semvec/pypi-packages huggingface dataset:
TCRnumba 0.2.6
some description
License: BSD 2-clause

# Installation
To install on Linux type into the commandline:
    
    pip install TCRnumba

It requires CUDA and is not yet tested for MacOS or Windows.

# Usage
In Python you can import

    import TCRnumba

Then, test the script, by running the additional commands  

    import TCRnumba.graph_numba as gn
    import TCRnumba.convert_pure as cp
    adjacency_sparse = gn.adjacency_matrix(['ABC', 'DEF', 'EFG', 'ABC', 'ABD', 'EEE', 'EEF', 'EFF', 'GGE', 'GAS'], N_part=2, len_xy=5, direct_output=True)
    cp.convert_pure(adjacency_sparse, 2, 5)

# Use SONIA to create files
    sonia-generate --humanTRB -n 1000000 --pre -o pre_example.txt

# Functions 

    convert_pure(sparse, single_sidelength, len_x)

Takes list of single digit indices and convert into a dense matrix.

    dense_matrix(data, len_xy)

Takes list of two digit indices and convert that into a dense matrix.

    total_idx(el, N_part, i, j, len_x, len_y)

Converts indices from single-number indices to two-number indices. "N_part" is the number of strings 
in each block. i and j are int numbers, referring to the index of the block. len_x and len_y 
are int numbers denoting the number of blocks in height and length. 

    adjacency_matrix(seq, name="sparse.txt", idx_max=0, name_params="data/sparse_params.txt", N_part=None, len_xy=None, direct_output=False)

Inputs a list of strings "seq" and calculate the distance matrix. Save under the filename "name". 
// define the other params of all the presented functions

# robustness distribution 
![](https://raw.githubusercontent.com/Paul44444/TCR_numba/main/robustness.png)

(a) The relative size of the largest connected component (LCC) 
as a function of $1-p$, where $p$ is the fraction of removed 
nodes. The TCR network has \NSeqRobustness nodes  with 
lmax = 1.
The TCR network does not exhibit a  
percolation phase transition. Moreover, the system 
is robust against  small node removal. 
(b) The corresponding degree distribution of the same 
network is plotted for (0.01, 0.49, 0.69, 0.89) with darker 
green indicating a smaller p. With increasing p, the curve 
shifts downwards and becomes steeper as the number of nodes decreases.