I have this Python code to plotting a figure:
matplotlib.rcParams['axes.unicode_minus'] = False
fig = plt.figure()
ax = fig.add_subplot(111)
I draw each plot running a loop along x and y like this:
ax.plot(x, y, dotFormat)
ax.errorbar(x, y, yerr=errorStd, fmt=dotFormat)
Finally, I set the axes and show the chart with the interactive navigation:
ax.grid(True)
ax.set_title(chartTitle)
fontsize=10
ax.set_ylabel(yLabel, fontsize=fontsize+2)
ax.set_xlabel(xLabel+'\n', fontsize=fontsize+2)
ax.set_yticklabels(ax.get_yticks(), fontsize=fontsize)
ax.set_xticks(range(len(xMinorLabels)), minor=True)
ax.set_xticklabels(xMinorLabels, minor=True, rotation=90, fontsize=fontsize-5)
ax.set_xticks(xMajorPosition, minor=False)
ax.set_xticklabels(xMajorLabels, minor=False, rotation=90, fontsize=fontsize-4)
plt.show()
If I use the tool zoom-to-rectangle and the Y-axis doesn't work property, because the same dot before zooming in is under 5, and after zooming in it is over 5.
What is happening with the y-axis when I use the zoom tool? Is a bug in the interactive navigation of matplotlib library? Without this tool, the library is useless for huge charts.
Thanks in advance!
The problem is this
ax.set_yticklabels(ax.get_yticks(), fontsize=fontsize)
section. set_yticklabels sets the value of the tick independent of the data. That is the third tick will always be the third entry of what ever you passed in.
set_*ticklabels should be considered dangerous and only used in very specialized situations.
You can set the font size via ax.tick_params(...) doc, example
Related
I try to figure out how to create scatter plot in matplotlib with two different y-axis values.
Now i have one and need to add second with index column values on y.
points1 = plt.scatter(r3_load["TimeUTC"], r3_load["r3_load_MW"],
c=r3_load["r3_load_MW"], s=50, cmap="rainbow", alpha=1) #set style options
plt.rcParams['figure.figsize'] = [20,10]
#plt.colorbar(points)
plt.title("timeUTC vs Load")
#plt.xlim(0, 400)
#plt.ylim(0, 300)
plt.xlabel('timeUTC')
plt.ylabel('Load_MW')
cbar = plt.colorbar(points1)
cbar.set_label('Load')
Result i expect is like this:
So second scatter set should be for TimeUTC vs index. Colors are not the subject;) also in excel y-axes are different sites, but doesnt matter.
Appriciate your help! Thanks, Paulina
Continuing after the suggestions in the comments.
There are two ways of using matplotlib.
Via the matplotlib.pyplot interface, like you were doing in your original code snippet with .plt
The object-oriented way. This is the suggested way to use matplotlib, especially when you need more customisation like in your case. In your code, ax1 is an Axes instance.
From an Axes instance, you can plot your data using the Axes.plot and Axes.scatter methods, very similar to what you did through the pyplot interface. This means, you can write a Axes.scatter call instead of .plot and use the same parameters as in your original code:
fig, ax1 = plt.subplots()
ax2 = ax1.twinx()
ax1.scatter(r3_load["TimeUTC"], r3_load["r3_load_MW"],
c=r3_load["r3_load_MW"], s=50, cmap="rainbow", alpha=1)
ax2.plot(r3_dda249["TimeUTC"], r3_dda249.index, c='b', linestyle='-')
ax1.set_xlabel('TimeUTC')
ax1.set_ylabel('r3_load_MW', color='g')
ax2.set_ylabel('index', color='b')
plt.show()
I am trying to make my plots a bit more readable and have come across a feature where the axes are automatically scaled by factors of tens (so instead of the y axis reading 0.00000005, 0.00000007, 0.00000009, it reads 0.5,0.7,0.9 and then says 1e-7 at the top of the axis). However some of my plots don't scale the axes automatically, and I would like to get advise of how to do that manually.
I have found threads on manually setting the tick marks, however I haven't been able to find threads on scaling only.
I can't imbed pictures but here is a link to a picture of what I would like to do: Ideal y axis and here's link to a picture of what I want to avoid: Current y axis.
I'm using seaborn formatting and matplotlib for plots and my code looks like this:
plt.plot(x_j_n,y_j_n, label='Scanning i negativ retning', color='grey', ls='dashed')
plt.plot(x_j_p,y_j_p, label='Scanning i positiv retning', color='black', ls='dashed')
plt.errorbar(x_j_n,y_j_n, yerr=std_j_n, fmt='o', color='black', mfc='white', label = 'Usikkerhed')
plt.errorbar(x_j_p,y_j_p, yerr=std_j_p, fmt='o', color='grey', mfc='white', label = 'Usikkerhed')
plt.ylabel('Målt spænding i volt (V)')
plt.xlabel('Påtrykt felt i tesla (T)')
plt.legend()
plt.show;
Set the y axis to scientific:
plt.gca().yaxis.get_major_formatter().set_scientific(True)
For example:
x = [1100000,2200000,3300000]
y = [1100000,2200000,3300000]
plt.plot(x,y)
plt.gca().xaxis.get_major_formatter().set_scientific(False)
plt.gca().yaxis.get_major_formatter().set_scientific(True)
plt.show()
will give:
I have the following plot:
dfA.plot.bar(stacked=True, color=[colorDict.get(x, '#333333') for x in
dfA.columns],figsize=(10,8))
plt.legend(loc='upper right', bbox_to_anchor=(1.4, 1))
Which displays this:
I want to remove all of the borders of the chart and legend i.e. the box around the chart (leaving the axis numbers like 2015 and 6000 etc)
All of the examples I find refer to spines and 'ax', however I have not built my chart using fig = plt.figure() etc.
Anyone know how to do it?
You can remove the border of the legend by using the argument frameon=False in the call to plt.legend().
If you only have one figure and axes active, then you can use plt.gca() to get the current axes. Alternatively df.plot.bar returns an axes object (which I would suggest using because plt.gca() might get confusing when working with multiple figures). Therefore you can set the visibility of the spines to False:
ax = dfA.plot.bar(stacked=True, color=[colorDict.get(x, '#333333') for x in
dfA.columns],figsize=(10,8))
plt.legend(loc='upper right', bbox_to_anchor=(1.4, 1), frameon=False)
for spine in ax.spines:
ax.spines[spine].set_visible(False)
# Color of the spines can also be set to none, suggested in the comments by ScoutEU
# ax.spines[spine].set_color("None")
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 a figure without tickmarks or numbers on either of the axes (I use axes in the traditional sense, not the matplotlib nomenclature!). An issue I have come across is where matplotlib adjusts the x(y)ticklabels by subtracting a value N, then adds N at the end of the axis.
This may be vague, but the following simplified example highlights the issue, with '6.18' being the offending value of N:
import matplotlib.pyplot as plt
import random
prefix = 6.18
rx = [prefix+(0.001*random.random()) for i in arange(100)]
ry = [prefix+(0.001*random.random()) for i in arange(100)]
plt.plot(rx,ry,'ko')
frame1 = plt.gca()
for xlabel_i in frame1.axes.get_xticklabels():
xlabel_i.set_visible(False)
xlabel_i.set_fontsize(0.0)
for xlabel_i in frame1.axes.get_yticklabels():
xlabel_i.set_fontsize(0.0)
xlabel_i.set_visible(False)
for tick in frame1.axes.get_xticklines():
tick.set_visible(False)
for tick in frame1.axes.get_yticklines():
tick.set_visible(False)
plt.show()
The three things I would like to know are:
How to turn off this behaviour in the first place (although in most cases it is useful, it is not always!) I have looked through matplotlib.axis.XAxis and cannot find anything appropriate
How can I make N disappear (i.e. X.set_visible(False))
Is there a better way to do the above anyway? My final plot would be 4x4 subplots in a figure, if that is relevant.
Instead of hiding each element, you can hide the whole axis:
frame1.axes.get_xaxis().set_visible(False)
frame1.axes.get_yaxis().set_visible(False)
Or, you can set the ticks to an empty list:
frame1.axes.get_xaxis().set_ticks([])
frame1.axes.get_yaxis().set_ticks([])
In this second option, you can still use plt.xlabel() and plt.ylabel() to add labels to the axes.
If you want to hide just the axis text keeping the grid lines:
frame1 = plt.gca()
frame1.axes.xaxis.set_ticklabels([])
frame1.axes.yaxis.set_ticklabels([])
Doing set_visible(False) or set_ticks([]) will also hide the grid lines.
If you are like me and don't always retrieve the axes, ax, when plotting the figure, then a simple solution would be to do
plt.xticks([])
plt.yticks([])
I've colour coded this figure to ease the process.
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
You can have full control over the figure using these commands, to complete the answer I've add also the control over the spines:
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
# X AXIS -BORDER
ax.spines['bottom'].set_visible(False)
# BLUE
ax.set_xticklabels([])
# RED
ax.set_xticks([])
# RED AND BLUE TOGETHER
ax.axes.get_xaxis().set_visible(False)
# Y AXIS -BORDER
ax.spines['left'].set_visible(False)
# YELLOW
ax.set_yticklabels([])
# GREEN
ax.set_yticks([])
# YELLOW AND GREEN TOGHETHER
ax.axes.get_yaxis().set_visible(False)
I was not actually able to render an image without borders or axis data based on any of the code snippets here (even the one accepted at the answer). After digging through some API documentation, I landed on this code to render my image
plt.axis('off')
plt.tick_params(axis='both', left=False, top=False, right=False, bottom=False, labelleft=False, labeltop=False, labelright=False, labelbottom=False)
plt.savefig('foo.png', dpi=100, bbox_inches='tight', pad_inches=0.0)
I used the tick_params call to basically shut down any extra information that might be rendered and I have a perfect graph in my output file.
Somewhat of an old thread but, this seems to be a faster method using the latest version of matplotlib:
set the major formatter for the x-axis
ax.xaxis.set_major_formatter(plt.NullFormatter())
One trick could be setting the color of tick labels as white to hide it!
plt.xticks(color='w')
plt.yticks(color='w')
or to be more generalized (#Armin Okić), you can set it as "None".
When using the object oriented API, the Axes object has two useful methods for removing the axis text, set_xticklabels() and set_xticks().
Say you create a plot using
fig, ax = plt.subplots(1)
ax.plot(x, y)
If you simply want to remove the tick labels, you could use
ax.set_xticklabels([])
or to remove the ticks completely, you could use
ax.set_xticks([])
These methods are useful for specifying exactly where you want the ticks and how you want them labeled. Passing an empty list results in no ticks, or no labels, respectively.
You could simply set xlabel to None, straight in your axis. Below an working example using seaborn
from matplotlib import pyplot as plt
import seaborn as sns
tips = sns.load_dataset("tips")
ax = sns.boxplot(x="day", y="total_bill", data=tips)
ax.set(xlabel=None)
plt.show()
Just do this in case you have subplots
fig, axs = plt.subplots(1, 2, figsize=(16, 8))
ax[0].set_yticklabels([]) # x-axis
ax[0].set_xticklabels([]) # y-axis