def pivot_detail(request, syllabus_tag=None, pivot_type=None, pivot_id=None):
"""
Describes a single pivot in detail.
"""
if pivot_type not in ['k', 'w'] or pivot_id is None:
raise Http404
pivot_id = int(pivot_id)
context = {}
context['pivot_type'] = pivot_type
context['syllabus'] = usermodel_models.Syllabus.objects.get(
tag=syllabus_tag.replace('_', ' '))
if pivot_type == 'k':
pivot = usermodel_models.PartialKanji.objects.get(id=pivot_id)
else:
assert pivot_type == 'w'
pivot = usermodel_models.PartialLexeme.objects.get(id=pivot_id)
context['pivot'] = pivot
base_response_stats = stats.get_pivot_response_stats(pivot.id, pivot_type)
context['response_dists'] = [(l, charts.PieChart(d.items(),
size='750x200'))
for (l, d) in base_response_stats]
return render_to_response('analysis/pivot_detail.html', context,
RequestContext(request))
def _build_user_graph(name):
if name.endswith('lang'):
name = name[:-len('lang')]
dist = stats.get_language_data(name)
return charts.PieChart(dist.items(), max_options=8)
elif name == 'dropout':
data = stats.get_dropout_figures()
approx_data = stats.approximate(data)
chart = charts.MultiLineChart(approx_data,
data_name='histogram',
x_axis=(0.4, 1.0, 0.1),
y_axis=(0, 1.0, 0.1))
chart.add_data('raw', data)
return chart
elif name == 'prepostdiff':
data = [
r['pre_post_diff'] for r in stats.get_global_rater_stats()
if r['n_tests'] > 2 and r['pre_post_diff']
]
hist_data = stats.histogram(data,
n_bins=11,
normalize=False,
x_min=-0.7,
x_max=0.7)
chart = charts.LineChart(hist_data,
data_name='histogram',
x_axis=(-0.8, 0.8, 0.2))
chart.add_data('raw', data)
return chart
elif name == 'abilityjlpt3':
data = stats.get_user_scores('jlpt 3')
hist_data = stats.histogram(data,
x_min=0.0,
x_max=1.0,
normalize=False)
chart = charts.LineChart(hist_data,
data_name='histogram',
x_axis=(0.0, 1.0, 0.1))
chart.add_data('raw', data)
return chart
elif name == 'abilityjlpt4':
data = stats.get_user_scores('jlpt 4')
hist_data = stats.histogram(data,
x_min=0.0,
x_max=1.0,
normalize=False)
chart = charts.LineChart(hist_data,
data_name='histogram',
x_axis=(0.0, 1.0, 0.1))
chart.add_data('raw', data)
return chart
raise KeyError(name)
def _build_plugin_graph(name):
if '_' in name:
name, subpart = name.split('_', 1)
else:
subpart = None
if name == 'questions':
data = []
for plugin in models.QuestionPlugin.objects.all():
data.append((
plugin.name + \
((plugin.is_adaptive) and ' [adaptive]' or ' [simple]'),
plugin.question_set.count(),
))
return charts.PieChart(data)
elif name == 'error':
raw_data = stats.get_mean_error_by_plugin()
data = _accumulate_plugin_errors(raw_data)
k = (1 + settings.N_DISTRACTORS)
data.append(('[random guess]', (k - 1) / float(k)))
chart = charts.BarChart(data, y_axis=(0, 1.0, 0.2))
chart.add_data('raw', raw_data)
return chart
elif name == 'errorbin':
data_sets = stats.get_power_binned_error_by_plugin()
is_error_chart = True
if subpart == 'early':
raw_data = data_sets[0]
data = _accumulate_plugin_errors(raw_data)
elif subpart == 'mid':
raw_data = data_sets[1]
data = _accumulate_plugin_errors(raw_data)
elif subpart == 'late':
raw_data = data_sets[2]
data = _accumulate_plugin_errors(raw_data)
elif subpart == 'volume':
is_error_chart = False
data = zip(['Early', 'Mid', 'Late'], map(len, data_sets))
else:
raise KeyError(name)
if is_error_chart:
chart = charts.BarChart(data) #, y_axis=(0, 0.5, 0.1))
chart.add_data('raw', raw_data)
else:
chart = charts.BarChart(data)
return chart
raise KeyError(name)
def _build_test_graph(name):
if name == 'mean':
score_data = stats.get_mean_score_nth_test()
data = stats.group_by_points(score_data, y_max=1.0, y_min=0.0)
chart = charts.MultiLineChart(data,
y_axis=(0, 1, 0.1),
data_name='grouped')
chart.add_data('raw', score_data)
return chart
elif name == 'volume':
user_data = stats.get_users_by_n_tests()
return charts.LineChart(user_data)
elif name == 'length':
return charts.PieChart(stats.get_test_length_volume())
elif name == 'normtime':
user_data = stats.get_score_over_norm_time()
return charts.LineChart(user_data)
elif name == 'time':
base_data = stats.get_score_over_time()
data = stats.approximate(base_data)
chart = charts.MultiLineChart(data,
y_axis=(0, 1.05, 0.1),
data_name='approximate')
chart.add_data('raw', base_data)
two_colours = charts.color_desc(2).split(',')
three_colours = ','.join(
(two_colours[0], two_colours[1], two_colours[1]))
chart['chco'] = three_colours
return chart
elif name == 'dropout':
return charts.LineChart(stats.get_mean_score_by_n_tests())
elif name == 'firstlast':
data = stats.get_first_last_test()
hist_data = stats.histogram(data,
n_bins=11,
normalize=False,
x_min=-0.5,
x_max=0.5)
chart = charts.LineChart(hist_data,
data_name='histogram',
x_axis=(-0.5, 0.5, 0.1))
chart.add_data('raw', data)
return chart
raise KeyError(name)
def _build_syllabus_graph(name):
if name == 'volume':
return charts.PieChart(stats.get_syllabus_volume())
raise KeyError(name)