# Data representation improvements

Good evening.

For some time now I have managed an application which acquires data from
bespoke hardware & optionally plots upto seven channels. Matplotlib is
embedded within a QT4 application & the "plotting" interface is almost
fully for matplotlib.

I have managed to free up some spare time from my main role & I would like
to vastly improve the representation of the data to the users in light of
some in-use observation.

The data is being acquired from a motor-drive & so there is some sinus data
( the motor's currents... 50Hz, 100A peak), data like speed (mostly DC
around 3000rpm with some ripple...) and then some digital flag (16bit
data...)

The fixed point data are (luckily) all relatively low in magnitude ... and
so plotting a 3000-value and a 100pk value is still visible, be it small
amplitude BUT the moment I plot one of the digital flag registers, these
dwarf the fixed-point data.

To help with the I have provided means to "hide" channels but I was
wondering if there is any better way?

I have considered 7 stacked x-y subplots but this will eat into vertical
re-estate very quickly.

I have played with secondary y-axis but this equally ends up taking a lot
of horizontal re-estate.

I have considered plotting the digital registers akin to a digital vector
so they are small, heightwise, plots as this will alleviate part of the
concerns.

Any advice with regards to a usable presentation

Example code for multiple y-axis

import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import host_subplot
import mpl_toolkits.axisartist as AA
import numpy as np

t = np.arange(0,1,0.00001)
data = [5000*np.sin(t*2*np.pi*10),
10*np.sin(t*2*np.pi*20),
20*np.sin(t*2*np.pi*30),
np.sin(t*2*np.pi*40)+5000,
np.sin(t*2*np.pi*50)-5000,
np.sin(t*2*np.pi*60),
np.sin(t*2*np.pi*70),
]

fig = plt.figure()
host = host_subplot(111, axes_class=AA.Axes)

axis_list = [None]*7
for i in range(len(axis_list)):
axis_list[i] = host.twinx()
new_axis = axis_list[i].get_grid_helper().new_fixed_axis
axis_list[i].axis['right'] = new_axis(loc='right',
axes=axis_list[i],
offset=(60*i,0))
axis_list[i].axis['right'].toggle(all=True)
axis_list[i].plot(t,data[i])

plt.show()

for i in data:
plt.plot(t,i)
plt.show()

Yours,

JonRB
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Good evening.

For some time now I have managed an application which acquires data from bespoke hardware & optionally plots upto seven channels. Matplotlib is embedded within a QT4 application & the "plotting" interface is almost fully for matplotlib.

I have managed to free up some spare time from my main role & I would like to vastly improve the representation of the data to the users in light of some in-use observation.

The data is being acquired from a motor-drive & so there is some sinus data ( the motor's currents... 50Hz, 100A peak), data like speed (mostly DC around 3000rpm with some ripple...) and then some digital flag (16bit data...)

The fixed point data are (luckily) all relatively low in magnitude ... and so plotting a 3000-value and a 100pk value is still visible, be it small amplitude BUT the moment I plot one of the digital flag registers, these dwarf the fixed-point data.

Please tell us a bit more about those digital flag registers. Are they 16 independent status flags, for example, or something more correlated? If they really are 16 independent flags, the traditional way to deal with that sort of thing is to plot them as 16 separate strip-chart lines, with vertical values of 1 or 0. They can be fairly close together, consume relatively little vertical plotting space, and still clearly show when one of the states has changed.

Bill

···

On Jul 31, 2016, at 4:08 PM, Jon Roadley-Battin <jon.roadleybattin at gmail.com> wrote:

To help with the I have provided means to "hide" channels but I was wondering if there is any better way?

I have considered 7 stacked x-y subplots but this will eat into vertical re-estate very quickly.

I have played with secondary y-axis but this equally ends up taking a lot of horizontal re-estate.

I have considered plotting the digital registers akin to a digital vector so they are small, heightwise, plots as this will alleviate part of the concerns.

Any advice with regards to a usable presentation

Example code for multiple y-axis

import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import host_subplot
import mpl_toolkits.axisartist as AA
import numpy as np

t = np.arange(0,1,0.00001)
data = [5000*np.sin(t*2*np.pi*10),
10*np.sin(t*2*np.pi*20),
20*np.sin(t*2*np.pi*30),
np.sin(t*2*np.pi*40)+5000,
np.sin(t*2*np.pi*50)-5000,
np.sin(t*2*np.pi*60),
np.sin(t*2*np.pi*70),
]

fig = plt.figure()
host = host_subplot(111, axes_class=AA.Axes)

axis_list = [None]*7
for i in range(len(axis_list)):
axis_list[i] = host.twinx()
new_axis = axis_list[i].get_grid_helper().new_fixed_axis
axis_list[i].axis['right'] = new_axis(loc='right',
axes=axis_list[i],
offset=(60*i,0))
axis_list[i].axis['right'].toggle(all=True)
axis_list[i].plot(t,data[i])

plt.show()

for i in data:
plt.plot(t,i)
plt.show()

Yours,

JonRB
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