nanoparticlesims/cnapQuenchMelting.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "58765919",
   "metadata": {},
   "source": [
    "Define pySnow location and allow python to look it up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "48a9927a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "from pathlib import Path\n",
    "# 1. Define the absolute path to the directory containing the 'snow' folder\n",
    "# (Replace this with the actual path where your 'snow' directory lives)\n",
    "local_packages_path = Path(\"/home/tmlkyza/Documents/Universita/Master/nanopants/lcm/pySNOW\")\n",
    "\n",
    "# 2. Convert to an absolute string and inject it into Python's search path\n",
    "sys.path.append(str(local_packages_path.resolve()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0afb3497",
   "metadata": {},
   "source": [
    "Get all the sims dir and look at all the runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "31b9d741",
   "metadata": {},
   "outputs": [],
   "source": [
    "script_dir= Path('.')\n",
    "\n",
    "# Find all directories in the current folder ending with 'Sims'\n",
    "sims_root = [\n",
    "    p for p in script_dir.iterdir() \n",
    "    if p.is_dir() and p.name.endswith('quenchMeltingSims')\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "49bf3ba8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract the real, absolute path of every subfolder inside the Sims directories\n",
    "sims_runs = []\n",
    "for sim in sims_root:\n",
    "    sims_runs.append([\n",
    "        run.resolve() for run in sim.iterdir() \n",
    "        if run.is_dir()\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "78fa3bb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n",
      "[]\n"
     ]
    }
   ],
   "source": [
    "# Safely print the true absolute paths of every single item inside those runs\n",
    "movies = []\n",
    "outs = []\n",
    "for root in sims_runs:\n",
    "    for run in root:\n",
    "        for item in run.glob('*.xyz'):\n",
    "            if item.name == \"movie.xyz\":\n",
    "                movies.append(item)\n",
    "        # 2. Grab all out[number].xyz files in this folder\n",
    "        out_files = list(run.glob('out*.xyz'))\n",
    "        \n",
    "        if out_files:\n",
    "            # Find the file with the maximum numeric value\n",
    "            # item.stem gives us 'out11', [3:] strips 'out' to leave '11', int() makes it 11\n",
    "            highest_out = max(out_files, key=lambda item: int(item.stem[3:]))\n",
    "            outs.append(highest_out)\n",
    "\n",
    "print(movies)\n",
    "print(outs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "680746c9",
   "metadata": {},
   "source": [
    "Start by computing the coordination number and appending it to xyz, such that we can read it with ovito. The output dir is cns/[processType]/run_natoms.xyz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ddc8d398",
   "metadata": {},
   "outputs": [],
   "source": [
    "#define some useful functions, you can ignore this\n",
    "\n",
    "def getProcess(file):\n",
    "    return file.parent.parent.name\n",
    "\n",
    "def FileOut(file, subDirName, filename):\n",
    "    run_folder = file.parent       # This gives 'run_14'\n",
    "    process_folder = run_folder.parent  # This gives 'growthSims'\n",
    "\n",
    "    process_type = process_folder.name  # Extracts the text string 'growthSims'\n",
    "    run_name = run_folder.name          # Extracts the text string 'run_14'\n",
    "\n",
    "    output_dir = script_dir / subDirName / process_type\n",
    "    output_dir.mkdir(parents=True, exist_ok=True) # Automatically creates the cns/ and [processtype]/ folders if they don't exist\n",
    "\n",
    "    #return output_dir / f\"{run_name}.xyz\"\n",
    "    return output_dir / filename\n",
    "\n",
    "def writeDict(data_dict, file_path):\n",
    "    field_names = list(data_dict.keys())\n",
    "    columns = [data_dict[key] for key in field_names]\n",
    "    with open(file_path, \"w\") as outfile:\n",
    "        header_line = \"# \" + \" \".join(field_names) + \"\\n\"\n",
    "        outfile.write(header_line)\n",
    "        for row in zip(*columns):\n",
    "            row_line = \" \".join(str(val) for val in row) + \"\\n\"\n",
    "            outfile.write(row_line)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a7abba6",
   "metadata": {},
   "source": [
    "Generate PDDF RDF in .dat files and append CNS and CNAPS to existing .xyz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "863a210c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from snow.io.xyz import read_xyz, write_xyz\n",
    "from snow.descriptors.coordination import coordination_number\n",
    "from snow.descriptors import cna\n",
    "from snow.descriptors.distributions import pddf_calculator, com_rdf_calculator\n",
    "from collections import Counter\n",
    "\n",
    "alat   = 4.087077141 # a lattice for Ag also checksout with https://reference.wolfram.com/language/ref/ElementData.html\n",
    "cutoff = 0.85*alat\n",
    "\n",
    "for file in outs:\n",
    "    el, coords = read_xyz(file)\n",
    "    cns   = coordination_number(coords, cutoff)\n",
    "    cnaps = cna.cnap_peratom(coords, cutoff)\n",
    "\n",
    "    write_xyz(FileOut(file, \"GCN\", f\"{file.parent.name}.xyz\"), el, coords, additional_data={\"cns\":cns,\"cnaps\":cnaps})\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "55a63173",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1400x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# 1. Your specified directory path\n",
    "target_dir = Path(\n",
    "    \"/home/tmlkyza/Documents/Universita/Master/nanopants/lcm/growth_Melting_Silver_ICO/growth_Melting_Silver_ICO_3269/GCN/quenchMeltingSims\"\n",
    ")\n",
    "\n",
    "# Initialize a list to store data from each file\n",
    "data_summary = []\n",
    "\n",
    "# 2. Read and parse all .xyz files\n",
    "for file_path in target_dir.glob(\"*.xyz\"):\n",
    "    with open(file_path, \"r\") as f:\n",
    "        lines = f.readlines()\n",
    "\n",
    "    if not lines:\n",
    "        continue\n",
    "\n",
    "    # The 1st line contains the total number of atoms\n",
    "    num_atoms = int(lines[0].strip())\n",
    "\n",
    "    # Collect cnaps values (the last column, index -1)\n",
    "    # Skip the first two lines (header metadata)\n",
    "    cnaps_values = []\n",
    "    for line in lines[2:]:\n",
    "        parts = line.split()\n",
    "        if parts:  # Ensure the line isn't empty\n",
    "            try:\n",
    "                # Convert float string to int (e.g., 14.000000 -> 14)\n",
    "                cnaps = int(float(parts[-1]))\n",
    "                cnaps_values.append(cnaps)\n",
    "            except (ValueError, IndexError):\n",
    "                continue\n",
    "\n",
    "    if not cnaps_values:\n",
    "        continue\n",
    "\n",
    "    # Count frequencies of each cnaps value\n",
    "    cnaps_series = pd.Series(cnaps_values)\n",
    "    cnaps_counts = cnaps_series.value_counts()\n",
    "\n",
    "    # Calculate percentages based on total atom count from line 1\n",
    "    cnaps_percentages = (cnaps_counts / num_atoms)\n",
    "\n",
    "    # Store results along with the atom number (X-axis value)\n",
    "    for cnaps_val, percentage in cnaps_percentages.items():\n",
    "        data_summary.append(\n",
    "            {\n",
    "                \"Atoms_Number\": num_atoms,\n",
    "                \"CNAPS_Value\": cnaps_val,\n",
    "                \"Percentage\": percentage,\n",
    "            }\n",
    "        )\n",
    "\n",
    "# 3. Create a DataFrame\n",
    "df = pd.DataFrame(data_summary)\n",
    "\n",
    "if not df.empty:\n",
    "    # Pivot the data so:\n",
    "    # - Rows (Index) = Atoms_Number (X-axis)\n",
    "    # - Columns = CNAPS_Value (The stacks)\n",
    "    # - Values = Percentage (Y-axis)\n",
    "    # aggfunc='mean' handles cases if multiple files share the exact same atom count\n",
    "    df_pivot = df.pivot_table(\n",
    "        index=\"Atoms_Number\",\n",
    "        columns=\"CNAPS_Value\",\n",
    "        values=\"Percentage\",\n",
    "        aggfunc=\"mean\",\n",
    "    ).fillna(0)\n",
    "# --- CUT DATA UP TO 50 ATOMS ---\n",
    "    # This filters the index (Atoms_Number) to only include values <= 50\n",
    "    df_pivot = df_pivot[df_pivot.index <= 60]\n",
    "    # Sort the index so the X-axis grows sequentially\n",
    "    df_pivot = df_pivot.sort_index()\n",
    "\n",
    "    # 4. Plotting the Stacked Histogram / Bar Chart\n",
    "    # You can change the color map via `cmap` if you want a different palette (e.g., 'viridis', 'tab10')\n",
    "    ax = df_pivot.plot(kind=\"bar\", stacked=True, figsize=(14, 7), width=0.8)\n",
    "\n",
    "    # Formatting the graph\n",
    "    plt.xlabel(\"Number of Atoms (System Size)\")\n",
    "    plt.ylabel(\"CNAPS#/Total Atoms\")\n",
    "    plt.title(\"Stacked Distribution of CNAPS Percentages by cluster size\")\n",
    "    plt.grid(axis=\"y\", linestyle=\"--\", alpha=0.5)\n",
    "\n",
    "    # Place the legend outside the plot area so it doesn't cover data points\n",
    "    plt.legend(title=\"CNAPS Type\", bbox_to_anchor=(1.02, 1), loc=\"upper left\")\n",
    "\n",
    "    # Optional: If you have too many unique atom numbers, the X-axis text can overlap.\n",
    "    # This line thins out the labels to show every 5th label (uncomment if needed):\n",
    "    # ax.set_xticklabels([label.get_text() if i % 5 == 0 else '' for i, label in enumerate(ax.get_xticklabels())])\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "else:\n",
    "    print(\"No valid data was extracted. Check if the directory contains .xyz files.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "6c3c00b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Starting Stage 1: Processing Simulations ---\n",
      "Stage 1 complete. Files written to GCN folders.\n",
      "\n",
      "--- Starting Stage 2: Aggregating & Averaging Statistical Runs ---\n",
      "Total unique sizes found: 86\n",
      "Filtering to display the first 50 sizes (From 14 to 60 atoms)\n",
      "\n",
      "--- Summary of Consolidated Statistical Averages (First 50 Sizes) ---\n",
      "    System Size  CNAPS Type  Average Percentage\n",
      "0            14          14            0.571429\n",
      "1            14           0            0.428571\n",
      "2            15           0            0.866667\n",
      "3            15           8            0.066667\n",
      "4            15          14            0.066667\n",
      "5            16           0            0.750000\n",
      "6            16          14            0.250000\n",
      "7            17           0            0.588235\n",
      "8            17          14            0.352941\n",
      "9            17           5            0.058824\n",
      "10           18           0            0.611111\n",
      "11           18          16            0.055556\n",
      "12           18           2            0.166667\n",
      "13           18           8            0.055556\n",
      "14           18          11            0.055556\n",
      "15           18          10            0.055556\n",
      "16           19          14            0.210526\n",
      "17           19           0            0.789474\n",
      "18           20           0            0.550000\n",
      "19           20          15            0.150000\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sys\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter\n",
    "\n",
    "# ==============================================================================\n",
    "# 0. DEFINE ENVIRONMENT & PATHS\n",
    "# ==============================================================================\n",
    "local_packages_path = Path(\"/home/tmlkyza/Documents/Universita/Master/nanopants/lcm/pySNOW\")\n",
    "sys.path.append(str(local_packages_path.resolve()))\n",
    "\n",
    "from snow.io.xyz import read_xyz, write_xyz\n",
    "from snow.descriptors.coordination import coordination_number\n",
    "from snow.descriptors import cna\n",
    "\n",
    "alat = 4.087077141  \n",
    "cutoff = 0.85 * alat\n",
    "root_dir = Path('.') \n",
    "\n",
    "statistical_runs = [\n",
    "    p for p in root_dir.iterdir()\n",
    "    if p.is_dir() and any(sub.is_dir() and sub.name == 'quenchMeltingSims' for sub in p.iterdir())\n",
    "]\n",
    "\n",
    "def FileOut(file, subDirName, filename):\n",
    "    run_folder = file.parent          \n",
    "    process_folder = run_folder.parent \n",
    "    irand_folder = process_folder.parent \n",
    "\n",
    "    output_dir = irand_folder / subDirName\n",
    "    output_dir.mkdir(parents=True, exist_ok=True)\n",
    "    return output_dir / filename\n",
    "\n",
    "# ==============================================================================\n",
    "# STAGE 1: COMPUTE & WRITE CNAPS TO GCN FOLDER\n",
    "# ==============================================================================\n",
    "print(\"--- Starting Stage 1: Processing Simulations ---\")\n",
    "\n",
    "for irand in statistical_runs:\n",
    "    quench_dir = irand / \"quenchMeltingSims\"\n",
    "    if not quench_dir.exists():\n",
    "        continue\n",
    "    \n",
    "    for run_folder in quench_dir.iterdir():\n",
    "        if not run_folder.is_dir():\n",
    "            continue\n",
    "            \n",
    "        out_files = list(run_folder.glob('out*.xyz'))\n",
    "        \n",
    "        if out_files:\n",
    "            highest_out = max(out_files, key=lambda f: int(f.stem[3:]))\n",
    "            el, coords = read_xyz(highest_out)\n",
    "            cns = coordination_number(coords, cutoff)\n",
    "            cnaps = cna.cnap_peratom(coords, cutoff)\n",
    "            \n",
    "            output_file_path = FileOut(highest_out, \"GCN\", f\"{run_folder.name}.xyz\")\n",
    "            write_xyz(output_file_path, el, coords, additional_data={\"cns\": cns, \"cnaps\": cnaps})\n",
    "\n",
    "print(\"Stage 1 complete. Files written to GCN folders.\")\n",
    "\n",
    "# ==============================================================================\n",
    "# STAGE 2: READ GCN FOLDERS, AVERAGE FREQUENCIES, AND COMPUTE STATS\n",
    "# ==============================================================================\n",
    "print(\"\\n--- Starting Stage 2: Aggregating & Averaging Statistical Runs ---\")\n",
    "\n",
    "master_data = {}\n",
    "\n",
    "for irand in statistical_runs:\n",
    "    gcn_dir = irand / \"GCN\"\n",
    "    if not gcn_dir.exists():\n",
    "        continue\n",
    "        \n",
    "    irand_name = irand.name\n",
    "    \n",
    "    for gcn_file in gcn_dir.glob(\"*.xyz\"):\n",
    "        with open(gcn_file, 'r') as f:\n",
    "            lines = f.readlines()\n",
    "            \n",
    "        if not lines:\n",
    "            continue\n",
    "            \n",
    "        try:\n",
    "            num_atoms = int(lines[0].strip())\n",
    "        except ValueError:\n",
    "            continue  \n",
    "            \n",
    "        cnaps_in_file = []\n",
    "        for line in lines[2:2 + num_atoms]:\n",
    "            parts = line.split()\n",
    "            if parts:\n",
    "                try:\n",
    "                    cnap_value = int(float(parts[-1]))\n",
    "                    cnaps_in_file.append(cnap_value)\n",
    "                except ValueError:\n",
    "                    continue\n",
    "                \n",
    "        if num_atoms not in master_data:\n",
    "            master_data[num_atoms] = {}\n",
    "            \n",
    "        if irand_name not in master_data[num_atoms]:\n",
    "            master_data[num_atoms][irand_name] = Counter()\n",
    "            \n",
    "        master_data[num_atoms][irand_name].update(cnaps_in_file)\n",
    "\n",
    "all_unique_sizes = sorted(master_data.keys())\n",
    "first_50_sizes = set(all_unique_sizes[:47])\n",
    "\n",
    "print(f\"Total unique sizes found: {len(all_unique_sizes)}\")\n",
    "print(f\"Filtering to display the first 50 sizes (From {min(first_50_sizes)} to {max(first_50_sizes)} atoms)\")\n",
    "\n",
    "final_averaged_records = []\n",
    "\n",
    "for num_atoms, irand_dict in sorted(master_data.items()):\n",
    "    if num_atoms not in first_50_sizes:\n",
    "        continue  \n",
    "    \n",
    "    cnap_percentages_per_seed = {}\n",
    "    \n",
    "    for irand_name, counts in irand_dict.items():\n",
    "        total_cnaps_counted = sum(counts.values())\n",
    "        if total_cnaps_counted == 0:\n",
    "            continue\n",
    "            \n",
    "        for cnap_type, occurencies in counts.items():\n",
    "            pct = occurencies / total_cnaps_counted\n",
    "            \n",
    "            if cnap_type not in cnap_percentages_per_seed:\n",
    "                cnap_percentages_per_seed[cnap_type] = []\n",
    "            cnap_percentages_per_seed[cnap_type].append(pct)\n",
    "            \n",
    "    for cnap_type, pct_list in cnap_percentages_per_seed.items():\n",
    "        total_seeds_for_size = len(irand_dict)\n",
    "        missing_seeds = total_seeds_for_size - len(pct_list)\n",
    "        full_pct_list = pct_list + [0.0] * missing_seeds\n",
    "        \n",
    "        avg_pct = np.mean(full_pct_list)\n",
    "        \n",
    "        final_averaged_records.append({\n",
    "            \"System Size\": num_atoms,\n",
    "            # --- FIXED: Store purely as an integer to maintain numerical sorting ---\n",
    "            \"CNAPS Type\": int(cnap_type), \n",
    "            \"Average Percentage\": avg_pct\n",
    "        })\n",
    "\n",
    "df_results = pd.DataFrame(final_averaged_records)\n",
    "print(\"\\n--- Summary of Consolidated Statistical Averages (First 50 Sizes) ---\")\n",
    "print(df_results.head(20))\n",
    "\n",
    "# ==============================================================================\n",
    "# STAGE 3: VISUALIZATION (STAKED BAR CHART)\n",
    "# ==============================================================================\n",
    "if not df_results.empty:\n",
    "    # Pivot the data \n",
    "    df_pivot = df_results.pivot(index=\"System Size\", columns=\"CNAPS Type\", values=\"Average Percentage\").fillna(0)\n",
    "    \n",
    "    # --- FIXED: Explicitly sort columns numerically so 2 comes before 10 ---\n",
    "    df_pivot = df_pivot.reindex(columns=sorted(df_pivot.columns))\n",
    "    \n",
    "    # Render the bar chart\n",
    "    ax = df_pivot.plot(kind=\"bar\", stacked=True, figsize=(14, 6), width=0.8, cmap=\"tab20\")\n",
    "\n",
    "    plt.xlabel(\"$N$\", fontsize=14)\n",
    "    plt.ylabel(\"Fractions of each CNAP type\", fontsize=14)\n",
    "    #plt.title('Stacked Distribution of CNAPs', fontsize=12, fontweight='bold', pad=15)\n",
    "    plt.grid(axis=\"y\", linestyle=\"--\", alpha=0.5)\n",
    "    plt.tick_params(axis='both', which='major', labelsize=13)\n",
    "\n",
    "    # Updated Legend Title to match plain numbers\n",
    "    #plt.legend(title=\"CNAP types\", bbox_to_anchor=(1.02, 1), loc=\"upper left\", fontsize=13)\n",
    "    legend=plt.legend(title=\"CNAP types\", loc=\"upper left\", bbox_to_anchor=(1.0,1.02), fontsize=12)\n",
    "\n",
    "    plt.setp(plt.gca().get_legend().get_texts(), fontsize='13') #legend 'list' fontsize\n",
    "    \n",
    "    # --- Align ticks with actual multiples of 10 ---\n",
    "    data_index_floats = df_pivot.index.astype(float)\n",
    "    min_val = int(data_index_floats.min())\n",
    "    max_val = int(data_index_floats.max())\n",
    "    \n",
    "    start_tick = min_val + (10 - min_val % 10) if min_val % 10 != 0 else min_val\n",
    "    target_values = np.arange(start_tick, max_val + 1, 10)\n",
    "    \n",
    "    tick_indices = [np.where(data_index_floats == val)[0][0] for val in target_values if val in data_index_floats]\n",
    "    tick_labels = [int(data_index_floats[i]) for i in tick_indices]\n",
    "    \n",
    "    ax.set_xticks(tick_indices)\n",
    "    ax.set_xticklabels(tick_labels, rotation=0) # Kept at 0 rotation since 10-step numbers fit comfortably\n",
    "    # --------------------------------------------------------\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    \n",
    "    \n",
    "    output_img = \"CNAPSperSize.png\"\n",
    "    plt.savefig(output_img, dpi=300)\n",
    "    plt.show()\n",
    "else:\n",
    "    print(\"No structural data resolved for the selected subset.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "676a4d41",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Cluster Configurations Ranked by Custom Structural Purity Score (Strict Max Size 50) ---\n",
      " System Size Diversity (Entropy) Non-Zero Concentration Custom Score\n",
      "          14              0.9852                 0.5714       0.5800\n",
      "          21              0.9587                 0.3810       0.3974\n",
      "          17              1.2210                 0.4118       0.3372\n",
      "          16              0.8113                 0.2500       0.3082\n",
      "          28              0.7496                 0.2143       0.2859\n",
      "          27              1.0378                 0.2963       0.2855\n",
      "          19              0.7425                 0.2105       0.2835\n",
      "          24              1.4136                 0.3750       0.2653\n",
      "          38              0.9089                 0.2368       0.2606\n",
      "          37              1.4042                 0.3514       0.2502\n",
      "          40              0.6098                 0.1500       0.2460\n",
      "          43              0.5830                 0.1395       0.2393\n",
      "          22              0.5746                 0.1364       0.2373\n",
      "          30              1.4256                 0.3333       0.2338\n",
      "          20              1.9815                 0.4500       0.2271\n",
      "          50              0.5294                 0.1200       0.2267\n",
      "          60              0.8853                 0.2000       0.2259\n",
      "          36              0.7584                 0.1667       0.2198\n",
      "          29              0.7877                 0.1724       0.2189\n",
      "          58              0.9509                 0.2069       0.2176\n",
      "          18              1.7917                 0.3889       0.2171\n",
      "          61              0.8369                 0.1803       0.2155\n",
      "          56              0.8343                 0.1786       0.2140\n",
      "          42              0.9046                 0.1905       0.2106\n",
      "          57              1.0121                 0.2105       0.2080\n",
      "          35              0.6948                 0.1429       0.2056\n",
      "          23              0.4262                 0.0870       0.2040\n",
      "          63              0.8595                 0.1746       0.2032\n",
      "          33              1.5280                 0.3030       0.1983\n",
      "          44              1.5061                 0.2955       0.1962\n",
      "          47              2.1931                 0.4255       0.1940\n",
      "          41              0.8948                 0.1707       0.1908\n",
      "          15              0.6998                 0.1333       0.1905\n",
      "          45              1.2843                 0.2444       0.1903\n",
      "          51              0.9276                 0.1765       0.1902\n",
      "          49              1.7223                 0.3265       0.1896\n",
      "          62              1.9577                 0.3710       0.1895\n",
      "          52              1.3200                 0.2500       0.1894\n",
      "          48              1.5414                 0.2917       0.1892\n",
      "          25              0.6396                 0.1200       0.1876\n",
      "          39              0.7283                 0.1282       0.1760\n",
      "          34              1.0108                 0.1765       0.1746\n",
      "          59              1.2988                 0.2203       0.1697\n",
      "          46              0.6614                 0.1087       0.1643\n",
      "          31              0.4096                 0.0645       0.1575\n",
      "          32              0.3998                 0.0625       0.1563\n",
      "          53              0.7267                 0.1132       0.1558\n",
      "          55              0.3555                 0.0545       0.1534\n",
      "          54              0.8463                 0.1296       0.1532\n",
      "          26             -0.0000                 0.0000       0.0000\n"
     ]
    }
   ],
   "source": [
    "# ==============================================================================\n",
    "# CELL: SCORE CLUSTERS (LOW DIVERSITY & HIGH NON-ZERO CNAP FREQUENCIES)\n",
    "# ==============================================================================\n",
    "import numpy as np\n",
    "\n",
    "# 1. Filter out system sizes above 50\n",
    "df_filtered = df_results[(df_results[\"System Size\"] >= 14) & (df_results[\"System Size\"] <= 63)].copy()\n",
    "if not df_filtered.empty:\n",
    "    scores_record = []\n",
    "    \n",
    "    # Group by system size to look at the full distribution of each cluster\n",
    "    for num_atoms, group in df_filtered.groupby(\"System Size\"):\n",
    "        # Extract types and percentages\n",
    "        group = group[group[\"Average Percentage\"] > 0] # drop 0% occurrences for entropy math\n",
    "        \n",
    "        percentages = group[\"Average Percentage\"].values\n",
    "        types = group[\"CNAPS Type\"].values\n",
    "        \n",
    "        # Calculate Shannon Entropy (measures diversity/fragmentation across ALL types)\n",
    "        entropy = -np.sum(percentages * np.log2(percentages))\n",
    "        \n",
    "        # Calculate the sum of squares of ONLY non-zero CNAPs\n",
    "        non_zero_sum_squares = 0.0\n",
    "        for pct, cnap_type in zip(percentages, types):\n",
    "            if cnap_type != \"cnap_0\":\n",
    "                #non_zero_sum_squares += pct **2 \n",
    "                non_zero_sum_squares += pct \n",
    "                \n",
    "        # Calculate the custom score (higher score = better)\n",
    "        # We add 1e-6 to avoid division by zero if entropy is perfectly 0\n",
    "        score = non_zero_sum_squares / (entropy + 1e-6)\n",
    "        \n",
    "        scores_record.append({\n",
    "            \"System Size\": num_atoms,\n",
    "            \"Non-Zero Concentration\": non_zero_sum_squares,\n",
    "            \"Diversity (Entropy)\": entropy,\n",
    "            \"Custom Score\": score\n",
    "        })\n",
    "        \n",
    "    df_scores = pd.DataFrame(scores_record)\n",
    "    \n",
    "    # 2. Sort by Custom Score in descending order (highest score / best configurations first)\n",
    "    df_scores_sorted = df_scores.sort_values(by=\"Custom Score\", ascending=False)\n",
    "    \n",
    "    print(\"--- Cluster Configurations Ranked by Custom Structural Purity Score (Strict Max Size 50) ---\")\n",
    "    print(df_scores_sorted[[\"System Size\", \"Diversity (Entropy)\", \"Non-Zero Concentration\", \"Custom Score\"]].to_string(\n",
    "        index=False, \n",
    "        formatters={\n",
    "            \"Diversity (Entropy)\": lambda x: f\"{x:.4f}\",\n",
    "            \"Non-Zero Concentration\": lambda x: f\"{x:.4f}\",\n",
    "            \"Custom Score\": lambda x: f\"{x:.4f}\"\n",
    "        }\n",
    "    ))\n",
    "else:\n",
    "    print(\"No data found. Verify Stage 2 executed successfully.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e17fc955",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Cluster Configurations Ranked by Custom Purity Score (Sizes 30 to 50) ---\n",
      " System Size Diversity (Entropy) Non-Zero Concentration Custom Score\n",
      "          14              0.9852                 0.5714       0.5800\n",
      "          21              0.9587                 0.3810       0.3974\n",
      "          17              1.2210                 0.4118       0.3372\n",
      "          16              0.8113                 0.2500       0.3082\n",
      "          28              0.7496                 0.2143       0.2859\n",
      "          27              1.0378                 0.2963       0.2855\n",
      "          19              0.7425                 0.2105       0.2835\n",
      "          24              1.4136                 0.3750       0.2653\n",
      "          38              0.9089                 0.2368       0.2606\n",
      "          37              1.4042                 0.3514       0.2502\n",
      "          40              0.6098                 0.1500       0.2460\n",
      "          43              0.5830                 0.1395       0.2393\n",
      "          22              0.5746                 0.1364       0.2373\n",
      "          30              1.4256                 0.3333       0.2338\n",
      "          20              1.9815                 0.4500       0.2271\n",
      "          50              0.5294                 0.1200       0.2267\n",
      "          60              0.8853                 0.2000       0.2259\n",
      "          36              0.7584                 0.1667       0.2198\n",
      "          29              0.7877                 0.1724       0.2189\n",
      "          58              0.9509                 0.2069       0.2176\n",
      "          18              1.7917                 0.3889       0.2171\n",
      "          61              0.8369                 0.1803       0.2155\n",
      "          56              0.8343                 0.1786       0.2140\n",
      "          42              0.9046                 0.1905       0.2106\n",
      "          57              1.0121                 0.2105       0.2080\n",
      "          35              0.6948                 0.1429       0.2056\n",
      "          23              0.4262                 0.0870       0.2040\n",
      "          63              0.8595                 0.1746       0.2032\n",
      "          33              1.5280                 0.3030       0.1983\n",
      "          44              1.5061                 0.2955       0.1962\n",
      "          47              2.1931                 0.4255       0.1940\n",
      "          41              0.8948                 0.1707       0.1908\n",
      "          15              0.6998                 0.1333       0.1905\n",
      "          45              1.2843                 0.2444       0.1903\n",
      "          51              0.9276                 0.1765       0.1902\n",
      "          49              1.7223                 0.3265       0.1896\n",
      "          62              1.9577                 0.3710       0.1895\n",
      "          52              1.3200                 0.2500       0.1894\n",
      "          48              1.5414                 0.2917       0.1892\n",
      "          25              0.6396                 0.1200       0.1876\n",
      "          39              0.7283                 0.1282       0.1760\n",
      "          34              1.0108                 0.1765       0.1746\n",
      "          59              1.2988                 0.2203       0.1697\n",
      "          46              0.6614                 0.1087       0.1643\n",
      "          31              0.4096                 0.0645       0.1575\n",
      "          32              0.3998                 0.0625       0.1563\n",
      "          53              0.7267                 0.1132       0.1558\n",
      "          55              0.3555                 0.0545       0.1534\n",
      "          54              0.8463                 0.1296       0.1532\n",
      "          26             -0.0000                 0.0000       0.0000\n",
      "\n",
      "================================================================================\n",
      "CENTER OF THE DISTRIBUTION (Expected System Size): 36.4262 atoms\n",
      "================================================================================\n",
      "Interpretation: Crystalline structural order in this range is centered around this cluster size scale.\n"
     ]
    }
   ],
   "source": [
    "# --------------------------------------------------------------------------\n",
    "# NEW: Compute the center of the distribution using: (natoms * score) / sum(natoms * score)\n",
    "# --------------------------------------------------------------------------\n",
    "df_scores[\"Weight_Component\"] = df_scores[\"System Size\"] * df_scores[\"Custom Score\"]\n",
    "total_weight_component = df_scores[\"Weight_Component\"].sum()\n",
    "total_scores = df_scores[\"Custom Score\"].sum()\n",
    "\n",
    "# Calculate the singular center of the distribution for this range\n",
    "# (i.e. Expected system size weighted by structural purity)\n",
    "if total_scores > 0:\n",
    "    center_of_distribution = total_weight_component / total_scores\n",
    "else:\n",
    "    center_of_distribution = 0.0\n",
    "\n",
    "# 2. Sort by Custom Score in descending order (highest score first)\n",
    "df_scores_sorted = df_scores.sort_values(by=\"Custom Score\", ascending=False)\n",
    "\n",
    "print(\"--- Cluster Configurations Ranked by Custom Purity Score (Sizes 30 to 50) ---\")\n",
    "print(df_scores_sorted[[\"System Size\", \"Diversity (Entropy)\", \"Non-Zero Concentration\", \"Custom Score\"]].to_string(\n",
    "    index=False, \n",
    "    formatters={\n",
    "        \"Diversity (Entropy)\": lambda x: f\"{x:.4f}\",\n",
    "        \"Non-Zero Concentration\": lambda x: f\"{x:.4f}\",\n",
    "        \"Custom Score\": lambda x: f\"{x:.4f}\"\n",
    "    }\n",
    "))\n",
    "\n",
    "print(\"\\n\" + \"=\"*80)\n",
    "print(f\"CENTER OF THE DISTRIBUTION (Expected System Size): {center_of_distribution:.4f} atoms\")\n",
    "print(\"=\"*80)\n",
    "print(\"Interpretation: Crystalline structural order in this range is centered around this cluster size scale.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48d8033c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:102: SyntaxWarning: \"\\m\" is an invalid escape sequence. Such sequences will not work in the future. Did you mean \"\\\\m\"? A raw string is also an option.\n",
      "<>:102: SyntaxWarning: \"\\m\" is an invalid escape sequence. Such sequences will not work in the future. Did you mean \"\\\\m\"? A raw string is also an option.\n",
      "/tmp/ipykernel_424539/216917900.py:102: SyntaxWarning: \"\\m\" is an invalid escape sequence. Such sequences will not work in the future. Did you mean \"\\\\m\"? A raw string is also an option.\n",
      "  ax1.set_ylabel('$T_\\mathrm{m}$ [K]', fontsize=12)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Cluster Configurations Ranked by Custom Structural Purity Score (Strict Max Size 50) ---\n",
      " System Size Diversity (Entropy) Non-Zero Concentration Custom Score\n",
      "          26             -0.0000                 1.0000 1000000.0000\n",
      "          55              0.3555                 1.0000       2.8130\n",
      "          32              0.3998                 1.0000       2.5013\n",
      "          31              0.4096                 1.0000       2.4412\n",
      "          23              0.4262                 1.0000       2.3462\n",
      "          50              0.5294                 1.0000       1.8891\n",
      "          22              0.5746                 1.0000       1.7402\n",
      "          43              0.5830                 1.0000       1.7152\n",
      "          40              0.6098                 1.0000       1.6398\n",
      "          25              0.6396                 1.0000       1.5636\n",
      "          46              0.6614                 1.0000       1.5120\n",
      "          35              0.6948                 1.0000       1.4392\n",
      "          15              0.6998                 1.0000       1.4289\n",
      "          53              0.7267                 1.0000       1.3761\n",
      "          39              0.7283                 1.0000       1.3731\n",
      "          19              0.7425                 1.0000       1.3468\n",
      "          28              0.7496                 1.0000       1.3341\n",
      "          36              0.7584                 1.0000       1.3186\n",
      "          29              0.7877                 1.0000       1.2696\n",
      "          16              0.8113                 1.0000       1.2326\n",
      "          56              0.8343                 1.0000       1.1986\n",
      "          54              0.8463                 1.0000       1.1817\n",
      "          60              0.8853                 1.0000       1.1296\n",
      "          41              0.8948                 1.0000       1.1176\n",
      "          42              0.9046                 1.0000       1.1054\n",
      "          38              0.9089                 1.0000       1.1002\n",
      "          51              0.9276                 1.0000       1.0781\n",
      "          58              0.9509                 1.0000       1.0517\n",
      "          21              0.9587                 1.0000       1.0431\n",
      "          14              0.9852                 1.0000       1.0150\n",
      "          34              1.0108                 1.0000       0.9893\n",
      "          57              1.0121                 1.0000       0.9880\n",
      "          27              1.0378                 1.0000       0.9636\n",
      "          17              1.2210                 1.0000       0.8190\n",
      "          45              1.2843                 1.0000       0.7786\n",
      "          59              1.2988                 1.0000       0.7700\n",
      "          52              1.3200                 1.0000       0.7576\n",
      "          37              1.4042                 1.0000       0.7121\n",
      "          24              1.4136                 1.0000       0.7074\n",
      "          30              1.4256                 1.0000       0.7015\n",
      "          44              1.5061                 1.0000       0.6639\n",
      "          33              1.5280                 1.0000       0.6545\n",
      "          48              1.5414                 1.0000       0.6488\n",
      "          49              1.7223                 1.0000       0.5806\n",
      "          18              1.7917                 1.0000       0.5581\n",
      "          20              1.9815                 1.0000       0.5047\n",
      "          47              2.1931                 1.0000       0.4560\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x550 with 2 Axes>"
      ]
     },
     "metadata": {},
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   ],
   "source": [
    "# ==============================================================================\n",
    "# CELL: SCORE CLUSTERS & GRAPH TEMPERATURE WITH SCORES\n",
    "# ==============================================================================\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import re\n",
    "\n",
    "# 1. Filter out system sizes above 50\n",
    "df_filtered = df_results[(df_results[\"System Size\"] >= 14) & (df_results[\"System Size\"] <= 60)].copy()\n",
    "if not df_filtered.empty:\n",
    "    scores_record = []\n",
    "    \n",
    "    # Group by system size to look at the full distribution of each cluster\n",
    "    for num_atoms, group in df_filtered.groupby(\"System Size\"):\n",
    "        # Extract types and percentages\n",
    "        group = group[group[\"Average Percentage\"] > 0] # drop 0% occurrences for entropy math\n",
    "        \n",
    "        percentages = group[\"Average Percentage\"].values\n",
    "        types = group[\"CNAPS Type\"].values\n",
    "        \n",
    "        # Calculate Shannon Entropy (measures diversity/fragmentation across ALL types)\n",
    "\n",
    "        entropy = -np.sum(percentages * np.log2(percentages))\n",
    "        \n",
    "        nonzero_entropy=0\n",
    "        for pct, cnap_type in zip(percentages, types):\n",
    "            if cnap_type != \"cnap_0\":\n",
    "                nonzero_entropy=nonzero_entropy-np.sum(pct*np.log2(pct))\n",
    "\n",
    "        # Calculate the sum of squares of ONLY non-zero CNAPs\n",
    "        non_zero_sum_squares = 0.0\n",
    "        for pct, cnap_type in zip(percentages, types):\n",
    "            if cnap_type != \"cnap_0\":\n",
    "                #non_zero_sum_squares += pct **2 \n",
    "                non_zero_sum_squares += pct \n",
    "                \n",
    "        # Calculate the custom score (higher score = better)\n",
    "        # We add 1e-6 to avoid division by zero if entropy is perfectly 0\n",
    "        score = non_zero_sum_squares / (entropy + 1e-6)\n",
    "        \n",
    "        scores_record.append({\n",
    "            \"System Size\": num_atoms,\n",
    "            \"Non-Zero Concentration\": non_zero_sum_squares,\n",
    "            \"Diversity (Entropy)\": entropy,\n",
    "            \"nonzeroentropy\": nonzero_entropy,\n",
    "            \"Custom Score\": score\n",
    "        })\n",
    "        \n",
    "    df_scores = pd.DataFrame(scores_record)\n",
    "    \n",
    "    # 2. Sort by Custom Score in descending order (highest score / best configurations first)\n",
    "    df_scores_sorted = df_scores.sort_values(by=\"Custom Score\", ascending=False)\n",
    "    \n",
    "    print(\"--- Cluster Configurations Ranked by Custom Structural Purity Score (Strict Max Size 50) ---\")\n",
    "    print(df_scores_sorted[[\"System Size\", \"Diversity (Entropy)\", \"Non-Zero Concentration\", \"Custom Score\"]].to_string(\n",
    "        index=False, \n",
    "        formatters={\n",
    "            \"Diversity (Entropy)\": lambda x: f\"{x:.4f}\",\n",
    "            \"Non-Zero Concentration\": lambda x: f\"{x:.4f}\",\n",
    "            \"Custom Score\": lambda x: f\"{x:.4f}\"\n",
    "        }\n",
    "    ))\n",
    "\n",
    "    # ==============================================================================\n",
    "    # ADDED: LOAD TEMPERATURE DATA AND GENERATE PLOT\n",
    "    # ==============================================================================\n",
    "    try:\n",
    "        # Load and parse the temperature text file\n",
    "        with open(\"/home/tmlkyza/Documents/Universita/Master/nanopants/lcm/growth_Melting_Silver_ICO/statistical_tempbysize.txt\", \"r\") as f:\n",
    "            temp_text = f.read()\n",
    "        \n",
    "        # Strip out any rows starting with #\n",
    "        lines = [line.strip() for line in temp_text.splitlines() if line.strip() and not line.strip().startswith('#')]\n",
    "        \n",
    "        # Tokenize whitespace-separated numbers and shape into groups of 5 columns\n",
    "        tokens = (\" \".join(lines)).split()\n",
    "        rows = [tokens[i:i+5] for i in range(0, len(tokens), 5)]\n",
    "        \n",
    "        # Create a matching DataFrame and convert values to float\n",
    "        df_temp = pd.DataFrame(rows, columns=['System Size', 'Tmelt', 'TmeltError', 'Tfreeze', 'TfreezeError']).astype(float)\n",
    "        \n",
    "        # Merge custom scores with temperature data on System Size\n",
    "        df_plot = pd.merge(df_scores, df_temp, on='System Size', how='inner').sort_values(by='System Size')\n",
    "        \n",
    "        if not df_plot.empty:\n",
    "            # ------------------------------------------------------------------\n",
    "            # LAYOUT 1: Dual-Axis Overlaid Graph (Histogram layer behind the line layer)\n",
    "            # ------------------------------------------------------------------\n",
    "            fig, ax1 = plt.subplots(figsize=(8,5.5))\n",
    "            ax2 = ax1.twinx()\n",
    "            \n",
    "            # Plot the scores as background bar/histogram layer (plotted first/lower zorder)\n",
    "            ax2.bar(df_plot['System Size'], df_plot['Diversity (Entropy)'], color='royalblue', alpha=0.35, width=0.8, label='Entropy')\n",
    "            ax2.set_ylabel('Shannon entropy', fontsize=12, rotation=270, labelpad=18)\n",
    "            ax2.tick_params(axis='y')\n",
    "            \n",
    "            # Plot the melting temperature on top with errorbars\n",
    "            ax1.errorbar(df_plot['System Size'], df_plot['Tmelt'], yerr=df_plot['TmeltError'], \n",
    "                         fmt='o-', color='crimson', ecolor='crimson', capsize=3, elinewidth=1.2, markersize=5, label='Melting Temp.', linewidth=0)\n",
    "            ax1.set_xlabel('$N$', fontsize=12)\n",
    "            ax1.set_ylabel('$T_\\mathrm{m}$ [K]', fontsize=12)\n",
    "            ax1.tick_params(axis='y')\n",
    "            \n",
    "            #ax1.set_title('Melting Temperature and Entropy by System Size', fontsize=12, fontweight='bold', pad=15)\n",
    "            ax1.grid(True, linestyle='--', alpha=0.3)\n",
    "            \n",
    "            # Combine the legends safely from both axes\n",
    "            lines1, labels1 = ax1.get_legend_handles_labels()\n",
    "            lines2, labels2 = ax2.get_legend_handles_labels()\n",
    "            ax1.legend(lines1 + lines2, labels1 + labels2, loc='upper right')\n",
    "            \n",
    "            plt.tight_layout()\n",
    "            plt.savefig('melting_temp_and_scores_dual_axis.png', dpi=300)\n",
    "            plt.show()\n",
    "            plt.close()\n",
    "        else:\n",
    "            print(\"\\n[Warning] No matching system sizes found between the cluster scores and the temperature dataset.\")\n",
    "            \n",
    "    except FileNotFoundError:\n",
    "        print(\"\\n[Error] The file 'statistical_tempbysize.txt' was not found in the workspace directory.\")\n",
    "    except Exception as e:\n",
    "        print(f\"\\n[Error] Failed to generate visualizations: {e}\")\n",
    "\n",
    "else:\n",
    "    print(\"No data found. Verify Stage 2 executed successfully.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4d5b842",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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