Analyze & Visualize Spring Constants with Python Code
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- Python-Based Data Analysis and Visualization
- Block 1: Importing Necessary Libraries
- Block 2: Function to Extract Spring Constants
- Block 3:Reading .docx Files and Creating DataFrames
- Block 4: Saving Data to CSV File
- Block 5: Creating a Scatter Plot
- Block 6: Calculating Mean and Standard Deviation
- Conclusion
This Python code facilitates the analysis and visualization of spring constants extracted from two specific .docx files. It begins by importing essential libraries, including docx, pandas, and matplotlib, enabling data processing and plotting. The extract_spring_constants function parses the XML content of the documents to retrieve spring constants and their respective IDs. These values are organized into a DataFrame. The resulting data is saved to a CSV file for further reference. A scatter plot is generated to visualize variations in spring constants, and mean and standard deviation statistics are computed for each file. This code offers a comprehensive solution for studying and comparing spring constants efficiently.
Python-Based Data Analysis and Visualization
This Python script serves as a powerful tool for conducting data analysis and visualization, making it an ideal choice if you need help with your Python assignment. With a focus on spring constants extracted from specific .docx files, this Python-driven solution streamlines the entire process. By leveraging Python libraries such as docx, pandas, and matplotlib, it offers a comprehensive framework for handling and presenting data. The key function, extract_spring_constants, enables the extraction of valuable information. The resulting data is structured into a DataFrame, which can be exported to a CSV file, providing a solid foundation for further Python-based research or analysis. Additionally, the code generates an informative scatter plot and computes essential statistical metrics, enhancing the capabilities of Python in understanding complex datasets.
Block 1: Importing Necessary Libraries
from docx import Document
import re
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns- The code begins by importing the necessary Python libraries using the import statements.
- It imports libraries such as Document from docx, regular expressions (re), pandas for data manipulation, matplotlib.pyplot for data visualization, and seaborn for enhancing the appearance of plots.
Block 2: Function to Extract Spring Constants
def extract_spring_constants(docx_file):
document = Document(docx_file)
xml_content = "\n".join([para.text for para in document.paragraphs])
muscle_data = re.findall(r']*ID=\"([^\"]*)\"[^>]*SpringConstant=\"([^\"]*)\"', xml_content)
spring_constants = [(id_, float(constant)) for id_, constant in muscle_data]
return spring_constants- A function named extract_spring_constants is defined. This function takes a .docx file as input, extracts data from it, and returns a list of spring constants and their corresponding IDs.
Block 3:Reading .docx Files and Creating DataFrames
file1_spring_constants = extract_spring_constants("Raw XML subdivided_cylinder_damped.gaitsym.docx")
file2_spring_constants = extract_spring_constants("Raw XML subdivided_cylinder_damped.gaitsym10.9.4.docx")- The code then uses the extract_spring_constants function to read two specific .docx files and extract spring constants.
- It stores the spring constants in two separate lists, file1_spring_constants and file2_spring_constants.
- Subsequently, these lists are used to create a pandas DataFrame (df) to display the spring constants and their corresponding IDs.
Block 4: Saving Data to CSV File
df.to_csv('output.csv', index=False)- The code saves the df DataFrame to a CSV file named "output.csv."
Block 5: Creating a Scatter Plot
fig, ax = plt.subplots(figsize=(15, 10))
ax.scatter(df.index, df["File1"], label="File1", marker="<", color="blue", s=10)
ax.scatter(df.index, df["File2"], label="File2", marker=">", color="red", s=10)
# Other plot settings...- The code creates a scatter plot to visualize the changes in spring constants between the two files.
- It uses matplotlib and sets various plot attributes like labels, titles, and markers to create the plot.
- The x-axis is labeled with muscle indices, and the y-axis represents spring constants.
- The plot is displayed using plt.show().
Block 6: Calculating Mean and Standard Deviation
file1_mean = df['File1'].mean()
file1_std = df['File1'].std()
file2_mean = df['File2'].mean()
file2_std = df['File2'].std()
print(f"File1 - Mean: {file1_mean}, Standard Deviation: {file1_std}")
print(f"File2 - Mean: {file2_mean}, Standard Deviation: {file2_std}")- The code calculates and prints the mean and standard deviation of spring constants for both File1 and File2.
Conclusion
In conclusion, the Python code presented here is a versatile and powerful resource for those seeking a streamlined approach to analyze and visualize spring constants from specific .docx files. It harnesses the capabilities of Python, leveraging essential libraries to simplify data processing and visualization. This code is an invaluable tool for research, academic assignments, or any scenario where understanding and presenting spring constants is vital. It offers a user-friendly experience, from data extraction to the generation of informative scatter plots and statistical analysis. By using this code, users can unlock new possibilities in efficiently exploring and presenting spring constant data, making it an excellent addition to any Python project or assignment.
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