I am trying to plot multiple time series in one plot. The scales are different, so they need separate y-axis, and I want a specific time series to have its y-axis on the right. I also want that time series to be behind the others. But I find that when I use secondary_y=True, this time series is always brought to the front, even if the code to plot it comes before the others. How can I control the order of the plots when using secondary_y=True (or is there an alternative)?
Furthermore, when I use secondary_y=True the y-axis on the left no longer adapts to appropriate values. Is there a fixed for this?
# imports
import numpy as np
import matplotlib.pyplot as plt
# dummy data
lenx = 1000
x = range(lenx)
np.random.seed(4)
y1 = np.random.randn(lenx)
y1 = pd.Series(y1, index=x)
y2 = 50.0 + y1.cumsum()
# plot time series.
# use ax to make Pandas plot them in the same plot.
ax = y2.plot.area(secondary_y=True)
y1.plot(ax=ax)
So what I would like is to have the blue area plot behind the green time series, and to have the left y-axis take appropriate values for the green time series:
https://i.stack.imgur.com/6QzPV.png
Perhaps something like the following using matplotlib.axes.Axes.twinx instead of using secondary_y, and then following the approach in this answer to move the twinned axis to the background:
# plot time series.
fig, ax = plt.subplots()
y1.plot(ax=ax, color='green')
ax.set_zorder(10)
ax.patch.set_visible(False)
ax1 = ax.twinx()
y2.plot.area(ax=ax1, color='blue')
Related
I'm trying to set xlimits and keep the margins.
In a simplified code, the dataset contains 50 values. When plotting the whole data set, it is fine. However, I only want to plot values 20-40. The plot starts and ends without having any margins.
How do I plot values 20-40 but keep the margins?
Online I found to ways to play with the margin/padding
1) plt.tight_layout(pad=1.08, h_pad=None, w_pad=None, rect=None)
2) ax1.margins(0.05)
Both, however, do not seem to work when using xlimits.
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(1, 200, 50)
y = np.random.random(len(x))
fig_1 = plt.figure(figsize=(8, 4))
ax1 = plt.subplot(1,1,1)
ax1.plot(x, y)
ax1.set_xlim(x[19], x[40])
# ax1.plot(x[19:40], y[19:40])
# would create exactly the plot I want. But it is not the solution I am looking for.
# I cannot change/slice the data. I want to change the figure.
I am new to this module. I have time series data for movement of particle against time. The movement has its X and Y component against the the time T. I want to plot these 3 parameters in the graph. The sample data looks like this. The first coloumn represent time, 2nd- Xcordinate , 3rd Y-cordinate.
1.5193 618.3349 487.5595
1.5193 619.3349 487.5595
2.5193 619.8688 489.5869
2.5193 620.8688 489.5869
3.5193 622.9027 493.3156
3.5193 623.9027 493.3156
If you want to add a 3rd info to a 2D curve, one possibility is to use a color mapping instituting a relationship between the value of the 3rd coordinate and a set of colors.
In Matplotlib we have not a direct way of plotting a curve with changing color, but we can fake one using matplotlib.collections.LineCollection.
In the following I've used some arbitrary curve but I have no doubt that you could adjust my code to your particular use case if my code suits your needs.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
# e.g., a Lissajous curve
t = np.linspace(0, 2*np.pi, 6280)
x, y = np.sin(4*t), np.sin(5*t)
# to use LineCollection we need an array of segments
# the canonical answer (to upvote...) is https://stackoverflow.com/a/58880037/2749397
points = np.array([x, y]).T.reshape(-1,1,2)
segments = np.concatenate([points[:-1],points[1:]], axis=1)
# instantiate the line collection with appropriate parameters,
# the associated array controls the color mapping, we set it to time
lc = LineCollection(segments, cmap='nipy_spectral', linewidth=6, alpha=0.85)
lc.set_array(t)
# usual stuff, just note ax.autoscale, not needed here because we
# replot the same data but tipically needed with ax.add_collection
fig, ax = plt.subplots()
plt.xlabel('x/mm') ; plt.ylabel('y/mm')
ax.add_collection(lc)
ax.autoscale()
cb = plt.colorbar(lc)
cb.set_label('t/s')
# we plot a thin line over the colormapped line collection, especially
# useful when our colormap contains white...
plt.plot(x, y, color='black', linewidth=0.5, zorder=3)
plt.show()
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).
I'm drawing the bloxplot shown below using python and matplotlib. Is there any way I can reduce the distance between the two boxplots on the X axis?
This is the code that I'm using to get the figure above:
import matplotlib.pyplot as plt
from matplotlib import rcParams
rcParams['ytick.direction'] = 'out'
rcParams['xtick.direction'] = 'out'
fig = plt.figure()
xlabels = ["CG", "EG"]
ax = fig.add_subplot(111)
ax.boxplot([values_cg, values_eg])
ax.set_xticks(np.arange(len(xlabels))+1)
ax.set_xticklabels(xlabels, rotation=45, ha='right')
fig.subplots_adjust(bottom=0.3)
ylabels = yticks = np.linspace(0, 20, 5)
ax.set_yticks(yticks)
ax.set_yticklabels(ylabels)
ax.tick_params(axis='x', pad=10)
ax.tick_params(axis='y', pad=10)
plt.savefig(os.path.join(output_dir, "output.pdf"))
And this is an example closer to what I'd like to get visually (although I wouldn't mind if the boxplots were even a bit closer to each other):
You can either change the aspect ratio of plot or use the widths kwarg (doc) as such:
ax.boxplot([values_cg, values_eg], widths=1)
to make the boxes wider.
Try changing the aspect ratio using
ax.set_aspect(1.5) # or some other float
The larger then number, the narrower (and taller) the plot should be:
a circle will be stretched such that the height is num times the width. aspect=1 is the same as aspect=’equal’.
http://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes.set_aspect
When your code writes:
ax.set_xticks(np.arange(len(xlabels))+1)
You're putting the first box plot on 0 and the second one on 1 (event though you change the tick labels afterwards), just like in the second, "wanted" example you gave they are set on 1,2,3.
So i think an alternative solution would be to play with the xticks position and the xlim of the plot.
for example using
ax.set_xlim(-1.5,2.5)
would place them closer.
positions : array-like, optional
Sets the positions of the boxes. The ticks and limits are automatically set to match the positions. Defaults to range(1, N+1) where N is the number of boxes to be drawn.
https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.boxplot.html
This should do the job!
As #Stevie mentioned, you can use the positions kwarg (doc) to manually set the x-coordinates of the boxes:
ax.boxplot([values_cg, values_eg], positions=[1, 1.3])
I would like to plot a curve in the (x,y) plane, where the color of the curve depends on a value of another variable T. x is a 1D numpy array, y is a 1D numpy array.
T=np.linspace(0,1,np.size(x))**2
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(x,y)
I want the line to change from blue to red (using RdBu colormap) depending on the value of T (one value of T exists for every (x,y) pair).
I found this, but I don't know how to warp it to my simple example. How would I use the linecollection for my example? http://matplotlib.org/examples/pylab_examples/multicolored_line.html
Thanks.
One idea could be to set the color using color=(R,G,B) then split your plot into n segments and continuously vary either one of the R, G or B (or a combinations)
import pylab as plt
import numpy as np
# Make some data
n=1000
x=np.linspace(0,100,n)
y=np.sin(x)
# Your coloring array
T=np.linspace(0,1,np.size(x))**2
fig = plt.figure()
ax = fig.add_subplot(111)
# Segment plot and color depending on T
s = 10 # Segment length
for i in range(0,n-s,s):
ax.plot(x[i:i+s+1],y[i:i+s+1],color=(0.0,0.5,T[i]))
Hope this is helpful