new eval_tools and new df_marks_manip

notes_tools/tools/__init__.py

@@ -4,6 +4,7 @@
 
 from .extract import extract_flat_marks, get_class_ws
 from .df_marks_manip import digest_flat_df, students_pov
+from .eval_tools import select_eval, get_present_absent, keep_only_presents
 
 
 

notes_tools/tools/df_marks_manip.py

@@ -68,6 +68,53 @@ def note_to_mark(x):
         return x["Note"] * x["Bareme"] / 3
     return x["Note"]
 
+def question_uniq_formater(row):
+    """ Create a kind of unique description of the question 
+
+    >>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
+    ...    "Nom": ["N1"]*4+["N2"]*2 + ["N1"]*4+["N2"]*2,
+    ...    "Exercice":["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"] + ["Ex1"]*2+["Ex2"]*2+["Ex1"]+["Ex2"],
+    ...    "Question":["Q1"]+["Q2"]+["Q1"]+["Q2"]+["Q1"]+["Q1"] + ["Q1"]+["Q2"]+["Q1"]+["Q2"]+["Q1"]+["Q1"],
+    ...    "Date":["16/09/2016"]*4+["01/10/2016"]*2 + ["16/09/2016"]*4+["01/10/2016"]*2,
+    ...    "Trimestre": ["1"]*12,
+    ...    "Bareme":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
+    ...    "Niveau":[0]*4+[1]*2 + [0]*4+[1]*2,
+    ...    "Note":[1, 0.33, 2, 1.5, 1, 3,   0.666, 1, 1.5, 1, 2, 3],
+    ...    }
+    >>> df = pd.DataFrame(d)
+    >>> question_uniq_formater(df.loc[0])
+    'Ex1 Q1'
+    >>> question_uniq_formater(df.loc[10])
+    'Ex1 Q1'
+
+    """
+    ans = ""
+    try:
+        int(row['Exercice'])
+    except ValueError:
+        ans += str(row["Exercice"])
+    else:
+        ans += "Exo"+str(row["Exercice"])
+
+    ans += " "
+
+    try:
+        int(row["Question"])
+    except ValueError:
+        if not pd.isnull(row["Question"]):
+            ans += str(row["Question"])
+    else:
+        ans += "Qu"+str(row["Question"])
+
+    try:
+        row["Commentaire"]
+    except KeyError:
+        pass
+    else:
+        if not pd.isnull(row["Commentaire"]):
+            ans += " ({})".format(row["Commentaire"])
+    return ans
+
 # DataFrame columns manipulations
 
 def compute_marks(df):
@@ -170,6 +217,10 @@ def compute_normalized(df):
     """
     return df["Mark"] / df["Bareme"]
 
+def compute_question_description(df):
+    """ Compute the unique description of a question """
+    return df.apply(question_uniq_formater, axis = 1)
+
 # Computing custom values
 
 def compute_exo_marks(df):
@@ -273,6 +324,7 @@ def digest_flat_df(flat_df):
     df["Mark"] = compute_marks(flat_df)
     df["Latex_rep"] = compute_latex_rep(flat_df)
     df["Normalized"] = compute_normalized(df)
+    df["Uniq_quest"] = compute_question_description(df)
 
     exo_df = compute_exo_marks(df)
     exo_df["Normalized"] = compute_normalized(exo_df)

notes_tools/tools/eval_tools.py

@@ -0,0 +1,43 @@
+#!/usr/bin/env python
+# encoding: utf-8
+
+import pandas as pd
+import numpy as np
+
+
+def select_eval(quest_df, exo_df, eval_df, evalname):
+    """ Return quest, exo and eval rows which correspond to evalname
+
+    :param quest_df: TODO
+    :param exo_df: TODO
+    :param eval_df: TODO
+
+    """
+    qu = quest_df[quest_df["Nom"] == evalname]
+    exo = exo_df[exo_df["Nom"] == evalname]
+    ev = eval_df[eval_df["Nom"] == evalname]
+    return qu, exo, ev
+
+def get_present_absent(eval_df):
+    """ Return list of student who where present (Mark > 0) and 
+    the list of those who weren't
+
+    """
+    presents = eval_df[eval_df["Mark"] > 0]["Eleve"]
+    absents = eval_df[eval_df["Mark"] == 0]["Eleve"]
+    return {"presents": presents, "absents": absents}
+
+def keep_only_presents(quest_df, exo_df, eval_df, presents):
+    """ Return quest, exo and eval rows of presents students """
+    qu = quest_df[quest_df["Eleve"].isin(presents)]
+    exo = exo_df[exo_df["Eleve"].isin(presents)]
+    ev = eval_df[eval_df["Eleve"].isin(presents)]
+    return qu, exo, ev
+
+
+
+
+# -----------------------------
+# Reglages pour 'vim'
+# vim:set autoindent expandtab tabstop=4 shiftwidth=4:
+# cursor: 16 del