189 lines
4.6 KiB
Plaintext
189 lines
4.6 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"%matplotlib inline\n",
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"import matplotlib.pyplot as plt\n",
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"plt.style.use(\"seaborn-notebook\")\n",
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"\n",
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"from ipywidgets import interact, interactive, fixed\n",
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"import ipywidgets as widgets\n",
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"from IPython.display import display\n",
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"\n",
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"import notes_tools.tools as tools\n",
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"from notes_tools.generate_bilan import texenv\n",
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"from notes_tools.tools.df_marks_manip import round_half_point"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"classe = \"509\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"ws = tools.get_class_ws(classe)\n",
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"flat = tools.extract_flat_marks(ws)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"quest_pov, exo_pov, eval_pov = tools.digest_flat_df(flat)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Création des bilans trimestriels"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"def create_summary_table(pv_table, coefs = \"\"):\n",
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" if coefs:\n",
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" cols = [(\"Evaluations\",[\"Note\", \"Note sur 20\", \"Coefficient\"])]\n",
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" else:\n",
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" cols = [(\"Evaluations\",[\"Note\", \"Note sur 20\"])]\n",
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" for k,v in pv_table.iterrows():\n",
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" if coefs:\n",
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" cols.append((k[1], [\"{}/{}\".format(v.iloc[1], v.iloc[0]), \"\", coefs[k[1]]]))\n",
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" else:\n",
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" cols.append((k[1], [\"{}/{}\".format(v.iloc[1], v.iloc[0]), \"\"]))\n",
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" #cols[k[1]] = [\"{}/{}\".format(v.iloc[1], v.iloc[0]), \"\"]\n",
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" #df = pd.DataFrame.from_dict(cols)\n",
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" df = pd.DataFrame.from_items(cols)\n",
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" #return df.set_index('Evaluations')\n",
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" return df"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"def build_latex_content(eval_pov, coefs=\"\"):\n",
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" table = pd.pivot_table(eval_pov,\n",
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" index = [\"Eleve\", \"Nom\"],\n",
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" values = [\"Mark\", \"Bareme\"],\n",
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" #columns = [\"Nom\"],\n",
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" )\n",
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" content = {}\n",
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" content[\"students\"] = []\n",
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" for e in eval_pov[\"Eleve\"].unique():\n",
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" eleve = {\"name\":e}\n",
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" e_table = table.query('Eleve == \"{}\"'.format(e))\n",
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" summary = create_summary_table(e_table, coefs)\n",
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" \n",
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" col_format = \"|\"+\"c|\"*len(summary.columns)\n",
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" #print(len(summary.columns))\n",
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" eleve['latex_table'] = summary.to_latex(index=False, column_format=col_format)\n",
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" content[\"students\"].append(eleve)\n",
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" return content"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"coefs = {\"DM1\":0.5, \"DM2\":0.5, \"DS1\":1, \"DS2\":1, \"ConnT1\":1}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"#build_latex_content(eval_pov)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"#content = build_latex_content(eval_pov, coefs)\n",
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"content = build_latex_content(eval_pov)\n",
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"tpl = texenv.get_template(\"./tpl_bilan_trim.tex\")\n",
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"with open(\"./{}/trim1.tex\".format(classe), \"w\") as f:\n",
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" f.write(tpl.render(content))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.5.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}
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