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Analyzing the data yourself
You can download the raw data directly from individual update sites. Each site has 8 different statistics files available, plus a metadata index:
Data Types:
stats-unique-daily.txt.gz
- Unique IP addresses per daystats-total-daily.txt.gz
- Total update checks per daystats-unique-monthly.txt.gz
- Unique IP addresses per monthstats-total-monthly.txt.gz
- Total update checks per monthstats-unique-yearly.txt.gz
- Unique IP addresses per yearstats-total-yearly.txt.gz
- Total update checks per yearstats-unique-ever.txt.gz
- Cumulative unique IP addresses by daystats-total-ever.txt.gz
- Cumulative total checks by daysites.json
- Metadata index with site list and summary statistics
URL Format: https://sites.imagej.net/{SITE_NAME}/{STATS_FILE}
Sites Index: https://sites.imagej.net/sites.json
Example URLs:
- https://sites.imagej.net/Java-8/stats-unique-daily.txt.gz
- https://sites.imagej.net/Fiji/stats-total-monthly.txt.gz
Data Format: Each line contains a datestamp and count value separated by a space:
20250723 458
20250724 672
20250725 543
Date formats:
- Daily/Ever:
YYYYMMDD
(e.g.,20250723
) - Monthly:
YYYYMM
(e.g.,202507
) - Yearly:
YYYY
(e.g.,2025
)
Sites Metadata Format:
The sites.json
file contains metadata for each update site:
{
"Java-8": {
"date_range": {
"start": "20151220",
"end": "20250904"
},
"total_unique_ips": 12534,
"total_requests": 89472,
"days_with_data": 3546,
"last_generated": "2025-09-05"
}
}
Here is an example Python script to analyze total yearly downloads for a specific site:
import gzip
import urllib.request
def fetch_yearly_stats(site_name, stat_type='total'):
"""Fetch and parse yearly statistics for a site."""
url = f'https://sites.imagej.net/{site_name}/stats-{stat_type}-yearly.txt.gz'
with urllib.request.urlopen(url) as response:
with gzip.open(response, 'rt') as f:
data = {}
for line in f:
if line.strip():
year, count = line.strip().split()
data[int(year)] = int(count)
return data
# Example: Get Java-8 yearly download statistics
site = 'Java-8'
yearly_stats = fetch_yearly_stats(site, 'unique')
print(f"Yearly unique IP statistics for {site}:")
for year in sorted(yearly_stats.keys()):
print(f" {year}: {yearly_stats[year]:,} unique IPs")
# Find the most popular year
best_year = max(yearly_stats.keys(), key=lambda y: yearly_stats[y])
print(f"\nBest year: {best_year} with {yearly_stats[best_year]:,} unique IPs")
And another example to list all sites sorted by total unique IPs:
import json
import urllib.request
with urllib.request.urlopen('https://sites.imagej.net/sites.json') as response:
sites_data = json.load(response)
# Sort sites by total unique IPs
ranked_sites = sorted(
sites_data.items(),
key=lambda x: x[1]['total_unique_ips'],
reverse=True
)
print("Sites ranked by total unique IPs:")
for site_name, metadata in ranked_sites:
print(f" {site_name}: {metadata['total_unique_ips']:,} unique IPs "
f"({metadata['days_with_data']} days of data)")