Re: [Matplotlib-users] depth longitude plot

[Sudheer J. <sud...@ya...>]

Hi Phil,

Though iris looked to be promising it needed many other libraries, so I chose the below suggestion. But is there a way to overlay contours on this ? also is it possible to specify the levels?
In [23]: plt.pcolormesh?? did not give much help

with best regards,
Sudheer

________________________________
 From: Phil Elson <pel...@gm...>
To: "Mat...@li..." <mat...@li...> 
Sent: Saturday, 2 March 2013 3:40 PM
Subject: Re: [Matplotlib-users] depth longitude plot
 

Incidentally, if you wanted to do this a little more expressively than indexing, you could look into using iris (http://scitools.org.uk/iris/docs/latest/index.html). It doesn't currently support DAP, but if you had the NetCDF file (from http://www.marine.csiro.au/dods-data/climatology-netcdf/levitus_monthly_temp_98.nc) you would do:


importiris
import iris.quickplot as qplt
import matplotlib.pyplot as plt


temp_cube = iris.load_cube('levitus_monthly_temp_98.nc')

# Sort out some of the bad metadata. Firstly, set the unit,
# secondly rename the dimension 1 coordinate to 'depth'.
temp_cube.unit = 'C'
temp_cube.coord(dimensions=1, dim_coords=True).rename('depth')

# Extract a spatial sub-domain.
sub_temp_cube = temp_cube.extract(iris.Constraint(latitude=0.5,
                              longitude=lambda v: 45 < v < 100))

# Iterate over all the depth-longitude sections (in this case it
# iterates over time)
for cross_sect_cube in sub_temp_cube.slices(['depth', 'longitude']):
    qplt.pcolormesh(cross_sect_cube)
    plt.gca().invert_yaxis()
    plt.show()
    break


Hope that helps!

Phil








On 2 March 2013 09:37, Phil Elson <pel...@gm...> wrote:

Perhaps something like:
>
>
>
>
>from matplotlib import pyplot as plt
>from netCDF4 import Dataset
>import numpy as np
>
>
>
>
>url='http://www.marine.csiro.au/dods/nph-dods/dods-data/climatology-netcdf/levitus_monthly_temp_98.nc'
>ds = Dataset(url)
>
>
>
>
>temp = ds.variables['TEMP']
>lats = ds.variables['lat']
>lons = ds.variables['lon']
>depths = ds.variables['z']
>
>
>
>
># filter all but one latitude
>lat_index = np.where(lats[:] == 0.5)[0][0]
>lats = lats[lat_index]
>
>
># filter a range of longitudes
>lon_lower_index = np.where(lons[:] == 44.5)[0][0]
>lon_upper_index = np.where(lons[:] == 100.5)[0][0]
>lons = lons[lon_lower_index:lon_upper_index]
>
>
>temp = temp[0, :, lat_index, lon_lower_index:lon_upper_index]
>
>
>
>
>plt.pcolormesh(lons, depths[:], temp)
>plt.gca().invert_yaxis()
>
>
>plt.show()
>
>
>
>
>
>
>
>
>The indexing approach used here is quite flakey, so I certainly wouldn't use this in anything operational.
>
>Hope this helps,
>
>Phil
>
>

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