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wave_plot_app.py
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wave_plot_app.py
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import streamlit as st
import fdasrsf as fs
import plotly.figure_factory as ff
import pandas as pd
import numpy as np
from scipy.signal import find_peaks
import os
import tempfile
from scipy.interpolate import CubicSpline
import plotly.graph_objects as go
import struct
import datetime
from skfda import FDataGrid
from skfda.preprocessing.dim_reduction import FPCA
from sklearn.cluster import DBSCAN
from kneed import KneeLocator
from sklearn.neighbors import NearestNeighbors
from scipy.ndimage import gaussian_filter1d
import torch
import torch.nn as nn
import torch.optim as optim
import keras
from tensorflow.keras.models import load_model
import warnings
warnings.filterwarnings('ignore')
def plot_scatter_waves(df, freq, db, background_curves=False, smoothing_method='None', sigma=3, n=15):
fig = go.Figure()
khz = df[(df['Freq(Hz)'].astype(float) == freq) & (df['Level(dB)'].astype(float) == db)]
if not khz.empty:
index = khz.index.values[0]
final = df.loc[index, '0':]
final = pd.to_numeric(final, errors='coerce')
# Find highest peaks separated by at least n data points
peaks, _ = find_peaks(final, distance=n)
highest_peaks = peaks[np.argsort(final[peaks])[-5:]]
if multiply_y_factor != 1:
y_values = final * multiply_y_factor
else:
y_values = final
fig.update_layout(width=700, height=450)
# Plot scatter plot instead of line plot
fig.add_trace(go.Scatter(x=np.arange(len(final)), y=y_values, mode='markers', name='Scatter Plot'))
# Mark the highest peaks with red markers
fig.add_trace(go.Scatter(x=highest_peaks, y=y_values[highest_peaks], mode='markers', marker=dict(color='red'), name='Peaks'))
# Annotate the peaks with red color, smaller font, and closer to the peaks
for peak in highest_peaks:
fig.add_annotation(
x=peak,
y=y_values[peak],
text=f'{y_values[peak]:.4f}',
showarrow=True,
arrowhead=2,
arrowcolor='red',
arrowwidth=2,
ax=0,
ay=-30,
font=dict(color='red', size=10)
)
return fig
def plotting_waves_cubic_spline(df, freq=16000, db=90, n=45):
fig = go.Figure()
i=0
for df in dfs:
khz = df[df['Freq(Hz)'] == freq]
dbkhz = khz[khz['Level(dB)'] == db]
if not khz.empty:
index = dbkhz.index.values[0]
original_waveform = df.loc[index, '0':].dropna()
original_waveform = pd.to_numeric(original_waveform, errors='coerce')[:-1]
if multiply_y_factor != 1:
original_waveform *= multiply_y_factor
# Apply cubic spline interpolation
smooth_time = np.linspace(0, len(original_waveform) - 1, 244)
cs = CubicSpline(np.arange(len(original_waveform)), original_waveform)
smooth_amplitude = cs(smooth_time)
# Find highest peaks separated by at least n data points in the smoothed curve
n = 15
peaks, _ = find_peaks(smooth_amplitude, distance=n)
troughs, _ = find_peaks(-smooth_amplitude, distance=n)
highest_peaks = peaks[np.argsort(smooth_amplitude[peaks])[-5:]]
highest_peaks = np.sort(highest_peaks)
relevant_troughs = np.array([])
for p in range(len(highest_peaks)):
c = 0
for t in troughs:
if t > highest_peaks[p]:
if p != 4:
if t < highest_peaks[p+1]:
relevant_troughs = np.append(relevant_troughs, int(t))
break
else:
relevant_troughs = np.append(relevant_troughs, int(t))
break
relevant_troughs = relevant_troughs.astype('i')
# Plot the original ABR waveform
fig.add_trace(go.Scatter(x=np.linspace(0, 10, len(original_waveform)), y=original_waveform, mode='lines', name='Original ABR', opacity=0.8))
# Plot the cubic spline interpolation
fig.add_trace(go.Scatter(x=np.linspace(0,10,len(smooth_time)), y=smooth_amplitude, mode='lines', name='Cubic Spline Interpolation'))
# Mark the highest peaks with red markers
fig.add_trace(go.Scatter(x=np.linspace(0,10,len(smooth_time))[highest_peaks], y=smooth_amplitude[highest_peaks], mode='markers', marker=dict(color='red'), name='Peaks'))
# Mark the relevant troughs with blue markers
fig.add_trace(go.Scatter(x=np.linspace(0,10,len(smooth_time))[relevant_troughs], y=smooth_amplitude[relevant_troughs], mode='markers', marker=dict(color='blue'), name='Troughs'))
# Set layout options
fig.update_layout(title=f'{uploaded_files[i].name}', xaxis_title='Time (ms)', yaxis_title='Voltage (mV)', legend=dict(x=0, y=1, traceorder='normal'))
i+=1
# Show the plot using Streamlit
return fig
def update_title_and_legend_if_single_frequency(fig, selected_freqs):
if len(set(selected_freqs)) == 1:
fig.update_layout(title=f'{uploaded_file.name} - Freq: {selected_freqs[0]} Hz')
for trace in fig.data:
if 'Freq' in trace.name:
trace.name = trace.name.replace(f'Freq: {trace.name.split(" ")[1]} Hz, ', '')
return fig
def plot_waves_single_frequency(df, freq, y_min, y_max, plot_time_warped=False):
if level:
db_column = 'Level(dB)'
else:
db_column = 'PostAtten(dB)'
if len(selected_dfs) == 0:
st.write("No files selected.")
return
for idx, file_df in enumerate(selected_dfs):
fig = go.Figure()
# Filter DataFrame to include only data for the specified frequency
df_filtered = file_df[file_df['Freq(Hz)'] == freq]
# Get unique dB levels for the filtered DataFrame
db_levels = sorted(df_filtered[db_column].unique())
if plot_time_warped:
original_waves = [] # Only store original waves if not plotting time warped
for i, db in enumerate(db_levels):
khz = df_filtered[df_filtered[db_column] == db]
if not khz.empty:
index = khz.index.values[0]
final = df_filtered.loc[index, '0':]
final = pd.to_numeric(final, errors='coerce')
if multiply_y_factor != 1:
y_values = final * multiply_y_factor
else:
y_values = final
if plot_time_warped:
original_waves.append(y_values.to_list())
else:
# Define color scale from dark red to light red based on dB level
col_diff = np.linspace(255, 125, len(db_levels))
color_scale = f'rgb(0, {col_diff[i]}, {col_diff[i]})' # Adjust color intensity for each dB level
fig.add_trace(go.Scatter(x=np.linspace(0,10, len(y_values)), y=y_values, mode='lines', name=f'db: {db} dB', line=dict(color=color_scale)))
if plot_time_warped:
# Convert original waves to a 2D numpy array
original_waves_array = np.array([wave[:-1] for wave in original_waves])
try:
# Apply time warping to all waves in the array
time = np.linspace(0, 10, original_waves_array.shape[1])
obj = fs.fdawarp(original_waves_array.T, time)
obj.srsf_align(parallel=True)
warped_waves_array = obj.fn.T # Use the time-warped curves
# Plot time-warped curves
for i, db in enumerate(db_levels):
col_diff = np.linspace(255, 125, len(db_levels))
color_scale = f'rgb(0, {col_diff[i]}, {col_diff[i]})' # Adjust color intensity for each dB level
fig.add_trace(go.Scatter(x=np.linspace(0,10, len(warped_waves_array[i])), y=warped_waves_array[i], mode='lines', name=f'dB: {db} dB', line=dict(color=color_scale)))
except IndexError:
pass
fig.update_layout(title=f'{selected_files[idx].split("/")[-1]} - Frequency: {freq} Hz', xaxis_title='Time (ms)', yaxis_title='Voltage (mV)')
fig.update_layout(annotations=annotations)
fig.update_layout(yaxis_range=[y_min, y_max])
custom_width = 700
custom_height = 450
fig.update_layout(width=custom_width, height=custom_height)
st.plotly_chart(fig)
def plot_waves_single_db(df, db, y_min, y_max):
fig = go.Figure()
if level:
d = 'Level(dB)'
else:
d = 'PostAtten(dB)'
if len(selected_dfs) > 1:
st.write("Can only process one file at a time.")
return
else:
df = selected_dfs[0]
for freq in sorted(df['Freq(Hz)'].unique()):
khz = df[(df['Freq(Hz)'] == freq) & (df[d] == db)]
if not khz.empty:
index = khz.index.values[0]
final = df.loc[index, '0':]
final = pd.to_numeric(final, errors='coerce')
if multiply_y_factor != 1:
y_values = final * multiply_y_factor
else:
y_values = final
fig.add_trace(go.Scatter(x=np.linspace(0,10, len(y_values)), y=y_values, mode='lines', name=f'Frequency: {freq} Hz'))
fig.update_layout(width=700, height=450)
fig.update_layout(title=f'{selected_files[0].split("/")[-1]} - dB Level: {db}', xaxis_title='Index', yaxis_title='Voltage (mV)')
fig.update_layout(annotations=annotations)
fig.update_layout(yaxis_range=[y_min, y_max])
return fig
def plot_waves_single_tuple(df, freq, db, y_min, y_max):
fig = go.Figure()
i=0
if level:
d = 'Level(dB)'
else:
d = 'PostAtten(dB)'
for df in selected_dfs:
khz = df[(df['Freq(Hz)'] == freq) & (df[d] == db)]
if not khz.empty:
index = khz.index.values[0]
final = df.loc[index, '0':].dropna()
final = pd.to_numeric(final, errors='coerce')[:-1]
time_axis = np.linspace(0, 10, len(final))
# Find highest peaks separated by at least n data points
if multiply_y_factor != 1:
y_values = final * multiply_y_factor
else:
y_values = final
# Apply Gaussian smoothing to the original ABR waveform
smoothed_waveform = gaussian_filter1d(y_values, sigma=1.8125)
# Find highest peaks separated by at least n data points in the smoothed curve
n = 20
# Find highest peaks separated by at least n data points in the smoothed curve
smoothed_peaks, _ = find_peaks(smoothed_waveform[26:], distance=n)
smoothed_troughs, _ = find_peaks(-smoothed_waveform, distance=12)
sorted_indices = np.argsort(smoothed_waveform[smoothed_peaks+26])
# Get the indices of the highest peaks (top 5 in this case)
highest_smoothed_peaks = smoothed_peaks[sorted_indices[-5:]] + 26
relevant_troughs = np.array([])
for p in range(len(highest_smoothed_peaks)):
c = 0
for t in smoothed_troughs:
if t > highest_smoothed_peaks[p]:
if p != 4:
try:
if t < highest_smoothed_peaks[p+1]:
relevant_troughs = np.append(relevant_troughs, int(t))
break
except IndexError:
pass
else:
relevant_troughs = np.append(relevant_troughs, int(t))
break
relevant_troughs = relevant_troughs.astype('i')
fig.add_trace(go.Scatter(x=np.linspace(0,10, len(y_values)), y=y_values, mode='lines', name=f'{selected_files[i].split("/")[-1]}'))
# Mark the highest peaks with red markers
fig.add_trace(go.Scatter(x=np.linspace(0,10,len(y_values))[highest_smoothed_peaks], y=y_values[highest_smoothed_peaks], mode='markers', marker=dict(color='red'), name='Peaks'))
# Mark the relevant troughs with blue markers
fig.add_trace(go.Scatter(x=np.linspace(0,10,len(y_values))[relevant_troughs], y=y_values[relevant_troughs], mode='markers', marker=dict(color='blue'), name='Troughs'))
i+=1
fig.update_layout(width=700, height=450)
fig.update_layout(title=f'Freq = {freq}, dB = {db}', xaxis_title='Time (ms)', yaxis_title='Voltage (mV)')
fig.update_layout(annotations=annotations)
fig.update_layout(yaxis_range=[y_min, y_max])
return fig
def plotting_waves_gauss(df, freq, db, n=15, sigma=3):
fig = go.Figure()
if level:
d = 'Level(dB)'
else:
d = 'PostAtten(dB)'
for df in selected_dfs:
khz = df[df['Freq(Hz)'] == freq]
dbkhz = khz[khz[d] == db]
if not dbkhz.empty:
index = dbkhz.index.values[0]
original_waveform = df.loc[index, '0':]
original_waveform = pd.to_numeric(original_waveform, errors='coerce')
# Apply Gaussian smoothing to the original ABR waveform
smoothed_waveform = gaussian_filter1d(original_waveform, sigma=sigma)
# Find highest peaks separated by at least n data points in the original curve
original_peaks, _ = find_peaks(original_waveform, distance=n)
highest_original_peaks = original_peaks[np.argsort(original_waveform[original_peaks])[-5:]]
# Find highest peaks separated by at least n data points in the smoothed curve
smoothed_peaks, _ = find_peaks(smoothed_waveform, distance=n)
highest_smoothed_peaks = smoothed_peaks[np.argsort(smoothed_waveform[smoothed_peaks])[-5:]]
# Plot the original ABR waveform
fig.add_trace(go.Scatter(x=np.arange(len(original_waveform)), y=original_waveform, mode='lines', name='Original ABR'))
# Plot the smoothed ABR waveform
fig.add_trace(go.Scatter(x=np.arange(len(smoothed_waveform)), y=smoothed_waveform, mode='lines', name=f'Gaussian Smoothed (sigma={sigma})'))
if highest_original_peaks.size > 0: # Check if highest_original_peaks is not empty
first_original_peak = np.sort(highest_original_peaks)[0]
fig.add_trace(go.Scatter(x=[first_original_peak], y=[original_waveform[first_original_peak]], mode='markers', marker=dict(color='red'), name='Original Peaks'))
if highest_smoothed_peaks.size > 0: # Check if highest_smoothed_peaks is not empty
first_smoothed_peak = np.sort(highest_smoothed_peaks)[0]
fig.add_trace(go.Scatter(x=[first_smoothed_peak], y=[smoothed_waveform[first_smoothed_peak]], mode='markers', marker=dict(color='blue'), name='Smoothed Peaks'))
#fig.update_layout(title=f'Sheet: {filename}', xaxis_title='Index', yaxis_title='Voltage (mV)', legend=dict(x=0, y=1, traceorder='normal'))
return fig
def plot_3d_surface(df, freq, y_min, y_max):
if level:
db_column = 'Level(dB)'
else:
db_column = 'PostAtten(dB)'
if len(selected_dfs) == 0:
st.write("No files selected.")
return
for idx, file_df in enumerate(selected_dfs):
fig = go.Figure()
# Filter DataFrame to include only data for the specified frequency
df_filtered = file_df[file_df['Freq(Hz)'] == freq]
# Get unique dB levels for the filtered DataFrame
db_levels = sorted(df_filtered[db_column].unique())
original_waves = [] # List to store original waves
wave_colors = [f'rgb(255, 0, 255)' for b in np.linspace(0, 0, len(db_levels))]
connecting_line_color = 'rgba(0, 255, 0, 0.3)'
for db in db_levels:
khz = df_filtered[df_filtered[db_column] == db]
if not khz.empty:
index = khz.index.values[0]
final = df_filtered.loc[index, '0':]
final = pd.to_numeric(final, errors='coerce')
if multiply_y_factor != 1:
y_values = final * multiply_y_factor
else:
y_values = final
original_waves.append(y_values.to_list())
# Convert original waves to a 2D numpy array
original_waves_array = np.array([wave[:-1] for wave in original_waves])
try:
# Apply time warping to all waves in the array
time = np.linspace(0, 10, original_waves_array.shape[1])
obj = fs.fdawarp(original_waves_array.T, time)
obj.srsf_align(parallel=True)
warped_waves_array = obj.fn.T # Use the time-warped curves
except IndexError:
pass
# Plot all time-warped waves in the array
for i, (db, warped_waves) in enumerate(zip(db_levels, warped_waves_array)):
fig.add_trace(go.Scatter3d(x=[db] * len(warped_waves), y=np.linspace(0, 10, len(warped_waves)), z=warped_waves, mode='lines', name=f'dB: {db}', line=dict(color=wave_colors[i])))
# Create surface connecting the curves at each time point
for i in range(len(time)):
z_values_at_time = [warped_waves_array[j, i] for j in range(len(db_levels))]
fig.add_trace(go.Scatter3d(x=db_levels, y=[time[i]] * len(db_levels), z=z_values_at_time, mode='lines', name=f'Time: {time[i]:.2f} ms', line=dict(color=connecting_line_color)))
fig.update_layout(width=700, height=450)
fig.update_layout(title=f'{selected_files[idx].split("/")[-1]} - Frequency: {freq} Hz', scene=dict(xaxis_title=f'dB {is_level}', yaxis_title='Time (ms)', zaxis_title='Voltage (mV)'))
fig.update_layout(annotations=annotations)
fig.update_layout(scene=dict(zaxis=dict(range=[y_min, y_max])))
khz = file_df[(file_df['Freq(Hz)'] == freq)]
if not khz.empty:
st.plotly_chart(fig)
def display_metrics_table(df, freq, db, baseline_level):
if level:
d = 'Level(dB)'
else:
d = 'PostAtten(dB)'
khz = df[(df['Freq(Hz)'] == freq) & (df[d] == db)]
if not khz.empty:
index = khz.index.values[0]
final = df.loc[index, '0':]
final = pd.to_numeric(final, errors='coerce')
if multiply_y_factor != 1:
y_values = final * multiply_y_factor
else:
y_values = final
# Adjust the waveform by subtracting the baseline level
y_values -= baseline_level
# Find highest peaks separated by at least n data points
peaks, _ = find_peaks(y_values, distance=15)
troughs, _ = find_peaks(-y_values, distance=15)
highest_peaks = peaks[np.argsort(final[peaks])[-5:]]
highest_peaks = np.sort(highest_peaks)
relevant_troughs = np.array([])
for p in range(len(highest_peaks)):
c = 0
for t in troughs:
if t > highest_peaks[p]:
if p != 4:
if t < highest_peaks[p+1]:
relevant_troughs = np.append(relevant_troughs, int(t))
break
else:
relevant_troughs = np.append(relevant_troughs, int(t))
break
relevant_troughs = relevant_troughs.astype('i')
if highest_peaks.size > 0: # Check if highest_peaks is not empty
first_peak_amplitude = y_values[highest_peaks[0]] - y_values[relevant_troughs[0]]
latency_to_first_peak = highest_peaks[0] * (10 / len(y_values)) # Assuming 10 ms duration for waveform
if len(highest_peaks) >= 4:
amplitude_ratio = (y_values[highest_peaks[0]] - y_values[relevant_troughs[0]]) / (y_values[highest_peaks[3]] - y_values[relevant_troughs[3]])
else:
amplitude_ratio = np.nan
metrics_table = pd.DataFrame({
'Metric': ['First Peak Amplitude (mV)', 'Latency to First Peak (ms)', 'Amplitude Ratio (Peak1/Peak4)', 'Estimated Threshold'],
'Value': [first_peak_amplitude, latency_to_first_peak, amplitude_ratio, calculate_hearing_threshold(df, freq)],
})
#st.table(metrics_table)
return metrics_table
def display_metrics_table_all_db(selected_dfs, freq, db_levels, baseline_level, level=True, multiply_y_factor=1):
if level:
db_column = 'Level(dB)'
else:
db_column = 'PostAtten(dB)'
metrics_data = {'File Name': [], 'Frequency (Hz)': [], 'dB Level': [], 'First Peak Amplitude (mV)': [], 'Latency to First Peak (ms)': [], 'Amplitude Ratio (Peak1/Peak4)': []}
for file_df, file_name in zip(selected_dfs, selected_files):
for db in db_levels:
khz = file_df[(file_df['Freq(Hz)'] == freq) & (file_df[db_column] == db)]
if not khz.empty:
index = khz.index.values[0]
final = file_df.loc[index, '0':]
final = pd.to_numeric(final, errors='coerce')
if multiply_y_factor != 1:
y_values = final * multiply_y_factor
else:
y_values = final
# Adjust the waveform by subtracting the baseline level
y_values -= baseline_level
# Find highest peaks separated by at least n data points
peaks, _ = find_peaks(y_values, distance=int((15 / 243) * len(y_values)))
troughs, _ = find_peaks(-y_values, distance=int((15 / 243) * len(y_values)))
highest_peaks = peaks[np.argsort(final[peaks])[-5:]]
highest_peaks = np.sort(highest_peaks)
relevant_troughs = np.array([])
for p in range(len(highest_peaks)):
c = 0
for t in troughs:
if t > highest_peaks[p]:
if p != 4:
if t < highest_peaks[p + 1]:
relevant_troughs = np.append(relevant_troughs, int(t))
break
else:
relevant_troughs = np.append(relevant_troughs, int(t))
break
relevant_troughs = relevant_troughs.astype('i')
if highest_peaks.size > 0: # Check if highest_peaks is not empty
first_peak_amplitude = y_values[highest_peaks[0]] - y_values[relevant_troughs[0]]
latency_to_first_peak = highest_peaks[0] * (10 / len(y_values)) # Assuming 10 ms duration for waveform
if len(highest_peaks) >= 4 and len(relevant_troughs) >= 4:
amplitude_ratio = (y_values[highest_peaks[0]] - y_values[relevant_troughs[0]]) / (
y_values[highest_peaks[3]] - y_values[relevant_troughs[3]])
else:
amplitude_ratio = np.nan
metrics_data['File Name'].append(file_name.split("/")[-1])
metrics_data['Frequency (Hz)'].append(freq)
metrics_data['dB Level'].append(db)
metrics_data['First Peak Amplitude (mV)'].append(first_peak_amplitude)
metrics_data['Latency to First Peak (ms)'].append(latency_to_first_peak)
metrics_data['Amplitude Ratio (Peak1/Peak4)'].append(amplitude_ratio)
metrics_table = pd.DataFrame(metrics_data)
st.table(metrics_table)
def plot_waves_stacked(df, freq, y_min, y_max, plot_time_warped=False):
if level:
db_column = 'Level(dB)'
else:
db_column = 'PostAtten(dB)'
if len(selected_dfs) == 0:
st.write("No files selected.")
return
for idx, file_df in enumerate(selected_dfs):
fig = go.Figure()
# Get unique dB levels
unique_dbs = sorted(file_df[db_column].unique())
# Calculate the vertical offset for each waveform
num_dbs = len(unique_dbs)
vertical_spacing = 25 / num_dbs
# Initialize an offset for each dB level
db_offsets = {db: y_min + i * vertical_spacing for i, db in enumerate(unique_dbs)}
# Find the highest dB level
max_db = max(unique_dbs)
threshold = calculate_hearing_threshold(file_df, freq)
# Process and plot each waveform
for db in sorted(file_df[db_column].unique(), reverse=True):
khz = file_df[(file_df['Freq(Hz)'] == freq) & (file_df[db_column] == db)]
if not khz.empty:
index = khz.index.values[0]
final = file_df.loc[index, '0':]
final = pd.to_numeric(final, errors='coerce')[:-1]
# Normalize the waveform
if db == max_db:
max_value = final.abs().max() # Find the maximum absolute value
final_normalized = final / max_value # Normalize
# Scale relative to the highest decibel wave
#final_scaled = final_normalized * (db / max_db)
# Apply the vertical offset
y_values = final_normalized + db_offsets[db]
# Optionally apply time warping
if plot_time_warped:
# ... (your time warping code here)
pass
# Plot the waveform
fig.add_trace(go.Scatter(x=np.linspace(0, 10, y_values.shape[0]),
y=y_values,
mode='lines',
name=f'dB: {db}',
#line=dict(color='black')
))
fig.update_layout(title=f'{selected_files[idx].split("/")[-1]} - Frequency: {freq} Hz, Estimated Threshold: {threshold}',
xaxis_title='Time (ms)',
yaxis_title='Voltage (mV)')
fig.update_layout(yaxis_range=[y_min, y_max])
# Set custom width and height (in pixels)
custom_width = 400
custom_height = 700
fig.update_layout(width=custom_width, height=custom_height)
fig.update_layout(yaxis=dict(showticklabels=False, showgrid=False, zeroline=False))
fig.update_layout(xaxis=dict(showgrid=False, zeroline=False))
khz = file_df[(file_df['Freq(Hz)'] == freq)]
if not khz.empty:
st.plotly_chart(fig)
def arfread(PATH, **kwargs):
# defaults
PLOT = kwargs.get('PLOT', False)
RP = kwargs.get('RP', False)
isRZ = not RP
data = {'RecHead': {}, 'groups': []}
# open file
with open(PATH, 'rb') as fid:
# open RecHead data
data['RecHead']['ftype'] = struct.unpack('h', fid.read(2))[0]
data['RecHead']['ngrps'] = struct.unpack('h', fid.read(2))[0]
data['RecHead']['nrecs'] = struct.unpack('h', fid.read(2))[0]
data['RecHead']['grpseek'] = struct.unpack('200i', fid.read(4*200))
data['RecHead']['recseek'] = struct.unpack('2000i', fid.read(4*2000))
data['RecHead']['file_ptr'] = struct.unpack('i', fid.read(4))[0]
data['groups'] = []
bFirstPass = True
for x in range(data['RecHead']['ngrps']):
# jump to the group location in the file
fid.seek(data['RecHead']['grpseek'][x], 0)
# open the group
data['groups'].append({
'grpn': struct.unpack('h', fid.read(2))[0],
'frecn': struct.unpack('h', fid.read(2))[0],
'nrecs': struct.unpack('h', fid.read(2))[0],
'ID': get_str(fid.read(16)),
'ref1': get_str(fid.read(16)),
'ref2': get_str(fid.read(16)),
'memo': get_str(fid.read(50)),
})
# read temporary timestamp
if bFirstPass:
if isRZ:
ttt = struct.unpack('q', fid.read(8))[0]
fid.seek(-8, 1)
data['fileType'] = 'BioSigRZ'
else:
ttt = struct.unpack('I', fid.read(4))[0]
fid.seek(-4, 1)
data['fileType'] = 'BioSigRP'
data['fileTime'] = datetime.datetime.utcfromtimestamp(ttt/86400 + datetime.datetime(1970, 1, 1).timestamp()).strftime('%Y-%m-%d %H:%M:%S')
bFirstPass = False
if isRZ:
grp_t_format = 'q'
beg_t_format = 'q'
end_t_format = 'q'
read_size = 8
else:
grp_t_format = 'I'
beg_t_format = 'I'
end_t_format = 'I'
read_size = 4
data['groups'][x]['beg_t'] = struct.unpack(beg_t_format, fid.read(read_size))[0]
data['groups'][x]['end_t'] = struct.unpack(end_t_format, fid.read(read_size))[0]
data['groups'][x].update({
'sgfname1': get_str(fid.read(100)),
'sgfname2': get_str(fid.read(100)),
'VarName1': get_str(fid.read(15)),
'VarName2': get_str(fid.read(15)),
'VarName3': get_str(fid.read(15)),
'VarName4': get_str(fid.read(15)),
'VarName5': get_str(fid.read(15)),
'VarName6': get_str(fid.read(15)),
'VarName7': get_str(fid.read(15)),
'VarName8': get_str(fid.read(15)),
'VarName9': get_str(fid.read(15)),
'VarName10': get_str(fid.read(15)),
'VarUnit1': get_str(fid.read(5)),
'VarUnit2': get_str(fid.read(5)),
'VarUnit3': get_str(fid.read(5)),
'VarUnit4': get_str(fid.read(5)),
'VarUnit5': get_str(fid.read(5)),
'VarUnit6': get_str(fid.read(5)),
'VarUnit7': get_str(fid.read(5)),
'VarUnit8': get_str(fid.read(5)),
'VarUnit9': get_str(fid.read(5)),
'VarUnit10': get_str(fid.read(5)),
'SampPer_us': struct.unpack('f', fid.read(4))[0],
'cc_t': struct.unpack('i', fid.read(4))[0],
'version': struct.unpack('h', fid.read(2))[0],
'postproc': struct.unpack('i', fid.read(4))[0],
'dump': get_str(fid.read(92)),
'recs': [],
})
for i in range(data['groups'][x]['nrecs']):
record_data = {
'recn': struct.unpack('h', fid.read(2))[0],
'grpid': struct.unpack('h', fid.read(2))[0],
'grp_t': struct.unpack(grp_t_format, fid.read(read_size))[0],
#'grp_d': datetime.utcfromtimestamp(data['groups'][x]['recs'][i]['grp_t']/86400 + datetime(1970, 1, 1).timestamp()).strftime('%Y-%m-%d %H:%M:%S'),
'newgrp': struct.unpack('h', fid.read(2))[0],
'sgi': struct.unpack('h', fid.read(2))[0],
'chan': struct.unpack('B', fid.read(1))[0],
'rtype': get_str(fid.read(1)),
'npts': struct.unpack('H' if isRZ else 'h', fid.read(2))[0],
'osdel': struct.unpack('f', fid.read(4))[0],
'dur_ms': struct.unpack('f', fid.read(4))[0],
'SampPer_us': struct.unpack('f', fid.read(4))[0],
'artthresh': struct.unpack('f', fid.read(4))[0],
'gain': struct.unpack('f', fid.read(4))[0],
'accouple': struct.unpack('h', fid.read(2))[0],
'navgs': struct.unpack('h', fid.read(2))[0],
'narts': struct.unpack('h', fid.read(2))[0],
'beg_t': struct.unpack(beg_t_format, fid.read(read_size))[0],
'end_t': struct.unpack(end_t_format, fid.read(read_size))[0],
'Var1': struct.unpack('f', fid.read(4))[0],
'Var2': struct.unpack('f', fid.read(4))[0],
'Var3': struct.unpack('f', fid.read(4))[0],
'Var4': struct.unpack('f', fid.read(4))[0],
'Var5': struct.unpack('f', fid.read(4))[0],
'Var6': struct.unpack('f', fid.read(4))[0],
'Var7': struct.unpack('f', fid.read(4))[0],
'Var8': struct.unpack('f', fid.read(4))[0],
'Var9': struct.unpack('f', fid.read(4))[0],
'Var10': struct.unpack('f', fid.read(4))[0],
'data': [] #list(struct.unpack(f'{data["groups"][x]["recs"][i]["npts"]}f', fid.read(4*data['groups'][x]['recs'][i]['npts'])))
}
# skip all 10 cursors placeholders
fid.seek(36*10, 1)
record_data['data'] = list(struct.unpack(f'{record_data["npts"]}f', fid.read(4*record_data['npts'])))
record_data['grp_d'] = datetime.datetime.utcfromtimestamp(record_data['grp_t'] / 86400 + datetime.datetime(1970, 1, 1).timestamp()).strftime('%Y-%m-%d %H:%M:%S')
data['groups'][x]['recs'].append(record_data)
if PLOT:
import matplotlib.pyplot as plt
# determine reasonable spacing between plots
d = [x['data'] for x in data['groups'][x]['recs']]
plot_offset = max(max(map(abs, [item for sublist in d for item in sublist]))) * 1.2
plt.figure()
for i in range(data['groups'][x]['nrecs']):
plt.plot([item - plot_offset * i for item in data['groups'][x]['recs'][i]['data']])
plt.hold(True)
plt.title(f'Group {data["groups"][x]["grpn"]}')
plt.axis('off')
plt.show()
return data
def get_str(data):
# return string up until null character only
ind = data.find(b'\x00')
if ind > 0:
data = data[:ind]
return data.decode('utf-8')
def calculate_hearing_threshold(df, freq):
if level:
db_column = 'Level(dB)'
else:
db_column = 'PostAtten(dB)'
if len(selected_dfs) == 0:
st.write("No files selected.")
return
threshold_dict = {}
# Filter DataFrame to include only data for the specified frequency
df_filtered = df[df['Freq(Hz)'] == freq]
# Get unique dB levels for the filtered DataFrame
db_levels = sorted(df_filtered[db_column].unique())
lowest_db = None
waves = []
for i, db in enumerate(db_levels):
khz = df_filtered[df_filtered[db_column] == db]
if not khz.empty:
index = khz.index.values[0]
final = df_filtered.loc[index, '0':]
final = pd.to_numeric(final, errors='coerce')
final = np.array(final, dtype=np.float64)
if multiply_y_factor != 1:
y_values = final * multiply_y_factor
else:
y_values = final
waves.append(y_values)
waves = np.array(waves)
waves = np.expand_dims(waves, axis=2)
# Perform prediction
prediction = thresholding_model.predict(waves)
y_pred = (prediction > 0.5).astype(int)
y_pred = list(y_pred.flatten())
print(y_pred)
for p, d in zip(y_pred, db_levels):
if p == 0:
lowest_db = d
continue
else:
lowest_db = d
break
# Store the lowest dB level where signal was detected
threshold = lowest_db
return threshold
# Streamlit UI
st.title("Wave Plotting App")
st.sidebar.header("Upload File")
uploaded_files = st.sidebar.file_uploader("Choose a file", type=["csv", "arf"], accept_multiple_files=True)
is_rz_file = st.sidebar.radio("Select ARF File Type:", ("RP", "RZ"))
is_level = st.sidebar.radio("Select dB You Are Studying:", ("Attenuation", "Level"))
annotations = []
thresholding_model = load_model('./abr_cnn.keras')
thresholding_model.steps_per_execution = 1
if uploaded_files:
dfs = []
selected_files = []
selected_dfs = []
for idx, file in enumerate(uploaded_files):
# Use tempfile
temp_file_path = os.path.join(tempfile.gettempdir(), file.name)
with open(temp_file_path, 'wb') as temp_file:
temp_file.write(file.read())
#st.sidebar.markdown(f"**File Name:** {file.name}")
selected = st.sidebar.checkbox(f"{file.name}", key=f"file_{idx}")
if selected:
selected_files.append(temp_file_path)
if file.name.endswith(".arf"):
# Read ARF file
if is_rz_file == 'RP':
data = arfread(temp_file.name, RP=True)
else:
data = arfread(temp_file.name)
# Process ARF data
rows = []
freqs = []
dbs = []
for group in data['groups']:
for rec in group['recs']:
# Extract data
freq = rec['Var1']
db = rec['Var2']
# Construct row
wave_cols = list(enumerate(rec['data']))
wave_data = {f'{i}':v*1e6 for i, v in wave_cols}
if is_level == 'Level':
row = {'Freq(Hz)': freq, 'Level(dB)': db, **wave_data}
rows.append(row)
if is_level == 'Attenuation':
row = {'Freq(Hz)': freq, 'PostAtten(dB)': db, **wave_data}
rows.append(row)
df = pd.DataFrame(rows)
elif file.name.endswith(".csv"):
# Process CSV
if pd.read_csv(temp_file_path).shape[1] > 1:
df = pd.read_csv(temp_file_path)
else:
df = pd.read_csv(temp_file_path, skiprows=2)
# Append df to list
dfs.append(df)
if temp_file_path in selected_files:
selected_dfs.append(df)
level = (is_level == 'Level')
# Get distinct frequency and dB level values across all files
distinct_freqs = sorted(pd.concat([df['Freq(Hz)'] for df in dfs]).unique())
distinct_dbs = sorted(pd.concat([df['Level(dB)'] if level else df['PostAtten(dB)'] for df in dfs]).unique())
multiply_y_factor = st.sidebar.number_input("Multiply Y Values by Factor", value=1.0)
# Frequency dropdown options
freq = st.sidebar.selectbox("Select Frequency (Hz)", options=distinct_freqs, index=0)
# dB Level dropdown options
db = st.sidebar.selectbox(f'Select dB {is_level}', options=distinct_dbs, index=0)
y_min = st.sidebar.number_input("Y-axis Minimum", value=-5.0)
y_max = st.sidebar.number_input("Y-axis Maximum", value=5.0)
baseline_level_str = st.sidebar.text_input("Set Baseline Level", "0.0")
baseline_level = float(baseline_level_str)
plot_time_warped = st.sidebar.checkbox("Plot Time Warped Curves", False)
# Create a plotly figure
fig = go.Figure()
#scatter_plot_option = st.sidebar.checkbox("Plot Waves as Scatter Plot", False)
if st.sidebar.button("Plot Waves at Single Frequency"):
if plot_time_warped:
fig = plot_waves_single_frequency(df, freq, y_min, y_max, plot_time_warped=True)
else:
fig = plot_waves_single_frequency(df, freq, y_min, y_max, plot_time_warped=False)
display_metrics_table_all_db(selected_dfs, freq, distinct_dbs, baseline_level)
if st.sidebar.button("Plot Waves at Single dB Level"):
fig = plot_waves_single_db(df, db, y_min, y_max)
st.plotly_chart(fig)
display_metrics_table(df, freq, db, baseline_level)
if st.sidebar.button("Plot Waves at Single Wave (Frequency, dB)"):
fig = plot_waves_single_tuple(df, freq, db, y_min, y_max)
st.plotly_chart(fig)
metrics_df = display_metrics_table(df, freq, db, baseline_level)
if metrics_df is not None:
st.table(metrics_df)
if st.button("Download metrics"):
csv = metrics_df.to_csv(index=False)
b64 = base64.b64encode(csv.encode()).decode() # Some strings
link = f'<a href="data:file/csv;base64,{b64}" download="metrics_table.csv">Download Metrics Table CSV</a>'
st.markdown(link, unsafe_allow_html=True)
if st.sidebar.button("Plot Stacked Waves at Single Frequency"):
if plot_time_warped:
plot_waves_stacked(df, freq, y_min, y_max, plot_time_warped=True)
else:
plot_waves_stacked(df, freq, y_min, y_max, plot_time_warped=False)
#if st.sidebar.button("Plot Waves with Cubic Spline"):
# fig = plotting_waves_cubic_spline(df, freq, db)
# fig.update_layout(yaxis_range=[y_min, y_max])
# st.plotly_chart(fig)
if st.sidebar.button("Plot 3D Surface"):
plot_3d_surface(df, freq, y_min, y_max)
#if st.sidebar.button("Plot Waves with Gaussian Smoothing"):
# fig_gauss = plotting_waves_gauss(dfs, freq, db)
# st.plotly_chart(fig_gauss)
#st.markdown(get_download_link(fig), unsafe_allow_html=True)