Reproducible LMA research with the IPython notebook and brawl4d

This notebook demonstrates how to display data included in the lmatools repository.

If you haven't yet, process and view sample data included with lmatools

python ~/code/lmatools/testing/ /path/to/output/files/

Then, edit the data_path in the second cell to include /path/to/output/files/ as defined above. Run all the cells prior to the "Charge Analysis" and try interacting with the plot. You should see some data.

In [1]:
import matplotlib
%matplotlib qt4
# import matplotlib
# matplotlib.use('nbagg')
#import matplotlib.pyplot as plt
from brawl4d.brawl4d import B4D_startup, redraw
import os

In [2]:
data_path = '/data/DC3/flash_sort/'
lma_file = os.path.join(data_path, 'h5_files/2012/Jun/02/LYLOUT_120602_215001_0600.dat.flash.h5')

In the cell below, note that the basedate has been set to match the dataset we specified above.

If you are not using data from the WTLMA, then you'll also need to pass ctr_lon=value and ctr_lat=value to B4D_startup.

In [3]:
from datetime import datetime
panels = B4D_startup(basedate=datetime(2012,6,2), ctr_lat=40.4463980, ctr_lon=-104.6368130)

/Users/ebruning/anaconda/lib/python2.7/site-packages/matplotlib/ UserWarning: findfont: Font family [u'Helvetica'] not found. Falling back to Bitstream Vera Sans
  (prop.get_family(), self.defaultFamily[fontext]))

In [4]:
import matplotlib.pyplot as plt;

In [5]:
from brawl4d.LMA.controller import LMAController
lma_ctrl = LMAController()
d, post_filter_brancher, scatter_ctrl, charge_lasso = lma_ctrl.load_hdf5_to_panels(panels, lma_file)

found flash data

Zoom to overview of first minute

In [7]:
h,m,s,dt = 21, 50, 0, 60
panels.panels['tz'].axis((h*3600 + m*60 + s, h*3600 + m*60 + s+dt, 1, 18))
panels.panels['xy'].axis((-150, 150, -150, 150))

(-150, 150, -150, 150)

In [6]:
from brawl4d.LMA.widgets import LMAwidgetController
from IPython.display import display
from brawl4d.LMA.controller import LMAController

lma_tools = LMAwidgetController(panels, lma_ctrl, scatter_ctrl, charge_lasso, d)

Zoom in on a few cells of interest. The smaller, western and northern cells here are anomalously electrified, while the larger cluster is normally electrified.

Hereford storm

In [9]:
h,m,s,dt = 21, 50, 0, 60*10
panels.panels['tz'].axis((h*3600 + m*60 + s, h*3600 + m*60 + s+dt, 1, 18))
panels.panels['xy'].axis((35, 60, 45, 70))

(35, 60, 45, 70)

In [8]:

[('x', (34.920718816067655, 60.079281183932345)),
 ('y', (45.0, 70.0)),
 ('z', (1.0, 18.0)),
 ('time', (78600.0, 79200.0))]

In [ ]: