135 lines
3.4 KiB
Python
135 lines
3.4 KiB
Python
#!/usr/bin/env python
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# encoding: utf-8
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import pandas as pd
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import numpy as np
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import xlrd
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from path import Path
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notes_path = Path("./")
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notStudent = ["Trimestre", "Nom", "Date", "Exercice", "Question", "Competence", "Domaine", "Commentaire", "Bareme", "Niveau"]
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pure_marks = ["Malus", "Bonus", "Presentation"]
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def list_classes(path = notes_path):
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"""
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List classes available in notes_path
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>>> list_classes()
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['509', '503', '308', '312']
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>>> p = Path("./")
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>>> list_classes(p)
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['509', '503', '308', '312']
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>>> list_classes("./")
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['509', '503', '308', '312']
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"""
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try:
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return [n.namebase for n in path.files("*.xlsx")]
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except AttributeError:
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p = Path(path)
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return [n.namebase for n in p.files("*.xlsx")]
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def get_class_ws(classe, path = notes_path):
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"""
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From the name of a classe, returns pd.ExcelFile
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"""
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if classe in list_classes(path):
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return pd.ExcelFile(notes_path/classe+".xlsx")
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else:
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raise ValueError("This class is not disponible in {p}. You have to choose in {c}".format(p = path, c = list_classes(path)))
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def extract_students(df, notStudent = notStudent):
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""" Extract the list of students from df """
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students = df.columns.difference(notStudent)
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return students
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def check_students(dfs, notStudent = notStudent):
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""" Build students list """
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dfs_students = [extract_students(df) for df in dfs]
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if not are_equal(dfs_students):
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raise ValueError("Not same list of students between df1 = {} ans df2 = {}".format(df1, df2))
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return dfs_students[0]
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def are_equal(elems):
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""" Test if item of elems are equal
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>>> L = [[1, 2, 3], [1, 3, 2], [1, 3, 2]]
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>>> are_equal(L)
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True
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>>> L = [[0, 2, 3], [1, 3, 2], [1, 3, 2]]
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>>> are_equal(L)
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False
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"""
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first = sorted(elems[0])
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others = [sorted(e) for e in elems[1:]]
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diff = [e == first for e in others]
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if False in diff:
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return False
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return True
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def flat_df_students(df, students):
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""" Flat the ws for students """
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flat_df = pd.DataFrame()
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flat_data = []
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dfT = df.T
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for n in dfT:
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pre_di = dfT[n][notStudent].to_dict()
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for e in students:
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data = pre_di.copy()
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data["Eleve"] = e
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data["Note"] = dfT[n].loc[e]
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flat_data.append(data)
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return pd.DataFrame.from_dict(flat_data)
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def get_all_marks(ws, marks_sheetnames = ["Notes", "Connaissances", "Calcul mental"]):
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""" Extract marks from marks_sheetnames
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:param ws: TODO
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:returns: TODO
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"""
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for sheetname in marks_sheetnames:
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try:
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marks = ws.parse(sheetname)
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except xlrd.biffh.XLRDError:
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pass
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def extract_flat_marks(ws):
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""" Extract, flat and contact marks from the worksheet
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:param ws: TODO
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:returns: TODO
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"""
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marks_sheetnames = ["Notes", "Connaissances", "Calcul mental"]
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sheets = []
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for sheetname in marks_sheetnames:
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try:
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sheets.append(ws.parse(sheetname))
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except xlrd.biffh.XLRDError:
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pass
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students = check_students(sheets)
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flat_df = pd.DataFrame()
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for sheet in sheets:
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flat = flat_df_students(sheet, students)
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flat_df = pd.concat([flat_df, flat])
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return flat_df
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# -----------------------------
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# Reglages pour 'vim'
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# vim:set autoindent expandtab tabstop=4 shiftwidth=4:
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# cursor: 16 del
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