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Changed function

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Diana 2024-10-22 12:02:41 +02:00
parent a90f35b8b9
commit 04c43bbfcf
1 changed files with 115 additions and 26 deletions
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141
code/GP_Code.py
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@ -1,10 +1,10 @@
# -*- coding: utf-8 -*-
"""
Created on Thu Oct 17 09:23:10 2024
Created on Tue Oct 22 11:43:41 2024
@author: diana
"""
# -*- coding: utf-8 -*-
import glob
import os
@ -101,11 +101,12 @@ def prepare_harmonics(frequencies, categories, num_harmonics, colors):
return points, color_mapping, points_categories
def plot_power_spectrum_with_integrals(frequency, power, points, delta, color_mapping, points_categories):
"""Create a figure of the power spectrum with integrals highlighted around specified points.
def plot_power_spectrum_with_integrals(frequency, power, points, delta):
"""
Create a figure of the power spectrum and calculate integrals around specified points.
This function creates a plot of the power spectrum and shades areas around
specified harmonic points to indicate the calculated integrals.
This function generates the plot of the power spectrum and calculates integrals
around specified harmonic points, but it does not color the regions or add vertical lines.
Parameters
----------
@ -117,37 +118,121 @@ def plot_power_spectrum_with_integrals(frequency, power, points, delta, color_ma
A list of harmonic frequencies to highlight.
delta : float
Half-width of the range for integration around each point.
color_mapping : dict
A mapping of point categories to colors.
points_categories : dict
A mapping of categories to lists of points.
Returns
-------
integrals : list
List of calculated integrals for each point.
local_means : list
List of local mean values (adjacent integrals).
fig : matplotlib.figure.Figure
The created figure object.
The created figure object with the power plot.
ax : matplotlib.axes.Axes
The axes object for further modifications.
"""
fig, ax = plt.subplots()
ax.plot(frequency, power)
ax.plot(frequency, power) # Plot power spectrum
integrals = []
local_means = []
for point in points:
# Define indices for the integration window
indices = (frequency >= point - delta) & (frequency <= point + delta)
# Calculate integral around the point
integral = np.trapz(power[indices], frequency[indices])
integrals.append(integral)
# Get color based on point category
color = next((c for cat, c in color_mapping.items() if point in points_categories[cat]), 'gray')
ax.axvspan(point - delta, point + delta, color=color, alpha=0.3, label=f'{point:.2f} Hz')
print(f"Integral around {point:.2f} Hz: {integral:.5e}")
ax.set_xlim([0, 1200])
# Calculate adjacent region integrals for local mean
left_indices = (frequency >= point - 5 * delta) & (frequency < point - delta)
right_indices = (frequency > point + delta) & (frequency <= point + 5 * delta)
l_integral = np.trapz(power[left_indices], frequency[left_indices])
r_integral = np.trapz(power[right_indices], frequency[right_indices])
local_mean = np.mean([l_integral, r_integral])
local_means.append(local_mean)
ax.set_xlim([0, 1200]) # Set x-axis limit
ax.set_xlabel('Frequency (Hz)')
ax.set_ylabel('Power')
ax.set_title('Power Spectrum with marked Integrals')
ax.legend()
ax.set_title('Power Spectrum with Integrals (Uncolored)')
return integrals, local_means, fig, ax
def highlight_integrals_with_threshold(frequency, power, points, delta, threshold, integrals, local_means, color_mapping, points_categories, fig_orig, ax_orig):
"""
Create a new figure by highlighting integrals that exceed the threshold.
This function generates a new figure with colored shading around points where the integrals exceed
the local mean by a given threshold and adds vertical lines at the boundaries of adjacent regions.
It leaves the original figure unchanged.
Parameters
----------
frequency : np.array
An array of frequencies corresponding to the power values.
power : np.array
An array of power spectral density values.
points : list
A list of harmonic frequencies to highlight.
delta : float
Half-width of the range for integration around each point.
threshold : float
Threshold value to compare integrals with local mean.
integrals : list
List of calculated integrals for each point.
local_means : list
List of local mean values (adjacent integrals).
color_mapping : dict
A mapping of point categories to colors.
points_categories : dict
A mapping of categories to lists of points.
fig_orig : matplotlib.figure.Figure
The original figure object (remains unchanged).
ax_orig : matplotlib.axes.Axes
The original axes object (remains unchanged).
Returns
-------
fig_new : matplotlib.figure.Figure
The new figure object with color highlights and vertical lines.
"""
# Create a new figure based on the original power spectrum
fig_new, ax_new = plt.subplots()
ax_new.plot(frequency, power) # Plot the same power spectrum
# Loop through each point and check if the integral exceeds the threshold
for i, point in enumerate(points):
exceeds = integrals[i] > (local_means[i] * threshold)
if exceeds:
# Define color based on the category of the point
color = next((c for cat, c in color_mapping.items() if point in points_categories[cat]), 'gray')
# Shade the region around the point where the integral was calculated
ax_new.axvspan(point - delta, point + delta, color=color, alpha=0.3, label=f'{point:.2f} Hz')
print(f"Integral around {point:.2f} Hz: {integrals[i]:.5e}")
# Define left and right boundaries of adjacent regions
left_boundary = frequency[np.where((frequency >= point - 5 * delta) & (frequency < point - delta))[0][0]]
right_boundary = frequency[np.where((frequency > point + delta) & (frequency <= point + 5 * delta))[0][-1]]
# Add vertical dashed lines at the boundaries of the adjacent regions
ax_new.axvline(x=left_boundary, color="k", linestyle="--")
ax_new.axvline(x=right_boundary, color="k", linestyle="--")
# Update plot legend and return the new figure
ax_new.set_xlim([0, 1200])
ax_new.set_xlabel('Frequency (Hz)')
ax_new.set_ylabel('Power')
ax_new.set_title('Power Spectrum with Highlighted Integrals')
ax_new.legend()
return fig_new
return fig
@ -184,9 +269,13 @@ categories = ["AM", "EODf", "Stimulus frequency"]
num_harmonics = [4, 2, 2]
colors = ["green", "orange", "red"]
delta = 2.5
threshold = 10
### Peaks im Powerspektrum finden ###
###
points, color_mapping, points_categories = prepare_harmonics(frequencies, categories, num_harmonics, colors)
fig = plot_power_spectrum_with_integrals(frequency, power, points, delta, color_mapping, points_categories)
plt.show()
# First, create the power spectrum plot with integrals (without coloring)
integrals, local_means, fig1, ax1 = plot_power_spectrum_with_integrals(frequency, power, points, delta)
# Then, create a new separate figure where integrals exceeding the threshold are highlighted
fig2 = highlight_integrals_with_threshold(frequency, power, points, delta, threshold, integrals, local_means, color_mapping, points_categories, fig1, ax1)