This works for me, using matplotlib 1.1: import numpy as np import matplotlib.pyplot as plt x = np.arange(10) y = np.sin(x) plt.scatter(x, y, marker=”+”, s=150, linewidths=4, c=y, cmap=plt.cm.coolwarm) plt.show() Result: Alternatively, for n points, make an array of RGB color values with shape (n, 3), and assign it to the edgecolors keyword argument of scatter(): … Read more
Translate from user data coordinate system to display coordinate system. and use edgecolors=”none” to plot faces with no outlines. import numpy as np fig = figure() ax = fig.add_subplot(111) dx_in_points = np.diff(ax.transData.transform(zip([0]*len(dx), dx))) scatter(x,y,c=z,s=dx_in_points**2,marker=”s”, edgecolors=”none”)
Alright, I found one way to do it, that looks relatively clean: (using the ColorBar object from the question) color_bar.set_alpha(1) color_bar.draw_all() # pylab.draw() or pyplot.draw() might be necessary It would be great to get a confirmation that this is the most robust way to proceed, though! 🙂
There are a number of ways to create DataFrames. Given 1-dimensional column vectors, you can create a DataFrame by passing it a dict whose keys are column names and whose values are the 1-dimensional column vectors: import numpy as np import pandas as pd x = np.random.randn(5) y = np.sin(x) df = pd.DataFrame({‘x’:x, ‘y’:y}) df.plot(‘x’, … Read more
as explained here With help from numpy one can calculate for example a linear fitting. # plot the data itself pylab.plot(x,y,’o’) # calc the trendline z = numpy.polyfit(x, y, 1) p = numpy.poly1d(z) pylab.plot(x,p(x),”r–“) # the line equation: print “y=%.6fx+(%.6f)”%(z[0],z[1])
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
From the documentation for scatter: Optional kwargs control the Collection properties; in particular: edgecolors: The string ‘none’ to plot faces with no outlines facecolors: The string ‘none’ to plot unfilled outlines Try the following: import matplotlib.pyplot as plt import numpy as np x = np.random.randn(60) y = np.random.randn(60) plt.scatter(x, y, s=80, facecolors=”none”, edgecolors=”r”) plt.show() Note: … Read more
This can be a somewhat confusing way of defining the size but you are basically specifying the area of the marker. This means, to double the width (or height) of the marker you need to increase s by a factor of 4. [because A = WH => (2W)(2H)=4A] There is a reason, however, that the … Read more