{ "cells": [ { "cell_type": "code", "execution_count": 76, "id": "c7396882-5bc6-4db2-b5b3-36ec779a4642", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "import pandas as pd\n", "import xlsxwriter" ] }, { "cell_type": "code", "execution_count": 187, "id": "8f83398f-4fed-4a84-9c6e-6b04e6d28ddf", "metadata": {}, "outputs": [], "source": [ "max_score = 100\n", "cuts_all = {\n", " 'Spanish': {'2026': {'C': [49, 50], 'B': [73, 63], 'T': [99, 80], 'L': [0, 20], 'U': [100, 80]}},\n", " 'French': {'2010': {'C': [40, 50], 'B': [68, 59], 'T': [99, 80], 'L': [0, 20], 'U': [100, 80]}},\n", " 'German': {'2010': {'C': [34, 50], 'B': [56, 60], 'T': [86, 80], 'L': [0, 20], 'U': [87, 80]}}\n", "}\n", "\n", "col_dict = {\n", " 'raw': 'Raw Score', \n", " 'ss_lin':'SS Linear',\n", " 'ss_elb':'SS Segments',\n", " 'ss_lin_ur':'SS Linear (unrounded)',\n", " 'ss_elb_ur':'SS Segments (unrounded)',\n", "}\n", "\n", "def ab(top, bot, cuts):\n", " _a = (cuts[top][1] - cuts[bot][1]) / (cuts[top][0] - cuts[bot][0])\n", " _b = cuts[top][1] - _a * cuts[top][0]\n", "\n", " return _a, _b\n", "\n", "\n", "def round_clip(a, low, high):\n", " return np.clip(np.round(a, 0), low, high)\n", "\n", "\n", "def conversion(cuts):\n", " a, b = ab('T', 'C', cuts)\n", " a1, b1 = ab('B', 'C', cuts)\n", " a2, b2 = ab('T', 'B', cuts)\n", " raw = np.arange(cuts['L'][0], cuts['U'][0]+1) \n", " raw1 = np.arange(cuts['L'][0], cuts['B'][0])\n", " raw2 = np.arange(cuts['B'][0], cuts['U'][0]+1)\n", " ss_lin_ur = raw * a + b\n", " ss_elb_ur = np.append(raw1 * a1 + b1, raw2 * a2 + b2)\n", " data = pd.DataFrame({\n", " 'raw': raw,\n", " 'ss_lin': round_clip(ss_lin_ur, cuts['L'][1], cuts['U'][1]),\n", " 'ss_elb': round_clip(ss_elb_ur, cuts['L'][1], cuts['U'][1]),\n", " 'ss_lin_ur': ss_lin_ur,\n", " 'ss_elb_ur': ss_elb_ur\n", " })\n", " data = data.sort_values(by=\"raw\", ascending=False)\n", "\n", " return data \n", "\n", " \n", "def plot(data, subject, year, cuts):\n", " fig, ax = plt.subplots()\n", " ax.plot(data.raw, data.ss_lin_ur, label='Linear')\n", " ax.plot(data.raw, data.ss_elb_ur, color='magenta', label='Segments')\n", " ax.plot([cuts['L'][0], cuts['U'][0]], [cuts['C'][1], cuts['C'][1]], color='green', linewidth=0.5) \n", " ax.plot([cuts['L'][0], cuts['U'][0]], [cuts['B'][1], cuts['B'][1]], color='orange', linewidth=0.5)\n", " ax.set_xlabel('Raw Score')\n", " ax.set_ylabel('Scale Score')\n", " ax.set_title(f'Conversions: CLEP {subject} ({year})')\n", " ax.legend()\n", " plt.tight_layout()\n", " plt.savefig(f'output/CLEP_{subject}_conversion_{year}.pdf')\n", "\n", "\n", "def save_excel(data, subject, year, cuts):\n", " workbook = xlsxwriter.Workbook(f'output/CLEP_{subject}_conversion_{year}.xlsx')\n", " worksheet = workbook.add_worksheet('conversion')\n", " bold = workbook.add_format({'bold': True})\n", " cut_red0 = workbook.add_format({'bold': True, 'font_color': 'red'})\n", " cut_red2 = workbook.add_format({'bold': True, 'font_color': 'red', 'num_format': '#.00'})\n", " dec0 = workbook.add_format({'num_format': '#0'})\n", " dec2 = workbook.add_format({'num_format': '#.0'})\n", " i, j = 0, 0\n", " for col in data.columns:\n", " for row in data[col]:\n", " fmt = dec0 \n", " val = data[col].iloc[j]\n", " raw = data.raw.iloc[j]\n", " if j == 0: # column header\n", " worksheet.write(j, i, col_dict[col], bold)\n", " if (raw == cuts['C'][0] or raw == cuts['B'][0]):\n", " if i<3:\n", " fmt = cut_red0\n", " else:\n", " fmt = cut_red2\n", " else:\n", " if i>2:\n", " fmt = dec2\n", " worksheet.write(j+1, i, val, fmt)\n", " j += 1\n", " i += 1\n", " j = 0\n", " workbook.close()" ] }, { "cell_type": "code", "execution_count": 191, "id": "834a50e9-0f6c-44af-ba44-28d0e713744e", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def main(subject, year):\n", " cuts = cuts_all[subject][year]\n", " data = conversion(cuts)\n", " plot(data, subject, year, cuts)\n", " save_excel(data, subject, year, cuts)\n", "\n", "if __name__ == \"__main__\":\n", " main('Spanish', '2026')" ] }, { "cell_type": "code", "execution_count": null, "id": "5bc143b6-febc-4b05-8a6d-b16be926ac50", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "bc723af0-c890-43c8-b60c-ad94a15120bb", "metadata": {}, "outputs": [], "source": [] } ], "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": "3.14.3" } }, "nbformat": 4, "nbformat_minor": 5 }