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t-cell-data-1

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Please generate data in excel time in second for stepsize one using code "import numpy as np import pandas as pd import matplotlib.pyplot as plt from scipy.constants import N_A # Avogadro's number # Parameters initial_cells = 1e4 # Initial T-cell count growth_rate = 0.15 # per hour blood_volume = 5 # in liters noise_level = 0.05 # 5% noise # Generate time points (5-minute intervals for 24 hours) time_seconds = np.arange(0, 24*3600 + 300, 300) time_hours = time_seconds / 3600 # Exponential growth model growth_rate_per_second = growth_rate / 3600 ideal_counts = initial_cells * np.exp(growth_rate_per_second * time_seconds) # Add realistic noise noise = np.random.lognormal(mean=0, sigma=noise_level, size=len(time_seconds)) noisy_counts = ideal_counts * noise # Convert to molar concentration molar_ideal = ideal_counts / (N_A * blood_volume) molar_noisy = noisy_counts / (N_A * blood_volume) # Create figure with two subplots plt.figure(figsize=(12, 8)) # Plot 1: Linear scale plt.subplot(2, 1, 1) plt.plot(time_hours, molar_ideal, 'b--', label='Ideal Growth') plt.plot(time_hours, molar_noisy, 'r-', alpha=0.7, label='Noisy Measurements') plt.xlabel('Time (hours)') plt.ylabel('Molar Concentration (M)') plt.title('T-cell Molar Concentration Over Time (Linear Scale)') plt.legend() plt.grid(True) # Plot 2: Log scale plt.subplot(2, 1, 2) plt.semilogy(time_hours, molar_ideal, 'b--', label='Ideal Growth') plt.semilogy(time_hours, molar_noisy, 'r-', alpha=0.7, label='Noisy Measurements') plt.xlabel('Time (hours)') plt.ylabel('Molar Concentration (M)') plt.title('T-cell Molar Concentration Over Time (Log Scale)') plt.legend() plt.grid(True, which="both", ls="--") plt.tight_layout() plt.show()" - Initial Deployment