One way is to can things with a simple tuple when creating a figure: figure(…, x_range=(left, right), y_range=(bottom, top)) But you can also set the x_range and y_range properties of a created figure directly. (I had been looking for something like set_xlim or set_ylim from matplotlib.) from bokeh.models import Range1d fig = make_fig() left, right, … Read more
Calling p.plot after setting the limits is why it is rescaling. You are correct in that turning autoscaling off will get the right answer, but so will calling xlim() or ylim() after your plot command. I use this quite a lot to invert the x axis, I work in astronomy and we use a magnitude … Read more
I wrote a function very similar to plt.axes. You could use it for plotting yours sub-subplots. There is an example… import matplotlib.pyplot as plt import numpy as np #def add_subplot_axes(ax,rect,facecolor=”w”): # matplotlib 2.0+ def add_subplot_axes(ax,rect,axisbg=’w’): fig = plt.gcf() box = ax.get_position() width = box.width height = box.height inax_position = ax.transAxes.transform(rect[0:2]) transFigure = fig.transFigure.inverted() infig_position = … Read more
I had this same issue when working in iPython notebook. I think it can be re-created as follows: import matplotlib.pyplot as plt plt.ylabel=”somestring” # oh wait this isn’t the right syntax. … plt.ylabel(‘somestring’) # now this breaks because the function has been turned into a string Re-starting the kernel or re-importing the libraries restores plt.ylabel … Read more
Common grounds Both, add_axes and add_subplot add an axes to a figure. They both return a (subclass of a) matplotlib.axes.Axes object. However, the mechanism which is used to add the axes differs substantially. add_axes The calling signature of add_axes is add_axes(rect), where rect is a list [x0, y0, width, height] denoting the lower left point … Read more
Use the gca (“get current axes”) helper function: ax = plt.gca() Example: import matplotlib.pyplot as plt import matplotlib.finance quotes = [(1, 5, 6, 7, 4), (2, 6, 9, 9, 6), (3, 9, 8, 10, 8), (4, 8, 8, 9, 8), (5, 8, 11, 13, 7)] ax = plt.gca() h = matplotlib.finance.candlestick(ax, quotes) plt.show()
You could always hide the axes which you do not need. For example, the following code turns off the 6th axes completely: import matplotlib.pyplot as plt hf, ha = plt.subplots(3,2) ha[-1, -1].axis(‘off’) plt.show() and results in the following figure: Alternatively, see the accepted answer to the question Hiding axis text in matplotlib plots for a … Read more
I think you are looking for this: require(ggplot2) df <- data.frame(x=seq(1, 1e9, length.out=100), y=sample(100)) # displays x-axis in scientific notation p <- ggplot(data = df, aes(x=x, y=y)) + geom_line() + geom_point() p # displays as you require require(scales) p + scale_x_continuous(labels = comma)
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
You can create a big subplot that covers the two subplots and then set the common labels. import random import matplotlib.pyplot as plt x = range(1, 101) y1 = [random.randint(1, 100) for _ in range(len(x))] y2 = [random.randint(1, 100) for _ in range(len(x))] fig = plt.figure() ax = fig.add_subplot(111) # The big subplot ax1 = … Read more