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Bertrand Benjamin 2020-10-04 07:20:08 +02:00
parent 04a1ed9378
commit 400fb0a690
1 changed files with 22 additions and 8 deletions
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recopytex/df_marks_manip.py
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@ -6,7 +6,9 @@ import numpy as np
from math import ceil, floor from math import ceil, floor
from .config import COLUMNS, VALIDSCORE from .config import COLUMNS, VALIDSCORE
# Values manipulations """
Functions for manipulate score dataframes
"""
def round_half_point(val): def round_half_point(val):
@ -19,12 +21,13 @@ def round_half_point(val):
def score_to_mark(x): def score_to_mark(x):
""" Compute the mark """Compute the mark
if the item is leveled then the score is multiply by the score_rate if the item is leveled then the score is multiply by the score_rate
otherwise it copies the score otherwise it copies the score
:param x: dictionnary with COLUMNS["is_leveled"], COLUMNS["score"] and COLUMNS["score_rate"] keys :param x: dictionnary with COLUMNS["is_leveled"], COLUMNS["score"] and COLUMNS["score_rate"] keys
:return: the mark
>>> d = {"Eleve":["E1"]*6 + ["E2"]*6, >>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
... COLUMNS["score_rate"]:[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2, ... COLUMNS["score_rate"]:[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
@ -43,7 +46,9 @@ def score_to_mark(x):
if x[COLUMNS["is_leveled"]]: if x[COLUMNS["is_leveled"]]:
if x[COLUMNS["score"]] not in [0, 1, 2, 3]: if x[COLUMNS["score"]] not in [0, 1, 2, 3]:
raise ValueError(f"The evaluation is out of range: {x[COLUMNS['score']]} at {x}") raise ValueError(
f"The evaluation is out of range: {x[COLUMNS['score']]} at {x}"
)
return round_half_point(x[COLUMNS["score"]] * x[COLUMNS["score_rate"]] / 3) return round_half_point(x[COLUMNS["score"]] * x[COLUMNS["score_rate"]] / 3)
if x[COLUMNS["score"]] > x[COLUMNS["score_rate"]]: if x[COLUMNS["score"]] > x[COLUMNS["score_rate"]]:
@ -54,9 +59,10 @@ def score_to_mark(x):
def score_to_level(x): def score_to_level(x):
""" Compute the level (".",0,1,2,3). """Compute the level (".",0,1,2,3).
:param x: dictionnary with COLUMNS["is_leveled"], COLUMNS["score"] and COLUMNS["score_rate"] keys :param x: dictionnary with COLUMNS["is_leveled"], COLUMNS["score"] and COLUMNS["score_rate"] keys
:return: the level
>>> d = {"Eleve":["E1"]*6 + ["E2"]*6, >>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
... COLUMNS["score_rate"]:[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2, ... COLUMNS["score_rate"]:[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
@ -91,7 +97,9 @@ def score_to_level(x):
def compute_mark(df): def compute_mark(df):
""" Add Mark column to df """Compute the mark for the dataframe
apply score_to_mark to each row
:param df: DataFrame with COLUMNS["score"], COLUMNS["is_leveled"] and COLUMNS["score_rate"] columns. :param df: DataFrame with COLUMNS["score"], COLUMNS["is_leveled"] and COLUMNS["score_rate"] columns.
@ -122,9 +130,12 @@ def compute_mark(df):
def compute_level(df): def compute_level(df):
""" Add Mark column to df """Compute level for the dataframe
Applies score_to_level to each row
:param df: DataFrame with COLUMNS["score"], COLUMNS["is_leveled"] and COLUMNS["score_rate"] columns. :param df: DataFrame with COLUMNS["score"], COLUMNS["is_leveled"] and COLUMNS["score_rate"] columns.
:return: Columns with level
>>> d = {"Eleve":["E1"]*6 + ["E2"]*6, >>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
... COLUMNS["score_rate"]:[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2, ... COLUMNS["score_rate"]:[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
@ -153,9 +164,10 @@ def compute_level(df):
def compute_normalized(df): def compute_normalized(df):
""" Compute the normalized mark (Mark / score_rate) """Compute the normalized mark (Mark / score_rate)
:param df: DataFrame with "Mark" and COLUMNS["score_rate"] columns :param df: DataFrame with "Mark" and COLUMNS["score_rate"] columns
:return: column with normalized mark
>>> d = {"Eleve":["E1"]*6 + ["E2"]*6, >>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
... COLUMNS["score_rate"]:[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2, ... COLUMNS["score_rate"]:[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
@ -186,7 +198,9 @@ def compute_normalized(df):
def pp_q_scores(df): def pp_q_scores(df):
""" Postprocessing questions scores dataframe """Postprocessing questions scores dataframe
Add 3 columns: mark, level and normalized
:param df: questions-scores dataframe :param df: questions-scores dataframe
:return: same data frame with mark, level and normalize columns :return: same data frame with mark, level and normalize columns