I was using jupyterlab to create a graph for highest daily temperature. This is the part of my code that does the graphing. I tried to use 'autofmt_xdate()' attribute to draw the date labels diagonally, but it doesn't work. The date labels were still drawn horizontally, overlapping each other.
from matplotlib import pyplot as plt
fig=plt.figure(dpi=128,figsize=(10,6))
plt.show()
plt.plot(dates,highs,c='Red')
plt.title('highest temperature,July 2018')
plt.xlabel('date',fontsize=16)
fig.autofmt_xdate()
plt.ylabel('temperature C',fontsize=16)
plt.tick_params(axis='both',which='major',labelsize=16)
plt.show()
Related
When plotting matrices using matplotlib's imshow function the lines of the axes can overlap the actual plot, see the following minimal example (matshow is just a simple wrapper around imshow):
import numpy as np
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(3,3))
ax.matshow(np.random.random((50, 50)), interpolation="none", cmap="Blues")
plt.savefig("example.png", dpi=300)
I would expect every entry of the matrix to be represented by a square, but in the top row it is quite obvious that the axis is hiding a bit of the plot resulting in non-square entries. The same is happening for the last column. Since I want the complete matrix to be seen - every entry with the same importance - is there any way this can be fixed?
To me, this is just a visualisation issue. If I run your code and maximise the window, I do not see the overlapping you are talking about:
Otherwise, remove the spines but without hiding the ticks:
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(False)
EDIT
Reduce the thickness of the borders:
[x.set_linewidth(0.3) for x in ax.spines.values()]
The following is the exported image:
With 0.2 the exported image looks like this:
Trying to plot a surface using matplotlib. However, the plotted surface has lower grid density than the meshgrid.
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
xgrid = np.arange(0,1,1/100)
ygrid = xgrid.copy()
xx, yy = np.meshgrid(xgrid,ygrid)
fig = plt.figure()
ax = fig.add_subplot(111,projection='3d')
ax.plot_surface(xx,yy,np.square(xx)+np.square(yy))
The meshgrid was defined to have 100 segments in each direction. However, the surface in the figure doesn't have 100 segments. Is there anyway to render the surface with the same density?
Don't worry, you don't have to count the number of segments, so I overlap the plot with a scatter plot with the same points
ax.scatter3D(xx,yy,np.square(xx)+np.square(yy))
Upon zooming in, you can see that each grid of the surface plot has a scatter point in the middle, which shows that the plot_surface changed the density of the points.
Usually it doesn't matter for smooth plots such as these, but I have singular points in my data which sometimes disappear in the surface plot because of this
import matplotlib
import matplotlib.pyplot as plt
import numpy as nm
x = nm.linspace(start=0,stop=20,num=30)
fig=plt.figure()
ax1 = fig.add_axes([0,0.6,0.6,0.4])
ax2 = fig.add_axes([0,0,0.8,0.4])
ax1.plot(x,nm.sin(x))
ax1.set_xlabel('x',fontsize=15,color='r')
ax1.set_ylabel('sin(x)',fontsize=15,color='r')
ax2.plot(x,nm.cos(x))
ax2.set_xlabel('x',fontsize=15,color='r')
ax2.set_ylabel('cos(x)',fontsize=15,color='r')
plt.show()
The output I am not able to see the xlabel for ax2 and not able to see both y label for ax1 and ax2..The image is present below:
enter code hereenter image description here
This is expected as you are asking to create an axes that is aligned with the left edge of the figure by using fig.add_axes([0,...]). Same thing for the bottom axes, which you have aligned to the bottom-left of the figure using fig.add_axes([0,0,...]).
Increase the first value e.g. fig.add_axes([0.125,...]) to leave room for the axes decorations on the left or bottom of the axes.
It is generally recommended to use the subplots functions (such as add_subplot, plt.subplots or GridSpec) so that these details are handled automatically.
Searching easily reveals how to plot multiple charts on one figure, whether using the same plotting axes, a second y axis or subplots. Much harder to uncover is how to overlay one figure onto another, something like this:
That image was prepared using a bitmap editor to overlay the images. I have no difficulty creating the individual plots, but cannot figure out how to combine them. I expect a single line of code will suffice, but what is it? Here is how I imagine it:
bigFig = plt.figure(1, figsize=[5,25])
...
ltlFig = plt.figure(2)
...
bigFig.overlay(ltlFig, pos=[x,y], size=[1,1])
I've established that I can use figure.add_axes, but it is quite challenging getting the position of the overlaid plot correct, since the parameters are fractions, not x,y values from the first plot. It also [it seems to me - am I wrong?] places constraints on the order in which the charts are plotted, since the main plot must be completed before the other plots are added in turn.
What is the pyplot method that achieves this?
To create an inset axes you may use mpl_toolkits.axes_grid1.inset_locator.inset_axes.
Position of inset axes in axes coordinates
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
fig, ax= plt.subplots()
inset_axes = inset_axes(ax,
width=1, # inch
height=1, # inch
bbox_transform=ax.transAxes, # relative axes coordinates
bbox_to_anchor=(0.5,0.5), # relative axes coordinates
loc=3) # loc=lower left corner
ax.axis([0,500,-.1,.1])
plt.show()
Position of inset axes in data coordinates
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
fig, ax= plt.subplots()
inset_axes = inset_axes(ax,
width=1, # inch
height=1, # inch
bbox_transform=ax.transData, # data coordinates
bbox_to_anchor=(250,0.0), # data coordinates
loc=3) # loc=lower left corner
ax.axis([0,500,-.1,.1])
plt.show()
Both of the above produce the same plot
(For a possible drawback of this solution see specific location for inset axes)
I've drawn a plot that looks something like the following:
It was created using the following code:
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
# 1. Plot a figure consisting of 3 separate axes
# ==============================================
plotNames = ['Plot1','Plot2','Plot3']
figure, axisList = plt.subplots(len(plotNames), sharex=True, sharey=True)
tempDF = pd.DataFrame()
tempDF['date'] = pd.date_range('2015-01-01','2015-12-31',freq='D')
tempDF['value'] = np.random.randn(tempDF['date'].size)
tempDF['value2'] = np.random.randn(tempDF['date'].size)
for i in range(len(plotNames)):
axisList[i].plot_date(tempDF['date'],tempDF['value'],'b-',xdate=True)
# 2. Create a new single axis in the figure. This new axis sits over
# the top of the axes drawn previously. Make all the components of
# the new single axis invisibe except for the x and y labels.
big_ax = figure.add_subplot(111)
big_ax.set_axis_bgcolor('none')
big_ax.set_xlabel('Date',fontweight='bold')
big_ax.set_ylabel('Random normal',fontweight='bold')
big_ax.tick_params(labelcolor='none', top='off', bottom='off', left='off', right='off')
big_ax.spines['right'].set_visible(False)
big_ax.spines['top'].set_visible(False)
big_ax.spines['left'].set_visible(False)
big_ax.spines['bottom'].set_visible(False)
# 3. Plot a separate figure
# =========================
figure2,ax2 = plt.subplots()
ax2.plot_date(tempDF['date'],tempDF['value2'],'-',xdate=True,color='green')
ax2.set_xlabel('Date',fontweight='bold')
ax2.set_ylabel('Random normal',fontweight='bold')
# Save plot
# =========
plt.savefig('tempPlot.png',dpi=300)
Basically, the rationale for plotting the whole picture is as follows:
Create the first figure and plot 3 separate axes using a loop
Plot a single axis in the same figure to sit on top of the graphs
drawn previously. Label the x and y axes. Make all other aspects of
this axis invisible.
Create a second figure and plot data on a single axis.
The plot displays just as I want when using jupyter-notebook but when the plot is saved, the file contains only the second figure.
I was under the impression that plots could have multiple figures and that figures could have multiple axes. However, I suspect I have a fundamental misunderstanding of the differences between plots, subplots, figures and axes. Can someone please explain what I'm doing wrong and explain how to get the whole image to save to a single file.
Matplotlib does not have "plots". In that sense,
plots are figures
subplots are axes
During runtime of a script you can have as many figures as you wish. Calling plt.save() will save the currently active figure, i.e. the figure you would get by calling plt.gcf().
You can save any other figure either by providing a figure number num:
plt.figure(num)
plt.savefig("output.png")
or by having a refence to the figure object fig1
fig1.savefig("output.png")
In order to save several figures into one file, one could go the way detailed here: Python saving multiple figures into one PDF file.
Another option would be not to create several figures, but a single one, using subplots,
fig = plt.figure()
ax = plt.add_subplot(611)
ax2 = plt.add_subplot(612)
ax3 = plt.add_subplot(613)
ax4 = plt.add_subplot(212)
and then plot the respective graphs to those axes using
ax.plot(x,y)
or in the case of a pandas dataframe df
df.plot(x="column1", y="column2", ax=ax)
This second option can of course be generalized to arbitrary axes positions using subplots on grids. This is detailed in the matplotlib user's guide Customizing Location of Subplot Using GridSpec
Furthermore, it is possible to position an axes (a subplot so to speak) at any position in the figure using fig.add_axes([left, bottom, width, height]) (where left, bottom, width, height are in figure coordinates, ranging from 0 to 1).