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
|
import datetime
import pandas
from sklearn.linear_model import Ridge
import numpy as np
import os
from member_colors import member_groups
# Pure logic for creating a database connection
def create_database_connection():
from sql.pg_handler import PostgresHandler
hostname = os.environ.get("POSTGRES_HOST")
user = os.environ.get("POSTGRES_USER")
password = os.environ.get("POSTGRES_PASSWORD")
database = os.environ.get("POSTGRES_DATABASE")
return PostgresHandler(host_name=hostname, username=user, password=password, database=database, port=5432)
def get_subscribers_data():
server = create_database_connection()
query = 'SELECT sd.*, h.* FROM subscriber_data sd INNER JOIN "24h_historical" h ON sd.channel_id = h.channel_id ORDER BY sd.subscriber_count DESC'
data = server.execute_query(query)
channel_data_list = [{
"channel_name": row[3],
"profile_pic": row[2],
"subscribers": row[4],
"sub_org": row[5],
"video_count": row[6],
"views": row[8],
"day_diff": int(row[4] - int(row[11]))
} for row in data]
return {"timestamp": datetime.datetime.now(), "channel_data": channel_data_list}
def get_twitch_data():
server = create_database_connection()
query = '''
SELECT sd.*, h.*, ts.follower_count
FROM subscriber_data sd
INNER JOIN "24h_historical" h ON sd.channel_id = h.channel_id
LEFT JOIN twitch_stats ts ON sd.channel_id = ts.channel_id
ORDER BY sd.subscriber_count DESC
'''
data = server.execute_query(query)
channel_data_list = []
for row in data:
youtube_subs = row[4]
twitch_followers = row[-1] if row[-1] is not None else 0
total_followers = youtube_subs + twitch_followers
channel_data_list.append({
"channel_name": row[3],
"profile_pic": row[2],
"subscribers": youtube_subs,
"sub_org": row[5],
"twitch_followers": twitch_followers,
"total_sum": total_followers,
})
return {"timestamp": datetime.datetime.now(), "channel_data": channel_data_list}
def get_channel_timeseries(channel_name):
server = create_database_connection()
query = "SELECT * FROM subscriber_data_historical WHERE name = %s AND timestamp > %s ORDER BY TO_CHAR(timestamp, 'YYYY-MM-DD')"
data = server.execute_query(query, (channel_name, os.environ.get("START_DATE"),))
labels = []
data_points = []
seen_dates = set()
for row in data:
date_string = row[5].strftime("%Y-%m-%d")
if date_string in seen_dates:
continue
labels.append(date_string)
data_points.append(row[4])
seen_dates.add(date_string)
return {"labels": labels, "datasets": data_points}
def get_channel_7d(channel_name):
server = create_database_connection()
query = "SELECT * FROM subscriber_data_historical WHERE name = %s ORDER BY TO_CHAR(timestamp, 'YYYY-MM-DD')"
data = server.execute_query(query, (channel_name,))
labels = []
data_points = []
seen_dates = set()
for row in data:
date_string = row[5].strftime("%Y-%m-%d")
if date_string in seen_dates:
continue
labels.append(date_string)
data_points.append(row[4])
seen_dates.add(date_string)
return {"labels": labels[-7:], "datasets": data_points[-7:]}
def get_channel_milestones(channel_name, milestone_increment=10000):
server = create_database_connection()
current_milestone = 10000
query = """
SELECT subscriber_count, MIN(timestamp)
FROM subscriber_data_historical
WHERE name = %s
GROUP BY subscriber_count
ORDER BY subscriber_count ASC
"""
data = server.execute_query(query, (channel_name,))
dates = []
milestones = []
for row in data:
subscriber_count = row[0]
while subscriber_count >= current_milestone:
dates.append(row[1].strftime("%Y-%m-%d"))
milestones.append(current_milestone)
current_milestone += milestone_increment
return {"milestones": milestones, "dates": dates}
def get_channel_diffs(channel_name):
server = create_database_connection()
query = "SELECT * FROM subscriber_data_historical WHERE name = %s ORDER BY timestamp DESC"
data = server.execute_query(query, (channel_name,))
if not data:
return {"diff_1d": None, "diff_7d": None, "diff_30d": None}
now = datetime.datetime.now()
latest = data[0][4]
sub_1d = next((r[4] for r in data if (now - r[5]).days >= 1), None)
sub_7d = next((r[4] for r in data if (now - r[5]).days >= 7), None)
sub_30d = next((r[4] for r in data if (now - r[5]).days >= 30), None)
return {
"diff_1d": latest - sub_1d if sub_1d is not None else None,
"diff_7d": latest - sub_7d if sub_7d is not None else None,
"diff_30d": latest - sub_30d if sub_30d is not None else None,
}
def get_channel_info(channel_name):
def find_next_milestone(sc):
return ((sc // 10000) + 1) * 10000 if sc < 100000 else ((sc // 100000) + 1) * 100000 if sc < 1000000 else ((sc // 1000000) + 1) * 1000000
server = create_database_connection()
data = server.execute_query("SELECT * FROM subscriber_data WHERE name = %s", (channel_name,))[0]
historical = server.execute_query("SELECT * FROM subscriber_data_historical WHERE name = %s ORDER BY timestamp DESC", (channel_name,))
current = historical[0][4]
result = {
"channel_id": data[1], "channel_name": data[3], "profile_pic": data[2], "subscribers": data[4],
"sub_org": data[5], "video_count": data[6], "view_count": data[8],
"diff_1d": None, "diff_7d": None, "diff_30d": None
}
now = datetime.datetime.now()
for days, key in [(1, 'diff_1d'), (7, 'diff_7d'), (30, 'diff_30d')]:
past_val = next((r[4] for r in historical if (now - r[5]).days >= days), None)
result[key] = current - past_val if past_val is not None else None
subs, dates, seen = [], [], set()
for r in historical:
ds = r[5].strftime("%Y-%m-%d")
if ds in seen: continue
subs.append(r[4])
dates.append(ds)
seen.add(ds)
df = pandas.DataFrame({"subscribers": subs, "dates": pandas.to_datetime(dates)}).set_index("dates").sort_index()
df = df[df.index > (now - datetime.timedelta(days=90))]
try:
model = Ridge(alpha=100)
X = np.arange(len(df)).reshape(-1, 1)
model.fit(X, df["subscribers"])
next_m = find_next_milestone(current)
days_left = int(((next_m - model.intercept_) / model.coef_)[0])
result.update({
"next_milestone": str(next_m),
"days_until_next_milestone": str(days_left),
"next_milestone_date": str((now.date() + datetime.timedelta(days=days_left))) if days_left >= 0 else "N/A"
})
except:
result.update({"next_milestone": "N/A", "days_until_next_milestone": "N/A", "next_milestone_date": "N/A"})
return result
def get_group_mappings():
group_mappings = {}
for name, group in member_groups.items():
group_mappings.setdefault(group, []).append(name)
return group_mappings
|
