From 806bf1a0c5bbe7b244046eaf80204cd6056e66d7 Mon Sep 17 00:00:00 2001
From: Klaas van Schelven <klaas@vanschelven.com>
Date: Mon, 18 Dec 2023 23:32:08 +0100
Subject: [PATCH] Tool to test w/ bursty data

---
 performance/bursty_data.py | 66 ++++++++++++++++++++++++++++++++++++++
 performance/some_script.py |  3 ++
 2 files changed, 69 insertions(+)
 create mode 100644 performance/bursty_data.py

diff --git a/performance/bursty_data.py b/performance/bursty_data.py
new file mode 100644
index 0000000..664142d
--- /dev/null
+++ b/performance/bursty_data.py
@@ -0,0 +1,66 @@
+import datetime
+import math
+import random
+
+
+# a way to generate some bursty streams of points-in-time.
+# I'm sure there's a 100 things wrong with this, but at least it's
+#
+# * not simply distributed at random
+# * has some form of periodic pattern in it as real data surely has
+# * has bursts (errors come in bursts!)
+#
+# this will give us at least some base to test in somewhat natural settings.
+
+
+def generate_bursty_data(nr_of_waves=1, base_amplitude=1, expected_nr_of_bursts=1, burst_amplitude=5, num_buckets=1000):
+    """returns `num_buckets` histogram-like buckets"""
+
+    burst_prob = expected_nr_of_bursts / num_buckets
+    period = num_buckets / nr_of_waves
+
+    buckets = [0] * num_buckets
+
+    for i in range(num_buckets):
+        # We pick math.sin as an arbitrary periodic pattern. Normalize for period and >0
+        periodic_pattern = (1 + math.sin(i / period * 2 * math.pi)) / 2
+
+        # Introduce burst with probability 'burst_prob'
+        if random.random() < burst_prob:
+            burst = abs(random.gauss(0, burst_amplitude))
+            buckets[i] = periodic_pattern + burst
+        else:
+            buckets[i] = periodic_pattern
+
+    return buckets
+
+
+def buckets_to_points_in_time(buckets, begin, end, total_points):
+    """given:
+
+    * histogram-like list of 'buckets', where each bucket is a float that is a relative business of that period
+    * a begin and an end (both datetime)
+    * a total amount of points
+
+    generates a list of points of length `total_points` that conforms to the distribution denoted by the buckets, and
+    where the points-in-time are distributed at random within the buckets.
+    """
+
+    total_weight = sum(buckets)
+
+    time_range_size = end - begin
+    bucket_size = time_range_size.total_seconds() / len(buckets)
+
+    points = []
+
+    rounding_difference = 0
+
+    for i, bucket_weight in enumerate(buckets):
+        bucket_points = (bucket_weight / total_weight) * total_points + rounding_difference
+        rounding_difference = bucket_points - round(bucket_points)
+        bucket_points = round(bucket_points)
+
+        for j in range(bucket_points):
+            points.append(begin + datetime.timedelta(seconds=bucket_size * (i + random.uniform(0, 1))))
+
+    return sorted(points)
diff --git a/performance/some_script.py b/performance/some_script.py
index d897289..a4cd1b7 100644
--- a/performance/some_script.py
+++ b/performance/some_script.py
@@ -30,4 +30,7 @@ slow results for a check in.
 """)
 
 
+def print_thoughts_about_inc():
+
+
 print_thoughts_about_prev_tup()