PythonCourse/Assignment_3 at main · ie8701va/PythonCourse · GitHub

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# -*- coding: utf-8 -*-
"""
Created on Tue Apr 11 16:07:03 2023

@author: Ieva
"""
import numpy as np
import pandas as  pd
import matplotlib.pyplot as plt
plt.rcParams["font.family"] = "Times New Roman"
plt.rcParams["font.size"] = "14"
plt.rcParams["font.weight"] = "bold"
plt.rcParams["axes.labelweight"] = "bold"

# Task 3.1.
eye_velocity = np.array(pd.read_csv("C:/Users/Leva/Documents/NewComputer/Master's programme/Python Course/eye_velocity.csv")).flatten() # Importing the data set
eye_velocity_short = eye_velocity[0:999] # 1st second of Eye_velocity dataframe
eye_velocity_short_short = eye_velocity[0:99]

fig = plt.figure()
ax = fig.add_subplot(111)

# Plotting on the axes.
ax.plot(eye_velocity_short, c = 'r')

ax.axhline(y = np.mean(eye_velocity_short), color='g', linestyle="dashed")
ax.set_xlabel('Time (ms)', fontname="Times New Roman", fontsize=14, fontweight="bold")
ax.set_ylabel('Velocity ($^\circ$/s)', fontname="Times New Roman", fontsize=14, fontweight="bold")
plt.annotate('Average velocity', xy = (600,50), xytext = (700,180),
             arrowprops=dict(facecolor='black', arrowstyle= "->, head_length = 0.3, head_width = .20", lw=1), fontname="Times New Roman", fontsize=14, fontweight="bold")

plt.annotate()
plt.show()

# Saving the figure as .png format
plt.savefig('my_figure.png')

# Plot a histogram of the velocities using 100 bins.

plt.hist(eye_velocity, bins=100)
plt.xlabel('Velocity ($^\circ$/s)')
plt.ylabel('Frequency')
plt.title('Eye movement velocity')
plt.show() # Answer - Most of eye movement activity contains the velocity of less than 20 degrees


# Assigment 3 part 2

import matplotlib.pyplot as plt
import numpy as np
import matplotlib.image as mpimg
from PIL import Image

img1 = mpimg.imread('1.png')
imgplot1 = plt.imshow(img1)
plt.show()

img2 = Image.open('2.png')
imgplot2 = plt.imshow(img2)
plt.show()

img3 = mpimg.imread('3.png')
imgplot3 = plt.imshow(img3)
plt.show()

img4 = mpimg.imread('4.png')
imgplot4 = plt.imshow(img4)
plt.show()


figB, ax = plt.subplots(nrows = 2, ncols = 2)
figB.tight_layout() # This is added to make a bit more space between the subplots
ax[0, 0].imshow(img1)
ax[0, 0].set_title('Image 1')

ax[0, 1].imshow(img2)
ax[0, 1].set_title('Image 2')

ax[1, 0].imshow(img3)
ax[1, 0].set_title('Image 3')

ax[1, 1].imshow(img4)
ax[1, 1].set_title('Image 4')

# Adjusting the rotation and scale of images
# Putting the puzzle pieces together

import cv2 # I will conduct all the rearrangement with CV2 so opening the images using this package first
import numpy as np
cvimg1 = cv2.imread('1.png')
cvimg1 = np.rot90(cvimg1, 3) # Rotating

cvimg2 = cv2.imread('2.png')
cvimg2 = cv2.resize(cvimg2, (250,240)) # Resizing

cvimg3 = cv2.imread('3.png')
cvimg4 = cv2.imread('4.png')


cvimg12 = cv2.hconcat([cvimg1, cvimg2]) # Concatenating the first two images horizontally
cvimg34 = cv2.hconcat([cvimg3, cvimg4]) # Concatenating the 3rd and 4th images horizontally
cvimgall = cv2.vconcat([cvimg12, cvimg34]) # Concatenating 1 and 2, 3 and 4 images vertically

cv2.imshow('all', cvimgall) # Displaying the whole image
cv2.waitKey(0)
cv2.detroyAllWindows()


cv2.imwrite(r"C:\Users\Leva\Documents\baboon.jpg", cvimgall)
from PIL import Image

img_from_jpg_to_pdf = Image.open(r'C:\Users\Leva\Documents\baboon.jpg')
cvimgall_pdf = img_from_jpg_to_pdf.convert('RGB')
cvimgall_pdf.save(r'C:\Users\Leva\Documents\baboon.pdf')