CODEWITHSUNDEEP
pythongeolocationbounding-box
user27321
user27321

Reputation: 11

extracting data from an array within a bounding box defined by latitude and longitude

I have 'rh_irr’ which is an array of size 744*171*162 where 171 is latitude, 162 is longitude and 744 is time dimension. I also have arrays lat and lon both have size of 171*162 and contains corresponding latitude and longitudes of the data. I need to extract data within the bounding box -115.3 to -115.5 longitude, and 32.5 to 33.1 latitude.

import numpy as np

# Define the latitude and longitude ranges
lat_min, lat_max = 32.5, 33.1
lon_min, lon_max = -115.5, -115.3

# Find the indices of latitudes and longitudes within the specified range
lat_indices = np.where((lat >= lat_min) & (lat <= lat_max))[0]
lon_indices = np.where((lon >= lon_min) & (lon <= lon_max))[1]

# Extract the data within the specified range
extracted_data = rh_irr[:, lat_indices, :][:, :, lon_indices]

# Print the shape of the extracted data
print("Shape of extracted data:", extracted_data.shape)

The above code executes and hopefully it will do what I want to do but is crashing. How can we make it more memory efficient? Please suggest if there is another better way.

Upvotes: 0

Views: 159

Answers (1)

Dan Nagle
Dan Nagle

Reputation: 5425

The np.where() function uses additional memory for storing indices where a condition is True but it shouldn't impact on the memory usage significantly. If you apply boolean masks directly to the rh_irr array to extract the subset, it will avoid unnecessary memory allocations. You can use the memory-profiler module to monitor the memory consumption.

import numpy as np

# Define the latitude and longitude ranges
lat_min, lat_max = 32.5, 33.1
lon_min, lon_max = -115.5, -115.3

# Create boolean masks for latitude and longitude
lat_mask = (lat >= min_lat) & (lat <= max_lat)
lon_mask = (lon >= min_lon) & (lon <= max_lon)

# Apply boolean masks directly on the array
selected_data = rh_irr[:, lat_mask, :][:, :, lon_mask]

# Check the shape of selected_data
print("Shape of selected_data:", selected_data.shape)

Upvotes: 0

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