matplotlib-checkins

SF.net SVN: matplotlib:[8971] trunk/toolkits/basemap/examples/lic_demo.py

[<js...@us...>]

Revision: 8971
          http://matplotlib.svn.sourceforge.net/matplotlib/?rev=8971&view=rev
Author:   jswhit
Date:     2011-02-11 20:29:37 +0000 (Fri, 11 Feb 2011)

Log Message:
-----------
remove un-needed imports

Modified Paths:
--------------
    trunk/toolkits/basemap/examples/lic_demo.py

Modified: trunk/toolkits/basemap/examples/lic_demo.py
===================================================================
--- trunk/toolkits/basemap/examples/lic_demo.py	2011-02-11 19:30:12 UTC (rev 8970)
+++ trunk/toolkits/basemap/examples/lic_demo.py	2011-02-11 20:29:37 UTC (rev 8971)
@@ -5,7 +5,7 @@
     from netCDF4 import Dataset as NetCDFFile
 except ImportError:
     from mpl_toolkits.basemap import NetCDFFile
-from mpl_toolkits.basemap import Basemap, cm, shiftgrid
+from mpl_toolkits.basemap import Basemap
 import numpy as np
 import matplotlib.pyplot as plt
 try:
@@ -26,10 +26,9 @@
 
 fig = plt.figure(figsize=(8,8))
 m = Basemap(projection='stere',lat_0=lat0,lon_0=lon0,width=1.e6,height=1.e6,resolution='i')
-nxv = 501; nyv = 501
+nxv = 301; nyv = 301; kernellen=41
 udat, vdat, xv, yv = m.transform_vector(u,v,lons1,lats1,nxv,nyv,returnxy=True)
 texture = np.random.rand(udat.shape[0],udat.shape[1]).astype(np.float32)
-kernellen=51
 kernel = np.sin(np.arange(kernellen)*np.pi/kernellen).astype(np.float32)
 image = lic_internal.line_integral_convolution(udat.astype(np.float32),\
         vdat.astype(np.float32), texture, kernel)


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SF.net SVN: matplotlib:[8973] trunk/toolkits/basemap/examples/lic_demo.py

[<js...@us...>]

Revision: 8973
          http://matplotlib.svn.sourceforge.net/matplotlib/?rev=8973&view=rev
Author:   jswhit
Date:     2011-02-11 21:04:16 +0000 (Fri, 11 Feb 2011)

Log Message:
-----------
no regridding

Modified Paths:
--------------
    trunk/toolkits/basemap/examples/lic_demo.py

Modified: trunk/toolkits/basemap/examples/lic_demo.py
===================================================================
--- trunk/toolkits/basemap/examples/lic_demo.py	2011-02-11 20:52:28 UTC (rev 8972)
+++ trunk/toolkits/basemap/examples/lic_demo.py	2011-02-11 21:04:16 UTC (rev 8973)
@@ -15,27 +15,26 @@
 
 # H*wind data from http://www.aoml.noaa.gov/hrd/data_sub/wind.html
 ncfile = NetCDFFile('rita.nc')
-u = ncfile.variables['sfc_u'][0,:,:]
-v = ncfile.variables['sfc_v'][0,:,:]
+udat = ncfile.variables['sfc_u'][0,:,:]
+vdat = ncfile.variables['sfc_v'][0,:,:]
 lons1 = ncfile.variables['longitude'][:]
 lats1 = ncfile.variables['latitude'][:]
 lat0 = lats1[len(lats1)/2]; lon0 = lons1[len(lons1)/2]
-print lat0,lon0
 lons, lats = np.meshgrid(lons1,lats1)
 ncfile.close()
 
+
 fig = plt.figure(figsize=(8,8))
-m = Basemap(projection='stere',lat_0=lat0,lon_0=lon0,width=1.e6,height=1.e6,resolution='i')
-nxv = 301; nyv = 301; kernellen=41
-udat, vdat, xv, yv = m.transform_vector(u,v,lons1,lats1,nxv,nyv,returnxy=True)
+m = Basemap(projection='cyl',llcrnrlat=lats1[0],llcrnrlon=lons1[0],urcrnrlat=lats1[-1],urcrnrlon=lons1[-1],resolution='i')
+kernellen=31
 texture = np.random.rand(udat.shape[0],udat.shape[1]).astype(np.float32)
 kernel = np.sin(np.arange(kernellen)*np.pi/kernellen).astype(np.float32)
 image = lic_internal.line_integral_convolution(udat.astype(np.float32),\
         vdat.astype(np.float32), texture, kernel)
 im = m.imshow(image,plt.cm.gist_stern)
 m.drawcoastlines()
-m.drawmeridians(np.arange(0,360,2),labels=[0,0,0,1])
-m.drawparallels(np.arange(-30,30,2),labels=[1,0,0,0])
+m.drawmeridians(np.arange(-120,-60,2),labels=[0,0,0,1])
+m.drawparallels(np.arange(0,30,2),labels=[1,0,0,0])
 plt.title('Hurricane Rita flow field visualized with Line Integral Convolution',\
         fontsize=13)
 plt.show()


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SF.net SVN: matplotlib:[8974] trunk/toolkits/basemap/examples/lic_demo.py

[<js...@us...>]

Revision: 8974
          http://matplotlib.svn.sourceforge.net/matplotlib/?rev=8974&view=rev
Author:   jswhit
Date:     2011-02-12 04:35:09 +0000 (Sat, 12 Feb 2011)

Log Message:
-----------
fix import

Modified Paths:
--------------
    trunk/toolkits/basemap/examples/lic_demo.py

Modified: trunk/toolkits/basemap/examples/lic_demo.py
===================================================================
--- trunk/toolkits/basemap/examples/lic_demo.py	2011-02-11 21:04:16 UTC (rev 8973)
+++ trunk/toolkits/basemap/examples/lic_demo.py	2011-02-12 04:35:09 UTC (rev 8974)
@@ -9,7 +9,7 @@
 import numpy as np
 import matplotlib.pyplot as plt
 try:
-    from vectorplot import lic_internal
+    from scikits.vectorplot import lic_internal
 except ImportError:
     raise  ImportError('need vectorplot scikit for this example')
 


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SF.net SVN: matplotlib:[8981] trunk/toolkits/basemap/examples/lic_demo.py

[<js...@us...>]

Revision: 8981
          http://matplotlib.svn.sourceforge.net/matplotlib/?rev=8981&view=rev
Author:   jswhit
Date:     2011-02-12 16:08:44 +0000 (Sat, 12 Feb 2011)

Log Message:
-----------
interpolate to finer grid for nicer looking plot

Modified Paths:
--------------
    trunk/toolkits/basemap/examples/lic_demo.py

Modified: trunk/toolkits/basemap/examples/lic_demo.py
===================================================================
--- trunk/toolkits/basemap/examples/lic_demo.py	2011-02-12 16:01:55 UTC (rev 8980)
+++ trunk/toolkits/basemap/examples/lic_demo.py	2011-02-12 16:08:44 UTC (rev 8981)
@@ -2,7 +2,7 @@
 # flow field (from Hurricane Earl).  Produces something akin to streamlines.
 # Requires vectorplot scikit (http://scikits.appspot.com/vectorplot).
 from netCDF4 import Dataset as NetCDFFile
-from mpl_toolkits.basemap import Basemap
+from mpl_toolkits.basemap import Basemap, interp
 import numpy as np
 import matplotlib.pyplot as plt
 try:
@@ -20,7 +20,15 @@
 lons, lats = np.meshgrid(lons1,lats1)
 ncfile.close()
 
+# downsample to finer grid.
+nlats = 2*udat.shape[0]; nlons = 2*udat.shape[1]
+lons = np.linspace(lons1[0],lons1[-1],nlons)
+lats = np.linspace(lats1[0],lats1[-1],nlats)
+lons, lats = np.meshgrid(lons, lats)
+udat = interp(udat,lons1,lats1,lons,lats,order=3)
+vdat = interp(vdat,lons1,lats1,lons,lats,order=3)
 
+
 fig = plt.figure(figsize=(8,8))
 m = Basemap(projection='cyl',llcrnrlat=lats1[0],llcrnrlon=lons1[0],urcrnrlat=lats1[-1],urcrnrlon=lons1[-1],resolution='i')
 kernellen=31


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SF.net SVN: matplotlib:[8982] trunk/toolkits/basemap/examples/lic_demo.py

[<js...@us...>]

Revision: 8982
          http://matplotlib.svn.sourceforge.net/matplotlib/?rev=8982&view=rev
Author:   jswhit
Date:     2011-02-12 16:10:15 +0000 (Sat, 12 Feb 2011)

Log Message:
-----------
add comments.

Modified Paths:
--------------
    trunk/toolkits/basemap/examples/lic_demo.py

Modified: trunk/toolkits/basemap/examples/lic_demo.py
===================================================================
--- trunk/toolkits/basemap/examples/lic_demo.py	2011-02-12 16:08:44 UTC (rev 8981)
+++ trunk/toolkits/basemap/examples/lic_demo.py	2011-02-12 16:10:15 UTC (rev 8982)
@@ -8,7 +8,7 @@
 try:
     from scikits.vectorplot import lic_internal
 except ImportError:
-    raise  ImportError('need vectorplot scikit for this example')
+    raise ImportError('need vectorplot scikit for this example')
 
 # H*wind data from http://www.aoml.noaa.gov/hrd/data_sub/wind.html
 ncfile = NetCDFFile('rita.nc')
@@ -20,7 +20,7 @@
 lons, lats = np.meshgrid(lons1,lats1)
 ncfile.close()
 
-# downsample to finer grid.
+# downsample to finer grid for nicer looking plot.
 nlats = 2*udat.shape[0]; nlons = 2*udat.shape[1]
 lons = np.linspace(lons1[0],lons1[-1],nlons)
 lats = np.linspace(lats1[0],lats1[-1],nlats)
@@ -31,11 +31,13 @@
 
 fig = plt.figure(figsize=(8,8))
 m = Basemap(projection='cyl',llcrnrlat=lats1[0],llcrnrlon=lons1[0],urcrnrlat=lats1[-1],urcrnrlon=lons1[-1],resolution='i')
+# pass texture, kernel and data to LIC function from vectorplot.
 kernellen=31
 texture = np.random.rand(udat.shape[0],udat.shape[1]).astype(np.float32)
 kernel = np.sin(np.arange(kernellen)*np.pi/kernellen).astype(np.float32)
 image = lic_internal.line_integral_convolution(udat.astype(np.float32),\
         vdat.astype(np.float32), texture, kernel)
+# plot the resulting image.
 im = m.imshow(image,plt.cm.gist_stern)
 m.drawcoastlines()
 m.drawmeridians(np.arange(-120,-60,2),labels=[0,0,0,1])


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