This code shows an Attribute error:
I am plotting errorbar plot for let's say 10 different datasets (huge datasets) from a file containing multiple datasets (let's say for different days), and I am showing the user an option (Checkbox) to remove or include a plot of the particular dataset (through GUI).
So for this, I just want to erase the current axes and at a later time want to redraw it again.
How can I do this?
Below is a simplified example to show what I need.
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots(1)
x, y, yerr = np.random.rand(3,10)
l = ax.errorbar(x, y, yerr, marker='s', mfc='red', mec='green', ms=20, mew=4)
canvas = fig.canvas
canvas.draw()
bkg = canvas.copy_from_bbox(ax.bbox)
plt.show()
plt.pause(1)
ax.clear()
canvas.restore_region(bkg)
ax.draw_artist(l)
# here it throws an AttributeError: 'ErrorbarContainer'
#object has no attribute 'draw'
Related
I am using subplots in a function which is using a slider widget inputs to calculate some stuff and plotting results.
I want to turn on the grid for all subplots of ax1. But somehow jupternotebooks only turns it on only on the last plot...
import numpy as np
from matplotlib import pyplot as plt
import ipywidgets as widgets
from IPython.html.widgets import interact
%matplotlib inline
## Plot
fig, ax1 = plt.subplots(6,2)
plt.subplots_adjust(right = 2, top = 8 )
# Show the major grid lines with dark grey lines
plt.grid(b=True, which='major', color='#666666', linestyle='-')
# Show the minor grid lines with very faint and almost transparent grey lines
plt.minorticks_on()
plt.grid(b=True, which='minor', color='#999999', linestyle='-', alpha=0.2)
## Giergeschwindigkeit über v und ay
ax1[0,0].plot(v_ms, omega)
ax1[0,0].set_ylabel('Giergeschwindigkeit [rad/s]')
ax1[0,0].set_xlabel('Geschwindigkeit [m/s]')
ax1[0,0].set_title('Giergeschwindigkeit über Geschwindigkeit')
# ... more subplots
plt.show()
It looks like this:
And can you explain to me why in my case
ax1.grid()
throws an error?
AttributeError: 'numpy.ndarray' object has no attribute 'grid'
This is because plt will only operate on the last-created axes object.
And the reason you're getting that error is that ax1 is a numpy n-dimensional array, not an axes object.
You can do this to iterate over the numpy n-dimensional array to create the grids:
for row in axes:
for ax in row:
ax.grid(b=True, which='major', color='#666666', linestyle='-')
ax.minorticks_on()
ax.grid(b=True, which='minor', color='#999999', linestyle='-',alpha=0.2)
Result (without plt.subplots_adjust()):
You can set grid for every ax object, so in your case you should set like this:
ax1[0,0].grid()
ax1[0,1].grid()
this is my first question here so let me know if I should make any improvements regarding e.g. formulation of the question, code and so on.
So I am creating several 3-D Scatter Plots in Python and want to safe them for later re usage and comparability. I am using Qt5 as Graphics Backend in Spyder, which perfectly displays my interactive (so I can rotate over the axes and flip the plot) 3-D Scatter plot using the origin Code.
Now I am able to successfully save the created plot and also load it into a new script, which opens the Plot in Qt5 as well. But somehow the interactivity is gone, meaning I am not able to rotate over the axes and flip the plot anymore.
I was unable to find any guidance to that issue or find any person with a similar problem. Do you guys have any idea? I'll put the relevant part of my sample Code below:
""" First script """
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import pandas as pd
import pickle
testdf = pd.DataFrame({"X" : x, "Y" : y, "Z" : z}) #x and y are the criteria, z the values, stored as lists
# Create 3d scatter plot
fig = plt.figure(figsize=(12, 12))
ax = fig.add_subplot(111, projection="3d")
ax.scatter(x, y, z, c=z, marker="o")
ax.set_xlabel("Initial Notional Cluster")
ax.set_ylabel("Laufzeit in Month Cluster")
ax.set_zlabel("Vol. Weighted Margin")
plt.show()
# Save the figure object as binary file
file = open(r"Init_Lfz_VolWeightedMargin.pkl", "wb")
pickle.dump(fig, file)
file.close()
""" Second script """
import matplotlib.pyplot as plt
figx = pickle.load(open(r"Init_Lfz_VolWeightedMargin.pkl", "rb"))
plt.show()
Any idea, why the interactivity is gone? According to the pickle library and other usercases, this shall not happen.
Many thanks.
I created a scatter plot in seaborn using seaborn.relplot, but am having trouble putting the legend all in one graph.
When I do this simple way, everything works fine:
import pandas as pd
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
df2 = df[df.ln_amt_000s < 700]
sns.relplot(x='ln_amt_000s', y='hud_med_fm_inc', hue='outcome', size='outcome', legend='brief', ax=ax, data=df2)
The result is a scatter plot as desired, with the legend on the right hand side.
However, when I try to generate a matplotlib figure and axes objects ahead of time to specify the figure dimensions I run into problems:
a4_dims = (10, 10) # generating a matplotlib figure and axes objects ahead of time to specify figure dimensions
df2 = df[df.ln_amt_000s < 700]
fig, ax = plt.subplots(figsize = a4_dims)
sns.relplot(x='ln_amt_000s', y='hud_med_fm_inc', hue='outcome', size='outcome', legend='brief', ax=ax, data=df2)
The result is two graphs -- one that has the scatter plots as expected but missing the legend, and another one below it that is all blank except for the legend on the right hand side.
How do I fix this such? My desired result is one graph where I can specify the figure dimensions and have the legend at the bottom in two rows, below the x-axis (if that is too difficult, or not supported, then the default legend position to the right on the same graph would work too)? I know the problem lies with "ax=ax", and in the way I am specifying the dimensions as matplotlib figure, but I'd like to know specifically why this causes a problem so I can learn from this.
Thank you for your time.
The issue is that sns.relplot is a "Figure-level interface for drawing relational plots onto a FacetGrid" (see the API page). With a simple sns.scatterplot (the default type of plot used by sns.relplot), your code works (changed to use reproducible data):
df = pd.read_csv("https://vincentarelbundock.github.io/Rdatasets/csv/datasets/iris.csv", index_col=0)
fig, ax = plt.subplots(figsize = (5,5))
sns.scatterplot(x = 'Sepal.Length', y = 'Sepal.Width',
hue = 'Species', legend = 'brief',
ax=ax, data = df)
plt.show()
Further edits to legend
Seaborn's legends are a bit finicky. Some tweaks you may want to employ:
Remove the default seaborn title, which is actually a legend entry, by getting and slicing the handles and labels
Set a new title that is actually a title
Move the location and make use of bbox_to_anchor to move outside the plot area (note that the bbox parameters need some tweaking depending on your plot size)
Specify the number of columns
fig, ax = plt.subplots(figsize = (5,5))
sns.scatterplot(x = 'Sepal.Length', y = 'Sepal.Width',
hue = 'Species', legend = 'brief',
ax=ax, data = df)
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles=handles[1:], labels=labels[1:], loc=8,
ncol=2, bbox_to_anchor=[0.5,-.3,0,0])
plt.show()
I am trying to make an animation using ArtistAnimation like this:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
fig, ax = plt.subplots()
ims = []
for i in range(60):
x = np.linspace(0,i,1000)
y = np.sin(x)
im = ax.plot(x,y, color='black')
ims.append(im)
ani = animation.ArtistAnimation(fig, ims, interval=50, blit=True,
repeat_delay=1000)
plt.show()
This animates a sine wave growing across the figure. Currently I'm just adding the Lines2D object returned by ax.plot() to ims. However, I would like to potentially draw multiple overlapping plots on the Axes and adjust the title, legend and x-axis range for each frame. How do I get an object that I can add to ims after plotting and making all the changes I want for each frame?
The list you supply to ArtistAnimation should be a list of lists of artists, one list per frame.
artist_list = [[line1a, line1b, title1], [line2a, line2b, title2], ...]
where the first list is shown in the first frame, the second list in the second frame etc.
The reason your code works is that ax.plot returns a list of lines (in your case only a list of a single line).
In any case, the following might be a more understandable version of your code where an additional text is animated.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
fig, ax = plt.subplots()
artist_list = []
for i in range(60):
x = np.linspace(0,i,1000)
y = np.sin(x)
line, = ax.plot(x,y, color='black')
text = ax.text(i,0,i)
artist_list.append([line, text])
ani = animation.ArtistAnimation(fig, artist_list, interval=50, blit=True,
repeat_delay=1000)
plt.show()
In general, it will be hard to animate changing axes limits with ArtistAnimation, so if that is an ultimate goal consider using a FuncAnimation instead.
python 3.6 om mac
matplotlib 2.1.0
using matplotlib.pyplot (as plt)
Let's say i have a few plt.figures() that i appended into a list called figures as objects. When in command line i do: figures[0]it produces the plot for the index 0 of the list figures.
However, how can i arrange to have all the plots in figures to be in a subplot.
# Pseudo code:
plt.figure()
for i, fig in enumerate(figures): # figures contains the plots
plt.subplot(2, 2, i+1)
fig # location i+1 of the subplot is filled with the fig plot element
So as a result, i would a 2 by 2 grid that contains each plot found in figures.
hoping this makes sense.
A figure is a figure. You cannot have a figure inside a figure. The usual approach is to create a figure, create one or several subplots, plot something in the subplots.
In case it may happen that you want to plot something in different axes or figures, it might make sense to wrap the plotting in a function which takes the axes as argument.
You could then use this function to plot to an axes of a new figure or to plot to an axes of a figure with many subplots.
import numpy as np
import matplotlib.pyplot as plt
def myplot(ax, data_x, data_y, color="C0"):
ax.plot(data_x, data_y, color=color)
ax.legend()
x = np.linspace(0,10)
y = np.cumsum(np.random.randn(len(x),4), axis=0)
#create 4 figures
for i in range(4):
fig, ax = plt.subplots()
myplot(ax, x, y[:,i], color="C{}".format(i))
# create another figure with each plot as subplot
fig, ax = plt.subplots(2,2)
for i in range(4):
myplot(ax.flatten()[i], x, y[:,i], color="C{}".format(i))
plt.show()