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Optimizing NumPy Code: Estimating Memory Usage Before Creating MaskedArrays


It's important to distinguish between regular NumPy arrays and MaskedArrays. Regular NumPy arrays only store data elements, whereas MaskedArrays store both the data and a mask with the same shape as the data array. This mask indicates whether the corresponding data element is valid or missing (masked).

  • Mask
    The mask is typically a boolean array, so it consumes one byte per element.
  • Data
    The number of bytes used to store the data elements depends on the data type of the array. For instance, an array of integers (e.g., int32) will use less memory compared to an array of floating-point numbers (e.g., float64).

Therefore, the total memory usage (nbytes) of a MaskedArray is the sum of the data and mask memory usage.

import numpy.ma as ma

# Create a masked array
arr = ma.array([1, 2, 3, 4, 5], mask=[0, 0, 1, 0, 1])

# Get the number of bytes in the data part of the masked array
data_nbytes = arr.data.nbytes

# Get the number of bytes in the mask part of the masked array
mask_nbytes = arr.mask.nbytes

# Get the total number of bytes in the masked array
total_nbytes = arr.nbytes

# Print the size information
print("Data size (bytes):", data_nbytes)
print("Mask size (bytes):", mask_nbytes)
print("Total size (bytes):", total_nbytes)

This code will output something like:

Data size (bytes): 40
Mask size (bytes): 5
Total size (bytes): 40

In this example, the data has 5 elements (assuming 32-bit integers), and the mask also has 5 elements (each being a boolean value). The total size (40 bytes) matches the sum of data size (40 bytes) and mask size (5 bytes).


Comparing memory usage of MaskedArray vs. Regular Array

import numpy as np
import numpy.ma as ma

# Create a regular array and a MaskedArray with the same data and shape
data = np.array([1, 2, 3, 4, 5])
masked_arr = ma.array(data, mask=[0, 0, 1, 0, 1])

# Print the data type and size of both arrays
print("Regular array data type:", data.dtype)
print("Regular array size (bytes):", data.nbytes)
print("Masked array data type:", masked_arr.dtype)
print("Masked array size (bytes):", masked_arr.nbytes)

This code compares the memory usage of a regular NumPy array and a corresponding MaskedArray. The regular array only stores data, while the MaskedArray stores both data and the mask. You'll likely see the MaskedArray having a slightly larger size due to the additional mask.

Estimating memory usage before creating a MaskedArray

import numpy.ma as ma

# Define the data shape and number of masked elements
data_shape = (1000, 1000)
num_masked = 200

# Calculate the estimated size of the data (assuming float64)
data_size = data_shape[0] * data_shape[1] * 8  # 8 bytes for float64

# Calculate the estimated size of the mask (assuming boolean)
mask_size = data_shape[0] * data_shape[1] * 1  # 1 byte for boolean

# Estimate the total memory usage
estimated_nbytes = data_size + mask_size

print("Estimated total size of MaskedArray (bytes):", estimated_nbytes)

# Create the MaskedArray (actual size might differ slightly)
masked_arr = ma.masked_array(np.random.rand(data_shape), mask=np.random.rand(data_shape) < num_masked/data_shape[0])

# Print the actual size of the MaskedArray
print("Actual size of MaskedArray (bytes):", masked_arr.nbytes)

This code demonstrates how you can estimate the memory footprint of a MaskedArray before creating it. Here, we calculate the size based on the data type, shape, and expected number of masked elements. You can then compare this estimate with the actual size obtained using nbytes after creating the array.


Using size and itemsize attributes

These attributes provide information about the number of elements and the size of each element in the MaskedArray, respectively. You can then calculate the total memory usage by multiplying these two values.

import numpy.ma as ma

arr = ma.array([1, 2, 3, 4, 5], mask=[0, 0, 1, 0, 1])

total_nbytes = arr.size * arr.dtype.itemsize

print("Total size (bytes) using size and itemsize:", total_nbytes)

This approach works for both regular NumPy arrays and MaskedArrays. However, it doesn't explicitly account for the mask memory usage.

Using sys.getsizeof (for informational purposes)

The sys.getsizeof function from the sys module can be used to get an estimate of the total object size in bytes. However, it's important to note that this includes not just the data and mask but also the overhead associated with the NumPy object itself. This might not be the most precise measure of memory used by the data elements themselves.

import sys
import numpy.ma as ma

arr = ma.array([1, 2, 3, 4, 5], mask=[0, 0, 1, 0, 1])

total_size = sys.getsizeof(arr)

print("Total size (bytes) using sys.getsizeof:", total_size)
  • Use sys.getsizeof with caution, as it might not reflect the exact data memory usage.
  • If you just need a general idea of the total size (including overhead), size and itemsize can be sufficient.
  • If you need the precise memory footprint of the data elements (excluding overhead), stick with ma.MaskedArray.nbytes.