Plotting ZDF data files

To Plot ZDF data files you must first import the ZDF module



In [1]:

    
from zdf import zdf_read_grid, zdf_read_particles

Next you need to read the data. You should also read the metadata while you are at it.



In [2]:

    
(data, info) = zdf_read_grid( "J3-000500.zdf" )

data is a NumPy ndarray, info is a dictionary with all the Metadata.



In [3]:

    
print(type(data))

print(info)









    



<class 'numpy.ndarray'>
{'grid': {'units': 'e \\omega_p^2 / c', 'label': 'J3', 'nx': [128, 128], 'has_axis': 1, 'axis': [{'units': 'c/\\omega_p', 'label': 'x_1', 'max': 12.800000190734863, 'min': 0.0, 'type': 0}, {'units': 'c/\\omega_p', 'label': 'x_2', 'max': 12.800000190734863, 'min': 0.0, 'type': 0}], 'ndims': 2}, 'iteration': {'t': 35.0, 'n': 500, 'tunits': '1/\\omega_p'}}

You can plot the data with any of your favorite tools.

Plotting with Bokeh



In [4]:

    
import numpy as np
from bokeh.io import push_notebook, show, output_notebook
output_notebook()

from bokeh.plotting import figure
from bokeh.models import LinearColorMapper, BasicTicker, ColorBar
from bokeh.core.enums import TextBaseline

p = figure(x_range=(info['grid']['axis'][0]['min'], info['grid']['axis'][0]['max']), 
           y_range=(info['grid']['axis'][1]['min'], info['grid']['axis'][1]['max']),
           toolbar_sticky=False)
p.title.text = info['grid']['label']

p.xaxis.axis_label = info['grid']['axis'][0]['label']
p.yaxis.axis_label = info['grid']['axis'][1]['label']

color_map = LinearColorMapper(palette="Viridis256", low = np.amin(data), high = np.amax(data))

p.image(image=[data], x = 0, y = 0, 
        dw = info['grid']['axis'][0]['max'], dh = info['grid']['axis'][1]['max'], 
        color_mapper = color_map )

color_bar = ColorBar(color_mapper = color_map, ticker = BasicTicker(),
                     location = (0,0))
p.add_layout( color_bar, 'right')

t = show(p, notebook_handle = True)









    





    
        
        Loading BokehJS ...

Plotting with MatplotLib



In [5]:

    
import matplotlib.pyplot as plt
%matplotlib notebook

fig = plt.figure( figsize = (8,6), dpi = 80)
fig.subplots_adjust( top = 0.85 )
fig.set_facecolor("#FFFFFF") 

timeLabel = r'$\sf{t = ' + str( info['iteration']['t'] ) + \
                     ' ['+info['iteration']['tunits']+r']}$'
plotTitle = r'$\sf{' + info['grid']['label'] + r'}$' + '\n' + timeLabel

plotArea = fig.add_subplot(1,1,1)
plotArea.set_title(plotTitle, fontsize = 16)

colorMap = plotArea.imshow(data, cmap = plt.cm.jet, interpolation = 'nearest', 
                           origin = 'lower')

colorBar = fig.colorbar(colorMap)
colorBar.set_label(r'$\sf{'+info['grid']['label'] +
        ' [' + info['grid']['units'] + r']}$', fontsize = 14)

xlabel = info['grid']['axis'][0]['label'] + '[' + info['grid']['axis'][0]['units'] + ']'
ylabel = info['grid']['axis'][1]['label'] + '[' + info['grid']['axis'][1]['units'] + ']'
plt.xlabel(r'$\sf{'+xlabel+r'}$', fontsize = 14)
plt.ylabel(r'$\sf{'+ylabel+r'}$', fontsize = 14)









    














    











    Out[5]:





<matplotlib.text.Text at 0x1127e1630>

Working with particle data

The routines also work with particle data, that can be read using the zdf_read_particles command:



In [2]:

    
(particles, info) = zdf_read_particles("particles-electrons-001200.zdf")

Particles is a dictionary of NumPy arrays containing all the particles quantities



In [7]:

    
print(type(particles))
print(type(particles['x1']))









    



<class 'dict'>
<class 'numpy.ndarray'>

Again, you can plot the data with any of your favorite tools. Here's an example using Matplotlib



In [50]:

    
import matplotlib.pyplot as plt
%matplotlib notebook

x = particles['x1']
y = particles['u1']

plt.plot(x, y, 'r.', ms=1,alpha=0.5)

t = str(info["iteration"]["t"])
tunits = str(info["iteration"]["tunits"])

title = info['particles']['name'] + '  u_1 x_1'

timeLabel = r'$\sf{t = ' + t + ' [' + tunits + r']}$'
plt.title(r'$\sf{' + title + r'}$' + '\n' + timeLabel)

xlabel = 'x_1' + '[' + info['particles']['units']['x1'] + ']'
ylabel = 'u_1' + '[' + info['particles']['units']['u1'] + ']'

plt.xlabel(r'$\sf{' + xlabel + r'}$', fontsize=14)
plt.ylabel(r'$\sf{' + ylabel + r'}$', fontsize=14)









    














    











    Out[50]:





<matplotlib.text.Text at 0x819b2bef0>



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