IWSC04_03_BCA_polynomial.ipynb

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   "execution_count": null,
   "id": "eaacb570",
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   "source": [
    "from scipy.optimize import curve_fit\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "# Importing data\n",
    "# Laptop\n",
    "test_dataset = pd.read_excel(r'C:\\Users\\Iwanw\\OneDrive - Cardiff University\\Documents\\programs\\python\\python_data\\BCA_results.xlsx')\n",
    "# Uni\n",
    "#test_dataset = pd.read_excel(r'C:\\Users\\User\\OneDrive - Cardiff University\\Documents\\programs\\python\\python_data\\BCA_results.xlsx')\n",
    "\n",
    "# Define the standard curve\n",
    "standard_concentrations = np.array([0, 2.5, 5, 10, 25, 37.5, 50, 75])\n",
    "pre_standard_absorbances = test_dataset[[0, 2.5, 5, 10, 25, 37.5, 50, 75]].mean().values\n",
    "\n",
    "# Define the sample dilutions\n",
    "sample_dilutions = np.array([0.025, 0.05, 0.1, 0.25, 0.5, 1])\n",
    "pre_sample_absorbances = test_dataset[[0.025, 0.05, 0.1, 0.25, 0.5, 1]].mean().values\n",
    "\n",
    "offset = pre_standard_absorbances[0]\n",
    "standard_absorbances = pre_standard_absorbances - offset\n",
    "sample_absorbances = pre_sample_absorbances - offset\n",
    "\n",
    "# Removing 0 ( # if you would like graph to cross 0,0)\n",
    "#standard_concentrations = standard_concentrations[standard_concentrations != 0]\n",
    "#standard_absorbances = standard_absorbances[standard_absorbances != 0]\n",
    "\n",
    "# Fit a polynomial regression model to the standard data\n",
    "def polynomial_fit(x, a, b, c, d):\n",
    "    return a * x**3 + b * x**2 + c * x + d\n",
    "# Regression to calculate concentrations\n",
    "params, params_covariance = curve_fit(polynomial_fit, standard_absorbances, standard_concentrations)\n",
    "a, b, c, d = params\n",
    "# Use the regression equation to calculate the corresponding concentrations for the sample absorbances\n",
    "sample_concentrations = a * sample_absorbances**3 + b * sample_absorbances**2 + c * sample_absorbances + d\n",
    "# Correct for the sample dilutions\n",
    "corrected_sample_concentrations = sample_concentrations / sample_dilutions * 10\n",
    "print(\"\\nRegression equation: y = {:.10f}x^3 + {:.10f}x^2 + {:.10f}x + {:.10f}\".format(a, b, c, d))\n",
    "print(\"\\n\\nSample Concentrations (ug/mL):\",corrected_sample_concentrations, \"\\n\")\n",
    "# Inverted regression to image\n",
    "params, params_covariance = curve_fit(polynomial_fit, standard_concentrations, standard_absorbances)\n",
    "a, b, c, d = params\n",
    "\n",
    "# Plotting\n",
    "plt.scatter(standard_concentrations, standard_absorbances, color='red')\n",
    "plt.plot(standard_concentrations, polynomial_fit(standard_concentrations, a, b, c, d), color='orange')\n",
    "#plt.scatter(sample_concentrations, sample_absorbances, color='blue')\n",
    "plt.xlabel('Standard Concentration')\n",
    "plt.ylabel('Standard Absorbance')\n",
    "plt.xlim([np.min(0), np.max(sample_concentrations * 1.1)])\n",
    "plt.ylim([np.min(0), np.max(sample_absorbances * 1.1)])\n",
    "for i, (c, a) in enumerate(zip(sample_concentrations, sample_absorbances)):\n",
    "    plt.vlines(c, 0, a, color='green', linestyle='--')\n",
    "    plt.hlines(a, 0, c, color='green', linestyle='--')\n",
    "current_time = datetime.datetime.now().strftime(\"%Y-%m-%d_%H-%M-%S\")\n",
    "filename = \"BCA_results_\" + current_time + \".pdf\"\n",
    "plt.savefig(filename)\n",
    "filename1 = \"BCA_results_\" + current_time + \".jpg\"\n",
    "plt.savefig(filename1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a334da0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#value1 = corrected_sample_concentrations[0]\n",
    "value2 = corrected_sample_concentrations[1]\n",
    "value3 = corrected_sample_concentrations[2]\n",
    "value4 = corrected_sample_concentrations[3]\n",
    "value5 = corrected_sample_concentrations[4]\n",
    "value6 = corrected_sample_concentrations[5]\n",
    "for_averages = []\n",
    "for i in range(1, 7):\n",
    "    variable_name = \"value\" + str(i)\n",
    "    try:\n",
    "        value = eval(variable_name)\n",
    "        for_averages.append(value)\n",
    "    except NameError:\n",
    "        pass\n",
    "average = sum(for_averages) / len(for_averages)\n",
    "print(\"\\nThe average of the defined values is: {:.2f} µg/mL\".format(average))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49a6116c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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