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How to Create Nuclide Data Analysis Using Python

In this guide, we'll explore the fascinating domain of nuclide data analysis using Python. We'll take you on an extensive code walkthrough, demonstrating how to create, manipulate, and scale nuclide data. Whether you're a student engaged in programming assignments within the nuclear physics field or a data enthusiast passionate about scientific analysis, this guide is meticulously designed to provide you with the knowledge and skills you require. Prepare to immerse yourself in the intriguing world of nuclide data and uncover Python's data analysis prowess. By the end of this guide, you'll have acquired a strong foundation in handling intricate data sets, empowering you to address real-world challenges and conduct research in nuclear science and beyond.

Demystifying Nuclide Data Analysis with Python

Explore our comprehensive guide on creating nuclide data analysis with Python. We empower you to write your Python assignment with confidence, offering step-by-step insights into data manipulation, scaling, and in-depth analysis. Whether you're a student tackling nuclear physics assignments or a data enthusiast, this guide equips you with the expertise to handle complex data sets and excel in scientific analysis. Dive into the world of nuclide data, harness Python's power, and embark on real-world research with confidence.

Analyzing and Modifying Nuclide Data

The provided code performs a series of operations to analyze and modify nuclide data. It begins with the creation of a list of initial key nuclides, constructs a DataFrame with columns 'Co', 'Cs', and 'Am', and then proceeds to manipulate this data based on certain conditions. Here's a breakdown of the code:

Importing Libraries

```python import matplotlib import numpy import pandas as pd import psutil ```

The code begins by importing necessary libraries, including Matplotlib, NumPy, Pandas, and psutil.

Creating the Initial Nuclide List and DataFrame

```python NV_ini_list = ["Co-Cs-Am", "Co-Cs-Am-Eu", "Co-Cs-Am-Sr", "Co-Cs-Am-Xg", "Co-Cs-Am-Eu-Xg" ] df_ini = pd.DataFrame(columns=['Co', 'Cs', 'Am']) ```

The code creates a list `NV_ini_list` containing combinations of nuclides and initializes an empty Pandas DataFrame `df_ini` with columns 'Co', 'Cs', and 'Am'.

Loop for Nuclide Combinations

```python for x in NV_ini_list: ```

The code enters a loop, iterating through each combination in `NV_ini_list`.

Generating Initial Nuclide Tables

```python NV_ini = [] nNuc = 3 incr = 100 if nNuc == 3: for i in range(101): for j in range(101-i): NV_ini.append([i, j, 100-i-j]) elif nNuc == 4: for i in range(101): for j in range(101-i): for k in range(101-i-j): NV_ini.append([i, j, k, 100-i-j-k]) ```

Within the loop, the code generates the initial nuclide tables based on the value of `nNuc`. It creates combinations of nuclide values and appends them to the `NV_ini` list.

Creating DataFrames from NV_ini

```python df_temp = pd.DataFrame(NV_ini) df_temp = df_temp.rename(columns={0: 'Co', 1: 'Cs', 2: 'Am'}) df_ini = pd.concat([df_ini, df_temp]) df_ini = df_ini.reset_index(drop=True) ```

The code converts the `NV_ini` list into a DataFrame, renames the columns, and appends this DataFrame to the `df_ini` DataFrame. It then resets the index of `df_ini`.

Reading Data from a File

```python df_HRF = pd.read_fwf('HRF_zu_NV_2023.txt') df_HRF.columns =['i', 'j', 'mw', 'uei', 'mat', 'sp3', 'sp5', 'sp6', 'sp7', 'sp8', 'sp9', 'sp10', 'sp11', 'sp12', 'sp13', 'sp14'] ```

The code reads data from a text file, 'HRF_zu_NV_2023.txt', and assigns column names to the resulting DataFrame.

Filtering Data

```python df_HRF_anlagenweit = df_HRF[df_HRF['mat'] == 'anlagenweit'].copy() ```

The code filters data based on a specific condition, creating a new DataFrame `df_HRF_anlagenweit` that contains only rows where the 'mat' column is equal to 'anlagenweit'.

Processing Data for Different Nuclide Values

```python j_value = list(df_HRF_anlagenweit['j'].unique()) df_ini_1 = df_ini.copy() df_ini_2 = df_ini.copy() df_ini_3 = df_ini.copy() ```

The code extracts unique values from the 'j' column of `df_HRF_anlagenweit` and creates three copies of the initial nuclide DataFrame, `df_ini_1`, `df_ini_2`, and `df_ini_3`.

Iterating over j_values

```python for val in j_value: ```

The code enters a loop, iterating through different values of 'j' from the `j_value` list.

Modifying Nuclide Data Based on Conditions

The code within this loop modifies the nuclide data in `df_ini_1`, `df_ini_2`, and `df_ini_3` based on conditions defined for different values of 'j'. It calculates new values for the 'Co', 'Cs', and 'Am' columns in these DataFrames using 'mw' and 'uei' values from `df_HRF_anlagenweit` based on specific conditions for 'val'.

Similar Processing for Other Data Categories

After processing 'anlagenweit' data, the code performs similar data processing for data categories 'Gruppe_1' and 'Gruppe_2'.

Final Scaling of Data

```python df_ini_1 = df_ini_1 * 0.9 df_ini_2 = df_ini_2 * 0.9 df_ini_3 = df_ini_3 * 0.9 ```

The code scales down the values in `df_ini_1`, `df_ini_2`, and `df_ini_3` by multiplying each DataFrame by 0.9.


In conclusion, this guide has navigated the complexities of nuclide data analysis using Python, offering a comprehensive understanding of data manipulation and scaling. Whether you're a student or a data enthusiast, you're now well-equipped to tackle assignments or scientific projects within nuclear physics. You've witnessed the transformative power of Python in analyzing nuclide data. As you venture beyond this guide, you're poised to handle intricate data sets and embark on real-world research, armed with the skills and knowledge needed to excel in nuclear science and beyond.