The scale consists of two Transform classes, each of which needs to provide a transform_non_affine method. One class needs to transform from data to display coordinates, which would be log(a+1), the other is the inverse and needs to transform from display to data coordinates, which would in this case be exp(a)-1. Those methods need to … 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
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
You could explicitly set where you want to tick marks with plt.xticks: plt.xticks(np.arange(min(x), max(x)+1, 1.0)) For example, import numpy as np import matplotlib.pyplot as plt x = [0,5,9,10,15] y = [0,1,2,3,4] plt.plot(x,y) plt.xticks(np.arange(min(x), max(x)+1, 1.0)) plt.show() (np.arange was used rather than Python’s range function just in case min(x) and max(x) are floats instead of ints.) … Read more
The axes size is determined by the figure size and the figure spacings, which can be set using figure.subplots_adjust(). In reverse this means that you can set the axes size by setting the figure size taking into acount the figure spacings: import matplotlib.pyplot as plt def set_size(w,h, ax=None): “”” w, h: width, height in inches … Read more
Sans legend, setting the preferred size of the ChartPanel works pretty well: private static final int SIZE = 456; chartPanel.setPreferredSize(new Dimension(SIZE, SIZE)); See also Should I avoid the use of set(Preferred|Maximum|Minimum)Size() methods in Java Swing? and this answer regarding chart size. import java.awt.BasicStroke; import java.awt.BorderLayout; import java.awt.Color; import java.awt.Dimension; import java.awt.EventQueue; import java.awt.event.ActionEvent; import java.awt.geom.Ellipse2D; … Read more
You can subclass matplotlib.ticker.ScalarFormatter and fix the orderOfMagnitude attribute to the number you like (in this case -4). In the same way you can fix the format to be used. import numpy as np import matplotlib.pyplot as plt import matplotlib.ticker class OOMFormatter(matplotlib.ticker.ScalarFormatter): def __init__(self, order=0, fformat=”%1.1f”, offset=True, mathText=True): self.oom = order self.fformat = fformat matplotlib.ticker.ScalarFormatter.__init__(self,useOffset=offset,useMathText=mathText) … 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)
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
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