i wrote this code to produce the chart in python, can any one reproduce it in Matlab to produce the same figure, i need it urgently, please. this chart i made it using Gemini Ai in colab but the quality of images is very bad and i want to produce it in Matlab so that the quality be better, please. i wrote the following code using python and i want it in Matlab, please, urgently, help.
import matplotlib.pyplot as plt
import numpy as np
# Define the number of groups and categories per group
num_groups = 7 # Changed from 4 to 7
num_categories_per_group = 7
# Define 7 distinct colors for categories (reused per group)
colors = [
'#007F7F', '#800000', '#000080', '#808000', '#800080',
'#008000', '#FF4500'
]
# Define custom 'before' and 'after' values for each group
custom_group_data = [
{
'before': np.array([20, 30, 25, 40, 15, 35, 22]),
'after': np.array([25, 28, 30, 35, 18, 38, 28])
},
{
'before': np.array([10, 20, 18, 25, 12, 28, 15]),
'after': np.array([12, 22, 20, 28, 15, 30, 18])
},
{
'before': np.array([30, 35, 28, 45, 20, 40, 32]),
'after': np.array([32, 38, 30, 48, 23, 42, 35])
},
{
'before': np.array([18, 22, 20, 30, 10, 25, 15]),
'after': np.array([20, 25, 22, 33, 12, 28, 18])
},
{
'before': np.array([22, 28, 32, 38, 17, 30, 25]), # New data for Group 5
'after': np.array([24, 30, 35, 40, 19, 33, 27])
},
{
'before': np.array([15, 19, 23, 27, 11, 26, 19]), # New data for Group 6
'after': np.array([17, 21, 25, 29, 13, 28, 21])
},
{
'before': np.array([35, 40, 33, 50, 25, 45, 38]), # New data for Group 7
'after': np.array([37, 42, 36, 52, 27, 48, 40])
}
]
plt.figure(figsize=(15, 5)) # Decreased height and increased width for Word document
# Generate and plot data for each group
for group_idx in range(num_groups):
group_name = f'Group {group_idx + 1}'
categories = [f'Category {i+1}' for i in range(num_categories_per_group)]
# Use custom values for the current group
before_values = custom_group_data[group_idx]['before']
after_values = custom_group_data[group_idx]['after']
# Ensure 'after' values are not negative (though with custom values, this might be handled directly)
after_values[after_values < 0] = 0
# Plot each line within the current group
for i, category in enumerate(categories):
# Label each category only once (when group_idx is 0) to avoid duplicate legend entries
plt.plot(
[group_idx - 0.3, group_idx + 0.3], # Increased offset from 0.2 to 0.3
[before_values[i], after_values[i]],
marker='o',
linestyle='-',
color=colors[i],
label=f'{category}' if group_idx == 0 else '', # Use only 7 labels for categories
alpha=0.7
)
# Get current axes to modify tick parameters and set ylim
ax = plt.gca()
# Adjust y-axis limits to create space for 'Before'/'After' labels below the plot area
current_ymin, current_ymax = ax.get_ylim()
# Extend y-axis downwards by a percentage of the total y-range to make space
new_ymin = current_ymin - (current_ymax - current_ymin) * 0.15
ax.set_ylim(bottom=new_ymin)
# Re-get y_label_pos after setting new y-limit to ensure it's at the very bottom
y_label_pos = ax.get_ylim()[0]
for group_idx in range(num_groups):
# Add 'before' and 'after' labels to the x-axis for each group
# Placed at the new lower y-limit with increased font size
plt.text(group_idx - 0.3, y_label_pos, 'Before', ha='center', va='top', fontsize=12, color='gray') # Increased offset
plt.text(group_idx + 0.3, y_label_pos, 'After', ha='center', va='top', fontsize=12, color='gray') # Increased offset
# Add labels and title
plt.xticks(range(num_groups), [f'Group {i+1}' for i in range(num_groups)], fontsize=14) # Increased x-axis label font size
plt.ylabel('Value', fontsize=14) # Increased y-axis label font size
plt.title('Before and After Comparison for Multiple Groups and Categories', fontsize=16) # Increased title font size
plt.legend(title='Category', bbox_to_anchor=(1.05, 1), loc='upper left') # Ensure legend is displayed
plt.grid(True, linestyle='--', alpha=0.7)
# Adjust x-axis tick label padding to move them further down
ax.tick_params(axis='x', which='both', pad=15) # Increased pad to move x-tick labels down
plt.tight_layout()
plt.show()
