Metadata-Version: 2.1
Name: GeneVecTools
Version: 1.27
Summary: UNKNOWN
Home-page: https://github.com/danielum16/SecondProject
Author: Daniel Um
Author-email: danielum.16@gmail.com
License: danielum16license
Description: 
        
        GeneVecTools
        ===============
        Reading in Variety of Genetic File Types
        
        Vector Embedding Algorithms
        
        Byte Array Encoders
        
        Clustering and Preprocessing Steps for Compression
        
        Similarity Search Tools for FASTA/FASTQ files
        
        Installing
        
        Tester files: https://tinyurl.com/cDNALibraryExampleFiles
        ============
        
        .. code-block:: bash
        
            pip install GeneVecTools
        
        Usage
        =====
        
        .. code-block:: bash
        
            >>> from GeneVecTools import SimSearch
        
            """
            file is location of the "small_cDNA_Sequences_pbmc_1k_v2_S1_L002_R2_001.fastq" 
            that you downloaded from https://tinyurl.com/cDNALibraryExampleFiles
            if it is in current directory, just use file name
            """
            >>> file = "small_cDNA_Sequences_pbmc_1k_v2_S1_L002_R2_001.fastq"
        
        .. code-block:: bash
        
            """
            f is the file location and name
            length is the number of sequences we want in our scope
            encoding is one of three choices: "one-hot-encoding", "standard", or "no-encoding"
            bits is one of three choices: 2, 4, or 8
            """
            >>> VECSS = SimSearch.VecSS(f=dir, length=10000, encoding="one-hot-encoding",bits=8)
            >>> sequences = VECSS.readq()
        
        .. code-block:: bash
            # embed produces the vector embedding of the sequence
            >>> embedded = VECSS.embed(VECSS.s)
            >>> print(embedded)
        
        .. code-block:: bash
            """
            similarity search
            D is the 
            I is the 
            time is the time it takes to perform this similarity search query
            """
            >>> D, I, time = VECSS.run_search()
            >>> print(D,I,time)
        
        .. code-block:: bash
            #Testing the embedding and umembedding process
            >>> print(VECSS.unembed(VECSS.embed(VECSS.s)) == VECSS.s)
           'True'
        
Keywords: example project
Platform: UNKNOWN
Description-Content-Type: text/markdown
