我们从Python开源项目中，提取了以下8个代码示例，用于说明如何使用pylab.hold()。
def plot(self, logarithmic=False): """Plot a graphical representation of the peaks Arguments: (none) """ import pylab as pl pl.figure() pl.plot(self.x) pl.hold('on') pl.plot(self.pos[self.keep], self.val[self.keep], 'og') pl.plot(self.pos[np.logical_not(self.keep)], self.val[np.logical_not(self.keep)], 'om') if hasattr(self, 'bounds'): lmins = np.unique(self.bounds.flatten()) lminvals = self.x[lmins] pl.plot(lmins, lminvals, 'or') if hasattr(self, 'fpos'): pl.plot(self.fpos[self.keep], self.fval[self.keep], 'dg') pl.hold('off') if logarithmic: pl.gca().set_yscale('log')
def two_plot_time_freq_mag(self, minlen=10): part = [pp for pp in self.partial if len(pp.f) > minlen] pl.figure() ax1 = pl.subplot(211) pl.hold(True) ax2 = pl.subplot(212, sharex=ax1) pl.hold(True) for pp in part: ax1.plot(pp.start_idx + np.arange(len(pp.f)), np.array(pp.f)) ax2.plot(pp.start_idx + np.arange(len(pp.f)), 20*np.log10(np.array(pp.mag))) ax1.hold(False) # ax1.xlabel('Time (s)') ax1.set_ylabel('Frequency (Hz)') ax2.set_xlabel('Time (s)') ax2.set_ylabel('Frequency (Hz)') # pl.show() return pl.gca()
def plot_time_freq_mag(self, minlen=10, cm=pl.cm.rainbow): cadd = 30 cmax = 256 ccur = 0 part = [pp for pp in self.partial if len(pp.f) > minlen] pl.figure() pl.hold(True) for pp in part: # pl.plot(pp.start_idx + np.arange(len(pp.f)), np.array(pp.f)) mag = 100 + 20*np.log10(np.array(pp.mag)) pl.scatter(pp.start_idx + np.arange(len(pp.f)), np.array(pp.f), s=mag, c=cm(ccur), lw=0) ccur = np.mod(ccur + cadd, cmax) pl.hold(False) pl.xlabel('Time (s)') pl.ylabel('Frequency (Hz)') pl.show()
def test_arms_via_lognormal (self): """ Test by sampling from a lognormal distribution.""" N = 5000 params = [ (0.0, 1.0, numpy.inf), (-2.0, 0.5, numpy.inf), (2.0, 2.0, 1000.0) ] for mu, sig, x_max in params: print "==========================\nSampling lognormal mean %.4f sd %.4f" % (mu, sig) ln = distributions.LogNormal (mu, sig) s_ex = [] while len(s_ex) < N: x = ln.sample (1) if x < x_max: s_ex.append (x) sampling.reset_statistics() s_ars = sampling.arms (ln.lpdf, ln.dx_lpdf, 0.0, x_max, n=N, num_points=5) pylab.hold(False) pylab.plot (s_ars) pylab.savefig('iter.png') pylab.plot (s_ars[1:N-1], s_ars[2:N], 'ro') pylab.savefig('lag1.png') s_ex.sort() s_ars.sort() stats = sampling.statistics() print "ARMS: Num MH rejections = %d" % (stats['mh_rejects']) self.assertEquals (N, stats['values_generated']) self.assertTrue (0 < stats['mh_rejects']) netutils.check_quantiles (self, s_ex, s_ars, N)
def _test_graph(): i = 10000 x = np.linspace(0,3.7*pi,i) y = (0.3*np.sin(x) + np.sin(1.3 * x) + 0.9 * np.sin(4.2 * x) + 0.06 * np.random.randn(i)) y *= -1 x = range(i) _max, _min = peakdetect(y,x,750, 0.30) xm = [p[0] for p in _max] ym = [p[1] for p in _max] xn = [p[0] for p in _min] yn = [p[1] for p in _min] plot = pylab.plot(x,y) pylab.hold(True) pylab.plot(xm, ym, 'r+') pylab.plot(xn, yn, 'g+') _max, _min = peak_det_bad.peakdetect(y, 0.7, x) xm = [p[0] for p in _max] ym = [p[1] for p in _max] xn = [p[0] for p in _min] yn = [p[1] for p in _min] pylab.plot(xm, ym, 'y*') pylab.plot(xn, yn, 'k*') pylab.show()
def plot(self): """Plot a graphical representation of the peaks Arguments: (none) """ import pylab as pl pl.figure() pl.plot(self.x) pl.hold('on') pl.plot(self.pos,self.val,'o') pl.hold('off')
def plot_time_freq(self, minlen=10): part = [pp for pp in self.partial if len(pp.f) > minlen] pl.figure() pl.hold(True) for pp in part: pl.plot(pp.start_idx + np.arange(len(pp.f)), np.array(pp.f)) pl.hold(False) pl.xlabel('Time (s)') pl.ylabel('Frequency (Hz)') # pl.show() return pl.gca()
def peakdetect_parabole(y_axis, x_axis, points = 9): """ Function for detecting local maximas and minmias in a signal. Discovers peaks by fitting the model function: y = k (x - tau) ** 2 + m to the peaks. The amount of points used in the fitting is set by the points argument. Omitting the x_axis is forbidden as it would make the resulting x_axis value silly if it was returned as index 50.234 or similar. will find the same amount of peaks as the 'peakdetect_zero_crossing' function, but might result in a more precise value of the peak. keyword arguments: y_axis -- A list containg the signal over which to find peaks x_axis -- A x-axis whose values correspond to the y_axis list and is used in the return to specify the postion of the peaks. points -- (optional) How many points around the peak should be used during curve fitting, must be odd (default: 9) return -- two lists [max_peaks, min_peaks] containing the positive and negative peaks respectively. Each cell of the lists contains a list of: (position, peak_value) to get the average peak value do: np.mean(max_peaks, 0)[1] on the results to unpack one of the lists into x, y coordinates do: x, y = zip(*max_peaks) """ # check input data x_axis, y_axis = _datacheck_peakdetect(x_axis, y_axis) # make the points argument odd points += 1 - points % 2 #points += 1 - int(points) & 1 slower when int conversion needed # get raw peaks max_raw, min_raw = peakdetect_zero_crossing(y_axis) # define output variable max_peaks = [] min_peaks = [] max_ = _peakdetect_parabole_fitter(max_raw, x_axis, y_axis, points) min_ = _peakdetect_parabole_fitter(min_raw, x_axis, y_axis, points) max_peaks = map(lambda x: [x[0], x[1]], max_) max_fitted = map(lambda x: x[-1], max_) min_peaks = map(lambda x: [x[0], x[1]], min_) min_fitted = map(lambda x: x[-1], min_) #pylab.plot(x_axis, y_axis) #pylab.hold(True) #for max_p, max_f in zip(max_peaks, max_fitted): # pylab.plot(max_p[0], max_p[1], 'x') # pylab.plot(max_f[0], max_f[1], 'o', markersize = 2) #for min_p, min_f in zip(min_peaks, min_fitted): # pylab.plot(min_p[0], min_p[1], 'x') # pylab.plot(min_f[0], min_f[1], 'o', markersize = 2) #pylab.show() return [max_peaks, min_peaks]
