I've been working hard on www.eswap.com. It's
matplotlib powered. I would like to thank the
matplotlib community and especially John H. for making
this possible. Some of the features are:
1. See 20 (professional looking) charts per page
2. You can tag stocks with keywords
3. You can create your own sets and portfolios
4. keyboard navigation. You can use the right arrow
key and left arrow key
Scanning, Modelling and Back Testing are planned for
release early next year. Can you give it a spin and
let me know how you like it?
Thanks,
Vineet
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I’ve been working hard on www.eswap.com. It’s
matplotlib powered. I would like to thank the
matplotlib community and especially John H. for making
this possible. Some of the features are:
See 20 (professional looking) charts per page
You can tag stocks with keywords
You can create your own sets and portfolios
keyboard navigation. You can use the right arrow
key and left arrow key
Scanning, Modelling and Back Testing are planned for
release early next year. Can you give it a spin and
let me know how you like it?
Thanks,
Vineet
···
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Any change you could share back with the community the approach
you followed - web framework used? integration strategy? deploy-
ment and setup issues? graph creation methodology?
web framework used: cherrypy
deploymnet os: ubuntu
setup issues: none
I am looking to tackle a project with multi-chart reporting
component features and would really love to use Matplotlib
with it.... but am not quite sure where to start.
matplotlib comes with a lot of examples and that's probably the best
place to start. There is an example for how to save charts to a file
(which is what I do) and another one for streaming the charts back to
the user.
The only issue I have faced is that matplotlib is slow for my chart
types (takes about 0.8 seconds to do a chart). I'm hoping that future
version of matplotlib will be faster.
Any change you could share back with the community
the approach
you followed - web framework used? integration
strategy? deploy-
ment and setup issues? graph creation methodology?
web framework used: cherrypy
deploymnet os: ubuntu
setup issues: none
I am looking to tackle a project with multi-chart
reporting
component features and would really love to use
Matplotlib
with it.... but am not quite sure where to start.
matplotlib comes with a lot of examples and that's
probably the best
place to start. There is an example for how to save
charts to a file
(which is what I do) and another one for streaming the
charts back to
the user.
The only issue I have faced is that matplotlib is slow
for my chart types (takes about 0.8 seconds to do a
chart). I'm hoping that future version of matplotlib
will be faster.
Guys at stockcharts.com generate charts in about 0.3
seconds. So matplotlib (for the charts I generate) is
about 2.5 slower than what they use. For most people
that might not be an issue, but since I pre-generate
100,000s of graphs daily it can cause a lot of pain.
Vineet
···
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Sorry - I was not too clear on my question; I *am* currently able
to use matplotlib - what I am not sure how to is to integrate
it into a web framework. I have been looking at using Pylons -
as that is WSGI compliant - but probably any one will do.
I would like to be able to generate graphs "on the fly" from database
data (and I assume your graphs are done a similar way), and so I
was wondering what are the specifics of the approach you used
for this - is it just a case of installing Python onto a server,
plus the web framework code, and then importing the matplotlib
code - if so, what command is used to "pull" the graph into the
web page - the matplotlib "show" command? - or do you store the
graph somewhere on a temporary basis and then delete again when
done??
Thanks
Derek
PS <1 sec to make a chart sounds quick - given that it takes much
longer
than 1 second to load the chart across the web into one's browser...
Any change you could share back with the community
the approach
you followed - web framework used? integration
strategy? deploy-
ment and setup issues? graph creation methodology?
web framework used: cherrypy
deploymnet os: ubuntu
setup issues: none
I am looking to tackle a project with multi-chart
reporting
component features and would really love to use
Matplotlib
with it.... but am not quite sure where to start.
matplotlib comes with a lot of examples and that's
probably the best
place to start. There is an example for how to save
charts to a file
(which is what I do) and another one for streaming the
charts back to
the user.
The only issue I have faced is that matplotlib is slow
for my chart types (takes about 0.8 seconds to do a
chart). I'm hoping that future version of matplotlib
will be faster.
Guys at stockcharts.com generate charts in about 0.3
seconds. So matplotlib (for the charts I generate) is
about 2.5 slower than what they use. For most people
that might not be an issue, but since I pre-generate
100,000s of graphs daily it can cause a lot of pain.
Vineet
···
__________________________________________________
Do You Yahoo!?
Tired of spam? Yahoo! Mail has the best spam protection around
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This message is subject to the CSIR's copyright, terms and conditions and
e-mail legal notice. Views expressed herein do not necessarily represent the
views of the CSIR.
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Legal Notice send a blank message with REQUEST LEGAL in the subject line to
CallCentre@...1230...
This message has been scanned for viruses and dangerous content by MailScanner,
and is believed to be clean.
what command is used to "pull" the graph into the
web page - the matplotlib "show" command?
There are a bunch of ways to do this. One way is to pregenerate the charts you need and store them as .png or .gif files on the server. If you need truly dynamic images, then you need to make a Python program (WSGI, CGI, whatever) that returns the image/png on the fly. It's not easy to do this with matplotlib, but it's not too hard; search the mailing list archives for notes about getting the image bits in memory rather than drawing on screen or writing to disk.
PS <1 sec to make a chart sounds quick - given that it takes much
longer than 1 second to load the chart across the web into one's browser...
All depends on your load and expectations. If the 1 second is all CPU time, then 1 second means you can only generate 100,000 or so graphs a day per server.