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.
Related
I am just looking for some kind of working example which will allow me to have a slider and a sine wave plot whereby I can vary the frequency in a Jupyter Notebook. All the online demos that I have Googled don't work for me. The issue seems to be that the old matplotlib graph is not erased before the new one is created.
Here is my minimal example
import ipywidgets as widgets
from IPython.display import display, clear_output
import matplotlib.pyplot as plt
import numpy as np
def f(a):
clear_output(wait=True)
fig, ax = plt.subplots()
x = np.linspace(0, 2*np.pi, 100)
y = np.sin(a*x)
ax.grid()
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.plot(x,y)
ax.set_title("y = sin(x)")
%matplotlib inline
from time import sleep
for i in range(1,10):
clear_output(wait=True)
f(i)
sleep(1)
I have also tried
out = widgets.Output(layout={'border': '1px solid black'})
with out:
widgets.interact(f, a=1)
out.clear_output()
display(out)
However nothing I try will erase the previous matplotlib graph. They just all pile up on top of each other. I admit I am floundering as I don't really understand the API that well and the example in the documentation don't work for me.
I don't expect people to fix my code as it is probably rubbish. I am just looking for a working example which will allow me to interactively control the frequency and redraw the sine wave on a Jupyter notebook.
Here is a minimal example that works for me:
from ipywidgets import interact
import ipywidgets as widgets
import matplotlib.pyplot as plt
import numpy as np
def plot(freq):
x = np.linspace(0, 2*np.pi)
y = np.sin(x * freq)
plt.plot(x, y)
interact(plot, freq = widgets.FloatSlider(value=2, min=0.1, max=5, step=0.1))
default plot:
Screenshot of notebook after moving the slider to the right:
The following figure (exported as a pdf) contains overly detailed vector images that load slowly in a PDF viewer:
I'm trying to rasterize the outlines of the polygons displayed. However, when I use ax.set_rasterization_zorder(0), the following occurs:
I believe it is a backend issue but cannot figure out how to fix the problem.
import numpy as np
import matplotlib as mpl
from matplotlib.collections import PatchCollection
fig, ax = plt.subplots()
ax.imshow(dem, zorder=-2, rasterized=True)
parts = mpl.patches.Polygon(vertices)
polygon = PatchCollection(parts, zorder=-1)
ax.add_collection(polygon)
ax.set_rasterization_zorder(0)
I was not setting rasterized=True when creating the PatchCollection objects that were drawing the polygons. When using rasterized=True without ax.set_rasterization_zorder(), each polygon would be rasterized individually, creating a large file; however, it was at least in the right location.
By rasterizing the polygon initially at the PatchCollection level and combining it with ax.set_rasterization_zorder() prior to calling plt.savefig(), all of the polygon objects were combined with the underlying DEM into one raster, creating a file manageable by Adobe PDF viewers.
import numpy as np
import matplotlib as mpl
from matplotlib.collections import PatchCollection
fig, ax = plt.subplots()
ax.imshow(dem, zorder=-2, rasterized=True)
parts = mpl.patches.Polygon(vertices)
polygon = PatchCollection(parts, zorder=-1, rasterized=True)
ax.add_collection(polygon)
ax.set_rasterization_zorder(0)
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()
I am creating a scatter plot with color map based on some values and I am trying to make part of the x_axis label italic (inspired mostly by this post -> https://stackoverflow.com/a/8384685/1093485) but I am getting a LaTeX error that I can not explain myself, I would appreciate if anyone is able to explain what is going wrong with this chunk?
Minimum code required to reproduce problem here:
#! /usr/bin/env python
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rc
X = [1257.422648,1298.449197,1339.475746,1419.475471,1455.52309,1460.50202,1485.533655]
Y = [21.84637515,18.19617016,22.29456694,5.228978612,3.888695726,12.36598466,4.201838517]
Z = [44.02797944,9.758071204,21.58997772,64.53887544,53.09630431,8.461254471,291.4311435]
# Enable LaTeX style
rc('text',usetex=True)
# Plot the data
fig=plt.figure()
fig.patch.set_facecolor('white')
ax=fig.add_subplot(111)
s = ax.scatter(X,Y,c=np.log(Z))
ax.set_xlabel(r'Analyte \textit{m/z}')
ax.xaxis.labelpad = 7.5
cb = plt.colorbar(mappable=s,ax=ax)
plt.show()
Commenting the rc('text',usetex=True) causes the plot to show but obviously without italics. The whole traceback is rather large but seems to revolve around this part (if I read it correctly):
RuntimeError: LaTeX was not able to process the following string:
'$1450$'
Anyone have a suggestion on what to do to isolate the problem?
Hi I have a text file containing three columns of numbers; one column each for the x,y,z coordinates of a bunch of points. All numbers are between 0 ad 1.
I want to plot all these points in the unit cube [0,1]x[0,1]x[0,1].
Please let me know how I can do this in Octave or MatPlot lib, whichever prduces a better quality image.
If I understand your question correctly, this is how it looks in Matplotlib:
This is the code to produce this plot:
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
np.random.seed(101)
x,y,z = np.random.rand(3,20)
fig = plt.figure()
# version 1.0.x syntax:
#ax = fig.add_subplot(111, projection='3d')
# version 0.99.x syntax: (accepted by 1.0.x as well)
ax = Axes3D(fig)
ax.scatter(x,y,z)
fig.savefig('scatter3d.png')
As the code suggests, there are slight differences in how matplotlib version 0.99.1.1 and version 1.0.1 behave, as noted in this SO question/answer. I am using 0.99.1.1, and I had trouble using all the options available to 2D scatter plots, which should be the same for 3D plots as well. The full list of scatter features are listed here.
The above code resulted from looking at the matplotlib tutorial on 3D plotting.