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.
Related
I don't know where to start, as I think it is a new approach for me. Using matplotlib with python, I would like to plot a set of fuzzy numbers (for instance a set of triangular or bell curve fuzzy numbers) as in the picture below:
You can plot the curves recurrently. My try at reproducing your example (including the superposition of labels 1 and 6):
import matplotlib.pyplot as plt
import numpy as np
# creating the figure and axis
fig, ax = plt.subplots(1,1,constrained_layout=True)
# generic gaussian
y = np.linspace(-1,1,100)
x = np.exp(-5*y**2)
center_x = (0,2,4,1,3,0,5)
center_y = (6,2,3,4,5,6,7)
# loop for all the values
for i in range(len(center_x)):
x_c, y_c = center_x[i], center_y[i]
# plotting the several bells, relocated to (x_c, y_c)
ax.plot(x + x_c,y + y_c,
color='red',linewidth=2.0)
ax.plot(x_c,y_c,
'o',color='blue',markersize=3)
# adding label
ax.annotate(
str(i+1),
(x_c - 0.1,y_c), # slight shift in x
horizontalalignment='right',
verticalalignment='center',
color='blue',
)
ax.grid()
Every call to ax.plot() is adding points or curves (to be more precise, Artists) to the same axis. The same for ax.annotate() to create the labels.
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')
I am trying to plot multiple rgb images with matplotlib
the code I am using is:
import numpy as np
import matplotlib.pyplot as plt
for i in range(0, images):
test = np.random.rand(1080, 720,3)
plt.subplot(images,2,i+1)
plt.imshow(test, interpolation='none')
the subplots appear tiny though as thumbnails
How can I make them bigger?
I have seen solutions using
fig, ax = plt.subplots()
syntax before but not with plt.subplot ?
plt.subplots initiates a subplot grid, while plt.subplot adds a subplot. So the difference is whether you want to initiate you plot right away or fill it over time. Since it seems, that you know how many images to plot beforehand, I would also recommend going with subplots.
Also notice, that the way you use plt.subplot you generate empy subplots in between the ones you are actually using, which is another reason they are so small.
import numpy as np
import matplotlib.pyplot as plt
images = 4
fig, axes = plt.subplots(images, 1, # Puts subplots in the axes variable
figsize=(4, 10), # Use figsize to set the size of the whole plot
dpi=200, # Further refine size with dpi setting
tight_layout=True) # Makes enough room between plots for labels
for i, ax in enumerate(axes):
y = np.random.randn(512, 512)
ax.imshow(y)
ax.set_title(str(i), fontweight='bold')
I faced a serious problem when I was trying to add colorbar to scatter plot which indicates in which classes individual sample belongs to. The code works perfectly when classes are [0,1,2] but when the classes are for example [4,5,6] chooses colorbar automatically color values in the end of colormap and colorbar looks blue solid color. I'm missing something obvious but I just can't figure out what it is.
Here is the example code about the problem:
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots(1 , figsize=(6, 6))
plt.scatter(datapoints[:,0], datapoints[:,1], s=20, c=labels, cmap='jet', alpha=1.0)
plt.setp(ax, xticks=[], yticks=[])
cbar = plt.colorbar(boundaries=np.arange(len(classes)+1)-0.5)
cbar.set_ticks(np.arange(len(classes)))
cbar.set_ticklabels(classes)
plt.show()
Variables can be for example
datapoints = np.array([[1,1],[2,2],[3,3],[4,4],[5,5],[6,6],[7,7]])
labels = np.array([4,5,6,4,5,6,4])
classes = np.array([4,5,6])
Correct result is got when
labels = np.array([0,1,2,0,1,2,0])
In my case I want it to work also for classes [4,5,6]
The buoundaries need to be in data units. Meaning, if your classes are 4,5,6, you probably want to use boundaries of 3.5, 4.5, 5.5, 6.5.
import matplotlib.pyplot as plt
import numpy as np
datapoints = np.array([[1,1],[2,2],[3,3],[4,4],[5,5],[6,6],[7,7]])
labels = np.array([4,5,6,4,5,6,4])
classes = np.array([4,5,6])
fig, ax = plt.subplots(1 , figsize=(6, 6))
sc = ax.scatter(datapoints[:,0], datapoints[:,1], s=20, c=labels, cmap='jet', alpha=1.0)
ax.set(xticks=[], yticks=[])
cbar = plt.colorbar(sc, ticks=classes, boundaries=np.arange(4,8)-0.5)
plt.show()
If you wanted to have the boundaries determined automatically from the classes, some assumption must me made. E.g. if all classes are subsequent integers,
boundaries=np.arange(classes.min(), classes.max()+2)-0.5
In general, an alternative would be to use a BoundaryNorm, as shown e.g. in Create a discrete colorbar in matplotlib
or How to specify different color for a specific year value range in a single figure? (Python) or python colormap quantisation (matplotlib)
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).