Matlab用户转Python：基于元素级比较筛选目标数组元素

Hey there! Making the switch from MATLAB to Python with NumPy is a great move, and this element-wise filtering problem is super common—let's walk through exactly how to solve it.

First, let's recap your data:

• Radiation array: rad = [0,0,0,0,0,12,55,95,50,12,60,12,5,0,0,0]
• Relative humidity array: rhu = [90,91,95,94,93,90,88,89,85,83,81,80,80,85,90,92]
• Temperature array: tmp = [3,5,6,9,8,9,10,11,13,15,14,13,11,9,8,8]

You want to extract temperature values where radiation > 50 AND relative humidity > 87. Here's how to do this in NumPy:

Step 1: Import NumPy and define your arrays #

First, convert your lists into NumPy arrays (NumPy is built for this kind of element-wise operation):

import numpy as np

# Define your arrays as NumPy arrays
rad = np.array([0,0,0,0,0,12,55,95,50,12,60,12,5,0,0,0])
rhu = np.array([90,91,95,94,93,90,88,89,85,83,81,80,80,85,90,92])
tmp = np.array([3,5,6,9,8,9,10,11,13,15,14,13,11,9,8,8])

Step 2: Create element-wise boolean masks #

Just like in MATLAB, we'll make boolean arrays (masks) that mark which elements meet each condition:

# Mask for radiation > 50
mask_rad = rad > 50
# Mask for relative humidity > 87
mask_rhu = rhu > 87

If you print these masks, you'll see arrays of True/False values where the condition holds (e.g., mask_rad is True at indices 6,7,10 since those are the positions where rad > 50).

Step 3: Combine the masks and filter the temperature array #

We need both conditions to be true, so use the element-wise AND operator & (don't use Python's and—that works for single values, not arrays!). Then use this combined mask to index into the temperature array:

# Combine masks: both conditions must be True
combined_mask = mask_rad & mask_rhu

# Filter the temperature array using the combined mask
filtered_tmp = tmp[combined_mask]

Step 4: Check the result #

If you run print(filtered_tmp), you'll get:

[10 11]

Let's verify this:

• Index 6: rad=55 (>50), rhu=88 (>87) → tmp=10 is included
• Index7: rad=95 (>50), rhu=89 (>87) → tmp=11 is included
• Index10: rad=60 (>50), but rhu=81 (<=87) → excluded

That's exactly what we wanted!

Quick note for MATLAB users #

This is nearly identical to MATLAB's logical indexing. In MATLAB, you'd write:

filtered_tmp = tmp(rad > 50 & rhu > 87);

NumPy's syntax is very similar—just replace parentheses with square brackets, and ensure you're using NumPy arrays instead of MATLAB matrices.

内容的提问来源于stack exchange，提问作者scana