I'm trying to recreate the above style of plotting using matplotlib. The raw data is stored in a 2D numpy array, where the fast axis is time. Plotting the lines is the easy bit. I'm trying to get the shaded-in areas efficiently. My current attempt looks something like: import numpy as np from matplotlib import collections import matplotlib.pyplot as pylab #make some oscillating data panel = np.meshgrid(np.arange(1501), np.arange(284))[0] panel = np.sin(panel) #generate coordinate vectors. panel[:,-1] = np.nan #lazy prevents polygon wrapping x = panel.ravel() y = np.meshgrid(np.arange(1501), np.arange(284))[0].ravel() #find indexes of each zero crossing zero_crossings = np.where(np.diff(np.signbit(x)))[0]+1 #calculate scalars used to shift "traces" to plotting corrdinates trace_centers = np.linspace(1,284, panel.shape[-2]).reshape(-1,1) gain = 0.5 #scale traces #shift traces to plotting coordinates x = ((panel*gain)+trace_centers).ravel() #split coordinate vectors at each zero crossing xpoly = np.split(x, zero_crossings) ypoly = np.split(y, zero_crossings) #we only want the polygons which outline positive values if x[0] > 0: steps = range(0, len(xpoly),2) else: steps = range(1, len(xpoly),2) #turn vectors of polygon coordinates into lists of coordinate pairs polygons = [zip(xpoly, ypoly) for i in steps if len(xpoly) > 2] #this is so we can plot the lines as well xlines = np.split(x, 284) ylines = np.split(y, 284) lines = [zip(xlines[a],ylines[a]) for a in range(len(xlines))] #and plot fig = pylab.figure() ax = fig.add_subplot(111) col = collections.PolyCollection(polygons) col.set_color('k') ax.add_collection(col, autolim=True) col1 = collections.LineCollection(lines) col1.set_color('k') ax.add_collection(col1, autolim=True) ax.autoscale_view() pylab.xlim([0,284]) pylab.ylim([0,1500]) ax.set_ylim(ax.get_ylim()[::-1]) pylab.tight_layout() pylab.show() and the result is There are two issues: It does not fill perfectly because I am splitting on the array indexes closest to the zero crossings, not the exact zero crossings. I'm assuming that calculating each zero crossing will be a big computational hit. Performance. It's not that bad, given the size of the problem - around a second to render on my laptop, but i'd like to get it down to 100ms - 200ms. Because of the usage case I am limited to python with numpy/scipy/matplotlib. Any suggestions? Followup: Turns out linearly interpolating the zero crossings can be done with very little computational load. By inserting the interpolated values into the data, setting negative values to nans, and using a single call to pyplot.fill, 500,000 odd samples can be plotted in around 300ms. For reference, Tom's method below on the same data took around 8 seconds. The following code assumes an input of a numpy recarray with a dtype that mimics a seismic unix header/trace definition. def wiggle(frame, scale=1.0): fig = pylab.figure() ax = fig.add_subplot(111) ns = frame['ns'][0] nt = frame.size scalar = scale*frame.size/(frame.size*0.2) #scales the trace amplitudes relative to the number of traces frame['trace'][:,-1] = np.nan #set the very last value to nan. this is a lazy way to prevent wrapping vals = frame['trace'].ravel() #flat view of the 2d array. vect = np.arange(vals.size).astype(np.float) #flat index array, for correctly locating zero crossings in the flat view crossing = np.where(np.diff(np.signbit(vals)))[0] #index before zero crossing #use linear interpolation to find the zero crossing, i.e. y = mx + c. x1= vals[crossing] x2 = vals[crossing+1] y1 = vect[crossing] y2 = vect[crossing+1] m = (y2 - y1)/(x2-x1) c = y1 - m*x1 #tack these values onto the end of the existing data x = np.hstack([vals, np.zeros_like(c)]) y = np.hstack([vect, c]) #resort the data order = np.argsort(y) #shift from amplitudes to plotting coordinates x_shift, y = y[order].__divmod__(ns) ax.plot(x[order] *scalar + x_shift + 1, y, 'k') x[x<0] = np.nan x = x[order] *scalar + x_shift + 1 ax.fill(x,y, 'k', aa=True) ax.set_xlim([0,nt]) ax.set_ylim([ns,0]) pylab.tight_layout() pylab.show() The full code is published at https://github.com/stuliveshere/PySeis Continue reading...
