Script to generate a synthetic Tinode dataset.

tinode-db/generate_dataset.py

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+# Script that generates a synthetic Tinode dataset in the same format as data.json.
+# Run as:
+# $ python generate_dataset.py --num_users X
+#
+# User ids and passwords are: userN and userN123 where N is in [0..X).
+# The output will be dumped to stdout.
+
+import argparse as ap
+import json
+import numpy as np
+import random
+import sys
+
+parser = ap.ArgumentParser(description='Simple python script to generate synthetic Tinode datasets.')
+parser.add_argument('--num_users', type=int, default=50, help='Number of user accounts (default: 50).')
+args = parser.parse_args()
+
+random.seed()
+np.random.seed()
+
+data = dict()
+
+# Users.
+num_users = args.num_users
+users = list()
+ul = ['user%d' % i for i in range(num_users)]
+
+for i in range(num_users):
+  userid = ul[i]
+  u = {
+    'createdAt': '-%dh' % random.randint(1, 300),
+    'email': '%s@example.com' % userid,
+    'passhash': '%s123' % userid,
+    'private': {'comment': 'some comment 123'},
+    'public': {'fn': userid},
+    'tags': [userid],
+    'state': 'ok',
+    'status': {
+      'text': 'my status %s' % userid
+    },
+    'username': userid,
+  }
+  users.append(u)
+
+data['users'] = users
+
+# Groups.
+group_topics = list()
+# TODO: make it configurable.
+num_groups = random.randint(1, num_users >> 1)
+for i in range(num_groups):
+  owner = random.choice(ul)
+  g = { 
+    'createdAt': '-%dh' % random.randint(1, 300),
+    'name': '*ABCgroup%d' % i,
+    'owner': owner,
+    'tags': ['group%d' % i],
+    'public': {'fn': 'My group %d' % i}
+  }
+  group_topics.append(g)
+
+data['grouptopics'] = group_topics
+
+# P2P subs.
+p2p_subs = list()
+ids = set()
+
+# TODO: Poisson mean should be configurable.
+num_contacts = np.random.poisson(40, num_users).tolist()
+num_contacts = [min(max(2, int(s)), num_users - 1) for s in num_contacts]
+
+all_ids = [i for i in range(num_users)]
+
+for i in range(num_users):
+  contacts = [ul[x] for x in random.sample(all_ids, num_contacts[i]) if x > i]
+  for c in contacts:
+    p2p = { 
+      'createdAt': '-%dh' % random.randint(1, 300),
+      'users': [{'name': ul[i]}, {'name': c}]
+    }
+    p2p_subs.append(p2p)
+
+data['p2psubs'] = p2p_subs
+
+# Group subs.
+# TODO: Poisson mean should be configurable as well.
+sizes = np.random.poisson(4, num_groups).tolist()
+sizes = [min(max(2, int(s)), num_users) for s in sizes]
+group_subs = list()
+
+for i in range(num_groups): 
+  k = sizes[i]
+  gs = random.sample(ul, k) 
+  for uid in gs:
+    g = {
+      'createdAt': '-%dh' % random.randint(1, 300),
+      'topic': '*ABCgroup%d' % i,
+      'user': uid
+    }
+    group_subs.append(g)
+
+data['groupsubs'] = group_subs
+
+json.dump(data, sys.stdout, ensure_ascii=False, indent=4)