matplotlib-users Mailing List for matplotlib

Hi
I was asked off list how I created the little sparklines using Matplotlib.
There are two ways I create these:
The live graphs on the demo page (http://your.gridspy.co.nz/powertech/)
are created by a great little jquery app (so yeah, not matplotlib):
http://omnipotent.net/jquery.sparkline/
To get the data to the browser in order to render the sparkline, you
will need some sort of mechanism similar to Ajax (or at least a form of
it) called Comet. There is a great tutorial on using orbited for this here
http://cometdaily.com/2008/10/10/scalable-real-time-web-architecture-part-2-a-live-graph-with-orbited-morbidq-and-jsio/
If any of you need more help doing that, I am happy to provide some
source code examples.
If instead, you want to create static line graphs using matplotlib such
as those on this page:
http://your.gridspy.co.nz/powertech/history/04Nov2009.htm
http://your.gridspy.co.nz/powertech/graph/tiny/3-3-04Nov2009.png?c=2 (an
example)
To render static sparklines I use the following matplot lib code:
def render_simple_line(sensors, resolution = 'hour', span = 1,
 start=None, end=None, fig=None, column=0):
 """Builds a figure that shows the given sensors at the given
resolution and span in the given time period.
 """
 if fig is None:
 fig=Figure()
 fig.set_facecolor('white')
 fig.set_edgecolor('white')
 axes = fig.add_axes([0.00,0.00,1.0,1.0], axisbg='w', frame_on=False)
 axes.set_xticks([])
 axes.set_yticks([])
 axes.set_axis_off()
 if start is None:
 start = datetime.datetime.now()
 if end is None:
 end = start + datetime.timedelta(days=1)
 first_date = start.strftime('%Y-%m-%d')
 last_date = end.strftime('%Y-%m-%d')
 desc = [("mean", pk) for pk in sensors]
 np_table = data_table_matrix(desc, resolution, first_date,
last_date, span )
 #note that np_table[0] is datetime objects and [1] is data
 if np_table.size == 0:
 return None
 #replace nulls with 0
 np_table[1:][np_table[1:] == np.array([None])] = 0
 #replace -ve values
 np_table[1:][np_table[1:] < np.array([0])] = 0
 axes.xaxis.set_major_formatter(DateFormatter('%H'))
 fig.autofmt_xdate()
 base = np.zeros(np_table.shape[1])
 color = color_list[column % len(color_list)][1]
 axes.fill_between(np_table[0], base, np_table[column + 1], facecolor
= color)
 return fig
I pass fig in so it is easy to pass a figure from the ipython console,
since ipython makes special figures that are interactive.
-Tom
PS: Dan - I replied to your email directly but it bounced.

Hi
I was asked off list how I created the little sparklines using Matplotlib.
There are two ways I create these:
The live graphs on the demo page (http://your.gridspy.co.nz/powertech/) 
are created by a great little jquery app (so yeah, not matplotlib):
http://omnipotent.net/jquery.sparkline/
To get the data to the browser in order to render the sparkline, you
will need some sort of mechanism similar to Ajax (or at least a form of
it) called Comet. There is a great tutorial on using orbited for this here
http://cometdaily.com/2008/10/10/scalable-real-time-web-architecture-part-2-a-live-graph-with-orbited-morbidq-and-jsio/
If any of you need more help doing that, I am happy to provide some 
source code examples.
If instead, you want to create static line graphs using matplotlib such 
as those on this page:
http://your.gridspy.co.nz/powertech/history/04Nov2009.htm
http://your.gridspy.co.nz/powertech/graph/tiny/3-3-04Nov2009.png?c=2 (an
example)
To render static sparklines I use the following matplot lib code:
def render_simple_line(sensors, resolution = 'hour', span = 1,
 start=None, end=None, fig=None, column=0):
 """Builds a figure that shows the given sensors at the given
resolution and span in the given time period.
 """
 if fig is None:
 fig=Figure()
 fig.set_facecolor('white')
 fig.set_edgecolor('white')
 axes = fig.add_axes([0.00,0.00,1.0,1.0], axisbg='w', frame_on=False)
 axes.set_xticks([])
 axes.set_yticks([])
 axes.set_axis_off()
 if start is None:
 start = datetime.datetime.now()
 if end is None:
 end = start + datetime.timedelta(days=1)
 first_date = start.strftime('%Y-%m-%d')
 last_date = end.strftime('%Y-%m-%d')
 desc = [("mean", pk) for pk in sensors]
 np_table = data_table_matrix(desc, resolution, first_date,
last_date, span )
 #note that np_table[0] is datetime objects and [1] is data
 if np_table.size == 0:
 return None
 #replace nulls with 0
 np_table[1:][np_table[1:] == np.array([None])] = 0
 #replace -ve values
 np_table[1:][np_table[1:] < np.array([0])] = 0
 axes.xaxis.set_major_formatter(DateFormatter('%H'))
 fig.autofmt_xdate()
 base = np.zeros(np_table.shape[1])
 color = color_list[column % len(color_list)][1]
 axes.fill_between(np_table[0], base, np_table[column + 1], facecolor
= color)
 return fig
I pass fig in so it is easy to pass a figure from the ipython console,
since ipython makes special figures that are interactive.
-Tom
PS: Dan - I replied to your email directly but it bounced.