def get_json_response(self):
# Currently accepts: { "buckets", "all", "newest" }
to_show = self.request_string("show", "buckets")
past_days = self.request_int("past_days", 7)
refresh_secs = self.request_int("rsecs", 30)
today = dt.date.today()
# We don't use App Engine Query filters so as to avoid adding entries to index.yaml
days = [today - dt.timedelta(days=i) for i in range(0, past_days)]
if to_show == "all":
exercise_names = [ex.name for ex in Exercise.get_all_use_cache()]
return self.exercise_over_time_for_highcharts(exercise_names, days, "All Exercises", showLegend=True)
if to_show == "newest":
exercises = Exercise.get_all_use_cache()
exercises.sort(key=lambda ex: ex.creation_date, reverse=True)
exercise_names = [ex.name for ex in exercises]
num_newest = self.request_int("newest", 5)
exid = exercise_names[get_bucket_cursor(refresh_secs, num_newest)]
title = "Newest Exercises - %s" % Exercise.to_display_name(exid)
return self.exercise_over_time_for_highcharts([exid], days, title, showLegend=True)
num_buckets = self.request_int("buckets", NUM_BUCKETS)
bucket_index = self.request_int("ix", 0)
bucket_size = get_bucket_size(num_buckets, bucket_index)
bucket_cursor = get_bucket_cursor(refresh_secs, bucket_size)
exercise_names = exercises_in_bucket(num_buckets, bucket_index)
exid = exercise_names[bucket_cursor]
return self.exercise_over_time_for_highcharts([exid], days, Exercise.to_display_name(exid))
def get_use_cache(self):
params = self.get_request_params()
# Get the maximum so we know how the data label circles should be scaled
most_new_users = 1
ex_stat_dict = {}
for ex in Exercise.get_all_use_cache():
stat = ExerciseStatistic.get_by_date(ex.name,
params['interested_day'])
ex_stat_dict[ex.name] = stat
if stat:
most_new_users = max(most_new_users, stat.num_new_users())
data_points = []
min_y, max_y = -1, 1
for ex in Exercise.get_all_use_cache():
stat = ex_stat_dict[ex.name]
y, x = -int(ex.h_position), int(ex.v_position)
min_y, max_y = min(y, min_y), max(y, max_y)
# Set the area of the circle proportional to the data value
radius = 1
if stat:
radius = math.sqrt(
float(stat.num_new_users()) /
most_new_users) * MAX_POINT_RADIUS
point = {
'x': x,
'y': y,
'name': ex.display_name,
'marker': {
'radius': max(radius, 1)
},
}
data_points.append(point)
context = {
'title': 'Exercises map - First attempts',
'series': {
'name': 'First attempts',
'data_values': json.dumps(data_points),
},
'minYValue': min_y - 1,
'maxYValue': max_y + 1,
}
return self.render_jinja2_template_to_string(
'exercisestats/highcharts_scatter_map.json', context)
def get_use_cache(self):
params = self.get_request_params()
# Get the maximum so we know how the data label circles should be scaled
most_new_users = 1
ex_stat_dict = {}
for ex in Exercise.get_all_use_cache():
stat = ExerciseStatistic.get_by_date(ex.name, params['interested_day'])
ex_stat_dict[ex.name] = stat
if stat:
most_new_users = max(most_new_users, stat.num_new_users())
data_points = []
min_y, max_y = -1, 1
for ex in Exercise.get_all_use_cache():
stat = ex_stat_dict[ex.name]
y, x = -int(ex.h_position), int(ex.v_position)
min_y, max_y = min(y, min_y), max(y, max_y)
# Set the area of the circle proportional to the data value
radius = 1
if stat:
radius = math.sqrt(float(stat.num_new_users()) / most_new_users) * MAX_POINT_RADIUS
point = {
'x': x,
'y': y,
'name': ex.display_name,
'marker': {
'radius': max(radius, 1)
},
}
data_points.append(point)
context = {
'title': 'Exercises map - First attempts',
'series': {
'name': 'First attempts',
'data_values': json.dumps(data_points),
},
'minYValue': min_y - 1,
'maxYValue': max_y + 1,
}
return self.render_jinja2_template_to_string(
'exercisestats/highcharts_scatter_map.json', context)
def get_histogram_spline_for_highcharts(self, earliest_dt=dt.datetime.min):
histogram = {}
for ex in Exercise.get_all_use_cache():
if ex.creation_date:
creation_day = dt.datetime.combine(ex.creation_date, dt.time())
timestamp = to_unix_secs(creation_day) * 1000
histogram[timestamp] = histogram.get(timestamp, 0) + 1
total_exercises = {}
prev_value = 0
for day, value in sorted(histogram.items()):
prev_value = total_exercises[day] = prev_value + value
# Only retain recent dates
earliest_unix = to_unix_secs(earliest_dt) * 1000
histogram = [[k, v] for k, v in histogram.items() if k >= earliest_unix]
total_exercises = [[k, v] for k, v in total_exercises.items() if k >= earliest_unix]
context = {
"series": [
{
"name": "Histogram (created per day)",
"type": "column",
"data_values": json.dumps(histogram),
"axis": 0,
},
{"name": "Total exercises", "type": "spline", "data_values": json.dumps(total_exercises), "axis": 1},
],
# Let highcharts determine the scales for now.
"axes": [{"max": "null"}, {"max": "null"}],
}
return self.render_jinja2_template_to_string(
"exercisestats/highcharts_exercises_created_histogram.json", context
)
def exercise_counter_for_geckoboard_rag():
exercises = Exercise.get_all_use_cache()
exercises.sort(key=lambda ex: ex.creation_date, reverse=True)
last_exercise = exercises[0]
num_exercises = len(exercises)
last_exercise_author = last_exercise.author.nickname(
) if last_exercise.author else 'random person'
text = "Thanks %s for %s!" % (last_exercise_author,
last_exercise.display_name)
return {
'item': [
{
'value': None,
'text': '',
},
{
'value': None,
'text': '',
},
{
'value': num_exercises,
'text': text,
},
]
}
def exercise_counter_for_geckoboard_rag():
exercises = Exercise.get_all_use_cache()
exercises.sort(key=lambda ex: ex.creation_date, reverse=True)
last_exercise = exercises[0]
num_exercises = len(exercises)
last_exercise_author = last_exercise.author.nickname() if last_exercise.author else 'random person'
text = "Thanks %s for %s!" % (last_exercise_author, last_exercise.display_name)
return {
'item': [
{
'value': None,
'text': '',
},
{
'value': None,
'text': '',
},
{
'value': num_exercises,
'text': text,
},
]
}
def get(self):
date_to_set = self.request_date("date", "%Y/%m/%d")
exercises = Exercise.get_all_use_cache()
updated = []
for ex in exercises:
ex.creation_date = date_to_set
updated.append(ex)
db.put(updated)
def get_ip_addresses_for_geckoboard_map(date):
ip_addresses = []
for ex in Exercise.get_all_use_cache():
stat = ExerciseStatistic.get_by_date(ex.name, date)
if stat:
ip_addresses += stat.histogram.get("ip_addresses", [])
return {"points": {"point": [{"ip": addr} for addr in ip_addresses]}}
def get_ip_addresses_for_geckoboard_map(date):
ip_addresses = []
for ex in Exercise.get_all_use_cache():
stat = ExerciseStatistic.get_by_date(ex.name, date)
if stat:
ip_addresses += stat.histogram.get('ip_addresses', [])
return {'points': {'point': [{'ip': addr} for addr in ip_addresses]}}
def get(self):
date_to_set = self.request_date('date', "%Y/%m/%d")
exercises = Exercise.get_all_use_cache()
updated = []
for ex in exercises:
ex.creation_date = date_to_set
updated.append(ex)
db.put(updated)
def get_json_response(self):
# Currently accepts: { "buckets", "all", "newest" }
to_show = self.request_string('show', 'buckets')
past_days = self.request_int('past_days', 7)
refresh_secs = self.request_int('rsecs', 30)
today = dt.date.today()
# We don't use App Engine Query filters so as to avoid adding entries to index.yaml
days = [today - dt.timedelta(days=i) for i in range(0, past_days)]
if to_show == 'all':
exercise_names = [ex.name for ex in Exercise.get_all_use_cache()]
return self.exercise_over_time_for_highcharts(exercise_names,
days,
'All Exercises',
showLegend=True)
if to_show == 'newest':
exercises = Exercise.get_all_use_cache()
exercises.sort(key=lambda ex: ex.creation_date, reverse=True)
exercise_names = [ex.name for ex in exercises]
num_newest = self.request_int('newest', 5)
exid = exercise_names[get_bucket_cursor(refresh_secs, num_newest)]
title = 'Newest Exercises - %s' % Exercise.to_display_name(exid)
return self.exercise_over_time_for_highcharts([exid],
days,
title,
showLegend=True)
num_buckets = self.request_int('buckets', NUM_BUCKETS)
bucket_index = self.request_int('ix', 0)
bucket_size = get_bucket_size(num_buckets, bucket_index)
bucket_cursor = get_bucket_cursor(refresh_secs, bucket_size)
exercise_names = exercises_in_bucket(num_buckets, bucket_index)
exid = exercise_names[bucket_cursor]
return self.exercise_over_time_for_highcharts(
[exid], days, Exercise.to_display_name(exid))
def exercise_counter_for_geckoboard_rag():
exercises = Exercise.get_all_use_cache()
exercises.sort(key=lambda ex: ex.creation_date, reverse=True)
last_exercise = exercises[0]
num_exercises = len(exercises)
last_exercise_author = last_exercise.author.nickname() if last_exercise.author else "random person"
text = "Thanks %s for %s!" % (last_exercise_author, last_exercise.display_name)
return {
"item": [{"value": None, "text": ""}, {"value": None, "text": ""}, {"value": num_exercises, "text": text}]
}
def exercises_in_bucket(num_buckets, bucket_index):
exercise_names = [ex.name for ex in Exercise.get_all_use_cache()]
exercise_names.sort()
# These calculations evenly distribute exercises among buckets, with excess
# going to the first few buckets.
# In particular, max_capacity(buckets) - min_capacity(buckets) <= 1.
num_exercises = len(exercise_names)
min_bucket_size = num_exercises / num_buckets
bucket_rem = num_exercises % num_buckets
first = bucket_index * min_bucket_size + min(bucket_rem, bucket_index)
return exercise_names[first : first + get_bucket_size(num_buckets, bucket_index)]
def exercises_in_bucket(num_buckets, bucket_index):
exercise_names = [ex.name for ex in Exercise.get_all_use_cache()]
exercise_names.sort()
# These calculations evenly distribute exercises among buckets, with excess
# going to the first few buckets.
# In particular, max_capacity(buckets) - min_capacity(buckets) <= 1.
num_exercises = len(exercise_names)
min_bucket_size = num_exercises / num_buckets
bucket_rem = num_exercises % num_buckets
first = bucket_index * min_bucket_size + min(bucket_rem, bucket_index)
return exercise_names[first:first +
get_bucket_size(num_buckets, bucket_index)]
def get_ip_addresses_for_geckoboard_map(date):
ip_addresses = []
for ex in Exercise.get_all_use_cache():
stat = ExerciseStatistic.get_by_date(ex.name, date)
if stat:
ip_addresses += stat.histogram.get('ip_addresses', [])
return {
'points': {
'point': [{'ip': addr} for addr in ip_addresses]
}
}
def get_histogram_spline_for_highcharts(self, earliest_dt=dt.datetime.min):
histogram = {}
for ex in Exercise.get_all_use_cache():
if ex.creation_date:
creation_day = dt.datetime.combine(ex.creation_date, dt.time())
timestamp = to_unix_secs(creation_day) * 1000
histogram[timestamp] = histogram.get(timestamp, 0) + 1
total_exercises = {}
prev_value = 0
for day, value in sorted(histogram.items()):
prev_value = total_exercises[day] = prev_value + value
# Only retain recent dates
earliest_unix = to_unix_secs(earliest_dt) * 1000
histogram = [[k, v] for k, v in histogram.items()
if k >= earliest_unix]
total_exercises = [[k, v] for k, v in total_exercises.items()
if k >= earliest_unix]
context = {
'series': [{
'name': 'Histogram (created per day)',
'type': 'column',
'data_values': json.dumps(histogram),
'axis': 0,
}, {
'name': 'Total exercises',
'type': 'spline',
'data_values': json.dumps(total_exercises),
'axis': 1,
}],
# Let highcharts determine the scales for now.
'axes': [
{
'max': 'null'
},
{
'max': 'null'
},
],
}
return self.render_jinja2_template_to_string(
'exercisestats/highcharts_exercises_created_histogram.json',
context)
def get_histogram_spline_for_highcharts(self, earliest_dt=dt.datetime.min):
histogram = {}
for ex in Exercise.get_all_use_cache():
if ex.creation_date:
creation_day = dt.datetime.combine(ex.creation_date, dt.time())
timestamp = to_unix_secs(creation_day) * 1000
histogram[timestamp] = histogram.get(timestamp, 0) + 1
total_exercises = {}
prev_value = 0
for day, value in sorted(histogram.items()):
prev_value = total_exercises[day] = prev_value + value
# Only retain recent dates
earliest_unix = to_unix_secs(earliest_dt) * 1000
histogram = [[k,v] for k,v in histogram.items() if k >= earliest_unix]
total_exercises = [[k,v] for k,v in total_exercises.items() if k >= earliest_unix]
context = {
'series': [
{
'name': 'Histogram (created per day)',
'type': 'column',
'data_values': json.dumps(histogram),
'axis': 0,
},
{
'name': 'Total exercises',
'type': 'spline',
'data_values': json.dumps(total_exercises),
'axis': 1,
}
],
# Let highcharts determine the scales for now.
'axes': [
{ 'max': 'null' },
{ 'max': 'null' },
],
}
return self.render_jinja2_template_to_string(
'exercisestats/highcharts_exercises_created_histogram.json', context)
def get_bucket_size(num_buckets, bucket_index):
num_exercises = len(Exercise.get_all_use_cache())
bucket_rem = num_exercises % num_buckets
return (num_exercises / num_buckets) + (1 if bucket_index < bucket_rem else 0)
def get_bucket_size(num_buckets, bucket_index):
num_exercises = len(Exercise.get_all_use_cache())
bucket_rem = num_exercises % num_buckets
return (num_exercises /
num_buckets) + (1 if bucket_index < bucket_rem else 0)
def get(self):
number = self.request_int('num', len(Exercise.get_all_use_cache()))
self.render_json(self.number_facts_for_geckboard_text(number))
def class_progress_report_graph_context(user_data, list_students):
if not user_data:
return {}
list_students = sorted(list_students, key=lambda student: student.nickname)
student_email_pairs = [
(escape(s.email),
(s.nickname[:14] + '...') if len(s.nickname) > 17 else s.nickname)
for s in list_students
]
emails_escapejsed = [escapejs(s.email) for s in list_students]
exercises_all = Exercise.get_all_use_cache()
user_exercise_graphs = UserExerciseGraph.get(list_students)
exercises_found = []
for exercise in exercises_all:
for user_exercise_graph in user_exercise_graphs:
graph_dict = user_exercise_graph.graph_dict(exercise.name)
if graph_dict and graph_dict["total_done"]:
exercises_found.append(exercise)
break
exercise_names = [(e.name, e.display_name, escapejs(e.name))
for e in exercises_found]
exercise_list = [{
'name': e.name,
'display_name': e.display_name
} for e in exercises_found]
exercise_data = {}
for (student, _, _,
user_exercise_graph) in izip(list_students, student_email_pairs,
emails_escapejsed, user_exercise_graphs):
student_email = student.email
for (exercise, (_, _, _)) in izip(exercises_found, exercise_names):
exercise_name = exercise.name
graph_dict = user_exercise_graph.graph_dict(exercise_name)
status = ""
if graph_dict["proficient"]:
if graph_dict["reviewing"]:
status = "Review"
else:
status = "Proficient"
if not graph_dict["explicitly_proficient"]:
status = "Proficient (due to proficiency in a more advanced module)"
elif graph_dict["struggling"]:
status = "Struggling"
elif graph_dict["total_done"] > 0:
status = "Started"
if student_email not in exercise_data:
exercise_data[student_email] = {
'email': student.email,
'nickname': student.nickname,
'profile_root': student.profile_root,
'exercises': []
}
if len(status) > 0:
exercise_data[student_email]['exercises'].append({
"status":
status,
"progress":
graph_dict["progress"],
"total_done":
graph_dict["total_done"],
"last_done":
graph_dict["last_done"] if graph_dict["last_done"]
and graph_dict["last_done"].year > 1 else '',
"last_done_ago":
timesince_ago(graph_dict["last_done"])
if graph_dict["last_done"]
and graph_dict["last_done"].year > 1 else ''
})
else:
exercise_data[student_email]['exercises'].append({
"name":
exercise_name,
"status":
status,
})
student_row_data = [data for (_, data) in exercise_data.iteritems()]
return {
'exercise_names':
exercise_list,
'exercise_data':
student_row_data,
'coach_email':
user_data.email,
'c_points':
reduce(lambda a, b: a + b, map(lambda s: s.points, list_students), 0)
}
def class_progress_report_graph_context(user_data, list_students):
if not user_data:
return {}
list_students = sorted(list_students, key=lambda student: student.nickname)
student_email_pairs = [(escape(s.email), (s.nickname[:14] + '...')
if len(s.nickname) > 17
else s.nickname)
for s in list_students]
emails_escapejsed = [escapejs(s.email) for s in list_students]
exercises_all = Exercise.get_all_use_cache()
user_exercise_graphs = UserExerciseGraph.get(list_students)
exercises_found = []
for exercise in exercises_all:
for user_exercise_graph in user_exercise_graphs:
graph_dict = user_exercise_graph.graph_dict(exercise.name)
if graph_dict and graph_dict["total_done"]:
exercises_found.append(exercise)
break
exercise_names = [(e.name, e.display_name, escapejs(e.name))
for e in exercises_found]
exercise_list = [{'name': e.name, 'display_name': e.display_name}
for e in exercises_found]
exercise_data = {}
for (student, _, _, user_exercise_graph) in izip(list_students,
student_email_pairs,
emails_escapejsed,
user_exercise_graphs):
student_email = student.email
for (exercise, (_, _, _)) in izip(exercises_found, exercise_names):
exercise_name = exercise.name
graph_dict = user_exercise_graph.graph_dict(exercise_name)
status = ""
if graph_dict["proficient"]:
if graph_dict["reviewing"]:
status = "Herzien"
else:
status = "Gehaald"
if not graph_dict["explicitly_proficient"]:
status = ("Gehaald")
elif graph_dict["struggling"]:
status = "Moeite"
elif graph_dict["total_done"] > 0:
status = "Gestart"
if student_email not in exercise_data:
exercise_data[student_email] = {
'email': student.email,
'nickname': student.nickname,
'profile_root': student.profile_root,
'exercises': []
}
if len(status) > 0:
exercise_data[student_email]['exercises'].append({
"status": status,
"progress": graph_dict["progress"],
"total_done": graph_dict["total_done"],
"last_done": (graph_dict["last_done"]
if (graph_dict["last_done"] and
graph_dict["last_done"].year > 1)
else ''),
"last_done_ago": (timesince_ago(graph_dict["last_done"])
if (graph_dict["last_done"] and
graph_dict["last_done"].year > 1)
else '')
})
else:
exercise_data[student_email]['exercises'].append({
"name": exercise_name,
"status": status,
})
student_row_data = [data for (_, data) in exercise_data.iteritems()]
return {
'exercise_names': exercise_list,
'exercise_data': student_row_data,
'coach_email': user_data.email,
'c_points': reduce(lambda a, b: a + b,
map(lambda s: s.points, list_students),
0)
}
def get(self):
number = self.request_int("num", len(Exercise.get_all_use_cache()))
self.render_json(self.number_facts_for_geckboard_text(number))
