Feat: filter none numeric scores

recopytex/database/filesystem/default_config.yml

@@ -21,27 +21,34 @@ competences: # Competences
     name: Communiquer
     abrv: Com
 
-valid_scores: # 
+scores: # 
   BAD: # Everything is bad
     value: 0 
+    numeric_value: 0 
     color: "#E7472B"
   FEW: # Few good things
     value: 1 
+    numeric_value: 1 
     color: "#FF712B"
   NEARLY: # Nearly good but things are missing
     value: 2
+    numeric_value: 2
     color: "#F2EC4C"
   GOOD: # Everything is good
     value: 3
+    numeric_value: 3
     color: "#68D42F"
   NOTFILLED: # The item is not scored yet
     value: ""
+    numeric_value: None
     color: white
   NOANSWER: # Student gives no answer (count as 0)
     value: "."
+    numeric_value: 0
     color: black
   ABS: # Student has absent (this score won't be impact the final mark)
     value: a
+    numeric_value: None
     color: lightgray
 
 csv_fields: # dataframe_field: csv_field 

recopytex/database/filesystem/loader.py

@@ -31,10 +31,10 @@ class CSVLoader(Loader):
     :example:
     >>> loader = CSVLoader()
     >>> loader.get_config()
-    {'source': './', 'competences': {'Chercher': {'name': 'Chercher', 'abrv': 'Cher'}, 'Représenter': {'name': 'Représenter', 'abrv': 'Rep'}, 'Modéliser': {'name': 'Modéliser', 'abrv': 'Mod'}, 'Raisonner': {'name': 'Raisonner', 'abrv': 'Rai'}, 'Calculer': {'name': 'Calculer', 'abrv': 'Cal'}, 'Communiquer': {'name': 'Communiquer', 'abrv': 'Com'}}, 'valid_scores': {'BAD': {'value': 0, 'color': '#E7472B'}, 'FEW': {'value': 1, 'color': '#FF712B'}, 'NEARLY': {'value': 2, 'color': '#F2EC4C'}, 'GOOD': {'value': 3, 'color': '#68D42F'}, 'NOTFILLED': {'value': '', 'color': 'white'}, 'NOANSWER': {'value': '.', 'color': 'black'}, 'ABS': {'value': 'a', 'color': 'lightgray'}}, 'csv_fields': {'term': 'Trimestre', 'exam': 'Nom', 'date': 'Date', 'exercise': 'Exercice', 'question': 'Question', 'competence': 'Competence', 'theme': 'Domaine', 'comment': 'Commentaire', 'score_rate': 'Bareme', 'is_leveled': 'Est_nivele'}, 'id_templates': {'exam': '{name}_{tribe}', 'question': '{exam_id}_{exercise}_{question}_{comment}'}}
+    {'source': './', 'competences': {'Chercher': {'name': 'Chercher', 'abrv': 'Cher'}, 'Représenter': {'name': 'Représenter', 'abrv': 'Rep'}, 'Modéliser': {'name': 'Modéliser', 'abrv': 'Mod'}, 'Raisonner': {'name': 'Raisonner', 'abrv': 'Rai'}, 'Calculer': {'name': 'Calculer', 'abrv': 'Cal'}, 'Communiquer': {'name': 'Communiquer', 'abrv': 'Com'}}, 'scores': {'BAD': {'value': 0, 'numeric_value': 0, 'color': '#E7472B'}, 'FEW': {'value': 1, 'numeric_value': 1, 'color': '#FF712B'}, 'NEARLY': {'value': 2, 'numeric_value': 2, 'color': '#F2EC4C'}, 'GOOD': {'value': 3, 'numeric_value': 3, 'color': '#68D42F'}, 'NOTFILLED': {'value': '', 'numeric_value': 'None', 'color': 'white'}, 'NOANSWER': {'value': '.', 'numeric_value': 0, 'color': 'black'}, 'ABS': {'value': 'a', 'numeric_value': 'None', 'color': 'lightgray'}}, 'csv_fields': {'term': 'Trimestre', 'exam': 'Nom', 'date': 'Date', 'exercise': 'Exercice', 'question': 'Question', 'competence': 'Competence', 'theme': 'Domaine', 'comment': 'Commentaire', 'score_rate': 'Bareme', 'is_leveled': 'Est_nivele'}, 'id_templates': {'exam': '{name}_{tribe}', 'question': '{exam_id}_{exercise}_{question}_{comment}'}}
     >>> loader = CSVLoader("./test_config.yml")
     >>> loader.get_config()
-    {'source': './example', 'competences': {'Chercher': {'name': 'Chercher', 'abrv': 'Cher'}, 'Représenter': {'name': 'Représenter', 'abrv': 'Rep'}, 'Modéliser': {'name': 'Modéliser', 'abrv': 'Mod'}, 'Raisonner': {'name': 'Raisonner', 'abrv': 'Rai'}, 'Calculer': {'name': 'Calculer', 'abrv': 'Cal'}, 'Communiquer': {'name': 'Communiquer', 'abrv': 'Com'}}, 'valid_scores': {'BAD': {'value': 0, 'color': '#E7472B'}, 'FEW': {'value': 1, 'color': '#FF712B'}, 'NEARLY': {'value': 2, 'color': '#F2EC4C'}, 'GOOD': {'value': 3, 'color': '#68D42F'}, 'NOTFILLED': {'value': '', 'color': 'white'}, 'NOANSWER': {'value': '.', 'color': 'black'}, 'ABS': {'value': 'a', 'color': 'lightgray'}}, 'csv_fields': {'term': 'Trimestre', 'exam': 'Nom', 'date': 'Date', 'exercise': 'Exercice', 'question': 'Question', 'competence': 'Competence', 'theme': 'Domaine', 'comment': 'Commentaire', 'score_rate': 'Bareme', 'is_leveled': 'Est_nivele'}, 'id_templates': {'exam': '{name}_{tribe}', 'question': '{exam_id}_{exercise}_{question}_{comment}'}, 'output': './output', 'templates': 'templates/', 'tribes': {'Tribe1': {'name': 'Tribe1', 'type': 'Type1', 'students': 'tribe1.csv'}, 'Tribe2': {'name': 'Tribe2', 'students': 'tribe2.csv'}}}
+    {'source': './example', 'competences': {'Chercher': {'name': 'Chercher', 'abrv': 'Cher'}, 'Représenter': {'name': 'Représenter', 'abrv': 'Rep'}, 'Modéliser': {'name': 'Modéliser', 'abrv': 'Mod'}, 'Raisonner': {'name': 'Raisonner', 'abrv': 'Rai'}, 'Calculer': {'name': 'Calculer', 'abrv': 'Cal'}, 'Communiquer': {'name': 'Communiquer', 'abrv': 'Com'}}, 'scores': {'BAD': {'value': 0, 'numeric_value': 0, 'color': '#E7472B'}, 'FEW': {'value': 1, 'numeric_value': 1, 'color': '#FF712B'}, 'NEARLY': {'value': 2, 'numeric_value': 2, 'color': '#F2EC4C'}, 'GOOD': {'value': 3, 'numeric_value': 3, 'color': '#68D42F'}, 'NOTFILLED': {'value': '', 'numeric_value': 'None', 'color': 'white'}, 'NOANSWER': {'value': '.', 'numeric_value': 0, 'color': 'black'}, 'ABS': {'value': 'a', 'numeric_value': 'None', 'color': 'lightgray'}}, 'csv_fields': {'term': 'Trimestre', 'exam': 'Nom', 'date': 'Date', 'exercise': 'Exercice', 'question': 'Question', 'competence': 'Competence', 'theme': 'Domaine', 'comment': 'Commentaire', 'score_rate': 'Bareme', 'is_leveled': 'Est_nivele'}, 'id_templates': {'exam': '{name}_{tribe}', 'question': '{exam_id}_{exercise}_{question}_{comment}'}, 'output': './output', 'templates': 'templates/', 'tribes': {'Tribe1': {'name': 'Tribe1', 'type': 'Type1', 'students': 'tribe1.csv'}, 'Tribe2': {'name': 'Tribe2', 'students': 'tribe2.csv'}}}
     """
 
     CONFIG = DEFAULT_CONFIG

recopytex/datalib/on_score_column.py

@@ -10,7 +10,7 @@ def score_to_mark(x, score_max, rounding=lambda x: round(x, 2)):
     if the item is leveled then the score is multiply by the score_rate
     otherwise it copies the score
 
-    :param x: dictionnary with "is_leveled", "score" and "score_rate" keys
+    :param x: dictionnary with "is_leveled", "score"  (need to be number) and "score_rate" keys
     :param score_max:
     :param rounding: rounding mark function
     :return: the mark
@@ -125,6 +125,10 @@ def score_to_level(x, level_max=3):
     return int(ceil(x["score"] / x["score_rate"] * level_max))
 
 
+def score_to_numeric_score(x, score_config):
+    pass
+
+
 # -----------------------------
 # Reglages pour 'vim'
 # vim:set autoindent expandtab tabstop=4 shiftwidth=4:

recopytex/datalib/on_score_dataframe.py

@@ -2,6 +2,7 @@
 # encoding: utf-8
 
 from .on_score_column import score_to_mark, score_to_level
+import pandas as pd
 
 
 def compute_marks(df, score_max, rounding=lambda x: round(x, 2)):
@@ -9,7 +10,7 @@ def compute_marks(df, score_max, rounding=lambda x: round(x, 2)):
 
     apply score_to_mark to each row
 
-    :param df: DataFrame with "score", "is_leveled" and "score_rate" columns.
+    :param df: DataFrame with "score" (need to be number), "is_leveled" and "score_rate" columns.
 
     >>> import pandas as pd
     >>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
@@ -113,12 +114,12 @@ def compute_normalized(df, rounding=lambda x: round(x, 2)):
     >>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
     ...    "score_rate":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
     ...    "is_leveled":[0]*4+[1]*2 + [0]*4+[1]*2,
-    ...    "score":[1, 0.33, 2, 1.5, 1, 3,   0.666, 1, 1.5, 1, 2, 3],
+    ...    "score":[0, 0.33, 2, 1.5, 1, 3,   0.666, 1, 1.5, 1, 2, 3],
     ...    }
     >>> df = pd.DataFrame(d)
     >>> df["mark"] = compute_marks(df, 3)
     >>> compute_normalized(df)
-    0     1.00
+    0     0.00
     1     0.33
     2     1.00
     3     0.75
@@ -135,6 +136,60 @@ def compute_normalized(df, rounding=lambda x: round(x, 2)):
     return rounding(df["mark"] / df["score_rate"])
 
 
+def filter_none_score(df, score_config):
+    """Filter rows where scores have None numeric values
+
+    :example:
+
+    >>> import pandas as pd
+    >>> d = {"Eleve":["E1"]*7,
+    ...    "score_rate": [1]*7,
+    ...    "is_leveled":[0]+[1]*6,
+    ...    "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)
+    >>> filter_none_score(df, score_config)
+      Eleve  score_rate  is_leveled score
+    0    E1           1           0  0.33
+    2    E1           1           1     .
+    4    E1           1           1     1
+    5    E1           1           1     2
+    6    E1           1           1     3
+    """
+    not_leveled_df = df[df["is_leveled"] != 1]
+    leveled_df = df[df["is_leveled"] == 1]
+
+    not_none_values = [
+        v["value"]
+        for v in score_config.values()
+        if str(v["numeric_value"]).lower() != "none"
+    ]
+    filtered_leveled_df = leveled_df[leveled_df["score"].isin(not_none_values)]
+
+    return pd.concat([not_leveled_df, filtered_leveled_df])
+
+
+def score_to_numeric_score(df, score_config):
+    """Transform a score to the corresponding numeric value
+
+    >>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
+    ...    "score_rate":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
+    ...    "is_leveled":[0]*4+[1]*2 + [0]*4+[1]*2,
+    ...    "score":[0, 0.33, 2, 1.5, 1, 3,   0.666, 1, 1.5, 1, 2, 3],
+    ...    }
+    """
+    pass
+
+
 # -----------------------------
 # Reglages pour 'vim'
 # vim:set autoindent expandtab tabstop=4 shiftwidth=4: