Shading Using Patterns in VCS

This notebook shows how to use patterns in vcs.

Pattern can be used with isofill, boxfill, meshfill and fillarea object.

In this notebook we are using primirarly boxfill

The CDAT software was developed by LLNL. This tutorial was written by Charles Doutriaux. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Download Jupyter Notebook

Table of Contents

Prepare Notebook Elements

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In [1]:
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)

import cdms2
# and load data
f = cdms2.open("clt.nc")
clt = f("clt",time=slice(0,1),squeeze=1) # Get first month

/software/anaconda53/envs/cdms2/lib/python3.7/site-packages/numpy/ma/core.py:3174: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  dout = self.data[indx]

Create default Graphic Method

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In [2]:
import vcs
import cdms2
x=vcs.init(bg=True, geometry=(800,600))
gm = vcs.createboxfill()
gm.boxfill_type = "custom"
In [3]:
# Let's look at the data w/o pattern
x.plot(clt,gm)
Out[3]:

Mask some data

Now let's assume we are only interested in areas where clt is greater than 60% let's shade out areas where clt is < 60%

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In [4]:
import MV2
bad = MV2.less(clt,60.).astype("f")

Method 1: Regular Masking

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In [5]:
# let's create a second boxfill method 
gm2 = vcs.createboxfill()
gm2.boxfill_type = "custom"
# and a template for it
tmpl2 = vcs.createtemplate()
tmpl2.legend.priority=0
gm2.levels = [[0.5,1.]]  
gm2.fillareacolors = ["black",]
x.plot(bad,gm2,tmpl2)
Out[5]:

Method 2: Using Opacity

Let's use some opacity to "see" what's bellow

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In [6]:
gm2.fillareaopacity = [50]
x.clear()
x.plot(clt,gm)
x.plot(bad,gm2,tmpl2)
Out[6]:

Method 3: Using Patterns

Rather than opacity, we can use patterns, that let us see better what's "underneath"

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In [7]:
gm2.fillareastyle = "pattern"
gm2.fillareaindices = [10]
gm2.fillareaopacity = [100]
x.clear()
x.plot(clt,gm)
x.plot(bad,gm2,tmpl2)
Out[7]:
In [8]:
# we can control the size of patterns
gm2.fillareapixelscale = 2.
x.clear()
x.plot(clt,gm)
x.plot(bad,gm2,tmpl2)
Out[8]:

Controling Patterns Size

We can make the patterns bigger or smaller, using spacing

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In [9]:
# Bigger
gm2.fillareapixelspacing = [20,20]
gm2.fillareapixelscale=None
x.clear()
x.plot(clt,gm)
x.plot(bad,gm2,tmpl2)
Out[9]:
In [10]:
# or smaller
gm2.fillareapixelspacing = [5,5]
gm2.fillareapixelscale=None
x.clear()
x.plot(clt,gm)
x.plot(bad,gm2,tmpl2)
Out[10]:

Size and Opacity

We can still add opacity

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In [11]:
gm2.fillareaopacity = [25.]
x.clear()
x.plot(clt,gm)
x.plot(bad,gm2,tmpl2)
Out[11]:

Pattern Color can also be controled

Using hatch rather than pattern we can control the shading color

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In [12]:
gm2.fillareaopacity = [100.]
gm2.fillareastyle = "hatch"
gm2.fillareacolors = ["red"]
x.clear()
x.plot(clt,gm)
x.plot(bad,gm2,tmpl2)
Out[12]:

Patterns legend

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In [13]:
# could even have a legend
tmpl2.legend.x1 = .54
tmpl2.legend.x2 = .62
tmpl2.legend.y1 = .885
tmpl2.legend.y2 = .985
tmpl2.legend.priority=0
gm2.legend = {.5:" Bad"}
x.clear()
x.plot(clt,gm)
x.plot(bad,gm2,tmpl2)
Out[13]: