pyterse

Pyterse

Python package of TERSE/PROLIX diffraction and cryo-EM data compression algorithm

The pyterse python package uses the c++ TERSE/PROLIX(TRPX) compression algorithm scheme (https://github.com/senikm/trpx) and adds python binders to it's main class.

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Before you consider pyterse

• Your data is signed or unsigned integral type.
• Your data is grayscale.
• Preferably has high dynamic range.

How to install the package

Create a virtual environment

conda create -n pyterse python pip numpy pillow 

Install the package

pip install pyterse 

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Testing the functionality of the library

Basic commands

• Create terse object:

from pyterse import Terse

# Constructor 1
terse = Terse() 

# Constuctor 2
 terse = Terse(data) #nD NumPy array or slice from an array

 # Constuctor 3
 terse = Terse(data,  data.size, block_size) #Provide the frame size and the costum block size: default is 12
 - Example:
terse = Terse(data,  512* 512, 12)

• Add additional entry to Terse object:

terse.push_back(data) #The NumPy array or slice from a nD array should correspond to existing set shape (terse.shape).

• Save compressed data to a file:

terse.save('filename.trpx')

• Load compressed data from a file:

loaded_terse = Terse.load('filename.trpx')

• Decompress the data:

decompressed_frame = loaded_terse.prolix()

• Decompress a specific entry:

decompressed_frame = loaded_terse.prolix(n)
# You can check the number of entries by terse.number_of_entries()

• Add and retrieve metadata to the terse object:

terse.set_attribute('distance', '487.0')
terse.get_attribute('distance')
terse.get_attributes() #Retrieves all set atributes