I have created a plot with some data points (in blue), a fit (in purple) and I have managed to include the fit residuals (fit-datapoints) by using 'add_axes' as shown below:
#Plot and fit:
fig1 = plt.figure(1)
frame1 = fig1.add_axes((.1,.3,.8,.6))
plt.scatter(a/nshots,m/nshots,zorder=-1,s=1)
plt.plot(a/nshots,fit(a/nshots),color='purple')
plt.xlabel(r'$a_i/N_s$ (mV)')
plt.ylabel(r'$m_i/N_s$ (count/$N_s$)')
plt.tick_params(axis='both',which='both',direction='in',right=True,top=True)
#Residuals:
frame2=fig1.add_axes((.1,.1,.8,.2))
plt.scatter(a/nshots,m/nshots-fit(a/nshots),zorder=-1,s=1,color='pink')
plt.xlabel(r'$a_i/N_s$ (mV)')
plt.ylabel(r'residuals')
plt.tick_params(axis='both',which='both',direction='in',right=True,top=True)
However, I cannot seem to align the y labels on the resulting figure:
I have tried using things like plt.gca().yaxis.set_label_coords(-0.1,0.1) and plt.gca().yaxis.labelpad=20 but I would very much prefer an approach where alignment is automated and I need not align the labels by hand.
Thank you very much for your help.
Related
I am practicing with Python Pandas plotting functions and I am trying to plot the content of two series extracted from the same dataframe into one plot.
When I plot the two series individually the result is correct. However, when I plot them together, the one that I plot as second appears flat in the picture.
Here is my code:
# dailyFlow and smooth are created in the same way from the same dataframe
dailyFlow = pd.Series(dataFrame...
smooth = pd.Series(dataFrame...
# lower the noise in the signal with standard deviation = 6
smooth = smooth.resample('D').sum().rolling(31, center=True, win_type='gaussian').sum(std=6)
dailyFlow.plot(style ='-b')
plt.legend(loc = 'upper right')
plt.show()
smooth.plot(style ='-r')
plt.legend(loc = 'upper right')
plt.show()
plt.figure(figsize=(12,5))
smooth.plot(style ='-r')
dailyFlow.plot(style ='-b')
plt.legend(loc = 'upper right')
plt.show()
Here is the output of my function:
I already tried using the parameter secondary_y=True in the second plot, but then I lose the information on the second line in the legend and the scaling between the two plots is wrong.
Many sources on the Internet seem to suggest that plotting the two series like I am doing should be correct, but then why is the third plot incorrect?
Thank you very much for your help.
For the data you have, the 3rd plot is correct. Look at the scale of the y axis on your two plots: one goes up to 70,000 and the other to 60,000,000.
I suspect what you actually want is a .rolling(...).mean() which should have a range comparable to your original data.
If you would like to make both plots bigger, you cold try something like this
fig, ax1 = plt.subplots()
ax1.set_ylim([0, 75000])
# plot first graph
ax2 = ax1.twinx() # second axes that shares the same x-axis
ax2.set_ylim([0, 60000000])
#plot the second graph
I've been working hard on a package of functions for my work, and I'm stuck on a layout problem. Sometimes I need to work with a lot of columns subplots (1 row x N columns) and the standard matplotlib legend sometimes is not helpful and makes it hard to visualize all the data.
I've been trying to create something like the picture below. I already tried to create a subplot for the curves and another one for the legends (and display the x-axis scale as a horizontal plot). Also, I tried to spine the x-axis, but when I have a lot of curves plotted inside the same subplots the legend becomes huge.
The following image is from a software. I'd like to create a similar look. Notice that these legends are "static": it remains fixed independent of the zooming. Another observation is, I don't need all the ticks or anything like that.
What I'm already have is the following (the code is a mess, becouse I'm trying many different solutions and it is not organized nor pythonic yet.
import matplotlib.pyplot as plt
fig, ax = plt.subplots(1,2, sharey = True)
ax[0].semilogx(np.zeros_like(dados.Depth)+0.02, dados.Depth)
ax[0].semilogx(dados.AHT90, dados.Depth, label = 'aht90')
ax[0].set_xlim(0.2,2000)
ax[0].grid(True, which = 'both', axis = 'both')
axres1 = ax[0].twiny()
axres1.semilogx(dados.AHT90, dados.Depth, label = 'aht90')
axres1.set_xlim(0.2 , 2000)
axres1.set_xticks(np.logspace(np.log10(0.2),np.log10(2000),2))
axres1.spines["top"].set_position(("axes", 1.02))
axres1.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())
axres1.tick_params(axis='both', which='both', labelsize=6)
axres1.set_xlabel('sss')#, labelsize = 5)
axres2 = ax[0].twiny()
axres2.semilogx(dados.AHT10, dados.Depth, label = 'aht90')
axres2.set_xlim(0.2 , 2000)
axres2.set_xticks(np.logspace(np.log10(0.2),np.log10(2000),2))
axres2.spines["top"].set_position(("axes", 1.1))
axres2.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())
axres2.tick_params(axis='both', which='both', labelsize=6)
axres2.set_xlabel('aht10')#, labelsize = 5)
fig.show()
and the result is:
But well, I'm facing some issues on make a kind of make it automatic. If I add more curves, the prameter "set position" it is not practical to keep setting the position "by hand"
set_position(("axes", 1.02))
and another problem is, more curves I add, that kind of "legend" keep growing upward, and I have to adjust the subplot size with
fig.subplots_adjust(top=0.75)
And I'm also want to make the adjustment automatic, without keeping updating that parameter whenever I add more curves
I'm trying to plot two datasets (called Height and Temperature) on different y axes.
Both datasets have the same length.
Both datasets are linked together by a third dataset, RH.
I have tried to use matplotlib to plot the data using twiny() but I am struggling to align both datasets together on the same plot.
Here is the plot I want to align.
The horizontal black line on the figure is defined as the 0°C degree line that was found from Height and was used to test if both datasets, when plotted, would be aligned. They do not. There is a noticable difference between the black line and the 0°C tick from Temperature.
Rather than the two y axes changing independently from each other I would like to plot each index from Height and Temperature at the same y position on the plot.
Here is the code that I used to create the plot:
#Define number of subplots sharing y axis
f, ax1 = plt.subplots()
ax1.minorticks_on()
ax1.grid(which='major',axis='both',c='grey')
#Set axis parameters
ax1.set_ylabel('Height $(km)$')
ax1.set_ylim([np.nanmin(Height), np.nanmax(Height)])
#Plot RH
ax1.plot(RH, Height, label='Original', lw=0.5)
ax1.set_xlabel('RH $(\%)$')
ax2 = ax1.twinx()
ax2.plot(RH, Temperature, label='Original', lw=0.5, c='black')
ax2.set_ylabel('Temperature ($^\circ$C)')
ax2.set_ylim([np.nanmin(Temperature), np.nanmax(Temperature)])
Any help on this would be amazing. Thanks.
Maybe the atmosphere is wrong. :)
It sounds like you are trying to align the two y axes at particular values. Why are you doing this? The relationship of Height vs. Temperature is non-linear, so I think you are setting the stage for a confusing graph. Any particular line you plot can only be interpreted against one vertical axis.
If needed, I think you will be forced to "do some math" on the limits of the y axes. This link may be helpful:
align scales
I'm trying to visualize fashion MNIST dataset with different dimensional reduction techniques and I also want to attach the legend to resulted picture with so called real_labels which tells the real name of the label. For fashion MNIST real labels are:
real_labels = ['t-shirt','trouser','pullover','dress','coat','sandal','shirt','sneaker','bag','ankle boot']
I'm doing the plotting part inside of following fucntion:
def Draw_datasamples_to_figure(X_scaled, labels, axis):
y = ['${}$'.format(i) for i in labels]
num_cls = len(list(set(labels)))
for (X_plot, Y_plot, y1, label1) in zip(X_scaled[:,0], X_scaled[:,1], y, labels):
axis.scatter(X_plot, Y_plot, color=cm.gnuplot(int(label1)/num_cls),label=y1, marker=y1, s=60)
, where X_scaled tells x and y coordinate, labels are integer numbers (0-9) for class information and axis tells in which subplot window picture will be drawn.
The legend is drawn with following command:
ax3.legend(real_labels, loc='center left', bbox_to_anchor=(1, 0.5))
Everything seems to work pretty well until the legend is drawn to picture. As you can see from picture in below, instead of numbers goes from 0 to 9, the chosen numbers in legend are arbitrary.
I know that the problem is probably in scatter part and I should implement it in another way but I hope that there is still something simple that I miss which can fix my implementation. I don't want either to use hand-made legend in where markers and names are defined in the code because I have also other datasets with different classes and real label names. Thanks in advance!
I am starting to play around with creating polar plots in Matplotlib that do NOT encompass an entire circle - i.e. a "wedge" plot - by setting the thetamin and thetamax properties. This is something I was waiting for for a long time, and I am glad they have it done :)
However, I have noticed that the figure location inside the axes seem to change in a strange manner when using this feature; depending on the wedge angular aperture, it can be difficult to fine tune the figure so it looks nice.
Here's an example:
import numpy as np
import matplotlib.pyplot as plt
# get 4 polar axes in a row
fig, axes = plt.subplots(2, 2, subplot_kw={'projection': 'polar'},
figsize=(8, 8))
# set facecolor to better display the boundaries
# (as suggested by ImportanceOfBeingErnest)
fig.set_facecolor('paleturquoise')
for i, theta_max in enumerate([2*np.pi, np.pi, 2*np.pi/3, np.pi/3]):
# define theta vector with varying end point and some data to plot
theta = np.linspace(0, theta_max, 181)
data = (1/6)*np.abs(np.sin(3*theta)/np.sin(theta/2))
# set 'thetamin' and 'thetamax' according to data
axes[i//2, i%2].set_thetamin(0)
axes[i//2, i%2].set_thetamax(theta_max*180/np.pi)
# actually plot the data, fine tune radius limits and add labels
axes[i//2, i%2].plot(theta, data)
axes[i//2, i%2].set_ylim([0, 1])
axes[i//2, i%2].set_xlabel('Magnitude', fontsize=15)
axes[i//2, i%2].set_ylabel('Angles', fontsize=15)
fig.set_tight_layout(True)
#fig.savefig('fig.png', facecolor='skyblue')
The labels are in awkward locations and over the tick labels, but can be moved closer or further away from the axes by adding an extra labelpad parameter to set_xlabel, set_ylabel commands, so it's not a big issue.
Unfortunately, I have the impression that the plot is adjusted to fit inside the existing axes dimensions, which in turn lead to a very awkward white space above and below the half circle plot (which of course is the one I need to use).
It sounds like something that should be reasonably easy to get rid of - I mean, the wedge plots are doing it automatically - but I can't seem to figure it out how to do it for the half circle. Can anyone shed a light on this?
EDIT: Apologies, my question was not very clear; I want to create a half circle polar plot, but it seems that using set_thetamin() you end up with large amounts of white space around the image (especially above and below) which I would rather have removed, if possible.
It's the kind of stuff that normally tight_layout() takes care of, but it doesn't seem to be doing the trick here. I tried manually changing the figure window size after plotting, but the white space simply scales with the changes. Below is a minimum working example; I can get the xlabel closer to the image if I want to, but saved image file still contains tons of white space around it.
Does anyone knows how to remove this white space?
import numpy as np
import matplotlib.pyplot as plt
# get a half circle polar plot
fig1, ax1 = plt.subplots(1, 1, subplot_kw={'projection': 'polar'})
# set facecolor to better display the boundaries
# (as suggested by ImportanceOfBeingErnest)
fig1.set_facecolor('skyblue')
theta_min = 0
theta_max = np.pi
theta = np.linspace(theta_min, theta_max, 181)
data = (1/6)*np.abs(np.sin(3*theta)/np.sin(theta/2))
# set 'thetamin' and 'thetamax' according to data
ax1.set_thetamin(0)
ax1.set_thetamax(theta_max*180/np.pi)
# actually plot the data, fine tune radius limits and add labels
ax1.plot(theta, data)
ax1.set_ylim([0, 1])
ax1.set_xlabel('Magnitude', fontsize=15)
ax1.set_ylabel('Angles', fontsize=15)
fig1.set_tight_layout(True)
#fig1.savefig('fig1.png', facecolor='skyblue')
EDIT 2: Added background color to figures to better show the boundaries, as suggested in ImportanteOfBeingErnest's answer.
It seems the wedge of the "truncated" polar axes is placed such that it sits in the middle of the original axes. There seems so be some constructs called LockedBBox and _WedgeBbox in the game, which I have never seen before and do not fully understand. Those seem to be created at draw time, such that manipulating them from the outside seems somewhere between hard and impossible.
One hack can be to manipulate the original axes such that the resulting wedge turns up at the desired position. This is not really deterministic, but rather looking for some good values by trial and error.
The parameters to adjust in this case are the figure size (figsize), the padding of the labels (labelpad, as already pointed out in the question) and finally the axes' position (ax.set_position([left, bottom, width, height])).
The result could then look like
import numpy as np
import matplotlib.pyplot as plt
# get a half circle polar plot
fig1, ax1 = plt.subplots(1, 1, figsize=(6,3.4), subplot_kw={'projection': 'polar'})
theta_min = 1.e-9
theta_max = np.pi
theta = np.linspace(theta_min, theta_max, 181)
data = (1/6.)*np.abs(np.sin(3*theta)/np.sin(theta/2.))
# set 'thetamin' and 'thetamax' according to data
ax1.set_thetamin(0)
ax1.set_thetamax(theta_max*180./np.pi)
# actually plot the data, fine tune radius limits and add labels
ax1.plot(theta, data)
ax1.set_ylim([0, 1])
ax1.set_xlabel('Magnitude', fontsize=15, labelpad=-60)
ax1.set_ylabel('Angles', fontsize=15)
ax1.set_position( [0.1, -0.45, 0.8, 2])
plt.show()
Here I've set some color to the background of the figure to better see the boundary.