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from IPython.display import HTML

HTML('''<script>
code_show=true; 
function code_toggle() {
 if (code_show){
 $('div.input').hide();
 } else {
 $('div.input').show();
 }
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} 
$( document ).ready(code_toggle);
</script>
<form action="javascript:code_toggle()"><input type="submit" value="Click here to toggle on/off the raw code."></form>''')


    

    
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#The usual imports
import math
import glob
import os
from collections import OrderedDict

from pandas import read_csv
import numpy as np

from pymatgen.util.plotting import pretty_plot
from monty.string import remove_non_ascii

import seaborn as sns
import matplotlib.pyplot as plt

import datetime

%matplotlib inline

print("Last updated on %s" % datetime.datetime.today())


    

    




Last updated on 2017-03-21 21:39:24.307394

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# Load data from exported CSV from Ted Full Grade Center. 
# Some sanitization is performed to remove non-ascii characters and cruft.

files = glob.glob(os.path.join(os.environ["CENG114GC"], "*.csv"))
latest = sorted(files)[-1]
d = read_csv(latest)
d.columns = [remove_non_ascii(c) for c in d.columns]
d.columns = [c.split("[")[0].strip().strip("\"") for c in d.columns]
d["Weighted Total"] = [float(s.strip("%")) for s in d["Weighted Total"]]
d = d.fillna(0)


    

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# Define lower grade cutoffs in terms of number of standard deviations from mean.
grade_cutoffs = OrderedDict()
grade_cutoffs["A+"] = 1.5
grade_cutoffs["A"] = 1.0
grade_cutoffs["A-"] = 0.75
grade_cutoffs["B+"] = 0.5
grade_cutoffs["B"] = 0
grade_cutoffs["B-"] = -0.5
grade_cutoffs["C+"] = -0.75
grade_cutoffs["C"] = -1
grade_cutoffs["C-"] = -1.5
grade_cutoffs["F"] = float("-inf")

print("The cutoffs are:")
for k, v in grade_cutoffs.items():
    print(u"%s: > μ + %.2f σ" % (k, v))


    

    




The cutoffs are:
A+: > μ + 1.50 σ
A: > μ + 1.00 σ
A-: > μ + 0.75 σ
B+: > μ + 0.50 σ
B: > μ + 0.00 σ
B-: > μ + -0.50 σ
C+: > μ + -0.75 σ
C: > μ + -1.00 σ
C-: > μ + -1.50 σ
F: > μ + -inf σ

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# Define some general functions for computing grade statistics.

def bar_plot(dframe, data_key, offset=0, annotate=True):
    """
    Creates a historgram of the results.
    
    Args:
        dframe: DataFrame which is imported from CSV.
        data_key: Specific column to plot
        offset: Allows an offset for each grade. Defaults to 0.
        
    Returns:
        dict of cutoffs, {grade: (lower, upper)}
    """
    data = dframe[data_key]
    d = [d for d in data if (not np.isnan(d)) and d != 0]
    heights, bins = np.histogram(d, bins=20, range=(0, 100))
    import matplotlib.pyplot as plt
    sns.distplot(d, bins=np.arange(0, 105, 5), kde=False, rug=True)
    plt = pretty_plot(12, 8, plt)
    plt.xlabel("Score")
    plt.ylabel("Number of students")
    mean = np.mean(d)
    sigma = np.std(d)
    maxy = np.max(heights) + 5
    prev_cutoff = 100
    cutoffs = {}
    grade = ["A", "B+", "B", "B-", "C+", "C", "C-", "F"]
    for grade, cutoff in grade_cutoffs.items():
        if cutoff == float("-inf"):
            cutoff = 0
        else:
            cutoff = max(0, mean + cutoff * sigma) + offset
            if annotate:
                plt.plot([cutoff] * 2, [0, maxy], 'k--')
                plt.annotate("%.2f" % cutoff, [cutoff, maxy - 1], fontsize=18, horizontalalignment='left', rotation=45)
        n = len([d for d in data if cutoff <= d < prev_cutoff])
        #print "Grade %s (%.1f-%.1f): %d" % (grade, cutoff, prev_cutoff, n)
        if annotate:
            plt.annotate(grade, [(cutoff + prev_cutoff) / 2, maxy], fontsize=18, horizontalalignment='center')
        cutoffs[grade] = (cutoff, prev_cutoff)
        prev_cutoff = cutoff
    
    plt.xlim([0, 100])
    plt.ylim([0, maxy * 1.1])
    plt.annotate('$\\mu = %.2f$\n$\\sigma = %.2f$\n$max=%.2f$' % (mean, sigma, data.max()), xy=(10, 7), fontsize=30)
    title = data_key.split("[")[0].strip()
    plt.title(title, fontsize=30)
    plt.tight_layout()
    # plt.savefig("%s.png" % title)
    return cutoffs


def assign_grades(d, column_name, cutoffs):
    def compute_grade(pts):
        for g, c in cutoffs.items():
            if c[0] < pts <= c[1]:
                return g

    d["Final_Assigned_Egrade"] = [compute_grade(v) for v in d[column_name]]
    d.to_csv(os.path.join(os.path.join(os.environ["CENG114GC"], "Overall grades %s.csv" % datetime.datetime.today().date())))


    

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cutoffs = bar_plot(d, "PS1", annotate=True)


    

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cutoffs = bar_plot(d, "PS2", annotate=True)


    

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cutoffs = bar_plot(d, "PS3", annotate=True)


    

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cutoffs = bar_plot(d, "PS4", annotate=True)


    

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cutoffs = bar_plot(d, "PS5", annotate=True)


    

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cutoffs = bar_plot(d, "MT1", annotate=True)


    

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cutoffs = bar_plot(d, "MT2", annotate=True)


    

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cutoffs = bar_plot(d, "Final", annotate=True)


    

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d["Final Total"] = np.minimum(d["Final Total"], 100)


    

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cutoffs = bar_plot(d, "Final Total", annotate=True, offset=-1.9)


    

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# The command below is used to generate the overall grade assignments for all students and dump it into a CSV file.
assign_grades(d, "Final Total", cutoffs=cutoffs)