plot_performance_summary.py

"""Create comprehensive performance summary plots"""

import numpy as np
import matplotlib.pyplot as plt
import sys
from pathlib import Path

sys.path.insert(0, str(Path(__file__).parent.parent.parent))

from pintle_pipeline.io import load_config
from pintle_models.runner import PintleEngineRunner

# Load configuration
config_path = Path(__file__).parent / "config_minimal.yaml"
config = load_config(str(config_path))

# Initialize runner
runner = PintleEngineRunner(config)

# Test at target operating point
P_tank_O = 1305 * 6894.76  # psi to Pa
P_tank_F = 974 * 6894.76   # psi to Pa

# Run pipeline
results = runner.evaluate(P_tank_O, P_tank_F)
diagnostics = results.get('diagnostics', {})
if isinstance(diagnostics, list):
    diagnostics = diagnostics[0] if diagnostics else {}

# Create comprehensive plots
fig = plt.figure(figsize=(16, 12))

# 1. Mass Flow Rates
ax1 = plt.subplot(3, 3, 1)
mdot_O = results['mdot_O']
mdot_F = results['mdot_F']
mdot_total = mdot_O + mdot_F
bars = ax1.bar(['LOX', 'Fuel', 'Total'], 
               [mdot_O, mdot_F, mdot_total],
               color=['blue', 'orange', 'green'], alpha=0.7)
ax1.set_ylabel('Mass Flow Rate [kg/s]')
ax1.set_title('Mass Flow Rates')
ax1.grid(True, alpha=0.3)
for bar in bars:
    height = bar.get_height()
    ax1.text(bar.get_x() + bar.get_width()/2., height,
             f'{height:.3f}',
             ha='center', va='bottom', fontsize=9)

# 2. Pressure Breakdown
ax2 = plt.subplot(3, 3, 2)
Pc = results['Pc'] / 6894.76
P_tank_O_psi = P_tank_O / 6894.76
P_tank_F_psi = P_tank_F / 6894.76

from pintle_pipeline.feed_loss import delta_p_feed
delta_p_feed_O = delta_p_feed(mdot_O, config.fluids["oxidizer"].density, 
                               config.feed_system["oxidizer"], P_tank_O) / 6894.76
delta_p_feed_F = delta_p_feed(mdot_F, config.fluids["fuel"].density,
                               config.feed_system["fuel"], P_tank_F) / 6894.76

P_inj_O = P_tank_O_psi - delta_p_feed_O
P_inj_F = P_tank_F_psi - delta_p_feed_F

pressures = [P_tank_O_psi, P_inj_O, Pc, P_tank_F_psi, P_inj_F, Pc]
labels = ['P_tank_O', 'P_inj_O', 'Pc', 'P_tank_F', 'P_inj_F', 'Pc']
colors = ['blue', 'lightblue', 'red', 'orange', 'lightcoral', 'red']
ax2.barh(labels, pressures, color=colors, alpha=0.7)
ax2.set_xlabel('Pressure [psi]')
ax2.set_title('Pressure Breakdown')
ax2.grid(True, alpha=0.3)

# 3. Mixture Ratio
ax3 = plt.subplot(3, 3, 3)
MR = results['MR']
target_MR = 2.36
ax3.bar(['Actual', 'Target'], [MR, target_MR], 
        color=['blue', 'red'], alpha=0.7)
ax3.set_ylabel('O/F Ratio')
ax3.set_title(f'Mixture Ratio (Target: {target_MR:.2f})')
ax3.grid(True, alpha=0.3)
ax3.text(0, MR, f'{MR:.2f}', ha='center', va='bottom', fontsize=10, fontweight='bold')
ax3.text(1, target_MR, f'{target_MR:.2f}', ha='center', va='bottom', fontsize=10, fontweight='bold')

# 4. Thrust Components
ax4 = plt.subplot(3, 3, 4)
F = results['F'] / 1000
F_momentum = results.get('F_momentum', 0) / 1000
F_pressure = results.get('F_pressure', 0) / 1000
target_F = 5.308
ax4.bar(['Momentum', 'Pressure', 'Total', 'Target'],
        [F_momentum, F_pressure, F, target_F],
        color=['blue', 'green', 'purple', 'red'], alpha=0.7)
ax4.set_ylabel('Thrust [kN]')
ax4.set_title('Thrust Components')
ax4.grid(True, alpha=0.3)

# 5. Performance Metrics
ax5 = plt.subplot(3, 3, 5)
Isp = results['Isp']
target_Isp = 299.0
cstar = results['cstar_actual']
eta = diagnostics.get('eta_cstar', np.nan)
metrics = ['Isp [s]', 'c* [m/s]', 'η_c*']
values = [Isp, cstar/10, eta*100]  # Scale for visibility
targets = [target_Isp, None, None]
colors_plot = ['blue', 'green', 'orange']
bars = ax5.bar(metrics, values, color=colors_plot, alpha=0.7)
ax5.set_ylabel('Value')
ax5.set_title('Performance Metrics')
ax5.grid(True, alpha=0.3)
for i, (bar, val) in enumerate(zip(bars, values)):
    height = bar.get_height()
    label = f'{val:.1f}' if i == 0 else f'{val:.0f}'
    ax5.text(bar.get_x() + bar.get_width()/2., height,
             label, ha='center', va='bottom', fontsize=9)

# 6. Chamber Properties
ax6 = plt.subplot(3, 3, 6)
Tc = diagnostics.get('Tc', np.nan)
gamma = diagnostics.get('gamma', np.nan)
R = diagnostics.get('R', np.nan)
if not np.isnan(Tc) and not np.isnan(gamma) and not np.isnan(R):
    props = ['Tc [K]', 'γ', 'R [J/kg·K]']
    vals = [Tc/100, gamma*10, R/10]  # Scale for visibility
    ax6.bar(props, vals, color=['red', 'blue', 'green'], alpha=0.7)
    ax6.set_ylabel('Value (scaled)')
    ax6.set_title('Chamber Properties')
    ax6.grid(True, alpha=0.3)
    for prop, val in zip(props, vals):
        ax6.text(prop, val, f'{val:.1f}', ha='center', va='bottom', fontsize=9)

# 7. Injector Performance
ax7 = plt.subplot(3, 3, 7)
from pintle_models.geometry import get_effective_areas, get_hydraulic_diameters
from pintle_models.discharge import cd_from_re, calculate_reynolds_number

A_LOX, A_fuel = get_effective_areas(config.pintle_geometry)
rho_O = config.fluids["oxidizer"].density
rho_F = config.fluids["fuel"].density
u_O = mdot_O / (rho_O * A_LOX)
u_F = mdot_F / (rho_F * A_fuel)

d_hyd_O, d_hyd_F = get_hydraulic_diameters(config.pintle_geometry)
mu_O = config.fluids["oxidizer"].viscosity
mu_F = config.fluids["fuel"].viscosity
Re_O = calculate_reynolds_number(rho_O, u_O, d_hyd_O, mu_O)
Re_F = calculate_reynolds_number(rho_F, u_F, d_hyd_F, mu_F)
Cd_O = cd_from_re(Re_O, config.discharge["oxidizer"])
Cd_F = cd_from_re(Re_F, config.discharge["fuel"])

ax7.bar(['LOX', 'Fuel'], [u_O, u_F], color=['blue', 'orange'], alpha=0.7)
ax7.set_ylabel('Velocity [m/s]')
ax7.set_title('Injector Velocities')
ax7.grid(True, alpha=0.3)
ax7.text(0, u_O, f'{u_O:.1f}', ha='center', va='bottom', fontsize=10)
ax7.text(1, u_F, f'{u_F:.1f}', ha='center', va='bottom', fontsize=10)

# 8. Discharge Coefficients
ax8 = plt.subplot(3, 3, 8)
ax8.bar(['LOX', 'Fuel'], [Cd_O, Cd_F], color=['blue', 'orange'], alpha=0.7)
ax8.set_ylabel('Discharge Coefficient')
ax8.set_title(f'Cd (Re_O={Re_O:.0f}, Re_F={Re_F:.0f})')
ax8.grid(True, alpha=0.3)
ax8.text(0, Cd_O, f'{Cd_O:.3f}', ha='center', va='bottom', fontsize=10)
ax8.text(1, Cd_F, f'{Cd_F:.3f}', ha='center', va='bottom', fontsize=10)

# 9. Spray Diagnostics
ax9 = plt.subplot(3, 3, 9)
J = diagnostics.get('J', np.nan)
theta = diagnostics.get('theta', np.nan)
We_O = diagnostics.get('We_O', np.nan)
We_F = diagnostics.get('We_F', np.nan)
D32_O = diagnostics.get('D32_O', np.nan)
D32_F = diagnostics.get('D32_F', np.nan)

if not np.isnan(J) and not np.isnan(theta):
    spray_data = {
        'J': J,
        'θ [deg]': theta*180/np.pi,
        'We_O': We_O/1000,  # Scale
        'We_F': We_F/1000,  # Scale
    }
    ax9.bar(list(spray_data.keys())[:2], list(spray_data.values())[:2], 
            color=['blue', 'green'], alpha=0.7)
    ax9.set_ylabel('Value')
    ax9.set_title('Spray Parameters')
    ax9.grid(True, alpha=0.3)
    for i, (key, val) in enumerate(list(spray_data.items())[:2]):
        ax9.text(i, val, f'{val:.2f}', ha='center', va='bottom', fontsize=9)

plt.tight_layout()
output_path = Path(__file__).parent / 'performance_summary.png'
plt.savefig(str(output_path), dpi=150, bbox_inches='tight')
print("✅ Saved performance summary plot to examples/pintle_engine/performance_summary.png")
plt.show()


import numpy as np
import matplotlib.pyplot as plt
import sys
from pathlib import Path

sys.path.insert(0, str(Path(__file__).parent.parent.parent))

from pintle_pipeline.io import load_config
from pintle_models.runner import PintleEngineRunner

# Load configuration
config_path = Path(__file__).parent / "config_minimal.yaml"
config = load_config(str(config_path))

# Initialize runner
runner = PintleEngineRunner(config)

# Test at target operating point
P_tank_O = 1305 * 6894.76  # psi to Pa
P_tank_F = 974 * 6894.76   # psi to Pa

# Run pipeline
results = runner.evaluate(P_tank_O, P_tank_F)
diagnostics = results.get('diagnostics', {})
if isinstance(diagnostics, list):
    diagnostics = diagnostics[0] if diagnostics else {}

# Create comprehensive plots
fig = plt.figure(figsize=(16, 12))

# 1. Mass Flow Rates
ax1 = plt.subplot(3, 3, 1)
mdot_O = results['mdot_O']
mdot_F = results['mdot_F']
mdot_total = mdot_O + mdot_F
bars = ax1.bar(['LOX', 'Fuel', 'Total'], 
               [mdot_O, mdot_F, mdot_total],
               color=['blue', 'orange', 'green'], alpha=0.7)
ax1.set_ylabel('Mass Flow Rate [kg/s]')
ax1.set_title('Mass Flow Rates')
ax1.grid(True, alpha=0.3)
for bar in bars:
    height = bar.get_height()
    ax1.text(bar.get_x() + bar.get_width()/2., height,
             f'{height:.3f}',
             ha='center', va='bottom', fontsize=9)

# 2. Pressure Breakdown
ax2 = plt.subplot(3, 3, 2)
Pc = results['Pc'] / 6894.76
P_tank_O_psi = P_tank_O / 6894.76
P_tank_F_psi = P_tank_F / 6894.76

from pintle_pipeline.feed_loss import delta_p_feed
delta_p_feed_O = delta_p_feed(mdot_O, config.fluids["oxidizer"].density, 
                               config.feed_system["oxidizer"], P_tank_O) / 6894.76
delta_p_feed_F = delta_p_feed(mdot_F, config.fluids["fuel"].density,
                               config.feed_system["fuel"], P_tank_F) / 6894.76

P_inj_O = P_tank_O_psi - delta_p_feed_O
P_inj_F = P_tank_F_psi - delta_p_feed_F

pressures = [P_tank_O_psi, P_inj_O, Pc, P_tank_F_psi, P_inj_F, Pc]
labels = ['P_tank_O', 'P_inj_O', 'Pc', 'P_tank_F', 'P_inj_F', 'Pc']
colors = ['blue', 'lightblue', 'red', 'orange', 'lightcoral', 'red']
ax2.barh(labels, pressures, color=colors, alpha=0.7)
ax2.set_xlabel('Pressure [psi]')
ax2.set_title('Pressure Breakdown')
ax2.grid(True, alpha=0.3)

# 3. Mixture Ratio
ax3 = plt.subplot(3, 3, 3)
MR = results['MR']
target_MR = 2.36
ax3.bar(['Actual', 'Target'], [MR, target_MR], 
        color=['blue', 'red'], alpha=0.7)
ax3.set_ylabel('O/F Ratio')
ax3.set_title(f'Mixture Ratio (Target: {target_MR:.2f})')
ax3.grid(True, alpha=0.3)
ax3.text(0, MR, f'{MR:.2f}', ha='center', va='bottom', fontsize=10, fontweight='bold')
ax3.text(1, target_MR, f'{target_MR:.2f}', ha='center', va='bottom', fontsize=10, fontweight='bold')

# 4. Thrust Components
ax4 = plt.subplot(3, 3, 4)
F = results['F'] / 1000
F_momentum = results.get('F_momentum', 0) / 1000
F_pressure = results.get('F_pressure', 0) / 1000
target_F = 5.308
ax4.bar(['Momentum', 'Pressure', 'Total', 'Target'],
        [F_momentum, F_pressure, F, target_F],
        color=['blue', 'green', 'purple', 'red'], alpha=0.7)
ax4.set_ylabel('Thrust [kN]')
ax4.set_title('Thrust Components')
ax4.grid(True, alpha=0.3)

# 5. Performance Metrics
ax5 = plt.subplot(3, 3, 5)
Isp = results['Isp']
target_Isp = 299.0
cstar = results['cstar_actual']
eta = diagnostics.get('eta_cstar', np.nan)
metrics = ['Isp [s]', 'c* [m/s]', 'η_c*']
values = [Isp, cstar/10, eta*100]  # Scale for visibility
targets = [target_Isp, None, None]
colors_plot = ['blue', 'green', 'orange']
bars = ax5.bar(metrics, values, color=colors_plot, alpha=0.7)
ax5.set_ylabel('Value')
ax5.set_title('Performance Metrics')
ax5.grid(True, alpha=0.3)
for i, (bar, val) in enumerate(zip(bars, values)):
    height = bar.get_height()
    label = f'{val:.1f}' if i == 0 else f'{val:.0f}'
    ax5.text(bar.get_x() + bar.get_width()/2., height,
             label, ha='center', va='bottom', fontsize=9)

# 6. Chamber Properties
ax6 = plt.subplot(3, 3, 6)
Tc = diagnostics.get('Tc', np.nan)
gamma = diagnostics.get('gamma', np.nan)
R = diagnostics.get('R', np.nan)
if not np.isnan(Tc) and not np.isnan(gamma) and not np.isnan(R):
    props = ['Tc [K]', 'γ', 'R [J/kg·K]']
    vals = [Tc/100, gamma*10, R/10]  # Scale for visibility
    ax6.bar(props, vals, color=['red', 'blue', 'green'], alpha=0.7)
    ax6.set_ylabel('Value (scaled)')
    ax6.set_title('Chamber Properties')
    ax6.grid(True, alpha=0.3)
    for prop, val in zip(props, vals):
        ax6.text(prop, val, f'{val:.1f}', ha='center', va='bottom', fontsize=9)

# 7. Injector Performance
ax7 = plt.subplot(3, 3, 7)
from pintle_models.geometry import get_effective_areas, get_hydraulic_diameters
from pintle_models.discharge import cd_from_re, calculate_reynolds_number

A_LOX, A_fuel = get_effective_areas(config.pintle_geometry)
rho_O = config.fluids["oxidizer"].density
rho_F = config.fluids["fuel"].density
u_O = mdot_O / (rho_O * A_LOX)
u_F = mdot_F / (rho_F * A_fuel)

d_hyd_O, d_hyd_F = get_hydraulic_diameters(config.pintle_geometry)
mu_O = config.fluids["oxidizer"].viscosity
mu_F = config.fluids["fuel"].viscosity
Re_O = calculate_reynolds_number(rho_O, u_O, d_hyd_O, mu_O)
Re_F = calculate_reynolds_number(rho_F, u_F, d_hyd_F, mu_F)
Cd_O = cd_from_re(Re_O, config.discharge["oxidizer"])
Cd_F = cd_from_re(Re_F, config.discharge["fuel"])

ax7.bar(['LOX', 'Fuel'], [u_O, u_F], color=['blue', 'orange'], alpha=0.7)
ax7.set_ylabel('Velocity [m/s]')
ax7.set_title('Injector Velocities')
ax7.grid(True, alpha=0.3)
ax7.text(0, u_O, f'{u_O:.1f}', ha='center', va='bottom', fontsize=10)
ax7.text(1, u_F, f'{u_F:.1f}', ha='center', va='bottom', fontsize=10)

# 8. Discharge Coefficients
ax8 = plt.subplot(3, 3, 8)
ax8.bar(['LOX', 'Fuel'], [Cd_O, Cd_F], color=['blue', 'orange'], alpha=0.7)
ax8.set_ylabel('Discharge Coefficient')
ax8.set_title(f'Cd (Re_O={Re_O:.0f}, Re_F={Re_F:.0f})')
ax8.grid(True, alpha=0.3)
ax8.text(0, Cd_O, f'{Cd_O:.3f}', ha='center', va='bottom', fontsize=10)
ax8.text(1, Cd_F, f'{Cd_F:.3f}', ha='center', va='bottom', fontsize=10)

# 9. Spray Diagnostics
ax9 = plt.subplot(3, 3, 9)
J = diagnostics.get('J', np.nan)
theta = diagnostics.get('theta', np.nan)
We_O = diagnostics.get('We_O', np.nan)
We_F = diagnostics.get('We_F', np.nan)
D32_O = diagnostics.get('D32_O', np.nan)
D32_F = diagnostics.get('D32_F', np.nan)

if not np.isnan(J) and not np.isnan(theta):
    spray_data = {
        'J': J,
        'θ [deg]': theta*180/np.pi,
        'We_O': We_O/1000,  # Scale
        'We_F': We_F/1000,  # Scale
    }
    ax9.bar(list(spray_data.keys())[:2], list(spray_data.values())[:2], 
            color=['blue', 'green'], alpha=0.7)
    ax9.set_ylabel('Value')
    ax9.set_title('Spray Parameters')
    ax9.grid(True, alpha=0.3)
    for i, (key, val) in enumerate(list(spray_data.items())[:2]):
        ax9.text(i, val, f'{val:.2f}', ha='center', va='bottom', fontsize=9)

plt.tight_layout()
output_path = Path(__file__).parent / 'performance_summary.png'
plt.savefig(str(output_path), dpi=150, bbox_inches='tight')
print("✅ Saved performance summary plot to examples/pintle_engine/performance_summary.png")
plt.show()