-
Notifications
You must be signed in to change notification settings - Fork 0
/
points.py
212 lines (183 loc) · 6.46 KB
/
points.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
from dash_bootstrap_components._components.Collapse import Collapse
from dash_bootstrap_components._components.Select import Select
import dash_core_components as dcc
import dash_html_components as html
import dash_bootstrap_components as dbc
from dash.dependencies import Input, Output, State, ALL
from pandas._config.config import options
from global_vars import app, db
import plotly.express as px
import pandas as pd
from datetime import datetime, time, timedelta
import dash
from collections import defaultdict
layout = html.Div([
html.H1("Points", id="points_heading"),
dbc.Label("Graph Type"),
dbc.Select(
id="graph_type",
value = "average",
options = [
{"label": "Average Points Per day", "value": "average"},
{"label": "Total Points", "value": "total"},
{"label": "Total Time", "value": "time"},
]
),
dcc.Graph(id="ppd_graph"),
dbc.Form([
dbc.Row([
dbc.Col([
dbc.Label("Group by", className="mr-3"),
dbc.Select(id="group_by",
value="time_period",
options=[
{"label": "Time Period", "value": "time_period"},
{"label": "Task", "value": "task"},
],
className="mr-3"
),
], width=4),
dbc.Col(
dbc.Collapse([
dbc.Label("Period", className="mr-3"),
dbc.Input(id="group_days", value=7, type="number", className="mr-3")
], id="time_period_input", is_open=True),
width=4)
], className="mb-4"),
dbc.Row(
dbc.Col([
dbc.Label("Total days", className="mr-3"),
dbc.Input(id="total_days", value=28, type="number", className="mr-3")
], width=4)
)
]),
dcc.Graph(id="points_graph")
])
@app.callback(
Output("ppd_graph", "figure"),
Input("graph_type", "value")
)
def load_ppd_graph(graph_type):
users = list(db.users.find({"active": True}))
user_ids = set()
users_active_seconds = {}
id_to_display_name = {}
for user in users:
active_seconds = user["active_seconds"]
active_seconds += (datetime.now() - user["last_activated"]).total_seconds()
user_id = user["_id"]
users_active_seconds[user_id] = active_seconds
id_to_display_name[user_id] = user["name"]
user_ids.add(user_id)
records = db.records.find()
records = [record for record in records if record["user"] in user_ids]
total_points = 0
users_points = defaultdict(lambda: 0) # maps user_id to total points from all records
for record in records:
total_points += record["points"]
users_points[record["user"]] += record["points"]
# users_pps = {}
display_names = []
daily_points = []
total_points = []
total_time = []
for user_id in user_ids:
points = users_points[user_id]
total_points.append(100 * points)
active_seconds = users_active_seconds[user_id]
total_time.append(round(active_seconds / (60 * 60 * 24)))
display_names.append(id_to_display_name[user_id])
daily_points.append(100 * points / active_seconds * 60 * 60 * 24)
df = pd.DataFrame({
"User": display_names,
"Daily Points": daily_points,
"Total Points": total_points,
"Total Time": total_time
})
df.sort_values("User", inplace=True)
value = "Daily Points"
if graph_type == "total":
value = "Total Points"
elif graph_type == "time":
value = "Total Time"
return px.bar(
df,
x="User",
y=value,
color="User",
)
@app.callback(
Output("points_graph", "figure"),
Output("time_period_input", "is_open"),
Input("group_by", "value"),
Input("group_days", "value"),
Input("total_days", "value"),
)
def load_graph(group_by, group, total):
if group is None or total is None or group <= 0:
return dash.no_update, dash.no_update
start = datetime.now() - timedelta(days=total)
users = list(db.users.find({"active": True}))
# user_ids = set()
users_active_seconds = {}
for user in users:
active_seconds = user["active_seconds"]
active_seconds += (datetime.now() - user["last_activated"]).total_seconds()
user_id = user["_id"]
users_active_seconds[user_id] = active_seconds
# user_ids.add(user_id)
# records = db.records.find()
# records = [record for record in records if record["user"] in user_ids]
# total_points = 0
# users_points = defaultdict(lambda: 0) # maps user_id to total points from all records
# for record in records:
# total_points += record["points"]
# users_points[record["user"]] += record["points"]
# users_pps = {}
# for user_id in user_ids:
# points = users_points[user_id]
# active_seconds = users_active_seconds[user_id]
# users_pps[user_id] = points / active_seconds
points = []
weeks = []
users = []
tasks = []
times = []
max_seconds = max(users_active_seconds.values())
records = db.records.find({"time_completed": {"$gt": start}})
id_to_user = {}
for record in records:
if record["user"] in id_to_user:
user = id_to_user[record["user"]]
else:
user = db.users.find_one({"_id": record["user"]})
if not user["active"]:
continue
id_to_user[record["user"]] = user
users.append(user["name"])
time_modifier = max_seconds / users_active_seconds[record["user"]]
points.append(round(record["points"] * 100 * time_modifier))
days = (record["time_completed"] - start).total_seconds() / (60*60*24)
weeks.append(int(days/group)+1)
tasks.append(record["task_name"])
times.append(record["time_completed"].strftime("%-m/%d %-I:%M %p"))
period_label = "%d day periods" % group
df = pd.DataFrame({
"Points": points,
period_label: weeks,
"User": users,
"Task": tasks,
"Time": times
})
df.sort_values("User", inplace=True)
x_label = period_label
if group_by == "task":
x_label = "Task"
return px.bar(
df,
x=x_label,
y="Points",
color="User",
barmode="group",
hover_data=["Task", "Time"],
), group_by == "time_period"