I want to plot data with matplotlib where equal x and y increments have the same length. This works fine with
ax1.axis('equal')
where ax1 is a subfigure()
However setting the lower limits like this:
ax1.set_xlim(left=lowerlimit)
ax1.set_ylim(bottom=lowerlimit)
doesn't work. I also tried something like this which didn't work either:
ax1.axis('equal', xmin=lowerlimit,ymin=lowerlimit)
Can anyone help me?
Edit:
Here a minimal example to show the problem:
import matplotlib.pyplot as plt
fig, ax1 = plt.subplots()
x = [0,1,2,3,4,5]
y = [0,-1,-3,-2,-1,-2]
ax1.plot(x,y)
ax1.set_xlim(0,6)
ax1.set_ylim(0,-6)
ax1.axis('equal')
ax1.set_ylim(bottom=0)
plt.show()
Even though I explicitly set the lower limit of the y-axis to 0 after calling ax1.set_ylim(bottom=0) the lower limit of the plot is -1.
In your MWE, change:
ax1.axis('equal')
with:
ax1.set_aspect('equal',adjustable='box')
Related
I need help with setting the limits of y-axis on matplotlib. Here is the code that I tried, unsuccessfully.
import matplotlib.pyplot as plt
plt.figure(1, figsize = (8.5,11))
plt.suptitle('plot title')
ax = []
aPlot = plt.subplot(321, axisbg = 'w', title = "Year 1")
ax.append(aPlot)
plt.plot(paramValues,plotDataPrice[0], color = '#340B8C',
marker = 'o', ms = 5, mfc = '#EB1717')
plt.xticks(paramValues)
plt.ylabel('Average Price')
plt.xlabel('Mark-up')
plt.grid(True)
plt.ylim((25,250))
With the data I have for this plot, I get y-axis limits of 20 and 200. However, I want the limits 20 and 250.
Get current axis via plt.gca(), and then set its limits:
ax = plt.gca()
ax.set_xlim([xmin, xmax])
ax.set_ylim([ymin, ymax])
One thing you can do is to set your axis range by yourself by using matplotlib.pyplot.axis.
matplotlib.pyplot.axis
from matplotlib import pyplot as plt
plt.axis([0, 10, 0, 20])
0,10 is for x axis range.
0,20 is for y axis range.
or you can also use matplotlib.pyplot.xlim or matplotlib.pyplot.ylim
matplotlib.pyplot.ylim
plt.ylim(-2, 2)
plt.xlim(0,10)
Another workaround is to get the plot's axes and reassign changing only the y-values:
x1,x2,y1,y2 = plt.axis()
plt.axis((x1,x2,25,250))
You can instantiate an object from matplotlib.pyplot.axes and call the set_ylim() on it. It would be something like this:
import matplotlib.pyplot as plt
axes = plt.axes()
axes.set_ylim([0, 1])
Just for fine tuning. If you want to set only one of the boundaries of the axis and let the other boundary unchanged, you can choose one or more of the following statements
plt.xlim(right=xmax) #xmax is your value
plt.xlim(left=xmin) #xmin is your value
plt.ylim(top=ymax) #ymax is your value
plt.ylim(bottom=ymin) #ymin is your value
Take a look at the documentation for xlim and for ylim
This worked at least in matplotlib version 2.2.2:
plt.axis([None, None, 0, 100])
Probably this is a nice way to set up for example xmin and ymax only, etc.
To add to #Hima's answer, if you want to modify a current x or y limit you could use the following.
import numpy as np # you probably alredy do this so no extra overhead
fig, axes = plt.subplot()
axes.plot(data[:,0], data[:,1])
xlim = axes.get_xlim()
# example of how to zoomout by a factor of 0.1
factor = 0.1
new_xlim = (xlim[0] + xlim[1])/2 + np.array((-0.5, 0.5)) * (xlim[1] - xlim[0]) * (1 + factor)
axes.set_xlim(new_xlim)
I find this particularly useful when I want to zoom out or zoom in just a little from the default plot settings.
This should work. Your code works for me, like for Tamás and Manoj Govindan. It looks like you could try to update Matplotlib. If you can't update Matplotlib (for instance if you have insufficient administrative rights), maybe using a different backend with matplotlib.use() could help.
I have the following code for generating a time series plot
import numpy as np
fig = plt.figure()
ax = fig.add_subplot(111)
series = pd.Series([np.sin(ii*np.pi) for ii in range(30)],
index=pd.date_range(start='2019-01-01', end='2019-12-31',
periods=30))
series.plot(ax=ax)
I want to set an automatic limit for x and y, I tried using ax.margins() but it does not seem to work:
ax.margins(y=0.1, x=0.05)
# even with
# ax.margins(y=0.1, x=5)
What I am looking for is an automatic method like padding=0.1 (10% of whitespace around the graph)
Pandas and matplotlib seem to be confused rather often while collaborating when axes have dates. For some reason in this case ax.margins doesn't work as expected with the x-axis.
Here is a workaround which does seem to do the job, explicitely moving the xlims:
xmargins = 0.05
ymargins = 0.1
ax.margins(y=ymargins)
x0, x1 = plt.xlim()
plt.xlim(x0-xmargins*(x1-x0), x1+xmargins*(x1-x0))
Alternatively, you could work directly with matplotlib's plot, which does work as expected applying the margins to the date axis.
ax.plot(series.index, series)
ax.margins(y=0.1, x=0.05)
PS: This post talks about setting use_sticky_edges to False and calling autoscale_view after setting the margins, but also that doesn't seem to work here.
ax.use_sticky_edges = False
ax.autoscale_view(scaley=True, scalex=True)
You can use ax.set_xlim and ax.set_ylim to set the x and y limits of your plot respectively.
import numpy as np
fig = plt.figure()
ax = fig.add_subplot(111)
series = pd.Series([np.sin(ii*np.pi) for ii in range(30)],
index=pd.date_range(start='2019-01-01', end='2019-12-31',
periods=30))
# set xlim to be a between certain dates
ax.set_xlim((pd.to_datetime('2019-01-01'), pd.to_datetime('2019-01-31'))
# set ylim to be between certain values
ax.set_ylim((-0.5, 0.5))
series.plot(ax=ax)
I need help with setting the limits of y-axis on matplotlib. Here is the code that I tried, unsuccessfully.
import matplotlib.pyplot as plt
plt.figure(1, figsize = (8.5,11))
plt.suptitle('plot title')
ax = []
aPlot = plt.subplot(321, axisbg = 'w', title = "Year 1")
ax.append(aPlot)
plt.plot(paramValues,plotDataPrice[0], color = '#340B8C',
marker = 'o', ms = 5, mfc = '#EB1717')
plt.xticks(paramValues)
plt.ylabel('Average Price')
plt.xlabel('Mark-up')
plt.grid(True)
plt.ylim((25,250))
With the data I have for this plot, I get y-axis limits of 20 and 200. However, I want the limits 20 and 250.
Get current axis via plt.gca(), and then set its limits:
ax = plt.gca()
ax.set_xlim([xmin, xmax])
ax.set_ylim([ymin, ymax])
One thing you can do is to set your axis range by yourself by using matplotlib.pyplot.axis.
matplotlib.pyplot.axis
from matplotlib import pyplot as plt
plt.axis([0, 10, 0, 20])
0,10 is for x axis range.
0,20 is for y axis range.
or you can also use matplotlib.pyplot.xlim or matplotlib.pyplot.ylim
matplotlib.pyplot.ylim
plt.ylim(-2, 2)
plt.xlim(0,10)
Another workaround is to get the plot's axes and reassign changing only the y-values:
x1,x2,y1,y2 = plt.axis()
plt.axis((x1,x2,25,250))
You can instantiate an object from matplotlib.pyplot.axes and call the set_ylim() on it. It would be something like this:
import matplotlib.pyplot as plt
axes = plt.axes()
axes.set_ylim([0, 1])
Just for fine tuning. If you want to set only one of the boundaries of the axis and let the other boundary unchanged, you can choose one or more of the following statements
plt.xlim(right=xmax) #xmax is your value
plt.xlim(left=xmin) #xmin is your value
plt.ylim(top=ymax) #ymax is your value
plt.ylim(bottom=ymin) #ymin is your value
Take a look at the documentation for xlim and for ylim
This worked at least in matplotlib version 2.2.2:
plt.axis([None, None, 0, 100])
Probably this is a nice way to set up for example xmin and ymax only, etc.
To add to #Hima's answer, if you want to modify a current x or y limit you could use the following.
import numpy as np # you probably alredy do this so no extra overhead
fig, axes = plt.subplot()
axes.plot(data[:,0], data[:,1])
xlim = axes.get_xlim()
# example of how to zoomout by a factor of 0.1
factor = 0.1
new_xlim = (xlim[0] + xlim[1])/2 + np.array((-0.5, 0.5)) * (xlim[1] - xlim[0]) * (1 + factor)
axes.set_xlim(new_xlim)
I find this particularly useful when I want to zoom out or zoom in just a little from the default plot settings.
This should work. Your code works for me, like for Tamás and Manoj Govindan. It looks like you could try to update Matplotlib. If you can't update Matplotlib (for instance if you have insufficient administrative rights), maybe using a different backend with matplotlib.use() could help.
I have already binned data to plot a histogram. For this reason I'm using the plt.bar() function. I'd like to set both axes in the plot to a logarithmic scale.
If I set plt.bar(x, y, width=10, color='b', log=True) which lets me set the y-axis to log but I can't set the x-axis logarithmic.
I've tried plt.xscale('log') unfortunately this doesn't work right. The x-axis ticks vanish and the sizes of the bars don't have equal width.
I would be grateful for any help.
By default, the bars of a barplot have a width of 0.8. Therefore they appear larger for smaller x values on a logarithmic scale. If instead of specifying a constant width, one uses the distance between the bin edges and supplies this to the width argument, the bars will have the correct width. One would also need to set the align to "edge" for this to work.
import matplotlib.pyplot as plt
import numpy as np; np.random.seed(1)
x = np.logspace(0, 5, num=21)
y = (np.sin(1.e-2*(x[:-1]-20))+3)**10
fig, ax = plt.subplots()
ax.bar(x[:-1], y, width=np.diff(x), log=True,ec="k", align="edge")
ax.set_xscale("log")
plt.show()
I cannot reproduce missing ticklabels for a logarithmic scaling. This may be due to some settings in the code that are not shown in the question or due to the fact that an older matplotlib version is used. The example here works fine with matplotlib 2.0.
If the goal is to have equal width bars, assuming datapoints are not equidistant, then the most proper solution is to set width as
plt.bar(x, y, width=c*np.array(x), color='b', log=True) for a constant c appropriate for the plot. Alignment can be anything.
I know it is a very old question and you might have solved it but I've come to this post because I was with something like this but at the y axis and I manage to solve it just using ax.set_ylim(df['my data'].min()+100, df['my data'].max()+100). In y axis I have some sensible information which I thouhg the best way was to show in log scale but when I set log scale I couldn't see the numbers proper (as this post in x axis) so I just leave the idea of use log and use the min and max argment. It sets the scale of my graph much like as log. Still looking for another way for doesnt need use that -+100 at set_ylim.
While this does not actually use pyplot.bar, I think this method could be helpful in achieving what the OP is trying to do. I found this to be easier than trying to calibrate the width as a function of the log-scale, though it's more steps. Create a line collection whose width is independent of the chart scale.
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.collections as coll
#Generate data and sort into bins
a = np.random.logseries(0.5, 1000)
hist, bin_edges = np.histogram(a, bins=20, density=False)
x = bin_edges[:-1] # remove the top-end from bin_edges to match dimensions of hist
lines = []
for i in range(len(x)):
pair=[(x[i],0), (x[i], hist[i])]
lines.append(pair)
linecoll = coll.LineCollection(lines, linewidths=10, linestyles='solid')
fig, ax = plt.subplots()
ax.add_collection(linecoll)
ax.set_xscale("log")
ax.set_yscale("log")
ax.set_xlim(min(x)/10,max(x)*10)
ax.set_ylim(0.1,1.1*max(hist)) #since this is an unweighted histogram, the logy doesn't make much sense.
Resulting plot - no frills
One drawback is that the "bars" will be centered, but this could be changed by offsetting the x-values by half of the linewidth value ... I think it would be
x_new = x + (linewidth/2)*10**round(np.log10(x),0).
I need help with setting the limits of y-axis on matplotlib. Here is the code that I tried, unsuccessfully.
import matplotlib.pyplot as plt
plt.figure(1, figsize = (8.5,11))
plt.suptitle('plot title')
ax = []
aPlot = plt.subplot(321, axisbg = 'w', title = "Year 1")
ax.append(aPlot)
plt.plot(paramValues,plotDataPrice[0], color = '#340B8C',
marker = 'o', ms = 5, mfc = '#EB1717')
plt.xticks(paramValues)
plt.ylabel('Average Price')
plt.xlabel('Mark-up')
plt.grid(True)
plt.ylim((25,250))
With the data I have for this plot, I get y-axis limits of 20 and 200. However, I want the limits 20 and 250.
Get current axis via plt.gca(), and then set its limits:
ax = plt.gca()
ax.set_xlim([xmin, xmax])
ax.set_ylim([ymin, ymax])
One thing you can do is to set your axis range by yourself by using matplotlib.pyplot.axis.
matplotlib.pyplot.axis
from matplotlib import pyplot as plt
plt.axis([0, 10, 0, 20])
0,10 is for x axis range.
0,20 is for y axis range.
or you can also use matplotlib.pyplot.xlim or matplotlib.pyplot.ylim
matplotlib.pyplot.ylim
plt.ylim(-2, 2)
plt.xlim(0,10)
Another workaround is to get the plot's axes and reassign changing only the y-values:
x1,x2,y1,y2 = plt.axis()
plt.axis((x1,x2,25,250))
You can instantiate an object from matplotlib.pyplot.axes and call the set_ylim() on it. It would be something like this:
import matplotlib.pyplot as plt
axes = plt.axes()
axes.set_ylim([0, 1])
Just for fine tuning. If you want to set only one of the boundaries of the axis and let the other boundary unchanged, you can choose one or more of the following statements
plt.xlim(right=xmax) #xmax is your value
plt.xlim(left=xmin) #xmin is your value
plt.ylim(top=ymax) #ymax is your value
plt.ylim(bottom=ymin) #ymin is your value
Take a look at the documentation for xlim and for ylim
This worked at least in matplotlib version 2.2.2:
plt.axis([None, None, 0, 100])
Probably this is a nice way to set up for example xmin and ymax only, etc.
To add to #Hima's answer, if you want to modify a current x or y limit you could use the following.
import numpy as np # you probably alredy do this so no extra overhead
fig, axes = plt.subplot()
axes.plot(data[:,0], data[:,1])
xlim = axes.get_xlim()
# example of how to zoomout by a factor of 0.1
factor = 0.1
new_xlim = (xlim[0] + xlim[1])/2 + np.array((-0.5, 0.5)) * (xlim[1] - xlim[0]) * (1 + factor)
axes.set_xlim(new_xlim)
I find this particularly useful when I want to zoom out or zoom in just a little from the default plot settings.
This should work. Your code works for me, like for Tamás and Manoj Govindan. It looks like you could try to update Matplotlib. If you can't update Matplotlib (for instance if you have insufficient administrative rights), maybe using a different backend with matplotlib.use() could help.