Why this happens… With plot(x(i),y) you are plotting 100 single points (one in each iteration) and they are not shown by default. Therefore the plot looks empty. Solution 1: Vectorized calculation and direct plot I assume you meant to draw a continuous line. In that case no for-loop is needed because you can calculate and … Read more
There is an issue with pandas datetimes and matplotlib coming from the recent release of pandas 0.21, which does not register its converters any more at import. Once you use those converters once (within pandas) they’ll be registered and automatically used by matplotlib as well. A workaround would be to register them manually, import pandas.plotting._converter … Read more
Does plt.tricontourf(x,y,z) satisfy your requirements? It will plot filled contours for irregularly spaced data (non-rectilinear grid). You might also want to look into plt.tripcolor(). import numpy as np import matplotlib.pyplot as plt x = np.random.rand(100) y = np.random.rand(100) z = np.sin(x)+np.cos(y) f, ax = plt.subplots(1,2, sharex=True, sharey=True) ax[0].tripcolor(x,y,z) ax[1].tricontourf(x,y,z, 20) # choose 20 contour levels, … Read more
As you have noticed, xscale and yscale does not support a simple linear re-scaling (unfortunately). As an alternative to Hooked’s answer, instead of messing with the data, you can trick the labels like so: ticks = ticker.FuncFormatter(lambda x, pos: ‘{0:g}’.format(x*scale)) ax.xaxis.set_major_formatter(ticks) A complete example showing both x and y scaling: import numpy as np import … Read more
The base graphics behaviour can be reproduced using a custom breaks function: base_breaks <- function(n = 10){ function(x) { axisTicks(log10(range(x, na.rm = TRUE)), log = TRUE, n = n) } } Applying this to the example data gives the same result as using trans_breaks(‘log10’, function(x) 10^x): ggplot(M, aes(x = X, y = Y)) + geom_line() … Read more
data<-iris plot(data$Sepal.Length, data$Sepal.Width, col=data$Species) legend(7,4.3,unique(data$Species),col=1:length(data$Species),pch=1) should do it for you. But I prefer ggplot2 and would suggest that for better graphics in R.
Edit: I just saw that you pointed out one of your dimensions is a date. In that case, have a look at Jeff Ryan’s chartSeries3d which is designed to chart 3-dimensional time series. Here he shows the yield curve over time: Original Answer: As I understand it, you want a countour map to be the … Read more
Use the matplotlib savefig function with the keyword argument transparent=True to save the image as a png file. In [30]: x = np.linspace(0,6,31) In [31]: y = np.exp(-0.5*x) * np.sin(x) In [32]: plot(x, y, ‘bo-‘) Out[32]: [<matplotlib.lines.Line2D at 0x3f29750>] In [33]: savefig(‘demo.png’, transparent=True) Result: Of course, that plot doesn’t demonstrate the transparency. Here’s a screenshot … Read more
You might like to look at Lissajous curves; an example of a = 5, b = 4 (5:4) is shown below. Addendum: Once you see how to plot points in xy coordinates, then you should look at converting between polar and Cartesian coordinates. public class LissajousPanel extends JPanel { private static final int SIZE = … Read more
seaborn is used to draw the plot, but it’s just a high-level API for matplotlib. The functions called to remove the y-axis labels and ticks are matplotlib methods. After creating the plot, use .set(). .set(yticklabels=[]) should remove tick labels. This doesn’t work if you use .set_title(), but you can use .set(title=””) .set(ylabel=None) should remove the … Read more