``````

In :

%pylab inline
rcParams['figure.figsize'] = (10,6)
from lagranto import Tra

``````
``````

Populating the interactive namespace from numpy and matplotlib

``````

``````

In :

filename = 'data/lsl_20070119_12_ana_48'
trajs = Tra(filename)
print trajs
print trajs.shape

``````
``````

303 trajectories with 9 time steps.
Available fields: time/lon/lat/p/Q/RH/TH/PV/P/LABEL
total duration: 2880.0 minutes
(303, 9)

``````

## Play around with the data

• Explore the data using Notebook feature like !, tab-completation on trajs.
• print the time of the first trajectorie using trajs['time']
• print the pressure at the starting time of all trajectories
``````

In :

``````

## Interpolate PV on evenly spaced pressure level

Interpolate at each 50hPa pressure level between 1000hPa and 200hPa

Hint: Use the scipy.interpolate module

``````

In [ ]:

``````

## Plot the mean, mean + std, mean - std of PV interpolated on pressure levels

Use the decreasing pressure as x axis hint:

• use np.nanmean and np.nanstd
• use matplotlib fill_bewteen function
``````

In [ ]:

``````

## Plot the same paramaters as above for TH, RH, PV, Q in a 4x4 plot

Hint : use the AxesGrid class from the mpl_toolkits.axes_grid1 module

``````

In [ ]:

``````

## Do a scatter plot of Q versus PV, with points colored by pressure

``````

In [ ]:

``````

## Plot TH values at each P level in the form of a boxplot

``````

In [ ]:

``````

## Plot the trajectories on a map

hint : Use the Basemap class from the mpl_toolkits.basemap module

``````

In [ ]:

``````

## Plot the trajectories on a map, coloring them with pressure

hint : Use the LineCollection class from matplotlib.collections module

``````

In [ ]:

``````