lafrite/recopytex
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

FEat: add comments

Browse Source
This commit is contained in:
Bertrand Benjamin 2020-10-04 07:20:08 +02:00
parent 04a1ed9378
commit 400fb0a690
1 changed files with 22 additions and 8 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

22
recopytex/df_marks_manip.py
View File

@ -6,7 +6,9 @@ import numpy as np
from math import ceil, floor
from .config import COLUMNS, VALIDSCORE
# Values manipulations
"""
Functions for manipulate score dataframes
"""
def round_half_point(val):
@ -25,6 +27,7 @@ def score_to_mark(x):
otherwise it copies the score
:param x: dictionnary with COLUMNS["is_leveled"], COLUMNS["score"] and COLUMNS["score_rate"] keys
:return: the mark
>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
... 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["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)
if x[COLUMNS["score"]] > x[COLUMNS["score_rate"]]:
@ -57,6 +62,7 @@ def score_to_level(x):
"""Compute the level (".",0,1,2,3).
:param x: dictionnary with COLUMNS["is_leveled"], COLUMNS["score"] and COLUMNS["score_rate"] keys
:return: the level
>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
... 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):
""" 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.
@ -122,9 +130,12 @@ def compute_mark(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.
:return: Columns with level
>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
... COLUMNS["score_rate"]:[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
@ -156,6 +167,7 @@ def compute_normalized(df):
"""Compute the normalized mark (Mark / score_rate)
:param df: DataFrame with "Mark" and COLUMNS["score_rate"] columns
:return: column with normalized mark
>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
... COLUMNS["score_rate"]:[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
@ -188,6 +200,8 @@ def compute_normalized(df):
def pp_q_scores(df):
"""Postprocessing questions scores dataframe
Add 3 columns: mark, level and normalized
:param df: questions-scores dataframe
:return: same data frame with mark, level and normalize columns
"""