I make a graph using matplotlib and save it as a pdf. When I zoom in there is a gap where the x- and y-axis converge. Is there any way to get rid of this?
import matplotlib.pyplot as plt
import numpy as np
x = np.array([1, 2, 3])
y = np.array([1, 2, 3])
plt.scatter(x, y)
plt.savefig('Scatter_Plot.pdf')
Unfortunately I can not upload pictures here - but here is a link:
http://de.tinypic.com/r/25gckcw/8
Thanks
I've updated matplotlib 1.3.1 -> 1.4.3
Now everything looks perfect!
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:
I want to scale Y axis so that I can see values, as code below plots cant see anything other than a thin black line. Changing plot height doesn't expand the plot.
import numpy as np
import matplotlib.pyplot as plt
data=np.random.random((4,10000))
plt.rcParams["figure.figsize"] = (20,100)
#or swap line above with one below, still no change in plot height
#fig=plt.figure(figsize=(20, 100))
plt.matshow(data)
plt.show()
One way to do this is just repeat the values then plot result, but I would have thought it possible to just scale the height of the plot?
data_repeated = np.repeat(data, repeats=1000, axis=0)
You can do it like this:
import numpy as np
import matplotlib.pyplot as plt
data=np.random.random((4, 10000))
plt.figure(figsize=(40, 10))
plt.matshow(data, fignum=1, aspect='auto')
plt.show()
Output:
I am trying to find a way in matplotlib to draw a lineplot, except that I don't want to draw a line between points. Instead I want to draw a perpendicular line between each of my points and the x axis.
When I do a standard plot, I obtain the following :
import numpy as np
import matplotlib.pyplot as plt
data = np.array([0,1,3,2,3,1,4])
plt.plot(data)
plt.xlim([-0.2,6.2])
plt.ylim([-0.2,5])
Instead I want to obtain the following :
Any ideas how to do this ?
Thanks
There are two other options apart from stem and bar chart is the following using vlines() and LineCollection()
Option 1 -- Using vlines()
for x, y in enumerate(data):
plt.vlines(x=x, ymin=0, ymax=y, color='r')
Or in a single line without using loops
plt.vlines(x=range(data.size), ymin=0, ymax=data, color='r')
Option 2 -- Using LineCollection()
from matplotlib.collections import LineCollection
lines = [[(x, 0), (x, y)] for x, y in enumerate(data)]
linesCol = LineCollection(lines, linewidths=3, color='r')
fig, ax = plt.subplots()
ax.add_collection(linesCol)
plt.scatter(range(len(data)), data, s=0)
I'm trying to migrate from Basemap to Cartopy looking demo examples. I have a simple code using both coastlines() and contourf(). I can get both separately but not simultaneously. The data set is a netcdf file containing the sea surface temperature data of the west Med. The code is:
import numpy as np
from netCDF4 import Dataset
import cartopy
import matplotlib.pyplot as plt
# DATA
data = Dataset('20190715.0504.n19.nc','r')
lon = data.variables['lon'][:]
lat = data.variables['lat'][:]
sst = data.variables['mcsst'][0,:,:].squeeze()
xxT,yyT = np.meshgrid(lon,lat)
# PLOT
fig = plt.figure(figsize=(10, 5))
ax1 = fig.add_axes([0.01,0.01,0.98,0.98],projection=cartopy.crs.Mercator())
ax1.coastlines()
#ax1.contourf(xxT,yyT,sst)
ax1.set_extent([16.5, -15.0, 35.0, 46.5])
plt.show()
With this code I get:
If I use:
#ax1.coastlines()
ax1.contourf(xxT,yyT,sst)
ax1.set_extent([16.5, -15.0, 35.0, 46.5])
I get a white rectangle.
If I use:
#ax1.coastlines()
ax1.contourf(xxT,yyT,sst)
ax1.set_extent([16.5,-15.0,35.0,46.5],crs=cartopy.crs.Mercator())
I get the contoured data.
But with both:
ax1.coastlines()
ax1.contourf(xxT,yyT,sst)
ax1.set_extent([16.5,-15.0,35.0,46.5],crs=cartopy.crs.Mercator())
the contour is ok ! but without coastlines. And if finally
ax1.coastlines()
ax1.contourf(xxT,yyT,sst)
ax1.set_extent([16.5,-15.0,35.0,46.5])
only coastlines are shown, not contour !. I try to understand how I have to proceed because problems arose when trying to include this into a GUI with options show/hide for coatlines, features, etc. Just in case I'm using Python 3.7.4, Cartopy 0.17, proj4 5.2, matplotlib 3.1.1. Thanks !
Thanks to swatchai suggestion, although, I still don't understand why I need to use the transform keyword with the specific PlateCarree projection keyword, the code works fine if:
fig = plt.figure(figsize=(10, 5))
ax1 = fig.add_axes([0.01, 0.01, 0.98, 0.98],projection=cartopy.crs.Mercator())
ax1.coastlines('10m')
ax1.set_extent([16.5, -15.0, 35.0, 46.5])
ax1.contourf(xxT,yyT,sst,transform=cartopy.crs.PlateCarree())
Here the result:
I am writting a very simple script, one that plot a sin using jupyter notebook (python 3). when I put:
import numpy
import matplotlib.pyplot as plt
x=np.arange(0.0,5*np.pi,0.001)
y = np.sin(x)
plt.plot(x,y)
The plot is fine.
However if :
import numpy
import matplotlib.pyplot as plt
x=np.arange(0.0,5*np.pi,0.001)
np.random.shuffle(x)
y = np.sin(x)
plt.plot(x,y)
the image is
I don't understand why shuffling the x BEFORE I ran sin does it.
thank you
Let's first simplify things a bit. We plot 4 points and annote them with the order in which they are plotted.
import numpy as np; np.random.seed(42)
import matplotlib.pyplot as plt
x=np.arange(4)
y = np.sin(x)
plt.plot(x,y, marker="o")
for i, (xi,yi) in enumerate(zip(x,y)):
plt.annotate(str(i), xy=(xi,yi), xytext=(0,4),
textcoords="offset points", ha="center")
plt.show()
No if we shuffle x and plot the same graph,
x=np.arange(4)
np.random.shuffle(x)
y = np.sin(x)
we see that positions of the points are still are the same, but while e.g. previously the first point was the one at (0,0), it's now the third one appearing there. Due to this randomized order, the connecting lines go zickzack.
Now if you use enough points, all those lines will add up to look like a complete surface, which is what you get in your image.