def get_pivots_by_errors(n, syllabus_id, pivot_type):
"Get the top n pivots by the number of errors made on them."
syllabus_query = _get_syllabus_query(syllabus_id, pivot_type)
cursor = connection.cursor()
cursor.execute("""
SELECT q.pivot_id, COUNT(*) AS n_errors
FROM (
SELECT mco.question_id
FROM drill_multiplechoiceresponse AS mcr
INNER JOIN drill_multiplechoiceoption AS mco
ON mcr.option_id = mco.id
WHERE NOT mco.is_correct
) AS qr
INNER JOIN drill_question AS q
ON qr.question_id = q.id
WHERE pivot_type = "%(pivot_type)s"
AND pivot_id IN (%(syllabus_query)s)
GROUP BY pivot_id
ORDER BY n_errors DESC
LIMIT %(n)d
""" % {
'syllabus_query': syllabus_query,
'pivot_type': pivot_type,
'n': n,
})
base_results = cursor.fetchall()
pivot_ids = sequences.unzip(base_results)[0]
if pivot_type == 'k':
pivot_model = PartialKanji
else:
pivot_model = PartialLexeme
pivot_map = pivot_model.objects.in_bulk(pivot_ids)
return [(pivot_map[pivot_id], _get_n_responses(pivot_id), n_errors) for \
(pivot_id, n_errors) in base_results]
def get_pivots_by_errors(n, syllabus_id, pivot_type):
"Get the top n pivots by the number of errors made on them."
syllabus_query = _get_syllabus_query(syllabus_id, pivot_type)
cursor = connection.cursor()
cursor.execute("""
SELECT q.pivot_id, COUNT(*) AS n_errors
FROM (
SELECT mco.question_id
FROM drill_multiplechoiceresponse AS mcr
INNER JOIN drill_multiplechoiceoption AS mco
ON mcr.option_id = mco.id
WHERE NOT mco.is_correct
) AS qr
INNER JOIN drill_question AS q
ON qr.question_id = q.id
WHERE pivot_type = "%(pivot_type)s"
AND pivot_id IN (%(syllabus_query)s)
GROUP BY pivot_id
ORDER BY n_errors DESC
LIMIT %(n)d
""" % {
'syllabus_query': syllabus_query,
'pivot_type': pivot_type,
'n': n,
})
base_results = cursor.fetchall()
pivot_ids = sequences.unzip(base_results)[0]
if pivot_type == 'k':
pivot_model = PartialKanji
else:
pivot_model = PartialLexeme
pivot_map = pivot_model.objects.in_bulk(pivot_ids)
return [(pivot_map[pivot_id], _get_n_responses(pivot_id), n_errors) for \
(pivot_id, n_errors) in base_results]
def get_pivots_by_questions(n, syllabus_id, pivot_type):
"""
Fetches the n pivots of the given type and from the given syllabus which
have been used in the most questions.
"""
syllabus_query = _get_syllabus_query(syllabus_id, pivot_type)
cursor = connection.cursor()
cursor.execute("""
SELECT pivot_id, COUNT(*) AS n_exposures
FROM drill_question
WHERE pivot_type = "%(pivot_type)s"
AND pivot_id IN (%(syllabus_query)s)
GROUP BY pivot_id
ORDER BY n_exposures DESC
LIMIT %(n)d
""" % {
'syllabus_query': syllabus_query,
'pivot_type': pivot_type,
'n': n,
})
base_results = cursor.fetchall()
pivot_ids = sequences.unzip(base_results)[0]
if pivot_type == 'k':
pivot_model = PartialKanji
else:
pivot_model = PartialLexeme
pivot_map = pivot_model.objects.in_bulk(pivot_ids)
return [(pivot_map[pid], c, _get_n_errors(pid)) for \
(pid, c) in base_results]
def get_pivots_by_questions(n, syllabus_id, pivot_type):
"""
Fetches the n pivots of the given type and from the given syllabus which
have been used in the most questions.
"""
syllabus_query = _get_syllabus_query(syllabus_id, pivot_type)
cursor = connection.cursor()
cursor.execute("""
SELECT pivot_id, COUNT(*) AS n_exposures
FROM drill_question
WHERE pivot_type = "%(pivot_type)s"
AND pivot_id IN (%(syllabus_query)s)
GROUP BY pivot_id
ORDER BY n_exposures DESC
LIMIT %(n)d
""" % {
'syllabus_query': syllabus_query,
'pivot_type': pivot_type,
'n': n,
})
base_results = cursor.fetchall()
pivot_ids = sequences.unzip(base_results)[0]
if pivot_type == 'k':
pivot_model = PartialKanji
else:
pivot_model = PartialLexeme
pivot_map = pivot_model.objects.in_bulk(pivot_ids)
return [(pivot_map[pid], c, _get_n_errors(pid)) for \
(pid, c) in base_results]
def __init__(self, data, x_axis=None, y_axis=None, **kwargs):
if len(data) < 2:
raise ValueError('need at least two columns of data')
if settings.DEBUG:
# Check data format
assert isinstance(data, (list, tuple, numpy.ndarray))
for row in data:
assert isinstance(row, (list, tuple, numpy.ndarray))
for val in row:
assert isinstance(val, (int, float, numpy.number))
super(MultiLineChart, self).__init__(data, **kwargs)
columns = unzip(data)
self.x_data = columns[0]
self.y_data = columns[1:]
self.x_axis = x_axis or automatic_axis(self.x_data)
self.y_axis = y_axis or automatic_axis(*self.y_data)
self['cht'] = 'lxy'
self['chxt'] = 'x,y'
self['chxr'] = '0,%f,%f,%f|1,%f,%f,%f' % (self.x_axis + self.y_axis)
self['chco'] = color_desc(len(self.y_data))
x_t = Transform(0, 100, self.x_axis[0], self.x_axis[1], strict=True)
y_t = Transform(0, 100, self.y_axis[0], self.y_axis[1], strict=True)
x_values = x_t.transform(self.x_data)
y_values = [y_t.transform(col) for col in self.y_data]
self['chd'] = 't:' + '|'.join(
'|'.join((self._stringify(x_values), self._stringify(vec)))
for vec in y_values)
def __init__(self, data, y_axis=None, **kwargs):
labels, points = unzip(data)
self.y_axis = y_axis or automatic_axis(points)
super(BarChart, self).__init__(data, **kwargs)
self['cht'] = 'bvg'
self['chxl'] = '1:' + ('|%s|' % '|'.join(labels))
self['chxt'] = 'y,x'
self['chxr'] = '0,%s,%s,%s' % tuple((map(smart_str, self.y_axis)))
self['chbh'] = 'a'
t = Transform(0, 100, self.y_axis[0], self.y_axis[1], strict=True)
norm_points = t.transform(points)
self['chd'] = 't:' + (','.join(map(smart_str, norm_points)))
def __init__(self, data, y_axis=None, **kwargs):
labels, points = unzip(data)
self.y_axis = y_axis or automatic_axis(points)
super(BarChart, self).__init__(data, **kwargs)
self['cht'] = 'bvg'
self['chxl'] = '1:' + ('|%s|' % '|'.join(labels))
self['chxt'] = 'y,x'
self['chxr'] = '0,%s,%s,%s' % tuple((map(smart_str, self.y_axis)))
self['chbh'] = 'a'
t = Transform(0, 100, self.y_axis[0], self.y_axis[1], strict=True)
norm_points = t.transform(points)
self['chd'] = 't:' + (','.join(map(smart_str, norm_points)))
def __init__(self, data, **kwargs):
try:
max_options = kwargs.pop('max_options')
except KeyError:
max_options = None
super(PieChart, self).__init__(data, **kwargs)
self['cht'] = 'p'
self['chxt'] = 'x'
sorted_data = sorted(data, key=lambda p: p[1], reverse=True)
if max_options is not None and len(sorted_data) > max_options:
self.__truncate_options(sorted_data, max_options)
normalized_data = self.__normalize(sorted_data)
labels, values = unzip(normalized_data)
self['chd'] = 't:' + ','.join(smart_str(x) for x in values)
self['chl'] = '|'.join(map(smart_str, labels))
def __init__(self, data, x_axis=None, y_axis=None, **kwargs):
self.x_data, self.y_data = unzip(data)
self.x_axis = x_axis or automatic_axis(self.x_data)
self.y_axis = y_axis or automatic_axis(self.y_data)
super(LineChart, self).__init__(data, **kwargs)
self['cht'] = 'lxy'
self['chxt'] = 'x,y'
self['chxr'] = '0,%f,%f,%f|1,%f,%f,%f' % (self.x_axis + self.y_axis)
self['chco'] = color_desc(1)
x_t = Transform(0, 100, self.x_axis[0], self.x_axis[1], strict=True)
y_t = Transform(0, 100, self.y_axis[0], self.y_axis[1], strict=True)
x_values = x_t.transform(self.x_data)
y_values = y_t.transform(self.y_data)
self['chd'] = 't:' + self._stringify(x_values) + '|' + \
self._stringify(y_values)
def __init__(self, data, **kwargs):
try:
max_options = kwargs.pop('max_options')
except KeyError:
max_options = None
super(PieChart, self).__init__(data, **kwargs)
self['cht'] = 'p'
self['chxt'] = 'x'
sorted_data = sorted(data, key=lambda p: p[1], reverse=True)
if max_options is not None and len(sorted_data) > max_options:
self.__truncate_options(sorted_data, max_options)
normalized_data = self.__normalize(sorted_data)
labels, values = unzip(normalized_data)
self['chd'] = 't:' + ','.join(smart_str(x) for x in values)
self['chl'] = '|'.join(map(smart_str, labels))
def __init__(self, data, x_axis=None, y_axis=None, **kwargs):
self.x_data, self.y_data = unzip(data)
self.x_axis = x_axis or automatic_axis(self.x_data)
self.y_axis = y_axis or automatic_axis(self.y_data)
super(LineChart, self).__init__(data, **kwargs)
self['cht'] = 'lxy'
self['chxt'] = 'x,y'
self['chxr'] = '0,%f,%f,%f|1,%f,%f,%f' % (self.x_axis + self.y_axis)
self['chco'] = color_desc(1)
x_t = Transform(0, 100, self.x_axis[0], self.x_axis[1], strict=True)
y_t = Transform(0, 100, self.y_axis[0], self.y_axis[1], strict=True)
x_values = x_t.transform(self.x_data)
y_values = y_t.transform(self.y_data)
self['chd'] = 't:' + self._stringify(x_values) + '|' + \
self._stringify(y_values)
def __init__(self, data, x_axis=None, y_axis=None, **kwargs):
if len(data) < 2:
raise ValueError('need at least two columns of data')
if settings.DEBUG:
# Check data format
assert isinstance(data, (list, tuple, numpy.ndarray))
for row in data:
assert isinstance(row, (list, tuple, numpy.ndarray))
for val in row:
assert isinstance(val, (int, float, numpy.number))
super(MultiLineChart, self).__init__(data, **kwargs)
columns = unzip(data)
self.x_data = columns[0]
self.y_data = columns[1:]
self.x_axis = x_axis or automatic_axis(self.x_data)
self.y_axis = y_axis or automatic_axis(*self.y_data)
self['cht'] = 'lxy'
self['chxt'] = 'x,y'
self['chxr'] = '0,%f,%f,%f|1,%f,%f,%f' % (self.x_axis + self.y_axis)
self['chco'] = color_desc(len(self.y_data))
x_t = Transform(0, 100, self.x_axis[0], self.x_axis[1], strict=True)
y_t = Transform(0, 100, self.y_axis[0], self.y_axis[1], strict=True)
x_values = x_t.transform(self.x_data)
y_values = [y_t.transform(col) for col in self.y_data]
self['chd'] = 't:' + '|'.join(
'|'.join((
self._stringify(x_values), self._stringify(vec)
)) for vec in y_values
)
