Feat: add "." in csv example and fix issues

example/Tribe1/210112_DS.csv

@@ -1,5 +1,5 @@
 Trimestre,Nom,Date,Exercice,Question,Competence,Domaine,Commentaire,Bareme,Est_nivele,Star Tice,Umberto Dingate,Starlin Crangle,Humbert Bourcq,Gabriella Handyside,Stewart Eaves,Erick Going,Ase Praton,Rollins Planks,Dunstan Sarjant,Stacy Guiton,Ange Stanes,Amabelle Elleton,Darn Broomhall,Dyan Chatto,Keane Rennebach,Nari Paulton,Brandy Wase,Jaclyn Firidolfi,Violette Lockney
-1,DS,12/01/2021,Exercice 1,1,Calculer,Plop,Coucou,1,1,,,1.0,0,1.0,2.0,3.0,0.0,3.0,3.0,2.0,,1.0,,,,,,,
+1,DS,12/01/2021,Exercice 1,1,Calculer,Plop,Coucou,1,1,,,1,0,1,2,3,0,3,3,2,,1,,,,,,,
-1,DS,12/01/2021,Exercice 1,2,Calculer,C'est trop chouette!,Coucou,1,1,,,1.0,2,,,3.0,3.0,,,,,2.0,,,,,,,
+1,DS,12/01/2021,Exercice 1,2,Calculer,C'est trop chouette!,Coucou,1,1,,,1,2,,,3,3,,,,,2,,,,,,,
-1,DS,12/01/2021,Exercice 1,3,Calculer,Null,Coucou,1,1,,,,3,2.0,,,,,,,,3.0,,,,,,,
+1,DS,12/01/2021,Exercice 1,3,Calculer,Null,Coucou,1,1,,,,3,2,,,,,,,,3,,,,,,,
-1,DS,12/01/2021,Exercice 1,3,Calculer,Nié,DChic,1,1,,,,2,,,,,,,,,,,,,,,,
+1,DS,12/01/2021,Exercice 1,3,Calculer,Nié,DChic,1,1,,,,2,.,,,,,,,,,,,,,,,

recopytex/dashboard/pages/exams_scores/app.py

@@ -68,6 +68,7 @@ layout = html.Div(
                             children=[
                                 dcc.Graph(
                                     id="fig_exam_histo",
+                                    config={"displayModeBar": False},
                                 )
                             ],
                             id="fig_exam_histo_container",
@@ -76,6 +77,7 @@ layout = html.Div(
                             children=[
                                 dcc.Graph(
                                     id="fig_questions_bar",
+                                    config={"displayModeBar": False},
                                 )
                             ],
                             id="fig_questions_bar_container",

recopytex/dashboard/pages/exams_scores/callbacks.py

@@ -205,5 +205,12 @@ def update_questions_bar(finale_scores):
     fig.update_layout(
         height=500,
         margin=dict(l=5, r=5, b=5, t=5),
-    )
+        legend=dict(
+            orientation="h",
+            yanchor="bottom",
+            y=1.02,
+            xanchor="right",
+            x=1
+            )
+        )
     return [fig]

recopytex/dashboard/pages/exams_scores/models.py

@@ -72,22 +72,31 @@ def get_score_colors():
 
 def get_level_color_bar():
     return [
-        {"score": s["value"], "name": s["comment"], "color": s["color"]}
+        {"score": str(s["value"]), "name": s["comment"], "color": s["color"]}
         for s in SCORES_CONFIG.values()
     ]
 
 
 is_none_score = lambda x: on_column.is_none_score(x, SCORES_CONFIG)
+format_score = lambda x: on_column.format_score(x, SCORES_CONFIG)
 score_to_numeric_score = lambda x: on_column.score_to_numeric_score(x, SCORES_CONFIG)
 score_to_mark = lambda x: on_column.score_to_mark(
     x, max([v["value"] for v in SCORES_CONFIG.values() if isinstance(v["value"], int)])
 )
 
 
+def filter_clean_score(scores):
+    filtered_scores = scores[~scores.apply(is_none_score, axis=1)]
+    filtered_scores = filtered_scores.assign(
+        score=filtered_scores.apply(format_score, axis=1)
+    )
+    return filtered_scores
+
+
 def score_to_final_mark(scores):
     """ Compute marks then reduce to final mark per student """
 
-    filtered_scores = scores[~scores.apply(is_none_score, axis=1)]
+    filtered_scores = filter_clean_score(scores)
     filtered_scores = filtered_scores.assign(
         score=filtered_scores.apply(score_to_numeric_score, axis=1)
     )
@@ -106,9 +115,10 @@ def pivot_score_on(scores, index, columns, aggfunc="size"):
     It assumes thant scores are levels
 
     """
-    filtered_scores = scores[~scores.apply(is_none_score, axis=1)]
+    filtered_scores = filter_clean_score(scores)
+    filtered_scores["score"] = filtered_scores["score"].astype(str)
     pt = pd.pivot_table(
-        scores,
+        filtered_scores,
         index=index,
         columns=columns,
         aggfunc=aggfunc,

recopytex/datalib/on_score_column.py

@@ -43,6 +43,57 @@ def is_none_score(x, score_config):
     return x["score"] in none_values or pd.isnull(x["score"])
 
 
+def format_score(x, score_config):
+    """Make sure that score have the appropriate format
+
+    >>> import pandas as pd
+    >>> d = {"Eleve":["E1"]*6,
+    ...    "score_rate": [1]*6,
+    ...    "is_leveled":[0]+[1]*5,
+    ...    "score":[0.33,  ".", "a", 1, 2, 3],
+    ...    }
+    >>> score_config = {
+    ...   'BAD': {'value': 0, 'numeric_value': 0},
+    ...   'FEW': {'value': 1, 'numeric_value': 1},
+    ...   'NEARLY': {'value': 2, 'numeric_value': 2},
+    ...   'GOOD': {'value': 3, 'numeric_value': 3},
+    ...   'NOTFILLED': {'value': '', 'numeric_value': 'None'},
+    ...   'NOANSWER': {'value': '.', 'numeric_value': 0},
+    ...   'ABS': {'value': 'a', 'numeric_value': 'None'}
+    ...    }
+    >>> df = pd.DataFrame(d)
+    >>> df.apply(lambda x:format_score(x, score_config), axis=1)
+    0    0.33
+    1       .
+    2       a
+    3       1
+    4       2
+    5       3
+    dtype: object
+    >>> format_score({"score": "1.0", "is_leveled": 1}, score_config)
+    1
+    >>> format_score({"score": "3.0", "is_leveled": 1}, score_config)
+    3
+    >>> format_score({"score": 4, "is_leveled": 1}, score_config)
+    Traceback (most recent call last):
+        ...
+    ValueError: 4 (<class 'int'>) can't be a score
+
+    """
+    if not x["is_leveled"]:
+        return float(x["score"])
+
+    try:
+        score = int(float(x["score"]))
+    except ValueError:
+        score = str(x["score"])
+
+    if score in [v["value"] for v in score_config.values()]:
+        return score
+
+    raise ValueError(f"{x['score']} ({type(x['score'])}) can't be a score")
+
+
 def score_to_numeric_score(x, score_config):
     """Convert a score to the corresponding numeric value
 
@@ -81,7 +132,7 @@ def score_to_numeric_score(x, score_config):
 
 
 def score_to_mark(x, score_max, rounding=lambda x: round(x, 2)):
-    """Compute the mark from the score
+    """Compute the mark from "score" which have to be filtered and in numeric form
 
     if the item is leveled then the score is multiply by the score_rate
     otherwise it copies the score
@@ -92,39 +143,38 @@ def score_to_mark(x, score_max, rounding=lambda x: round(x, 2)):
     :return: the mark
 
     >>> import pandas as pd
-    >>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
+    >>> d = {"Eleve":["E1"]*7,
-    ...    "score_rate":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
+    ...    "score_rate": [1]*7,
-    ...    "is_leveled":[0]*4+[1]*2 + [0]*4+[1]*2,
+    ...    "is_leveled":[0]+[1]*6,
-    ...    "score":[1, 0.33, 2, 1.5, 1, 3,   0.666, 1, 1.5, 1.2, 2, 3],
+    ...    "score":[0.33, "", ".", "a", 1, 2, 3],
+    ...    }
+    >>> score_config = {
+    ...   'BAD': {'value': 0, 'numeric_value': 0},
+    ...   'FEW': {'value': 1, 'numeric_value': 1},
+    ...   'NEARLY': {'value': 2, 'numeric_value': 2},
+    ...   'GOOD': {'value': 3, 'numeric_value': 3},
+    ...   'NOTFILLED': {'value': '', 'numeric_value': 'None'},
+    ...   'NOANSWER': {'value': '.', 'numeric_value': 0},
+    ...   'ABS': {'value': 'a', 'numeric_value': 'None'}
     ...    }
     >>> df = pd.DataFrame(d)
-    >>> df.loc[0]
+    >>> df = df[~df.apply(lambda x:is_none_score(x, score_config), axis=1)]
-    Eleve          E1
+    >>> df["score"] = df.apply(lambda x:score_to_numeric_score(x, score_config), axis=1)
-    score_rate      1
+    >>> df.apply(lambda x:score_to_mark(x, 3), axis=1)
-    is_leveled      0
+    0    0.33
-    score         1.0
+    2    0.00
-    Name: 0, dtype: object
+    4    0.33
-    >>> score_to_mark(df.loc[0], 3)
+    5    0.67
-    1.0
+    6    1.00
-    >>> df.loc[10]
+    dtype: float64
-    Eleve          E2
-    score_rate      2
-    is_leveled      1
-    score         2.0
-    Name: 10, dtype: object
-    >>> score_to_mark(df.loc[10], 3)
-    1.33
     >>> from .on_value import round_half_point
-    >>> score_to_mark(df.loc[10], 3, round_half_point)
+    >>> df.apply(lambda x:score_to_mark(x, 3, round_half_point), axis=1)
-    1.5
+    0    0.5
-    >>> df.loc[1]
+    2    0.0
-    Eleve           E1
+    4    0.5
-    score_rate       1
+    5    0.5
-    is_leveled       0
+    6    1.0
-    score         0.33
+    dtype: float64
-    Name: 1, dtype: object
-    >>> score_to_mark(df.loc[1], 3)
-    0.33
     """
     if x["is_leveled"]:
         if x["score"] not in list(range(score_max + 1)):