day_1_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a3f83985-a674-49da-8492-e0c24a9f171e",
   "metadata": {},
   "source": [
    "# Writing and Reading Data in Python\n",
    "# HDF5 Files\n",
    "\n",
    "HDF5 is a popular file format for storing and managing large amounts of scientific data. In Python, you can use the h5py library to read and write HDF5 files. Here's an example of how to write an array of data to an HDF5 file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ec01b870-4d2a-415e-afb6-91a01358b633",
   "metadata": {},
   "outputs": [],
   "source": [
    "import h5py\n",
    "\n",
    "data = [1, 2, 3, 4, 5]\n",
    "\n",
    "with h5py.File('data.h5', 'w') as f:\n",
    "    dset = f.create_dataset('my_data', data=data)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba408fb5-dc24-42bf-a5d3-3bc2f8900146",
   "metadata": {},
   "source": [
    "This creates a new HDF5 file called data.h5 and stores the data array as a dataset called my_data. To read the data back from the file, you can use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6be80b5f-fdad-4f46-a693-a05bdef40d33",
   "metadata": {},
   "outputs": [],
   "source": [
    "import h5py\n",
    "\n",
    "with h5py.File('data.h5', 'r') as f:\n",
    "    data = f['my_data'][()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e39727d-f50b-4c82-9332-61df1a69d7f2",
   "metadata": {},
   "source": [
    "This reads the my_data dataset from the data.h5 file and stores it in the data variable. Printing the data works just fine:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4e39c07f-1f18-41a4-bee1-7976ec0d93ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4 5]\n"
     ]
    }
   ],
   "source": [
    "print(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a26146ea-f96e-44f5-8dd0-6d2e04cc0005",
   "metadata": {},
   "source": [
    "# Pickle Files\n",
    "\n",
    "Pickle is a built-in Python module that allows you to serialize Python objects into a byte stream, which can be written to a file. Here's an example of how to write a Python dictionary to a pickle file:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "50961bea-0b68-4a70-8a81-f51dbb193be4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "data = {'a': 1, 'b': 2, 'c': 3}\n",
    "\n",
    "with open('data.pickle', 'wb') as f:\n",
    "    pickle.dump(data, f)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddeddceb-085b-4fb1-b17f-baa25a7ed363",
   "metadata": {},
   "source": [
    "This creates a new file called data.pickle and stores the data dictionary in it. To read the data back from the file, you can use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "96ac2240-8ed8-40c1-8235-0e33c0c0dde5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "with open('data.pickle', 'rb') as f:\n",
    "    data = pickle.load(f)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90280d76-2bf1-4cf2-9752-67a4e64c7b95",
   "metadata": {},
   "source": [
    "This reads the data.pickle file and deserializes the byte stream back into a Python object, which is stored in the data variable."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7eab7c2d-6d9c-4e81-81ed-a136d143658a",
   "metadata": {},
   "source": [
    "# CSV Files\n",
    "\n",
    "CSV (Comma-Separated Values) is a simple file format for storing tabular data. In Python, you can use the built-in csv module to read and write CSV files. Here's an example of how to write a list of lists to a CSV file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c9371432-b4d2-4ed9-94b3-56ab42505b15",
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "data = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]\n",
    "\n",
    "with open('data.csv', 'w', newline='') as f:\n",
    "    writer = csv.writer(f)\n",
    "    for row in data:\n",
    "        writer.writerow(row)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c3b826f-318f-41d9-99fd-89d124b5568b",
   "metadata": {},
   "source": [
    "This creates a new file called data.csv and writes the data list of lists to it as rows. To read the data back from the file, you can use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "372d1b5d-b1d3-4e73-b500-544f18f4c117",
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "data = []\n",
    "\n",
    "with open('data.csv', 'r') as f:\n",
    "    reader = csv.reader(f)\n",
    "    for row in reader:\n",
    "        data.append([int(x) for x in row])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3afbcfe-45df-4f3c-b489-cc689c8285f0",
   "metadata": {},
   "source": [
    "This reads the data.csv file and parses each row as a list of strings. It then converts each string to an integer and appends the resulting list to the data list of lists."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "190eebb0-e4ce-4c51-8a2a-829897413778",
   "metadata": {},
   "source": [
    "# Let's download some data for the course!\n",
    "\n",
    "Ok let's take some really interesting data and see how we can work with it"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21c58aca-beb4-4a59-96e7-05b13e264b48",
   "metadata": {},
   "source": [
    "# Images- Composition - Spectra\n",
    "\n",
    "In the search for new materials for photoelectrochemistry applications such as solar fuels (sometimes referred to as \"sunlight to liquid\"), having a comprehensive dataset of images and optical absorption properties can be invaluable. Photoabsorbers, which absorb light and convert it into electrical energy, are a key component in these applications, and their efficiency is strongly dependent on their optical absorption properties. Therefore, a dataset that includes information on the optical absorption properties of a large number of materials can help researchers identify promising photoabsorber candidates for further investigation.\n",
    "\n",
    "In addition to optical absorption properties, having a dataset that includes images of the materials can also be useful in identifying potential candidates for photoabsorbers. For example, if a particular material has a unique color or good bandgap and absorption properties that makes it well-suited for light absorption, this information can be gleaned from images of the material. Images are however much simpler to measure than optical absorption spectra. Additionally, by combining information on the optical absorption properties and the structural/morphological features of the materials, researchers can gain a more profound understanding of the underlying physical mechanisms that govern photoabsorption in these materials.\n",
    "\n",
    "Overall, having a comprehensive dataset of images and optical absorption properties for a large number of materials can greatly accelerate the search for new photoabsorbers in photoelectrochemistry and facilitate the discovery of new materials for green hydrogen production.\n",
    "\n",
    "There is a large HDF5 file available at https://data.caltech.edu/records/ksy2t-w6215/files/synthesis_imaging_spectroscopy_metal_oxides.h5. This dataset contains the largest publicly available curated materials science dataset as of December 2018. The dataset includes information on near-infrared to near-UV (UV-Vis) light absorbance, composition, and processing properties of metal oxides. The data was collected using optical absorption spectroscopy, an important materials characterization technique for applications such as solar fuels generation.\n",
    "\n",
    "The dataset includes 179,072 samples from 99,965 unique compositions, making it a comprehensive resource for researchers in materials science. The complete synthesis and processing history is supplied for each sample, allowing researchers to develop predictive models for materials, such as prediction of optical properties based on composition and processing. The dataset is also a valuable resource for identifying materials composition and synthesis to attain specific optical properties. The dataset was generated to train the model in https://doi.org/10.1039/C8SC03077D and has been published as a data descriptor here: https://www.nature.com/articles/s41597-019-0019-4\n",
    "\n",
    "With its comprehensive coverage and curated nature, the dataset is expected to serve as a benchmark for continued integration of machine learning in materials science. To access the dataset, researchers can download the HDF5 file from https://data.caltech.edu/records/ksy2t-w6215/files/synthesis_imaging_spectroscopy_metal_oxides.h5.\n",
    "\n",
    "Run the code below (this might take some time):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "83fc0821-6ccc-419e-b503-211f6cddbc6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading file...\n",
      "Download complete.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import urllib.request\n",
    "\n",
    "url = 'https://data.caltech.edu/records/ksy2t-w6215/files/synthesis_imaging_spectroscopy_metal_oxides.h5'\n",
    "filename = 'synthesis_imaging_spectroscopy_metal_oxides.h5'\n",
    "\n",
    "# Check if the file already exists in the current directory\n",
    "if not os.path.exists(filename):\n",
    "    # If the file does not exist, download it\n",
    "    print('Downloading file...')\n",
    "    urllib.request.urlretrieve(url, filename)\n",
    "    print('Download complete.')\n",
    "else:\n",
    "    print('File already exists in current directory.')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09752b32-89e2-4826-b3f6-afa69bbe8288",
   "metadata": {},
   "source": [
    "Ok, let's look at some of the contents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "947b5b65-b7df-4ff1-9518-43394aec03d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<KeysViewHDF5 ['atfrac', 'atfrac_keys', 'energy_eV', 'gas_composition_string', 'images', 'intended_element', 'loading_keys', 'loadings', 'max_temperature', 'nominal_pressure', 'plate_id', 'plate_id_anneal', 'sample_id', 'soak_time_at_max_temperature', 'spectra', 'substrate']>\n"
     ]
    }
   ],
   "source": [
    "materials_icp = h5py.File(filename, 'r')\n",
    "print(materials_icp.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e0dc34c-ce0f-419d-b257-7827e9641d66",
   "metadata": {},
   "source": [
    "Ok what do these keys actually mean?\n",
    "\n",
    "- `atfrac`: A dataset that contains atomic fraction values for each element in the sample.\n",
    "- `atfrac_keys`: A list of keys that correspond to the elements in the sample.\n",
    "- `energy_eV`: A dataset that contains the UV-Vis energy in electronvolts (eV) for all optical absorption measurements\n",
    "- `gas_composition_string`: A string that indicates the gas composition used dring annealing.\n",
    "- `images`: A dataset that contains RGB image data for each sample.\n",
    "- `intended_element`: A string that indicates the intended element for the sample.\n",
    "- `loading_keys`: A list of keys that correspond to the loading values from XRF or Printing in the sample in mol.\n",
    "- `loadings`: A dataset that contains loading values for each element in the sample - useful when trying to calculate athe absorption per mol or nm.\n",
    "- `max_temperature`: A float that indicates the maximum temperature during annealing.\n",
    "- `nominal_pressure`: A float that indicates the nominal pressure during annealing.\n",
    "- `plate_id`: A string that indicates the ID of the plate used in the measurement. This comes from datamanagement as described here: https://www.nature.com/articles/s41524-019-0216-x\n",
    "- `plate_id_anneal`: A string that indicates the ID the annealing.\n",
    "- `sample_id`: A string that indicates the ID of the sample. This and the plate ID uniquely identifies a sample.\n",
    "- `soak_time_at_max_temperature`: A float that indicates the soak time at the maximum temperature used in the annealing oven.\n",
    "- `spectra`: A dataset that contains the UV-Vis absorption spectra data for each sample.\n",
    "- `substrate`: A string that indicates the substrate used in the measurement.\n",
    "\n",
    "## Reading Image Data for a Random Entry\n",
    "\n",
    "To read in the image data for a random entry in the HDF5 file, we can use the following code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "255c907f-813e-4e33-9b4a-7eadc3916d6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image shape: (64, 64, 3)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Get a random index from the length of the 'images' dataset\n",
    "index = np.random.randint(len(materials_icp['images']))\n",
    "\n",
    "# Get the image data for the randomly-selected index\n",
    "image_data = materials_icp['images'][index]\n",
    "\n",
    "# Print the shape of the image data\n",
    "print(f\"Image shape: {image_data.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "f55053dc-9d5a-4dde-8dbb-512b9fc2f9d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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acBaY8xS140ZMpDnTMVmNnaq+kbT6CACO/e+vUrH+3l6zbaa6xozPIOq4qmq7Qm6VUZAuoWoqX5z6c5mKt9ILF47X3AP3CSNxl7qJKQO9681QEvrEbvTTbNUM4idodHTqpO0z1/+u7W3IChpebHgY1tSytzpyIYgKkBaNs8K0CCcbCVHN2c3t6+z1mPT4cbqUhKW/eHQnJIQQIhpKQkIIIaKhJCSEECIaSkJCCCGioSQkhBAiGhWrjjP1cQ4zLyrkYHmXllh0dO4Qu40esnSVHVPkZTBixvv7bB+u3ncMpVFiL4NcbZ0Zt7zgACBL/OAsXzHuBcc8y5j3F7uejs9X1ECs9MqYVAjlqgY8ngCpdMc66vvG/NAsX7URe12Ry8AFhiSeMYsh2+fkJFHN9b7zthmvyaXVmxc228rNpIaoLgtkfZoDh6kQ8157Hmbve2XwcqMeeaVjqfQ8Fax1JySEECIaSkJCCCGioSQkhBAiGkpCQggholHBwoQ05Smq5OsjMVUCdtsCs9DxOe6Y9h101CNDZvhk37t28+H0A+eaGcRuJ2fHs4YND8ALm1kihAR22wwRLHC7lNIfgHoelgLj2aiY0gRX37Q2mqOQntdXiJ9DY72xB/Be8Q07ZHW6/yrY6y1XsA86cJJZU/1vKlbfYBS6A1BfbYtvqBCIiD4sbQddET43n/FUVummVMPgfe9MT4gX47MGUvrxdCckhBAiGkpCQgghoqEkJIQQIhpKQkIIIaKhJCSEECIaFauOCyFBOMNSgmuBDHWPu8Bc6Uoo1tZtxcLixuBDsBVCg/lTZnzg+HG7c0M+k62xVUmsaFhg3i3UEskomkaKhvFzQrqmKjPrHNLKhSRqxy0VJHVyKjgLm3kcXRwKpPGxbFeYfZLdAxGwIWSZ3ZSxtkjb6hq784Kh9ASAU/m0zU/fMbswXm19oz2+artwI10rxhD51XEWUXQo28oiIIb9HsddiEyfsZKPpTshIYQQ0VASEkIIEQ0lISGEENFQEhJCCBENJSEhhBDRqFh1HEJIKSy43qL0wnOByXgIph+c1wuOKKQKRPFmqu8KthJo6NQJMz44mDfjGcP3LVttLwPq40b9s0r3MmOiMd4FK8jm9OsrvWufMxtVqpFe2AlwmMp5FaAevzruYUc+txKvuWSErZV0+0zG9iSsqiLqOLJurbV/or/PbJvP2/5zM4hitECKwHkUhgy3v5vHC5Fee0/hxtLXrGcuuhMSQggRDSUhIYQQ0VASEkIIEQ0lISGEENFQEhJCCBGNylXHmZTuw+WvJMikbY5+nF3Qni3lVGHYbHpqoN/uYsRuX2WofrIZsgyclUi5nsxSDvkUXIGpz5yqHw/UT9AME5Wet+IqwWUT5/SUM62/XD1wn72E+AwWDJVqgQnvsvYOVuE3m02v5+FBWwWXH0j7zAFArs72lCMFgakXogk1QiTNSTeJ1Y973XvfnIymE7OO052QEEKIeCgJCSGEiIaSkBBCiGgoCQkhhIiGKwl1dnbihhtuQENDA2bPno077rgDhw8fHtMmhID29na0tLSgtrYWS5cuxaFDh8o6aCGEENMDlzpuz549uOeee3DDDTdgeHgY69evx/Lly/Haa6+hvr4eALBhwwZs3LgRjz/+OC677DI8/PDDWLZsGQ4fPoyGhobSD5YgrRZxqM+cAhSuYHMpoYgXHPOIY75nRj9heMhsy9Q9zCEvsaqlskqpzFfK4TdF497KouQcOh3eXAflFXFLPyYdNz2HpJqrVbrT7RFXejVb5nfIYONmhGCsOXOOQGBVXrO2VC1blW4/NGR7Lw6eslVz9Lo5Sv+y17dXuemyJaSXjXj4OfzgJgtXEnruuefG/Puxxx7D7NmzsX//fnziE59ACAGbNm3C+vXrsWLFCgDA9u3b0dTUhB07duDuu+8u38iFEEJMeSb0TKi3txcAcMEFFwAAjhw5gu7ubixfvrzYJpfLYcmSJdi7d6/ZRz6fR19f35hNCCHEucFZJ6EQAu6//37cfPPNmD9/PgCgu7sbANDU1DSmbVNTU3HfmXR2dqKxsbG4zZ0792yHJIQQYopx1kno3nvvxauvvop/+qd/Su078zvPEAL9HnTdunXo7e0tbl1dXWc7JCGEEFOMs7Lt+epXv4pnnnkGL774Ii655JJivLm5GcDoHdGcOXOK8Z6entTd0WlyuRxyOaOAVEDpz8bMh2vkob/zObbb/cegQJ4sFhwPxAvDdpG6wTwpXkfEBpZFD7VcYUW52INVxwNX/uDbWRyOnFuPowm35yEP8o1juuuRkThbE5bUhJ9t+4E9FX0Y88mMI8uwoyTuqiHJrjERJhgCBADIGIKFMGgLe4ZI8ccRZnuVrTHj1rnNMCGIs6CjR0xF7a2IVIm+9q3jsXE42lq47oRCCLj33nvx1FNP4Uc/+hFaW1vH7G9tbUVzczN2795djA0ODmLPnj1YvHix51BCCCHOAVx3Qvfccw927NiBf/mXf0FDQ0PxOU9jYyNqa2uRJAnWrFmDjo4OtLW1oa2tDR0dHairq8PKlSsnZQJCCCGmLq4ktGXLFgDA0qVLx8Qfe+wxfPnLXwYArF27FgMDA1i9ejWOHTuGhQsXYteuXb7fCAkhhDgncCWhUsojJEmC9vZ2tLe3n+2YhBBCnCPIO04IIUQ0KraoXQiFtHUGU5uYRe1Ix0YxrfHaJ4bOg9rtsDhzbmHHNP5gZMhW64wM2/EksRVSGUOCQ+1cyHyoqo/EM8Y5zDBVm9eGh1f8SjclijxuZ1N6wS+3iJIuUIcVD1UpkutG528cjl6f0tWIo52TuLnDZxPF2mer0ms/yQyabYeHbHVcIOo4ZudjKSkDk7ux15XtLAR6r2B0T+2TvNfTPJzdt/WeYsUYuhMSQggRDSUhIYQQ0VASEkIIEQ0lISGEENFQEhJCCBGNylXHwVBoOLzWqFqJKtIcf0AVdqwQFouX7sbEvKwCkdRQHypDaeQtC0fbly4mwwhRarH6evzysDVhSb5Y3xEKezkLm1ljcZ8TxzGphx0txudVX5nGZ2Q0RJVFPOWy2XSc+bgNDTHVnO01V5VjZnjpYxZGnGpMprwjp4XN3+7cDnM/RSPG1oTxfsjelyx0JySEECIaSkJCCCGioSQkhBAiGkpCQgghoqEkJIQQIhoVq46zYAocO8za+pRQliLEXROUSFCoAsk8pqtEpav6KRMluQVcrEKppwfn9Rmno9J7oCfAEebmg/YhvfJNq2tf8VN3VWHzmHTJ+q691Q/3ZCRqVNK3VVmWqsCI6pSpUctxbukKd1ZcNRWTbhWcoxoyrWKcjvMKwWl0JySEECIaSkJCCCGioSQkhBAiGkpCQgghoqEkJIQQIhoVq45LkFbWUPWMGfR5WdH2ZtynBPKo4FicVQXlyjbmT2XFPKpD0Im6VDz0Otifi7iGx7EmGFTyxSquOo5I+qBrha0t63qSNeGV5Fl9uzzf+BHHUXyl/6LAfAMLrOIqWStm9WC7bYF4nLE49Ye0zgB5QVDFIBmjTxfqq3xLVXPWexC7lsaMrEqzDN0JCSGEiIaSkBBCiGgoCQkhhIiGkpAQQohoVKwwIYQwjoVLqrUR8T7Om7jogRavcxr9eB6He4tVlX68s8DxnNwsOodxLFr8HkKlDON0776OHM0TZnXC2rsO6vTtoec83Z6Ow/EgGxhPIFP6MdknZaJXsBc/aVsYJuttuPSibID9/kEfzvtq941zUOOcs6KDXIFQ8uFo4U9zh2x7hBBCTAGUhIQQQkRDSUgIIUQ0lISEEEJEQ0lICCFENCpWHZcgpIrY8UJJlhcNaem0qLE0JV4XHq8cxlTaOFUvTE1mtvdWR3MUAOTtmWrMHgv9tDRx0c941e5K78LpfMQtjlwlAB1ty4PrvMJXkI4qJulFLn2N86Ju9vgKBV8RSat7Nh+vCK4MwtDyFYs0+/YcL43uhIQQQkRDSUgIIUQ0lISEEEJEQ0lICCFENJSEhBBCRKNi1XEFhFSBL25/ZO2w1S1eHZjlB8eVNmTHCFMIMe84Qx2XKb2AF8DVV+VQ2oxzIeywQ4HDvcbY9bTPS8b0s/Kqr3wFwkgnrj48Hl/UysutdiyDL59XpWgWTfMq0jyFBJ2GbeT1xudjvE9kfZ/xqQKW/oExDGeRR6bgK5O8tCR0JySEECIaSkJCCCGioSQkhBAiGkpCQgghoqEkJIQQIhoVq45LkBZ/cD+i0tU9Hk8jwFdZlfbNlGoucQ/zybI/RxQKw3bfhicW6WIcKaHvOpj+e9zczzUUWs3WOF9UOeQW9zj+gF5j1gdTiJVeLXQc6R37AyPifJ2QMqfskFacqRSpao6qyRxVW7NZu4+MHaevZUc1V35mna8r442Cq1+dhpcO5R1dyiWiOyEhhBDRUBISQggRDSUhIYQQ0VASEkIIEQ1XEtqyZQuuvvpqzJo1C7NmzcKiRYvw7LPPFveHENDe3o6WlhbU1tZi6dKlOHTo0FkNLAmlb6MPc8/cgrklGZhbUQlx5maPztySBObG+7bHOHpZxm6ZTNbcEvJfKBTsLaS3BGwjQw/B3Nh8QkhvbNzsoKx3RiiE1IZgb6cLKJ650TWUJKktk8Dc+OUn8+eHLXmjM2KnwDyv4y3ckl8o41y40tZJCMF+UY2zhYyxkXOSzVaRLWtunjVO15uxfpKErzg6T8frgR2TXU5zddL334nhSkKXXHIJHnnkEbz88st4+eWX8Sd/8if4sz/7s2Ki2bBhAzZu3IjNmzdj3759aG5uxrJly9Df3z/xkQohhJh2uJLQ7bffjj/90z/FZZddhssuuwzf+ta3MHPmTLz00ksIIWDTpk1Yv349VqxYgfnz52P79u04efIkduzYMVnjF0IIMYU562dCIyMj2LlzJ06cOIFFixbhyJEj6O7uxvLly4ttcrkclixZgr1799J+8vk8+vr6xmxCCCHODdxJ6ODBg5g5cyZyuRxWrVqFp59+GldccQW6u7sBAE1NTWPaNzU1FfdZdHZ2orGxsbjNnTvXOyQhhBBTFHcS+uhHP4oDBw7gpZdewle+8hXcddddeO2114r7z/zlcPjdgzjGunXr0NvbW9y6urq8QxJCCDFFcdv21NTU4CMf+QgAYMGCBdi3bx+++93v4utf/zoAoLu7G3PmzCm27+npSd0dvZdcLodcLpeKW2oPJsRw2a4wqw9mxWO2ZxYY5JBeCxRrLLSoHSm+xXsvfRzeYlWkeSZj7fAV8OLFukrvhxXw4uuHXVAjTryP2LVnh2RFClFI/4VdzBHcWoYcs/SrM17nZJ7s5FpLnBY0tPsosNesWUTSbss+H5OXFb8+7FrYje2+WWuHlZWniOBoJ55xe15rpTPh3wmFEJDP59Ha2orm5mbs3r27uG9wcBB79uzB4sWLJ3oYIYQQ0xDXndADDzyAW2+9FXPnzkV/fz927tyJF154Ac899xySJMGaNWvQ0dGBtrY2tLW1oaOjA3V1dVi5cuVkjV8IIcQUxpWEfvOb3+BLX/oSjh49isbGRlx99dV47rnnsGzZMgDA2rVrMTAwgNWrV+PYsWNYuHAhdu3ahYaGhkkZvBBCiKmNKwl9//vfH3d/kiRob29He3v7RMYkhBDiHEHecUIIIaJRsUXtTKiyzQraXRRIgSxafMvomwlhMlR9ReLsoMYxs4l9qbJVNWZ8KH+SjKXUII8zRR6XL1pBVqiMFBnzVEeDrXakKiNn39Z58RZLZCcrGCq40aGkx+JVk1F1k2fspS/Z8ZrbO8jc2TrMOJSH7LxmqOqUrEMuazRCHo2YT1E22j79F1zR6uvd7GfiQjoT3QkJIYSIhpKQEEKIaCgJCSGEiIaSkBBCiGgoCQkhhIhGxarjQmKo0Kjww5BiUEXNxP3D3CoWopLJFMhYDMVOyFabbatztWb85PF3zXhhZNjoxFbY0U8oTMXE1Fp2YztKPa6YIq/0sXg9/FwWgU6/Nu4z6FmftBPSB3tNeHAqDAkOR0Z6jdl8gqW8ZGs2Q94CiUqTvgUZY0lcfnLgSk96bo017l7ipV9Pfr5LizF0JySEECIaSkJCCCGioSQkhBAiGkpCQgghoqEkJIQQIhoVq45Lfvffe+FKjnTMa+XFxT1eLVwaVv2TimcKaXVPkrU/L9TkZphx5n01MjKSHkdIxwCu7gkJ8X0jn2lsVZpPCUSVOV45kHlMEqd+Y5Znlw+XzR64as5DlqoXSx+9pzorANBCpJY1mbNoaTBeJ6Px9HouEK/C6mpbdZqtYm+NZN1aEy2DYnAUx/VxKVR9Q2R9W1Hma2ihOyEhhBDRUBISQggRDSUhIYQQ0VASEkIIEY2KFSaMPu5K0iED87E3KVbFHsLTB44lHm9cWDE1R7G7DLGtqa7JmXFWrCsYtj3Wg1w2jtEd7LNL6Y+tmYUOP7ce859xOzLaeu1vjKalO6uMH/cUX3Pa87gECM6H6t4ifZa1DhcTMSGM/QeFYUOYQLqorrGFPUyYQAs6WhfDaeXkLUdnXSJ62TwWVHAKYSxxkIraCSGEmAooCQkhhIiGkpAQQohoKAkJIYSIhpKQEEKIaFSsOi5JDKWHo/Ac1wGxvDt51iVcfcVsiKwx2jY8VdW2Oq4qa1/aoeFTqZhZ6A5cCZRhPiouCx1WqMyWMTF1oEsG51Z80Y6MkE8zSdVHrHafp3+3sM2jhGJ9sPZMllV6KTSm9gNRdRZGhowoU5fa6rgMKWrnUo05vXK81mGmHZjXr8ylCmbX0hqHbHuEEEJMAZSEhBBCRENJSAghRDSUhIQQQkRDSUgIIUQ0KlYdZ3nHUdsmQ4lRICorj38WwDy0aJUtuw/qY8f8tgxfLfJ5IZslRbmqa8z4qZPpvpk6LkMUdmwsCVX7OYranUW5N3LQUltSbJUiWUNO0y7mG8gwz6C3gNnExYt0R8LWMlVrGXH2+iHxEVMFBwwOGvHEVsEx70VewI0pXY315vQNZMo75uFoRp3riioSzVjpZnieYehOSAghRDSUhIQQQkRDSUgIIUQ0lISEEEJEQ0lICCFENCpYHReQ0mi4S5qmcVeM9BzTK4dxeMpRHzdSAbKq2lYDWWMZGbbVcdkqUnGVjIWpe+y4o0zuOM0d4jg33CLP4x3nUDZhPLsxS33lO4dMGeo7V7QkLGnNVHOlBsep/EtUnYWR9FgyOVst6lbHUUViOs5e3hl2wsuxaJ1dcJWmVQ259IN6PPZ0JySEECIaSkJCCCGioSQkhBAiGkpCQggholGxwoQkSUoWEZDHrY5ouXDaqFAbovQOy8pntA/7Elbn6uy+M+liXUyYYBcHAzLMP4k9tDXiGfZMnRQTowUAXde5dNuRcXE8QPZa69DVbPXDdC2kZ/pA3FFgrkBVD2xGxD7LsuJxFakDCkN2fGQkPZaaKluAkCVFIdmLk1rXmF2w10nJXfyueenr1llbESHje12ZfZjiBgkThBBCTAGUhIQQQkRDSUgIIUQ0lISEEEJEQ0lICCFENCaUhDo7O5EkCdasWVOMhRDQ3t6OlpYW1NbWYunSpTh06NBZ9J6ktyRjbqeVdGM3mJvZ7zib9R8dcZIxN2Rgb+ywnvFlsuaWq60zt6qqGaltpBDMrTAyYm+FYXMLhRFzQyikthCCuSVsQ8HcXOeQXAd6ds11ZW98wZHr5u3ntIvVezfSPV/7Vif2xv6zruVoMboRcwuFgrklYSS1hTBsbhgZMrfhQXsbKSC11cyoN7dsTc7c3K9N44Szaxxgb863Jtf65Bt5z7InPymcdRLat28ftm7diquvvnpMfMOGDdi4cSM2b96Mffv2obm5GcuWLUN/f/+EByuEEGJ6cVZJ6Pjx47jzzjuxbds2nH/++cV4CAGbNm3C+vXrsWLFCsyfPx/bt2/HyZMnsWPHjrINWgghxPTgrJLQPffcg9tuuw2f+tSnxsSPHDmC7u5uLF++vBjL5XJYsmQJ9u7da/aVz+fR19c3ZhNCCHFu4HZM2LlzJ/bv34+XX345ta+7uxsA0NTUNCbe1NSEN9980+yvs7MTDz30kHcYQgghpgGuO6Guri7cd999+MEPfoAZM1i9GqQsRkII1L5k3bp16O3tLW5dXV2eIQkhhJjCuO6E9u/fj56eHlx//fXF2MjICF588UVs3rwZhw8fBjB6RzRnzpxim56entTd0WlyuRxyOcO/6feSnt/DvLKM/MY9nnwqj+AwY6JHJIWjPGXQLP+10ca211p1rt6M18xIx08M2KKRIeLNlcmSonasEFjB8MIjbTO21Rg9uUli+41ZH68y5DMXPbeOioZ0VdHChXaY+rtZY3F5wY238tPtC5a3G4AQ7POdEN83EM/DQiHtV1gYttfb8KAdHxqyPQ+RqU2FZsycZTatytpvgQn7fE5fh+kQew9i15gXHSSvN7O5b73R1ebyRzRijtsb153QLbfcgoMHD+LAgQPFbcGCBbjzzjtx4MABfOhDH0JzczN2795d/JvBwUHs2bMHixcv9hxKCCHEOYDrTqihoQHz588fE6uvr8eFF15YjK9ZswYdHR1oa2tDW1sbOjo6UFdXh5UrV5Zv1EIIIaYFZS/lsHbtWgwMDGD16tU4duwYFi5ciF27dqGhoaHchxJCCDHFmXASeuGFF8b8O0kStLe3o729faJdCyGEmObIO04IIUQ0plRlVaYesaKlVmUt9kGFUJb0g7RkiidDHQYABXLQAtJKI1qlkaiYqi3FIYC6hvNTsZO9vzXbjgwO2PGsU/VjxAOpuAmiVqoiCkN6MYxTWygw9ZGtMKRryFRCOSqigimbQK9nYpSiZVVOzaql4/Rtniz2gmBqtxHSt6GCG20/mIoND6VjADA4mDfjp4bsMVY3pJVwM+ptdRyy5NrbrbnC0JKDsaVJO2GvcY9ikvXNemCv2XQsw9qaa6X0gehOSAghRDSUhIQQQkRDSUgIIUQ0lISEEEJEQ0lICCFENCpYHZdWm7GqppbCg3lcjXc8M24ez8alsBsvnEkrdhKiJisE4iuVrTbj9bPS6ri+uvPMtid7bVVScsr28mIeeYlxxpKkxmzLPhcVktJVYwCI1RpbP8wnzaHwIepFa+6jxySQHZbiLRC1H++bdW7Mn3nHjdivq1Cw10QYseMjhuJtKE9UcCftPgoZ2x9x5oXNqdiMerstU3xxTzU7boU971dnhdmNr2+qALXWClUFp3d41Mm6ExJCCBENJSEhhBDRUBISQggRDSUhIYQQ0ahYYUIhjG7vhWZMl21E6YXKeGtf37R4nePhXSAChEyGWbfYfefq027mDRfaBQdPnjxuxgeH7CJ4mQwRLBhDz7JCXdW2jUogdjG8sJsRZ7Y95Bwy0YNlucPG534ITcQDljCBF9JjXZMxGmMPxG6HCX4KTIAwdMqMWyKE/IDd96khe03Uz7bX7XkXpYUJVVVECEN1LU5RgenuxdYP64OIb+j7hEc2RXAUXbTteezGXKSVRndCQgghoqEkJIQQIhpKQkIIIaKhJCSEECIaSkJCCCGiUbHqOAtWxKs8eFRM3nEQlYxL3sTsRVicqMky6UvecP7FZtN83i4y9m7PL+32Q7aaDklaaVXDir1R/yT7nPNPUaUXIxzHiIlErY7ISOi4J265Q114vEXtjHiBqeOGiQpu2F4rQ4N2PH8q3f/gMCnEeMEcM37xB1rNeG1dXSpWJqMcX0dURMuKC5LmnoKJDqup073bfZfacrwdpaE7ISGEENFQEhJCCBENJSEhhBDRUBISQggRDSUhIYQQ0ahYdVySJCn1B/UuKkNxJzoOI0ZVScxviiikPKoXXnuKSVOIN5vhh5arrTXbXtT0AbtrIu459r9MNWd4zSWkCJrdNewSfRhnBad7ymTtxhlaHI55eRleZq61CYyQ9lRQZarjHEXqxolbPnFM7VYYsgvPDZOCdAMD9nXOD6W93GrPv8RsO/vSD5nxhsZZZjxrFBh02DSOS+IxRaOWb75CelRLZ4wlYZ6MtAvmVVh6L1ZTj2BOd0JCCCGioSQkhBAiGkpCQgghoqEkJIQQIhpKQkIIIaJRueo4WFoMpj6zguWoiWorU6hfG1HBkbCr6iZT0vmxlHf2Z5GaGbZq7oImW8WUGL50AND3zq9TsVODfWbbkQKp3FmwK3RW19jtLSVcqLLHF8i4E0NlNRo3qpwSxZPHC24UpmzzqOOI4on4wY0MpZVww4O22m2I+Anm8/YxRzDTjNdfmFZeXjR3ntl2ZmO6GjAAZEjlW49tIH9dOeV05ShySiV8jo7YtfcNxYU1as/Z052QEEKIaCgJCSGEiIaSkBBCiGgoCQkhhIhGxQoTAoyHadx7ovSOy/Hsjz4Pddr2eJxefHXXyvEskz5sn1E3w4xfNMe2+anOpQUOvb/9jdk2f/y3Znx4gAgTiI1MdXXaLiZbbdjtgNv5ZJnNTzbdDy3GR6CF58iFK1iCDfYQesTue2jIttAZHkzHhwbtvgeH7XOIattCZ9bFl5rxC5tbUrHaelsIU8XqBVIvKytEF7mrby4GSbcPRDjB3icYrvbOwo3UWcjYw973zHPrGLLuhIQQQkRDSUgIIUQ0lISEEEJEQ0lICCFENJSEhBBCRKNi1XEmDjUZFYcxNQwt7lS6zMxtjUFdV0rviRfC8lm6mH0zhRBR+1Xl7OXUeNGFqViurs5se7z3fDN+sv9dMz58yiiYB2A4P5CKZfK2bU02ayvsWDyTTX92YxYyTMBVYIX0SNxqXyACO+LOg+GCPZgC0tciqbKvT33jeWZ85oWzzXhDY6MZz9Wmi9ply2WhM0G1FuAt9gZYzlf8leZTqrkq8vE3PlffpuKNKnGNPpiUzkB3QkIIIaKhJCSEECIaSkJCCCGioSQkhBAiGkpCQgghouFSx7W3t+Ohhx4aE2tqakJ3dzeAUUXJQw89hK1bt+LYsWNYuHAhHn30UVx55ZXlGa3Lz4mpjMozFA9UacOK4zn0flSEwg3hjOORLrxqJRKuqkkvs/qsXewsV2v70jU0XmDGT544bsZPnUir5vIn7bZDQyfN+CAp4AakvdYSUoyOqhepAWG1HTYL75GXbzZnhquIIjFXny4aV9dgF5Krq6834zUz0mo3AMiywnMWTLnK2tPl6VXTOfBMhxXWJEUkuVyWHWHipeoms9hdqbjvhK688kocPXq0uB08eLC4b8OGDdi4cSM2b96Mffv2obm5GcuWLUN/vy2jFUIIcW7j/p1QVVUVmpubU/EQAjZt2oT169djxYoVAIDt27ejqakJO3bswN133232l8/nkc///vcYfX122WchhBDTD/ed0Ouvv46Wlha0trbi85//PN544w0AwJEjR9Dd3Y3ly5cX2+ZyOSxZsgR79+6l/XV2dqKxsbG4zZ079yymIYQQYiriSkILFy7EE088geeffx7btm1Dd3c3Fi9ejHfeeaf4XKipqWnM37z3mZHFunXr0NvbW9y6urrOYhpCCCGmIq6v42699dbi/1911VVYtGgRPvzhD2P79u248cYbAaStXkII4xb8yuVyyOXsB6lCCCGmNxPyjquvr8dVV12F119/HXfccQcAoLu7G3PmzCm26enpSd0dlYZVW5Wp49jfl9qW920lUKZ2S6gnlLfCYlppxZRqtNAj6dn+A895HU81R+Yf0jfcIWO3zZIymjPqbdWc5UEGAMPnpT3LmNptiMSHB23vuEEjXhixDdvY5y9b7QZU1djzqapKf1CzKrwCQKbaVthVk76rDfViVZXdN5tPNlsGJSX9Xsa3Pi2lYjDW4LiUwWuOqeD4uMn7BBl6sBSW3ICOxJmCLx0reIzzHArFCf1OKJ/P42c/+xnmzJmD1tZWNDc3Y/fu3cX9g4OD2LNnDxYvXjyRwwghhJimuO6E/vZv/xa33347Lr30UvT09ODhhx9GX18f7rrrLiRJgjVr1qCjowNtbW1oa2tDR0cH6urqsHLlyskavxBCiCmMKwn98pe/xBe+8AW8/fbbuPjii3HjjTfipZdewrx58wAAa9euxcDAAFavXl38sequXbvQQH74JoQQ4twmCZ7iNX8A+vr60NjYiOee/1fUp36dTeqhmDYI9i/Y/c+ErD58z4TcNT6C45kQ6ZleVjPscaIAAjm3nmdCBXZ9iKWFcUrGbT88PJKK6ZnQZD4TYs8AyDf+RnNak8n7TMisdzW5z4QsMtQZwecMwZ8JGW1jPBMyzu2JEyfwfz79GfT29mLWrFlsUOSvhRBCiD8QlVtZ1RLH0axtKdhYfmUeX+Wo6uj7tBHox/t0e65H8x3TLJhIKqVyYZP3zqn0Ko3s0zC9E8zY17mmKt1PDan8GoLth0aKnKIwkr5urFIqU0hl7BsN+rHX9GBjN9Ksa/IxOWP5CZI14a1Y7KmG7Lcq9HyJ4+ycNi+9H/qNidPbjn/zYvXj83V0f0tj9mG8j7H3NgPdCQkhhIiGkpAQQohoKAkJIYSIhpKQEEKIaFSsMCFB+tEYfwxpPMh3+tm4bG7KYOkxbtyI8UN67YkskYDvITSVkpJCbQWzJ3JM+jyTPUB2iCGcJMRaqMoSCVAhjO/hNBu3pb9gbbkdlI1nvWXI51YqVnGMw2t7VY76lBmH0GA0XLpe2hJ8jPZRnp9yBLNAZXne9yzRlGkThImr2XUnJIQQIhpKQkIIIaKhJCSEECIaSkJCCCGioSQkhBAiGhWrjkOSpK1AmI+Koc+gzhhMlUTVdKVbznhNRj1QtRe1RSl9LPysepV3BMuGyNkJVc151E1uhdD7DOq9R6PjYH/gCptQO0myxqkizxSAOn1eyPVJiA2T1Q29xO5XkMPOpjyXzbTPYm9XVEfJrhszPDUtuEjnBPY+WXB0ZF1jet0NdCckhBAiGkpCQgghoqEkJIQQIhpKQkIIIaKhJCSEECIaFauOC0jSihOHFRNXkzlLc3tqO9EdzPuJHNJR1M47GPu82I25Wsl1SJ8X3iQq2MqjUwQRXzElYRnqRMNXyjkQWZZnKPTaO/3QuGjQoWBz9MD3+K6Ps0Yf6cPnD8g7YmPxOfOZfZAXkK2wZH6PxvsVVTIbxyq5pRBCCFFmlISEEEJEQ0lICCFENJSEhBBCRENJSAghRDQqVh03qsQYq7DweXn5lClMxeOq3uipZopxKpRa1RuZeRr1fvJWbywdrlQrvXOu7PHWc3VUrXUWuvRUs/X66dFzRdVnJQ1j3B2+iqtObz/n51lbfeWrnsuXROljpx6TzK+NHdIR9fiqjXZTjvlMvAIxbzkxpaPuhIQQQkRDSUgIIUQ0lISEEEJEQ0lICCFENJSEhBBCRKOC1XEJ0goLj0rGp0izKiNyfMohpkrivZRBsVIO8yun/x7vpnT3OF6d1nvOzU4I3vmUfES/75dniTsrcXKV2cT97Sy/w98Nhgwl3Z5XQyZ9OBRsTC1LvfCcOjirG+9yK8Nl8FMOEe0EbQB1JySEECIaSkJCCCGioSQkhBAiGkpCQggholHBwoS0bY/lZgPYDzkzXjGAy+7C9wSR1XdKeMWv0o9JRQKskJ7xQNgxitH2E3/wzWxrEva5iNkWEczenYIK61yNkp4Pc2IJPuMnXgTOU2SMdeF42E6vgxuHYIM+mXeKHt5vSCXhtY8yWlLrMHZIn92SvSKcr2aPLsOltFBROyGEEFMAJSEhhBDRUBISQggRDSUhIYQQ0VASEkIIEY0KVscZuIqm+eIcj0yEWYP4jugpDuet1GZ96vDYn5z+C7u9/RcFSx7ovRBeCZ9H1FiOAoDlslyhYjJrMOwzpM9CyDV0dk4yZCxkHZqXh6nDvK+fMrSmSkJPEbwy2fN4mjv1heP0YyhamXRzgnJE3QkJIYSIhpKQEEKIaCgJCSGEiIaSkBBCiGi4k9CvfvUrfPGLX8SFF16Iuro6fPzjH8f+/fuL+0MIaG9vR0tLC2pra7F06VIcOnSorIMWQggxPXCp444dO4abbroJn/zkJ/Hss89i9uzZ+O///m+cd955xTYbNmzAxo0b8fjjj+Oyyy7Dww8/jGXLluHw4cNoaGgo/WBp6zgkxDzOUg4VnEW2mMIjY/qekT6IeoSpYUzVGGnvdbLitnTpv6Dnyll4zlUejKmpCsRrzesr5tAUcTEimadlFEcOlyFrliogHUojer7LoNSj3n7O60D901yWjL4JWcou63Xs73m8Y6Yn5FXosjF6xLK2itJfzNNWL5I+gvWGVfqZdSWhb3/725g7dy4ee+yxYuyDH/zgewYTsGnTJqxfvx4rVqwAAGzfvh1NTU3YsWMH7r77bs/hhBBCTHNcX8c988wzWLBgAT772c9i9uzZuPbaa7Ft27bi/iNHjqC7uxvLly8vxnK5HJYsWYK9e/eafebzefT19Y3ZhBBCnBu4ktAbb7yBLVu2oK2tDc8//zxWrVqFr33ta3jiiScAAN3d3QCApqamMX/X1NRU3HcmnZ2daGxsLG5z5849m3kIIYSYgriSUKFQwHXXXYeOjg5ce+21uPvuu/FXf/VX2LJly5h2Z34PHEKg3w2vW7cOvb29xa2rq8s5BSGEEFMVVxKaM2cOrrjiijGxyy+/HG+99RYAoLm5GQBSdz09PT2pu6PT5HI5zJo1a8wmhBDi3MAlTLjppptw+PDhMbGf//znmDdvHgCgtbUVzc3N2L17N6699loAwODgIPbs2YNvf/vbroEliaHyITIRS4jBFUI+r7VgKdhY52x8XhMp65B2S7e8x+VLxxRftFioQ03mqiA67h+U3g1R7IQsuW60mqtD2sVUZlS8ydanodK0W9I9tPqrOR33wiJxux/Pp1+vmiwx3xBIJ1SR5vRHNF+0ZO5lqwg7eQpQa+ye91SuokzjSkJ//dd/jcWLF6OjowN//ud/jp/85CfYunUrtm7dWjzwmjVr0NHRgba2NrS1taGjowN1dXVYuXKl51BCCCHOAVxJ6IYbbsDTTz+NdevW4Zvf/CZaW1uxadMm3HnnncU2a9euxcDAAFavXo1jx45h4cKF2LVrl+83QkIIIc4JkuCrGzDp9PX1obGxEc8///+jvr5+7E7XHb93WuzrOCNIv0azv75hX8fRM1+OS8K+GjR+DOr9YW/CflBK5llw2NzzrzTpd4ClQ38f6v06zsD7Y0jv13HGdzi0BEfpXYz2Y34d5ysTwb5+SciPks3W9Csc8rUWmZD5dRyrNOH+Oo78+Hgyv45z/PDc+aSAd24MMkNfJ+m2J06cwKc//Rn09va+73N+eccJIYSIRuUWtTOVCaxtOkOzT5TumwzrCTK9c2D+PL6Dej5Ts09s3InH+vTk7YPEyUNoWyPgtBeZuDsPeAEzhuMzGp1PuYxhrL69x5x4VTt6x0P/gsoKjD58d1n8m5HS3w845PVGvWvKUHrObZVkvrJIH+SQ7Hp67MomiO6EhBBCRENJSAghRDSUhIQQQkRDSUgIIUQ0lISEEEJEo2LVcSFYaoxyWOuwI5b+mwv+Exefai5xyU2YJY6ruXmuuCrJNx/ucmMdkzSlhQGdP4AwrZyc4/aI47zqK7dobuK/CXGtT/cPnJzKNrNxeeLWMbla1im7dKhOeZE6e8G5CgBinNdnOXC8N02wpp3uhIQQQsRDSUgIIUQ0lISEEEJEQ0lICCFENCpOmHD6QeGJEyetveP+zXvxWn1QjId/7ge/VCXgjFvQh9OlG5j6cZi90rE4ay/RoTiECawLNu4yPFMvH+Uwq2Rdl16Qy2utY9aS+l1PJffBJhpDmMAwFEKTL0wobWjjwda4p2treKffv0vxx644F+1f/vKXmDt3buxhCCGEmCBdXV245JJLxm1TcUmoUCjg17/+NRoaGtDf34+5c+eiq6trWpf97uvr0zynEefCPM+FOQKa59kSQkB/fz9aWlqQIeU8TlNxX8dlMpli5jx9ezpr1qxpvQBOo3lOL86FeZ4LcwQ0z7OhsbGxpHYSJgghhIiGkpAQQohoVHQSyuVyePDBB5HL5WIPZVLRPKcX58I8z4U5AprnH4KKEyYIIYQ4d6joOyEhhBDTGyUhIYQQ0VASEkIIEQ0lISGEENFQEhJCCBGNik5C3/ve99Da2ooZM2bg+uuvx3/8x3/EHtKEePHFF3H77bejpaUFSZLgn//5n8fsDyGgvb0dLS0tqK2txdKlS3Ho0KE4gz1LOjs7ccMNN6ChoQGzZ8/GHXfcgcOHD49pMx3muWXLFlx99dXFX5gvWrQIzz77bHH/dJjjmXR2diJJEqxZs6YYmw7zbG9vR5IkY7bm5ubi/ukwx9P86le/whe/+EVceOGFqKurw8c//nHs37+/uD/KXEOFsnPnzlBdXR22bdsWXnvttXDfffeF+vr68Oabb8Ye2lnzwx/+MKxfvz48+eSTAUB4+umnx+x/5JFHQkNDQ3jyySfDwYMHw+c+97kwZ86c0NfXF2fAZ8GnP/3p8Nhjj4Wf/vSn4cCBA+G2224Ll156aTh+/HixzXSY5zPPPBP+7d/+LRw+fDgcPnw4PPDAA6G6ujr89Kc/DSFMjzm+l5/85Cfhgx/8YLj66qvDfffdV4xPh3k++OCD4corrwxHjx4tbj09PcX902GOIYTw29/+NsybNy98+ctfDv/1X/8Vjhw5Ev793/89/OIXvyi2iTHXik1Cf/RHfxRWrVo1Jvaxj30sfOMb34g0ovJyZhIqFAqhubk5PPLII8XYqVOnQmNjY/j7v//7CCMsDz09PQFA2LNnTwhh+s4zhBDOP//88A//8A/Tbo79/f2hra0t7N69OyxZsqSYhKbLPB988MFwzTXXmPumyxxDCOHrX/96uPnmm+n+WHOtyK/jBgcHsX//fixfvnxMfPny5di7d2+kUU0uR44cQXd395g553I5LFmyZErPube3FwBwwQUXAJie8xwZGcHOnTtx4sQJLFq0aNrN8Z577sFtt92GT33qU2Pi02mer7/+OlpaWtDa2orPf/7zeOONNwBMrzk+88wzWLBgAT772c9i9uzZuPbaa7Ft27bi/lhzrcgk9Pbbb2NkZARNTU1j4k1NTeju7o40qsnl9Lym05xDCLj//vtx8803Y/78+QCm1zwPHjyImTNnIpfLYdWqVXj66adxxRVXTKs57ty5E/v370dnZ2dq33SZ58KFC/HEE0/g+eefx7Zt29Dd3Y3FixfjnXfemTZzBIA33ngDW7ZsQVtbG55//nmsWrUKX/va1/DEE08AiHc9K66Uw3s5s9JgCIFXWZwmTKc533vvvXj11Vfxn//5n6l902GeH/3oR3HgwAG8++67ePLJJ3HXXXdhz549xf1TfY5dXV247777sGvXLsyYMYO2m+rzvPXWW4v/f9VVV2HRokX48Ic/jO3bt+PGG28EMPXnCIzWaluwYAE6OjoAANdeey0OHTqELVu24C/+4i+K7f7Qc63IO6GLLroI2Ww2lX17enpSWXq6cFqNM13m/NWvfhXPPPMMfvzjH4+prDid5llTU4OPfOQjWLBgATo7O3HNNdfgu9/97rSZ4/79+9HT04Prr78eVVVVqKqqwp49e/B3f/d3qKqqKs5lqs/zTOrr63HVVVfh9ddfnzbXEgDmzJmDK664Ykzs8ssvx1tvvQUg3muzIpNQTU0Nrr/+euzevXtMfPfu3Vi8eHGkUU0ura2taG5uHjPnwcFB7NmzZ0rNOYSAe++9F0899RR+9KMfobW1dcz+6TJPixAC8vn8tJnjLbfcgoMHD+LAgQPFbcGCBbjzzjtx4MABfOhDH5oW8zyTfD6Pn/3sZ5gzZ860uZYAcNNNN6V+LvHzn/8c8+bNAxDxtTlpkocJclqi/f3vfz+89tprYc2aNaG+vj78z//8T+yhnTX9/f3hlVdeCa+88koAEDZu3BheeeWVouz8kUceCY2NjeGpp54KBw8eDF/4whemnBT0K1/5SmhsbAwvvPDCGMnryZMni22mwzzXrVsXXnzxxXDkyJHw6quvhgceeCBkMpmwa9euEML0mKPFe9VxIUyPef7N3/xNeOGFF8Ibb7wRXnrppfCZz3wmNDQ0FN9rpsMcQxiV2VdVVYVvfetb4fXXXw8/+MEPQl1dXfjHf/zHYpsYc63YJBRCCI8++miYN29eqKmpCdddd11R5jtV+fGPfxwApLa77rorhDAqkXzwwQdDc3NzyOVy4ROf+EQ4ePBg3EE7seYHIDz22GPFNtNhnn/5l39ZXJsXX3xxuOWWW4oJKITpMUeLM5PQdJjn6d/CVFdXh5aWlrBixYpw6NCh4v7pMMfT/Ou//muYP39+yOVy4WMf+1jYunXrmP0x5qp6QkIIIaJRkc+EhBBCnBsoCQkhhIiGkpAQQohoKAkJIYSIhpKQEEKIaCgJCSGEiIaSkBBCiGgoCQkhhIiGkpAQQohoKAkJIYSIhpKQEEKIaPw/OTNkc3/iA5EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(image_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45aed00a-f546-4e36-b9ca-e866c56c1449",
   "metadata": {},
   "source": [
    "## Explanation of Using `__array__()` with HDF5 Files in Python\n",
    "\n",
    "When reading data from an HDF5 file using the `h5py` library in Python, you may encounter an error that looks something like this:\n",
    "\n",
    "\n",
    "This error occurs when the HDF5 dataset contains objects that cannot be directly converted to an HDF5 datatype. One solution to this error is to read the data into memory and convert it to a NumPy array using the `__array__()` method.\n",
    "\n",
    "When you call the `__array__()` method on a dataset in an HDF5 file, it converts the dataset into a NumPy array that can be stored in memory. This allows you to manipulate the data using NumPy functions and avoids issues with object datatypes.\n",
    "\n",
    "However, it's important to note that reading large datasets into memory can be memory-intensive, especially if the dataset is very large. This means that it may not be practical to read in all of the data at once, and you may need to read the data in chunks or use other memory-saving techniques to avoid running out of memory.\n",
    "\n",
    "In summary, using the `__array__()` method can be a useful way to convert HDF5 datasets to NumPy arrays for further manipulation, but it's important to be aware of the memory requirements and use appropriate techniques to manage memory when working with large datasets.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "a440a53b-5e48-46e1-896e-1c66da23a9ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Absorption [a.U.]')"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the spectrum for the randomly-selected index\n",
    "spectrum_data = materials_icp['spectra'][index].__array__()\n",
    "eV = materials_icp['energy_eV'].__array__()\n",
    "\n",
    "plt.plot(eV,spectrum_data)\n",
    "plt.xlabel('Energy [eV]')\n",
    "plt.ylabel('Absorption [a.U.]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "171a3172-7741-45b6-b48a-a0391f99dddd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the atomic fraction data and keys from the materials_icp dictionary\n",
    "atfrac = materials_icp['atfrac'].__array__().T\n",
    "atfrac_keys = materials_icp['atfrac_keys'][:]\n",
    "\n",
    "# Find the indices of the non-zero entries in the atomic fractions at the selected index\n",
    "nonzero_indices = atfrac[index].nonzero()[0]\n",
    "\n",
    "# Initialize the shorthand string\n",
    "shorthand = ''\n",
    "\n",
    "# Loop over the non-zero indices in the atomic fractions at the selected index\n",
    "for idx in nonzero_indices:\n",
    "    # Get the element symbol and amount for the current index\n",
    "    el = str(atfrac_keys[idx].decode('utf-8')).split('.PM')[0]\n",
    "    amnt = atfrac[index][idx]\n",
    "    # Add the element symbol and amount to the shorthand string\n",
    "    shorthand += el+f'_{amnt:.2f}'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "979ed2b3-387e-470a-aadd-5e02c9739930",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the material shown is a metal oxide witht he following composition:Bi_0.05V_0.60Mn_0.30La_0.05Ox\n"
     ]
    }
   ],
   "source": [
    "print('the material shown is a metal oxide witht he following composition:'+ shorthand+ 'Ox')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a504c703-99e7-4161-8a66-66e21c1c7a86",
   "metadata": {},
   "source": [
    "# Let's download catalyst data\n",
    "\n",
    "The JCAP (Joint Center for Artificial Photosynthesis) at Caltech has collected a high-throughput dataset containing composition data and corresponding overpotentials required for oxygen evolution reaction (OER) measurements. The dataset was obtained using combinatorial synthesis, and contains information on thousands of samples with various compositions. For each composition, researchers measured the overpotential vs. OER necessary to generate 10mAh/cm2. The dataset was aquired as part of https://doi.org/10.1039/C9SC05999G \n",
    "\n",
    "The measurements were conducted using chronoamperometric techniques, also known as chronoamperometry or chronoamperometric measurements. This technique involves applying a constant potential to an electrode and monitoring the resulting current as a function of time. By analyzing the resulting current versus time curves, researchers can extract information about the electrocatalytic activity of the electrode, such as the overpotential required for a given reaction rate.\n",
    "\n",
    "In the context of electrocatalysis, an overpotential represents the difference between the measured potential and the thermodynamic potential required to drive a particular reaction. The overpotential is typically a measure of the effectiveness of an electrocatalyst, with lower overpotentials indicating more efficient catalysis. Therefore, the JCAP high-throughput dataset provides valuable information for researchers looking to identify and optimize effective electrocatalysts for various applications."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "6380764a-8aee-454e-8f58-5bfbdc46164c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import urllib.request\n",
    "import pickle\n",
    "\n",
    "# Check if the file has already been downloaded\n",
    "if not os.path.isfile('tri_data_share.pck'):\n",
    "    # If the file hasn't been downloaded, download it\n",
    "    url = 'https://github.com/SantoshSuram-TRI/ACE-I/raw/master/tri_data_share.pck'\n",
    "    urllib.request.urlretrieve(url, 'tri_data_share.pck')\n",
    "\n",
    "# Load the data from the downloaded file into a variable called 'materials_OER'\n",
    "with open('tri_data_share.pck', 'rb') as f:\n",
    "    materials_OER = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37c1d49a-8385-4e93-b1f6-67f6e730304a",
   "metadata": {},
   "source": [
    "ok we need to have some additional information on the dataset. The data was aquired on a number of libraries where the plate ID's had the following composition:\n",
    "\n",
    "| Plate ID | Label | Composition system |\n",
    "|---------|-------|--------------------|\n",
    "| 3496 | A | Mn–Fe–Co–Ni–La–Ce |\n",
    "| 3851 | B | Mn–Fe–Co–Ni–Cu–Ta |\n",
    "| 3860 | C | Mn–Fe–Co–Cu–Sn–Ta |\n",
    "| 4098 | D | Ca–Mn–Co–Ni–Sn–Sb |\n",
    "\n",
    "Again we need to turn these lists into compositional strings like such:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "552e2445-f6cc-4803-8bc3-d80509e47480",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the plate ID\n",
    "plate_id = '3496'\n",
    "\n",
    "# Get the keys in the materials_OER dictionary for the specified plate ID\n",
    "materials_OER[plate_id].keys()\n",
    "\n",
    "# Get the OER overpotential and composition data for the specified plate ID\n",
    "OER_overpotential = materials_OER[plate_id]['fom']\n",
    "composition_OER = materials_OER[plate_id]['comp']\n",
    "\n",
    "# Define the keys for the atomic fractions in the materials_OER dictionary\n",
    "atfrac_keys_OER = 'Mn-Fe-Co-Ni-La-Ce'\n",
    "\n",
    "# Select a random index in the composition data\n",
    "index = np.random.randint(len(OER_overpotential))\n",
    "\n",
    "# Find the indices of the non-zero entries in the composition at the selected index\n",
    "nonzero_indices = composition_OER[index].nonzero()[0]\n",
    "\n",
    "# Split the atfrac keys string into individual element symbols\n",
    "atfrac_keys_OER_split = atfrac_keys_OER.split('-')\n",
    "\n",
    "# Initialize the shorthand string\n",
    "shorthand = ''\n",
    "\n",
    "# Loop over the non-zero indices in the composition at the selected index\n",
    "for idx in nonzero_indices:\n",
    "    # Get the element symbol and amount for the current index\n",
    "    el = atfrac_keys_OER_split[idx]\n",
    "    amnt = composition_OER[index][idx]\n",
    "    # Add the element symbol and amount to the shorthand string\n",
    "    shorthand += el+f'_{amnt:.1f}'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "31c434fa-fe77-4d9e-b2f5-8c5a0b04a5f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Co_0.3Ni_0.6Ce_0.1\n",
      "Overpotential: 0.635603 V vs. OER @ 10 mA/cm2\n"
     ]
    }
   ],
   "source": [
    "print(shorthand)\n",
    "print('Overpotential: '+ str(OER_overpotential[index]) + ' V vs. OER @ 10 mA/cm2')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79a8d46d-81a5-49c8-a8bc-63bdc3ae9732",
   "metadata": {},
   "source": [
    "# A X-ray diffraction dataset\n",
    "\n",
    "Phase region analysis is an important tool for materials scientists and researchers seeking to discover new materials with desirable properties. Phase regions describe the extend of chemical compositions in a system which exhibit desireable materials properties, and understanding the extend and innner and intra region functional property changes of these phase regions can help researchers identify new materials with new and improved properties. However, accurately mapping the phase regions for a given set of elements can be a challenging and time-consuming task, especially for complex ternary or higher-dimensional systems.\n",
    "\n",
    "The phase region analysis can be very complicated and the data aquisition even more so. This is why in:\n",
    "\n",
    "Le Bras, R., Bernstein, R., Gregoire, J. M., Suram, S. K., Gomes, C. P., Selman, B., & van Dover, R. B. (2014). \n",
    "    A Computational Challenge Problem in Materials Discovery: Synthetic Problem Generator and Real-World Datasets. \n",
    "    In Twenty-Eighth International Conference on Artificial Intelligence (AAAI'14).\n",
    "    \n",
    "The scientists have come up with a synthetic dataset generator that allows us to test our algorithms in a \"safe\" environment.\n",
    "\n",
    "The data is available here: https://www.udiscover.it/downloads/materials/synthetic_instance_data.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "662c6984-3b8b-4f75-8418-6f08f8117d0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import urllib.request\n",
    "import zipfile\n",
    "\n",
    "# Define the URL for the zip file\n",
    "url = 'https://www.udiscover.it/downloads/materials/synthetic_instance_data.zip'\n",
    "\n",
    "# Define the local file name for the zip file\n",
    "filename = 'synthetic_instance_data.zip'\n",
    "\n",
    "# Check if the file has already been downloaded\n",
    "if not os.path.isfile(filename):\n",
    "    # Download the file\n",
    "    urllib.request.urlretrieve(url, filename)\n",
    "\n",
    "# Extract the contents of the zip file\n",
    "with zipfile.ZipFile(filename, 'r') as zip_ref:\n",
    "    zip_ref.extractall('.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93047074-620f-431e-a247-12506e0cd854",
   "metadata": {},
   "source": [
    "Ok now it gets a little complicated because we need to read in the data and it is not in the ... let say most simple format. see the following code as an exercise if you can read some wonky python code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "38e0a163-0bb0-4e58-87f5-038f80903ab3",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"synthinst1_t0_n5_p100_s1_inst.txt\", \"r\") as f:\n",
    "    data = f.read()\n",
    "intensities = []\n",
    "lines = data.split(\"\\n\")\n",
    "for line in lines:\n",
    "    if line.startswith(\"I\"):\n",
    "        intens = [float(l) for l in line.split('=')[1].split(',')]\n",
    "        intensities.append(intens)\n",
    "    elif line.startswith(\"Q\"):\n",
    "        Qax = [float(l) for l in line.split('=')[1].split(',')]\n",
    "\n",
    "y = np.array(intensities).T\n",
    "x = Qax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "33b0e714-fb8b-40f0-bda7-72aaf92fc3a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Intensity')"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.plot(x,y,color='black',alpha=0.4)\n",
    "plt.xlabel('Q, scattering vector magnitude [1/nm]')\n",
    "plt.ylabel('Intensity')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ab95a3d-1c40-411d-99e3-4fad53075eb0",
   "metadata": {},
   "source": [
    "While we are at it let's download some experimental data as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "cf8cfdec-b712-46d0-903a-a581ccadc84e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import urllib.request\n",
    "import zipfile\n",
    "\n",
    "# Define the URL for the zip file\n",
    "url = 'https://www.udiscover.it/downloads/materials/2016_suram_phase_mapping_vmnnbo_data.zip'\n",
    "\n",
    "# Define the local file name for the zip file\n",
    "filename = '2016_suram_phase_mapping_vmnnbo_data.zip'\n",
    "\n",
    "# Check if the file has already been downloaded\n",
    "if not os.path.isfile(filename):\n",
    "    # Download the file\n",
    "    urllib.request.urlretrieve(url, filename)\n",
    "\n",
    "# Extract the contents of the zip file\n",
    "with zipfile.ZipFile(filename, 'r') as zip_ref:\n",
    "    zip_ref.extractall('.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "049203a5-cc73-47b9-8173-dfb08ad3ab85",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "with open(\"./data_Si/24297_NbMnVO_v02.txt\", \"r\") as f:\n",
    "    data = f.read()\n",
    "\n",
    "intensities = []\n",
    "Qax = []\n",
    "\n",
    "lines = data.split(\"\\n\")\n",
    "\n",
    "for line in lines:\n",
    "    if line.startswith(\"I\"):\n",
    "        intens = [float(l) for l in line.split('=')[1].split(',')]\n",
    "        intensities.append(intens)\n",
    "    elif line.startswith(\"Q\"):\n",
    "        Qax = [float(l) for l in line.split('=')[1].split(',')]\n",
    "\n",
    "y = np.array(intensities).T\n",
    "x = Qax\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "675fd9ac-3cc1-4286-84bb-618c8fc817bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Intensity')"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.plot(x,y,color='black',alpha=0.4)\n",
    "plt.xlabel('Q, scattering vector magnitude [1/nm]')\n",
    "plt.ylabel('Intensity')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "443fb5a6-cf51-4bd9-a690-9e9be38b9825",
   "metadata": {},
   "source": [
    "Ok that looks much more complex doesn't it? Now we can go for the secnd to last dataset from battery cycling!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3cf4454-2dda-4404-aaa2-522ba3b39d0a",
   "metadata": {},
   "source": [
    "# Time series data - battery cycling!\n",
    "\n",
    "Time series data is a critical data type that is increasingly encountered in many fields. It is especially important in machine learning since many real-world systems generate data that evolves over time. Battery cycling is one such example where a battery is repeatedly charged and discharged, and the performance of the battery changes with each cycle. The data generated from battery cycling experiments is time series data, which means it includes a sequence of observations taken at a specific time interval. The ability to analyze and model this type of data can provide significant insights into the behavior and performance of the battery over time.\n",
    "\n",
    "Machine learning models designed for time series data are essential for predictive maintenance, process optimization, and anomaly detection. They can help identify patterns and trends in the data that may not be easily observable in simple visualizations or statistical analyses. Moreover, these models can also learn from past experiences to predict the future behavior of the system accurately. However, dealing with time series data is often challenging because of its sequential and temporal nature. As a result, it requires specialized techniques and models that can handle the time dependencies in the data. In conclusion, understanding how to deal with time series data is becoming increasingly crucial for machine learning practitioners to unlock its full potential in various fields, including energy storage, finance, healthcare, and many others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "41e4676e-ab32-41e6-b663-a3158b3b215b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████| 1.45G/1.45G [07:58<00:00, 3.04MiB/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Download complete\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "#because I felt fancy here is a download with a progress bar:\n",
    "\n",
    "import os\n",
    "import requests\n",
    "from tqdm import tqdm\n",
    "\n",
    "# Set the URL for the file\n",
    "url = 'https://zenodo.org/record/7299473/files/AutoBassPapercells.hdf5'\n",
    "\n",
    "# Set the filename to save the file as\n",
    "filename = 'AutoBassPapercells.hdf5'\n",
    "\n",
    "# Check if file exists before downloading\n",
    "if os.path.exists(filename):\n",
    "    print(\"File already exists\")\n",
    "else:\n",
    "    # Start the download\n",
    "    with requests.get(url, stream=True) as r:\n",
    "        # Set the chunk size for downloading\n",
    "        chunk_size = 1024\n",
    "        \n",
    "        # Get the file size in bytes\n",
    "        total_size = int(r.headers.get('content-length', 0))\n",
    "        \n",
    "        # Initialize the progress bar\n",
    "        progress = tqdm(total=total_size, unit='iB', unit_scale=True)\n",
    "\n",
    "        # Open the file and start writing the downloaded content\n",
    "        with open(filename, 'wb') as f:\n",
    "            for chunk in r.iter_content(chunk_size=chunk_size):\n",
    "                # Update the progress bar\n",
    "                progress.update(len(chunk))\n",
    "                \n",
    "                # Write the downloaded chunk to the file\n",
    "                f.write(chunk)\n",
    "\n",
    "        # Close the progress bar\n",
    "        progress.close()\n",
    "        \n",
    "        # Print a message indicating that the download is complete\n",
    "        print('Download complete')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d978233-9785-4ff7-9250-f407b200e640",
   "metadata": {},
   "source": [
    "Ok now let's open this file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "0d0d914d-bf47-435e-8826-6de43eff42a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "batteries = h5py.File(filename, 'r')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8461884b-b82f-4101-bb0f-4bd9a2a88ea3",
   "metadata": {},
   "source": [
    "Ok what does this dataset entail?\n",
    "\n",
    "- `Current(A)`: The current in Amperes (A) flowing through the battery.\n",
    "- `Cycle_Index`: The index of the cycling cycle.\n",
    "- `Step_Index`: The index of the step within the current cycle.\n",
    "- `Testtime(s)`: The time in seconds (s) that the battery has been cycling for.\n",
    "- `Voltage(V)`: The voltage in Volts (V) of the battery.\n",
    "- `charge_Energy(Wh)`: The energy in Watt-hours (Wh) that the battery has received during charging.\n",
    "- `discharge_Energy(Wh)`: The energy in Watt-hours (Wh) that the battery has released during discharging.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "a99f7238-4b03-40dd-bd42-1fc9ce8c4e7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#let's look at it\n",
    "t = batteries['cell_42']['raw']['Testtime(s)'].__array__()\n",
    "V = batteries['cell_42']['raw']['Voltage(V)'].__array__()\n",
    "I = batteries['cell_42']['raw']['Current(A)'].__array__()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "0426b81d-8f25-4112-bfc6-c22df6a271f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "start, end = 5000, 15000\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "color = 'tab:red'\n",
    "ax1.set_xlabel('Time [s]')\n",
    "ax1.set_ylabel('Potential [V]', color=color)\n",
    "ax1.plot(t[start:end], V[start:end], color=color)\n",
    "ax1.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n",
    "\n",
    "color = 'tab:blue'\n",
    "ax2.set_ylabel('Current [mA]', color=color)\n",
    "ax2.plot(t[start:end], I[start:end]*1000, color=color)\n",
    "ax2.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "plt.title('Raw Battery Cycling Data')\n",
    "fig.tight_layout()  # otherwise the right y-label is slightly clipped\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6794881a-df77-4810-98d7-86c39ae155ba",
   "metadata": {},
   "source": [
    "Now I understand that this might be a little cumbersome to work with because very often you need data from idividual cycles. Here is how you can do the same plot for cycle 123. Be aware that here the keys changed slightly!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "f10b87d6-18da-44ff-a58e-472838f14af0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#the split data contains both the charge and discharge\n",
    "cycle = 123\n",
    "cell = 42\n",
    "\n",
    "t = batteries['cell_'+str(cell)]['split']['Cycle_'+str(cycle)]['t'].__array__()\n",
    "V = batteries['cell_'+str(cell)]['split']['Cycle_'+str(cycle)]['V'].__array__()\n",
    "I = batteries['cell_'+str(cell)]['split']['Cycle_'+str(cycle)]['I'].__array__()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "a1724596-5bd9-4d30-a9b5-ceb320f4b991",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "color = 'tab:red'\n",
    "ax1.set_xlabel('Time [s]')\n",
    "ax1.set_ylabel('Potential [V]', color=color)\n",
    "ax1.plot(t, V, color=color)\n",
    "ax1.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n",
    "\n",
    "color = 'tab:blue'\n",
    "ax2.set_ylabel('Current [mA]', color=color)\n",
    "ax2.plot(t, I*1000, color=color)\n",
    "ax2.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "plt.title('Split Battery Cycling Data')\n",
    "fig.tight_layout()  # otherwise the right y-label is slightly clipped\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b9eb5be-a0f1-4e63-807a-4eb3b0362e3d",
   "metadata": {},
   "source": [
    "# Final dataset: Molecules and melting point\n",
    "\n",
    "SMILES (Simplified Molecular Input Line Entry System) is a string representation of a chemical structure that can be read by both humans and machines. SMILES strings use a series of symbols to represent the atoms, bonds, and functional groups of a molecule.\n",
    "\n",
    "Molecules can be represented in many ways, but SMILES strings are particularly useful because they are simple and can be easily parsed by computers. They can be used for a variety of applications, including searching large databases of chemical structures, comparing molecular structures, and predicting properties of molecules.\n",
    "\n",
    "One example of how SMILES strings can be used in machine learning is in predicting melting points. By using molecular fingerprints derived from SMILES strings, we can train a machine learning model to predict the melting point of a molecule based on its structure. This is a neat example of the intersection of chemistry and machine learning, and it can have practical applications in drug discovery and materials science."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "b644fd49-45a0-4003-a59a-596ad2ed6009",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import urllib.request\n",
    "\n",
    "url = 'https://raw.githubusercontent.com/flawnson/Melting_point_predictor_NN_Regression/master/BradleyMeltingPointDatasetClean.csv'\n",
    "\n",
    "molecules = pd.read_csv(url,index_col=0,parse_dates=[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "d45c0031-fca1-42c3-bee6-27e90b8ef8ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['smiles', 'mpC', 'csid', 'link', 'source'], dtype='object')"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "molecules.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ea6d1c7-14e8-47a2-8eea-ac098b25a21b",
   "metadata": {},
   "source": [
    "Usefull for later we can easily generate Morgan Fingerprints. This is not reccomended but a dirty fix since some smiles are broken in this record"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "74c205b8-6fcd-4593-bba2-d9cd3821cce2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[14:40:51] Explicit valence for atom # 20 C, 5, is greater than permitted\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 16 17 18 19 20 21 22 23 24\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 10 11 12 13 14 15 16 17 23\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 2 3 4 5 6\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4 5 6 7 8\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5 6 7 8 9\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4 5 6 7 8\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4 5 6 7 8\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4 5 6 7 8\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 3 4 5 6 8\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5 6 8 9 10 11 12 13 14\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5 6 7 8 9\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5 7 8 9 10\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5 6 7 8 9\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 3 4 5 6 7 8 9 10 11\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4 5 6 7 8 9 10 11 12\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 24 25 26 27 28 31 32 33 34\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 9 10 11 12 13 14 15 16 17\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 9 10 11 12 13 14 15 16 17\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 12 13 14 15 16\n",
      "[14:40:51] Explicit valence for atom # 1 Br, 5, is greater than permitted\n",
      "[14:40:51] Explicit valence for atom # 1 Br, 3, is greater than permitted\n",
      "[14:40:51] Explicit valence for atom # 1 Cl, 3, is greater than permitted\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 17 18 19 20 21 22 23 24 32\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 21 22 23 24 25\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 11 12 13 14 15\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 7 8 9 10 11 12 13 14 15\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 6 7 8 9 10 11 12 13 14\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 32 33 34 35 36\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 5 6 7 8 9 10 11 12 13\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 14 15 16 17 18 19 20 21 22\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 27 28 29 30 31\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 6 7 8 9 10 11 12 13 14\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 5 6 7 8 9 10 11 13 14 15 16 17 18\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 16 17 18 19 20 21 22 23 24\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 10 11 12 13 14\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 2 3 4 5 6 12 13 14 15\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 45 46 47 48 49 50 51 52 53\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 24 25 26 27 28 31 32 33 34\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 16 17 18 19 20 21 22 23 24\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 15 16 17 18 19 20 21 22 23\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 10 11 12 13 14 15 16 17 18\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 5 6 7 8 9\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 6 7 8 9 10 11 12 13 16\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 3 4 5 6 7\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5 6 7 8 9\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 9 10 11 12 13 14 15 16 17\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 13 14 15 16 17 18 19 20 21\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 2 3 4 5 6\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 9 10 11 12 13\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 6 7 8 9 10 11 12 13 14\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 5 6 7 8 9 10 11 12 13\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 17 18 19 20 21 22 23 24 25 26 27 28 29\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 6 7 8 9 10 11 12 13 14\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 6 7 8 9 10 11 12 13 14\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 12 13 14 15 16 17 18 19 20\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 5 6 7 8 9 10 11 13 14\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 10 11 12 13 14 15 16 17 18\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 13 14 15 16 17 18 19 20 21\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 10 11 12 13 14 15 16 17 23\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 11 12 13 14 15 16 17 18 24\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 5 6 7 9 12\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 25 26 27 28 29\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 3 4 5 6 7\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5 6 7 8 9\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 6 7 8 9 10 11 12 13 21\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 5 6 7 8 9 10 11 12 13\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 5 6 7 8 9\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 22 23 24 25 26 27 28 29 30\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 16 17 18 19 20 21 22 23 24\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 14 15 16 17 18 19 20 21 22\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 10 11 12 13 14 15 16 17 18\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 22 23 24 25 26 29 30 31 32\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 5 6 7 8 9 10 11 12 13\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4 5 6 7 8\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 3 4 5 6 7\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5 6 7 8 9\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 10 11 12 13 14 15 16 17 18\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 11 12 13 14 15\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 2 3 4 5 6\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 9 10 11 12 13 14 15 16 17\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 8 9 10 11 12\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 13 14 15 16 17 18 19 20 21\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 2 3 4 5 6\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 1 2 3 5 6 7 8 9 10\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5 6 7 8 10\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 2 3 4 5 6 7 8 9 10\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5 6 7 8 9\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 5 6 7 9 12\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 6\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 3 4 5 6 7 8 9 10 11\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5 6 7 8 9 10 11 12 13\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 4 5 6 7 8 9 10 12 14\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5 6 7 8 9\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 2 3 4 5 6 7 8 9 10\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 6\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4 5 6 7 8\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 3 4 5 6 7\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 2 3 4 5 6 7 8 9 10\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 2 3 4 5 6\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4 5 6 7 14\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 4 5 6 7 8 9 10 11 12\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5 6 7 8 9\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 6 7 8 9 10 11 12 13 14\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 3 4 5 6 7 8 9 10 11\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 3 4 5 6 8\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 6\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4 5 6 7 8\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5 6 7 8 9\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5 6 7 8 9\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5 6 7 8 9\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 4 5 6 7 8 9 10 11 12\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 3 4 5 6 7 8 9 10 11\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 5 6 7 8 9 10 11 12 13\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 6\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 5 6 7 8 9 10 11 13 14\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 2 3 4 5 6 7 8 9 10\n",
      "[14:40:51] Can't kekulize mol.  Unkekulized atoms: 6 7 8 9 10\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 11 12 13 14 15\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 4 5 6 7 8 9 10 11 12\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4 5 6 7 8\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4 5 6 7 8\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4 5 6 7 8\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 14 15 16 17 18 19 20 21 22\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5 6 7 8 9\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 6 7 8 9 10\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 6 7 8 9 10 11 12 13 14\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 5 6 7 8 9 10 11 12 13\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5 6 7 8 9\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4 5 6 7 8\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 3 4 5 6 7 8 9 10 11\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5 6 7 8 9\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 4 5 6 7 8 9 10 11 12\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4 5 6 7 8 9 10 11 12\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5 6 7 8 9\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 1 2 3 5 6\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 5 6 7 8 9 10 11 12 13\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 4 5 6 7 8 9 10 11 12\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4 5 6 7 8\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 22 23 24 25 26 29 30 31 32\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4 5 6 7 8\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 26 27 28 29 30 33 34 35 36\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 6 7 8 9 10 11 12 13 14\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 7 8 9 10 11 12 13 14 15\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5 6 7 8 9\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 16 17 18 19 20 21 22 23 24\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 5 6 7 8 9 10 11 12 14\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 9 10 11 12 13 14 15 16 17\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 8 9 10 11 12 13 14 15 16\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 5 6 7 8 9 10 11 12 13\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 13 14 15 16 17 18 19 20 21\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 13 14 15 16 17 18 19 20 21\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 5 6 7 8 9 10 11 12 13\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 12 13 14 15 16 17 18 19 20\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 25 26 27 28 29\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4 5 6 7 8\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 5 6 7 8 9\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 5 6 7 8 9 10 11 12 13\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 2 3 4 5 6 7 8 9 10\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 10 11 12 13 14 15 16 17 18\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 12 13 14 15 16 17 18 19 20\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 14 15 16 17 18 19 20 21 22\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 14 15 16 17 18 19 20 21 22\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 17 18 19 20 21 22 23 24 25 26 27 28 29\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 5 6 7 8 9 10 11 13 14 15 16 17 18\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 18 19 20 21 22\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 10 11 12 13 14 15 16 17 18\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 2 3 4 5 6\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5 6 7 8 9\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 5 6 7 8 9 10 11 12 13\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 9 10 11 12 13 14 15 16 17\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 33 34 35 36 37\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 2 3 4 5 6 7 8 9 10\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 5 6 7 8 9 10 11 12 13\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 9 10 11 12 13\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 3 4 5 6 7 8 9 10 11\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4 5 6 7 8\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 6 7 8 9 10 11 12 13 14\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 4 5 6 7 8 9 10 11 12\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4 5 6 7 8\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 3 4 5 6 7\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5 6 7 8 9\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 6 7 8 9 10 11 12 14 15\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 9 10 11 12 13 19 20 21 22\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 5 6 7 8 9 10 11 12 13\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 12 13 14 15 16 17 18 19 20\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 3 4 5 6 7 8 9 10 11\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 12 13 14 15 16\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5 6 7 8 9 10 11 12 13\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 2 3 4 5 6 7 10 11 12 13 14 15 16 17 18\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 4 5 6 7 8 9 10 11 12\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 3 4 5 6 7\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 6 7 8 9 10\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 3 4 5 6 7 8 9 10 11\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 6\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 7\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5 6 7 8 9\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 1 2 3 5 7\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5 7 8 9 10\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 4 5 6 7 8 9 10 11 12\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 5 6 7 8 11 12 13 14 15\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 9 10 11 12 13 14 15 17 18\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 6 7 8 9 10\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 3 4 5 6 7 8 9 10 11\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 5 6 7 8 9 10 11 12 13\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 3 4 5 6 7 9 10 11 12\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5 6 7 8 9\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5 6 7 8 9\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5 6 7 8 9\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 2 3 4 5 6\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5 6 7 8 9\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5 6 7 8 9\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 8 9 10 11 12\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 5 6 7 8 9 10 11 12 13\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5 6 7 8 9 10 11 12 13\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 6 7 8 9 10 11 12 13 14\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5 6 7 8 9\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 6 7 8 9 10 11 12 14 15\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5 6 7 8 9\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 2 3 4 5 6 7 8 9 10\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5 6 7 8 9\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 1 2 3 5 6\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 3 4 5 6 7 8 9 10 11\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 15 16 17 18 19 22 23 24 25\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 16 17 18 19 20 21 22 23 24\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 22 23 24 25 26 27 28 29 30\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 3 4 5 6 7 8 9 10 11\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 4 5 6 7 8 9 10 11 12\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 6 7 8 9 10 11 12 13 19\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 12 13 15 16 17 18 19 20 21\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 6\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 7 8 9 10 11 12 13 14 15\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5 6 7 8 9\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 5 6 7 8 9\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 16 17 18 19 20 21 22 23 24\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 2 3 4 5 6 7 20 21 22\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 2 3 4 5 6 7 8 9 10 11 12 13 14\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5 6 7 8 9 10 11 12 13\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 11 12 13 14 15 16 17 20 21\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 14 15 16 17 18 19 20 21 22\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 13 14 15 16 17 18 19 20 21\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 2 3 4 5 6 7 8 9 10\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 6 7 8 9 10 11 12 13 18\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 1 2 3 4 5 6 8 9 10\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 9 10 11 12 13 14 15 16 17\n",
      "[14:40:52] Explicit valence for atom # 6 N, 5, is greater than permitted\n",
      "[14:40:52] Explicit valence for atom # 20 N, 5, is greater than permitted\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 7 8 9 10 11 12 13 14 15\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 3 4 5 6 7\n",
      "[14:40:52] Explicit valence for atom # 1 N, 4, is greater than permitted\n",
      "[14:40:52] Explicit valence for atom # 4 N, 4, is greater than permitted\n",
      "[14:40:52] Explicit valence for atom # 5 N, 4, is greater than permitted\n",
      "[14:40:52] Explicit valence for atom # 1 B, 4, is greater than permitted\n",
      "[14:40:52] Explicit valence for atom # 12 N, 4, is greater than permitted\n",
      "[14:40:52] Explicit valence for atom # 10 N, 4, is greater than permitted\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 3 4 5 6 7 8 9 10 11\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 4\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 4 5 8 9 10\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 3 4 5 6 7\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 0 1 2 3 4 5 6 7 8\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 5 6 7 8 9 10 11 12 13\n",
      "[14:40:52] Can't kekulize mol.  Unkekulized atoms: 5 6 7 8 9\n",
      "[14:40:53] Can't kekulize mol.  Unkekulized atoms: 4 5 6 7 8 9 10 11 12\n",
      "[14:40:53] Can't kekulize mol.  Unkekulized atoms: 6 7 8 9 10\n"
     ]
    }
   ],
   "source": [
    "from rdkit import Chem\n",
    "from rdkit.Chem import AllChem\n",
    "\n",
    "# Create a list to hold the fingerprints\n",
    "fingerprints = []\n",
    "melting_points = []\n",
    "\n",
    "# Iterate over the 'smiles' column in the dataframe\n",
    "for smi,melt in zip(molecules['smiles'],molecules['mpC']):\n",
    "    try:\n",
    "        # Convert the SMILES string to a molecule object\n",
    "        mol = Chem.MolFromSmiles(smi)\n",
    "        # Generate the fingerprint for the molecule\n",
    "        fp = AllChem.GetMorganFingerprintAsBitVect(mol, 2, nBits=64)\n",
    "        # Append the fingerprint to the list\n",
    "        fingerprints.append(fp)\n",
    "        melting_points.append(melt)\n",
    "    except:\n",
    "        pass\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "c40b0107-f43f-4098-bd00-f5d3de499cc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#let's at least have a look at the melting point distribution\n",
    "plt.hist(melting_points,100,range=(-220,550))\n",
    "plt.xlabel('Melting Point [Celsius]')\n",
    "plt.ylabel('Frequency')"
   ]
  }
 ],
 "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}