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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) | ||
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# Number of numerical columns | ||
num_columns = data.shape[1] | ||
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# Create a subplot for each column | ||
fig, axes = plt.subplots(num_columns, 1, figsize=(10, 6 * num_columns)) | ||
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# In case there is only one column, axes will not be an array, so we convert it | ||
if num_columns == 1: | ||
axes = [axes] | ||
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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) | ||
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plt.tight_layout() | ||
plt.suptitle(f'Data from {os.path.basename(file_path)}') | ||
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# Save the plot to a file | ||
plt.savefig(file_path.replace('.txt', '.png')) | ||
plt.close() | ||
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def scan_and_plot(directory): | ||
# Scan for .txt files in the given directory | ||
txt_files = glob.glob(os.path.join(directory, '*.txt')) | ||
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# 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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