{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"/home/sg666/MDpLM/data/uniref50/100k_seqs\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(path + \"/train.csv\")\n",
    "test = pd.read_csv(path + \"/test.csv\")\n",
    "val = pd.read_csv(path + '/val.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_residues = ['A','R','N','D','C','E','Q','G','H','I','L','K','M','F','P','S','T','W','Y','V']\n",
    "\n",
    "for df in [train, test, val]:\n",
    "    df['Length'] = df['Sequence'].str.len()\n",
    "\n",
    "    for residue in valid_residues:\n",
    "        df[residue] = 0\n",
    "\n",
    "    for idx, row in df.iterrows():\n",
    "        sequence = row['Sequence']\n",
    "\n",
    "        for residue in valid_residues:\n",
    "            df.at[idx, residue] = sequence.count(residue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "amino_acid_frequencies = {}\n",
    "\n",
    "datasets = {'Train': train, 'Test': test, 'Val': val}\n",
    "\n",
    "\n",
    "for name, df in datasets.items():\n",
    "    # Count total occurrences of each amino acid in each dataset\n",
    "    amino_acid_frequencies[name] = df[valid_residues].sum() / df['Length'].sum()\n",
    "\n",
    "# Convert frequencies to a dataframe for easier manipulation\n",
    "freq_df = pd.DataFrame(amino_acid_frequencies)\n",
    "\n",
    "# Plot the histogram comparing amino acid frequencies\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "# Set bar width\n",
    "bar_width = 0.2\n",
    "\n",
    "# Generate positions for the bars\n",
    "amino_acids = list(valid_residues)\n",
    "x = np.arange(len(amino_acids))  # positions for the amino acids\n",
    "\n",
    "# Plot the bars for each dataset with an offset\n",
    "plt.bar(x - bar_width, freq_df['Train'], width=bar_width, label='Train', align='center')\n",
    "plt.bar(x, freq_df['Test'], width=bar_width, label='Test', align='center')\n",
    "plt.bar(x + bar_width, freq_df['Val'], width=bar_width, label='Val', align='center')\n",
    "\n",
    "plt.xticks(x, amino_acids)\n",
    "\n",
    "plt.title('UniRef50 (100k Sequences) - Train, Test, and Val Residue Frequencies')\n",
    "plt.xlabel('Amino Acid')\n",
    "plt.ylabel('Frequency')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2. Sequence lengths histogram\n",
    "plt.figure(figsize=(12, 6))\n",
    "for name, df in datasets.items():\n",
    "    plt.hist(df['Length'], bins=30, alpha=0.7, label=name, density=True)\n",
    "\n",
    "plt.title('UniRef50 (100k Sequences) - Train, Test, and Val Sequence Lengths')\n",
    "plt.xlabel('Sequence Length')\n",
    "plt.ylabel('Frequency')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}