def see_question_answers(request, question):
#load csv
df = utils.get_whole_survey()
#load questionchoices txt
file2_ = open(
os.path.join(settings.BASE_DIR, 'clusters/RSQquestionchoices.txt'))
dic_source = rt.read(file2_)
about = dic_source['about']
questions_txt = dic_source['questions']
file2_.close()
charts = []
try:
chart = get_question_chart(df, questions_txt[question])
charts.append(chart)
except KeyError:
question_obj = questions_txt[question]
choices = question_obj.choices
letters = choices.keys()
for letter in letters:
try:
chart = get_question_chart(df,
question_obj,
letter=letter,
choice=choices[letter])
charts.append(chart)
except KeyError:
continue
context = {
'charts': charts,
'question': question,
'about': about,
}
return render(request, 'clusters/question.html', context)
def group_compare(request):
cluster = get_object_or_404(Cluster, pk=3)
#load csv into pandas.DataFrame
dic = rc.filter_by_cluster(utils.get_whole_survey(), cluster)
df = dic['df']
size_str = "%d / %d" % (len(df.index), dic['orig_size'])
clusters_dict = clustering.get_subclusters_length(cluster, dic['df'])
#load questions textfile into list
file2_ = open(
os.path.join(settings.BASE_DIR, 'clusters/RSQquestionchoices.txt'))
dic_source = rt.read(file2_)
about = dic_source['about']
questions_txt = dic_source['questions']
file2_.close()
#get cluster questions as strings
questions_strs = get_questions_as_str(questions_txt)
#get charts
charts = get_charts(df, questions_txt, cluster, 'a')
context = {
'cluster': cluster,
'charts': charts,
#'compare_chart': compare_chart,
'df_size': size_str, #no of rows in df
'questions_strs': questions_strs,
'about': about,
'subcluster_values': list(clusters_dict.values()),
'num_of_clusters': cluster.num_of_clusters,
'compare': False,
}
return render(request, 'clusters/group_compare.html', context)
def json3(request, cluster_id, question_id):
cluster = get_object_or_404(Cluster, pk=cluster_id)
#load csv into pandas.DataFrame
dic = rc.filter_by_cluster(utils.get_whole_survey(), cluster)
df = dic['df']
output = rc2.get_data_for_map3(df, question_id)
return JsonResponse(output, safe=False)
def json4(request, cluster_id, subcluster_id, question_id, choice_id):
"""for ward chart change
"""
cluster = get_object_or_404(Cluster, pk=cluster_id)
#load csv into pandas.DataFrame
df = rc.filter_by_cluster_only(utils.get_whole_survey(), cluster)
if subcluster_id != 'a': #if is subcluster, then filter subcluster
#TODO need to make permanent, subcluster id on df
tempdf = clustering.get_subcluster_list(cluster, df)
df = clustering.filter_by_subcluster(tempdf, subcluster_id)
output = rc2.get_data_for_map4(df, question_id, choice_id)
return JsonResponse(output, safe=False)
def json2(request, cluster_id, subcluster_id, question_id, choice_id):
""" creates json for map change
"""
cluster = get_object_or_404(Cluster, pk=cluster_id)
#load csv into pandas.DataFrame
dic = rc.filter_by_cluster(utils.get_whole_survey(), cluster)
df = dic['df']
if subcluster_id != 'a': #if is subcluster, then filter subcluster
tempdf = clustering.get_subcluster_list(cluster, df)
df = clustering.filter_by_subcluster(tempdf, subcluster_id)
output = rc2.get_data_for_map2(df, question_id, choice_id)
return JsonResponse(output, safe=False)
def survey_questions(request):
#load csv
df = utils.get_whole_survey()
#load questionchoices txt
file2_ = open(
os.path.join(settings.BASE_DIR, 'clusters/RSQquestionchoices.txt'))
dic_source = rt.read(file2_)
about = dic_source['about']
questions_txt = dic_source['questions']
file2_.close()
context = {
'questions': questions_txt.values(),
'about': about,
}
return render(request, 'clusters/survey.html', context)
def get_data_for_group_compare(question, choice):
""" outputs list for Group Compare view
Group = model.Cluster
Value = value of group filtered by question and choice
output = [[Group, Value], ..., ...]
"""
output = []
#column names
output.append(['Group', 'Orig Value', 'Percent', '% Diff from Avg'])
if question == '0' and choice == '0':
cluster_models = Cluster.objects.all()
for cluster in cluster_models:
output.append([cluster.name, 0, 0, 0])
return output
df = utils.get_whole_survey()
"""
for each cluster
v = get value(c)
output.append(c, v)
"""
cluster_all = Cluster.objects.get(pk=3)
value = df[question].value_counts()[int(choice)]
average = float(value) / df.shape[0]
cluster_models = Cluster.objects.all()
for cluster in cluster_models:
temp = rc.filter_by_cluster_only(df, cluster)
try:
value = temp[question].value_counts()[int(choice)]
percent = float(value) / temp.shape[0] #percent
except KeyError:
value = 0
percent = 0
except IndexError:
""" when value_counts only gives #NULL! """
value = 0
percent = 0
diff_from_avg = (percent - average) * 100
output.append([cluster.name, value, percent, diff_from_avg])
return output
def json1(request, cluster_id, subcluster_id):
""" used for map """
cluster = get_object_or_404(Cluster, pk=cluster_id)
#load csv into pandas.DataFrame
dic = rc.filter_by_cluster(utils.get_whole_survey(), cluster)
df = dic['df']
if subcluster_id != 'a':
df = clustering.get_subclusters(cluster, df)[int(subcluster_id)]
#load questions textfile into list
file2_ = open(
os.path.join(settings.BASE_DIR, 'clusters/RSQquestionchoices.txt'))
dic_source = rt.read(file2_)
about = dic_source['about']
questions_txt = dic_source['questions']
file2_.close()
output = rc2.get_data_for_map(df, questions_txt['WARD'])
return JsonResponse(output, safe=False)
def detail(request, cluster_id):
cluster = get_object_or_404(Cluster, pk=cluster_id)
#load csv into pandas.DataFrame
dic = rc.filter_by_cluster(utils.get_whole_survey(), cluster)
df = dic['df']
group_size = len(df.index)
survey_size = dic['orig_size']
if group_size == 0:
return render(request, 'clusters/emptygroup.html',
{'cluster': cluster})
clusters_dict = clustering.get_subclusters_length(cluster, dic['df'])
#load questions textfile into list
file2_ = open(
os.path.join(settings.BASE_DIR, 'clusters/RSQquestionchoices.txt'))
dic_source = rt.read(file2_)
file2_.close()
about = dic_source['about']
questions_txt = dic_source['questions']
#get cluster questions as strings
questions_strs = get_questions_as_str(questions_txt)
#get charts
charts = get_charts(df, questions_txt, cluster, 'a')
context = {
'cluster': cluster,
'subcluster_id': 'a',
'charts': charts,
'group_size': group_size, #no of rows in df
'survey_size': survey_size,
'questions_strs': questions_strs,
'about': about,
'subcluster_values': list(clusters_dict.values()),
'num_of_clusters': cluster.num_of_clusters,
'compare': False,
}
return render(request, 'clusters/detail.html', context)
def subcluster_detail(request, cluster_id, subcluster_id):
cluster = get_object_or_404(Cluster, pk=cluster_id)
#load csv into pandas.DataFrame
dic = rc.filter_by_cluster(utils.get_whole_survey(), cluster)
df = dic['df']
df = clustering.get_subclusters(cluster, df)[0] #dicitonary
size_str = "%d / %d" % (len(df.index), dic['orig_size'])
clusters_dict = clustering.get_subclusters_length(cluster, dic['df'])
#load questions textfile into list
file2_ = open(
os.path.join(settings.BASE_DIR, 'clusters/RSQquestionchoices.txt'))
dic_source = rt.read(file2_)
about = dic_source['about']
questions_txt = dic_source['questions']
file2_.close()
#get cluster questions as strings
questions_strs = get_questions_as_str(questions_txt)
#get charts
charts = get_charts(df, questions_txt, cluster, subcluster_id)
context = {
'cluster': cluster,
'subcluster_id': subcluster_id,
'charts': charts,
'group_size': len(df.index), #no of rows in df
'survey_size': dic['orig_size'],
'questions_strs': questions_strs,
'about': about,
'subcluster_values': list(clusters_dict.values()),
'num_of_clusters': cluster.num_of_clusters,
'issubcluster': True,
}
return render(request, 'clusters/detail.html', context)