anaysis_len.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Count the length of incomplete sequences and anaysis\n",
    "## Generate it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = []\n",
    "with open(\"MPoxBR(incomplete).fasta\", \"r\") as f:\n",
    "    this_len = 0\n",
    "    for line in f:\n",
    "        if line[0] == '>':\n",
    "            if this_len > 0: data.append(this_len)\n",
    "            this_len = 0\n",
    "            continue\n",
    "        else:\n",
    "            this_len += len(line.strip())\n",
    "    if this_len > 0: data.append(this_len)\n",
    "with open(\"seq_incomplete_len.txt\", \"w\") as g:\n",
    "    for i in sorted(data):\n",
    "        print(i, file = g)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"seq_incomplete_len.txt\", \"r\") as f:\n",
    "    data = [int(x) for x in f]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Draw it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x2500 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = [0, 0, 0, 0]\n",
    "y = [1, 2, 3, 4]\n",
    "for item in data:\n",
    "    if item < 2500:\n",
    "        x[0] += 1\n",
    "    elif item < 50000:\n",
    "        x[1] += 1\n",
    "    elif item < 150000:\n",
    "        x[2] += 1\n",
    "    else:\n",
    "        x[3] += 1\n",
    "\n",
    "plt.bar(y, x)\n",
    "plt.yscale('log')\n",
    "plt.title('linear')\n",
    "plt.grid(True)\n",
    "for a, b in zip(y, x):\n",
    "    plt.text(a, b, b, ha='center', va='bottom', fontsize=20)\n",
    "\n",
    "plt.figure(figsize=(20, 25))\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.11.3"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}