• to fix/unfix, set, reset and load ans save parameters;
• export parameters or computed datasets;
• launch the fit;
• select fit engines and configure them;
• list of all the fits that were run in the session with their starting and finishing parameters (see picture), and reuse them;
• run scripts;
• fit data completely non-interactively;
• and, last but not least, perform parameter space exploration, which consists in running (automatically) a number of fits with different (random) starting parameters to maximize the chances of finding out the best parameters for the fit, avoiding local minima.

• running the command from the menu now gives a dialog box for selecting all the arguments and options, which is very useful to discover the possibilities of the various commands;
• features that make the statistics and meta-data much easier to use with commands like apply-formula, like automatic code completion;
• a new system for permanently storing meta-data using record-meta;
• a new fit for fitting implicit equations, in which one cannot express \(y = f(x)\), but only \(f(x,y)=0\);
• and, of course, a brand shiny new logo by Marta Meneghello !

-----BEGIN PGP SIGNED MESSAGE-----
Hash: SHA512
OpenPGP Key Transition Statement for Simon Josefsson
I have created a new OpenPGP key and will be transitioning away from
my old key.  The old key has not been compromised and will continue to
be valid for some time, but I prefer all future correspondence to be
encrypted to the new key, and will be making signatures with the new
key going forward.
I would like this new key to be re-integrated into the web of trust.
This message is signed by both keys to certify the transition.  My new
and old keys are signed by each other.  If you have signed my old key,
I would appreciate signatures on my new key as well, provided that
your signing policy permits that without re-authenticating me.
The old key, which I am transitioning away from, is:
pub   1280R/B565716F 2002-05-05
      Key fingerprint = 0424 D4EE 81A0 E3D1 19C6  F835 EDA2 1E94 B565 716F
The new key, to which I am transitioning, is:
pub   3744R/54265E8C 2014-06-22
      Key fingerprint = 9AA9 BDB1 1BB1 B99A 2128  5A33 0664 A769 5426 5E8C
The entire key may be downloaded from: https://josefsson.org/54265e8c.txt
To fetch the full new key from a public key server using GnuPG, run:
  gpg --keyserver keys.gnupg.net --recv-key 54265e8c
If you already know my old key, you can now verify that the new key is
signed by the old one:
  gpg --check-sigs 54265e8c
If you are satisfied that you've got the right key, and the User IDs
match what you expect, I would appreciate it if you would sign my key:
  gpg --sign-key 54265e8c
You can upload your signatures to a public keyserver directly:
  gpg --keyserver keys.gnupg.net --send-key 54265e8c
Or email simon@josefsson.org (possibly encrypted) the output from:
  gpg --armor --export 54265e8c
If you'd like any further verification or have any questions about the
transition please contact me directly.
To verify the integrity of this statement:
  wget -q -O- https://josefsson.org/key-transition-2014-06-22.txt gpg --verify
/Simon
-----BEGIN PGP SIGNATURE-----
Version: GnuPG v1.4.12 (GNU/Linux)
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Photo from the Boston Python Workshop a similar workshop run in Boston that has inspired and provided a template for the CDSW.

• Are new contributors to an article in Wikipedia sticking around longer or contributing more than people who joined last year?
• Who are the most active or influential users of a particular Twitter hashtag?
• Are people who participated in a Wikipedia outreach event staying involved? How do they compare to people that joined the project outside of the event?

;;;; testing.hy
(import-from sunlight openstates)
(defn get-legislators [state]
  "Get some Legislators from a state"
  (kwapply (.legislators openstates)  "state" state ))
(defn print-legislator-count [state]
  "Print the Legislative count for a state"
  (print (len (get-legislators state))))

#!/usr/bin/env python
import hy
import testing
# import pdb; pdb.set_trace()
testing.print_legislator_count("ma")

> /home/tag/tmp/invoke.py(8)()
-> testing.print_legislator_count("ma")
(Pdb) 

(Pdb) l
  3     import hy
  4     import testing
  5     
  6     import pdb; pdb.set_trace()
  7     
  8  ->  testing.print_legislator_count("ma")
[EOF]

(Pdb) s
--Call--
> /home/tag/tmp/testing.hy(12)print_legislator_count()
-> (defn print-legislator-count [state]

> /home/tag/tmp/testing.hy(14)print_legislator_count()
-> (print (len (get-legislators state))))
(Pdb) 
--Call--
> /home/tag/tmp/testing.hy(7)get_legislators()
-> (defn get-legislators [state]
(Pdb) 
> /home/tag/tmp/testing.hy(9)get_legislators()->[ (SNIP) ]
-> (kwapply (.legislators openstates)  "state" state ))
(Pdb) l
  4     (import-from sunlight openstates)
  5     
  6     
  7     (defn get-legislators [state]
  8       "Get some Legislators from a state"
  9  ->    (kwapply (.legislators openstates)  "state" state ))
 10     
 11     
 12     (defn print-legislator-count [state]
 13       "Print the Legislative count for a state"
 14       (print (len (get-legislators state))))
(Pdb) 

-> (kwapply (.legislators openstates)  "state" state ))
(Pdb) state
'ma'

(Pdb) openstates
(Pdb) s

(Pdb) s
198
--Return--
> /home/tag/tmp/testing.hy(14)print_legislator_count()->None
-> (print (len (get-legislators state))))
(Pdb) s
--Return--
> /home/tag/tmp/invoke.py(8)()->None
-> testing.print_legislator_count("ma")

• Highly paid workers who have consistently saved over the years. There are examples in the aforementioned book about people with relatively low salaries who happened to be very frugal and invest well (and have had some luck in their investments too). These people would probably tend to be older, as it takes a while to save up that kind of money, and since this industry is so young with so much turnover, I would not think there would be a lot of people out there like this, but who knows, maybe there are a bunch of IBMers with this kind of story.
• Those who got in on the right IPO, like Google or Facebook or something like that. These events not only generate billions for those at the top of the heap, but for the right person at the right place at the right time, can mean significant wealth even without being in the upper echelons of the company. My suspicion is that this kind of IPO, where everyone cashes out, is not common enough to have a lot of people in this category, but who knows, maybe it adds up over the years.
• Those who own or started software firms that do something that's not very visible, but nonetheless dominates some particular niche. This is where I'd guess most of them would be, but I certainly have no data or even anecdotes to back this up.