khider
commented
4 months ago - To adjust the plot, you can use some of the arguments of the plot function directly to do what you need:
plot(in_loglog=True, in_period=True, label=None, xlabel=None, ylabel='PSD', title=None, marker=None, markersize=None, color=None, linestyle=None, linewidth=None, transpose=False, xlim=None, ylim=None, figsize=[10, 4], savefig_settings=None, ax=None, legend=True, lgd_kwargs=None, xticks=None, yticks=None, alpha=None, zorder=None, plot_kwargs=None, signif_clr='red', signif_linestyles=['--', '-.', ':'], signif_linewidth=1, plot_beta=True, beta_kwargs=None)
Have a look at the doc: https://pyleoclim-util.readthedocs.io/en/latest/core/api.html#pyleoclim.core.psds.PSD.plot
In particular, look at signif_clr, signif_linestyles, and signif_linewidth. If you need something more granular and specific to your use case, make sure you return fig and ax:
fig, ax = psd_signif.plot()
They are Matplotlib objects that you can use.
- To get the data:
You can do: psd_signif.signif_qs to get the information. Have a look at the object itself in the doc: https://pyleoclim-util.readthedocs.io/en/latest/core/api.html#pyleoclim.core.psds.PSD
CommonClimate
thelightonmyway
I have finished an power spectrum density analysis recently by using .spectral(), and making significant analysis by using .signif_test(). However, I'm facing a problem about how I could plot optionally. Particularly, I want to adjust the 95% threshold curve's characters, such as linewidth, colors, and so on. But, through my search, neither could I print the 95% threshold data to make a plot, nor could I change the parameters to adjust it. I‘d greatly appreciate if you could answer my question on how I could print the 95% threshold data or change the parameters to adjust the 95% threshold curve. My code is as follows:
Thanks, sincerely!
def Z_Score(data): mean = np.mean(data) # 均值 std_dev = np.std(data) # 标准差 norm_data = (data - mean) / std_dev return norm_data fg=xr.open_dataset("/mnt/e/wind_global/obs/masked/E-OBS_wind_monthly_mean_1×1_masked.nc").fg[:-6,:,:] fg_spatial=np.nanmean(fg,(1,2)) fg_de_spatial=signal.detrend(fg_spatial,axis=0,type="linear",bp=0,overwrite_data=False) fg_de_std_spatial=Z_Score(fg_de_spatial) time=np.arange(516) wind_speed=pyleo.Series(time=time,value=fg_de_std_spatial,time_name="cycles per year",time_unit="month") psd=wind_speed.spectral(method="mtm") psd_signif=psd.signif_test(qs=[0.95],number=5) fig, ax = psd_signif.plot(label="original",lgd_kwargs={"ncol":1},color="k",linewidth=2))This is my plot: