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
Just use axes.get_ylim(), it is very similar to set_ylim. From the docs: get_ylim() Get the y-axis range [bottom, top]
Given li in the question: li = list(zip(range(1, 14), range(14, 27))) To unpack the data from pairs into lists use zip: x, y = zip(*li) x → (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13) y → (14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26) … Read more
You could use scipy.interpolate.spline to smooth out your data yourself: from scipy.interpolate import spline # 300 represents number of points to make between T.min and T.max xnew = np.linspace(T.min(), T.max(), 300) power_smooth = spline(T, power, xnew) plt.plot(xnew,power_smooth) plt.show() spline is deprecated in scipy 0.19.0, use BSpline class instead. Switching from spline to BSpline isn’t a … Read more
Remove numbering on x-axis or y-axis: plot(1:10, xaxt=”n”) plot(1:10, yaxt=”n”) If you want to remove the labels as well: plot(1:10, xaxt=”n”, ann=FALSE) plot(1:10, yaxt=”n”, ann=FALSE)
Not sure if this is what you mean, but try setting las=1. Here’s an example: require(grDevices) tN <- table(Ni <- stats::rpois(100, lambda=5)) r <- barplot(tN, col=rainbow(20), las=1) That represents the style of axis labels. (0=parallel, 1=all horizontal, 2=all perpendicular to axis, 3=all vertical)
I really agree with the other posters: Tufte’s books are fantastic and well worth reading. First, I would point you to a very nice tutorial on ggplot2 and ggobi from “Looking at Data” earlier this year. Beyond that I would just highlight one visualization from R, and two graphics packages (which are not as widely … Read more
You want something like the cex=1.5 argument to scale fonts 150 percent. But do see help(par) as there are also cex.lab, cex.axis, …
xlim and ylim don’t cut it here. You need to use expand_limits, scale_x_continuous, and scale_y_continuous. Try: df <- data.frame(x = 1:5, y = 1:5) p <- ggplot(df, aes(x, y)) + geom_point() p <- p + expand_limits(x = 0, y = 0) p # not what you are looking for p + scale_x_continuous(expand = c(0, 0)) … Read more
There’s no need to manually set the colors. Instead, specify a grayscale colormap… import numpy as np import matplotlib.pyplot as plt # Generate data… x = np.random.random(10) y = np.random.random(10) # Plot… plt.scatter(x, y, c=y, s=500) plt.gray() plt.show() Or, if you’d prefer a wider range of colormaps, you can also specify the cmap kwarg to … Read more