land sea masks tutorials

Author: doutriaux1

Jupyter/Masking_Data_Land-Sea_masks/Masking_Data_Land-Sea_masks.ipynb

@@ -0,0 +1,155 @@
+---
+layout: default
+title: Masking_Data_Land-Sea_masks Tutorial
+---
+
+# Masking_Data_Land-Sea_masks Tutorial
+[download iPython Notebook](Masking_Data_Land-Sea_masks.ipynb)
+
+# In this example we will show how to generate masks, including lans/sea masks
+
+Notebook can be accessed [here](notebook.ipynb)
+
+## Preparing the notebook
+
+
+```python
+import requests
+r = requests.get("https://uvcdat.llnl.gov/cdat/sample_data/clt.nc",stream=True)
+with open("clt.nc","wb") as f:
+    for chunk in r.iter_content(chunk_size=1024):
+        if chunk:  # filter local_filename keep-alive new chunks
+            f.write(chunk)
+
+# and load data
+import cdms2
+f = cdms2.open("clt.nc")
+clt = f("clt", time=slice(0,1), squeeze=1) # Get first month
+u = f("u", level=slice(0,1), squeeze=1)
+v = f("v", level=slice(0,1), squeeze=1)
+clt = clt.regrid(u.getGrid(), regridTool="regrid2") # Put data on same grid
+
+# computes wind speed
+import MV2
+speed = MV2.sqrt(u**2+v**2)
+print "Max speed:", speed.max()
+print "Mean speed:",speed.mean()
+print "Min speed:",speed.min()
+
+# Prepare graphics
+import vcs
+x=vcs.init()
+```
+
+    Max speed: 68.9132
+    Mean speed: 16.2591233086
+    Min speed: 0.0611087
+
+
+## Value based  masks
+
+
+```python
+# Let's mask out area where wind speed is greater than twice mean
+mask = MV2.greater(speed,speed.mean()*2.)
+
+# Let's apply this mask
+clt_masked = MV2.masked_where(mask,clt)
+x.plot(clt_masked)
+```
+
+    /Users/doutriaux1/anaconda2/envs/2.12-nox/lib/python2.7/site-packages/vcs/VTKPlots.py:998: MaskedArrayFutureWarning: setting an item on a masked array which has a shared mask will not copy the mask and also change the original mask array in the future.
+    Check the NumPy 1.11 release notes for more information.
+      data[:] = numpy.ma.masked_invalid(data, numpy.nan)
+    /Users/doutriaux1/anaconda2/envs/2.12-nox/lib/python2.7/site-packages/numpy/ma/core.py:6385: MaskedArrayFutureWarning: In the future the default for ma.maximum.reduce will be axis=0, not the current None, to match np.maximum.reduce. Explicitly pass 0 or None to silence this warning.
+      return self.reduce(a)
+    /Users/doutriaux1/anaconda2/envs/2.12-nox/lib/python2.7/site-packages/numpy/ma/core.py:6385: MaskedArrayFutureWarning: In the future the default for ma.minimum.reduce will be axis=0, not the current None, to match np.minimum.reduce. Explicitly pass 0 or None to silence this warning.
+      return self.reduce(a)
+
+
+
+
+
+![png](Masking_Data_Land-Sea_masks_files/Masking_Data_Land-Sea_masks_3_1.png)
+
+
+
+## Land-sea Masks
+
+### Generating a landsea mask on any grid
+
+Conveniently CDAT can generate masks for you (for regular grids only).
+
+The observed data set used here as the basis for creating realistic model land/sea masks was obtained from the U.S. Navy on a 1/6 degree longitude-latitude grid.
+
+more on the technique used can be read [here](https://www-pcmdi.llnl.gov/publications/pdf/58.pdf)
+
+
+```python
+import cdutil
+mask = cdutil.generateLandSeaMask(clt)
+mask2 = MV2.where(mask._mask,1.,mask) # Not needed for cdutil versions >= 2.12.2017.9.25
+mask2.setAxisList(mask.getAxisList()) # Not needed for cdutil versions >= 2.12.2017.9.25
+clt_masked = MV2.masked_where(mask2,clt)
+x.clear()
+x.plot(clt_masked)
+```
+
+
+
+
+![png](Masking_Data_Land-Sea_masks_files/Masking_Data_Land-Sea_masks_5_0.png)
+
+
+
+### Surface type by region masks
+
+CDAT also provide capabilities to mask regions. Original regions and their numbers come from [EzGet](http://github.com/uv-cdat/ezget)
+
+The function requires both a land/sea mask and a file reporting "regions", default "region" mask is as follow:
+<img src="colorgeog.png">
+
+Regions tables is:
+<img src="table.png">
+
+
+
+```python
+regions, guess = cdutil.generateSurfaceTypeByRegionMask(mask2*100., verbose=False)
+```
+
+    Done : |                                       | 0.00Done : ##                                      | 4.76Done : ####                                    | 9.Done : ######                                  | 14.29Done : ########                                | 19.Done : ##########                              | 23.Done : ############                            | 28.Done : ##############                          | 33.33Done : ################                        | 38.10Done : ##################                      | 42.86Done : ####################                    | 47.62Done : #####################                   | 52.Done : #######################                 | 57.Done : #########################               | 61.Done : ###########################             | 66.Done : #############################           | 71.Done : ###############################         | 76.Done : #################################       | 80.95Done : ###################################     | 85.71Done : #####################################   | 90.Done : ####################################### | 95.24Done : ########################################| 100.00%
+
+
+
+```python
+# let's take a look
+x.clear()
+x.plot(regions)
+```
+
+
+
+
+![png](Masking_Data_Land-Sea_masks_files/Masking_Data_Land-Sea_masks_8_0.png)
+
+
+
+
+```python
+# Now let's extract the indian ocean which according to table are area 205 and 206
+ind1 = MV2.equal(regions,205)
+ind2 = MV2.equal(regions,206)
+indian_ocean = MV2.logical_or(ind1,ind2)
+
+clt_indian_ocean = MV2.masked_where(MV2.logical_not(indian_ocean),clt)
+x.clear()
+x.plot(clt_indian_ocean(longitude=(15,150),latitude=(-90,35)),ratio="autot")
+```
+
+
+
+
+![png](Masking_Data_Land-Sea_masks_files/Masking_Data_Land-Sea_masks_9_0.png)
+
+

Jupyter/Masking_Data_Land-Sea_masks/Masking_Data_Land-Sea_masks.md

@@ -95,6 +95,12 @@ <h3>cdutil and genutil</h3>
                 <p>Easily Creating Strings With Templating tutorial</p>
             </a>
         </div>
+        <div class="example">
+            <a href="Jupyter/Masking_Data_Land-Sea_masks/Masking_Data_Land-Sea_masks.html">
+                <div class="img-wrapper"><img src="Jupyter/Masking_Data_Land-Sea_masks/Masking_Data_Land-Sea_masks_files/Masking_Data_Land-Sea_masks_5_0.png"/></div>
+                <p>Masking Data, Creating and Using Masks tutorial</p>
+            </a>
+        </div>
     </div>
 </div>