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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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Tank you very much for both solutions Phil,
with best regards,
Sudheer 
***************************************************************
Sudheer Joseph 
Indian National Centre for Ocean Information Services
Ministry of Earth Sciences, Govt. of India
POST BOX NO: 21, IDA Jeedeemetla P.O.
Via Pragathi Nagar,Kukatpally, Hyderabad; Pin:5000 55
Tel:+91-40-23886047(O),Fax:+91-40-23895011(O),
Tel:+91-40-23044600(R),Tel:+91-40-9440832534(Mobile)
E-mail:sjo...@gm...;sud...@ya...
Web- http://oppamthadathil.tripod.com
***************************************************************
________________________________
 From: Phil Elson <pel...@gm...>
To: "Mat...@li..." <mat...@li...> 
Sent: Saturday, 2 March 2013 3:07 PM
Subject: Re: [Matplotlib-users] depth longitude plot
 
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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_______________________________________________
Matplotlib-users mailing list
Mat...@li...
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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:
import iris
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
[image: Inline images 1]
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
>
>
>

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

Dear Users,
               I am trying to make vertical section plot along equator of Indian ocean from 45E to 100E and latitude 0.
from levitus climatology using below code. What is the best and simple way to achieve this? np.meshgrid allows only 2 variables so I am not able to make x,y,z= using np.meshgrid(lat,depth,temp)
how to go about this? Is there a set of codes for handling such graphs?
url='http://www.marine.csiro.au/dods/nph-dods/dods-data/climatology-netcdf/levitus_monthly_temp_98.nc'
import cdms2
from matplotlib import pyplot as plt
from netCDF4 import Dataset as ncd
f=cdms2.open(url)
f.variables()
x=f.getAxis('lon')
y=f.getAxis('lat')
z=f.getAxis('z')
depth=z.getData()
lat=y.getData()
lon=x.getData()
temp=f('TEMP')
lonmin=np.where(lon==45)
lonmax=np.where(lon==100)
nlon=lon[lonmin:lonmax]
lat0=np.where(lat==0.5)
ntemp=temp[:,lat0,lonmin:lonmax]
basically I wanted to make a plot plt.plot(nlon,depth,ntemp)
Please help.
with best regards,
Sudheer
***************************************************************
Sudheer Joseph 
Indian National Centre for Ocean Information Services
Ministry of Earth Sciences, Govt. of India
POST BOX NO: 21, IDA Jeedeemetla P.O.
Via Pragathi Nagar,Kukatpally, Hyderabad; Pin:5000 55
Tel:+91-40-23886047(O),Fax:+91-40-23895011(O),
Tel:+91-40-23044600(R),Tel:+91-40-9440832534(Mobile)
E-mail:sjo...@gm...;sud...@ya...
Web- http://oppamthadathil.tripod.com
***************************************************************

Thank you Pierre.
 with best regards,
Sudheer
From: Pierre Haessig <pie...@cr...>
To: 
Cc: "mat...@li..." <mat...@li...> 
Sent: Thursday, 28 February 2013 7:15 PM
Subject: Re: [Matplotlib-users] cross correlation
 
Hi,
Le 27/02/2013 10:01, Sudheer Joseph a écrit :
> I was checking the plt.xcorr and it calls the np.correlate in side it.
> It calls np.correlate(ts1,ts2, mode=2).
Just as a side note, mode=2 is the old fashioned way to specify
mode='full' [1]. This may help in reading the numpy.correlate doc.
This being said, I'm really unfamiliar with cross-correlations. I just
kind of know the usual 95% confidence interval for autocorrelation at
1.96/sqrt(n). Just as a quick check, this is what R uses by default, but
there are options like ci.type get more appropriate intervals for an MA
series
(http://stat.ethz.ch/R-manual/R-patched/library/stats/html/plot.acf.html)
best,
Pierre
[1] https://github.com/numpy/numpy/blob/master/numpy/core/numeric.py#L678
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