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alpss_main.py
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alpss_main.py
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from datetime import datetime
import traceback
from IPython.display import display
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
import pandas as pd
import numpy as np
import os
from scipy.fft import fft, ifft, fftfreq
from scipy.fftpack import fftshift
from scipy.optimize import curve_fit
from scipy import signal
import findiff
import cv2 as cv
from scipy.signal import ShortTimeFFT
# main function to link together all the sub-functions
def alpss_main(**inputs):
# get the current working directory
cwd = os.getcwd()
# attempt to run the program in full
try:
# begin the program timer
start_time = datetime.now()
# function to find the spall signal domain of interest
sdf_out = spall_doi_finder(**inputs)
# function to find the carrier frequency
cen = carrier_frequency(sdf_out, **inputs)
# function to filter out the carrier frequency after the signal has started
cf_out = carrier_filter(sdf_out, cen, **inputs)
# function to calculate the velocity from the filtered voltage signal
vc_out = velocity_calculation(sdf_out, cen, cf_out, **inputs)
# function to estimate the instantaneous uncertainty for all points in time
iua_out = instantaneous_uncertainty_analysis(sdf_out, vc_out, cen, **inputs)
# function to find points of interest on the velocity trace
sa_out = spall_analysis(vc_out, iua_out, **inputs)
# function to calculate uncertainties in the spall strength and strain rate due to external uncertainties
fua_out = full_uncertainty_analysis(cen, sa_out, iua_out, **inputs)
# end the program timer
end_time = datetime.now()
# function to generate the final figure
fig = plotting(
sdf_out,
cen,
cf_out,
vc_out,
sa_out,
iua_out,
fua_out,
start_time,
end_time,
**inputs,
)
# function to save the output files if desired
if inputs["save_data"] == "yes":
saving(
sdf_out,
cen,
vc_out,
sa_out,
iua_out,
fua_out,
start_time,
end_time,
fig,
**inputs,
)
# end final timer and display full runtime
end_time2 = datetime.now()
print(
f"\nFull program runtime (including plotting and saving):\n{end_time2 - start_time}\n"
)
# in case the program throws an error
except Exception:
# print the traceback for the error
print(traceback.format_exc())
# attempt to plot the voltage signal from the imported data
try:
# import the desired data. Convert the time to skip and turn into number of rows
t_step = 1 / inputs["sample_rate"]
rows_to_skip = (
inputs["header_lines"] + inputs["time_to_skip"] / t_step
) # skip the header lines too
nrows = inputs["time_to_take"] / t_step
# change directory to where the data is stored
os.chdir(inputs["exp_data_dir"])
data = pd.read_csv(
inputs["filename"], skiprows=int(rows_to_skip), nrows=int(nrows)
)
# rename the columns of the data
data.columns = ["Time", "Ampl"]
# put the data into numpy arrays. Zero the time data
time = data["Time"].to_numpy()
time = time - time[0]
voltage = data["Ampl"].to_numpy()
# calculate the sample rate from the experimental data
fs = 1 / np.mean(np.diff(time))
# calculate the short time fourier transform
f, t, Zxx = stft(voltage, fs, **inputs)
# calculate magnitude of Zxx
mag = np.abs(Zxx)
# plotting
fig, (ax1, ax2) = plt.subplots(1, 2, num=2, figsize=(11, 4), dpi=300)
ax1.plot(time / 1e-9, voltage / 1e-3)
ax1.set_xlabel("Time (ns)")
ax1.set_ylabel("Voltage (mV)")
ax2.imshow(
10 * np.log10(mag**2),
aspect="auto",
origin="lower",
interpolation="none",
extent=[t[0] / 1e-9, t[-1] / 1e-9, f[0] / 1e9, f[-1] / 1e9],
cmap=inputs["cmap"],
)
ax2.set_xlabel("Time (ns)")
ax2.set_ylabel("Frequency (GHz)")
fig.suptitle("ERROR: Program Failed", c="r", fontsize=16)
plt.tight_layout()
plt.show()
# if that also fails then print the traceback and stop running the program
except Exception:
print(traceback.format_exc())
# move back to the original working directory
os.chdir(cwd)
# function to filter out the carrier frequency
def carrier_filter(sdf_out, cen, **inputs):
# unpack dictionary values in to individual variables
time = sdf_out["time"]
voltage = sdf_out["voltage"]
t_start_corrected = sdf_out["t_start_corrected"]
fs = sdf_out["fs"]
order = inputs["order"]
wid = inputs["wid"]
f_min = inputs["freq_min"]
f_max = inputs["freq_max"]
t_doi_start = sdf_out["t_doi_start"]
t_doi_end = sdf_out["t_doi_end"]
# get the index in the time array where the signal begins
sig_start_idx = np.argmin(np.abs(time - t_start_corrected))
# filter the data after the signal start time with a gaussian notch
freq = fftshift(
np.arange(-len(time[sig_start_idx:]) / 2, len(time[sig_start_idx:]) / 2)
* fs
/ len(time[sig_start_idx:])
)
filt_2 = (
1
- np.exp(-((freq - cen) ** order) / wid**order)
- np.exp(-((freq + cen) ** order) / wid**order)
)
voltage_filt = ifft(fft(voltage[sig_start_idx:]) * filt_2)
# pair the filtered voltage from after the signal starts with the original data from before the signal starts
voltage_filt = np.concatenate((voltage[0:sig_start_idx], voltage_filt))
# perform a stft on the filtered voltage data. Only the real part as to not get a two sided spectrogram
f_filt, t_filt, Zxx_filt = stft(np.real(voltage_filt), fs, **inputs)
# calculate the power
power_filt = 10 * np.log10(np.abs(Zxx_filt) ** 2)
# cut the data to the domain of interest
f_min_idx = np.argmin(np.abs(f_filt - f_min))
f_max_idx = np.argmin(np.abs(f_filt - f_max))
t_doi_start_idx = np.argmin(np.abs(t_filt - t_doi_start))
t_doi_end_idx = np.argmin(np.abs(t_filt - t_doi_end))
Zxx_filt_doi = Zxx_filt[f_min_idx:f_max_idx, t_doi_start_idx:t_doi_end_idx]
power_filt_doi = power_filt[f_min_idx:f_max_idx, t_doi_start_idx:t_doi_end_idx]
# save outputs to a dictionary
cf_out = {
"voltage_filt": voltage_filt,
"f_filt": f_filt,
"t_filt": t_filt,
"Zxx_filt": Zxx_filt,
"power_filt": power_filt,
"Zxx_filt_doi": Zxx_filt_doi,
"power_filt_doi": power_filt_doi,
}
return cf_out
# calculate the carrier frequency as the frequency with the max amplitude within the frequency range of interest
# specified in the user inputs
def carrier_frequency(spall_doi_finder_outputs, **inputs):
# unpack dictionary values in to individual variables
fs = spall_doi_finder_outputs["fs"]
time = spall_doi_finder_outputs["time"]
voltage = spall_doi_finder_outputs["voltage"]
freq_min = inputs["freq_min"]
freq_max = inputs["freq_max"]
# calculate frequency values for fft
freq = fftfreq(int(fs * time[-1]) + 1, 1 / fs)
freq2 = freq[: int(freq.shape[0] / 2) - 1]
# find the frequency indices that mark the range of interest
freq_min_idx = np.argmin(np.abs(freq2 - freq_min))
freq_max_idx = np.argmin(np.abs(freq2 - freq_max))
# find the amplitude values for the fft
ampl = np.abs(fft(voltage))
ampl2 = ampl[: int(freq.shape[0] / 2) - 1]
# cut the frequency and amplitude to the range of interest
freq3 = freq2[freq_min_idx:freq_max_idx]
ampl3 = ampl2[freq_min_idx:freq_max_idx]
# find the carrier as the frequency with the max amplitude
cen = freq3[np.argmax(ampl3)]
# return the carrier frequency
return cen
# program to calculate the uncertainty in the spall strength and strain rate
def full_uncertainty_analysis(cen, sa_out, iua_out, **inputs):
"""
Based on the work of Mallick et al.
Mallick, D.D., Zhao, M., Parker, J. et al. Laser-Driven Flyers and Nanosecond-Resolved Velocimetry for Spall Studies
in Thin Metal Foils. Exp Mech 59, 611–628 (2019). https://doi.org/10.1007/s11340-019-00519-x
"""
# unpack dictionary values in to individual variables
rho = inputs["density"]
C0 = inputs["C0"]
lam = inputs["lam"]
delta_rho = inputs["delta_rho"]
delta_C0 = inputs["delta_C0"]
delta_lam = inputs["delta_lam"]
theta = inputs["theta"]
delta_theta = inputs["delta_theta"]
delta_freq_tb = sa_out["peak_velocity_freq_uncert"]
delta_freq_td = sa_out["max_ten_freq_uncert"]
delta_time_c = iua_out["tau"]
delta_time_d = iua_out["tau"]
freq_tb = (sa_out["v_max_comp"] * 2) / lam + cen
freq_td = (sa_out["v_max_ten"] * 2) / lam + cen
time_c = sa_out["t_max_comp"]
time_d = sa_out["t_max_ten"]
# assuming time c is the same as time b
freq_tc = freq_tb
delta_freq_tc = delta_freq_tb
# convert angles to radians
theta = theta * (np.pi / 180)
delta_theta = delta_theta * (np.pi / 180)
# calculate the individual terms for spall uncertainty
term1 = (
-0.5
* rho
* C0
* (lam / 2)
* np.tan(theta)
* (1 / np.cos(theta))
* (freq_tb - freq_td)
* delta_theta
)
term2 = 0.5 * rho * C0 * (lam / (2 * np.cos(theta))) * delta_freq_tb
term3 = -0.5 * rho * C0 * (lam / (2 * np.cos(theta))) * delta_freq_td
term4 = 0.5 * rho * C0 * (1 / (2 * np.cos(theta))) * (freq_tb - freq_td) * delta_lam
term5 = 0.5 * rho * (lam / (2 * np.cos(theta))) * (freq_tb - freq_td) * delta_C0
term6 = 0.5 * C0 * (lam / (2 * np.cos(theta))) * (freq_tb - freq_td) * delta_rho
# calculate spall uncertainty
delta_spall = np.sqrt(
term1**2 + term2**2 + term3**2 + term4**2 + term5**2 + term6**2
)
# calculate the individual terms for strain rate uncertainty
d_f = freq_tc - freq_td
d_t = time_d - time_c
term7 = (-lam / (4 * C0**2 * np.cos(theta))) * (d_f / d_t) * delta_C0
term8 = (1 / (4 * C0 * np.cos(theta))) * (d_f / d_t) * delta_lam
term9 = (
((lam * np.tan(theta)) / (4 * C0 * np.cos(theta))) * (d_f / d_t) * delta_theta
)
term10 = (lam / (4 * C0 * np.cos(theta))) * (1 / d_t) * delta_freq_tc
term11 = (-lam / (4 * C0 * np.cos(theta))) * (1 / d_t) * delta_freq_td
term12 = (-lam / (4 * C0 * np.cos(theta))) * (d_f / d_t**2) * delta_time_c
term13 = (lam / (4 * C0 * np.cos(theta))) * (d_f / d_t**2) * delta_time_d
# calculate strain rate uncertainty
delta_strain_rate = np.sqrt(
term7**2 + term8**2 + term9**2 + term10**2 + term11**2 + term12**2 + term13**2
)
# save outputs to a dictionary
fua_out = {"spall_uncert": delta_spall, "strain_rate_uncert": delta_strain_rate}
return fua_out
# general function for a sinusoid
def sin_func(x, a, b, c, d):
return a * np.sin(2 * np.pi * b * x + c) + d
# get the indices for the upper and lower envelope of the voltage signal
# https://stackoverflow.com/questions/34235530/how-to-get-high-and-low-envelope-of-a-signal
def hl_envelopes_idx(s, dmin=1, dmax=1, split=False):
"""
Input :
s: 1d-array, data signal from which to extract high and low envelopes
dmin, dmax: int, optional, size of chunks, use this if the size of the input signal is too big
split: bool, optional, if True, split the signal in half along its mean, might help to generate the envelope in some cases
Output :
lmin,lmax : high/low envelope idx of input signal s
"""
# locals min
lmin = (np.diff(np.sign(np.diff(s))) > 0).nonzero()[0] + 1
# locals max
lmax = (np.diff(np.sign(np.diff(s))) < 0).nonzero()[0] + 1
if split:
# s_mid is zero if s centered around x-axis or more generally mean of signal
s_mid = np.mean(s)
# pre-sorting of locals min based on relative position with respect to s_mid
lmin = lmin[s[lmin] < s_mid]
# pre-sorting of local max based on relative position with respect to s_mid
lmax = lmax[s[lmax] > s_mid]
# global min of dmin-chunks of locals min
lmin = lmin[
[i + np.argmin(s[lmin[i : i + dmin]]) for i in range(0, len(lmin), dmin)]
]
# global max of dmax-chunks of locals max
lmax = lmax[
[i + np.argmax(s[lmax[i : i + dmax]]) for i in range(0, len(lmax), dmax)]
]
return lmin, lmax
# gaussian distribution
def gauss(x, amp, sigma, mu):
f = (amp / (sigma * np.sqrt(2 * np.pi))) * np.exp(-0.5 * ((x - mu) / sigma) ** 2)
return f
# calculate the fwhm of a gaussian distribution
def fwhm(
smoothing_window, smoothing_wid, smoothing_amp, smoothing_sigma, smoothing_mu, fs
):
# x points for the gaussian weights
x = np.linspace(-smoothing_wid, smoothing_wid, smoothing_window)
# calculate the gaussian weights
weights = gauss(x, smoothing_amp, smoothing_sigma, smoothing_mu)
# calculate the half max
half_max = ((np.max(weights) - np.min(weights)) / 2) + np.min(weights)
# calculate the fwhm of the gaussian weights for the normalized x points
fwhm_norm = 2 * np.abs(x[np.argmin(np.abs(weights - half_max))])
# scale the fwhm to the number of points being used for the smoothing window
fwhm_pts = (fwhm_norm / (smoothing_wid * 2)) * smoothing_window
# calculate the time span of the fwhm of the gaussian weights
fwhm = fwhm_pts / fs
return fwhm
# function to estimate the instantaneous uncertainty for all points in time
def instantaneous_uncertainty_analysis(sdf_out, vc_out, cen, **inputs):
# unpack needed variables
lam = inputs["lam"]
smoothing_window = inputs["smoothing_window"]
smoothing_wid = inputs["smoothing_wid"]
smoothing_amp = inputs["smoothing_amp"]
smoothing_sigma = inputs["smoothing_sigma"]
smoothing_mu = inputs["smoothing_mu"]
fs = sdf_out["fs"]
time = sdf_out["time"]
time_f = vc_out["time_f"]
voltage_filt = vc_out["voltage_filt"]
time_start_idx = vc_out["time_start_idx"]
time_end_idx = vc_out["time_end_idx"]
carrier_band_time = inputs["carrier_band_time"]
# take only real component of the filtered voltage signal
voltage_filt = np.real(voltage_filt)
# amount of time from the beginning of the voltage signal to analyze for noise
t_take = carrier_band_time
steps_take = int(t_take * fs)
# get the data for only the beginning section of the signal
time_cut = time[0:steps_take]
voltage_filt_early = voltage_filt[0:steps_take]
try:
# fit a sinusoid to the data
popt, pcov = curve_fit(
sin_func, time_cut, voltage_filt_early, p0=[0.1, cen, 0, 0]
)
except Exception:
# if sin fitting doesn't work set the fitting parameters to be zeros
print(traceback.format_exc())
popt = [0, 0, 0, 0]
pcov = [0, 0, 0, 0]
# calculate the fitted curve
volt_fit = sin_func(time_cut, popt[0], popt[1], popt[2], popt[3])
# calculate the residuals
noise = voltage_filt_early - volt_fit
# get data for only the doi of the voltage
voltage_filt_doi = voltage_filt[time_start_idx:time_end_idx]
# calculate the envelope indices of the originally imported voltage data (and now filtered) using the stack
# overflow code
lmin, lmax = hl_envelopes_idx(voltage_filt_doi, dmin=1, dmax=1, split=False)
# interpolate the voltage envelope to every time point
env_max_interp = np.interp(time_f, time_f[lmax], voltage_filt_doi[lmax])
env_min_interp = np.interp(time_f, time_f[lmin], voltage_filt_doi[lmin])
# calculate the estimated peak to peak amplitude at every time
inst_amp = env_max_interp - env_min_interp
# calculate the estimated noise fraction at every time
# https://doi.org/10.1063/12.0000870
inst_noise = np.std(noise) / (inst_amp / 2)
# calculate the frequency and velocity uncertainty
# https://doi.org/10.1063/12.0000870
# take the characteristic time to be the fwhm of the gaussian weights used for smoothing the velocity signal
tau = fwhm(
smoothing_window,
smoothing_wid,
smoothing_amp,
smoothing_sigma,
smoothing_mu,
fs,
)
freq_uncert_scaling = (1 / np.pi) * (np.sqrt(6 / (fs * (tau**3))))
freq_uncert = inst_noise * freq_uncert_scaling
vel_uncert = freq_uncert * (lam / 2)
# dictionary to return outputs
iua_out = {
"time_cut": time_cut,
"popt": popt,
"pcov": pcov,
"volt_fit": volt_fit,
"noise": noise,
"env_max_interp": env_max_interp,
"env_min_interp": env_min_interp,
"inst_amp": inst_amp,
"inst_noise": inst_noise,
"tau": tau,
"freq_uncert_scaling": freq_uncert_scaling,
"freq_uncert": freq_uncert,
"vel_uncert": vel_uncert,
}
return iua_out
# function to take the numerical derivative of input array phas (central difference with a 9-point stencil).
# phas is padded so that after smoothing the final velocity trace matches the length of the domain of interest.
# this avoids issues with handling the boundaries in the derivative and later in smoothing.
# https://github.com/maroba/findiff/tree/master
def num_derivative(phas, window, time_start_idx, time_end_idx, fs):
# set 8th order accuracy to get a 9-point stencil. can change the accuracy order if desired
acc = 8
# calculate how much padding is needed. half_space padding comes from the length of the smoothing window.
half_space = int(np.floor(window / 2))
pad = int(half_space + acc / 2)
# get only the section of interest
phas_pad = phas[time_start_idx - pad : time_end_idx + pad]
# calculate the phase angle derivative
ddt = findiff.FinDiff(0, 1 / fs, 1, acc=acc)
dpdt_pad = ddt(phas_pad) * (1 / (2 * np.pi))
# this is the hard coded 9-point central difference code. this can be used in case the findiff package ever breaks
# dpdt_pad = np.zeros(phas_pad.shape)
# for i in range(4, len(dpdt_pad) - 4):
# dpdt_pad[i] = ((1 / 280) * phas_pad[i - 4]
# + (-4 / 105) * phas_pad[i - 3]
# + (1 / 5) * phas_pad[i - 2]
# + (-4 / 5) * phas_pad[i - 1]
# + (4 / 5) * phas_pad[i + 1]
# + (-1 / 5) * phas_pad[i + 2]
# + (4 / 105) * phas_pad[i + 3]
# + (-1 / 280) * phas_pad[i + 4]) \
# * (fs / (2 * np.pi))
# output both the padded and un-padded derivatives
dpdt = dpdt_pad[pad:-pad]
dpdt_pad = dpdt_pad[int(acc / 2) : -int(acc / 2)]
return dpdt, dpdt_pad
# function to generate the final figure
def plotting(
sdf_out,
cen,
cf_out,
vc_out,
sa_out,
iua_out,
fua_out,
start_time,
end_time,
**inputs,
):
# create the figure and axes
fig = plt.figure(num=1, figsize=inputs["plot_figsize"], dpi=inputs["plot_dpi"])
ax1 = plt.subplot2grid((3, 5), (0, 0)) # voltage data
ax2 = plt.subplot2grid((3, 5), (0, 1)) # noise distribution histogram
ax3 = plt.subplot2grid((3, 5), (1, 0)) # imported voltage spectrogram
ax4 = plt.subplot2grid((3, 5), (1, 1)) # thresholded spectrogram
ax5 = plt.subplot2grid((3, 5), (2, 0)) # spectrogram of the ROI
ax6 = plt.subplot2grid((3, 5), (2, 1)) # filtered spectrogram of the ROI
ax7 = plt.subplot2grid((3, 5), (0, 2), colspan=2) # voltage in the ROI
ax8 = plt.subplot2grid(
(3, 5), (1, 2), colspan=2, rowspan=2
) # velocity overlaid with spectrogram
ax9 = ax8.twinx() # spectrogram overlaid with velocity
ax10 = plt.subplot2grid((3, 5), (0, 4)) # noise fraction
ax11 = ax10.twinx() # velocity uncertainty
ax12 = plt.subplot2grid((3, 5), (1, 4)) # velocity trace and spall points
ax13 = plt.subplot2grid((3, 5), (2, 4), colspan=1, rowspan=1) # results table
# voltage data
ax1.plot(
sdf_out["time"] / 1e-9,
sdf_out["voltage"] * 1e3,
label="Original Signal",
c="tab:blue",
)
ax1.plot(
sdf_out["time"] / 1e-9,
np.real(vc_out["voltage_filt"]) * 1e3,
label="Filtered Signal",
c="tab:orange",
)
ax1.plot(
iua_out["time_cut"] / 1e-9,
iua_out["volt_fit"] * 1e3,
label="Sine Fit",
c="tab:green",
)
ax1.axvspan(
sdf_out["t_doi_start"] / 1e-9,
sdf_out["t_doi_end"] / 1e-9,
ymin=-1,
ymax=1,
color="tab:red",
alpha=0.35,
ec="none",
label="ROI",
zorder=4,
)
ax1.set_xlabel("Time (ns)")
ax1.set_ylabel("Voltage (mV)")
ax1.set_xlim([sdf_out["time"][0] / 1e-9, sdf_out["time"][-1] / 1e-9])
ax1.legend(loc="upper right")
ax1.set_title("Voltage Data")
# noise distribution histogram
ax2.hist(iua_out["noise"] * 1e3, bins=50, rwidth=0.8)
ax2.set_xlabel("Noise (mV)")
ax2.set_ylabel("Counts")
ax2.set_title("Voltage Noise")
# imported voltage spectrogram and a rectangle to show the ROI
plt3 = ax3.imshow(
10 * np.log10(sdf_out["mag"] ** 2),
aspect="auto",
origin="lower",
interpolation="none",
extent=[
sdf_out["t"][0] / 1e-9,
sdf_out["t"][-1] / 1e-9,
sdf_out["f"][0] / 1e9,
sdf_out["f"][-1] / 1e9,
],
cmap=inputs["cmap"],
)
fig.colorbar(plt3, ax=ax3, label="Power (dBm)")
anchor = [sdf_out["t_doi_start"] / 1e-9, sdf_out["f_doi"][0] / 1e9]
width = sdf_out["t_doi_end"] / 1e-9 - sdf_out["t_doi_start"] / 1e-9
height = sdf_out["f_doi"][-1] / 1e9 - sdf_out["f_doi"][0] / 1e9
win = Rectangle(
anchor,
width,
height,
edgecolor="r",
facecolor="none",
linewidth=0.75,
linestyle="-",
)
ax3.add_patch(win)
ax3.set_xlabel("Time (ns)")
ax3.set_ylabel("Frequency (GHz)")
ax3.minorticks_on()
ax3.set_title("Spectrogram Original Signal")
# plotting the thresholded spectrogram on the ROI to show how the signal start time is found
ax4.imshow(
sdf_out["th3"],
aspect="auto",
origin="lower",
interpolation="none",
extent=[
sdf_out["t"][0] / 1e-9,
sdf_out["t"][-1] / 1e-9,
sdf_out["f_doi"][0] / 1e9,
sdf_out["f_doi"][-1] / 1e9,
],
cmap=inputs["cmap"],
)
ax4.axvline(sdf_out["t_start_detected"] / 1e-9, ls="--", c="r")
ax4.axvline(sdf_out["t_start_corrected"] / 1e-9, ls="-", c="r")
if inputs["start_time_user"] == "none":
ax4.axhline(sdf_out["f_doi"][sdf_out["f_doi_carr_top_idx"]] / 1e9, c="r")
ax4.set_ylim([inputs["freq_min"] / 1e9, inputs["freq_max"] / 1e9])
ax4.set_xlim([sdf_out["t_doi_start"] / 1e-9, sdf_out["t_doi_end"] / 1e-9])
ax4.set_xlabel("Time (ns)")
ax4.set_ylabel("Frequency (GHz)")
ax4.minorticks_on()
ax4.set_title("Thresholded Spectrogram")
# plotting the spectrogram of the ROI with the start-time line to see how well it lines up
plt5 = ax5.imshow(
10 * np.log10(sdf_out["mag"] ** 2),
aspect="auto",
origin="lower",
interpolation="none",
extent=[
sdf_out["t"][0] / 1e-9,
sdf_out["t"][-1] / 1e-9,
sdf_out["f"][0] / 1e9,
sdf_out["f"][-1] / 1e9,
],
cmap=inputs["cmap"],
)
fig.colorbar(plt5, ax=ax5, label="Power (dBm)")
ax5.axvline(sdf_out["t_start_detected"] / 1e-9, ls="--", c="r")
ax5.axvline(sdf_out["t_start_corrected"] / 1e-9, ls="-", c="r")
if inputs["start_time_user"] == "none":
ax5.axhline(sdf_out["f_doi"][sdf_out["f_doi_carr_top_idx"]] / 1e9, c="r")
ax5.set_ylim([inputs["freq_min"] / 1e9, inputs["freq_max"] / 1e9])
ax5.set_xlim([sdf_out["t_doi_start"] / 1e-9, sdf_out["t_doi_end"] / 1e-9])
plt5.set_clim([np.min(sdf_out["power_doi"]), np.max(sdf_out["power_doi"])])
ax5.set_xlabel("Time (ns)")
ax5.set_ylabel("Frequency (GHz)")
ax5.minorticks_on()
ax5.set_title("Spectrogram ROI")
# plotting the filtered spectrogram of the ROI
plt6 = ax6.imshow(
cf_out["power_filt"],
aspect="auto",
origin="lower",
interpolation="none",
extent=[
cf_out["t_filt"][0] / 1e-9,
cf_out["t_filt"][-1] / 1e-9,
cf_out["f_filt"][0] / 1e9,
cf_out["f_filt"][-1] / 1e9,
],
cmap=inputs["cmap"],
)
fig.colorbar(plt6, ax=ax6, label="Power (dBm)")
ax6.axvline(sdf_out["t_start_detected"] / 1e-9, ls="--", c="r")
ax6.axvline(sdf_out["t_start_corrected"] / 1e-9, ls="-", c="r")
ax6.set_ylim([inputs["freq_min"] / 1e9, inputs["freq_max"] / 1e9])
ax6.set_xlim([sdf_out["t_doi_start"] / 1e-9, sdf_out["t_doi_end"] / 1e-9])
plt6.set_clim([np.min(cf_out["power_filt_doi"]), np.max(cf_out["power_filt_doi"])])
ax6.set_xlabel("Time (ns)")
ax6.set_ylabel("Frequency (GHz)")
ax6.minorticks_on()
ax6.set_title("Filtered Spectrogram ROI")
# voltage in the ROI and the signal envelope
ax7.plot(
sdf_out["time"] / 1e-9,
np.real(vc_out["voltage_filt"]) * 1e3,
label="Filtered Signal",
c="tab:blue",
)
ax7.plot(
vc_out["time_f"] / 1e-9,
iua_out["env_max_interp"] * 1e3,
label="Signal Envelope",
c="tab:red",
)
ax7.plot(vc_out["time_f"] / 1e-9, iua_out["env_min_interp"] * 1e3, c="tab:red")
ax7.set_xlabel("Time (ns)")
ax7.set_ylabel("Voltage (mV)")
ax7.set_xlim([sdf_out["t_doi_start"] / 1e-9, sdf_out["t_doi_end"] / 1e-9])
ax7.legend(loc="upper right")
ax7.set_title("Voltage ROI")
# plotting the velocity and smoothed velocity curves to be overlaid on top of the spectrogram
ax8.plot(
(vc_out["time_f"]) / 1e-9,
vc_out["velocity_f"],
"-",
c="grey",
alpha=0.65,
linewidth=3,
label="Velocity",
)
ax8.plot(
(vc_out["time_f"]) / 1e-9,
vc_out["velocity_f_smooth"],
"k-",
linewidth=3,
label="Smoothed Velocity",
)
ax8.plot(
vc_out["time_f"] / 1e-9,
vc_out["velocity_f_smooth"] + iua_out["vel_uncert"] * inputs["uncert_mult"],
"r-",
alpha=0.5,
label=rf'$1\sigma$ Uncertainty (x{inputs["uncert_mult"]})',
)
ax8.plot(
vc_out["time_f"] / 1e-9,
vc_out["velocity_f_smooth"] - iua_out["vel_uncert"] * inputs["uncert_mult"],
"r-",
alpha=0.5,
)
ax8.set_xlabel("Time (ns)")
ax8.set_ylabel("Velocity (m/s)")
ax8.legend(loc="lower right", fontsize=9, framealpha=1)
ax8.set_zorder(1)
ax8.patch.set_visible(False)
ax8.set_title("Filtered Spectrogram ROI with Velocity")
# plotting the final spectrogram to go with the velocity curves
plt9 = ax9.imshow(
cf_out["power_filt"],
extent=[
cf_out["t_filt"][0] / 1e-9,
cf_out["t_filt"][-1] / 1e-9,
cf_out["f_filt"][0] / 1e9,
cf_out["f_filt"][-1] / 1e9,
],
aspect="auto",
origin="lower",
interpolation="none",
cmap=inputs["cmap"],
)
ax9.set_ylabel("Frequency (GHz)")
vel_lim = np.array([-300, np.max(vc_out["velocity_f_smooth"]) + 300])
ax8.set_ylim(vel_lim)
ax8.set_xlim([cf_out["t_filt"][0] / 1e-9, cf_out["t_filt"][-1] / 1e-9])
freq_lim = (vel_lim / (inputs["lam"] / 2)) + cen
ax9.set_ylim(freq_lim / 1e9)
ax9.set_xlim([sdf_out["t_doi_start"] / 1e-9, sdf_out["t_doi_end"] / 1e-9])
ax9.minorticks_on()
plt9.set_clim([np.min(cf_out["power_filt_doi"]), np.max(cf_out["power_filt_doi"])])
# plot the noise fraction on the ROI
ax10.plot(vc_out["time_f"] / 1e-9, iua_out["inst_noise"] * 100, "r", linewidth=2)
ax10.set_xlabel("Time (ns)")
ax10.set_ylabel("Noise Fraction (%)")
ax10.set_xlim([vc_out["time_f"][0] / 1e-9, vc_out["time_f"][-1] / 1e-9])
ax10.minorticks_on()
ax10.grid(axis="both", which="both")
ax10.set_title("Noise Fraction and Velocity Uncertainty")
# plot the velocity uncertainty on the ROI
ax11.plot(vc_out["time_f"] / 1e-9, iua_out["vel_uncert"], linewidth=2)
ax11.set_ylabel("Velocity Uncertainty (m/s)")
ax11.minorticks_on()
# plotting the final smoothed velocity trace and uncertainty bounds with spall point markers (if they were found
# on the signal)
ax12.fill_between(
(vc_out["time_f"] - sdf_out["t_start_corrected"]) / 1e-9,
vc_out["velocity_f_smooth"] + 2 * iua_out["vel_uncert"] * inputs["uncert_mult"],
vc_out["velocity_f_smooth"] - 2 * iua_out["vel_uncert"] * inputs["uncert_mult"],
color="mistyrose",
label=rf'$2\sigma$ Uncertainty (x{inputs["uncert_mult"]})',
)
ax12.fill_between(
(vc_out["time_f"] - sdf_out["t_start_corrected"]) / 1e-9,
vc_out["velocity_f_smooth"] + iua_out["vel_uncert"] * inputs["uncert_mult"],
vc_out["velocity_f_smooth"] - iua_out["vel_uncert"] * inputs["uncert_mult"],
color="lightcoral",
alpha=0.5,
ec="none",
label=rf'$1\sigma$ Uncertainty (x{inputs["uncert_mult"]})',
)
ax12.plot(
(vc_out["time_f"] - sdf_out["t_start_corrected"]) / 1e-9,
vc_out["velocity_f_smooth"],
"k-",
linewidth=3,
label="Smoothed Velocity",
)
ax12.set_xlabel("Time (ns)")
ax12.set_ylabel("Velocity (m/s)")
ax12.set_title("Velocity with Uncertainty Bounds")
if not np.isnan(sa_out["t_max_comp"]):
ax12.plot(
(sa_out["t_max_comp"] - sdf_out["t_start_corrected"]) / 1e-9,
sa_out["v_max_comp"],
"bs",
label=f'Velocity at Max Compression: {int(round(sa_out["v_max_comp"]))}',
)
if not np.isnan(sa_out["t_max_ten"]):
ax12.plot(
(sa_out["t_max_ten"] - sdf_out["t_start_corrected"]) / 1e-9,
sa_out["v_max_ten"],
"ro",
label=f'Velocity at Max Tension: {int(round(sa_out["v_max_ten"]))}',
)
if not np.isnan(sa_out["t_rc"]):
ax12.plot(
(sa_out["t_rc"] - sdf_out["t_start_corrected"]) / 1e-9,
sa_out["v_rc"],
"gD",
label=f'Velocity at Recompression: {int(round(sa_out["v_rc"]))}',
)
# if not np.isnan(sa_out['t_max_comp']) or not np.isnan(sa_out['t_max_ten']) or not np.isnan(sa_out['t_rc']):
# ax12.legend(loc='lower right', fontsize=9)
ax12.legend(loc="lower right", fontsize=9)
ax12.set_xlim(
[
-inputs["t_before"] / 1e-9,
(vc_out["time_f"][-1] - sdf_out["t_start_corrected"]) / 1e-9,
]
)
ax12.set_ylim(
[
np.min(vc_out["velocity_f_smooth"]) - 100,
np.max(vc_out["velocity_f_smooth"]) + 100,
]
)
if np.max(iua_out["inst_noise"]) > 1.0:
ax10.set_ylim([0, 100])
ax11.set_ylim([0, iua_out["freq_uncert_scaling"] * (inputs["lam"] / 2)])
# table to show results of the run
run_data1 = {
"Name": [
"Date",
"Time",
"File Name",
"Run Time",
"Smoothing FWHM (ns)",
"Peak Shock Stress (GPa)",
"Strain Rate (x1e6)",
"Spall Strength (GPa)",
],
"Value": [
start_time.strftime("%b %d %Y"),
start_time.strftime("%I:%M %p"),
inputs["filename"],
(end_time - start_time),
round(iua_out["tau"] * 1e9, 2),
round(
(0.5 * inputs["density"] * inputs["C0"] * sa_out["v_max_comp"]) / 1e9, 6
),
rf"{round(sa_out['strain_rate_est'] / 1e6, 6)} $\pm$ {round(fua_out['strain_rate_uncert'] / 1e6, 6)}",
rf"{round(sa_out['spall_strength_est'] / 1e9, 6)} $\pm$ {round(fua_out['spall_uncert'] / 1e9, 6)}",
],
}
df1 = pd.DataFrame(data=run_data1)
cellLoc1 = "center"
loc1 = "center"
table1 = ax13.table(
cellText=df1.values, colLabels=df1.columns, cellLoc=cellLoc1, loc=loc1
)
table1.auto_set_font_size(False)
table1.set_fontsize(10)
table1.scale(1, 1.5)
ax13.axis("tight")
ax13.axis("off")
# fix the layout
plt.tight_layout()
# display the plots if desired. if this is turned off the plots will still save
if inputs["display_plots"] == "yes":
plt.show()
# return the figure so it can be saved if desired
return fig
# function for saving all the final outputs
def saving(
sdf_out, cen, vc_out, sa_out, iua_out, fua_out, start_time, end_time, fig, **inputs
):
# change to the output files directory
os.chdir(inputs["out_files_dir"])
# save the plots
fig.savefig(
fname=(inputs["filename"][0:-4] + "--plots.png"),
dpi="figure",
format="png",
facecolor="w",
)
# save the function inputs used for this run
inputs_df = pd.DataFrame.from_dict(inputs, orient="index", columns=["Input"])
inputs_df.to_csv(
inputs["filename"][0:-4] + "--inputs" + ".csv", index=True, header=False
)
# save the noisy velocity trace
velocity_data = np.stack((vc_out["time_f"], vc_out["velocity_f"]), axis=1)
np.savetxt(
inputs["filename"][0:-4] + "--velocity" + ".csv", velocity_data, delimiter=","
)
# save the smoothed velocity trace
velocity_data_smooth = np.stack(
(vc_out["time_f"], vc_out["velocity_f_smooth"]), axis=1
)
np.savetxt(
inputs["filename"][0:-4] + "--velocity--smooth" + ".csv",
velocity_data_smooth,
delimiter=",",
)
# save the filtered voltage data
voltage_data = np.stack(
(
sdf_out["time"],
np.real(vc_out["voltage_filt"]),
np.imag(vc_out["voltage_filt"]),
),
axis=1,
)
np.savetxt(
inputs["filename"][0:-4] + "--voltage" + ".csv", voltage_data, delimiter=","
)
# save the noise fraction
noise_data = np.stack((vc_out["time_f"], iua_out["inst_noise"]), axis=1)
np.savetxt(
inputs["filename"][0:-4] + "--noise--frac" + ".csv", noise_data, delimiter=","
)
# save the velocity uncertainty
vel_uncert_data = np.stack((vc_out["time_f"], iua_out["vel_uncert"]), axis=1)
np.savetxt(
inputs["filename"][0:-4] + "--vel--uncert" + ".csv",