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):
-        """TODO: to be defined1. """
-        pass
+    def __init__(self, quest_df, exo_df, eval_df):
+        """ Init of a class from quest, exo and eval """
+        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)