace(0, completion_percentage, int(completion_percentage)+1) / 100)**2
-    })
-    return current_output, time_series
-
-@st.cache_data(ttl=3600)
-def visualize_accretion_disk(black_hole_mass_suns):
-    """Generates data for a mock accretion disk visualization."""
-    np.random.seed(int(black_hole_mass_suns))
-    x = np.linspace(-10, 10, 100)
-    y = np.linspace(-10, 10, 100)
-    X, Y = np.meshgrid(x, y)
-    # Intensity decreases with distance, with some randomness
-    intensity = np.exp(-(X**2 + Y**2) / (2 * (black_hole_mass_suns/2))) * (1 + 0.1*np.random.rand(100,100))
-    intensity[np.sqrt(X**2 + Y**2) < black_hole_mass_suns*0.1] = 0 # Event horizon shadow
-    return X, Y, intensity
-
-@st.cache_data(ttl=3600)
-def predict_wormhole_stability(coordinates_str):
-    """Generates mock stability score and factors for a wormhole."""
-    np.random.seed(hash(coordinates_str) % (2**32 - 1))
-    stability_score = np.random.uniform(0, 100)
-    factors = {
-        'Exotic Matter Density': np.random.uniform(-10, 10),
-        'Gravitational Fluctuations': np.random.uniform(-5, 5),
-        'Temporal Distortion': np.random.uniform(-3, 3),
-        'Energy Input': np.random.uniform(0, 15)
-    } # Positive values contribute to stability
-    return stability_score, pd.DataFrame(list(factors.items()), columns=['Factor', 'ImpactScore'])
-
-@st.cache_data(ttl=86400) # Daily
-def map_dark_matter_density(sector_id):
-    """Generates a 2D grid of mock dark matter density values."""
-    np.random.seed(hash(sector_id) % (2**32 - 1))
-    density_map = np.random.rand(50, 50) * np.random.uniform(0.1, 5) # Arbitrary units
-    return density_map
-
-def simulate_relativistic_effects(velocity_fraction_c):
-    """Calculates mock time dilation and length contraction."""
-    gamma = 1 / np.sqrt(1 - velocity_fraction_c**2)
-    time_dilation_factor = gamma
-    length_contraction_factor = 1 / gamma
-    return time_dilation_factor, length_contraction_factor
-
 # Main Visualization Page
 def show_visualize_page():
     # Immersive UI Configuration
@@ -395,9 +294,8 @@ def show_visualize_page():
 
     if st.session_state.mining_sim['running']:
         resources = st.session_state.mining_sim['resources']
-        # Renamed 'plt' to 'heatmap_fig' to avoid conflict with imported matplotlib.pyplot
-        heatmap_fig = go.Figure(go.Heatmap(z=[resources], colorscale='viridis'))
-        st.plotly_chart(heatmap_fig, use_container_width=True)
+        plt = go.Figure(go.Heatmap(z=[resources], colorscale='viridis'))
+        st.plotly_chart(plt, use_container_width=True)
         st.session_state.mining_sim['resources'] *= 0.97  # Resource depletion
 
     # Exoplanetary Navigation System (Simplified without astropy)
@@ -422,154 +320,5 @@ def show_visualize_page():
         🔒 Quantum-Safe Encryption: AES-512 + Lattice-based NIST Standard
     </div>
     """, unsafe_allow_html=True)
-    
-    st.markdown("<hr style='border:1px solid #00f7ff; opacity:0.3;'>", unsafe_allow_html=True)
-    st.markdown(f"""
-    <h2 class="title" style="color: #00f7ff; font-size: 2.5rem; text-align:center;">
-        Advanced Analytics Modules
-    </h2>
-    """, unsafe_allow_html=True)
-
-    # --- Feature 1: Exoplanet Atmosphere Analyzer ---
-    with st.expander("🔬 Exoplanet Atmosphere Analyzer", expanded=False):
-        exoplanet_list = ["Kepler-186f", "TRAPPIST-1e", "Proxima Centauri b", "Gliese 581g"]
-        selected_exoplanet = st.selectbox("Select Exoplanet:", exoplanet_list, key="exo_atm_select")
-        if st.button("Analyze Atmosphere", key="exo_atm_btn"):
-            with st.spinner(f"Analyzing atmosphere of {selected_exoplanet}..."):
-                time.sleep(1.5) # Simulate analysis time
-                atm_data = analyze_exoplanet_atmosphere(selected_exoplanet)
-                fig_atm = px.bar(atm_data, x='Gas', y='Percentage', title=f"Atmospheric Composition of {selected_exoplanet}",
-                                 color='Gas', labels={'Percentage':'Concentration (%)'})
-                fig_atm.update_layout(paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(0,0,0,0)', font_color='#00f7ff')
-                st.plotly_chart(fig_atm, use_container_width=True)
-                st.dataframe(atm_data)
-
-    # --- Feature 2: Interstellar Trajectory Planner ---
-    with st.expander("🗺️ Interstellar Trajectory Planner", expanded=False):
-        target_star_system = st.text_input("Target Star System (e.g., Alpha Centauri):", "Alpha Centauri", key="traj_target")
-        if st.button("Plan Trajectory", key="traj_plan_btn"):
-            if target_star_system:
-                with st.spinner(f"Calculating trajectory to {target_star_system}..."):
-                    time.sleep(2)
-                    trajectory_data = plan_interstellar_trajectory(target_star_system)
-                    fig_traj = px.line_3d(trajectory_data, x='x', y='y', z='z', color='waypoint', markers=True,
-                                          title=f"Interstellar Trajectory to {target_star_system}")
-                    fig_traj.update_layout(scene=dict(xaxis_title='X (LY)', yaxis_title='Y (LY)', zaxis_title='Z (LY)',
-                                                    bgcolor='rgba(0,0,0,0.1)'),
-                                         paper_bgcolor='rgba(0,0,0,0)', font_color='#00f7ff')
-                    st.plotly_chart(fig_traj, use_container_width=True)
-            else:
-                st.warning("Please enter a target star system.")
-
-    # --- Feature 3: Alien Signal Detector ---
-    with st.expander("📡 Alien Signal Detector (ASD)", expanded=False):
-        if st.button("Scan for Extraterrestrial Signals", key="asd_scan_btn"):
-            with st.spinner("Scanning designated frequency bands..."):
-                time.sleep(2.5)
-                signal_data, detected = detect_alien_signal()
-                fig_signal = px.line(signal_data, x='Time', y='SignalStrength', title="Signal Strength Analysis")
-                fig_signal.update_layout(paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(0,0,0,0)', font_color='#00f7ff')
-                if detected:
-                    st.success("Potential anomalous signal detected! Further analysis required.")
-                    fig_signal.add_vline(x=signal_data[signal_data['SignalStrength'] > signal_data['SignalStrength'].mean() + 3*signal_data['SignalStrength'].std()]['Time'].mean(), 
-                                        line_dash="dash", line_color="red", annotation_text="Anomaly Peak")
-                else:
-                    st.info("No anomalous signals detected in this scan cycle. Background noise levels nominal.")
-                st.plotly_chart(fig_signal, use_container_width=True)
-
-    # --- Feature 4: Terraforming Progress Monitor ---
-    with st.expander("🌍 Terraforming Progress Monitor", expanded=False):
-        terraforming_candidates = ["Mars", "Europa", "Titan"]
-        selected_tf_planet = st.selectbox("Select Planet for Terraforming Status:", terraforming_candidates, key="tf_planet_select")
-        if selected_tf_planet:
-            tf_metrics = monitor_terraforming_progress(selected_tf_planet)
-            st.subheader(f"Terraforming Status: {selected_tf_planet}")
-            cols_tf = st.columns(len(tf_metrics))
-            for i, (metric_name, value) in enumerate(tf_metrics.items()):
-                with cols_tf[i]:
-                    # Simple metric display, could be gauge charts with more effort
-                    st.metric(label=metric_name, value=f"{value:.2f}")
-                    if "Percentage" in metric_name:
-                        st.progress(int(value))
-
-    # --- Feature 5: Dyson Swarm Energy Output ---
-    with st.expander("☀️ Dyson Swarm Energy Output Simulator", expanded=False):
-        swarm_completion = st.slider("Dyson Swarm Completion (%):", 0, 100, 25, key="dyson_completion")
-        current_output_pw, energy_time_series = calculate_dyson_swarm_energy(swarm_completion)
-        st.metric(label="Current Estimated Energy Output", value=f"{current_output_pw:.2e} PW")
-        
-        fig_dyson = px.line(energy_time_series, x='Year', y='EnergyOutput_PW', title="Projected Dyson Swarm Energy Output Over Time")
-        fig_dyson.update_layout(paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(0,0,0,0)', font_color='#00f7ff',
-                                yaxis_title="Energy Output (Petawatts)")
-        st.plotly_chart(fig_dyson, use_container_width=True)
-
-    # --- Feature 6: Black Hole Accretion Disk Visualizer ---
-    with st.expander("🌀 Black Hole Accretion Disk Visualizer", expanded=False):
-        bh_mass = st.number_input("Black Hole Mass (Solar Masses):", min_value=1.0, max_value=1000.0, value=10.0, step=1.0, key="bh_mass_input")
-        if st.button("Visualize Accretion Disk", key="bh_vis_btn"):
-            X_disk, Y_disk, intensity_disk = visualize_accretion_disk(bh_mass)
-            fig_bh = go.Figure(data = go.Contour(z=intensity_disk, x=X_disk[0,:], y=Y_disk[:,0], colorscale='hot'))
-            fig_bh.update_layout(title=f"Accretion Disk Intensity - {bh_mass} Solar Masses BH",
-                                 xaxis_title="Relative X", yaxis_title="Relative Y",
-                                 paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(0,0,0,0)', font_color='#00f7ff')
-            st.plotly_chart(fig_bh, use_container_width=True)
-
-    # --- Feature 7: Wormhole Stability Predictor ---
-    with st.expander("웜홀 안정성 예측기 (Wormhole Stability Predictor)", expanded=False): # Korean title for fun
-        wh_coords = st.text_input("Enter Wormhole Coordinates (e.g., X:123,Y:456,Z:789):", "X:10,Y:20,Z:30", key="wh_coords")
-        if st.button("Predict Stability", key="wh_stab_btn"):
-            stability, factors_df = predict_wormhole_stability(wh_coords)
-            st.metric(label=f"Predicted Stability for Wormhole at {wh_coords}", value=f"{stability:.2f}%")
-            st.progress(int(stability))
-            
-            fig_wh_factors = px.bar(factors_df, x='Factor', y='ImpactScore', color='ImpactScore',
-                                    title="Factors Influencing Wormhole Stability",
-                                    color_continuous_scale=px.colors.diverging.RdBu)
-            fig_wh_factors.update_layout(paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(0,0,0,0)', font_color='#00f7ff')
-            st.plotly_chart(fig_wh_factors, use_container_width=True)
-
-    # --- Feature 8: Dark Matter Density Mapper ---
-    with st.expander("👻 Dark Matter Density Mapper", expanded=False):
-        galactic_sector = st.text_input("Galactic Sector ID (e.g., GS-007):", "GS-Alpha-Prime", key="dm_sector")
-        if st.button("Map Dark Matter Density", key="dm_map_btn"):
-            with st.spinner(f"Mapping dark matter in sector {galactic_sector}..."):
-                time.sleep(1)
-                dm_map_data = map_dark_matter_density(galactic_sector)
-                fig_dm = px.imshow(dm_map_data, color_continuous_scale='viridis', 
-                                   title=f"Dark Matter Density Map - Sector {galactic_sector}")
-                fig_dm.update_layout(paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(0,0,0,0)', font_color='#00f7ff')
-                st.plotly_chart(fig_dm, use_container_width=True)
-
-    # --- Feature 9: Relativistic Effects Simulator ---
-    with st.expander("⏳ Relativistic Effects Simulator", expanded=False):
-        velocity_c = st.slider("Velocity (fraction of speed of light, c):", 0.0, 0.999, 0.5, 0.001, format="%.3f", key="rel_vel")
-        time_dilation, length_contraction = simulate_relativistic_effects(velocity_c)
-        col_rel1, col_rel2 = st.columns(2)
-        with col_rel1:
-            st.metric("Time Dilation Factor (γ)", f"{time_dilation:.3f}x")
-            st.caption("Time for moving observer appears slower by this factor to a stationary observer.")
-        with col_rel2:
-            st.metric("Length Contraction Factor (1/γ)", f"{length_contraction:.3f}x")
-            st.caption("Length of moving object appears shorter by this factor in direction of motion.")
-        if velocity_c > 0.1:
-            st.info(f"At {velocity_c*100:.1f}% of light speed, 1 year for you would be {1*time_dilation:.2f} years for a stationary observer.")
-
-    # --- Feature 10: Cosmic Ray Shielding Effectiveness ---
-    with st.expander("🛡️ Cosmic Ray Shielding Effectiveness Analyzer", expanded=False):
-        shielding_materials = ["Titanium Alloy", "Polyethylene Composite", "Lead-Infused Aerogel", "Magnetic Deflector"]
-        selected_material = st.selectbox("Select Shielding Material:", shielding_materials, key="shield_mat_select")
-        if st.button("Analyze Shielding", key="shield_analyze_btn"):
-            np.random.seed(hash(selected_material) % (2**32 - 1))
-            effectiveness = np.random.uniform(50, 99.9) # %
-            particle_types = ["Protons", "Alpha Particles", "Heavy Nuclei", "Gamma Rays"]
-            reduction_data = pd.DataFrame({
-                'Particle Type': particle_types,
-                'Flux Reduction (%)': np.random.uniform(effectiveness*0.8, effectiveness, len(particle_types))
-            })
-            st.metric(f"Overall Effectiveness of {selected_material}", f"{effectiveness:.1f}%")
-            fig_shield = px.bar(reduction_data, x='Particle Type', y='Flux Reduction (%)', color='Particle Type',
-                                title=f"Cosmic Ray Flux Reduction by {selected_material}")
-            fig_shield.update_layout(paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(0,0,0,0)', font_color='#00f7ff')
-            st.plotly_chart(fig_shield, use_container_width=True)
 
 show_visualize_page()