I'm working through a tutorial on MatPlotlib and I initially started using Spyder's IPython console. The very simple code:
import matplotlib.pyplot as plt
x = range(1, 5)
plt.plot(x, [xi*1.5 for xi in x])
plt.plot(x, [xi*3.0 for xi in x])
plt.plot(x, [xi/3.0 for xi in x])
plt.show()
in Ipython only produces a plot once plt.show() is executed, and that plot has 3 lines on it - as expected. But in Spyder's IPython console, I get a plot every time plt.plot() is executed, and plt.show() doesn't seem to have any effect at all. Each individual plot only has one line plotted on it rather than the 3 lines I see at the end of the code with IPython.
Is there a way to make Spyder behave like IPython?
Related
I want to dynamically update the plot of a cell. I.e. the plot is initialized at the beginning of the cell, and updated in a (computationally heavy) for-loop, showing how the computation is progressing. In jupyter notebook, this can be done using pneumatics solution in What is the currently correct way to dynamically update plots in Jupyter/iPython?
%matplotlib notebook
import numpy as np
import matplotlib.pyplot as plt
import time
def pltsin(ax, colors=['b']):
x = np.linspace(0,1,100)
if ax.lines:
for line in ax.lines:
line.set_xdata(x)
y = np.random.random(size=(100,1))
line.set_ydata(y)
else:
for color in colors:
y = np.random.random(size=(100,1))
ax.plot(x, y, color)
fig.canvas.draw()
fig,ax = plt.subplots(1,1)
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_xlim(0,1)
ax.set_ylim(0,1)
for f in range(5):
pltsin(ax, ['b', 'r'])
time.sleep(1)
I am looking for an equivalent way of doing it in jupyter lab. I tried replacing %matplotlib notebook with %matplotlib widget, using the ipympl library, but that didn't work: The figure only shows once the loop is finished.
What I do not want are solutions like the one proposed by Ziofil in or the one by Paidoo in jupyterlab interactive plot which clear the whole output, as I might print additional things such as e.g. a tqdm progress bar
This is a known for matplotlib for which there happily are workarounds.
The relevant issues are: https://github.com/matplotlib/matplotlib/issues/18596 and https://github.com/matplotlib/ipympl/issues/258
and probably the longest explanation is https://github.com/matplotlib/ipympl/issues/290#issuecomment-755377055
Both of these workarounds will work with ipympl.
Workaround 1
Use the async ipython event loop following this answer: https://stackoverflow.com/a/63517891/835607
Workaround 2
Split the plt.subplots and the updating plot code into two cells. If you wait for a second or two between executing the cells then the plot will have enough time to set itself up properly and it should all work. That looks like this:
Cell 1:
fig,ax = plt.subplots(1,1)
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_xlim(0,1)
ax.set_ylim(0,1)
wait until the plot shows up then execute:
Cell 2:
for f in range(5):
pltsin(ax, ['b', 'r'])
time.sleep(1)
Edit: the problem I describe here no longer shows with current versions of matplotlib (2.1.1, edit made on 10 Sep 2019): most certainly a bug that has been fixed since then
I want to have a line plot (drawn with Axis.plot()) that is partially covered by the surface generated by Axis.plot_surface(). I wrote the following script:
import numpy as np
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
resolution = 100
ax = plt.gca(projection="3d")
x, y = np.meshgrid( np.linspace(-1.0, 1.0, resolution) ,
np.linspace(-1.0, 1.0, resolution) )
phi = (1.0 - x)*(1.0 - y) / 4.0
ax.plot([-1.0,1.0], [-1.0,1.0], [0.0,0.0], color="red")
ax.plot_surface(x, y, phi, linewidth=0, antialiased=False)
plt.show()
and made sure to have the call to plot() before the one to plot_surface(). Nevertheless, it seems that the line plot always has the highest "zindex" and gets plotted over the surface. Here is what I obtain:
Here is what I would like to have instead:
How do I achieve this result? (Without using Gimp…)
I changed.
ax.plot_surface(x, y, phi, linewidth=0, antialiased=False)
with:
ax.plot_surface(x, y, phi, linewidth=0, antialiased=True)
and then I saw the whole red line
It shows the plot with red line
When I try to obtain plots in which the axis (both formulae and text) are written in LaTeX standard roman font, I keep not obtaining the plot, but the code runs without warnings. In particular, this simple scatter with TeX code in the axis labels, in which I have put my better understanding of the documentation:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rc
x = np.linspace(0,1,100)
y = np.random.rand(100,1)
plt.rc('text', usetex=True)
plt.rc('font', family='roman')
plt.scatter(x, y, c='b', s=10)
plt.xlabel(r'$\lambda$ ($\AA$)',size='12')
plt.ylabel(r'$F_\alpha (W/m^2)$ ',size='12')
plt.title(r'A title in \LaTeX typography')
plt.show()
keeps yielding a message like <matplotlib.figure.Figure at 0x1f75d4750>, which I have met before, but I keep failing when trying to remedy this one. In addition, saving the plot (png or pdf) would not solve the issue, and if the problem is related to TeX, I have definitely not found any resource that can help. I use MacOS Sierra.
I'm trying to make a notebook where the data produced by a long for loop is put in a graph point by point. However, when using %matplotlib notebook and fig.canvas.draw() the graph is tiny up until the cell finishes running. (In fact, I've got many of those graphs and they are even tinyer when using more subplots.)
Here my code reproducing the behaviour in a jupyter notebook, at least on OS X with (latest) jupyter-core 4.3.0 and (latest) matplotlib 2.0.2.
%matplotlib notebook
import time
import matplotlib.pyplot as plt
fig, ax = plt.subplots(1, 1)
for _ in range(5):
ax.plot([1,2,3], [1,2,3])
fig.canvas.draw()
time.sleep(1)
During the cell execution I get this plot
And when the cell finishes execution (after 5 seconds) I get this
I would like to get the larger image even during cell execution. What am I doing wrong?
As proposed by ImportanceOfBeingErnest, one solution is to put the figure creation in its own cell.
[1] %matplotlib notebook
import time
import matplotlib.pyplot as plt
[2] fig, ax = plt.subplots(1, 1)
[3] for _ in range(5):
ax.plot([1,2,3], [1,2,3])
fig.canvas.draw()
time.sleep(1)
Edit: This solutions does not work if you run all cells at once.
Is it possible to use Python matplotlib code to draw graph in RStudio?
e.g. below Python matplotlib code:
import numpy as np
import matplotlib.pyplot as plt
n = 256
X = np.linspace(-np.pi,np.pi,n,endpoint=True)
Y = np.sin(2*X)
plt.plot (X, Y+1, color='blue', alpha=1.00)
plt.plot (X, Y-1, color='blue', alpha=1.00)
plt.show()
Output graph will be:
Then I need to write a R Markdown to include these code and generate graph automatically after knitting the markdown.
install.packages('devtools') first, get install_github function
install_github("rstudio/reticulate") install the dev version of reticulate
in r markdown doc, use code below to enable the function.
```{r setup, include=FALSE}
library(knitr)
library(reticulate)
knitr::knit_engines$set(python = reticulate::eng_python)
```
Try it , you will get what you want and don't need to save any image.
One possible solution is save the plot as a image, then load the file to markdown.
### Call python code sample
```{r,engine='python'}
import numpy as np
import matplotlib.pyplot as plt
n = 256
X = np.linspace(-np.pi,np.pi,n,endpoint=True)
Y = np.sin(2*X)
fig, ax = plt.subplots( nrows=1, ncols=1 )
ax.plot (X, Y+1, color='blue', alpha=1.00)
ax.plot (X, Y-1, color='blue', alpha=1.00)
#plt.show()
fig.savefig('foo.png', bbox_inches='tight')
print "finished"
```
Output image:
#### The End
Output:
You can do that with reticulate, but most time in trying to follow a tutorial in doing that you may encounter some technicalities that weren't sufficiently explained.
My answer is a little late but I hope it's a thorough walkthrough of doing it the right way - not rendering it and then loading it as a png but have the python code executed more "natively".
Step 1: Configure Python from RStudio
You want to insert an R chunk, and run the following code to configure the path to the version of Python you want to use. The default python that comes shipped with most OS is usually the outdated python 2 and is not where you install your packages. That is the reason why it's important to do this, to make sure Rstudio will use the specified python instance where your matplotlib library (and the other libraries you will be using for that project) can be found:
library(reticulate)
# change the following to point to the desired path on your system
use_python('/Users/Samuel/anaconda3/bin/python')
# prints the python configuration
py_config()
You should expect to see that your session is configured with the settings you specified:
python: /Users/Samuel/anaconda3/bin/python
libpython: /Users/Samuel/anaconda3/lib/libpython3.6m.dylib
pythonhome: /Users/Samuel/anaconda3:/Users/Samuel/anaconda3
version: 3.6.3 |Anaconda custom (64-bit)| (default, Oct 6 2017, 12:04:38) [GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)]
numpy: /Users/Samuel/anaconda3/lib/python3.6/site-packages/numpy
numpy_version: 1.15.2
python versions found:
/Users/Samuel/anaconda3/bin/python
/usr/bin/python
/usr/local/bin/python
/usr/local/bin/python3
/Users/Samuel/.virtualenvs/r-tensorflow/bin/python
Step 2: The familiar plt.show
Add a Python chunk (not R!) in your R Markdown document (see attached screenshot) and you can now write native Python code. This means that the familiar plt.show() and plt.imshow() will work without any extra work. It will be rendered and can be compiled into HTML / PDF using knitr.
This will work:
plt.imshow(my_image, cmap='gray')
Or a more elaborated example:
import numpy as np
import matplotlib.pyplot as plt
import os
import cv2
DATADIR = '/Users/Samuel/Datasets/PetImages'
CATEGORIES = ['Dog', 'Cat']
for category in CATEGORIES:
path = os.path.join(DATADIR, category) # path to cat or dog dir
for img in os.listdir(path):
img_array = cv2.imread(os.path.join(path,img), cv2.IMREAD_GRAYSCALE)
plt.imshow(img_array, cmap='gray')
plt.show()
break
break
Output:
Step 3: Knit to HTML / PDF / Word etc
Proceed to knit as usual. The end product is a beautifully formatted document done in Python code using R Markdown. RStudio has come a long way and I'm surprised the level of support it has for Python code isn't more known so hoping anyone that stumbled upon this answer will find it informative and learned something new.
I have been working with reticulate and R Markdown and you should specify your virtual environment. For example my R Markdown starts as follows:
{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, cache.lazy = FALSE)
library(reticulate)
use_condaenv('pytorch') ## yes, you can run pytorch and tensor flow too
Then you can work in either language. So, for plotting with matplotlib, I have found that you need the PyQt5 module to make it all run smoothly. The following makes a nice plot inside R Markdown - it's a separate chunk.
{python plot}
import PyQt5
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
data = pd.read_csv('Subscriptions.csv',index_col='Date', parse_dates=True)
# make the nice plot
# set the figure size
fig = plt.figure(figsize = (15,10))
# the series
ax1 = fig.add_subplot(211)
ax1.plot(data.index.values, data.Opens, color = 'green', label = 'Opens')
# plot the legend for the first plot
ax1.legend(loc = 'upper right', fontsize = 14)
plt.ylabel('Opens', fontsize=16)
# Hide the top x axis
ax1.axes.get_xaxis().set_visible(False)
####### NOW PLOT THE OTHER SERIES ON A SINGLE PLOT
# plot 212 is the MI series
# plot series
ax2 = fig.add_subplot(212)
ax2.plot(data.index.values, data.Joiners, color = 'orange', label = 'Joiners')
# plot the legend for the second plot
ax2.legend(loc = 'upper right', fontsize = 14)
# set the fontsize for the bottom plot
plt.ylabel('Joiners', fontsize=16)
plt.tight_layout()
plt.show()
You get the following from this:
I don't have the reputation points to add a comment, but Bryan's answer above was the only one to work for me. Adding plt.tight_layout() made the difference. I added that line to the following simple code and the plot displayed.
{python evaluate}
plt.scatter(X_train, y_train, color = 'gray')
plt.plot(X_train, regresssion_model_sklearn.predict(X_train), color = 'red')
plt.ylabel('Salary')
plt.xlabel('Number of Years of Experience')
plt.title('Salary vs. Years of Experience')
plt.tight_layout()
plt.show()