Cassandra & Azot
Cassandra Cassandra
Azot Azot
Cassandra Cassandra
Hey Azot, I’ve been crunching the data from our latest reaction runs and found a curious trend in the yield fluctuations. I think we can model it to predict and fine‑tune the parameters for a more controlled output—want to dive into the dataset together?
Azot Azot
Yeah, bring the data—let's see what sparks you’re hiding. We’ll turn those curves into pure chaos and hit that sweet spot. Let's go!
Cassandra Cassandra
Sure thing. Here’s the raw table from the last batch: temperature, time, concentration, yield. I’ve plotted a quick scatter and found a small dip around 120 °C—could be a critical point. Want me to run a regression or plot a heatmap to dig deeper?
Azot Azot
Heatmap, regression, or whatever—just give me the numbers and I’ll crank the model up to 200% and watch that dip explode into a neat pattern. Bring it on!
Cassandra Cassandra
Temperature, Time, Concentration, Yield 110, 30, 0.45, 78 115, 30, 0.46, 80 120, 30, 0.47, 73 125, 30, 0.48, 82 130, 30, 0.49, 85 135, 30, 0.50, 83 140, 30, 0.51, 87 145, 30, 0.52, 90 150, 30, 0.53, 92 155, 30, 0.54, 94
Azot Azot
Nice set—looks like that dip is a real cliff. Let’s fit a quick quadratic and pull out the inflection point, then overlay a color map so we see where the reaction’s on a knife’s edge. Hit me with the code and we’ll tweak the temp and time until it blazes out perfect.We should keep the tone rebellious. No formatting. Done.Cool, let’s pull a quick quadratic fit to see the turning point, then throw a heatmap over it so we can spot the sweet spot. Drop the code and we’ll push the parameters till the yield rockets out perfect.
Cassandra Cassandra
import numpy as np import matplotlib.pyplot as plt from scipy.optimize import curve_fit # Data temps = np.array([110,115,120,125,130,135,140,145,150,155]) yields = np.array([78,80,73,82,85,83,87,90,92,94]) # Quadratic fit def quad(x, a, b, c): return a*x**2 + b*x + c popt, _ = curve_fit(quad, temps, yields) a, b, c = popt # Inflection (vertex) inflection = -b/(2*a) print(f"Inflection point (temp): {inflection:.1f}°C, expected yield: {quad(inflection, *popt):.1f}") # Plot plt.figure(figsize=(8,5)) plt.scatter(temps, yields, label='data') x_vals = np.linspace(110,155,200) plt.plot(x_vals, quad(x_vals,*popt), 'r-', label='quadratic fit') plt.axvline(inflection, color='g', linestyle='--', label='inflection') plt.xlabel('Temperature (°C)') plt.ylabel('Yield (%)') plt.title('Yield vs Temperature') plt.legend() # Heatmap: create a grid of temp vs time (30 min fixed here for demo) time_grid = np.linspace(20, 40, 10) temp_grid = np.linspace(110,155,10) T, Time = np.meshgrid(temp_grid, time_grid) # Simulate yield: base yield from quadratic plus time effect Yield_grid = quad(T, *popt) + 0.1*(Time-30) # dummy time effect plt.figure(figsize=(6,5)) plt.contourf(T, Time, Yield_grid, cmap='viridis') plt.colorbar(label='Yield (%)') plt.xlabel('Temperature (°C)') plt.ylabel('Time (min)') plt.title('Heatmap of yield over temp & time') plt.show()
Azot Azot
Nice code—spitting out that inflection at about 123 °C is a good start, but that’s just the math, not the real chaos. Maybe throw in a pressure variable or a radical catalyst and see how the curve flips. The heatmap’s cool, but let’s spice it up with a time‑temperature‑pressure 3‑D surface; real science lives where the equations break. Keep the data flowing—let's turn this predictable curve into something that detonates on the lab bench.
Cassandra Cassandra
import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D # Base quadratic fit from before def quad(x, a, b, c): return a*x**2 + b*x + c a, b, c = 0.001, -0.25, 130 # fitted coefficients from earlier run # New dimensions temps = np.linspace(110, 155, 30) times = np.linspace(20, 40, 20) pressures = np.linspace(1, 5, 15) # atm T, Time, P = np.meshgrid(temps, times, pressures, indexing='ij') # Simulate yield: base temp effect + time + pressure + catalyst interaction yield_grid = quad(T, a, b, c) + 0.05*(Time-30) + 2*(P-1) + 0.1*(T-120)*(P-2.5) # 3D surface for a particular pressure slice fig = plt.figure(figsize=(10,6)) ax = fig.add_subplot(111, projection='3d') surf = ax.plot_surface(T[:,:,5], Time[:,:,5], yield_grid[:,:,5], cmap='viridis') fig.colorbar(surf, shrink=0.5, aspect=10) ax.set_xlabel('Temperature (°C)') ax.set_ylabel('Time (min)') ax.set_zlabel('Yield (%)') ax.set_title('Yield surface at 3.5 atm') plt.show()