async def wod(ctx):
activity_data = get_activity_data(ctx.guild.id)
data = []
for user, user_data in activity_data.items():
for time, value in user_data:
name = user.split("#")[0]
data.append([name, pd.to_datetime(time, unit='s'), value])
df = pd.DataFrame(data, columns=["name", "time", "time_spent"])
df["time_spent"] = df["time_spent"].apply(lambda k: k / (60 * 60))
df = df.groupby(["time", "name"])[
"time_spent"].sum().unstack()
df = df.cumsum().filter(items=df.sum().nlargest(10).index)
df = df.interpolate(method="time")
ax = df.plot(title="Wasted on Discord")
for label in ax.get_xticklabels():
label.set_rotation(25)
label.set_horizontalalignment('right')
plt.ylabel("Hours")
plt.xlabel("Day")
locator = mdates.AutoDateLocator()
formatter = mdates.DateFormatter("%d-%m-%y")
ax.xaxis.set_major_locator(locator)
ax.xaxis.set_major_formatter(formatter)
ax.legend(loc="upper left", title_fontsize="xx-small",
fontsize="xx-small")
plt.savefig(fname='ranking_plot')
file_path = join(settings.BASE_DIR, "ranking_plot.png")
file = discord.File(file_path, filename="ranking_plot.png")
await ctx.send(file=file)
def single_chat(request,key):
msgs = get_chat_messages(key)
msgs_list = pagination(request,msgs,MESSAGES_PER_PAGE)
dic = {'activity': get_activity_data(key),
'chatmessages': msgs_list,
'gps': Messages.objects.using('msgstore').exclude((Q(longitude = '0.0') | Q(latitude = '0.0'))),
'media': Messages.objects.using('msgstore').exclude(media_url__isnull = True),
'PAG_TITLE': 'Conversation'
}
return render_to_response('whatsapp/chat.html',dic,context_instance=RequestContext(request))
def contact_profile(request, key):
"""
Shows the contact profile
"""
ret = {}
ret['number'] = key.split('@')[0][2:]
ret['jid'] = key
ret['activity'] = get_activity_data(key)
ret['whatsapp'] = 0
ret['messages'] = Messages.objects.using('msgstore').filter(key_remote_jid=key).count()
tstamp = Messages.objects.using('msgstore').filter(key_remote_jid=key).values('timestamp').order_by('timestamp')[0:1][0]['timestamp']
ret['first_seen'] = timestamp2utc(float(tstamp) / 1000)
tstamp = Messages.objects.using('msgstore').filter(key_remote_jid=key).values('timestamp').order_by('-timestamp')[0:1][0]['timestamp']
ret['last_seen'] = timestamp2utc(float(tstamp) / 1000)
ret['media_messages'] = Messages.objects.using('msgstore').filter(key_remote_jid=key).exclude(media_url__isnull=True).count()
ret['gps_messages'] = Messages.objects.using('msgstore').filter(key_remote_jid=key).exclude((Q(longitude='0.0') | Q(latitude='0.0'))).count()
# no wa_contacts table available
if not 'wa_contacts' in connection.introspection.table_names():
ret['name'] = 'Not in contacts'
ret['status'] = 'N/A'
if ret['messages'] > 0:
ret['whatsapp'] = 1
else:
ret['name'] = WaContacts.objects.filter(jid=key).values('display_name')[0]['display_name']
ret['whatsapp'] = WaContacts.objects.filter(jid=key).values('is_whatsapp_user')[0]['is_whatsapp_user']
ret['status'] = WaContacts.objects.filter(jid=key).values('status')[0]['status']
# if it's a group, get participants information
peers = None
if '-' in key:
peers = []
aux = Messages.objects.using('msgstore').filter(key_remote_jid=key).exclude(Q(remote_resource = '')).values('remote_resource').distinct()
for peer in aux:
peer = peer['remote_resource']
if not 'wa_contacts' in connection.introspection.table_names():
name = peer
else:
name = WaContacts.objects.filter(jid=peer).values('display_name')[0]
count = Messages.objects.using('msgstore').filter(Q(key_remote_jid=key) & Q(remote_resource = peer)).count()
peers.append({'id': peer , 'name': name , 'count': count})
return render_to_response('whatsapp/profile.html', {'contact': ret, 'activity': ret['activity'] , 'peers': peers }, context_instance=RequestContext(request))
def index(request):
"""
Default view
"""
try:
wusers = WaContacts.objects.filter(is_whatsapp_user=1).count()
nwusers = WaContacts.objects.filter(is_whatsapp_user=0).count()
wasize = getsize(DATABASES['default']['NAME'])
wafile = basename(DATABASES['default']['NAME'])
wamd5 = get_md5_file(DATABASES['default']['NAME'])
washa1 = get_sha1_file(DATABASES['default']['NAME'])
waperm = "<b><font color=\"red\">YES (check file permissons)</font></b>" if access(DATABASES['default']['NAME'],W_OK) is True else "<b><font color=\"blue\">No</font></b>"
except:
wusers = Messages.objects.using('msgstore').values('key_remote_jid').distinct().count()
nwusers = 0
wasize = 0
wafile = "Not found!"
wamd5 = "N/A"
washa1 = "N/A"
waperm = 'N/A'
msgperm = "<b><font color=\"red\">YES (check file permissons)</font></b>" if access(DATABASES['msgstore']['NAME'],W_OK) is True else "<b><font color=\"blue\">No</font></b>"
dic = {
'whatsappusers': wusers,
'nonwhatsappusers': nwusers,
'activity': get_activity_data(None),
'fromme': Messages.objects.using('msgstore').filter(key_from_me=1).count(),
'tome': Messages.objects.using('msgstore').filter(key_from_me=0).count(),
'latest': get_latest_peers(),
'toppeers': get_top_peers(),
'msgsize': getsize(DATABASES['msgstore']['NAME']),
'msgfile': basename(DATABASES['msgstore']['NAME']),
'msgperm': msgperm,
'msgmd5': get_md5_file(DATABASES['msgstore']['NAME']),
'msgsha1': get_sha1_file(DATABASES['msgstore']['NAME']),
'wasize': wasize,
'wafile': wafile,
'wamd5': wamd5,
'waperm': waperm,
'washa1': washa1,
}
return render_to_response('whatsapp/index.html', dic, context_instance=RequestContext(request))
def index(request):
try:
wusers = WaContacts.objects.filter(is_whatsapp_user = 1).count()
nwusers = WaContacts.objects.filter(is_whatsapp_user = 0).count()
wasize = getsize(DATABASES['default']['NAME'])
wafile = basename(DATABASES['default']['NAME'])
wamd5 = get_md5_file(DATABASES['default']['NAME'])
washa1 = get_sha1_file(DATABASES['default']['NAME'])
except:
wusers = Messages.objects.using('msgstore').values('key_remote_jid').distinct().count()
nwusers = 0
wasize = 0
wafile = "Not found!"
wamd5 = "N/A"
washa1 = "N/A"
dic = {
'whatsappusers': wusers,
'nonwhatsappusers': nwusers,
'activity': get_activity_data(None),
'fromme': Messages.objects.using('msgstore').filter(key_from_me = 1).count(),
'tome': Messages.objects.using('msgstore').filter(key_from_me = 0).count(),
'latest': get_latest_peers(),
'toppeers': get_top_peers(),
'msgsize': getsize(DATABASES['msgstore']['NAME']),
'msgfile': basename(DATABASES['msgstore']['NAME']),
'msgmd5': get_md5_file(DATABASES['msgstore']['NAME']),
'msgsha1': get_sha1_file(DATABASES['msgstore']['NAME']),
'wasize': wasize,
'wafile': wafile,
'wamd5': wamd5,
'washa1': washa1,
}
return render_to_response('whatsapp/index.html',dic,context_instance=RequestContext(request))
if __name__ == '__main__':
experiment_id = int(SystemRandom().random() * 100000)
print(args, experiment_id)
seed = args.seed
torch.manual_seed(seed)
np.random.seed(seed)
torch.cuda.manual_seed(seed)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if args.dataset == 'physionet':
data_obj = utils.get_physionet_data(args, 'cpu', args.quantization)
elif args.dataset == 'mimiciii':
data_obj = utils.get_mimiciii_data(args)
elif args.dataset == 'activity':
data_obj = utils.get_activity_data(args, 'cpu')
train_loader = data_obj["train_dataloader"]
test_loader = data_obj["test_dataloader"]
val_loader = data_obj["val_dataloader"]
dim = data_obj["input_dim"]
# model
if args.enc == 'mtan_enc':
rec = models.enc_mtan_classif(dim, torch.linspace(0, 1., 128),
args.rec_hidden, args.embed_time,
args.num_heads, args.learn_emb,
args.freq).to(device)
elif args.enc == 'mtan_enc_activity':
rec = models.enc_mtan_classif_activity(dim, args.rec_hidden,
parser.add_argument("--num-ref-points", type=int, default=128)
parser.add_argument("--classify-pertp", action="store_true")
args = parser.parse_args()
if __name__ == "__main__":
experiment_id = int(SystemRandom().random() * 100000)
print(args, experiment_id)
seed = args.seed
torch.manual_seed(seed)
np.random.seed(seed)
torch.cuda.manual_seed(seed)
random.seed(seed)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if args.dataset == "activity":
data_obj = utils.get_activity_data(args, "cpu")
train_loader = data_obj["train_dataloader"]
test_loader = data_obj["test_dataloader"]
val_loader = data_obj["val_dataloader"]
dim = data_obj["input_dim"]
if args.enc == "enc_rnn3":
rec = models.enc_rnn3(
dim,
torch.linspace(0, 1.0, 50),
args.latent_dim,
args.rec_hidden,
128,
learn_emb=args.learn_emb,
).to(device)