Category Archives: software – A web site helping you find music jam sessions near you

Announcing, a web service allows you to find music jam sessions near users location. The site launched on 2nd May 2018, currently it is in the test period and it hosts jam session events of Athens and Greece, but aspires to host events from all around the word, allowing travelling musicians to find places to jam. web site

By using some simple javascript+jquery functions to build a single page project, it needs no backend at all. It uses geolocation services to find your position and makes queries to the open data events calendar website: API, getting datasets of jamsession events. A such query is:,23*15&view=json

Where 38,23 is an the example position (latitude, longitude) gathered either by geolocation services or by GeoIP. Cover images are fetched with another asynchronous query:,2,3

Where 1,2,3 are some event ids.

Near upcoming events to the given position, tagged with #jamsession and with start dates up to 15 days after, are retrieved with the above query and displayed in Ther is a similar site, that uses the same technique for building a single page web service that takes advantage the API of The later is a calendar of feminist events near user’s location.

It is easy to build such services using the open data of User and communities (like the jamming musicians or the feminist organizations) can announce their events in, then anyone may build new services that take advantage of the free and open access of database.

To help communities, soon we will release some free software to ease the creation of such services.

Furthermore, user if wishes, can use the advanced search and displaying features of wikical, e.g. to create event maps like bellow (a map with the upcoming jam session events in Athens / Greece).

Upcomming jam session events in athens from

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Creating tag clouds using logarithmic interpolation in Python

Reports of feature a tag cloud representation. One of the most popular and useful ways to create a tag cloud is by calculating the logarithm of the usage for each tag. Using the logarithm rather than the original usage value results to a smooth gradation from the less to the most used tag.


In order to implement the feature, we searched for a suitable algorithm in usual resources such as stackoverflow, but couldn’t find an approach that works for every data case (e.g. with few or just one tag). So we needed to go back and refresh our memories with interpolation methods we studied in numerical analysis undergraduate courses, to build the (simple to be honest) algorithm ourselves. We offer this algorithm we used and works perfectly for us, for anyone interested in tag clouds.

Let’s assume that the tags usage is a vector \bf{u} = [u_1, u_2, ..., u_n] , then the minimum and maximum usage values are \min \bf{u} and \max \bf{u}.

Using the formula:

l_i = \frac{\log u_i - \log \min \bf{u}}{\log \max \bf{u} - \log \min \bf{u}}

we get a linearised value l_i for each tag varying from 0 to 1. Then  we can use l_i e.g. to calculate grey (rgb) values or to multiply a cardinality of css classes.

A Python implementation of the above methodology follows, code is also available for download.

#!/usr/bin/env python
# -*- encoding: utf-8 -*-
# vi:expandtab:tabstop=4 shiftwidth=4 textwidth=79

import math

Let's have three separate css classes for our example

This can be a css template, with ".tags-cloud-1" for the less used tag and
".tags-cloud-3" for the most used tag (assuming 3 distinct classes)

def get_tags_cloud(data):
    data should be a list containing tags items. Each item should be a
    dictionary containing at least the `name` of the tag and the number of tag
    occurrences named as `usage`.

    Returns a list sorted by `name` plus two fields: a `css_class` and the
    linear value resulting from logarithmic interpolation.
    if not data:

    # Find the maximum and minimum usage
    maximum = max(data, key=lambda x: x['usage'])['usage']
    minimum = min(data, key=lambda x: x['usage'])['usage']

    # Do the math, find the common subtractor and divider and calculate log
    # value for each tag. Then, assuming that log value is linearized, find the
    # integer class from 1 to TAGS_CLOUD_CSS_CLASSES
    subtractor = math.log(float(minimum))
    divider = math.log(float(maximum)) - subtractor or 1.0 # 1.0 if min==max
    for item in data:
        log_value = (math.log(float(item['usage']))-subtractor) / divider
        d = int(round(log_value*(TAG_CLOUD_CSS_CLASSES-1) + 1))
        item['css_class'] = TAG_CLOUD_CSS_CLASS_TEMPLATE%d
        item['log_value'] = log_value

    # Sort results by name for displaying tags in an alphabetical order
    return sorted(data, key=lambda x: x['name'])

# An example

if __name__ == '__main__':
    import pprint

    TEST_SET = [
            {'name': 'popular tag', 'usage': 100},
            {'name': 'medium popularity tag', 'usage': 10},
            {'name': 'another medium popularity tag', 'usage': 15},
            {'name': 'obscure tag', 'usage': 2}


# This is a potential Django application of the tag cloud. Django code is
# untested, it is just to prove the concept. Let's assume two models, first
# model represents blog posts and second models tags being used in posts.
# from django.db import models
# from django.db.models import Count
# class BlogPost(models.Model):
#     # Several blog fields defined here...
#     #
#     tags = models.ManyToManyField("Tag", related_name='blog_posts')
# class Tag(models.Model):
#     name = models.CharField(max_length=40)
#     @staticmethod
#     def tags_cloud_data(limit=50):
#         data = (Tag.objects.
#                 values('name').
#                 annotate(usage=Count('blog_bosts')).
#                 order_by('-usage')[:limit])
#         data = [item for item in data if item['usage']]
#         return data
#     @staticmethod
#     def tags_cloud(limit=50):
#         return get_tags_cloud(Tag.tags_cloud_data(limit))

By running the example above we get this result:

[{'css_class': '.tag-cloud-2',
  'log_value': 0.5150539804460444,
  'name': 'another medium popularity tag',
  'usage': 15},
 {'css_class': '.tag-cloud-2',
  'log_value': 0.41140808993222105,
  'name': 'medium popularity tag',
  'usage': 10},
 {'css_class': '.tag-cloud-1',
  'log_value': 0.0,
  'name': 'obscure tag',
  'usage': 2},
 {'css_class': '.tag-cloud-3',
  'log_value': 1.0,
  'name': 'popular tag',
  'usage': 100}]
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