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alpss_run.py
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alpss_run.py
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"""
ALPSS
Jake Diamond (2024)
Johns Hopkins University
Hopkins Extreme Materials Institute (HEMI)
Please report any bugs or comments to jdiamo15@jhu.edu
Key for input variables:
filename: str; filename for the data to run
save_data: str; 'yes' or 'no' to save output data
start_time_user: str or float; if 'none' the program will attempt to find the
signal start time automatically. if float then
the program will use that as the signal start time
header_lines: int; number of header lines to skip in the data file
time_to_skip: float; the amount of time to skip in the full data file before beginning to read in data
time_to_take: float; the amount of time to take in the data file after skipping time_to_skip
t_before: float; amount of time before the signal start time to include in the velocity calculation
t_after: float; amount of time after the signal start time to include in the velocity calculation
start_time_correction: float; amount of time to adjust the signal start time by
freq_min: float; minimum frequency for the region of interest
freq_max: float; maximum frequency for the region of interest
smoothing_window: int; number of points to use for the smoothing window. must be an odd number
smoothing_wid: float; half the width of the normal distribution used
to calculate the smoothing weights (recommend 3)
smoothing_amp: float; amplitude of the normal distribution used to calculate
the smoothing weights (recommend 1)
smoothing_sigma: float; standard deviation of the normal distribution used
to calculate the smoothing weights (recommend 1)
smoothing_mu: float; mean of the normal distribution used to calculate
the smoothing weights (recommend 0)
pb_neighbors: int; number of neighbors to compare to when searching
for the pullback local minimum
pb_idx_correction: int; number of local minima to adjust by if the program grabs the wrong one
rc_neighbors: int; number of neighbors to compare to when searching
for the recompression local maximum
rc_idx_correction: int; number of local maxima to adjust by if the program grabs the wrong one
sample_rate: float; sample rate of the oscilloscope used in the experiment
nperseg: int; number of points to use per segment of the stft
noverlap: int; number of points to overlap per segment of the stft
nfft: int; number of points to zero pad per segment of the stft
window: str or tuple or array_like; window function to use for the stft (recommend 'hann')
blur_kernel: tuple; kernel size for gaussian blur smoothing (recommend (5, 5))
blur_sigx: float; standard deviation of the gaussian blur kernel in the x direction (recommend 0)
blur_sigy: float; standard deviation of the gaussian blur kernel in the y direction (recommend 0)
carrier_band_time: float; length of time from the beginning of the imported data window to average
the frequency of the top of the carrier band in the thresholded spectrogram
cmap: str; colormap for the spectrograms (recommend 'viridis')
uncert_mult: float; factor to multiply the velocity uncertainty by when plotting - allows for easier
visulaization when uncertainties are small
order: int; order for the gaussian notch filter used to remove the carrier band (recommend 6)
wid: float; width of the gaussian notch filter used to remove the carrier band (recommend 1e8)
lam: float; wavelength of the target laser
C0: float; bulk wavespeed of the sample
density: float; density of the sample
delta_rho: float; uncertainty in density of the sample
delta_C0: float; uncertainty in the bulk wavespeed of the sample
delta_lam: float; uncertainty in the wavelength of the target laser
theta: float; angle of incidence of the PDV probe
delta_theta: float; uncertainty in the angle of incidence of the PDV probe
exp_data_dir: str; directory from which to read the experimental data file
out_files_dir: str; directory to save output data to
display_plots: str; 'yes' to display the final plots and 'no' to not display them. if save_data='yes'
and and display_plots='no' the plots will be saved but not displayed
spall_calculation: str; 'yes' to run the calculations for the spall analysis and 'no' to extract the velocity
without doing the spall analysis
plot_figsize: tuple; figure size for the final plots
plot_dpi: float; dpi for the final plots
"""
# %%
from alpss_main import *
import os
alpss_main(
filename="example_file.csv",
save_data="yes",
start_time_user="none",
header_lines=1,
time_to_skip=2.3e-6,
time_to_take=1.5e-6,
t_before=5e-9,
t_after=50e-9,
start_time_correction=0e-9,
freq_min=1.5e9,
freq_max=4e9,
smoothing_window=601,
smoothing_wid=3,
smoothing_amp=1,
smoothing_sigma=1,
smoothing_mu=0,
pb_neighbors=400,
pb_idx_correction=0,
rc_neighbors=400,
rc_idx_correction=0,
sample_rate=80e9,
nperseg=512,
noverlap=435,
nfft=5120,
window="hann",
blur_kernel=(5, 5),
blur_sigx=0,
blur_sigy=0,
carrier_band_time=250e-9,
cmap="viridis",
uncert_mult=100,
order=6,
wid=5e7,
lam=1547.461e-9,
C0=4540,
density=1730,
delta_rho=9,
delta_C0=23,
delta_lam=8e-18,
theta=0,
delta_theta=5,
exp_data_dir=(os.getcwd() + "/input_data"),
out_files_dir=(os.getcwd() + "/output_data"),
display_plots="yes",
spall_calculation="yes",
plot_figsize=(30, 10),
plot_dpi=300,
)