#!/usr/bin/env python """ some code to do a stick plot with MPL This version uses matplotlib's quiver. """ import matplotlib as mpl import matplotlib.pyplot as plt import numpy as np import datetime def stick(axes, times, speeds, directions, units=''): """ Create a stick plot of the given data on the given axes. Stick plots are commonlyu used to display time series of a vector quantity at a point, such as wind or ocean current observations. Call signature:: stick(axes, times, speed, direction, units='m/s') Arguments: *axes*: The axes object you want to plot on *times*: An array of datetime objects giving the time of the observations *speeds*: An array of the velocities of the observations *directions*: An array of the directions of the observations (in degrees from North, where North is up on the plot) *units*: A string with the units of the observation """ props = {'units' : "inches", 'width' : 0.02, 'headwidth': 0, 'headlength': 0, 'headaxislength': 0, #'scale' : .1, } label_scale = np.floor(speeds.max() / 10) * 10 if label_scale == 0: label_scale = 5.0 unit_label = "%3g %s"%(label_scale, units) times = mpl.dates.date2num(times) y = np.zeros_like(speeds) dir_rad = directions / 180. * np.pi u = np.sin(dir_rad) * speeds v = np.cos(dir_rad) * speeds Q = axes.quiver(times, y, u, v, **props) axes.quiverkey(Q, X=0.1, Y=0.95, U=label_scale, label=unit_label, coordinates='axes', labelpos='S') axes.yaxis.set_major_locator(mpl.ticker.NullLocator()) locator = mpl.dates.AutoDateLocator() axes.xaxis.set_major_locator(locator) axes.xaxis.set_major_formatter(mpl.dates.AutoDateFormatter(locator)) def vector(axes, times, speeds, directions, units=''): """ Create a "vector" plot of the given data on the given axes. A vector plot is a way to plot a time series of a vector quantity at a point, such as wind or ocean current observations. For antoher option, see "stick" Call signature:: stick(axes, times, speed, direction, units='m/s') Arguments: *axes*: The axes object you want to plot on *times*: An array of datetime objects giving the time of the observations *speeds*: An array of the velocities of the observations *directions*: An array of the directions of the observations (in degrees from North, where North is up on the plot) *units*: A string with the units of the observation """ props = {'units' : "dots", 'width' : 2, 'headwidth': 3, 'headlength': 6, 'headaxislength': 6, 'scale' : .05, } times = mpl.dates.date2num(times) dir_rad = directions / 180. * np.pi u = np.sin(dir_rad) v = np.cos(dir_rad) Q = axes.quiver(times, speeds, u, v, **props) axes.plot(times, speeds, 'o') xaxis = axes.xaxis xaxis.set_major_locator(mpl.dates.DayLocator()) Fmt = mpl.dates.DateFormatter('%m/%d') xaxis.set_major_formatter(Fmt) axes.set_ylabel(units, rotation='horizontal') axes.set_ylim(ymin=0) axes.grid('on') locator = mpl.dates.AutoDateLocator() axes.xaxis.set_major_locator(locator) axes.xaxis.set_major_formatter(mpl.dates.AutoDateFormatter(locator, tz=None)) if __name__ == '__main__': ## some sample data: # data = np.array([(10, 0.5), # (10, 22.5), # (10, 45.0), # (10, 67.5), # (10, 90.0), # (10, 112.5), # (10, 135.0), # (10, 157.5), # (10, 180.0), # (10, 202.5), # (10, 225.0), # (10, 247.5), # (10, 270.0), # (10, 292.5), # (10, 315.0), # (10, 337.5), # ( 8, 157.5), # (12, 180.0), # (15, 202.5), # ( 7, 225.0), # ( 5, 247.5), # (16, 270.0), # (20, 292.5), # (22, 315.0), # (15, 337.5), # (12, 22.5), # (17, 25.0), # (18, 30), # ], dtype=np.float) # speeds = data[:,0] # directions = data[:,1] data = np.array([[ 220., 3.], [ 200., 2.], [ 230., 3.], [ 220., 1.], [ 240., 2.], [ 240., 2.], [ 240., 3.], [ 270., 3.], [ 270., 4.], [ 270., 5.], [ 270., 4.], [ 290., 2.], [ 320., 3.], [ 300., 5.], [ 280., 6.], [ 260., 5.], [ 260., 5.], [ 270., 5.], [ 260., 5.], [ 250., 5.], [ 240., 4.], [ 260., 5.], [ 250., 4.], [ 250., 5.], [ 240., 4.], [ 230., 4.], [ 270., 5.], [ 250., 2.], [ 260., 3.], [ 240., 3.], [ 270., 4.], [ 270., 5.], [ 270., 4.], [ 280., 3.], [ 290., 3.], [ 290., 3.], [ 310., 5.], [ 290., 7.], [ 290., 8.], [ 280., 7.], [ 280., 7.], [ 270., 7.], [ 280., 7.], [ 280., 6.], [ 280., 4.], [ 300., 5.], [ 290., 5.], [ 290., 3.], [ 300., 4.], [ 290., 4.], [ 300., 4.], [ 280., 4.], [ 290., 4.], [ 290., 4.], [ 280., 5.], [ 270., 4.], [ 250., 4.], [ 250., 2.], [ 270., 3.], [ 280., 3.], [ 290., 5.], [ 290., 6.], [ 290., 6.], [ 290., 6.], [ 300., 6.], [ 290., 5.], [ 290., 5.], [ 300., 5.], [ 300., 5.], [ 300., 5.], [ 300., 4.], [ 300., 4.], [ 310., 3.], [ 290., 2.], [ 270., 2.], [ 270., 1.], [ 300., 1.], [ 290., 2.], [ 320., 2.], [ 310., 2.], [ 310., 3.], [ 280., 3.], [ 280., 3.], [ 280., 1.], [ 310., 3.], [ 320., 3.], [ 290., 5.], [ 300., 5.], [ 290., 6.], [ 290., 5.], [ 290., 6.], [ 290., 4.], [ 280., 5.], [ 290., 4.], [ 300., 3.], [ 300., 5.], [ 290., 4.], [ 270., 1.], [ 310., 2.], [ 340., 1.], [ 310., 3.], [ 290., 2.], [ 290., 1.], [ 290., 2.], [ 270., 2.], [ 260., 3.], [ 270., 1.], [ 260., 1.], [ 270., 1.], [ 280., 3.], [ 290., 4.], [ 290., 4.], [ 290., 5.], [ 280., 4.], [ 290., 4.], [ 270., 4.], [ 260., 4.], [ 250., 4.], [ 240., 2.], [ 210., 2.], ]) speeds = data[:,1] directions = data[:,0] times = range(len(speeds)) times = [datetime.datetime(2009, 5, 13, 0) + datetime.timedelta(hours=h*48) for h in times] fig = plt.figure(1) fig.clear() ax = fig.add_subplot(2,1,1) stick(ax, times, speeds, directions, units='m/s') ax = fig.add_subplot(2,1,2) vector(ax, times, speeds, directions, units='$m/s$') fig.autofmt_xdate(bottom=0.18) plt.draw() plt.show()