使用两个自变量计算numpy数据的最有效方法

Question

I'm fairly new to python/numpy and I'm calculating airflow based on a few different conditions. I have array x (consisting of 100 elements) and array y (consisting of 2 elements...but eventually will expand). I can't figure out how to properly use numpy to combine all x elements with each y element separately and plot multiple lines on the same axis.

The following Python code works, but isn't efficient for my larger applications.

import numpy as np
import matplotlib.pyplot as plt

#-------------------------------------------------------------------------
# Flow calculations
#-------------------------------------------------------------------------

# Input
ps = 14.4                           # static pressure (psi)
d = 47.25                           # ID (inch)
dp_inWC = np.linspace(0,15,100)     # pressure head (inH2O)

# Airflow Calculation
Fna = 0.0997      # Unit conversion factor for lbm/s
Faa = 1.0         # Thermal expansion factor
blck = 0.02856    # Probe blockage
gam = 1.401       # Ratio of specific heats
rho = 0.0729      # humid air density (lbm/ft3)
Ya = 1 - (0.011332*(1-blck)**2 - 0.00342)*dp_inWC/(ps*gam)  # Gas expansion factor
ks = [0.6748, 0.6615]   # K-factor of probe


m_dot0 = Fna * ks[0] * d**2 * Faa * Ya * np.sqrt(rho*dp_inWC)  # mass flow rate (lbm/s)
m_dot1 = Fna * ks[1] * d**2 * Faa * Ya * np.sqrt(rho*dp_inWC)  # mass flow rate (lbm/s)

#----------------------------------------------------------------------------
# Plots
#----------------------------------------------------------------------------

# Initialize plot axis
fig1, ax1 = plt.subplots()

# Flow vs dP
ax1.set_title('Delta Pressure vs Mass Flow Rate',
          color='tab:red',fontweight='bold')
ax1.set_xlabel('dP (inH2O)')
ax1.set_ylabel('Mass Flow (lbm/s)')
ax1.plot(dp_inWC, m_dot0, label='k={}'.format(ks[0]))
ax1.plot(dp_inWC, m_dot1, label='k={}'.format(ks[1]))
ax1.legend()

fig1.tight_layout()
plt.show()

The goal is to combine m_dot0 and m_dot1 into the same object and plot them against dp_inWC using the simplest possible method.

Answer 1

First, create a function that gets the mass flow rate based on the k-factor of the probe; this is bad practice (probably best to input everything into the function) but will serve your purpose:

def get_mdot(k_factor):
    mdot = Fna * k_factor * d **2 * Faa * Ya * np.sqrt(rho * dp_inWC)
    return mdot

Then, you can automate the process by using a list (provided you have a list of k-factors).

all_flow_rates = np.array([get_mdot(i) for i in ks])

Finally, to get a plot of all of them:

plt.plot(dp_inWC, all_flow_rates.T)

Let me know if you have any questions. I will let you figure out the legend by yourself.