I have some data that looks like:
data = [1,2,4,5,9] (random pattern of increasing integers)
And I want to plot it in a binary horizontal line so that y=1 for every x value specified in data and zero otherwise.
I have a few different data arrays that I'd like to stack, similar to this style (this is CCD clocking data but the plot format looks ideal)
I think I need to create a list of ones for my data array, but how do I specify the zero value for everything not in the array?
Thanks
You got the point. You can create a list with 1 in any position specified in data and 0 elsewhere. This can be done very easily with a list comprehension
def binary_data(data):
return [1 if x in data else 0 for x in range(data[-1] + 1)]
which will act like this:
>>> data = [1, 2, 4, 5, 9]
>>> bindata = binary_data(data)
>>> bindata
[0, 1, 1, 0, 1, 1, 0, 0, 0, 1]
Now all you have to do is plot it... or better step it since it's binary data and step() looks way better:
import numpy as np
from matplotlib.pyplot import step, show
def binary_data(data):
return [1 if x in data else 0 for x in range(data[-1] + 1)]
data = [1, 2, 4, 5, 9]
bindata = binary_data(data)
xaxis = np.arange(0, data[-1] + 1)
yaxis = np.array(bindata)
step(xaxis, yaxis)
show()
To plot multiple data arrays stacked on the same figure you could tweak binary_data() like this:
def binary_data(data, yshift=0):
return [yshift+1 if x in data else yshift for x in range(data[-1] + 1)]
so now you can set yshift parameter to shift data arrays on the y-axis. E.g,
>>> data = [1, 2, 4, 5, 9]
>>> bindata1 = binary_data(data)
>>> bindata1
[0, 1, 1, 0, 1, 1, 0, 0, 0, 1]
>>> bindata2 = binary_data(data, 2)
>>> bindata2
[2, 3, 3, 2, 3, 3, 2, 2, 2, 3]
Let's say you have data1, data2 and data3 to plot stacked, you'd go like:
import numpy as np
from matplotlib.pyplot import step, show
def binary_data(data, yshift=0):
return [yshift+1 if x in data else yshift for x in range(data[-1] + 1)]
data1 = [1, 2, 4, 5, 9]
bindata1 = binary_data(data1)
x1 = np.arange(0, data1[-1] + 1)
y1 = np.array(bindata1)
data2 = [1, 4, 9]
bindata2 = binary_data(data2, 2)
x2 = np.arange(0, data2[-1] + 1)
y2 = np.array(bindata2)
data3 = [1, 2, 8, 9]
bindata3 = binary_data(data3, 4)
x3 = np.arange(0, data3[-1] + 1)
y3 = np.array(bindata3)
step(x1, y1, x2, y2, x3, y3)
show()
that you can easily edit to make it work with an arbitrary amount of data arrays:
data = [ [1, 2, 4, 5, 9],
[1, 4, 9],
[1, 2, 8, 9] ]
for shift, d in enumerate(data):
bindata = binary_data(d, 2 * shift)
x = np.arange(0, d[-1] + 1)
y = np.array(bindata)
step(x, y)
show()
Finally if you are dealing with data arrays with different length (say [1,2] and [15,16]) and you don't like plots that vanish in the middle of the figure you can tweak binary_data() again to force its range to the maximum range of your data.
import numpy as np
from matplotlib.pyplot import step, show
def binary_data(data, limit, yshift=0):
return [yshift+1 if x in data else yshift for x in range(limit)]
data = [ [1, 2, 4, 5, 9, 12, 13, 14],
[1, 4, 10, 11, 20, 21, 22],
[1, 2, 3, 4, 15, 16, 17, 18] ]
# find out the longest data to plot
limit = max( [ x[-1] + 1 for x in data] )
x = np.arange(0, limit)
for shift, d in enumerate(data):
bindata = binary_data(d, limit, 2 * shift)
y = np.array(bindata)
step(x, y)
show()
Edit: As @ImportanceOfBeingErnest suggested, if you prefer to perform data to bindata conversion without having to define your own binary_data() function you could use numpy.zeros_like(). Just pay more attention when you stack them:
import numpy as np
from matplotlib.pyplot import step, show
data = [ [1, 2, 4, 5, 9, 12, 13, 14],
[1, 4, 10, 11, 20, 21, 22],
[1, 2, 3, 4, 15, 16, 17, 18] ]
# find out the longest data to plot
limit = max( [ x[-1] + 1 for x in data] )
x = np.arange(0, limit)
for shift, d in enumerate(data):
y = np.zeros_like(x)
y[d] = 1
# don't forget to shift
y += 2*shift
step(x, y)
show()
You can create an array with all zeros and assign 1 for those elements in data
import numpy as np
data = [1,2,4,5,9]
t = np.arange(0,data[-1]+1)
x = np.zeros_like(t)
x[data] = 1
You might then plot it with the step function
import matplotlib.pyplot as plt
plt.step(t,x, where="post")
plt.show()
or with where = "pre", depending on how to interprete your data
