mirror of
https://codeberg.org/Mo8it/AdvLabDB.git
synced 2024-11-06 21:17:43 +00:00
142 lines
3.6 KiB
Python
142 lines
3.6 KiB
Python
from base64 import b64encode
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from io import BytesIO
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import numpy as np
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from flask_login import current_user
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from matplotlib.figure import Figure
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from matplotlib.ticker import MaxNLocator
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from sqlalchemy import select
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from .models import MAX_MARK, MIN_MARK, Assistant, Semester, User, db
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def html_fig(fig):
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buf = BytesIO()
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fig.savefig(buf, format="png")
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return b64encode(buf.getbuffer()).decode("ascii")
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def mark_hist(data, title):
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fig = Figure()
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ax = fig.subplots()
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ax.set_xlim(MIN_MARK - 0.5, MAX_MARK + 0.5)
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ax.set_xticks(np.arange(MAX_MARK + 1))
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# Only integer ticks
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ax.yaxis.set_major_locator(MaxNLocator(integer=True))
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ax.set_xlabel("Mark")
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N = data.size
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title += f"\nN = {N}"
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if N > 0:
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ax.hist(
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data,
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bins=np.arange(MAX_MARK + 2) - 0.5,
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)
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title += f" | mean = {round(np.mean(data), 1)}"
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ax.set_title(title)
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return html_fig(fig)
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def get_experiment_marks(assistant, attr):
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data = []
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for experiment_mark in assistant.experiment_marks:
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mark = getattr(experiment_mark, attr)
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if mark is not None:
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data.append(mark)
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return np.array(data)
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def mark_hists(markType, active_assistants):
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attr = markType.lower() + "_mark"
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mark_type_title_addition = f" | {markType} marks"
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marks = [get_experiment_marks(assistant, attr) for assistant in active_assistants]
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hists = [
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mark_hist(
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data=marks[i],
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title=str(active_assistants[i]) + mark_type_title_addition,
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)
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for i in range(len(marks))
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]
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hists.append(
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mark_hist(
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data=np.hstack(marks),
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title="All" + mark_type_title_addition,
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)
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)
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return hists
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def assistant_marks_analysis(cls):
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active_assistants = db.session.scalars(select(Assistant).join(User).where(User.active is True)).all()
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oral_mark_hists = mark_hists("Oral", active_assistants)
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protocol_mark_hists = mark_hists("Protocol", active_assistants)
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return cls.render(
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"analysis/assistant_marks.jinja.html",
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hist_indices=range(len(oral_mark_hists)),
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oral_mark_hists=oral_mark_hists,
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protocol_mark_hists=protocol_mark_hists,
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)
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def get_final_part_marks(part):
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data = []
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for part_student in part.part_students:
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mark = part_student.final_part_mark
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if mark is not None:
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data.append(mark)
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return np.array(data)
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def final_part_marks_analysis(cls):
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parts = current_user.active_semester.parts
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active_semester_final_part_marks_hists = [
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mark_hist(
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data=get_final_part_marks(part),
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title=part.str(),
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)
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for part in parts
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]
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semesters = db.session.scalars(select(Semester)).all()
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mean_final_part_marks = np.array(
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[np.mean(np.hstack([get_final_part_marks(part) for part in semester.parts])) for semester in semesters]
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)
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fig = Figure()
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len_mean_final_part_marks = mean_final_part_marks.size
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ax = fig.subplots()
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x = range(len_mean_final_part_marks)
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ax.plot(
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x,
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mean_final_part_marks,
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marker="d",
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)
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ax.set_xticks(x, [semester.str() for semester in semesters])
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ax.set_xlabel("Semester")
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ax.set_ylabel("Mean final experiment mark")
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ax.set_title("Mean final experiment mark over all semesters")
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mean_final_part_mark_plot = html_fig(fig)
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return cls.render(
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"analysis/final_part_marks.jinja.html",
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active_semester_final_part_marks_hists=active_semester_final_part_marks_hists,
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mean_final_part_mark_plot=mean_final_part_mark_plot,
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)
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