def check_PSD(device="Sr20R21",
alpha='0',
phi='0',
z='10',
p=(-0.1, 0.1),
component='vx',
plot_name='test_psd.png',
sampling=10000,
NFFT=256):
from time_series_functions import get_spectral_power_density #,\
#butter_lowpass_filter
from matplotlib import pyplot as plt
import seaborn as sns
sns.__version__
time_series = load_time_series(device=device,
alpha=alpha,
phi=phi,
z=z,
p=p)
#time_series.vx = butter_lowpass_filter(time_series.vx,cutoff=2000,fs=10000)
#time_series.vy = butter_lowpass_filter(time_series.vy,cutoff=2000,fs=10000)
#time_series.vz = butter_lowpass_filter(time_series.vz,cutoff=2000,fs=10000)
Pxx, freqs = get_spectral_power_density(time_series.vx,
NFFT=NFFT,
sampling=sampling)
Pyy, freqs = get_spectral_power_density(time_series.vy,
NFFT=NFFT,
sampling=sampling)
#Pzz,freqs = get_spectral_power_density(time_series.vz,
# NFFT=NFFT,sampling=sampling)
if plot_name:
fig = plt.figure()
plt.plot(freqs, Pxx, label='$u$', alpha=0.6)
plt.plot(freqs, Pyy, label='$v$', alpha=0.6)
#plt.plot(freqs, Pzz, label='$z$', alpha=0.6)
plt.ylabel("Power spectral density [p$^2/$Hz]")
plt.xlabel("Frequency [Hz]")
plt.yscale('log')
plt.xscale('log')
plt.xlim(xmin=50, xmax=5000)
#plt.ylim(ymin=10e-4)
plt.legend(loc='upper right')
plt.savefig(plot_name)
fig.clear()
return [Pxx, Pyy, freqs]
def check_PSD(
device="Sr20R21",
alpha="0",
phi="0",
z="10",
p=(-0.1, 0.1),
component="vx",
plot_name="test_psd.png",
sampling=10000,
NFFT=256,
):
from time_series_functions import get_spectral_power_density # ,\
# butter_lowpass_filter
from matplotlib import pyplot as plt
import seaborn as sns
sns.__version__
time_series = load_time_series(device=device, alpha=alpha, phi=phi, z=z, p=p)
# time_series.vx = butter_lowpass_filter(time_series.vx,cutoff=2000,fs=10000)
# time_series.vy = butter_lowpass_filter(time_series.vy,cutoff=2000,fs=10000)
# time_series.vz = butter_lowpass_filter(time_series.vz,cutoff=2000,fs=10000)
Pxx, freqs = get_spectral_power_density(time_series.vx, NFFT=NFFT, sampling=sampling)
Pyy, freqs = get_spectral_power_density(time_series.vy, NFFT=NFFT, sampling=sampling)
# Pzz,freqs = get_spectral_power_density(time_series.vz,
# NFFT=NFFT,sampling=sampling)
if plot_name:
fig = plt.figure()
plt.plot(freqs, Pxx, label="$u$", alpha=0.6)
plt.plot(freqs, Pyy, label="$v$", alpha=0.6)
# plt.plot(freqs, Pzz, label='$z$', alpha=0.6)
plt.ylabel("Power spectral density [p$^2/$Hz]")
plt.xlabel("Frequency [Hz]")
plt.yscale("log")
plt.xscale("log")
plt.xlim(xmin=50, xmax=5000)
# plt.ylim(ymin=10e-4)
plt.legend(loc="upper right")
plt.savefig(plot_name)
fig.clear()
return [Pxx, Pyy, freqs]
def compare_SPD(device="Sr20R21",alpha=0,phi=0,z=10):
""" Gets the spectral power density for the 'popular' points
in the root folder (defined in the function)
"""
from os import listdir
from os.path import join
import pandas as pd
from numpy import argmin,linspace,log10
import time_series_functions as tdf
from re import findall
import matplotlib.pyplot as plt
import seaborn as sns
sns.__version__
all_data_points = sorted(
[f for f in listdir(data_root) \
if f.endswith('.p')\
and "a{0}".format(alpha) in f\
and "p{0}".format(phi) in f\
and "z{0:02d}".format(z) in f\
and device in f
]
)
px,py = art2fnc.popular_points()
data_points = art2fnc.check_quick_point_existence(
all_data_points,
root,
data_root
)
fig,ax = plt.subplots(len(py),1,sharey=True,figsize=(5,10))
for data in data_points:
df = pd.read_pickle(join(data_root,data))
spd_x,freq = tdf.get_spectral_power_density(df.vx)
spd_y,freq = tdf.get_spectral_power_density(df.vy)
x = float(findall('px[0-9]+.[0-9]+',data)[0].replace('px',''))
y = float(findall('py[0-9]+.[0-9]+',data)[0].replace('py',''))
y_ix = len(py)-1-argmin(map(abs,py-[y]*len(py)))
St = freq*delta/Uinf
slope_x = linspace(0.2,1,100)
slope_y = 10*log10(slope_x**(-5/3.))-40
spd = 10*log10(spd_x+spd_y)
if x == px[0]:
ax[y_ix].plot(slope_x,slope_y,color='k',lw=1)
ax[y_ix].text(0.6,-35,"$\\mathrm{St}^{-5/3}$")
ax[y_ix].text(-0.25,0.5,
"$y/\\delta = {0:.2f}$".format(y*4/1.5),
transform=ax[y_ix].transAxes,
ha='center',
va='center',
rotation=90
)
ax[y_ix].plot(St,spd,label="$x/2h=${0}".format(x))
ax[y_ix].set_xscale('log')
ax[y_ix].set_xlim( 5e-2 , 2 )
ax[y_ix].set_ylim( -55,-20 )
if not y_ix == len(ax)-1:
plt.setp( ax[y_ix].get_xticklabels(), visible=False )
else:
ax[y_ix].set_xlabel('$\\mathrm{St} = f\\delta/U_\\infty$')
ax[y_ix].set_ylabel(
'$10\\,\\mathrm{log}_{10}(\\Phi_x+\\Phi_y)$ \n[dB]'
)
ax[0].legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
fig.tight_layout()
fig.savefig(
"images/SPD_{0}_a{1}_p{2}_z{3}.png".format(
device,alpha,phi,z),
bbox_inches='tight'
)
fig.clear()
