Working with large datasets in Python can often result in memory utilization issues. To reduce the memory utilization, the variables that are no longer immediately needed can be written to the disk. These variables can then be removed from the memory, thereby reducing the memory utilization. When these variables are needed at a later stage in the program, it can be easily restored from the disk. This strategy works for majority of the use cases, where the disk space is cheaper and more readily available than the main memory. In Python, restoring a variable from the disk can be efficiently handled using Pickle. Here is a concise Python3 code example for using Pickle to manage memory utilization, by using the strategy of restoring a variable from the disk.
“If it could be demonstrated that any complex organ existed, which could not possibly have been formed by numerous, successive, slight modifications, my theory would absolutely break down. But I can find no such case.”
-- Charles Darwin, The Origin of Species
The SARS-CoV2 virus is the pathogen responsible for the COVID19 pandemic. Even though the SARS-CoV2 virus is a new pathogen, its origin follows the principles of evolution. Therefore, it is very likely that the SARS-CoV2 virus will have genomic similarities with other viruses. I became fascinated by the possible genomic similarities of the SARS-CoV2 virus with other viruses, especially with the Measles, Mumps Rubella (MMR) group of viruses. My focus on this particular group of viruses was due to the recent news that the MMR vaccine could be also effective against the SARS-CoV2.¹ My reasoning for the MMR vaccine to work against SARS-CoV2 being the shared genomic similarities between these viruses. These genomic similarities could have contributed to the shared immuno-physiological profiles, thus causing the MMR vaccine to be effective against SARS-CoV2.
"The goal of forecasting is not to predict the future but to tell you what you need to know to take meaningful action in the present."