I am trying to plot a figure with two x-axis, which are non-linear to each other, with matplotlib. The plot I want to get is like this:
Basically, the age is dependent on red shift. It's non linear and need to be calculated. I want to make both age and red shift as x-axis. How can I make it?
The function twiny() may be what you're looking for.
import matplotlib.pyplot as plt
plt.loglog(range(100))
ax1 = plt.gca()
ax2 = ax1.twiny()
ax2.set_xticks([100,80,50])
ax2.set_xticklabels(['0','1','2'])
ax1.set_xlabel('redshift')
ax2.set_xlabel('age')
plt.show()
I did this like this:
from mpl_toolkits.axes_grid.parasite_axes import SubplotHost
fig = plt.figure(1, figsize=(figwidth,figheight))
ax = SubplotHost(fig, 1,1,1)
fig.add_subplot(ax)
#plotting as usual
ax2 = ax.twin() # ax2 is responsible for "top" axis and "right" axis
ax2.axis["right"].toggle(ticklabels=False)
ax2.xaxis.set_major_formatter(FuncFormatter(fmt_zToEta)) #set eta coord format
#with a function for the Z to eta transform for plot labels
def fmt_zToEta(x, pos=None):
#...
return transformed_label
I also remember starting off with that redshift example ;-)
I think the SubPlotHost thing is necessary, but I'm not 100% sure, since I ripped this out of an existing (sub)plot of mine without checking if it runs nicely without.
Edit: also, see https://stackoverflow.com/a/10517481/599884
Related
I'm struggling to wrap my head around matplotlib with dataframes today. I see lots of solutions but I'm struggling to relate them to my needs. I think I may need to start over. Let's see what you think.
I have a dataframe (ephem) with 4 columns - Time, Date, Altitude & Azimuth.
I produce a scatter for alt & az using:
chart = plt.scatter(ephem.Azimuth, ephem.Altitude, marker='x', color='black', s=8)
What's the most efficient way to set the values in the Time column as the labels/ticks on the x axis?
So:
the scale/gridlines etc all remain the same
the chart still plots alt and az
the y axis ticks/labels remain as is
only the x axis ticks/labels are changed to the Time column.
Thanks
This isn't by any means the cleanest piece of code but the following works for me:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.scatter(ephem.Azimuth, ephem.Altitude, marker='x', color='black', s=8)
labels = list(ephem.Time)
ax.set_xticklabels(labels)
plt.show()
Here you will explicitly force the set_xticklabels to the dataframe Time column which you have.
In other words, you want to change the x-axis tick labels using a list of values.
labels = ephem.Time.tolist()
# make your plot and before calling plt.show()
# insert the following two lines
ax = plt.gca()
ax.set_xticklabels(labels = labels)
plt.show()
I am new to matplotlib, and I am finding it very confusing. I have spent quite a lot of time on the matplotlib tutorial website, but I still cannot really understand how to build a figure from scratch. To me, this means doing everything manually... not using the plt.plot() function, but always setting figure, axis handles.
Can anyone explain how to set up a figure from the ground up?
Right now, I have this code to generate a double y-axis plot. But my xlabels are disappearing and I dont' know why
fig, ax1 = plt.subplots()
ax1.plot(yearsTotal,timeseries_data1,'r-')
ax1.set_ylabel('Windspeed [m/s]')
ax1.tick_params('y',colors='r')
ax2 = ax1.twinx()
ax2.plot(yearsTotal,timeseries_data2,'b-')
ax2.set_xticks(np.arange(min(yearsTotal),max(yearsTotal)+1))
ax2.set_xticklabels(ax1.xaxis.get_majorticklabels(), rotation=90)
ax2.set_ylabel('Open water duration [days]')
ax2.tick_params('y',colors='b')
plt.title('My title')
fig.tight_layout()
plt.savefig('plots/my_figure.png',bbox_inches='tight')
plt.show()
Because you are using a twinx, it makes sense to operate only on the original axes (ax1).
Further, the ticklabels are not defined at the point where you call ax1.xaxis.get_majorticklabels().
If you want to set the ticks and ticklabels manually, you can use your own data to do so (although I wouldn't know why you'd prefer this over using the automatic labeling) by specifying a list or array
ticks = np.arange(min(yearsTotal),max(yearsTotal)+1)
ax1.set_xticks(ticks)
ax1.set_xticklabels(ticks)
Since the ticklabels are the same as the tickpositions here, you may also just do
ax1.set_xticks(np.arange(min(yearsTotal),max(yearsTotal)+1))
plt.setp(ax1.get_xticklabels(), rotation=70)
Complete example:
import matplotlib.pyplot as plt
import numpy as np; np.random.seed(1)
yearsTotal = np.arange(1977, 1999)
timeseries_data1 = np.cumsum(np.random.normal(size=len(yearsTotal)))+5
timeseries_data2 = np.cumsum(np.random.normal(size=len(yearsTotal)))+20
fig, ax1 = plt.subplots()
ax1.plot(yearsTotal,timeseries_data1,'r-')
ax1.set_ylabel('Windspeed [m/s]')
ax1.tick_params('y',colors='r')
ax1.set_xticks(np.arange(min(yearsTotal),max(yearsTotal)+1))
plt.setp(ax1.get_xticklabels(), rotation=70)
ax2 = ax1.twinx()
ax2.plot(yearsTotal,timeseries_data2,'b-')
ax2.set_ylabel('Open water duration [days]')
ax2.tick_params('y',colors='b')
plt.title('My title')
fig.tight_layout()
plt.show()
Based on your code, it is not disappear, it is set (overwrite) by these two functions:
ax2.set_xticks(np.arange(min(yearsTotal),max(yearsTotal)+1))
ax2.set_xticklabels(ax1.xaxis.get_majorticklabels(), rotation=90)
set_xticks() on the axes will set the locations and set_xticklabels() will set the xtick labels with list of strings labels.
I'm trying to plot the contour map of a given function f(x,y), but since the functions output scales really fast, I'm losing a lot of information for lower values of x and y. I found on the forums to work that out using vmax=vmax, it actually worked, but only when plotted for a specific limit of x and y and levels of the colormap.
Say I have this plot:
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
u = np.linspace(-2,2,1000)
x,y = np.meshgrid(u,u)
z = (1-x)**2+100*(y-x**2)**2
cont = plt.contour(x,y,z,500,colors='black',linewidths=.3)
cont = plt.contourf(x,y,z,500,cmap="jet",vmax=100)
plt.colorbar(cont)
plt.show
I want to uncover whats beyond the axis limits keeping the same scale, but if I change de x and y limits to -3 and 3 I get:
See how I lost most of my levels since my max value for the function at these limits are much higher. A work around to this problem is to increase the levels to 1000, but that takes a lot of computational time.
Is there a way to plot only the contour levels that I need? That is, between 0 and 100.
An example of a desired output would be:
With the white space being the continuation of the plot without resizing the levels.
The code I'm using is the one given after the first image.
There are a few possible ideas here. The one I very much prefer is a logarithmic representation of the data. An example would be
from matplotlib import ticker
fig = plt.figure(1)
cont1 = plt.contourf(x,y,z,cmap="jet",locator=ticker.LogLocator(numticks=10))
plt.colorbar(cont1)
plt.show()
fig = plt.figure(2)
cont2 = plt.contourf(x,y,np.log10(z),100,cmap="jet")
plt.colorbar(cont2)
plt.show()
The first example uses matplotlibs LogLocator functions. The second one just directly computes the logarithm of the data and plots that normally.
The third example just caps all data above 100.
fig = plt.figure(3)
zcapped = z.copy()
zcapped[zcapped>100]=100
cont3 = plt.contourf(x,y,zcapped,100,cmap="jet")
cbar = plt.colorbar(cont3)
plt.show()
I have a small issue with matplotlib.pyplot and I hope someone might have come across it before.
I have data that contain X,Y,e values that are the X, Y measurements of a variable and e are the errors of the measurements in Y. I need to plot them in a log log scale.
I use the plt.errorbars function to plot them and then set yscale and xscale to log and this works fine. But I need to also plot a line on the same graph that needs to be in linear scale.
I am able to have the plots done separately just fine but I would like to have them in the same image if possible. Do you have any ideas? I am posting what I have done for now.
Cheers,
Kimon
tdlist = np.array([0.01,0.02,0.05,0.1,0.2,0.3,0.4,0.5,0.8,1,2,5,10,15,20,25,30,40,60,80,100,150,200,250,300,400])
freqlist=np.array([30,40,50,60,70,80,90,100,110,120,140,160,180,200,220,250,300,350,400,450])
filename=opts.filename
data = reader(filename)
data2 = logconv(data)
#x,y,e the data. Calculating usefull sums
x = data2[0]
y = data2[1]
e = data2[2]
xoe2 = np.sum(x/e**2)
yoe2 = np.sum(y/e**2)
xyoe2 = np.sum(x*y/e**2)
oe2 = np.sum(1/e**2)
x2oe2 = np.sum(x**2/e**2)
aslope = (xoe2*yoe2-xyoe2*oe2)/(xoe2**2-x2oe2*oe2)
binter = (xyoe2-aslope*x2oe2)/xoe2
aerr = np.sqrt(oe2/(x2oe2*oe2-xoe2**2))
berr = np.sqrt(x2oe2/(x2oe2*oe2-xoe2**2))
print('slope is ',aslope,' +- ', aerr)
print('inter is ',binter,' +- ', berr)
fig = plt.figure()
ax1 = fig.add_subplot(1,1,1)
ax2 = fig.add_axes(ax1.get_position(), frameon=False)
ax1.errorbar(data[0],data[1],yerr=data[2],fmt='o')
ax1.set_xscale('log',basex=10)
ax1.set_yscale('log',basey=10)
ax1.set_yticks([])
ax1.set_xticks([])
ax2.plot(x,aslope*x+binter,'r')
ax2.plot(x,(aslope-aerr)*x+(binter+berr),'--')
ax2.plot(x,(aslope+aerr)*x+(binter-berr),'--')
ax2.set_xscale('linear')
ax2.set_yscale('linear')
plt.xticks(np.log10(freqlist),freqlist.astype('int'))
plt.yticks(np.log10(tdlist),tdlist.astype('float'))
plt.xlabel('Frequency (MHz)')
plt.ylabel('t_s (msec)')
fitndx1 = 'Fit slope '+"{0:.2f}".format(aslope)+u"\u00B1"+"{0:.2f}".format(aerr)
plt.legend(('Data',fitndx1))
plt.show()
Following Molly's suggestion I managed to get closer to my goal but still not there. I am adding a bit more info for what I am trying to do and it might clarify things a bit.
I am setting ax1 to the errobar plot that uses loglog scale. I need to use errorbar and not loglog plot so that I can display the errors with my points.
I am using ax2 to plot the linear fit in linealinear scale.
Moreover I do not want the x and y axes to display values that are 10,100,1000 powers of ten but my own axes labels that have the spacing I want therefore I am using the plt.xticks. I tried ax1.set_yticks and ax1.set_yticklabes but with no success. Below is the image I am getting.
I do not have enough reputation to post an image but here is the link of it uploaded
http://postimg.org/image/uojanigab/
The values of my points should be x range = 40 - 80 and y range = 5 -200 as the fit lines are now.
You can create two overlapping axes using the add_suplot method of figure. Here's an example:
from matplotlib import pyplot as plt
fig = plt.figure()
ax1 = fig.add_subplot(1,1,1)
ax2 = fig.add_axes(ax1.get_position(), frameon=False)
ax1.loglog([1,10,100,1000],[1000,1,100,10])
ax2.plot([5,10,11,13],'r')
plt.show()
You can then turn off the x and y ticks for the linear scale plot like this:
ax2.set_xticks([])
ax2.set_yticks([])
I was not able to get two sets of axis working with the errorbar function so I had to convert everything to log scale including my linear plot. Below is the code I use to get it might be useful to someone.
plt.errorbar(data[0],data[1],yerr=data[2],fmt='o')
plt.xscale('log',basex=10)
plt.yscale('log',basey=10)
plt.plot(data[0],data[0]**aslope*10**binter,'r')
plt.plot(data[0],data[0]**(aslope-aerr)*10**(binter+berr),'--')
plt.plot(data[0],data[0]**(aslope+aerr)*10**(binter-berr),'--')
plt.xticks(freqlist,freqlist.astype('int'))
plt.yticks(tdlist,tdlist.astype('float'))
plt.xlabel('Frequency (MHz)')
plt.ylabel('t_s (msec)')
fitndx1 = 'Fit slope '+"{0:.2f}".format(aslope)+u"\u00B1"+"{0:.2f}".format(aerr)
plt.legend(('Data',fitndx1))
plt.show()
And here is the link to the final image
http://postimg.org/image/bevj2k6nf/
I am building a bar chart using matplotlib using the code below. When my first or last column of data is 0, my first column is wedged against the Y-axis.
An example of this. Note that the first column is ON the x=0 point.
If I have data in this column, I get a huge padding between the Y-Axis and the first column as seen here. Note the additional bar, now at X=0. This effect is repeated if I have data in my last column as well.
My code is as follows:
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import MultipleLocator
binVals = [0,5531608,6475325,1311915,223000,609638,291151,449434,1398731,2516755,3035532,2976924,2695079,1822865,1347155,304911,3562,157,5,0,0,0,0,0,0,0,0]
binTot = sum(binVals)
binNorm = []
for v in range(len(binVals)):
binNorm.append(float(binVals[v])/binTot)
fig = plt.figure(figsize=(6,4))
ax1 = fig.add_subplot(1,1,1)
ax1.bar(range(len(binNorm)),binNorm,align='center', label='Values')
plt.legend(loc=1)
plt.title("Demo Histogram")
plt.xlabel("Value")
plt.xticks(range(len(binLabels)),binLabels,rotation='vertical')
plt.grid(b=True, which='major', color='grey', linestyle='--', alpha=0.35)
ax1.xaxis.grid(False)
plt.ylabel("% of Count")
plt.subplots_adjust(bottom=0.15)
plt.tight_layout()
plt.show()
How can I set a constant margin between the Y-axis and my first/last bar?
Additionally, I realize it's labeled "Demo Histogram", that is a because I missed it when correcting problems discussed here.
I can't run the code snippet you gave, and even with some modification I couldn't replicate the big space. Aside from that, if you need to enforce a border to matplotlib, you ca do somthing like this:
ax.set_xlim( min(your_data) - 10, None )
The first term tells the axis to put the border at 10 units of distance from the minimum of your data, the None parameter teels it to keep the present value.
to put it into contest:
from collections import Counter
from pylab import *
data = randint(20,size=1000)
res = Counter(data)
vals = arange(20)
ax = gca()
ax.bar(vals-0.4, [ res[i] for i in vals ], width=0.8)
ax.set_xlim( min(data)-1, None )
show()
searching around stackoverflow I just learned a new trick: you can call
ax.margins( margin_you_desire )
to let automatically let matplotlib put that amount of space around your plot. It can also be configured differently between x and y.
In your case the best solution would be something like
ax.margins(0.01, None)
The little catch is that the unit is in axes unit, referred to the size of you plot, so a margin of 1 will put space around your plot at both sizes big as your present plot
The problem is align='center'. Remove it.