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import os
import glob
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
def plot_data_from_file(file_path):
# Load the data, assuming the last column is text
data = pd.read_csv(file_path, header=None)
rep_size=len(set(data[data.columns[-1]]))
data.drop(data.columns[-1], axis=1, inplace=True) # Drop the last column (text)
# Number of numerical columns
num_columns = data.shape[1]
# Create a subplot for each column
fig, axes = plt.subplots(num_columns, 1, figsize=(10, 6 * num_columns))
# In case there is only one column, axes will not be an array, so we convert it
if num_columns == 1:
axes = [axes]
for i, ax in enumerate(axes):
idx=0
ax.scatter(np.asarray(data.index,dtype=np.int64)%rep_size, data[i], label=f'Column {i+1}')
ax.set_title(f'Column {i+1}')
ax.set_xlabel('ID Number')
ax.set_ylabel('Value')
ax.legend()
ax.grid(True)
plt.tight_layout()
plt.suptitle(f'Data from {os.path.basename(file_path)}')
# Save the plot to a file
plt.savefig(file_path.replace('.txt', '.png'))
plt.close()
def scan_and_plot(directory):
# Scan for .txt files in the given directory
txt_files = glob.glob(os.path.join(directory, '*.txt'))
# Process each file
for file in txt_files:
print(f'Processing {file}...')
plot_data_from_file(file)
print(f'Plot saved for {file}')
# Replace 'your_folder_path' with the path to the folder containing the .txt files
scan_and_plot('./')
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