My plot is not showing any indication of what the order of magnitude of my y-values are on the axis. How do I force python to indicate some values on the y-axis?
import numpy as np
import matplotlib.pyplot as plt
BERfinal = [0.4967843137254903, 0.49215686274509757, 0.4938823529411763,
0.49170588235294116, 0.48852941176470605, 0.48203921568627417,
0.4797058823529405, 0.47454901960784257, 0.4795686274509802,
0.474901960784313, 0.4732549019607838, 0.4703137254901953,
0.4705490196078425]
x = np.linspace(-4,8,len(BERfinal))
plt.semilogy(x,BERfinal)
plt.title("BER vs SNR")
plt.ylabel("Bit Error Rate(BER)")
plt.xlabel("Signal-to-Noise Ratio(SNR)[dB]")
plt.xlim(-4,8)
I ended up playing around with:
plt.ylim(4.7*10**-1, 5*10**-1)
and changed the values until I found an appropriate range. It now shows 5x10^-1 on the y-axis.
Related
I want to plot a xy plot where x axis contain temperature values(first column) and y axis contain time in hr:min:sec(second column) .
8.8900 06:09:95.50
9.4500 06:09:00.56
10.5800 08.06:95.48
11.6500 09:07:73.58
56.3650 00:08:00.47
85.7823 07:01:03.23
I just want to plot a xy plot.
I tried code
import numpy as np
import matplotlib.pyplot as plt
data=np.loadtxt("inpdata.txt")
plt.plot(data[:,0],data[:,1])
plt.show()
But it does not give plot.hope experts may help.Thanks.
The simplest approach would be to use pandas.
Load the file as whitespace delimited file into pandas.DataFrame object as:
import pandas as pd
from matplotlib import pyplot as plt
df = pd.read_csv('inpdata.txt', names=['temp', 'time'], delim_whitespace=True)
Then create a line plot with time as x axis:
df.plot.line(x='time')
and show the plot
plt.show()
I'm trying to define a colorbar for the following type of plot.
import matplotlib.pyplot as plt
import numpy as np
for i in np.arange(0,10,0.1):
plt.plot(range(10),np.ones(10)*i,c=[i/10.,0.5,0.25])
plt.show()
This is just a simplified version of my actual data, but basically, I'd like a series of lines plotted and colored by another variable with a colorbar key. This is easy to do in scatter, but I can't get scatter to plot connected lines. Points are too clunky. I know this sounds like basic stuff, but I'm having a helluva time finding a simple solution ... what obvious solution am I missing?
You can build a custom color map and a mappable from it, then pass to colorbar:
from matplotlib.cm import ScalarMappable
from matplotlib.colors import Normalize
import matplotlib.colors as mcolors
color_list = [(i/10, 0.5,0.25) for i in np.arange(0,10,0.1)]
cmap = mcolors.LinearSegmentedColormap.from_list("my_colormap", color_list)
cmappable = ScalarMappable(norm=Normalize(0,10), cmap=cmap)
plt.figure(figsize=(10,10))
for j,i in enumerate(np.arange(0,10,0.1)):
plt.plot(range(10),np.ones(10)*i,c=color_list[j])
plt.colorbar(cmappable)
plt.show()
Output:
So I was trying to map out some math functions in 3d using matplotlib when I noticed something... The 3d plot suddenly changed (more like broke) when I tried to fix a previous issue wherein I was encountering some 'missing surface' - a gap in the plot. The main question is this -- Is the 3d plot not showing the two peaks on higher precision due to some inherent computing limitations of Axes3d or some other reason? Also a secondary question -- Why am I encountering 'missing surfaces' near +1.25 and -1.25 in lower precision plot?
I have tried googling for it and referred a few posts but nothing came ot except more questions.
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib import cm
from mpl_toolkits.mplot3d import Axes3D
X=np.arange(-2,+2,0.025)
## Use np.arange(-5,+5,0.25) to experience the 'surface loss' I mention but otherwise correct 2 spike plot at each of (0,-1) and (0,+1) for both X and Y
Y=np.arange(-2,+2,0.025)
X,Y=np.meshgrid(X,Y)
R=1+X**2-Y**2
S=R**2+4*(X**2)*(Y**2)
Z=R/S
fig=plt.figure()
ax=Axes3D(fig)
ax.plot_surface(X,Y,Z,rstride=1,cstride=1,cmap=cm.viridis,norm=mpl.colors.Normalize(vmin=-1.,vmax=1.))
##NORMALIZE Was essential to get the proper color range
plt.show()
plt.savefig('art3d.jpeg',bbox_inches='tight')
plt.savefig('art3d.svg',bbox_inches='tight')
The ideal result should be like this (shows the func and the plot)
https://i.stack.imgur.com/kVnYc.png
The two plots I'm getting could be seen when the code is run as I can't seem to add images presumably because of low reputation :(
Any and all help is appreciated.Thanks in advance.
First note that the function in use is different from the wolfram alpha output. So let's use the function shown in the screenshot. Then you can limit the data to the range you want to show.
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib import cm
from mpl_toolkits.mplot3d import Axes3D
X = np.arange(-2,+2,0.025)
Y=np.arange(-2,+2,0.025)
X,Y=np.meshgrid(X,Y)
Z = -2*X*Y / ((2*X*Y)**2 + (X**2 - Y**2 + 1)**2)
Z[(Z < -1)] = -1
Z[(Z > 1)] = 1
fig=plt.figure()
ax=Axes3D(fig)
ax.plot_surface(X,Y,Z,rstride=1,cstride=1,cmap=cm.viridis,norm=mpl.colors.Normalize(vmin=-1.,vmax=1.))
plt.show()
I just discovered something really strange when using plot method of pandas.DataFrame. I am using pandas 0.19.1. Here is my MWE:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import pandas as pd
t = pd.date_range('1990-01-01', '1990-01-08', freq='1H')
x = pd.DataFrame(np.random.rand(len(t)), index=t)
fig, axe = plt.subplots()
x.plot(ax=axe)
plt.show(axe)
xt = axe.get_xticks()
When I try to format my xticklabels I get strange beahviours, then I insepcted objects to understand and I have found the following:
t[-1] - t[0] = Timedelta('7 days 00:00:00'), confirming the DateTimeIndex is what I expect;
xt = [175320, 175488], xticks are integers but they are not equals to a number of days since epoch (I do not have any idea about what it is);
xt[-1] - xt[0] = 168 there are more like index, there is the same amount that len(x) = 169.
This explains why I cannot succed to format my axe using:
axe.xaxis.set_major_locator(mdates.HourLocator(byhour=(0,6,12,18)))
axe.xaxis.set_major_formatter(mdates.DateFormatter("%a %H:%M"))
The first raise an error that there is to many ticks to generate
The second show that my first tick is Fri 00:00 but it should be Mon 00:00 (in fact matplotlib assumes the first tick to be 0481-01-03 00:00, oops this is where my bug is).
It looks like there is some incompatibility between pandas and matplotlib integer to date conversion but I cannot find out how to fix this issue.
If I run instead:
fig, axe = plt.subplots()
axe.plot(x)
axe.xaxis.set_major_formatter(mdates.DateFormatter("%a %H:%M"))
plt.show(axe)
xt = axe.get_xticks()
Everything works as expected but I miss all cool features from pandas.DataFrame.plot method such as curve labeling, etc. And here xt = [726468. 726475.].
How can I properly format my ticks using pandas.DataFrame.plot method instead of axe.plot and avoiding this issue?
Update
The problem seems to be about origin and scale (units) of underlying numbers for date representation. Anyway I cannot control it, even by forcing it to the correct type:
t = pd.date_range('1990-01-01', '1990-01-08', freq='1H', origin='unix', units='D')
There is a discrepancy between matplotlib and pandas representation. And I could not find any documentation of this problem.
Is this what you are going for? Note I shortened the date_range to make it easier to see the labels.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as dates
t = pd.date_range('1990-01-01', '1990-01-04', freq='1H')
x = pd.DataFrame(np.random.rand(len(t)), index=t)
# resample the df to get the index at 6-hour intervals
l = x.resample('6H').first().index
# set the ticks when you plot. this appears to position them, but not set the label
ax = x.plot(xticks=l)
# set the display value of the tick labels
ax.set_xticklabels(l.strftime("%a %H:%M"))
# hide the labels from the initial pandas plot
ax.set_xticklabels([], minor=True)
# make pretty
ax.get_figure().autofmt_xdate()
plt.show()
I have made a plot in jupyter that has an x-axis spanning for about 40 seconds. I want to pull out sections that are milliseconds long and re-display them as separate plots (so that they can be better viewed). How would I go about doing this?
You could use some subplots, and slice the original data arrays. For example:
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0,40,1000)
y = np.random.random(1000)
fig, [ax1,ax2,ax3] = plt.subplots(3,1)
ax1.plot(x,y)
ax2.plot(x[100:120],y[100:120])
ax3.plot(x[500:520],y[500:520])
plt.show()