I have searched a lot about this problem, but I haven't found the solution to my problem yet.
I want to generate a graph as my output after the cell:
x= np.linspace(0,20, 100)
plt.plot(x, np.sin(x))
plt.show()
I've stumbled on some answers saying to insert before my graph:
%matplotlib inline
This returns just
<Figure size 640x480 with 1 Axes>
How could that problem be solved?
Related
I'm trying to plot a geographical data using a shapefile and a dataframe,(following the instructions in this link:
https://towardsdatascience.com/lets-make-a-map-using-geopandas-pandas-and-matplotlib-to-make-a-chloropleth-map-dddc31c1983d)
by merging them and then try to plot the geodata.
Here map_df is the shapefile read by geopandas, and df is the dataframe for the statistics about geography.
merged = map_df.set_index('NAME_3').join(df.set_index('City'))
variable = 'Rate'
vmin, vmax = 120, 220
fig, ax = plt.subplots(1, figsize=(10, 6))
merged.plot(column=variable, cmap = 'Blues', linewidth=0.8, ax=ax)
plt.show()
In the link there was 'edgecolor='0.8'' variable but it produced an RGBA error with my code and that's why I deleted that part.
Now even when I use plt.show() it doesn't show the plot and only says:
<Figure size 432x288 with 0 Axes>
What can I do about it? Thank you.
I had this exact problem using a jupyter notebook. The only thing that solved this for me was to create a new notebook. There it suddenly worked.
If your matplotlib ax instance and merged.plot() are in the different code cells of the Jupyter Notebook with ax instance being generated first, consider putting the ax instance and merged.plot() in the same cell.
I'm sucessfully generating a 3D plot with Mayavi, but can't find any way to rescale the axes to a semilog representation.
Is this possible?
I also tried taking a screenshot (as suggested by another answer I read before), which I placed after my mayavi code
arr = mayavi.mlab.screenshot()
fig = plt.figure(figsize=(5, 5))
pylab.imshow(arr)
plt.semilogx()
plt.show()
However this just produces a segmentation fault.
Thanks in advance!
I am a recent migrant from Matlab to Python and have recently worked with Numpy and Matplotlib. I recoded one of my scripts from Matlab, which employs Matlab's contourf-function, into Python using matplotlib's corresponding contourf-function. I managed to replicate the output in Python, apart that the contourf-plots are not exacly the same, for a reason that is unknown to me. As I run the contourf-function in matplotlib, I get this otherwise nice figure but it has these sharp edges on the contour-levels on top and bottom, which should not be there (see Figure 1 below, matplotlib-output). Now, when I export the arrays I used in Python to Matlab (i.e. the exactly same data set that was used to generate the matplotlib-contourf-plot) and use Matlab's contourf-function, I get a slightly different output, without those sharp contour-level edges (see Figure 2 below, Matlab-output). I used the same number of levels in both figures. In figure 3 I have made a scatterplot of the same data, which shows that there are no such sharp edges in the data as shown in the contourf-plot (I added contour-lines just for reference). Example dataset can be downloaded through Dropbox-link given below. The data set contains three txt-files: X, Y, Z. Each of them are an 500x500 arrays, which can be directly used with contourf(), i.e. plt.contourf(X,Y,Z,...). The code that used was
plt.contourf(X,Y,Z,10, cmap=plt.cm.jet)
plt.contour(X,Y,Z,10,colors='black', linewidths=0.5)
plt.axis('equal')
plt.axis('off')
Does anyone have an idea why this happens? I would appreciate any insight on this!
Cheers,
Jussi
Below are the details of my setup:
Python 3.7.0
IPython 6.5.0
matplotlib 2.2.3
Matplotlib output
Matlab output
Matplotlib-scatter
Link to data set
The confusing thing about the matlab plot is that its colorbar shows much more levels than there are actually in the plot. Hence you don't see the actual intervals that are contoured.
You would achieve the same result in matplotlib by choosing 12 instead of 11 levels.
import numpy as np
import matplotlib.pyplot as plt
X, Y, Z = [np.loadtxt("data/roundcontourdata/{}.txt".format(i)) for i in list("XYZ")]
levels = np.linspace(Z.min(), Z.max(), 12)
cntr = plt.contourf(X,Y,Z,levels, cmap=plt.cm.jet)
plt.contour(X,Y,Z,levels,colors='black', linewidths=0.5)
plt.colorbar(cntr)
plt.axis('equal')
plt.axis('off')
plt.show()
So in conclusion, both plots are correct and show the same data. Just the levels being automatically chosen are different. This can be circumvented by choosing custom levels depending on the desired visual appearance.
Python version: 3.6.4 (Anaconda on Windows)
Seaborn: 0.8.1
Matplotlib: 2.1.2
I'm trying to create a 2D Kernel Density plot using Seaborn but I want each step in the colourmap to have a different alpha value. I had a look at this question to create a matplotlib colourmap with alpha values: Add alpha to an existing matplotlib colormap.
I have a problem in that the lines between contours are visible. The result I get is here:
I thought that I had found the answer when I found this question: Hide contour linestroke on pyplot.contourf to get only fills. I tried the method outlined in the answer (using set_edgecolor("face") but it did not work in this case. That question also seemed to be related to vector graphics formats and I am just writing out a PNG.
Here is my script:
import numpy as np
import seaborn as sns
import matplotlib.colors as cols
import matplotlib.pyplot as plt
def alpha_cmap(cmap):
my_cmap = cmap(np.arange(cmap.N))
# Set a square root alpha.
x = np.linspace(0, 1, cmap.N)
my_cmap[:,-1] = x ** (0.5)
my_cmap = cols.ListedColormap(my_cmap)
return my_cmap
xs = np.random.uniform(size=100)
ys = np.random.uniform(size=100)
kplot = sns.kdeplot(data=xs, data2=ys,
cmap=alpha_cmap(plt.cm.viridis),
shade=True,
shade_lowest=False,
n_levels=30)
plt.savefig("example_plot.png")
Guided by some comments on this question I have tried some other methods that have been successful when this problem has come up. Based on this question (Matplotlib Contourf Plots Unwanted Outlines when Alpha < 1) I have tried altering the plot call to:
sns.kdeplot(data=xs, data2=ys,
cmap=alpha_cmap(plt.cm.viridis),
shade=True,
shade_lowest=False,
n_levels=30,
antialiased=True)
With antialiased=True the lines between contours are replaced by a narrow white line:
I have also tried an approach similar to this question - Pyplot pcolormesh confused when alpha not 1. This approach is based on looping over the PathCollections in kplot.collections and tuning the parameters of the edges so that they become invisible. I have tried adding this code and tweaking the linewidth -
for thing in kplot.collections:
thing.set_edgecolor("face")
thing.set_linewidth(0.01)
fig.canvas.draw()
This results in a mix of white and dark lines - .
I believe that I will not be able to tune the line width to make the lines disappear because of the variable width of the contour bands.
Using both methods (antialiasing + linewidth) makes this version, which looks cool but isn't quite what I want:
I also found this question - Changing Transparency of/Remove Contour Lines in Matplotlib
This one suggests overplotting a second plot with a different number of contour levels on the same axis, like:
kplot = sns.kdeplot(data=xs, data2=ys,
ax=ax,
cmap=alpha_cmap(plt.cm.viridis),
shade=True,
shade_lowest=False,
n_levels=30,
antialiased=True)
kplot = sns.kdeplot(data=xs, data2=ys,
ax=ax,
cmap=alpha_cmap(plt.cm.viridis),
shade=True,
shade_lowest=False,
n_levels=35,
antialiased=True)
This results in:
This is better, and almost works. The problem here is I need variable (and non-linear) alpha throughout the colourmap. The variable banding and lines seem to be a result of the combinations of alpha when contours are plotted over each other. I also still see some clear/white lines in the result.
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?