For plotting 100,000 to 500,000 data point in a text file I use the following code.
The problem is:
If I copy and paste the data points in a plotting software, reaching the plot takes just 30 seconds but with the following code it may take 1 hour or more to plot by Python.
import numpy as np
import matplotlib.pyplot as plt
from math import *
cmin=502.8571071527562
c,O=np.genfromtxt('textfile.txt',unpack=True)
for i in range(len(O)):
q=exp(-0.5*(c[i]-cmin))
plt.plot(O[i], q, 'bo')
plt.show()
What is the problem? How could I solve it?
I appreciate your help.
Some general rules:
use numpy, not math
avoid for-loops
Do not create unnecessary artists.
Here you want to create a single artist with all points, instead of 500000 single artists with one point each.
import numpy as np
import matplotlib.pyplot as plt
cmin=502.8571071527562
c,O=np.genfromtxt('textfile.txt',unpack=True)
q=np.exp(-0.5*(c-cmin))
plt.plot(O, q, 'bo')
plt.show()
Related
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
fig,ax=plt.subplots(2,2,figsize=(15,10))
x=np.linspace(-3,3)
ax[0,0].plot(x,foo-function)
now I need a way to save each of the 4 plots into one file like this:
plt1=topleft_plot.saveNOTfigBUTplot('quadfunction.pdf')
how?
Using the answer here: https://stackoverflow.com/a/4328608/16299117
We can do the following to save a SINGLE subplot from the overall figure:
import matplotlib.pyplot as plt
import numpy as np
fig,ax=plt.subplots(2,2,figsize=(15,10))
x=np.linspace(-3,3)
ax[0,0].plot(x,x**2) # This is just to make an actual plot.
# I am not using jupyter notebook, so I use this to show it instead of %inline
plt.show()
# Getting only the axes specified by ax[0,0]
extent = ax[0,0].get_window_extent().transformed(fig.dpi_scale_trans.inverted())
# Saving it to a pdf file.
fig.savefig('ax2_figure.pdf', bbox_inches=extent.expanded(1.1, 1.2))
EDIT: I believe I may have misunderstood what you want. If you want to save EACH plot individually, say as 4 different pages in a pdf, you can do the following adapted from this answer: https://stackoverflow.com/a/29435953/16299117
This will save each subplot from the figure as a different page in a single pdf.
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.backends.backend_pdf import PdfPages
fig,ax=plt.subplots(2,2,figsize=(15,10))
x=np.linspace(-3,3)
ax[0,0].plot(x,x**2) # This is just to make an actual plot.
with PdfPages('foo.pdf') as pdf:
for x in range(ax.shape[0]):
for y in range(ax.shape[1]):
extent = ax[x, y].get_window_extent().transformed(fig.dpi_scale_trans.inverted())
pdf.savefig(bbox_inches=extent.expanded(1.1, 1.2))
I have exported a large Matrix from Matlab to a data.dat file, which is tab delimited. I am importing this data into a iPython script to use seaborn to create a heatmap of the matrix using the following script:
import numpy as np
import seaborn as sns
import matplotlib.pylab as plt
uniform_data = np.loadtxt("data.dat", delimiter="\t")
ax = sns.heatmap(uniform_data, linewidth=0.0)
plt.show()
This code runs fine and outputs a correct heatmap. For small matrices, the output has a nice variation indicating the matrix elements:
However, if the size of the matrix increases in size, the result seems to have a uniform colour, which indicates that the result needs to be normalised:
which does not seem to contain any extractable information. How can I address this?
I am writting a script to plot some data.
I am using python 3.7.1 on windows and have the following code to plot:
import pandas as pd
import matplotlib.pyplot as plt
files=['path']
for i in range(len(files)):
data = pd.read_csv(files[i], sep=';', skiprows=17, header=None,engine='python', decimal=",")
c=files[0].split('\\')
path='\\'.join(c[:-1])
x= data.loc[:,0].values
y= data.loc[:,1].values
c,data=None,None
plt.ioff() #turns off the plotting
plt.plot(x,y)
plt.xlabel('x]')
plt.ylabel('y')
plt.savefig(path+'\\ title123') #saves image
I want to transform the dataframe from pandas into a numpy array dtype float64.
Currently, the code I have transforms the data into an object type. I cannot plot this because the code is taking too long to run.
An example of what I am trying to achieve is:
import numpy as np
import matplotlib.pyplot as plt
x=np.linspace(0,10,1000000)
y=np.sin(x)
plt.plot(x,y)
I will leave a link to the file.
https://drive.google.com/open?id=1kir-cGlk3bZSLmvD_tfnbGUaTYzvcW-3
Can anyone give me a help?
Kind Regards!
I just noticed that it was a problem with ',' and '.'. Sort of a math "language" conflict.
However, the for loop runs extremely slow when more than one file is loaded.
Kind regards to all!
I would like to create a pdf file [by using plt.savefig("~~~.pdf")]
containing lots of (about 20) subplots
each of which is drawing timeseries data.
I am using a matplotlib library with python language.
Each subplot may be long, and I want to put the subplots
horizontally.
Therefore, the figure should be very long (horizontally), so the horizontal scroll bar should be needed!
Is there any way to do this?
some example code will be appreciated!
The following is my sample code.
I just wanted to draw 10 sine graphs arranged horizontally
and save it as pdf file.
(but I'm not pretty good at this. so the code may looks to be weird to you.. :( )
from matplotlib import pyplot as plt
import numpy as np
x=np.linspace(0,100,1000)
y=np.sin(x)
numplots=10
nr=1
nc=numplots
size_x=nc*50
size_y=size_x*3/4
fig=plt.figure(1,figsize=(size_x,size_y))
for i in range(nc):
ctr=i+1
ax=fig.add_subplot(nr,nc,ctr)
ax.plot(x,y)
plt.savefig("longplot.pdf")
plt.clf()
Thank you!
You should do that using the backend "matplotlib.backends.backend_pdf". This enables you to save matplotlib graphs in pdf format.
I have simplified your code a bit, here is a working example:
from matplotlib import pyplot as plt
import numpy as np
from matplotlib.backends.backend_pdf import PdfPages
x = np.linspace(0,100,1000)
y = np.sin(x)
nr = 10
nc = 1
for i in range(nr):
plt.subplot(nr, nc, i + 1)
plt.plot(x, y)
pdf = PdfPages('longplot.pdf')
pdf.savefig()
pdf.close()
I hope this helps.
In the link below there is a solution, which can help you, since it was helpful to me either.
Scrollbar on Matplotlib showing page
But if you have many subplots, I am afraid your problem won't be solved. Since it will shrink each graph anyway. In that case it will be better to break your graphs into smaller and separate parts.
Plotting a very, very simple map of only europe in matplotlib / basemap takes so much time (around 10 seconds!). This is just unreal!?
Setting of resolution is only "l" (low).
Here is the very simple code:
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
m = Basemap(projection='stere',lon_0=5,lat_0=90.0,rsphere=6371200.,\
llcrnrlon=-25.0,urcrnrlon=72.0,llcrnrlat=26.0,urcrnrlat=65.0,resolution='l')
m.drawcoastlines(linewidth=0.2)
m.drawcountries(linewidth=0.2)
plt.savefig('/var/www/map.png')
I need to plot hundreds of these maps every 2 hours. This would be impossible? :( Only idea is: Create an empty Basemap and try to draw boundaries with a shapefile.
Regards,
John