Feat: Final mark for students

recopytex/dashboard/pages/exams_scores/app.py

@@ -7,62 +7,71 @@ from .models import get_tribes, get_exams
 from .callbacks import *
 
 layout = html.Div(
-        children=[
-            html.Header(
-                children=[
-                    html.H1("Analyse des notes"),
-                    html.P("Dernière sauvegarde", id="lastsave"),
+    children=[
+        html.Header(
+            children=[
+                html.H1("Analyse des notes"),
+                html.P("Dernière sauvegarde", id="lastsave"),
+            ],
+        ),
+        html.Main(
+            children=[
+                html.Section(
+                    children=[
+                        html.Div(
+                            children=[
+                                "Classe: ",
+                                dcc.Dropdown(
+                                    id="tribe",
+                                    options=[
+                                        {"label": t["name"], "value": t["name"]}
+                                        for t in get_tribes().values()
+                                    ],
+                                    value=next(iter(get_tribes().values()))["name"],
+                                ),
+                            ],
+                        ),
+                        html.Div(
+                            children=[
+                                "Evaluation: ",
+                                dcc.Dropdown(id="exam_select"),
+                            ],
+                        ),
                     ],
+                    id="selects",
                 ),
-            html.Main(
-                children=[
-                    html.Section(
-                        children=[
-                            html.Div(
-                                children=[
-                                    "Classe: ",
-                                    dcc.Dropdown(
-                                        id="tribe",
-                                        options=[
-                                            {"label": t["name"], "value": t["name"]}
-                                            for t in get_tribes().values()
-                                            ],
-                                        value=next(iter(get_tribes().values()))["name"],
-                                        ),
+                html.Section(
+                    children=[
+                        html.Div(
+                            children=[
+                                dash_table.DataTable(
+                                    id="final_score_table",
+                                    columns=[
+                                        {"name": "Étudiant", "id": "student_name"},
+                                        {"name": "Note", "id": "mark"},
+                                        {"name": "Barème", "id": "score_rate"},
                                     ],
-                                ),
-                            html.Div(
-                                children=[
-                                    "Evaluation: ",
-                                    dcc.Dropdown(id="exam_select"),
-                                    ],
-                                ),
-                            ],
-                        id="selects",
-                        ),
-                    html.Section(
-                        children=[
-                            html.Div(
-                                children=[],
-                                id="final_score_table_container",
-                                ),
-                            ],
-                        id="analysis",
-                        ),
-                    html.Section(
-                        children=[
-                            dash_table.DataTable(
-                                id="scores_table",
-                                columns=[],
-                                style_data_conditional=[],
-                                fixed_columns={},
-                                editable=True,
                                 )
                             ],
-                        id="edit",
+                            id="final_score_table_container",
                         ),
                     ],
+                    id="analysis",
+                ),
+                html.Section(
+                    children=[
+                        dash_table.DataTable(
+                            id="scores_table",
+                            columns=[],
+                            style_data_conditional=[],
+                            fixed_columns={},
+                            editable=True,
+                        )
+                    ],
+                    id="edit",
                 ),
-            dcc.Store(id="scores"),
             ],
-        )
+        ),
+        dcc.Store(id="scores"),
+    ],
+)

recopytex/dashboard/pages/exams_scores/callbacks.py

@@ -17,6 +17,7 @@ from .models import (
     get_unstack_scores,
     get_students_from_exam,
     get_score_colors,
+    score_to_final_mark,
 )
 
 
@@ -77,3 +78,17 @@ def update_scores_store(exam):
         highlight_scores(students, score_color),
         {"headers": True, "data": len(fixed_columns)},
     ]
+
+
+@app.callback(
+    [
+        Output("final_score_table", "data"),
+    ],
+    [
+        Input("scores_table", "data"),
+    ],
+)
+def update_scores_store(scores):
+    scores_df = pd.DataFrame.from_records(scores)
+    # print(scores_df)
+    return score_to_final_mark(scores_df)

recopytex/dashboard/pages/exams_scores/models.py

@@ -1,10 +1,44 @@
-#!/usr/bin/env python
+#!/use/bin/env python
 # encoding: utf-8
 
 from recopytex.database.filesystem.loader import CSVLoader
 from recopytex.datalib.dataframe import column_values_to_column
+import recopytex.datalib.on_score_column as on_column
+import pandas as pd
 
-LOADER = CSVLoader("./test_config.yml")
+LOADER = CSVLoader("./test_confia.ml")
+SCORES_CONFIG = LOADER.get_config()["scores"]
+
+
+def unstack_scores(scores):
+    """Put student_name values to columns
+
+    :param scores: Score dataframe with one line per score
+    :returns: Scrore dataframe with student_name in columns
+
+    """
+    kept_columns = [col for col in LOADER.score_columns if col != "score"]
+    return column_values_to_column("student_name", "score", kept_columns, scores)
+
+
+def stack_scores(scores):
+    """Student columns are melt to rows with student_name column
+
+    :param scores: Score dataframe with student_name in columns
+    :returns: Scrore dataframe with one line per score
+
+    """
+    kept_columns = [
+        c for c in LOADER.score_columns if c not in ["score", "student_name"]
+    ]
+    student_names = [c for c in scores.columns if c not in kept_columns]
+    return pd.melt(
+        scores,
+        id_vars=kept_columns,
+        value_vars=student_names,
+        var_name="student_name",
+        value_name="score",
+    )
 
 
 def get_tribes():
@@ -21,8 +55,7 @@ def get_record_scores(exam):
 
 def get_unstack_scores(exam):
     flat_scores = LOADER.get_exam_scores(exam)
-    kept_columns = [col for col in LOADER.score_columns if col != "score"]
-    return column_values_to_column("student_name", "score", kept_columns, flat_scores)
+    return unstack_scores(flat_scores)
 
 
 def get_students_from_exam(exam):
@@ -31,8 +64,32 @@ def get_students_from_exam(exam):
 
 
 def get_score_colors():
-    scores_config = LOADER.get_config()["valid_scores"]
     score_color = {}
-    for key, score in scores_config.items():
+    for key, score in SCORES_CONFIG.items():
         score_color[score["value"]] = score["color"]
     return score_color
+
+
+is_none_score = lambda x: on_column.is_none_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 score_to_final_mark(scores):
+    """ Compute marks then reduce to final mark per student """
+
+    melted_scores = stack_scores(scores)
+    filtered_scores = melted_scores[~melted_scores.apply(is_none_score, axis=1)]
+    filtered_scores = filtered_scores.assign(
+        score=filtered_scores.apply(score_to_numeric_score, axis=1)
+    )
+    filtered_scores = filtered_scores.assign(
+        mark=filtered_scores.apply(score_to_mark, axis=1)
+    )
+    final_score = filtered_scores.groupby(["student_name"])[
+        ["mark", "score_rate"]
+    ].sum()
+    return [final_score.reset_index().to_dict("records")]
+

recopytex/datalib/on_score_column.py

@@ -2,6 +2,82 @@
 # encoding: utf-8
 
 from math import ceil
+import numpy as np
+
+
+def is_none_score(x, score_config):
+    """Is a score correspond to a None numeric_value which
+
+    >>> 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)
+    >>> df.apply(lambda x:is_none_score(x, score_config), axis=1)
+    0    False
+    1     True
+    2    False
+    3     True
+    4    False
+    5    False
+    6    False
+    dtype: bool
+
+    """
+    none_values = [
+        v["value"]
+        for v in score_config.values()
+        if str(v["numeric_value"]).lower() == "none"
+    ]
+    return x["score"] in none_values or x["score"] is None or np.isnan(x["score"])
+
+
+def score_to_numeric_score(x, score_config):
+    """Convert a score to the corresponding numeric value
+
+    >>> 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)
+    >>> df.apply(lambda x:score_to_numeric_score(x, score_config), axis=1)
+    0    0.33
+    1    None
+    2       0
+    3    None
+    4       1
+    5       2
+    6       3
+    dtype: object
+
+    """
+    if x["is_leveled"]:
+        replacements = {v["value"]: v["numeric_value"] for v in score_config.values()}
+        return replacements[x["score"]]
+
+    return x["score"]
 
 
 def score_to_mark(x, score_max, rounding=lambda x: round(x, 2)):
@@ -125,10 +201,6 @@ 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

@@ -1,196 +0,0 @@
-#!/usr/bin/env python
-# 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)):
-    """Compute the mark for the dataframe
-
-    apply score_to_mark to each row
-
-    :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,
-    ...    "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],
-    ...    }
-    >>> df = pd.DataFrame(d)
-    >>> df
-       Eleve  score_rate  is_leveled  score
-    0     E1           1           0  1.000
-    1     E1           1           0  0.330
-    2     E1           2           0  2.000
-    3     E1           2           0  1.500
-    4     E1           2           1  1.000
-    5     E1           2           1  3.000
-    6     E2           1           0  0.666
-    7     E2           1           0  1.000
-    8     E2           2           0  1.500
-    9     E2           2           0  1.000
-    10    E2           2           1  2.000
-    11    E2           2           1  3.000
-    >>> compute_marks(df, 3)
-    0     1.00
-    1     0.33
-    2     2.00
-    3     1.50
-    4     0.67
-    5     2.00
-    6     0.67
-    7     1.00
-    8     1.50
-    9     1.00
-    10    1.33
-    11    2.00
-    dtype: float64
-    >>> from .on_value import round_half_point
-    >>> compute_marks(df, 3, round_half_point)
-    0     1.0
-    1     0.5
-    2     2.0
-    3     1.5
-    4     0.5
-    5     2.0
-    6     0.5
-    7     1.0
-    8     1.5
-    9     1.0
-    10    1.5
-    11    2.0
-    dtype: float64
-    """
-    return df[["score", "is_leveled", "score_rate"]].apply(
-        lambda x: score_to_mark(x, score_max, rounding), axis=1
-    )
-
-
-def compute_level(df, level_max=3):
-    """Compute level for the dataframe
-
-    Applies score_to_level to each row
-
-    :param df: DataFrame with "score", "is_leveled" and "score_rate" columns.
-    :return: Columns with level
-
-    >>> import pandas as pd
-    >>> import numpy as np
-    >>> 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],
-    ...    }
-    >>> df = pd.DataFrame(d)
-    >>> compute_level(df)
-    0     0
-    1     1
-    2     3
-    3     3
-    4     1
-    5     3
-    6     2
-    7     3
-    8     3
-    9     2
-    10    2
-    11    3
-    dtype: int64
-    """
-    return df[["score", "is_leveled", "score_rate"]].apply(
-        lambda x: score_to_level(x, level_max), axis=1
-    )
-
-
-def compute_normalized(df, rounding=lambda x: round(x, 2)):
-    """Compute the normalized mark (Mark / score_rate)
-
-    :param df: DataFrame with "Mark" and "score_rate" columns
-    :return: column with normalized mark
-
-    >>> import pandas as pd
-    >>> 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],
-    ...    }
-    >>> df = pd.DataFrame(d)
-    >>> df["mark"] = compute_marks(df, 3)
-    >>> compute_normalized(df)
-    0     0.00
-    1     0.33
-    2     1.00
-    3     0.75
-    4     0.34
-    5     1.00
-    6     0.67
-    7     1.00
-    8     0.75
-    9     0.50
-    10    0.66
-    11    1.00
-    dtype: float64
-    """
-    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:
-# cursor: 16 del