class POV for term

notes_tools/reports/term_pov.py

@@ -14,6 +14,8 @@ from notes_tools.tools.marks_plottings import (pie_pivot_table,
         parallel_on,
         radar_on,
         )
+import pandas as pd
+import numpy as np
 
 __all__ = ["Student", "Classe"]
 
@@ -112,11 +114,56 @@ class Student(object):
 
 class Classe(object):
 
-    """Docstring for Classe. """
+    """ 
+    Informations on a class which can be use inside template.
+    
+    Those informations should not be modify or use for compute analysis otherwise they won't be spread over other POV.
+    """
 
-    def __init__(self):
+    def __init__(self, quest_df, exo_df, eval_df):
-        """TODO: to be defined1. """
+        """ Init of a class from quest, exo and eval """
-        pass
+        self.quest_df = quest_df
+        self.exo_df = exo_df
+        self.eval_df = eval_df
+
+    @property
+    def evals_tabular(self):
+        """ Summary of all evaluations for all students """
+        try:
+            self._evals_tabular
+        except AttributeError:
+            self._evals_tabular = pd.pivot_table(self.eval_df,
+                    index = "Eleve",
+                    columns = "Nom", 
+                    values = "Mark_barem",
+                    aggfunc = lambda x: " ".join(x)).to_latex()
+        return self._evals_tabular
+
+
+    @property
+    def parallel_on_evals(self):
+        """ Parallel coordinate plot of the class """
+        return parallel_on(self.eval_df, "Nom")
+
+    @property
+    def pies_eff_pts_on_competence(self):
+        """ Pie charts on competence with repartition of evaluated times and attributed points """
+        return pie_pivot_table(self.quest_df,
+                index = "Competence",
+                #columns = "Level",
+                values = "Bareme",
+                aggfunc=[len,np.sum],
+                fill_value=0)
+
+    @property
+    def pies_eff_pts_on_domaine(self):
+        """ Pie charts on domaine with repartition of evaluated times and attributed points """
+        return pie_pivot_table(self.quest_df,
+                index = "Domaine",
+                #columns = "Level",
+                values = "Bareme",
+                aggfunc=[len,np.sum],
+                fill_value=0)
         
         
 

notes_tools/tools/df_marks_manip.py

@@ -17,6 +17,15 @@ def round_half_point(val):
     except TypeError:
         return val
 
+def num_format(num):
+    """ Tranform a number into an appropriate string """
+    try:
+        if int(num) == num:
+            return str(int(num))
+    except ValueError:
+        pass
+    return f"{num:.1f}".replace(".", ",")
+
 latex_caract = ["\\NoRep", "\\RepZ", "\\RepU", "\\RepD", "\\RepT"]
 def note_to_rep(x):
     r""" Transform a Note to the latex caracter
@@ -112,6 +121,10 @@ def note_to_level(x):
     else:
         return int(ceil(x["Note"] / x["Bareme"] * 3))
 
+def mark_bareme_formater(row):
+    """ Create m/b string """
+    return f"{num_format(row['Mark'])} / {num_format(row['Bareme'])}"
+
 def question_uniq_formater(row):
     """ Create a kind of unique description of the question 
 
@@ -294,6 +307,10 @@ def compute_normalized(df):
     """
     return df["Mark"] / df["Bareme"]
 
+def compute_mark_barem(df):
+    """ Build the string mark m/b """
+    return df.apply(mark_bareme_formater, axis=1)
+
 def compute_question_description(df):
     """ Compute the unique description of a question """
     return df.apply(question_uniq_formater, axis = 1)
@@ -458,8 +475,10 @@ def digest_flat_df(flat_df):
 
     exo_df = compute_exo_marks(df)
     exo_df["Normalized"] = compute_normalized(exo_df)
+    exo_df["Mark_barem"] = compute_mark_barem(exo_df)
     eval_df = compute_eval_marks(exo_df)
     eval_df["Normalized"] = compute_normalized(eval_df)
+    eval_df["Mark_barem"] = compute_mark_barem(eval_df)
 
     return df, exo_df, eval_df
 

notes_tools/tools/plottings.py

@@ -81,7 +81,7 @@ def my_autopct(values):
 def pivot_table_to_pie(pv, pies_per_lines = 3):
     nbr_pies = len(pv.columns)
     nbr_cols = min(pies_per_lines, nbr_pies)
-    nbr_rows = nbr_pies // nbr_cols + 1
+    nbr_rows = nbr_pies // nbr_cols 
     f, axs = plt.subplots(nbr_rows, nbr_cols, figsize = (4*nbr_cols,4*nbr_rows))
     for (c, ax) in zip(pv, axs.flatten()):
         datas = pv[c]