本文整理汇总了Python中timeseries.TimeSeries.peek_ps方法的典型用法代码示例。如果您正苦于以下问题：Python TimeSeries.peek_ps方法的具体用法？Python TimeSeries.peek_ps怎么用？Python TimeSeries.peek_ps使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类timeseries.TimeSeries的用法示例。
在下文中一共展示了TimeSeries.peek_ps方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1:
# 需要导入模块: from timeseries import TimeSeries [as 别名]
# 或者: from timeseries.TimeSeries import peek_ps [as 别名]
"""
plt.figure(1)
ts1.peek_ps()
ts2.peek_ps()
plt.figure(2)
plt.plot(ts1.pfreq, transfer_function12)
plt.figure(3)
plt.plot(ts1.pfreq, ts2.PowerSpectrum.ppower / ts1.PowerSpectrum.ppower)
"""
index3 = dt * (np.arange(0, 2 * window + 1) - window)
weight3 = np.zeros_like(index3) - w1
weight3[index3 == 0] = 1.0 - w1
angfreq = 2 * np.pi * ts1.pfreq
transfer_function13 = tsutils.transfer_function(index3, weight3, angfreq)
plt.figure(4)
plt.plot(1000 * ts1.pfreq, ts3.PowerSpectrum.ppower / ts1.PowerSpectrum.ppower, label='observed transfer function')
plt.plot(1000 * ts1.pfreq, transfer_function13, label='theoretical transfer function')
plt.axvline(3.333, label='300 s oscillation', color='k')
plt.xlabel('frequency (mHz)')
plt.ylabel('emission (arbitrary units)')
plt.legend()
plt.figure(6)
ts1.peek_ps()
ts3.peek_ps()
示例2: do_lstsqr
# 需要导入模块: from timeseries import TimeSeries [as 别名]
# 或者: from timeseries.TimeSeries import peek_ps [as 别名]
#.........这里部分代码省略.........
for f in findex:
plt.plot(h[1][1:] / np.log(10.0), hpwr[f, :], label='%7.5f Hz' % (freqs[f]))
plt.legend(loc=3, fontsize=10)
plt.savefig(savefig + '.power_spectra_distributions.png')
# plot out the time series
plt.figure(4)
full_ts.peek()
plt.savefig(savefig + '.full_ts_timeseries.png')
plt.close('all')
###############################################################
# Time series plots
# Plot all the analyzed time series
plt.figure(10)
for i in range(0, nx):
for j in range(0, ny):
plt.plot(t, dc_analysed[j, i, :])
plt.xlabel('time (seconds)')
plt.ylabel('analyzed emission ' + tsdetails)
plt.title(data_name)
plt.ylim(dc_analysed_minmax)
plt.xlim((t[0], t[-1]))
plt.savefig(savefig + '.all_analyzed_ts.png')
# Plot a histogram of the studied data at each time
bins = 50
hist_dc_analysed = np.zeros((bins, nt))
for this_time in range(0, nt):
hist_dc_analysed[:, this_time], bin_edges = np.histogram(dc_analysed[:, :, this_time], bins=bins, range=dc_analysed_minmax)
hist_dc_analysed = hist_dc_analysed / (1.0 * nx * ny)
plt.figure(12)
plt.xlabel('time (seconds)')
plt.ylabel('analyzed emission ' + tsdetails)
plt.imshow(hist_dc_analysed, aspect='auto', origin='lower',
extent=(t[0], t[-1], dc_analysed_minmax[0], dc_analysed_minmax[1]))
plt.colorbar()
plt.title(data_name)
plt.savefig(savefig + '.all_analyzed_ts_histogram.png')
###############################################################
# Fourier power plots
# Plot all the analyzed FFTs
plt.figure(11)
for i in range(0, nx):
for j in range(0, ny):
ts = TimeSeries(t, dc_analysed[j, i, :])
ts.peek_ps()
plt.loglog()
plt.axvline(five_min, color='k', linestyle='-.', label='5 mins.')
plt.axvline(three_min, color='k', linestyle='--', label='3 mins.')
plt.xlabel('frequency (Hz)')
plt.ylabel('FFT power ' + tsdetails)
plt.title(data_name)
plt.savefig(savefig + '.all_analyzed_fft.png')
# Plot a histogram of the studied FFTs at each time
bins = 50
minmax = [np.min(logpwr), np.max(logpwr)]
hist_dc_analysed_logpwr = np.zeros((bins, nposfreq))
for this_freq in range(0, nposfreq):
hist_dc_analysed_logpwr[:, this_freq], bin_edges = np.histogram(logpwr[:, :, this_freq], bins=bins, range=minmax)
hist_dc_analysed_logpwr = hist_dc_analysed_logpwr / (1.0 * nx * ny)
plt.figure(13)
plt.xlabel('frequency (Hz)')
plt.ylabel('FFT power ' + tsdetails)
plt.imshow(hist_dc_analysed_logpwr, aspect='auto', origin='lower',
extent=(freqs[0], freqs[-1], np.exp(minmax[0]), np.exp(minmax[1])))
plt.semilogy()
plt.colorbar()
plt.title(data_name)
plt.savefig(savefig + '.all_analyzed_fft_histogram.png')
###############################################################
# Save various data products
# Fourier Power of the analyzed data
ofilename = region_id
pkl_write(pkl_location,
'OUT.' + ofilename + '.fourier_power.pickle',
(freqs, pwr))
# Analyzed data
pkl_write(pkl_location,
'OUT.' + ofilename + '.dc_analysed.pickle',
(t, dc_analysed))
# Fourier transform
pkl_write(pkl_location,
'OUT.' + ofilename + '.fft_transform.pickle',
(freqs, fft_transform))
# Save the full time series to a CSV file
csv_timeseries_write(os.path.join(os.path.expanduser(scsv), window, manip),
'.'.join((data_name, 'average_analyzed_ts.csv')),
(t, full_data))
# Original data
csv_timeseries_write(os.path.join(os.path.expanduser(scsv)),
'.'.join((ident, 'average_original_ts.csv')),
(t, doriginal))示例3: range
# 需要导入模块: from timeseries import TimeSeries [as 别名]
# 或者: from timeseries.TimeSeries import peek_ps [as 别名]
dt = 12.0
nt = 300
data = np.zeros(nt)
alpha = 0.0001
data[0] = 1.0
for i in range(0, nt - 1):
data[i+1] = data[i] + alpha*np.random.normal()
ts = TimeSeries(dt * np.arange(0, nt), data)
plt.figure(1)
ts.peek_ps()
plt.loglog()
plt.figure(2)
ts.peek()
this = ([ts.pfreq, ts.ppower],)
norm_estimate = np.zeros((3,))
norm_estimate[0] = ts.ppower[0]
norm_estimate[1] = norm_estimate[0] / 1000.0
norm_estimate[2] = norm_estimate[0] * 1000.0
background_estimate = np.zeros_like(norm_estimate)
background_estimate[0] = np.mean(ts.ppower[-10:-1])