Parallel Coordinates ¶

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 the Jupyter Notebook

Work around to visualize plot in Jupyter Notebook¶

Back to Top

This class allow use to use vcsaddons plots

import tempfile
import base64

class VCSAddonsNotebook():
    def __init__(self, x):
        self.x = x
    def _repr_png_(self):
        fnm = tempfile.mktemp()+".png"
        x.png(fnm)
        encoded = base64.b64encode(open(fnm, "rb").read()).decode("ascii")
        return encoded
    def __call__(self):
        return self

Sample Data¶

Back to Top

These files are in the test directory of pcmdi_metrics repo at: http://github.com/PCMDI/pcmdi_metrics.git

!git clone http://github.com/PCMDI/pcmdi_metrics.git

fatal: destination path 'pcmdi_metrics' already exists and is not an empty directory.

# Prepare list of json files
import os

# Location on your computer
json_pth = "pcmdi_metrics/tests/graphics"
# Geenrate list ofjson files
json_files = glob.glob(
            os.path.join(
                json_pth,
                "json",
                "v2.0",
                "*.json"))
json_files += glob.glob(
            os.path.join(
                json_pth,
                "json",
                "v1.0",
                "*.json"))
# Read them in via pmp special json class
J = pcmdi_metrics.pcmdi.io.JSONs(json_files)

# Retrieve data we need for plot
# Annual mean RMS (XYT dimensions)
# All models and all variables
rms_xyt = J(statistic=["rms_xyt"],season=["ann"],region="global")(squeeze=1)

Let's take a look at the array generated. Note the axis are strings of varialbes used and models, The order of the axes is the order on the plot

rms_xyt.info()

*** Description of Slab variable_6 ***
id: variable_6
shape: (14, 17)
filename: 
missing_value: 1e+20
comments: 
grid_name: N/A
grid_type: N/A
time_statistic: 
long_name: 
units: 
tileIndex: None
No grid present.
** Dimension 1 **
   id: variable
   Length: 14
   First:  pr
   Last:   zg-500
   Other axis attributes:
      realtopology: linear
   Python id:  0x7ff7fb52ab00
** Dimension 2 **
   id: model
   Length: 17
   First:  0071-0100
   Last:   bcc-csm1-1-m
   Other axis attributes:
      realtopology: linear
   Python id:  0x7ff7fb52a2e8
*** End of description for variable_6 ***

# Ok now let's create a VCS pcoord graphic method

# initialize a canvas
x=vcs.init(geometry=(1200,600),bg=True)
import vcsaddons
gm = vcsaddons.createparallelcoordinates(x=x)

Preparing the plot¶

Back to Top

Data¶

Back to Top

'id' is used for variable in plot the JSON class returns var as "pmp", here "RMS" is more appropriate

'title' is used to draw the plot title (location/font controlled by template)

Template¶

Back to Top

The template section prepares where data will be rendered on plot, and the fonts used.

Fonts are controlled via textorientation and texttable VCS primary objects.

Here we need to angle a bit the xlabels (45 degrees).

We also want to turn off the boxes around the legend and the data area.

# Prepare the graphics
# Set variable name
rms_xyt.id = "RMS"
# Set units of each variables on axis
# This is a trick to have units listed on plot
rms_xyt.getAxis(-2).units = ["mm/day","mm/day","hPa","W/m2","W/m2","W/m2", "K","K","K","m/s","m/s","m/s","m/s","m"]
# Sets title on the variable
rms_xyt.title = "Annual Mean Error"

# Preprare the canvas areas
t = vcs.createtemplate()
# Create a text orientation object for xlabels
to=x.createtextorientation()
to.angle=-45
to.halign="right"
# Tell template to use this orientation for x labels
t.xlabel1.textorientation = to.name

# Define area where plot will be drawn in x direction
t.reset('x',0.05,0.9,t.data.x1,t.data.x2)
ln = vcs.createline()

# Turn off box around legend
ln.color = [[0,0,0,0]]
t.legend.line = ln
# turn off box around data area
t.box1.priority=0

# Define box where legend will be drawn
t.legend.x1 = .91
t.legend.x2 = .99
# use x/y of data drawn for legend height
t.legend.y1 = t.data.y1
t.legend.y2 = t.data.y2

# Plot with default values of graphic method
# Bug vcsaddons need to return a display
# as a result it does not show up in notebook
x.clear()
show = VCSAddonsNotebook(x)
gm.plot(rms_xyt,template=t,bg=True)
show()

Control various aspects of the graphic method¶

Back to Top

We want the first two model to be 'blue' and 'red' and a bit thicker.

All other plots will be 'grey' and 'dashed'.

x.clear()
import IPython
gm.linecolors = ["blue","red","grey"]
gm.linestyles=["solid","solid","dot"]
gm.linewidths=[5.,5.,1.]
gm.markercolors = ["blue","red","grey"]
gm.markertypes=["triangle_up","star","dot"]
gm.markersizes=[1,1,2]
gm.plot(rms_xyt,template=t,bg=True)
show()

# change order and number of models and variables
# We need to creaate a new gm
gm = vcsaddons.createparallelcoordinates(x=x)
axes = rms_xyt.getAxisList()
models = ['MIROC4h', 'HadGEM2-AO', 'GFDL-ESM2M',
 'GFDL-ESM2G', 'GFDL-CM3', 'FGOALS-g2', 'CSIRO-Mk3-6-0', 'CESM1-WACCM',
 'CESM1-FASTCHEM', 'CESM1-CAM5', 'CESM1-BGC', 'CCSM4', 'ACCESS1-3', 'ACCESS1-0',
 '0071-0100'] # invert them
variables = ['prw', 'psl', 'rltcre', 'rlut', 'rstcre', 'ta-200', 'ta-850', 'tas',
 'ua-850', 'va-850', 'zg-500']
rms_xyt = J(statistic=["rms_xyt"],season=["ann"],region="global",model=models,variable=variables)(squeeze=1)
x.clear()
gm.linecolors = ["blue","red","grey"]
gm.linestyles=["solid","solid","dot"]
gm.linewidths=[5.,5.,1.]
gm.markercolors = ["blue","red","grey"]
gm.markertypes=["triangle_up","star","dot"]
gm.markersizes=[1,2,2]
gm.plot(rms_xyt,template=t,bg=True)
show()

Parallel Coordinates ¶

Table of Contents

Work around to visualize plot in Jupyter Notebook¶

Sample Data¶

Preparing the plot¶

Data¶

Template¶

Control various aspects of the graphic method¶