Creating graph with date and time in axis labels with matplotlib

Question

I have my data in an array of the following structure,

[[1293606162197, 0, 0],  [1293605477994, 63, 0],  [1293605478057, 0, 0],  [1293605478072, 2735, 1249],  [1293606162213, 0, 0],  [1293606162229, 0, 0]] 

The first column is epoch time (in ms), second is y1 and third is y2. I need a plot with the time on the x-axis, and y1 and y2 on left and right y-axes.

I have been scouring through the documentation but couldn't find any way to get my x-axis ticks to display both date and time, like "28/12 16:48", i.e., "date/month hour:min". All the documentation helps me with is to display dates alone, but that is not what I want. Any help would be appreciated on this.

And, if it may seem like, this is not homework. It is actually a follow up to my previous question, Reading and graphing data read from huge files.

Answer 1

I hope this helps. I've always had a hard time with matplotlib's dates. Matplotlib requires a float format which is days since epoch. The helper functions num2date and date2num along with python builtin datetime can be used to convert to/from. The formatting business was lifted from this example. You can change an axis on any plot to a date axis using set_major_formatter.

import numpy as np from matplotlib import pyplot as plt from matplotlib import dates import datetime  a = np.array([     [1293605162197, 0, 0],     [1293605477994, 63, 0],     [1293605478057, 0, 0],     [1293605478072, 2735, 1249],     [1293606162213, 0, 0],     [1293606162229, 0, 0]])  d = a[:,0] y1 = a[:,1] y2 = a[:,2]  # convert epoch to matplotlib float format s = d/1000 ms = d-1000*s  # not needed? dts = map(datetime.datetime.fromtimestamp, s) fds = dates.date2num(dts) # converted  # matplotlib date format object hfmt = dates.DateFormatter('%m/%d %H:%M')  fig = plt.figure() ax = fig.add_subplot(111) ax.vlines(fds, y2, y1)  ax.xaxis.set_major_locator(dates.MinuteLocator()) ax.xaxis.set_major_formatter(hfmt) ax.set_ylim(bottom = 0) plt.xticks(rotation='vertical') plt.subplots_adjust(bottom=.3) plt.show()