docs/SeqPlotter_tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b370b9c6-bc23-4ec0-ad90-f209bab6defd",
   "metadata": {},
   "source": [
    "# SeqPlotter: Python package for sequence data analysis and visualization\n",
    "**SeqPlotter is a Python package developed to streamline the visualization and analysis of biological sequence data. It provides seamlessly integration into your Python workflow, with robust data handling capabilities, intuitive visualization, and useful analysis features. From parsing raw sequence files to creating stunning plots and charts.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bb31dbc-fa66-4fb3-8817-4909347011ff",
   "metadata": {},
   "source": [
    "# 1. Working with DNA sequences"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6ee14eb-a81b-4348-82ed-f32efd78f415",
   "metadata": {},
   "source": [
    "### 1.1. Importing DNA module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "23305f31-4f11-4129-9899-153b29ec66fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing module\n",
    "from seqplotter.nucl import DNA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d152fd54-6aba-4872-9076-e49a80b60818",
   "metadata": {},
   "source": [
    "### 1.2. Creating DNA objects\n",
    "```\n",
    ">>> dna_seq1 = DNA(\"seqID\", \"DNA_sequence\")\n",
    "```\n",
    "**Output:** retruns a DNA object with seqid and seq pair"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e3e40529-fd50-40e6-811d-02b5cb82ec1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating DNA object with seqID and sequence pair\n",
    "seq1 = DNA(\"seq1\", \"GTGTTTTGACTAATAATTGGTCAAGCCTAC\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90624bb3-36a2-49b0-ae3d-749f810c3f83",
   "metadata": {},
   "source": [
    "### 1.3. Sequence length\n",
    "```\n",
    ">>> dna_seq1.length()\n",
    "```\n",
    "**Output:** returns sequence length (int) in bp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b8396a85-49af-4d8f-958c-4c71f5da2c7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print sequence length\n",
    "seq1.length()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36d5ad4b-2955-49cb-8851-904e839a25b6",
   "metadata": {},
   "source": [
    "### 1.4. Sequence composition\n",
    "```\n",
    ">>> dna_seq1.comp()\n",
    "```\n",
    "**Output:** returns a dict object with sequence composition i.e. count of each nucleotides"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8b87b987-373c-435a-9bbd-8730b5a2866d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A': 26.666666666666668,\n",
       " 'T': 36.666666666666664,\n",
       " 'G': 20.0,\n",
       " 'C': 16.666666666666664}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print sequence composition\n",
    "seq1.comp()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3da63ad1-c727-4774-8e4a-3bb450110d13",
   "metadata": {},
   "source": [
    "### 1.5 Sequence composition barplot\n",
    "```\n",
    ">>> seq1_comp = dna_seq1.comp()\n",
    ">>> DNA.barplot(seq1_comp)\n",
    "```\n",
    "**Output:** generate barplot showing sequence composition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6b0d73d9-9e89-473f-80c7-23d81730c85c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot sequence composition\n",
    "comp = seq1.comp()\n",
    "DNA.barplot(comp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6de182b2-cd3d-4b61-a814-a647fe1afc5c",
   "metadata": {},
   "source": [
    "### 1.6. Sequence slicing\n",
    "```\n",
    ">>> # returns nucleotide for the given position\n",
    ">>> dna_seq1.slice(pos)\n",
    "\n",
    ">>> # returns nucleotides between start to end position\n",
    ">>> dna_seq1.slice(start_pos, end_pos)\n",
    "```\n",
    "**Output:** returns a string that contain the sliced sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b39b6081-46eb-4c42-a882-fe6821cd42ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'T'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# slice particular position\n",
    "seq1.slice(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "749bba26-2ffa-4f44-9711-99fb484c8264",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'TGTT'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# slice with start and end index\n",
    "seq1.slice(2,5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2555fdc4-02bb-4000-91c9-3192e2eba92e",
   "metadata": {},
   "source": [
    "### 1.7. GC content\n",
    "```\n",
    ">>> dna_seq1.gc_percent()\n",
    "```\n",
    "**Output:** returns GC percent (string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ec4fc9f8-ff78-422e-8e6c-f0c3f047bcf6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'36.67'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# GC percent\n",
    "seq1.gc_percent()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "872acb92-4d25-425c-ab6f-1a17f801ceae",
   "metadata": {},
   "source": [
    "### 1.8. K-mer abundance plot\n",
    "```\n",
    "# generated k-mer plot with default kmer size (k=3)\n",
    ">>> dna_seq1..kmer_abundance_plot()\n",
    "\n",
    "# generate k-mer plot with k=6\n",
    ">>> dna_seq1..kmer_abundance_plot(6)\n",
    "```\n",
    "**Output:** generate k-mer plot (histogram)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2ad4cc37-a5c7-4f48-9d7c-c4043b2aebfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# kmer abundance plot\n",
    "seq1.kmer_abundance_plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7a99f4a-2553-430e-9b19-f71d89261ae6",
   "metadata": {},
   "source": [
    "### 1.9. Translate gene sequence\n",
    "```\n",
    ">>> dna_seq1.translate()\n",
    "```\n",
    "**Output:** translate DNA sequence into amino acid sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c3f83b3f-a43f-4e92-be6d-be425f197abd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'VF*LIIGQAY'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# translate sequence\n",
    "seq1.translate()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "757f706a-7719-4bc0-84a1-82999ca76a4c",
   "metadata": {},
   "source": [
    "### 1.10. Readling FASTA file (DNA Sequence)\n",
    "```\n",
    ">>> fasta_records = DNA.read_fasta(\"/path/to/fasta/file\")\n",
    "```\n",
    "**Output:** returns a list containing DNA object(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cfc71467-9399-402c-90c1-d1ed0dc59f0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# reading a FASTA file\n",
    "data = DNA.read_fasta(\"seq.fasta\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c8feddc-210b-4258-b73a-614b77e3fe43",
   "metadata": {},
   "source": [
    "### 1.11. Using head() and tail() functions\n",
    "```\n",
    ">>> # return summary of first 5 sequence (default)\n",
    ">>> DNA.head()\n",
    "\n",
    ">>> # return summary of first 10 sequence\n",
    ">>> DNA.head(10)\n",
    "\n",
    ">>> # return summary of last 5 sequence (default)\n",
    ">>> DNA.tail()\n",
    "\n",
    ">>> # return summary of last 10 sequence\n",
    ">>> DNA.tail(10)\n",
    "```\n",
    "**Output:** print head and tail summary of parsed FASTA file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "db1bed99-6b24-4cab-87dd-3b8c409a2f26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seqID\tseq\n",
      "seq1:\t[CTTTTCTTTAGTGTTTTGACTAATAATTGGTCAAGCCTACCATTACAAACTATGTTCCATTACCAGTACA]\n",
      "seq2:\t[CTTTTCTTTAGTGTTTTGACTAATAATTGGTCAAGCCTACCATTACAAACTATGTTCCATTACCAGTACA]\n",
      "seq3:\t[CTTTTCTTTAGTGTTTTGACTAATAATTGGTCAAGCCTACCATTACAAACTATGTTCCATTACCAGTACA]\n",
      "seq4:\t[CTTTTCTTTAGTGTTTTGACTAATAATTGGAAACTATGTTCCATTACCAGTACA]\n",
      "seq5:\t[TCACAGAAGAGCAAGAGGCTCTTGTAGTGAAGTCTTGGAGTGTCATGAAGAAAAACTCAGCTGAATTAGG]\n"
     ]
    }
   ],
   "source": [
    "# print first five records (by default)\n",
    "DNA.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ea2f131b-bfde-40bd-be51-d1f3d5681ab4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seqID\tseq\n",
      "seq1:\t[CTTTTCTTTAGTGTTTTGACTAATAATTGGTCAAGCCTACCATTACAAACTATGTTCCATTACCAGTACA]\n",
      "seq2:\t[CTTTTCTTTAGTGTTTTGACTAATAATTGGTCAAGCCTACCATTACAAACTATGTTCCATTACCAGTACA]\n"
     ]
    }
   ],
   "source": [
    "# print first two records\n",
    "DNA.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c576120d-6704-424a-9c1a-22520c6db8b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seqID\tseq\n",
      "seq5:\t[TCACAGAAGAGCAAGAGGCTCTTGTAGTGAAGTCTTGGAGTGTCATGAAGAAAAACTCAGCTGAATTAGG]\n",
      "seq6:\t[TCTCAAACTCTTCATCAAGTAAGTAATGATCCCATTGATCTCTCTCTATTTTCTTTTTATGTATATAGCA]\n",
      "seq7:\t[TGAGATATGAACTACTATTTTGAACTGTAGGATCTTTGAGATTGCACCAACAACGA]\n",
      "seq8:\t[TTCTTGAGAGACTCACCAATTCCTGCTGAGCAAAATCCAAAGCTCAAGCCTCACGCAATGTCTGT]\n",
      "seq9:\t[TCATGGTAATAATCAATATCAAATAACATGATTTT]\n"
     ]
    }
   ],
   "source": [
    "# print last five records (by default)\n",
    "DNA.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e7bfbda5-ace3-418e-9196-179095e57d41",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seqID\tseq\n",
      "seq7:\t[TGAGATATGAACTACTATTTTGAACTGTAGGATCTTTGAGATTGCACCAACAACGA]\n",
      "seq8:\t[TTCTTGAGAGACTCACCAATTCCTGCTGAGCAAAATCCAAAGCTCAAGCCTCACGCAATGTCTGT]\n",
      "seq9:\t[TCATGGTAATAATCAATATCAAATAACATGATTTT]\n"
     ]
    }
   ],
   "source": [
    "# print last six records\n",
    "DNA.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c6084c1-cee3-4fb7-b55f-94735794134a",
   "metadata": {},
   "source": [
    "### 1.12. Count  records\n",
    "```\n",
    ">>> DNA.count(fasta_records)\n",
    "```\n",
    "**Output:** returns sequence count (int) i.e. number of records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7af77227-47c9-464f-b4aa-f2383bde876f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print record counts\n",
    "DNA.count(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e7b587e-5ed1-494c-a201-532f9885a0df",
   "metadata": {},
   "source": [
    "### 1.13. Find record\n",
    "```\n",
    ">>> DNA.where(seqid=\"seq1\")\n",
    "```\n",
    "**Output:** returns sequence for a given sequenceID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "45606997",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'CTTTTCTTTAGTGTTTTGACTAATAATTGGTCAAGCCTACCATTACAAACTATGTTCCATTACCAGTACACTTTTCTTTAGTGTTTTGACTAATAATTGGATTACCAGTACA'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find sequence\n",
    "DNA.where(seqid=\"seq1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a7c96e3-896f-478e-969f-6ba89040b376",
   "metadata": {},
   "source": [
    "### 1.14. GC percent per sequence plot\n",
    "```\n",
    ">>> DNA.gc_plot(fasta_records)\n",
    "```\n",
    "**Output**: generate barplot showing per sequence GC percent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d91e3118-aaba-4fd8-be4c-31572a6f8162",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# GC barplot\n",
    "DNA.gc_plot(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdc3caf2-933b-4677-a317-29fae9a30baa",
   "metadata": {},
   "source": [
    "### 1.15. Boxplot showing GC distribution\n",
    "```\n",
    ">>> DNA.gc_distribution_plot(fasta_records)\n",
    "```\n",
    "**Output**: generate boxplot showing overall GC distribution across all the sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f5bfaa62-ffeb-4e41-9ac3-e0ba6ca63f80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# GC distribution plot\n",
    "DNA.gc_distribution_plot(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51944d78-d4ba-43b2-b484-5d067fbc5cfc",
   "metadata": {},
   "source": [
    "### 1.16. Boxplot showing nucleotides distribution\n",
    "```\n",
    ">>> DNA.nt_distribution_plot(fasta_records)\n",
    "```\n",
    "**Output**: generate boxplot showing overall nucleotides distribution across all the sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fb7ccd49-5e44-40fb-86cf-80a8705bd9fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Nucleotides distribution plot\n",
    "DNA.nt_distribution_plot(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09ee4e0c-86fe-4570-aea0-b7d332143f7e",
   "metadata": {},
   "source": [
    "### 1.17. Stacked barplot showing per sequence base composition\n",
    "```\n",
    ">>> DNA.per_sequence_comp(fasta_records)\n",
    "```\n",
    "**Output:** generate stacked barplot showing per sequence nucleotide composition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "99941282-efb7-4109-8d7a-ecc3d0d16400",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Per sequence nucleotides composition\n",
    "DNA.per_sequence_comp(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fae62a4-edd7-4f56-ae85-9be090a26493",
   "metadata": {},
   "source": [
    "### 1.18. Barplot showing per sequence length\n",
    "```\n",
    ">>> DNA.length_plot(fasta_records)\n",
    "```\n",
    "**Output:** generate barplot showing per sequence length distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "719974bd-96c4-4ca5-8033-3fb47485503d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjsAAAGwCAYAAABPSaTdAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAuaElEQVR4nO3de1hVdb7H8c8GcUMKeOeSpGReUPNeRNlFodGs1GKaLDtaWk6FjUplMqaUWZonzfR4ScfLOOVxuppl3gZLS/FGpng5poWXUYFGE1IDFX7njznt004twL3Zm1/v1/Os53Gvtfbi8xXUj2utvbfDGGMEAABgqQBfBwAAAPAmyg4AALAaZQcAAFiNsgMAAKxG2QEAAFaj7AAAAKtRdgAAgNWq+TqAPygtLdWRI0cUGhoqh8Ph6zgAAKAMjDH6/vvvFR0drYCAi5+/oexIOnLkiGJiYnwdAwAAVMChQ4fUsGHDi26n7EgKDQ2V9O/frLCwMB+nAQAAZVFYWKiYmBjXv+MXQ9mRXJeuwsLCKDsAAFQxv3YLCjcoAwAAq1F2AACA1Sg7AADAapQdAABgNcoOAACwGmUHAABYjbIDAACsRtkBAABWo+wAAACrUXYAAIDVKDsAAMBqlB0AAGA1yg4AALAaZQcAAFiNsgMAAKxWzdcBbNd4xFJfR/hV+8ff7usIAAB4DWd2AACA1Sg7AADAapQdAABgNcoOAACwGmUHAABYjbIDAACsRtkBAABWo+wAAACrUXYAAIDVKDsAAMBqlB0AAGA1yg4AALAaZQcAAFiNsgMAAKxG2QEAAFaj7AAAAKtRdgAAgNUoOwAAwGqUHQAAYDXKDgAAsBplBwAAWI2yAwAArEbZAQAAVqPsAAAAq1F2AACA1Sg7AADAapQdAABgNcoOAACwGmUHAABYjbIDAACsRtkBAABWo+wAAACrUXYAAIDVfFp21q5dqzvvvFPR0dFyOBxavHix23ZjjEaPHq2oqCiFhIQoKSlJe/fuddvn+PHj6tu3r8LCwlSrVi0NHDhQJ0+erMQpAACAP/Np2Tl16pTatm2radOmXXD7hAkTNGXKFM2cOVMbN25UjRo11K1bNxUVFbn26du3r3bu3KlVq1bpo48+0tq1azVo0KDKGgEAAPi5ar784rfddptuu+22C24zxmjy5Ml69tln1atXL0nSggULFBERocWLF6tPnz7avXu3li9frs2bN6tTp06SpKlTp6pHjx565ZVXFB0dfcFjFxcXq7i42PW4sLDQw5MBAAB/4bf37OTk5Cg3N1dJSUmudeHh4YqPj1dmZqYkKTMzU7Vq1XIVHUlKSkpSQECANm7ceNFjjxs3TuHh4a4lJibGe4MAAACf8tuyk5ubK0mKiIhwWx8REeHalpubqwYNGrhtr1atmurUqePa50LS0tJUUFDgWg4dOuTh9AAAwF/49DKWrzidTjmdTl/HAAAAlcBvz+xERkZKkvLy8tzW5+XlubZFRkYqPz/fbfu5c+d0/Phx1z4AAOC3zW/LTmxsrCIjI5WRkeFaV1hYqI0bNyohIUGSlJCQoBMnTigrK8u1z+rVq1VaWqr4+PhKzwwAAPyPTy9jnTx5Uvv27XM9zsnJ0Zdffqk6deroiiuu0NChQzV27Fg1bdpUsbGxGjVqlKKjo9W7d29JUlxcnLp3765HHnlEM2fO1NmzZzV48GD16dPnoq/EAgAAvy0+LTtbtmxRly5dXI9TU1MlSf3799f8+fM1fPhwnTp1SoMGDdKJEyfUuXNnLV++XMHBwa7nvPnmmxo8eLASExMVEBCg5ORkTZkypdJnAQAA/slhjDG+DuFrhYWFCg8PV0FBgcLCwjx67MYjlnr0eN6wf/ztZdrPplmAysCfGcC7yvrvt9/eswMAAOAJlB0AAGA1yg4AALAaZQcAAFiNsgMAAKxG2QEAAFaj7AAAAKtRdgAAgNV+k596Dki84RsA/FZwZgcAAFiNsgMAAKxG2QEAAFaj7AAAAKtRdgAAgNUoOwAAwGqUHQAAYDXKDgAAsBplBwAAWI2yAwAArEbZAQAAVqPsAAAAq1F2AACA1Sg7AADAapQdAABgtWq+DgAAP9V4xFJfR/hV+8ff7usIAMqBMzsAAMBqlB0AAGA1yg4AALAaZQcAAFiNsgMAAKxG2QEAAFaj7AAAAKtRdgAAgNUoOwAAwGqUHQAAYDXKDgAAsBplBwAAWI2yAwAArEbZAQAAVqPsAAAAq1F2AACA1Sg7AADAapQdAABgNcoOAACwGmUHAABYjbIDAACsRtkBAABWo+wAAACrUXYAAIDVKDsAAMBqlB0AAGA1yg4AALCaX5edkpISjRo1SrGxsQoJCVGTJk30wgsvyBjj2scYo9GjRysqKkohISFKSkrS3r17fZgaAAD4E78uOy+//LJmzJih//qv/9Lu3bv18ssva8KECZo6daprnwkTJmjKlCmaOXOmNm7cqBo1aqhbt24qKiryYXIAAOAvqvk6wC9Zv369evXqpdtvv12S1LhxY/33f/+3Nm3aJOnfZ3UmT56sZ599Vr169ZIkLViwQBEREVq8eLH69OlzweMWFxeruLjY9biwsNDLkwAAAF/x6zM7119/vTIyMvTVV19JkrZt26bPP/9ct912myQpJydHubm5SkpKcj0nPDxc8fHxyszMvOhxx40bp/DwcNcSExPj3UEAAIDP+PWZnREjRqiwsFAtWrRQYGCgSkpK9OKLL6pv376SpNzcXElSRESE2/MiIiJc2y4kLS1NqamprseFhYUUHgAALOXXZeett97Sm2++qYULF6pVq1b68ssvNXToUEVHR6t///4VPq7T6ZTT6fRgUgAA4K/8uuw8/fTTGjFihOvem6uvvloHDhzQuHHj1L9/f0VGRkqS8vLyFBUV5XpeXl6e2rVr54vIAADAz/j1PTunT59WQIB7xMDAQJWWlkqSYmNjFRkZqYyMDNf2wsJCbdy4UQkJCZWaFQAA+Ce/PrNz55136sUXX9QVV1yhVq1aaevWrZo0aZIGDBggSXI4HBo6dKjGjh2rpk2bKjY2VqNGjVJ0dLR69+7t2/AAAMAv+HXZmTp1qkaNGqXHH39c+fn5io6O1h//+EeNHj3atc/w4cN16tQpDRo0SCdOnFDnzp21fPlyBQcH+zA5AADwF35ddkJDQzV58mRNnjz5ovs4HA6NGTNGY8aMqbxgAACgyvDre3YAAAAuFWUHAABYjbIDAACsRtkBAABWo+wAAACrUXYAAIDVKDsAAMBqlB0AAGA1yg4AALAaZQcAAFiNsgMAAKxG2QEAAFaj7AAAAKuV61PPS0tLtWbNGn322Wc6cOCATp8+rfr166t9+/ZKSkpSTEyMt3ICAABUSJnO7Pzwww8aO3asYmJi1KNHDy1btkwnTpxQYGCg9u3bp/T0dMXGxqpHjx7asGGDtzMDAACUWZnO7DRr1kwJCQmaPXu2br31VgUFBZ23z4EDB7Rw4UL16dNHI0eO1COPPOLxsAAAAOVVprKzcuVKxcXF/eI+jRo1Ulpamp566ikdPHjQI+EAAAAuVZkuY/1a0fmpoKAgNWnSpMKBAAAAPKlcNyj/6LvvvtOcOXO0e/duSf8uQwMGDFCdOnU8Gg4AAOBSlful52vXrlVsbKymTJmi7777Tt99952mTp2q2NhYrV271hsZAQAAKqzcZ3ZSUlL0hz/8QTNmzFBgYKAkqaSkRI8//rhSUlKUnZ3t8ZAAAAAVVe4zO/v27dOTTz7pKjqSFBgYqNTUVO3bt8+j4QAAAC5VuctOhw4dXPfq/NTu3bvVtm1bj4QCAADwlHJfxvrTn/6kIUOGaN++fbruuuskSRs2bNC0adM0fvx4bd++3bVvmzZtPJcUAACgAspddu677z5J0vDhwy+4zeFwyBgjh8OhkpKSS08IAABwCcpddnJycryRAwAAwCvKXXYaNWrkjRwAAABeUaE3FdyzZ4+mTp3q9qaCTzzxhJo3b+7RcAAAAJeq3K/Gevfdd9W6dWtlZWWpbdu2atu2rb744gu1bt1a7777rjcyAgAAVFi5z+wMHz5caWlpGjNmjNv69PR0DR8+XMnJyR4LBwAAcKnKfWbn6NGj6tev33nrH3jgAR09etQjoQAAADyl3GXnlltu0WeffXbe+s8//1w33nijR0IBAAB4SpkuYy1ZssT16549e+qZZ55RVlaW25sKvv3223r++ee9kxIAAKCCylR2evfufd666dOna/r06W7rUlJS9Oijj3okGAAAgCeUqeyUlpZ6OwcAAIBXlPueHQAAgKqEsgMAAKxG2QEAAFaj7AAAAKtV6LOxAACoyhqPWOrrCL9q//jbfR3BGhUqO6Wlpdq3b5/y8/PPe6XWTTfd5JFgAAAAnlDusrNhwwbdf//9OnDggIwxbtscDodKSko8Fg4AAOBSlbvsPProo+rUqZOWLl2qqKgoORwOb+QCAADwiHKXnb179+qdd97RVVdd5Y08AAAAHlXuV2PFx8dr37593sgCAADgceU+s/PEE0/oySefVG5urq6++moFBQW5bW/Tpo3HwgEAAFyqcped5ORkSdKAAQNc6xwOh4wx3KAMAAD8TrnLTk5OjjdyAAAAeEW5y06jRo28kQMAAMArylR2lixZottuu01BQUFasmTJL+7bs2dPjwQDAADwhDKVnd69eys3N1cNGjRQ7969L7of9+wAAAB/U6ay89OPhPj5x0MAAAD4M7//1PPDhw/rgQceUN26dRUSEqKrr75aW7ZscW03xmj06NGKiopSSEiIkpKStHfvXh8mBgAA/qRMZWfRokVlPuChQ4e0bt26Cgf6qe+++0433HCDgoKCtGzZMu3atUsTJ05U7dq1XftMmDBBU6ZM0cyZM7Vx40bVqFFD3bp1U1FRkUcyAACAqq1MZWfGjBmKi4vThAkTtHv37vO2FxQU6OOPP9b999+vDh066NixYx4J9/LLLysmJkbz5s3Ttddeq9jYWP3ud79TkyZNJP37rM7kyZP17LPPqlevXmrTpo0WLFigI0eOaPHixRc9bnFxsQoLC90WAABgpzKVnTVr1ujll1/WqlWr1Lp1a4WFhalp06a6+uqr1bBhQ9WtW1cDBgzQFVdcoR07dnjsFVlLlixRp06ddM8996hBgwZq3769Zs+e7dqek5Oj3NxcJSUludaFh4crPj5emZmZFz3uuHHjFB4e7lpiYmI8khcAAPifMr/PTs+ePdWzZ0/961//0ueff64DBw7ohx9+UL169dS+fXu1b99eAQGevQXom2++0YwZM5Samqo///nP2rx5s/70pz+pevXq6t+/v3JzcyVJERERbs+LiIhwbbuQtLQ0paamuh4XFhZSeAAAsFS531SwXr16v/jyc08qLS1Vp06d9NJLL0mS2rdvrx07dmjmzJnq379/hY/rdDrldDo9FRMAAPgxv341VlRUlFq2bOm2Li4uTgcPHpQkRUZGSpLy8vLc9snLy3NtAwAAv21+XXZuuOEG7dmzx23dV1995frIitjYWEVGRiojI8O1vbCwUBs3blRCQkKlZgUAAP6p3JexKtOwYcN0/fXX66WXXtIf/vAHbdq0SbNmzdKsWbMk/fsdm4cOHaqxY8eqadOmio2N1ahRoxQdHV1pl9oAAIB/8+uyc8011+j9999XWlqaxowZo9jYWE2ePFl9+/Z17TN8+HCdOnVKgwYN0okTJ9S5c2ctX75cwcHBPkwOAAD8hV+XHUm64447dMcdd1x0u8Ph0JgxYzRmzJhKTAUAAKqKcpedkpISzZ8/XxkZGcrPzz/vs7JWr17tsXAAAACXqtxlZ8iQIZo/f75uv/12tW7dWg6Hwxu5AAAAPKLcZWfRokV666231KNHD2/kAQAA8Khyv/S8evXquuqqq7yRBQAAwOPKXXaefPJJvfbaazLGeCMPAACAR5XpMtbdd9/t9nj16tVatmyZWrVqpaCgILdt7733nufSAQD8RuMRS30d4VftH3+7ryPAD5Wp7ISHh7s9vuuuu7wSBgAAwNPKVHbmzZvn7RwAAABeUe57drp27aoTJ06ct76wsFBdu3b1RCYAAACPKXfZ+fTTT3XmzJnz1hcVFemzzz7zSCgAAABPKfP77Gzfvt316127dik3N9f1uKSkRMuXL9fll1/u2XQAAACXqMxlp127dnI4HHI4HBe8XBUSEqKpU6d6NByAsuFVMgBwcWUuOzk5OTLG6Morr9SmTZtUv35917bq1aurQYMGCgwM9EpIAACAiipz2WnUqJEknffBnwAAAP6s3J+NtWTJkguudzgcCg4O1lVXXaXY2NhLDgYAAOAJ5S47vXv3lsPhOO/jIn5c53A41LlzZy1evFi1a9f2WFAAAICKKPdLz1etWqVrrrlGq1atUkFBgQoKCrRq1SrFx8fro48+0tq1a3Xs2DE99dRT3sgLAABQLuU+szNkyBDNmjVL119/vWtdYmKigoODNWjQIO3cuVOTJ0/WgAEDPBoUAACgIsp9Zufrr79WWFjYeevDwsL0zTffSJKaNm2qf/3rX5eeDgAA4BKVu+x07NhRTz/9tL799lvXum+//VbDhw/XNddcI0nau3evYmJiPJcSAACggsp9GWvOnDnq1auXGjZs6Co0hw4d0pVXXqkPPvhAknTy5Ek9++yznk0KAABQAeUuO82bN9euXbu0cuVKffXVV651t956qwIC/n2iqHfv3h4NCQAAUFHlLjuSFBAQoO7du6t79+6ezgMAAOBRFSo7GRkZysjIUH5+/nnvqDx37lyPBAMAAPCEcped559/XmPGjFGnTp0UFRUlh8PhjVwAAAAeUe6yM3PmTM2fP1//8R//4Y08AAAAHlXul56fOXPG7Q0FAQAA/Fm5y87DDz+shQsXeiMLAACAx5X7MlZRUZFmzZqlf/zjH2rTpo2CgoLctk+aNMlj4QAAAC5VucvO9u3b1a5dO0nSjh073LZxszIAAPA35S47n3zyiTdyAAAAeEW579n50b59+7RixQr98MMPkiRjjMdCAQAAeEq5y86xY8eUmJioZs2aqUePHjp69KgkaeDAgXryySc9HhAAAOBSlLvsDBs2TEFBQTp48KAuu+wy1/p7771Xy5cv92g4AACAS1Xue3ZWrlypFStWqGHDhm7rmzZtqgMHDngsGAAAgCeU+8zOqVOn3M7o/Oj48eNyOp0eCQUAAOAp5S47N954oxYsWOB67HA4VFpaqgkTJqhLly4eDQcAAHCpyn0Za8KECUpMTNSWLVt05swZDR8+XDt37tTx48e1bt06b2QEAACosHKf2WndurW++uorde7cWb169dKpU6d09913a+vWrWrSpIk3MgIAAFRYuc/sSFJ4eLhGjhzptu6f//ynBg0apFmzZnkkGAAAgCdU+E0Ff+7YsWOaM2eOpw4HAADgER4rOwAAAP6IsgMAAKxG2QEAAFYr8w3Kd9999y9uP3HixKVmAQAA8Lgyl53w8PBf3d6vX79LDgQAAOBJZS478+bN82YOAAAAr+CeHQAAYDXKDgAAsBplBwAAWI2yAwAArFalys748ePlcDg0dOhQ17qioiKlpKSobt26qlmzppKTk5WXl+e7kAAAwK9UmbKzefNmvf7662rTpo3b+mHDhunDDz/U22+/rTVr1ujIkSO/+p5AAADgt6NKlJ2TJ0+qb9++mj17tmrXru1aX1BQoDlz5mjSpEnq2rWrOnbsqHnz5mn9+vXasGGDDxMDAAB/USXKTkpKim6//XYlJSW5rc/KytLZs2fd1rdo0UJXXHGFMjMzL3q84uJiFRYWui0AAMBOZX5TQV9ZtGiRvvjiC23evPm8bbm5uapevbpq1arltj4iIkK5ubkXPea4ceP0/PPPezoqAADwQ359ZufQoUMaMmSI3nzzTQUHB3vsuGlpaSooKHAthw4d8tixAQCAf/HrspOVlaX8/Hx16NBB1apVU7Vq1bRmzRpNmTJF1apVU0REhM6cOXPeh5Dm5eUpMjLyosd1Op0KCwtzWwAAgJ38+jJWYmKisrOz3dY99NBDatGihZ555hnFxMQoKChIGRkZSk5OliTt2bNHBw8eVEJCgi8iAwAAP+PXZSc0NFStW7d2W1ejRg3VrVvXtX7gwIFKTU1VnTp1FBYWpieeeEIJCQm67rrrfBEZAAD4Gb8uO2Xx6quvKiAgQMnJySouLla3bt00ffp0X8cCAAB+osqVnU8//dTtcXBwsKZNm6Zp06b5JhAAAPBrfn2DMgAAwKWi7AAAAKtRdgAAgNUoOwAAwGqUHQAAYDXKDgAAsBplBwAAWI2yAwAArEbZAQAAVqty76AMAAD+X+MRS30d4VftH3+7T78+Z3YAAIDVKDsAAMBqlB0AAGA1yg4AALAaZQcAAFiNsgMAAKxG2QEAAFaj7AAAAKtRdgAAgNUoOwAAwGqUHQAAYDXKDgAAsBplBwAAWI2yAwAArEbZAQAAVqPsAAAAq1F2AACA1Sg7AADAapQdAABgNcoOAACwGmUHAABYjbIDAACsRtkBAABWo+wAAACrUXYAAIDVKDsAAMBqlB0AAGA1yg4AALAaZQcAAFiNsgMAAKxG2QEAAFaj7AAAAKtRdgAAgNUoOwAAwGqUHQAAYDXKDgAAsBplBwAAWI2yAwAArEbZAQAAVqPsAAAAq1F2AACA1Sg7AADAan5ddsaNG6drrrlGoaGhatCggXr37q09e/a47VNUVKSUlBTVrVtXNWvWVHJysvLy8nyUGAAA+Bu/Ljtr1qxRSkqKNmzYoFWrVuns2bP63e9+p1OnTrn2GTZsmD788EO9/fbbWrNmjY4cOaK7777bh6kBAIA/qebrAL9k+fLlbo/nz5+vBg0aKCsrSzfddJMKCgo0Z84cLVy4UF27dpUkzZs3T3FxcdqwYYOuu+46X8QGAAB+xK/P7PxcQUGBJKlOnTqSpKysLJ09e1ZJSUmufVq0aKErrrhCmZmZFz1OcXGxCgsL3RYAAGCnKlN2SktLNXToUN1www1q3bq1JCk3N1fVq1dXrVq13PaNiIhQbm7uRY81btw4hYeHu5aYmBhvRgcAAD5UZcpOSkqKduzYoUWLFl3ysdLS0lRQUOBaDh065IGEAADAH/n1PTs/Gjx4sD766COtXbtWDRs2dK2PjIzUmTNndOLECbezO3l5eYqMjLzo8ZxOp5xOpzcjAwAAP+HXZ3aMMRo8eLDef/99rV69WrGxsW7bO3bsqKCgIGVkZLjW7dmzRwcPHlRCQkJlxwUAAH7Ir8/spKSkaOHChfrggw8UGhrqug8nPDxcISEhCg8P18CBA5Wamqo6deooLCxMTzzxhBISEnglFgAAkOTnZWfGjBmSpFtuucVt/bx58/Tggw9Kkl599VUFBAQoOTlZxcXF6tatm6ZPn17JSQEAgL/y67JjjPnVfYKDgzVt2jRNmzatEhIBAICqxq/v2QEAALhUlB0AAGA1yg4AALAaZQcAAFiNsgMAAKxG2QEAAFaj7AAAAKtRdgAAgNUoOwAAwGqUHQAAYDXKDgAAsBplBwAAWI2yAwAArEbZAQAAVqPsAAAAq1F2AACA1Sg7AADAapQdAABgNcoOAACwGmUHAABYjbIDAACsRtkBAABWo+wAAACrUXYAAIDVKDsAAMBqlB0AAGA1yg4AALAaZQcAAFiNsgMAAKxG2QEAAFaj7AAAAKtRdgAAgNUoOwAAwGqUHQAAYDXKDgAAsBplBwAAWI2yAwAArEbZAQAAVqPsAAAAq1F2AACA1Sg7AADAapQdAABgNcoOAACwGmUHAABYjbIDAACsRtkBAABWo+wAAACrUXYAAIDVKDsAAMBqlB0AAGA1yg4AALAaZQcAAFjNmrIzbdo0NW7cWMHBwYqPj9emTZt8HQkAAPgBK8rO3//+d6Wmpio9PV1ffPGF2rZtq27duik/P9/X0QAAgI9ZUXYmTZqkRx55RA899JBatmypmTNn6rLLLtPcuXN9HQ0AAPhYNV8HuFRnzpxRVlaW0tLSXOsCAgKUlJSkzMzMCz6nuLhYxcXFrscFBQWSpMLCQo/nKy0+7fFjelpZ57ZpFsmueZilcv0WZ5HsmodZKpc3/n396XGNMb+8o6niDh8+bCSZ9evXu61/+umnzbXXXnvB56SnpxtJLCwsLCwsLBYshw4d+sWuUOXP7FREWlqaUlNTXY9LS0t1/Phx1a1bVw6Hw4fJfl1hYaFiYmJ06NAhhYWF+TrOJWEW/2TTLJJd8zCLf7JpFqlqzWOM0ffff6/o6Ohf3K/Kl5169eopMDBQeXl5buvz8vIUGRl5wec4nU45nU63dbVq1fJWRK8ICwvz+x/CsmIW/2TTLJJd8zCLf7JpFqnqzBMeHv6r+1T5G5SrV6+ujh07KiMjw7WutLRUGRkZSkhI8GEyAADgD6r8mR1JSk1NVf/+/dWpUydde+21mjx5sk6dOqWHHnrI19EAAICPWVF27r33Xn377bcaPXq0cnNz1a5dOy1fvlwRERG+juZxTqdT6enp512Gq4qYxT/ZNItk1zzM4p9smkWybx5Jchjza6/XAgAAqLqq/D07AAAAv4SyAwAArEbZAQAAVqPsAAAAq1F2LPWnP/1JHTt2lNPpVLt27Xwdp8K2bdum++67TzExMQoJCVFcXJxee+01X8eqkGPHjql79+6Kjo6W0+lUTEyMBg8e7LXPjKksx44dU8OGDeVwOHTixAlfx6kwh8Nx3rJo0SJfx6qw+fPnq02bNgoODlaDBg2UkpLi60jlNn/+/At+XxwOh/Lz830dr9w2b96sxMRE1apVS7Vr11a3bt20bds2X8eqsIyMDF1//fUKDQ1VZGSknnnmGZ07d87XsS7Iipee48IGDBigjRs3avv27b6OUmFZWVlq0KCB3njjDcXExGj9+vUaNGiQAgMDNXjwYF/HK5eAgAD16tVLY8eOVf369bVv3z6lpKTo+PHjWrhwoa/jVdjAgQPVpk0bHT582NdRLtm8efPUvXt31+Oq9s7qP5o0aZImTpyo//zP/1R8fLxOnTql/fv3+zpWud17771u3w9JevDBB1VUVKQGDRr4KFXFnDx5Ut27d1fPnj01ffp0nTt3Tunp6erWrZsOHTqkoKAgX0csl23btqlHjx4aOXKkFixYoMOHD+vRRx9VSUmJXnnlFV/HO59nPo4T5fX222+b1q1bm+DgYFOnTh2TmJhoTp48aYwxZvbs2aZFixbG6XSa5s2bm2nTprk9d+PGjaZdu3bG6XSajh07mvfee89IMlu3bj3v66Snp5u2bdtaMcuPHn/8cdOlSxcrZnnttddMw4YNq+ws06dPNzfffLPJyMgwksx3333nlVkqYx5J5v333/da/sqa5fjx4yYkJMT84x//qPKz/Fx+fr4JCgoyCxYsqHKzbN682UgyBw8edD1n+/btRpLZu3dvlZsnLS3NdOrUye05S5YsMcHBwaawsNAr81wKyo4PHDlyxFSrVs1MmjTJ5OTkmO3bt5tp06aZ77//3rzxxhsmKirKvPvuu+abb74x7777rqlTp46ZP3++McaY77//3tSvX9/cf//9ZseOHebDDz80V155pc/KTmXO8qO+ffua5OTkKj/L4cOHzc0332z69u1bJWfZuXOniYyMNAcOHDCffPKJV8tOZcwjyURHR5u6deuaa665xsyZM8eUlpZWuVn+/ve/G6fTaf7617+aFi1amMsvv9zcc889bv/IVpVZfu6VV14x4eHh5vTp01VulsLCQlO3bl2Tnp5uiouLzenTp82QIUNMXFycOXv2bJWbJzU11XTu3Nnta65atcpIMp988onH57lUlB0fyMrKMpLM/v37z9vWpEkTs3DhQrd1L7zwgklISDDGGPP666+bunXrmh9++MG1fcaMGT4rO5U5izHGrFu3zlSrVs2sWLHCc0P8n8qapU+fPiYkJMRIMnfeeafbc6rKLEVFRaZNmzbmb3/7mzHGeL3sVMb3ZsyYMebzzz83X3zxhRk/frxxOp3mtddeq3KzjBs3zgQFBZnmzZub5cuXm8zMTJOYmGiaN29uiouLq9QsPxcXF2cee+wxzw3wE5UxS3Z2tmnSpIkJCAgwAQEBpnnz5hf8elVhnhUrVpiAgACzcOFCc+7cOfPPf/7T3HjjjUbSecf2B5QdHzh37pxJTEw0oaGh5ve//72ZNWuWOX78uDl58qSRZEJCQkyNGjVci9PpNA0aNDDGGDN06NDzLuF8+eWXPis7lTlLdna2qVevnnnhhReq9CxHjx41u3fvNh988IFp2bKlV/7y9vYsw4YNM/fee69ru7fLTmX+nP1o1KhRXrnE6O1ZXnzxRSPJ7T8E+fn5JiAgwCxfvrxKzfJT69evN5LMli1bPDpDZc1y+vRpc+2115p+/fqZTZs2mczMTJOcnGxatWrllTNVlfG9mThxogkLCzOBgYHmsssuM+PGjTOSzKJFizw+z6XiBmUfCAwM1KpVq7R+/XqtXLlSU6dO1ciRI/Xhhx9KkmbPnq34+PjznuOPKmuWXbt2KTExUYMGDdKzzz7rkew/V1mzREZGKjIyUi1atFCdOnV04403atSoUYqKivLIHD/m8uYsq1evVnZ2tt555x1Jkvm/T52pV6+eRo4cqeeff95Dk/x/tsr+MxMfH68XXnhBxcXFHv2MIG/P8uPPUcuWLV3r6tevr3r16ungwYMemMA9V2V9X/7yl7+oXbt26tix4yXnvhBvz7Jw4ULt379fmZmZCggIcK2rXbu2PvjgA/Xp08dzw6hyvjepqakaNmyYjh49qtq1a2v//v1KS0vTlVde6bE5PMbXbQv/buCXX365mThxoomOjjZjxoy56L4XOr04c+ZMn96g/FPemGXHjh2mQYMG5umnn/Zm9PN48/vyozVr1hhJJicnx4PJz+fpWfbt22eys7Ndy9y5c40ks379epOXl+fVWYypnO/N2LFjTe3atT0Z+4I8PcuePXuMJLcblI8dO2YCAgK8cvn3p7z1ffn+++9NzZo1zdSpU70V/TyenmXKlCkmMjLS7T6ws2fPmho1apg333zTa3P8qDL+zIwaNcrExMSYc+fOeTK6R1B2fGDDhg3mxRdfNJs3bzYHDhwwb731lqlevbr5+OOPzezZs01ISIh57bXXzJ49e8z27dvN3LlzzcSJE40x//5DX69ePfPAAw+YnTt3mqVLl5qrrrrqvB/CvXv3mq1bt5o//vGPplmzZmbr1q1m69atHr9m7+1ZsrOzTf369c0DDzxgjh496lry8/M9OkdlzLJ06VIzd+5ck52dbXJycsxHH31k4uLizA033FDlZvk5b1/G8vY8S5YsMbNnzzbZ2dlm7969Zvr06eayyy4zo0ePrnKzGGNMr169TKtWrcy6detMdna2ueOOO0zLli3NmTNnqtwsxhjzl7/8xQQHB3v11X7enmX37t3G6XSaxx57zOzatcvs2LHDPPDAAyY8PNwcOXKkys1jjDETJkww27dvNzt27DBjxowxQUFBlfaKxvKi7PjArl27TLdu3Uz9+vWN0+k0zZo1c/sfy5tvvmnatWtnqlevbmrXrm1uuukm895777m2Z2ZmmrZt25rq1aubdu3amXffffe8H8Kbb77ZSDpv8fQZBG/Pkp6efsE5GjVq5NE5KmOW1atXm4SEBBMeHm6Cg4NN06ZNzTPPPOOVv8Ar42fsp7xddrw9z7Jly0y7du1MzZo1TY0aNUzbtm3NzJkzTUlJSZWbxRhjCgoKzIABA0ytWrVMnTp1zF133eWVV2NV1s9ZQkKCuf/++z2ev7JnWblypbnhhhtMeHi4qV27tunatavJzMyssvN06dLF9fdZfHy8+fjjj70yiyc4jPm/i+2osvbv36/Y2Fht3bq1Sr9bssQs/sqmWSS75mEW/2TTLFLVn4ePiwAAAFaj7AAAAKtxGQsAAFiNMzsAAMBqlB0AAGA1yg4AALAaZQcAAFiNsgMAAKxG2QEAAFaj7ADwuG+//VaPPfaYrrjiCjmdTkVGRqpbt25at26dr6MB+A2q5usAAOyTnJysM2fO6K9//auuvPJK5eXlKSMjQ8eOHfN1NAC/QZzZAeBRJ06c0GeffaaXX35ZXbp0UaNGjXTttdcqLS1NPXv2dO3z8MMPq379+goLC1PXrl21bds2t+OMHz9eERERCg0N1cCBAzVixAi3z+S55ZZbNHToULfn9O7dWw8++KDrcXFxsZ566ildfvnlqlGjhuLj4/Xpp5+6ts+fP1+1atXSihUrFBcXp5o1a6p79+46evSo23Hnzp2rVq1ayel0KioqSoMHD3ab95dm2bZtm7p06aLQ0FCFhYWpY8eO2rJlSwV/dwFUBGUHgEfVrFlTNWvW1OLFi1VcXHzBfe655x7l5+dr2bJlysrKUocOHZSYmKjjx49Lkt566y0999xzeumll7RlyxZFRUVp+vTp5c4yePBgZWZmatGiRdq+fbvuuecede/eXXv37nXtc/r0ab3yyiv629/+prVr1+rgwYN66qmnXNtnzJihlJQUDRo0SNnZ2VqyZImuuuqqMs/St29fNWzYUJs3b1ZWVpZGjBihoKCgcs8C4BL49kPXAdjonXfeMbVr1zbBwcHm+uuvN2lpaWbbtm3GGGM+++wzExYWZoqKitye06RJE/P6668bY4xJSEgwjz/+uNv2+Ph407ZtW9fjm2++2QwZMsRtn169epn+/fsbY4w5cOCACQwMNIcPH3bbJzEx0aSlpRljjJk3b56RZPbt2+faPm3aNBMREeF6HB0dbUaOHHnBOcsyS2hoqJk/f/4Fnw+gcnBmB4DHJScn68iRI1qyZIm6d++uTz/9VB06dND8+fO1bds2nTx5UnXr1nWdBapZs6ZycnL09ddfS5J2796t+Ph4t2MmJCSUK0N2drZKSkrUrFkzt6+zZs0a19eRpMsuu0xNmjRxPY6KilJ+fr4kKT8/X0eOHFFiYuIFv0ZZZklNTdXDDz+spKQkjR8/3u1rA6gc3KAMwCuCg4N166236tZbb9WoUaP08MMPKz09XY8//riioqLc7p35Ua1atcp8/ICAAJmffY7x2bNnXb8+efKkAgMDlZWVpcDAQLf9atas6fr1zy8pORwO13FDQkJ+McPJkyd/dZbnnntO999/v5YuXaply5YpPT1dixYt0l133fWrMwLwDMoOgErRsmVLLV68WB06dFBubq6qVaumxo0bX3DfuLg4bdy4Uf369XOt27Bhg9s+9evXd7uRuKSkRDt27FCXLl0kSe3bt1dJSYny8/N14403VihzaGioGjdurIyMDNdxf6oss0hSs2bN1KxZMw0bNkz33Xef5s2bR9kBKhGXsQB41LFjx9S1a1e98cYb2r59u3JycvT2229rwoQJ6tWrl5KSkpSQkKDevXtr5cqV2r9/v9avX6+RI0e6XqU0ZMgQzZ07V/PmzdNXX32l9PR07dy50+3rdO3aVUuXLtXSpUv1P//zP3rsscd04sQJ1/ZmzZqpb9++6tevn9577z3l5ORo06ZNGjdunJYuXVrmeZ577jlNnDhRU6ZM0d69e/XFF19o6tSpkvSrs/zwww8aPHiwPv30Ux04cEDr1q3T5s2bFRcXd+m/0QDKjDM7ADyqZs2aio+P16uvvqqvv/5aZ8+eVUxMjB555BH9+c9/lsPh0Mcff6yRI0fqoYce0rfffqvIyEjddNNNioiIkCTde++9+vrrrzV8+HAVFRUpOTlZjz32mFasWOH6OgMGDNC2bdvUr18/VatWTcOGDTvv7Mu8efM0duxYPfnkkzp8+LDq1aun6667TnfccUeZ5+nfv7+Kior06quv6qmnnlK9evX0+9//XpJ+dZbAwEAdO3ZM/fr1U15enurVq6e7775bzz//vAd+pwGUlcP8/KI3APih5557TosXL9aXX37p6ygAqhguYwEAAKtRdgAAgNW4jAUAAKzGmR0AAGA1yg4AALAaZQcAAFiNsgMAAKxG2QEAAFaj7AAAAKtRdgAAgNUoOwAAwGr/C9ypwY/rxNgwAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Per sequence length \n",
    "DNA.length_plot(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "831843de-0f07-4ad0-800b-e5c67c3fd541",
   "metadata": {},
   "source": [
    "### 1.19. Boxplot showing overall length distribution\n",
    "```\n",
    ">>> DNA.length_distribution_plot(fasta_records)\n",
    "```\n",
    "**Output:** generate boxplot showing overall length distribution across all the sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "86e5e0a7-0850-498e-825b-ddc5b90b74d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot showing length distribution\n",
    "DNA.length_distribution_plot(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4231204d-fe76-4ba4-a7fa-672706713408",
   "metadata": {},
   "source": [
    "### 1.20. Overall k-mer abundance pattern\n",
    "```\n",
    "# default k-mer size (k=3)\n",
    ">>> DNA.combined_kmer_abundance_plot(fasta_records)\n",
    "\n",
    "# kmer size (k=7)\n",
    ">>> DNA.combined_kmer_abundance_plot(fasta_records, 7)\n",
    "```\n",
    "**Output**: generate combined k-mer abundance plot (histogram) for all the sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4bf24a8e-e119-4477-8a38-cf11979fd7c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Combined kmers distribution\n",
    "DNA.combined_kmer_abundance_plot(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34430724-11a0-446e-b1b7-ede5f4b17de1",
   "metadata": {},
   "source": [
    "### 1.21. Converting sequence object to feature matrix\n",
    "```\n",
    ">>> from seqplotter_analysis import seq2feature\n",
    ">>> feature_matrix = seq2feature(fasta_records) # with default options (k=3, min_sample=1)\n",
    "\n",
    ">>> feature_matrix = seq2feature(fasta_records, 6, 2) # with (k=6, min_sample=2)\n",
    "```\n",
    "**Options:**\n",
    "- k: kmer size\n",
    "- min_sample: threshold of considering a k-mer as feature\n",
    "\n",
    "**Output:** returns a dict object containing following keys (\"matrix\", \"sample\", \"kmers\"), where \"kmers\" are columns and \"sample\" are rows\n",
    "\n",
    "*< dict{\"matrix\": numpy_array, \"sample\": seqID_list, \"kmers\": kmers_list} >*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a4dac9d1-9f99-4ee4-b7d6-f7eb338a8781",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CTT</th>\n",
       "      <th>TTT</th>\n",
       "      <th>TTC</th>\n",
       "      <th>TCT</th>\n",
       "      <th>TTA</th>\n",
       "      <th>TAG</th>\n",
       "      <th>AGT</th>\n",
       "      <th>GTG</th>\n",
       "      <th>TGT</th>\n",
       "      <th>GTT</th>\n",
       "      <th>...</th>\n",
       "      <th>GAA</th>\n",
       "      <th>AGA</th>\n",
       "      <th>GAG</th>\n",
       "      <th>GCA</th>\n",
       "      <th>AGG</th>\n",
       "      <th>GCT</th>\n",
       "      <th>CTC</th>\n",
       "      <th>CTG</th>\n",
       "      <th>TGC</th>\n",
       "      <th>ACG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>seq1</th>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seq2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seq3</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seq4</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seq5</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seq6</th>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seq7</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seq8</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seq9</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CTT  TTT  TTC  TCT  TTA  TAG  AGT  GTG  TGT  GTT  ...  GAA  AGA  GAG  \\\n",
       "seq1    4   10    3    2    5    2    4    2    3    3  ...    0    0    0   \n",
       "seq2    2    5    3    1    4    1    2    1    3    3  ...    0    0    0   \n",
       "seq3    2    5    3    1    4    1    3    1    3    3  ...    0    0    0   \n",
       "seq4    2    5    2    1    2    1    2    1    2    2  ...    1    0    0   \n",
       "seq5    2    0    0    2    2    3    3    2    2    0  ...    7    4    3   \n",
       "seq6    3   10    4   12    2    1    2    0    1    0  ...    0    0    0   \n",
       "seq7    1    3    0    1    0    1    0    0    1    0  ...    2    2    2   \n",
       "seq8    1    0    2    2    0    0    0    0    2    0  ...    0    2    3   \n",
       "seq9    0    2    0    0    0    0    0    0    0    0  ...    0    0    0   \n",
       "\n",
       "      GCA  AGG  GCT  CTC  CTG  TGC  ACG  \n",
       "seq1    0    0    0    0    0    0    0  \n",
       "seq2    0    0    0    0    0    0    0  \n",
       "seq3    0    0    0    0    0    0    0  \n",
       "seq4    0    0    0    0    0    0    0  \n",
       "seq5    1    3    3    3    2    0    0  \n",
       "seq6    1    0    0    8    0    0    0  \n",
       "seq7    1    1    0    0    1    1    1  \n",
       "seq8    2    0    2    3    3    1    1  \n",
       "seq9    0    0    0    0    0    0    0  \n",
       "\n",
       "[9 rows x 54 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing module\n",
    "from seqplotter_analysis import seq2feature\n",
    "\n",
    "# Converting sequence to feature matrix\n",
    "feature_matrix = seq2feature(data)\n",
    "\n",
    "# Visualizing feature matrix\n",
    "import pandas as pd\n",
    "pd.DataFrame(feature_matrix[\"matrix\"], index=feature_matrix[\"sample\"], columns=feature_matrix[\"kmers\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97b2d2e3-47d7-448a-9e4f-0b3420cb4c55",
   "metadata": {},
   "source": [
    "### 1.22. Principal component analysis\n",
    "```\n",
    ">>> from seqplotter_analysis import PCA\n",
    ">>> pca_matrix = PCA(feature_matrix) # return pca matrix with first 2 PCs\n",
    "\n",
    ">>> pca_matrix = PCA(feature_matrix, 3) # return pca matrix with first 3 PCs\n",
    "```\n",
    "**Output:** returns a dict object containing following keys (\"matrix\", \"sample\", \"components\"), where \"components\" are columns and \"sample\" are rows\n",
    "\n",
    "*< dict{\"matrix\": numpy_array, \"sample\": seqID_list, \"components\": PCA_components} >*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b2483eff-fd58-428b-99d4-15626cf2a433",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC-1</th>\n",
       "      <th>PC-2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>seq1</th>\n",
       "      <td>9.629055</td>\n",
       "      <td>2.243708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seq2</th>\n",
       "      <td>5.947428</td>\n",
       "      <td>3.774627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seq3</th>\n",
       "      <td>6.225862</td>\n",
       "      <td>4.230590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seq4</th>\n",
       "      <td>0.776820</td>\n",
       "      <td>1.919865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seq5</th>\n",
       "      <td>-9.639273</td>\n",
       "      <td>0.927017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seq6</th>\n",
       "      <td>3.503045</td>\n",
       "      <td>-15.066248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seq7</th>\n",
       "      <td>-4.161328</td>\n",
       "      <td>0.626651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seq8</th>\n",
       "      <td>-7.607692</td>\n",
       "      <td>0.437185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seq9</th>\n",
       "      <td>-4.673917</td>\n",
       "      <td>0.906603</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          PC-1       PC-2\n",
       "seq1  9.629055   2.243708\n",
       "seq2  5.947428   3.774627\n",
       "seq3  6.225862   4.230590\n",
       "seq4  0.776820   1.919865\n",
       "seq5 -9.639273   0.927017\n",
       "seq6  3.503045 -15.066248\n",
       "seq7 -4.161328   0.626651\n",
       "seq8 -7.607692   0.437185\n",
       "seq9 -4.673917   0.906603"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing module\n",
    "from seqplotter_analysis import PCA\n",
    "\n",
    "# Performing PCA\n",
    "pca_matrix = PCA(feature_matrix)\n",
    "\n",
    "# Visualizing PCA matrix\n",
    "import pandas as pd\n",
    "pd.DataFrame(pca_matrix[\"matrix\"], index=pca_matrix[\"sample\"], columns=pca_matrix[\"components\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e905078c-f50b-49fe-80c6-5bf4225510de",
   "metadata": {},
   "source": [
    "### 1.23. Plot scatter plot using PCs\n",
    "```\n",
    ">>> from seqplotter_analysis import plot_pca\n",
    "\n",
    "# Sequence IDs will be treated as class label (default)\n",
    ">>> plot_pca(pca_matrix)\n",
    "\n",
    "# Providing class labels as list\n",
    ">>> labels = [3, 3, 3, 2, 1, 4, 1, 1, 1]\n",
    ">>> plot_pca(pca_matrix, labels)\n",
    "```\n",
    "**Output:** generate PCA plot (2D scatter plot using first two components i.e. PC-1 and PC-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "95cbab11-e239-4f51-82dd-22103a93c4f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Importing module\n",
    "from seqplotter_analysis import plot_pca\n",
    "\n",
    "# Plotting 2D PCA plot\n",
    "labels = [3, 3, 3, 2, 1, 4, 1, 1, 1]\n",
    "plot_pca(pca_matrix, labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b99054b7-ea2d-44a2-ab8a-db6b61919bb1",
   "metadata": {},
   "source": [
    "### 1.24. Create sequence logo\n",
    "```\n",
    ">>> from seqplotter_analysis import seq_logo\n",
    "\n",
    "# Reading motif sequences file\n",
    ">>> logo_data = DNA.read_fasta(\"motif_seq.fasta\")\n",
    "\n",
    "# Generate sequence logo\n",
    ">>> seq_logo(logo_data, seq_type)\n",
    "```\n",
    "**Output:** generate DNA sequence logo using motif sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5ca1d246-0223-41fe-878c-8cb2d1fb0e1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Importing module\n",
    "from seqplotter_analysis import seq_logo\n",
    "\n",
    "# Reading motif sequences file\n",
    "data = DNA.read_fasta(\"dna_motif_seq.fasta\")\n",
    "\n",
    "# Generate sequence logo\n",
    "seq_logo(data, \"DNA\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63ae3225-6451-4867-8ae3-cb566b00943c",
   "metadata": {},
   "source": [
    "### 1.25 Generate random nucleotide sequence\n",
    "```\n",
    "# Generating random sequences with given length and GC content \n",
    ">>> DNA.generate_random_seq(lenght, GC_frequency, count)\n",
    "```\n",
    "**Output:** append random sequences object of given length and GC content to the DNA object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1a3faf2f-34d5-4f8d-ba23-7e6964e8d1bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seqID\tseq\n",
      "random_seq1:\t[ATACAAATCACAAGTATTCAATACAATAGACGTATGTAAGACTGATTGTTAATTCAGTAAATAGACGTGA]\n",
      "random_seq2:\t[TTCTTGTGTCATTATTTACACAGGAAATATGTAACCCAACACATAGATTTACAAACTATACTGGTAACAT]\n",
      "random_seq3:\t[CGTTTAGTGTTGCATGATGCGATCATTACATTTCTTATACAGTTGATATCAACAAATGCTACACTGACAA]\n",
      "random_seq4:\t[TAATGAGGTGAGCTTTAGATATAAAGGCTTCTTATAGCATACCATTGCAAAAGATCGCCATTATATAATG]\n",
      "random_seq5:\t[TTCATTCAGAAATCAATATACAAACATTATTGAAATACCTCATTTTGTGTTTTGAAATCTAGTCAGGTAT]\n"
     ]
    }
   ],
   "source": [
    "# Clearing records \n",
    "DNA.clear()\n",
    "\n",
    "# Generating 10 random sequences with 70 bp length and 30% GC content \n",
    "new_data = DNA.generate_random_seq(70, 0.3, 10)\n",
    "\n",
    "# Printing first 5 random sequences\n",
    "DNA.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa4decc1-6432-48bc-ab65-3215ae1397fa",
   "metadata": {},
   "source": [
    "# 2. Working with Protein sequences"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b97cd7a1-57a8-4391-93ae-2de5f89d3aa7",
   "metadata": {},
   "source": [
    "### 2.1. Importing PROT module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c4772da7-a480-4ddf-93ad-dc5119e537dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing module\n",
    "from seqplotter.prot import PROT"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b2f54ba-1c4a-4b01-8b25-650e19977c59",
   "metadata": {},
   "source": [
    "### 2.2 Creating PROT object\n",
    "```\n",
    ">>> prot_seq1 = PROT(\"seqID\", \"protein_sequence\")\n",
    "```\n",
    "**Output:** retruns a DNA object with seqid and seq pair"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "606d4a4f-f545-4086-99d8-163ec57d3fb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating Protein object with seqID and sequence pair\n",
    "seq1 = PROT(\"seq1\", \"VDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAV\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "847de826-9c72-417e-80fe-17b7cb7be3fc",
   "metadata": {},
   "source": [
    "### 2.3. Sequence length\n",
    "```\n",
    ">>> prot_seq1.length()\n",
    "```\n",
    "**Output:** returns sequence length (int) in bp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "cf8d4323-cb0a-49ec-b264-fc21a117ffd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print sequence length\n",
    "seq1.length()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba1e2d6e-a6ae-48ae-bf69-bfb7c1c49bdd",
   "metadata": {},
   "source": [
    "### 2.4. Sequence composition\n",
    "```\n",
    ">>> prot_seq1.comp()\n",
    "```\n",
    "**Output:** returns a dict object with sequence composition i.e. count of each amino acids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e19c1b1b-50da-42c4-a508-451a47d59fb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A': 5.714285714285714,\n",
       " 'R': 5.166051660516605,\n",
       " 'N': 0.0,\n",
       " 'D': 7.163266076755573,\n",
       " 'C': 0.0,\n",
       " 'Q': 2.1718543403148107,\n",
       " 'E': 6.353851345091472,\n",
       " 'G': 7.909936022986237,\n",
       " 'H': 0.0,\n",
       " 'I': 0.0,\n",
       " 'L': 7.342245635482005,\n",
       " 'K': 0.0,\n",
       " 'M': 0.0,\n",
       " 'F': 5.188382310331203,\n",
       " 'P': 3.3327849177003355,\n",
       " 'S': 3.260373880823663,\n",
       " 'T': 3.1947334484658114,\n",
       " 'W': 1.567453020447904,\n",
       " 'Y': 1.5536341292308162,\n",
       " 'V': 7.701922950367426}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print sequence composition\n",
    "seq1.comp()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a01dbd9-2bd6-4fd3-80e9-2afc9f7e2282",
   "metadata": {},
   "source": [
    "### 2.5. Sequence composition plot\n",
    "```\n",
    ">>> prot_comp = prot_seq1.comp()\n",
    ">>> PROT.barplot(prot_comp)\n",
    "```\n",
    "**Output:** generate barplot showing sequence composition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "5329adfa-ab60-4f9c-bf1f-94ebf3f6c30c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot sequence composition\n",
    "comp = seq1.comp()\n",
    "PROT.barplot(comp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33ea0cbf-dead-4b7e-8ce4-8e8950540619",
   "metadata": {},
   "source": [
    "### 2.6. Molecular weight in KDa\n",
    "```\n",
    ">>> prot_seq1.mol_weight()\n",
    "```\n",
    "**Output:** return molecular weight (int) of a protein sequence in KDa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "bf09ff7e-cd03-45e0-a0e9-6e6a42195561",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.87"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# molecular weight\n",
    "seq1.mol_weight()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2371a2a3-d306-4406-90f4-f03cc814aa93",
   "metadata": {},
   "source": [
    "### 2.7. Sequence slicing\n",
    "```\n",
    ">>> # returns amino acid for the given position\n",
    ">>> prot_seq1.slice(pos)\n",
    "\n",
    ">>> # returns amino acids between start to end position\n",
    ">>> prot_seq1.slice(start_pos, end_pos)\n",
    "```\n",
    "**Output:** returns a string that contain the sliced sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7598c3b5-15d4-43cd-9e6d-4be82c08fdac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'G'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# slice particular position\n",
    "seq1.slice(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "40796ab6-0353-40cd-be91-f727d91c9460",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'VGGEA'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# slice with start and end index\n",
    "seq1.slice(4,8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77eeddb6-de00-45da-967a-8f39ec2f5598",
   "metadata": {},
   "source": [
    "### 2.8. Reading FASTA file (Protein sequence)\n",
    "```\n",
    ">>> fasta_records = PROT.read_fasta(\"/path/to/fasta/file\")\n",
    "```\n",
    "**Output:** returns a list containing PROT object(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7ec5bd52-647c-4cec-9648-26d045eaeb4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# reading a FASTA file\n",
    "data = PROT.read_fasta(\"prot_seq.fasta\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19252936-d8f9-4872-9680-f48e42b058ab",
   "metadata": {},
   "source": [
    "### 2.9. Using head() and tail() functions\n",
    "```\n",
    ">>> # return summary of first 5 sequence (default)\n",
    ">>> PROT.head()\n",
    "\n",
    ">>> # return summary of first 10 sequence\n",
    ">>> PROT.head(10)\n",
    "\n",
    ">>> # return summary of last 5 sequence (default)\n",
    ">>> PROT.tail()\n",
    "\n",
    ">>> # return summary of last 10 sequence\n",
    ">>> PROT.tail(10)\n",
    "```\n",
    "**Output:** print head and tail summary of parsed FASTA file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "df8610a6-9923-482f-b755-e23cb3d1fe85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seqID\tseq\n",
      "seq1:\t[MVHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLG]\n",
      "seq2:\t[MLPFWKRLLYAAVIAGALVGADAQFWKTAGTAGSIQDSVKHYNRNEPKFPIDDSYDIVDSAGVARGDLPP]\n",
      "seq3:\t[TGQKGDRGEPGLNGLPGNPGQKGEPGRAGATGKPGLLGPPGPPGGGRGTPGPPGPKGPRGYVGAPGPQGL]\n",
      "seq4:\t[LPGATGEPGKPALCDLSLIEPLKGDKGYPGAPGAKGVQGFKGAEGLPGIPGPKGEFGFKGEKGLSGAPGN]\n"
     ]
    }
   ],
   "source": [
    "# Head\n",
    "PROT.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "952619d2-f7b8-41a6-9ebb-a167482eb01d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seqID\tseq\n",
      "seq1:\t[MVHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLG]\n",
      "seq2:\t[MLPFWKRLLYAAVIAGALVGADAQFWKTAGTAGSIQDSVKHYNRNEPKFPIDDSYDIVDSAGVARGDLPP]\n",
      "seq3:\t[TGQKGDRGEPGLNGLPGNPGQKGEPGRAGATGKPGLLGPPGPPGGGRGTPGPPGPKGPRGYVGAPGPQGL]\n",
      "seq4:\t[LPGATGEPGKPALCDLSLIEPLKGDKGYPGAPGAKGVQGFKGAEGLPGIPGPKGEFGFKGEKGLSGAPGN]\n"
     ]
    }
   ],
   "source": [
    "# Tail\n",
    "PROT.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53eb52d6-41ee-4326-b14f-6d0b79bbc7d0",
   "metadata": {},
   "source": [
    "### 2.10. Count records\n",
    "```\n",
    ">>> PROT.count(fasta_records)\n",
    "```\n",
    "**Output:** returns sequence count (int) i.e. number of records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4ce87bc7-22f9-4ef7-927a-1e7742c2fe62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sequence count\n",
    "PROT.count(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d12883f3-49de-479c-9213-7230b8b8070a",
   "metadata": {},
   "source": [
    "### 2.11. Find record\n",
    "```\n",
    ">>> PROT.where(seqid=\"seq2\")\n",
    "```\n",
    "**Output:** returns sequence for the given sequenceID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "68a92963-a9ce-44c3-afdb-a9ea598f825d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'MLPFWKRLLYAAVIAGALVGADAQFWKTAGTAGSIQDSVKHYNRNEPKFPIDDSYDIVDSAGVARGDLPPKNCTAGYAGCVPKCIAEKGNRGLPGPLGPTGLKGEMGFPGMEGPSGDKGQKGDPGPYGQRGDKGERGSPGLHGQAGVPGVQGPAGNPGAPGINGKDGCDGQDGIPGLEGLSGMPGPRGYAGQLGSKGEKGEPAKENGDYA'"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find sequence\n",
    "PROT.where(seqid=\"seq2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d8995d7-cff9-4e83-8c69-d357f01a31f1",
   "metadata": {},
   "source": [
    "### 2.12. Amino acids distribution plot\n",
    "```\n",
    ">>> PROT.aa_distribution_plot(fasta_records)\n",
    "```\n",
    "**Output**: generate boxplot showing overall amino acids distribution across all the sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "bd587a0d-b77d-4278-8e89-b10172e04de8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot aa distribution plot\n",
    "PROT.aa_distribution_plot(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cae988b8-5dd2-441c-85ae-ecc53495e86b",
   "metadata": {},
   "source": [
    "### 2.13. Stacked barplot showing per sequence amino acid composition\n",
    "```\n",
    ">>> PROT.per_sequence_comp(fasta_records)\n",
    "```\n",
    "**Output:** generate stacked barplot showing per sequence amino acids composition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c7482ae3-2891-4448-9be2-513d1f67517a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Per sequence nucleotides composition\n",
    "PROT.per_sequence_comp(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "069f9cf4-52fd-467c-94f9-ef8226ebe30d",
   "metadata": {},
   "source": [
    "### 2.14. Barplot showing per sequence length\n",
    "```\n",
    "PROT.length_plot(fasta_records)\n",
    "```\n",
    "**Output:** generate barplot showing per sequence length distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b63a4c83-51ab-46e8-8cb9-cdd5c7b7174b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Per sequence length \n",
    "PROT.length_plot(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb7e414b-b00a-41ca-9de8-d8abf02efa3d",
   "metadata": {},
   "source": [
    "### 2.15. Boxplot showing overall length distribution\n",
    "```\n",
    ">>> PROT.length_distribution_plot(fasta_records)\n",
    "```\n",
    "**Output:** generate boxplot showing overall length distribution across all the sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "7b364230-ad31-41a0-ac97-eb030ff373e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PROT.length_distribution_plot(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "426672e4-e973-42e3-96a7-5bfb59d91b7b",
   "metadata": {},
   "source": [
    "### 2.16. Converting sequence object to feature matrix\n",
    "```\n",
    ">>> from seqplotter_analysis import seq2feature\n",
    ">>> feature_matrix = seq2feature(fasta_records) # with default options (k=3, min_sample=1)\n",
    "\n",
    ">>> feature_matrix = seq2feature(fasta_records, 6, 2) # with (k=6, min_sample=2)\n",
    "```\n",
    "**Options:**\n",
    "- k: kmer size\n",
    "- min_sample: threshold of considering a k-mer as feature\n",
    "\n",
    "**Output:** returns a dict object containing following keys (\"matrix\", \"sample\", \"kmers\"), where \"kmers\" are columns and \"sample\" are rows\n",
    "\n",
    "*< dict{\"matrix\": numpy_array, \"sample\": seqID_list, \"kmers\": kmers_list} >*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a2ed333b-4231-407b-b23d-ff661275e089",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RLL</th>\n",
       "      <th>GDL</th>\n",
       "      <th>DLS</th>\n",
       "      <th>GNP</th>\n",
       "      <th>GKK</th>\n",
       "      <th>DGL</th>\n",
       "      <th>LKG</th>\n",
       "      <th>KGT</th>\n",
       "      <th>FWK</th>\n",
       "      <th>AGA</th>\n",
       "      <th>...</th>\n",
       "      <th>DAG</th>\n",
       "      <th>PGY</th>\n",
       "      <th>GYP</th>\n",
       "      <th>YPG</th>\n",
       "      <th>GAK</th>\n",
       "      <th>AKG</th>\n",
       "      <th>GFK</th>\n",
       "      <th>FKG</th>\n",
       "      <th>GRD</th>\n",
       "      <th>RDG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>seq1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seq2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seq3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seq4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 84 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      RLL  GDL  DLS  GNP  GKK  DGL  LKG  KGT  FWK  AGA  ...  DAG  PGY  GYP  \\\n",
       "seq1    1    1    1    1    1    1    1    1    0    0  ...    0    0    0   \n",
       "seq2    1    1    0    1    0    0    1    0    2    1  ...    0    0    0   \n",
       "seq3    0    0    0    1    1    1    1    1    0    1  ...    2    2    1   \n",
       "seq4    0    0    1    0    0    0    1    0    0    0  ...    0    0    2   \n",
       "\n",
       "      YPG  GAK  AKG  GFK  FKG  GRD  RDG  \n",
       "seq1    0    0    0    0    0    0    0  \n",
       "seq2    0    0    0    0    0    0    0  \n",
       "seq3    1    0    0    0    0    0    0  \n",
       "seq4    2    2    2    2    2    2    2  \n",
       "\n",
       "[4 rows x 84 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing module\n",
    "from seqplotter_analysis import seq2feature\n",
    "\n",
    "# Converting sequence to feature matrix\n",
    "feature_matrix = seq2feature(data)\n",
    "\n",
    "# Visualizing feature matrix\n",
    "import pandas as pd\n",
    "pd.DataFrame(feature_matrix[\"matrix\"], index=feature_matrix[\"sample\"], columns=feature_matrix[\"kmers\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1d494d3-86d6-4afe-ae9f-3e4e174cfefa",
   "metadata": {},
   "source": [
    "### 2.17 Principal component analysis\n",
    "```\n",
    ">>> from seqplotter_analysis import PCA\n",
    ">>> pca_matrix = PCA(feature_matrix) # return pca matrix with first 2 PCs\n",
    "\n",
    ">>> pca_matrix = PCA(feature_matrix, 3) # return pca matrix with first 3 PCs\n",
    "```\n",
    "**Output:** returns a dict object containing following keys (\"matrix\", \"sample\", \"components\"), where \"components\" are columns and \"sample\" are rows\n",
    "\n",
    "*< dict{\"matrix\": numpy_array, \"sample\": seqID_list, \"components\": PCA_components} >*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "93250b7d-cd2b-4246-a04b-988fbc820b38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC-1</th>\n",
       "      <th>PC-2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>seq1</th>\n",
       "      <td>5.069106</td>\n",
       "      <td>-3.616282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seq2</th>\n",
       "      <td>2.496495</td>\n",
       "      <td>-2.811089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seq3</th>\n",
       "      <td>-11.398262</td>\n",
       "      <td>-0.046961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seq4</th>\n",
       "      <td>3.832661</td>\n",
       "      <td>6.474332</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           PC-1      PC-2\n",
       "seq1   5.069106 -3.616282\n",
       "seq2   2.496495 -2.811089\n",
       "seq3 -11.398262 -0.046961\n",
       "seq4   3.832661  6.474332"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing module\n",
    "from seqplotter_analysis import PCA\n",
    "\n",
    "# Performing PCA\n",
    "pca_matrix = PCA(feature_matrix)\n",
    "\n",
    "# Visualizing PCA matrix\n",
    "import pandas as pd\n",
    "pd.DataFrame(pca_matrix[\"matrix\"], index=pca_matrix[\"sample\"], columns=pca_matrix[\"components\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb7d3894-a20a-4571-8855-667a652534d7",
   "metadata": {},
   "source": [
    "### 2.18. Plot scatter plot using PCs\n",
    "```\n",
    ">>> from seqplotter_analysis import plot_pca\n",
    "\n",
    "# Sequence IDs will be treated as class label (default)\n",
    ">>> plot_pca(pca_matrix)\n",
    "\n",
    "# Providing class labels as list\n",
    ">>> labels = [3, 3, 3, 2, 1, 4, 1, 1, 1]\n",
    ">>> plot_pca(pca_matrix, labels)\n",
    "```\n",
    "**Output:** generate PCA plot (2D scatter plot using first two components i.e. PC-1 and PC-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c21e0574-83f9-408d-af0f-464ae6e1a704",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Importing module\n",
    "from seqplotter_analysis import plot_pca\n",
    "\n",
    "# Plotting 2D PCA plot\n",
    "labels = [1, 1, 2, 3]\n",
    "plot_pca(pca_matrix, labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "137b2b36-24c9-4bc5-8aeb-21ae6042cc7e",
   "metadata": {},
   "source": [
    "### 2.19. Create sequence logo\n",
    "```\n",
    ">>> from seqplotter_analysis import seq_logo\n",
    "\n",
    "# Reading motif sequences file\n",
    ">>> logo_data = PROT.read_fasta(\"motif_seq.fasta\")\n",
    "\n",
    "# Generate sequence logo\n",
    ">>> seq_logo(logo_data, seq_type)\n",
    "```\n",
    "**Output:** generate protein sequence logo using motif sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "698a6d42-7476-4a5e-8865-3610a05211d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Importing module\n",
    "from seqplotter_analysis import seq_logo\n",
    "\n",
    "# Reading motif sequences file\n",
    "data = PROT.read_fasta(\"prot_motif_seq.fasta\")\n",
    "\n",
    "# Generate sequence logo\n",
    "seq_logo(data, \"PROT\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf22b642-8d0a-4c04-bd5d-26c197fb8996",
   "metadata": {},
   "source": [
    "### 2.20. Generate hydrophobicity plot\n",
    "```\n",
    ">>> from seqplotter_analysis import hydrophobicity_plot\n",
    "\n",
    "# Amino acid sequence for seq2 seqID\n",
    ">>> seq = PROT.where(seqid=\"seq2\")\n",
    "\n",
    "# Create hydrophobicity plot\n",
    ">>> hydrophobicity_plot(seq, window_size)\n",
    "```\n",
    "**Output:** generate hydrophobicity plot for a given protein sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b8d7be86-ad6d-470b-9805-70f3fe23a3ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from seqplotter_analysis import hydrophobicity_plot\n",
    "\n",
    "# Amino acid sequence for seq2 seqID\n",
    "seq = PROT.where(seqid=\"seq2\")\n",
    "\n",
    "# Create hydrophobicity plot\n",
    "hydrophobicity_plot(seq, 9)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}