I noticed a 'strange' behaviour when running the following code:
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import (MultipleLocator, AutoMinorLocator)
freqs = np.logspace(2,4)
freqs_ext = np.logspace(2, 10)
fig, ax = plt.subplots(1,2)
ax [0].plot(freqs , freqs**2)
#ax[0].xaxis.set_minor_locator(AutoMinorLocator(5))
ax[0].grid(which='both')
#ax[0].minorticks_on()
ax[0].set_xscale( 'log')
ax[1].plot(freqs_ext,freqs_ext**2)
#ax[l].xaxis.set_minor_locator(AutoMinorLocator(5))
ax[1].grid(which='both')
#ax[1].minorticks on()
ax[1].set_xscale('log')
The output is the following:
I have tried more variants than I care to report, (some are commented out in the code above), but I cannot get matplotlib to draw minor gridlines for the plot on the right side, as it does for the one on the left.
I think I have understood that the "problem" lies in where the ticks are located for the second plot, which has a much larger span. They are every two decades and I believe this might be the source of the minor grid lines not displaying.
I have played with xaxis.set_xticks and obtained ticks every decade, but still cannot get this to correctly produce the gridlines.
It is probably something stupid but I can't see it.
NOTE : I know that matplotlib doesn't turn the minor ticks on by default, and in this case this action is "triggered" by changing the scale to log (that's why axis.grid(which='both') actually only acts on the x axis)
OK, I have found this answer:
Matplotlib: strange double-decade axis ticks in log plot
which actually shows how the issue is a design choice for matplotlib starting with v2. Answer was given in 2017 so, not the newest issue :)
The following code correctly plots the minor grids as wanted:
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import LogLocator
freqs = np.logspace(2,4)
freqs_ext = np.logspace(2, 10)
fig, ax = plt.subplots(1,2)
ax[0].plot(freqs , freqs**2)
ax[0].grid(which='both')
ax[0].set_xscale( 'log')
ax[1].plot(freqs_ext,freqs_ext**2)
ax[1].set_xscale('log')
ax[1].xaxis.set_major_locator(LogLocator(numticks=15))
ax[1].xaxis.set_minor_locator(LogLocator(numticks=15,subs=np.arange(2,10)))
ax[1].grid(which='both')
Related
I am trying to change the appearance of a diagram created using plt.errorbar, but I can't get it to change in the ways I would like.
To demonstrate the problem, I have made up some example data:
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import matplotlib.axes as axes
import numpy as np
Temps=np.array([18000,15000,14000,12000,11750,11500,10000,5750,6000])
Powers=np.array([1.2,1.0,0.5,.35,0.4,0.2,.15,5.3,4.9])
Errors=100*np.array([2,2,2,2,2,2,2,3,3])
I have a function that turns the temperature values into colours:
def makecolour(t):
a=(t-min(Temps))/(max(Temps)-min(Temps))
return [[1-A,0,A] for A in a]
I have also changed some of the other properties of the diagram.
plt.axes(facecolor='black')
plt.yscale('log')
plt.xscale('log')
plt.xlim(2e4,5e3)
plt.errorbar(Temps,Powers,xerr=Errors,ecolor=makecolour(Temps),fmt='.')
I can't get the data points to change colour, only the error bars. When I try to change the colour of the actual points:
plt.errorbar(Temps,Powers,xerr=Errors,ecolor=makecolour(Temps),fmt='.',color=makecolour(Temps))
"Breaks because it fails to interpret the array of colours."
It doesn't work and I'm don't know how to fix it. The closest I have come to a solution is hiding the data points entirely:
plt.errorbar(Temps,Powers,xerr=Errors,ecolor=makecolour(Temps),fmt='.',markersize=0)
"Not showing where the data point is isn't acceptable."`
But this not good enough.
I have also been struggling with the way the axis ticks are displayed when using plt.xscale('log'). Ideally, I want to display the tick labels as a plain integer as opposed to scientific notation, but neither of the solutions I have tried worked. I have tried:
ticker.LogFormatter(base=1)
axes.ticklabel_format(style='plain')
I have searched around on here for previous answers, but I have not found any disussions of similar problems with plt.errorbar. Any help would be much appreciated.
Here is a partial answer. Just first plot without markers and on the same plot without the errorlines.
About the tickers, this post proposes:
ax=plt.gca()
ax.xaxis.set_minor_formatter(ticker.ScalarFormatter())
ax.xaxis.set_major_formatter(ticker.ScalarFormatter())
Demo code:
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import matplotlib.axes as axes
import numpy as np
Temps=np.array([18000,15000,14000,12000,11750,11500,10000,5750,6000])
Powers=np.array([1.2,1.0,0.5,.35,0.4,0.2,.15,5.3,4.9])
Errors=100*np.array([2,2,2,2,2,2,2,3,3])
def makecolour(t):
a=(t-min(Temps))/(max(Temps)-min(Temps))
return [[1-A,0,A] for A in a]
plt.axes(facecolor='black')
plt.yscale('log')
plt.xscale('log')
plt.xlim(2e4,5e3)
ax=plt.gca()
ax.xaxis.set_minor_formatter(ticker.ScalarFormatter())
ax.xaxis.set_major_formatter(ticker.ScalarFormatter())
plt.errorbar(Temps,Powers,xerr=Errors,ecolor=makecolour(Temps),fmt='.',markersize=0)
plt.errorbar(Temps,Powers,xerr=None,fmt='.')
plt.show()
I have been given a data for which I need to find a histogram. So I used pandas hist() function and plot it using matplotlib. The code runs on a remote server so I cannot directly see it and hence I save the image. Here is what the image looks like
Here is my code below
import matplotlib.pyplot as plt
df_hist = pd.DataFrame(np.array(raw_data)).hist(bins=5) // raw_data is the data supplied to me
plt.savefig('/path/to/file.png')
plt.close()
As you can see the x axis labels are overlapping. So I used this function plt.tight_layout() like so
import matplotlib.pyplot as plt
df_hist = pd.DataFrame(np.array(raw_data)).hist(bins=5)
plt.tight_layout()
plt.savefig('/path/to/file.png')
plt.close()
There is some improvement now
But still the labels are too close. Is there a way to ensure the labels do not touch each other and there is fair spacing between them? Also I want to resize the image to make it smaller.
I checked the documentation here https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html but not sure which parameter to use for savefig.
Since raw_data is not already a pandas dataframe there's no need to turn it into one to do the plotting. Instead you can plot directly with matplotlib.
There are many different ways to achieve what you'd like. I'll start by setting up some data which looks similar to yours:
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import gamma
raw_data = gamma.rvs(a=1, scale=1e6, size=100)
If we go ahead and use matplotlib to create the histogram we may find the xticks too close together:
fig, ax = plt.subplots(1, 1, figsize=[5, 3])
ax.hist(raw_data, bins=5)
fig.tight_layout()
The xticks are hard to read with all the zeros, regardless of spacing. So, one thing you may wish to do would be to use scientific formatting. This makes the x-axis much easier to interpret:
ax.ticklabel_format(style='sci', axis='x', scilimits=(0,0))
Another option, without using scientific formatting would be to rotate the ticks (as mentioned in the comments):
ax.tick_params(axis='x', rotation=45)
fig.tight_layout()
Finally, you also mentioned altering the size of the image. Note that this is best done when the figure is initialised. You can set the size of the figure with the figsize argument. The following would create a figure 5" wide and 3" in height:
fig, ax = plt.subplots(1, 1, figsize=[5, 3])
I think the two best fixes were mentioned by Pam in the comments.
You can rotate the labels with
plt.xticks(rotation=45
For more information, look here: Rotate axis text in python matplotlib
The real problem is too many zeros that don't provide any extra info. Numpy arrays are pretty easy to work with, so pd.DataFrame(np.array(raw_data)/1000).hist(bins=5) should get rid of three zeros off of both axes. Then just add a 'kilo' in the axes labels.
To change the size of the graph use rcParams.
from matplotlib import rcParams
rcParams['figure.figsize'] = 7, 5.75 #the numbers are the dimensions
I have to plot several curves with very high xtick density, say 1000 date strings. To prevent these tick labels overlapping each other I manually set them to be 60 dates apart. Code below:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
ts_index = pd.period_range(start="20060429", periods=1000).strftime("%Y%m%d")
fig = plt.figure(1)
ax = plt.subplot(1, 1, 1)
tick_spacing = 60
for i in range(5):
plt.plot(ts_index, 1 + i * 0.01 * np.arange(0, 1000), label="group %d"%i)
plt.legend(loc='best')
plt.title(r'net value curves')
xticks = ax.get_xticks()
xlabels = ax.get_xticklabels()
ax.set_xticks(xticks[::tick_spacing])
ax.set_xticklabels(xlabels[::tick_spacing])
plt.xticks(rotation="vertical")
plt.xlabel(r'date')
plt.ylabel('net value')
plt.grid(True)
plt.show()
fig.savefig(r".\net_value_curves.png", )
fig.clf()
I'm running this piece of code in PyCharm Community Edition 2017.2.2 with a Python 3.6 kernel. Now comes the funny thing: whenever I ran the code in the normal "run" mode (i.e. just hit the execution button and let the code run "freely" till interruption or termination), then the figure I got would always miss xticklabels:
However, if I ran the code in "debug" mode and ran it step by step then I would get an expected figure with complete xticklabels:
This is really weird. Anyway, I just hope to find a way that can ensure me getting the desired output (the second figure) in the normal "run" mode. How can I modify my current code to achieve this?
Thanks in advance!
Your x axis data are strings. Hence you will get one tick per data point. This is probably not what you want. Instead use the dates to plot. Because you are using pandas, this is easily converted,
dates = pd.to_datetime(ts_index, format="%Y%m%d")
You may then get rid of your manual xtick locating and formatting, because matplotlib will automatically choose some nice tick locations for you.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
ts_index = pd.period_range(start="20060429", periods=1000).strftime("%Y%m%d")
dates = pd.to_datetime(ts_index, format="%Y%m%d")
fig, ax = plt.subplots()
for i in range(5):
plt.plot(dates, 1 + i * 0.01 * np.arange(0, 1000), label="group %d"%i)
plt.legend(loc='best')
plt.title(r'net value curves')
plt.xticks(rotation="vertical")
plt.xlabel(r'date')
plt.ylabel('net value')
plt.grid(True)
plt.show()
However in case you do want to have some manual control over the locations and formats you may use matplotlib.dates locators and formatters.
# tick every 3 months
plt.gca().xaxis.set_major_locator(mdates.MonthLocator((1,4,7,10)))
# format as "%Y%m%d"
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter("%Y%m%d"))
In general, the Axis object computes and places ticks using a Locator object. Locators and Formatters are meant to be easily replaceable, with appropriate methods of Axis. The default Locator does not seem to be doing the trick for you so you can replace it with anything you want using axes.xaxis.set_major_locator. This problem is not complicated enough to write your own, so I would suggest that MaxNLocator fits your needs fairly well. Your example seems to work well with nbins=16 (which is what you have in the picture, since there are 17 ticks.
You need to add an import:
from matplotlib.ticker import MaxNLocator
You need to replace the block
xticks = ax.get_xticks()
xlabels = ax.get_xticklabels()
ax.set_xticks(xticks[::tick_spacing])
ax.set_xticklabels(xlabels[::tick_spacing])
with
ax.xaxis.set_major_locator(MaxNLocator(nbins=16))
or just
ax.xaxis.set_major_locator(MaxNLocator(16))
You may want to play around with the other arguments (all of which have to be keywords, except nbins). Pay especial attention to integer.
Note that for the Locator and Formatter APIs we work with an Axis object, not Axes. Axes is the whole plot, while Axis is the thing with the spines on it. Axes usually contains two Axis objects and all the other stuff in your plot.
You can set the visibility of the xticks-labels to False
for label in plt.gca().xaxis.get_ticklabels()[::N]:
label.set_visible(False)
This will set every Nth label invisible.
I've played with setting tick direction in rcParams using "in", "out", turning on and off the left, right, bottom, and top ticks but I have yet to find a way to do something that seems so simple. I just want both right and left y-axes ticks to face left, and top and bottom x-axes ticks to face down.
How do I do this?
Is there a way to change the Tick objects after they have been made?
Heres a snippet of me fooling around with things I may not fully understand:
from pylab import *
from numpy import *
import matplotlib.pyplot as plt
% matplotlib inline
plt.rc("font",size=18)
plt.rc("figure",figsize=(14,3.5))
matplotlib.rcParams.update({'ytick.left': True})
rc("ytick",direction="in")
matplotlib.rcParams.update({'ytick.right': True})
rc("ytick",direction="out")
#rc("ytick.major",left=True)
#rc("ytick.major",right=True)
#plt.rc("ytick",left=False)
# test
y = [2,3,4,5,6,7,8,9,12,13,4,23,23,34,3]
x = [i for i in range(len(y))]
plot(x,y)
Right now I'm trying to do this in a Jupyter notebook.
EDIT: I have found a fairly acceptable solution and put it in the comments.
So after some tinkering and input from clever colleagues, my best solution was this:
import matplotlib.pyplot as plt
import mpl_toolkits.axisartist as aa
% matplotlib inline
y = array([3,2,4,5,7,8])
x = [i for i in range(len(y))]
fig = plt.figure()
#*********************************************************
#****************** Here's the magic**********************
#*********************************************************
ax = aa.Subplot(fig,111) # 1 row, 1 column, 1st plot
fig.add_subplot(ax)
ax.axis["bottom","left"].major_ticks.set_tick_out(True)
ax.axis["bottom","left"].minor_ticks.set_tick_out(True)
ax.axis["top","right"].major_ticks.set_tick_out(False)
ax.axis["top","right"].minor_ticks.set_tick_out(False)
#*********************************************************
ax.plot(x,y)
ax.plot(x,y2)
#ax.set_ylim(0,30)
ax.set_yscale("log")
ax.set_xscale("log")
tickCtrl
The important part seems to be using axisartist to create the subplot, because when I try to use "plt.subplots()" I get errors when trying to call axis["bottom"].
This is strange to me, because I think axisartist is always used to draw axes with matplotlib. I'd love to hear a more detailed explanation by someone who has a more thorough understanding than me.
Normally if you plot two different figures using the default settings in pyplot, they will be exactly the same size, and if saved can be neatly aligned in PowerPoint or the like. I'd like to generate one figure, however, which has a legend outside of the figure. The script I'm using is shown below.
import numpy as np
import matplotlib.pyplot as plt
x=np.linspace(0,1,201)
y1=x**2
y2=np.sin(x)
fig1=plt.figure(1)
plt.plot(x,y1,label='y1')
handles1,labels1=plt.gca().get_legend_handles_labels()
lgd1=plt.gca().legend(handles1,labels1,bbox_to_anchor=(1.27,1),borderaxespad=0.)
fig2=plt.figure(2)
plt.plot(x,y2)
fig1.savefig('fig1',bbox_extra_artists=(lgd1,),bbox_inches='tight')
fig2.savefig('fig2')
plt.show()
The problem is that in PowerPoint, I can no longer align the two figures left and have their axes aligned. Due to the use of the 'extra artists' and 'bbox_inches=tight' arguments for the first figure, the width of its margins becomes different from the second figure.
Is there any way to 'transfer' the clip box from the first figure to the second figure, such that they can be aligned by 'align left' in PowerPoint?
I think an easier way to achieve what you want is to just construct one figure with two subplots, and let matplotlib align everything for you.
Do you think doing something like this is a good idea?
import matplotlib.pyplot as plt
import numpy as np
x=np.linspace(0,1,201)
y1=x**2
y2=np.sin(x)
fig = plt.figure()
a = fig.add_subplot(211)
a.plot(x,y1, label='y1')
lgd1 = a.legend(bbox_to_anchor = (1.27,1), borderaxespad=0.)
a = fig.add_subplot(212)
a.plot(x,y2)
fig.savefig('fig',bbox_extra_artists=(lgd1,),bbox_inches='tight')