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funct_for_SLEAP_analysis.py
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funct_for_SLEAP_analysis.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Jul 17 16:43:52 2024
@author: HCattan
"""
import numpy as np
import pandas as pd
from scipy.ndimage import gaussian_filter1d
def gaussian_smoothing(data, sigma=3, axis=0):
"""
Applies Gaussian smoothing to a 1D array or a 2D matrix.
Parameters:
data (numpy array): The input data array or matrix to be smoothed.
sigma (float): The standard deviation of the Gaussian kernel. Default is 3.
axis (int): The axis along which to apply the smoothing (0 for rows, 1 for columns). Default is 0.
Returns:
numpy array: The smoothed data.
"""
if data.ndim == 1:
# If data is a 1D array
smoothed_data = gaussian_filter1d(data, sigma)
elif data.ndim == 2:
# If data is a 2D matrix, apply smoothing along the specified axis
smoothed_data = np.apply_along_axis(gaussian_filter1d, axis, data, sigma)
else:
raise ValueError("Input data must be a 1D array or a 2D matrix.")
return smoothed_data
def find_indices(node_names, names_to_find):
"""
Find indices of specific names in a list of node names.
Parameters:
- node_names: List of node names.
- names_to_find: List of names to find in node_names.
Returns:
- Dictionary with names as keys and their indices in node_names as values.
"""
indices = {}
for name in names_to_find:
try:
indices[name] = node_names.index(name)
except ValueError:
indices[name] = None # Indicate that the item was not found
return indices
def sort_locations_by_node_order(locations_dict, node_names_dict):
"""
Sort locations in the dictionary by the node order.
Parameters:
- locations_dict: Dictionary with indices as keys and location arrays as values.
- node_names_dict: Dictionary with indices as keys and node name lists as values.
Returns:
- Tuple of (sorted node names dictionary, sorted locations dictionary).
"""
sorted_locations_dict = {}
for idx in locations_dict:
node_order = node_names_dict[idx]
node_index_map = {node_name: i for i, node_name in enumerate(node_order)}
locations_dict[idx] = locations_dict[idx][:, [node_index_map[node_name] for node_name in node_order], :]
node_names_dict[idx] = sorted(node_names_dict[idx])
return node_names_dict, locations_dict
def calculate_rectangle_center(x0, y0, length, width):
"""
Calculate the center of a rectangle.
Parameters:
- x0, y0: Coordinates of the bottom-left corner of the rectangle.
- length: Length of the rectangle.
- width: Width of the rectangle.
Returns:
- Tuple of (x, y) coordinates of the center.
"""
x_c = x0 + length / 2
y_c = y0 + width / 2
return x_c, y_c
def calculate_angle_between_vectors(v1, v2):
"""
Calculate the angle between two vectors.
Parameters:
- v1, v2: Input vectors.
Returns:
- Angle between the vectors in radians.
"""
dot_product = np.dot(v1, v2)
magnitude_v1 = np.linalg.norm(v1)
magnitude_v2 = np.linalg.norm(v2)
cos_angle = dot_product / (magnitude_v1 * magnitude_v2)
angle = np.arccos(np.clip(cos_angle, -1.0, 1.0)) # Clip to avoid numerical issues
return angle
def angle_between_point_and_line(line_point1, line_point2, mouse_angles):
"""
Calculate the angle between the mouse's head direction and the perpendicular to the line.
Parameters:
- line_point1: numpy array of shape (2,), first extremity point of the line (x1, y1).
- line_point2: numpy array of shape (2,), second extremity point of the line (x2, y2).
- mouse_angles: numpy array of shape (n,), angles of the mouse's head in radians.
Returns:
- angles_in_degrees: numpy array of shape (n,), angles between the mouse's head direction and the perpendicular to the line in degrees.
"""
# Calculate the direction vector of the line
direction_vector = line_point2 - line_point1
# Calculate the perpendicular vector to the line
perpendicular_vector = np.array([-(direction_vector[1]), direction_vector[0]])
# Normalize the perpendicular vector
perpendicular_vector = perpendicular_vector / np.linalg.norm(perpendicular_vector)
angles = []
for angle in mouse_angles:
# Calculate the mouse's direction vector
mouse_direction_vector = np.array([np.cos(angle), np.sin(angle)])
# Calculate the angle between the perpendicular vector and the mouse's direction vector
cos_theta = np.dot(perpendicular_vector, mouse_direction_vector)
theta = np.arccos(np.clip(cos_theta, -1.0, 1.0)) # Ensure the value is within [-1, 1] to avoid numerical issues
angles.append(theta)
# Convert the angles to degrees
angles_in_degrees = np.degrees(angles)
return np.array(angles_in_degrees)
def head_angles(nose, left_ear, right_ear):
"""
Calculate the head angles based on nose and ear positions.
Parameters:
- nose, left_ear, right_ear: numpy arrays of shape (n, 2), positions of the nose, left ear, and right ear respectively.
Returns:
- Tuple of (cos_angle, sin_angle, angle in degrees).
"""
head_direction = nose - (left_ear + right_ear) / 2
cos_angle = head_direction[:, 0] / np.linalg.norm(head_direction, axis=1)
sin_angle = head_direction[:, 1] / np.linalg.norm(head_direction, axis=1)
angle = np.degrees(np.arctan2(head_direction[:, 0], head_direction[:, 1]))
return cos_angle, sin_angle, angle
def velocity(locations, delta_t):
"""
Calculate the velocity based on location data.
Parameters:
- locations: numpy array of shape (n, 2), positions.
- delta_t: Time difference between frames.
Returns:
- numpy array of shape (n-1, 2), velocities.
"""
vel = np.diff(locations, axis=0) / delta_t
return vel
def crop_data_around_indices(data, LED_ON, cut_indices, timeStamps):
"""
Crop data around indices of interest.
Parameters:
- data: Input data array.
- LED_ON: Dictionary with labels and corresponding indices.
- cut_indices: List of start and end indices for cropping.
Returns:
- Tuple of (cropped data array, labels list).
"""
cropped_data = []
labels = []
for label, indices in LED_ON.items():
indices = np.squeeze(np.asarray(indices, dtype=int))
for idx in indices:
# find
find_first_trueindex = [
(timeStamps['Time Stamp (ms)'] > round(timeStamps['Time Stamp (ms)'][idx] + cut_indices[x])).idxmax()
for x in range(2)
]
start_idx = find_first_trueindex[0]
end_idx = find_first_trueindex[1]
cropped_segment = np.mean(data[start_idx:end_idx])
cropped_data.append(cropped_segment)
labels.append(label)
return np.array(cropped_data), labels
def interpolate_crop(data, LED_on, cut_indices, timeStamps):
"""
Interpolate data and crop it around specified indices.
Parameters:
- data: List of data arrays.
- LED_on: Dictionary with labels and corresponding indices.
- cut_indices: List of start and end indices for cropping.
Returns:
- Tuple of (interpolated and cropped data list, labels list).
"""
data_trials = []
for i in range(len(data)):
data_trials_1, labels = crop_data_around_indices(data[i], LED_on, cut_indices,timeStamps)
data_trials.append(data_trials_1)
return data_trials, labels