# Building Better Research Software - 2026-02-24-NCL

## This document: bit.ly/2026-02-24-NCL
### https://hackmd.io/@rseteam/2026-02-24-NCL/edit
(_**no need** to log in to Hackmd.io, edit mode: CTRL+ALT+B_)

[toc]

--------

### Links:

- [The UK Reproducibility Network (UKRN)](https://www.ukrn.org/) funded the [Software Sustainability Institute](https://software.ac.uk) to develop this training workshop and provided our catering today
- [Carpentries](https://carpentries.org)
- [RSE Team](https://rse.ncldata.dev)
- [Code of Conduct](https://docs.carpentries.org/policies/coc/)
- [Workshop website](https://nclrse-training.github.io/2026-02-24-NCL/)
- [Link to lesson website](https://carpentries-incubator.github.io/better-research-software/)
- [Software Used](https://carpentries-incubator.github.io/better-research-software/#software-setup) is pre-installed on our cluster PCs
- [Check your GitHub Account](https://github.com/) (SSH key on your H: drive or laptop local drive)
- [Link to lesson data and code](https://github.com/carpentries-incubator/better-research-software/raw/refs/heads/main/learners/spacewalks.zip)
- [Pre-workshop survey](https://forms.office.com/e/Qu8auyhQzS)
- [Post-workshop survey](https://forms.gle/NdtugmSvM9PS2k6T8)
- [Code Community](https://teams.microsoft.com/l/team/19%3aG79Rz7Mhk6rC0mhia04YCD-nj7WabLMxhnyb1YLp04A1%40thread.tacv2/conversations?groupId=7059214c-2200-4ad6-a739-9d350c74c7a9&tenantId=9c5012c9-b616-44c2-a917-66814fbe3e87)

### Attendance:
You will be asked to show the QR code we sent to you via email

## Your instructors
[Email us your questions](mailto://[e-mail]) to [e-mail]
Carol Booth ([e-mail])
Dr. Frances Turner (<[e-mail]>)
Dr. Robin Wardle (<[e-mail]>)

### GitHub Account: Paste your [<span style="color:red">GitHub login name</span>](https://github.com) on a new line
Please enter your github username below:

## Useful information:
_Everyone is welcome to add tips, tricks and comments to this document_ by editing text in the left pane of this
document in edit mode (CTRL+ALT+B)

### Git Default Branch
You can change your default branch name from `master` to `main`:

git config --global init.defaultBranch main

If you've already created a `master` branch, you can rename it:

git branch -m master main

### **Day2: Software Mangement and Collaboration - where to get a DOI?**
This workshop looks at Zenodo https://zenodo.org/ for DOIs.
#### We use https://sandbox.zenodo.org/ to practice
You may have heard of obtaining DOIs using https://data.ncl.ac.uk/, which is Newcastle University's institutional Figshare instance. Figshare was originally aimed at image and document sharing, but you can also use it like Zenodo with GitHub: https://info.figshare.com/user-guide/how-to-connect-figshare-with-your-github-account/

The research data management team provide guidance at: https://www.ncl.ac.uk/library/academics-and-researchers/lrs/rdm/working/organise/

### Renaming Files
- can use oneliner: `git mv <old_file.py> <new_file.py>` instead of the sequence `mv <old_file.py> <new_file.py>; git add <new_file.py>`

### Renaming Variables

- [the dangers of heedless global search and replace](https://selinker.livejournal.com/32929.html)

### Private email addresses preventing pushing to GitHub
["Your push would publish a private email address"](https://stackoverflow.com/questions/43863522/error-your-push-would-publish-a-private-email-address) - this happens when you have ["Block commands that expose my email"](https://github.com/settings/emails) set in GitHub. A solution is given in the linked Stack Overflow article.

For a local repository:

git config user.email "{ID}+{username}@users.noreply.github.com"

For all repositories on the computer

git config --global user.email "{ID}+{username}@users.noreply.github.com"

### Why make a copy of a dataframe inside a function?
https://stackoverflow.com/questions/46533197/understanding-variable-scope-and-changes-in-python#:~:text=I'm%20using%20Python%203.6%20and,dataframe%20to%20the%20original%20columns.
This Stackoverflow post covers the question of variable scope. We expect a variables to be isolated inside functions but the lines can be blurred because the variable is actually a pointer to your dataframe.

### pandas read_json input
The pandas read_json function can take a string or a file buffer as an input which is strange.

### how does Pytest know which test script to run?
the test scripts have to be in ./test and be named starting test_

### mkdocs with main script in a subdirectory
In the reference file use`:::mydir.myfile` rather than `:::mydir/myfile` or `:::myfile`
### Wrap Up: What are we trying to do?
Research software development principles explained by Software Sustainability Institute’s Director Neil Chue Hong during his [keynote at RSECon23](https://rsecon24.society-rse.org/about/research-software-development-principles/#neil-chue-hong-rse23-keynote) the section from minute 21 to minute 29 apx explains the Research Software Development Principles.

----------
# Code State

## Code state: Start of day 2

The below code is what your `eva_data_analysis.py` file should look like at the start of day 2.

<details>

You can copy the below code to your eva_data_analysis.py file, overwriting the previous contents:

Import matplotlib.pyplot as plt
import pandas as pd

def read_json_to_dataframe(input_file):
    """
    Read the data from a JSON file into a Pandas dataframe.
    Clean the data by removing any rows where the 'duration' value is missing.

    Args:
        input_file (file or str): The file object or path to the JSON file.

    Returns:
         eva_df (pd.DataFrame): The cleaned data as a dataframe structure
    """
    print(f'Reading JSON file {input_file}')
    # Read the data from a JSON file into a Pandas dataframe
    eva_df = pd.read_json(input_file, convert_dates=['date'], encoding='ascii')
    eva_df['eva'] = eva_df['eva'].astype(float)
    # Clean the data by removing any rows where duration is missing
    eva_df.dropna(axis=0, subset=['duration', 'date'], inplace=True)
    return eva_df


def write_dataframe_to_csv(df, output_file):
    """
    Write the dataframe to a CSV file.

    Args:
        df (pd.DataFrame): The input dataframe.
        output_file (file or str): The file object or path to the output CSV file.

    Returns:
        None
    """
    print(f'Saving to CSV file {output_file}')
    # Save dataframe to CSV file for later analysis
    df.to_csv(output_file, index=False, encoding='utf-8')

def plot_cumulative_time_in_space(df, graph_file):
    """
    Plot the cumulative time spent in space over years.

    Convert the duration column from strings to number of hours
    Calculate cumulative sum of durations
    Generate a plot of cumulative time spent in space over years and
    save it to the specified location

    Args:
        df (pd.DataFrame): The input dataframe.
        graph_file (file or str): The file object or path to the output graph file.

    Returns:
        None
    """
    print(f'Plotting cumulative spacewalk duration and saving to {graph_file}')
    df['duration_hours'] = df['duration'].str.split(":").apply(lambda x: int(x[0]) + int(x[1])/60)
    df['cumulative_time'] = df['duration_hours'].cumsum()
    plt.plot(df['date'], df['cumulative_time'], 'ko-')
    plt.xlabel('Year')
    plt.ylabel('Total time spent in space to date (hours)')
    plt.tight_layout()
    plt.savefig(graph_file)
    plt.show()

# Main code

print("--START--")

input_file = open('./eva-data.json', 'r', encoding='ascii')
output_file = open('./eva-data.csv', 'w', encoding='utf-8')
graph_file = './cumulative_eva_graph.png'

# Read the data from JSON file
eva_data = read_json_to_dataframe(input_file)

# Convert and export data to CSV file
write_dataframe_to_csv(eva_data, output_file)

# Sort dataframe by date ready to be plotted (date values are on x-axis)
eva_data.sort_values('date', inplace=True)

# Plot cumulative time spent in space over years
plot_cumulative_time_in_space(eva_data, graph_file)

print("--END--")

For the wider code state, at this point, the code in your local software project’s directory should be as in: https://github.com/carpentries-incubator/bbrs-software-project/tree/05-code-structure

</details>

-------------

### Code state: lesson 5, new functions

<details>

You can copy the below code to your eva_data_analysis.py file, overwriting the previous contents:

import matplotlib.pyplot as plt
import pandas as pd


def read_json_to_dataframe(input_file):
    """
    Read the data from a JSON file into a Pandas dataframe.
    Clean the data by removing any rows where the 'duration' value is missing.

    Args:
        input_file (file or str): The file object or path to the JSON file.

    Returns:
         eva_df (pd.DataFrame): The cleaned and sorted data as a dataframe structure
    """
    print(f'Reading JSON file {input_file}')
    # Read the data from a JSON file into a Pandas dataframe
    eva_df = pd.read_json(input_file, convert_dates=['date'], encoding='ascii')
    eva_df['eva'] = eva_df['eva'].astype(float)
    # Clean the data by removing any rows where duration is missing
    eva_df.dropna(axis=0, subset=['duration', 'date'], inplace=True)
    return eva_df


def write_dataframe_to_csv(df, output_file):
    """
    Write the dataframe to a CSV file.

    Args:
        df (pd.DataFrame): The input dataframe.
        output_file (file or str): The file object or path to the output CSV file.

    Returns:
        None
    """
    print(f'Saving to CSV file {output_file}')
    # Save dataframe to CSV file for later analysis
    df.to_csv(output_file, index=False, encoding='utf-8')


def plot_cumulative_time_in_space(df, graph_file):
    """
    Plot the cumulative time spent in space over years.

    Convert the duration column from strings to number of hours
    Calculate cumulative sum of durations
    Generate a plot of cumulative time spent in space over years and
    save it to the specified location

    Args:
        df (pd.DataFrame): The input dataframe.
        graph_file (file or str): The file object or path to the output graph file.

    Returns:
        None
    """
    print(f'Plotting cumulative spacewalk duration and saving to {graph_file}')
    df = add_duration_hours(df)
    df['cumulative_time'] = df['duration_hours'].cumsum()
    plt.plot(df['date'], df['cumulative_time'], 'ko-')
    plt.xlabel('Year')
    plt.ylabel('Total time spent in space to date (hours)')
    plt.tight_layout()
    plt.savefig(graph_file)
    plt.show()


def text_to_duration(duration):
    """
    Convert a text format duration "HH:MM" to duration in hours

    Args:
        duration (str): The text format duration

    Returns:
        duration_hours (float): The duration in hours
    """
    hours, minutes = duration.split(":")
    duration_hours = int(hours) + int(minutes)/6  # there is an intentional bug on this line (should divide by 60 not 6)
    return duration_hours


def add_duration_hours(df):
    """
    Add duration in hours (duration_hours) variable to the dataset

    Args:
        df (pd.DataFrame): The input dataframe.

    Returns:
        df_copy (pd.DataFrame): A copy of df with the new duration_hours variable added
    """
    df_copy = df.copy()
    df_copy["duration_hours"] = df_copy["duration"].apply(
        text_to_duration
    )
    return df_copy

# Main code

print("--START--")

input_file = open('./eva-data.json', 'r', encoding='ascii')
output_file = open('./eva-data.csv', 'w', encoding='utf-8')
graph_file = './cumulative_eva_graph.png'

# Read the data from JSON file
eva_data = read_json_to_dataframe(input_file)

# Convert and export data to CSV file
write_dataframe_to_csv(eva_data, output_file)

# Sort dataframe by date ready to be plotted (date values are on x-axis)
eva_data.sort_values('date', inplace=True)

# Plot cumulative time spent in space over years
plot_cumulative_time_in_space(eva_data, graph_file)

print("--END--")

</details>

### Code state: lesson 5 move to 'main'

<details>

import matplotlib.pyplot as plt
import pandas as pd


def main(input_file, output_file, graph_file):
    print("--START--")

    # Read the data from JSON file
    eva_data = read_json_to_dataframe(input_file)

    # Convert and export data to CSV file
    write_dataframe_to_csv(eva_data, output_file)

    # Sort dataframe by date ready to be plotted (date values are on x-axis)
    eva_data.sort_values('date', inplace=True)

    # Plot cumulative time spent in space over years
    plot_cumulative_time_in_space(eva_data, graph_file)

    print("--END--")


def read_json_to_dataframe(input_file):
    """
    Read the data from a JSON file into a Pandas dataframe.
    Clean the data by removing any rows where the 'duration' value is missing.

    Args:
        input_file (file or str): The file object or path to the JSON file.

    Returns:
         eva_df (pd.DataFrame): The cleaned and sorted data as a dataframe structure
    """
    print(f'Reading JSON file {input_file}')
    # Read the data from a JSON file into a Pandas dataframe
    eva_df = pd.read_json(input_file, convert_dates=['date'], encoding='ascii')
    eva_df['eva'] = eva_df['eva'].astype(float)
    # Clean the data by removing any rows where duration is missing
    eva_df.dropna(axis=0, subset=['duration', 'date'], inplace=True)
    return eva_df


def write_dataframe_to_csv(df, output_file):
    """
    Write the dataframe to a CSV file.

    Args:
        df (pd.DataFrame): The input dataframe.
        output_file (file or str): The file object or path to the output CSV file.

    Returns:
        None
    """
    print(f'Saving to CSV file {output_file}')
    # Save dataframe to CSV file for later analysis
    df.to_csv(output_file, index=False, encoding='utf-8')

def plot_cumulative_time_in_space(df, graph_file):
    """
    Plot the cumulative time spent in space over years.

    Convert the duration column from strings to number of hours
    Calculate cumulative sum of durations
    Generate a plot of cumulative time spent in space over years and
    save it to the specified location

    Args:
        df (pd.DataFrame): The input dataframe.
        graph_file (file or str): The file object or path to the output graph file.

    Returns:
        None
    """
    print(f'Plotting cumulative spacewalk duration and saving to {graph_file}')
    df = add_duration_hours(df)
    df['cumulative_time'] = df['duration_hours'].cumsum()
    plt.plot(df['date'], df['cumulative_time'], 'ko-')
    plt.xlabel('Year')
    plt.ylabel('Total time spent in space to date (hours)')
    plt.tight_layout()
    plt.savefig(graph_file)
    plt.show()


def text_to_duration(duration):
    """
    Convert a text format duration "HH:MM" to duration in hours

    Args:
        duration (str): The text format duration

    Returns:
        duration_hours (float): The duration in hours
    """
    hours, minutes = duration.split(":")
    duration_hours = int(hours) + int(minutes)/6  # there is an intentional bug on this line (should divide by 60 not 6)
    return duration_hours


def add_duration_hours(df):
    """
    Add duration in hours (duration_hours) variable to the dataset

    Args:
        df (pd.DataFrame): The input dataframe.

    Returns:
        df_copy (pd.DataFrame): A copy of df with the new duration_hours variable added
    """
    df_copy = df.copy()
    df_copy["duration_hours"] = df_copy["duration"].apply(
        text_to_duration
    )
    return df_copy

if __name__ == "__main__":

    input_file = './eva-data.json'
    output_file = './eva-data.csv'
    graph_file = './cumulative_eva_graph.png'
    main(input_file, output_file, graph_file)
    

</details>

### Lesson 6: New code from collaborator

<details>
This is to be added to eva_data_analysis.py

    import matplotlib.pyplot as plt
import pandas as pd
import sys
import re # added this line


def main(input_file, output_file, graph_file):
    print("--START--")

    # Read the data from JSON file
    eva_data = read_json_to_dataframe(input_file)

    # Calculate and add crew size to data
    eva_data = add_crew_size_column(eva_data) # added this line

    # Convert and export data to CSV file
    write_dataframe_to_csv(eva_data, output_file)

    # Sort dataframe by date ready to be plotted (date values are on x-axis)
    eva_data.sort_values('date', inplace=True)

    # Plot cumulative time spent in space over years
    plot_cumulative_time_in_space(eva_data, graph_file)

    print("--END--")

and also add the following:

def calculate_crew_size(crew):
    """
    Calculate the size of the crew for a single crew entry

    Args:
        crew (str): The text entry in the crew column containing a list of crew member names

    Returns:
        (int): The crew size
    """
    if crew.split() == []:
        return None
    else:
        return len(re.split(r';', crew))-1

def add_crew_size_column(df):
    """
    Add crew_size column to the dataset containing the value of the crew size

    Args:
        df (pd.DataFrame): The input data frame.

    Returns:
        df_copy (pd.DataFrame): A copy of the dataframe df with the new crew_size variable added
    """
    print('Adding crew size variable (crew_size) to dataset')
    df_copy = df.copy()
    df_copy["crew_size"] = df_copy["crew"].apply(
        calculate_crew_size
    )
    return df_copy
    
    

</details>

### Code state: end of lesson 6

<details>

Code for eva_data_analysis.py

import matplotlib.pyplot as plt
import pandas as pd
import sys
import re


def main(input_file, output_file, graph_file):
    print("--START--")

    # Read the data from JSON file
    eva_data = read_json_to_dataframe(input_file)

    # Calculate and add crew size to data
    eva_data = add_crew_size_column(eva_data)

    # Convert and export data to CSV file
    write_dataframe_to_csv(eva_data, output_file)

    # Sort dataframe by date ready to be plotted (date values are on x-axis)
    eva_data.sort_values('date', inplace=True)

    # Plot cumulative time spent in space over years
    plot_cumulative_time_in_space(eva_data, graph_file)

    print("--END--")


def read_json_to_dataframe(input_file):
    """
    Read the data from a JSON file into a Pandas dataframe.
    Clean the data by removing any rows where the 'duration' value is missing.

    Args:
        input_file (file or str): The file object or path to the JSON file.

    Returns:
         eva_df (pd.DataFrame): The cleaned data as a dataframe structure
    """
    print(f'Reading JSON file {input_file}')
    # Read the data from a JSON file into a Pandas dataframe
    eva_df = pd.read_json(input_file, convert_dates=['date'], encoding='ascii')
    eva_df['eva'] = eva_df['eva'].astype(float)
    # Clean the data by removing any rows where duration is missing
    eva_df.dropna(axis=0, subset=['duration', 'date'], inplace=True)
    return eva_df


def write_dataframe_to_csv(df, output_file):
    """
    Write the dataframe to a CSV file.

    Args:
        df (pd.DataFrame): The input dataframe.
        output_file (file or str): The file object or path to the output CSV file.

    Returns:
        None
    """
    print(f'Saving to CSV file {output_file}')
    # Save dataframe to CSV file for later analysis
    df.to_csv(output_file, index=False, encoding='utf-8')


def plot_cumulative_time_in_space(df, graph_file):
    """
    Plot the cumulative time spent in space over years.

    Convert the duration column from strings to number of hours
    Calculate cumulative sum of durations
    Generate a plot of cumulative time spent in space over years and
    save it to the specified location

    Args:
        df (pd.DataFrame): The input dataframe.
        graph_file (file or str): The file object or path to the output graph file.

    Returns:
        None
    """
    print(f'Plotting cumulative spacewalk duration and saving to {graph_file}')
    df = add_duration_hours(df)
    df['cumulative_time'] = df['duration_hours'].cumsum()
    plt.plot(df['date'], df['cumulative_time'], 'ko-')
    plt.xlabel('Year')
    plt.ylabel('Total time spent in space to date (hours)')
    plt.tight_layout()
    plt.savefig(graph_file)
    plt.show()


def text_to_duration(duration):
    """
    Convert a text format duration "HH:MM" to duration in hours

    Args:
        duration (str): The text format duration

    Returns:
        duration_hours (float): The duration in hours
    """
    hours, minutes = duration.split(":")
    duration_hours = int(hours) + int(minutes)/60
    return duration_hours


def add_duration_hours(df):
    """
    Add duration in hours (duration_hours) variable to the dataset

    Args:
        df (pd.DataFrame): The input dataframe.

    Returns:
        df_copy (pd.DataFrame): A copy of the dataframe df with the new crew_size variable added
    """
    df_copy = df.copy()
    df_copy["duration_hours"] = df_copy["duration"].apply(
        text_to_duration
    )
    return df_copy


def calculate_crew_size(crew):
    """
    Calculate the size of the crew for a single crew entry

    Args:
        crew (str): The text entry in the crew column containing a list of crew member names

    Returns:
        (int): The crew size
    """
    if crew.split() == []:
        return None
    else:
        return len(re.split(r';', crew))-1

def add_crew_size_column(df):
    """
    Add crew_size column to the dataset containing the value of the crew size

    Args:
        df (pd.DataFrame): The input data frame.

    Returns:
        df_copy (pd.DataFrame): A copy of the dataframe df with the new crew_size variable added
    """
    print('Adding crew size variable (crew_size) to dataset')
    df_copy = df.copy()
    df_copy["crew_size"] = df_copy["crew"].apply(
        calculate_crew_size
    )
    return df_copy


if __name__ == "__main__":

    if len(sys.argv) < 3:
        input_file = 'data/eva-data.json'
        output_file = 'results/eva-data.csv'
        print(f'Using default input and output filenames')
    else:
        input_file = sys.argv[1]
        output_file = sys.argv[2]
        print('Using custom input and output filenames')

    graph_file = 'results/cumulative_eva_graph.png'

    main(input_file, output_file, graph_file)

Test script test_eva_analysis.py:

from eva_data_analyis import text_to_duration, calculate_crew_size
import pytest

def test_text_to_duration_integer():
    """
    Test that text_to_duration returns expected values for typical
    durations with a zero minutes component
    """
    assert text_to_duration("10:00") == 10

def test_text_to_duration_float():
    """
    Test that text_to_duration returns expected ground truth values for
    typical durations with non-zero minutes components
    """
    assert text_to_duration("10:20") == pytest.approx(10.333333) 


@pytest.mark.parametrize("input_value, expected_result", [
    ("Valentina Tereshkova;", 1),
    ("Judith Smith; Sally Rider;", 2),
    ("", None)
])
def test_calculate_crew_size(input_value,expected_result):
    """
    Test that calculate_crew_size returns expected size for 
    typical crew values
    """
    actual_result = calculate_crew_size(input_value)
    assert actual_result == expected_result

                         

</details>

## Lesson 8: Adding new function

Add this new function to your code:

    
def summary_duration_by_astronaut(df):
    """
    Summarise the duration data by each astronaut and saves resulting table to a CSV file

    Args: 
        df (pd.DataFrame): Input dataframe to be summarised

    
    Returns:
        sum_by_astro (pd.DataFrame): Data frame with a row for each astronaut and a summarised column 
    """
    print(f'Calculating summary of total EVA time by astronaut')
    subset = df.loc[:,['crew', 'duration']] # subset to work with only relevant columns
    subset = add_duration_hours(subset) # need duration_hours for easier calcs
    subset = subset.drop('duration', axis=1) # dropping the extra 'duration' column as it contains string values not suitable for calulations
    subset = subset.groupby('crew').sum() 
    return subset
    

**Full code after adding new function:**
<details>

    
import matplotlib.pyplot as plt
import pandas as pd
import sys
import re


def main(input_file, output_file, duration_by_astronaut_output_file, graph_file):
    print("--START--")

    # Read the data from JSON file
    eva_data = read_json_to_dataframe(input_file)

    # Convert and export data to CSV file
    write_dataframe_to_csv(eva_data, output_file)

    # Calculate summary table for total EVA per astronaut
    duration_by_astronaut_df = summary_duration_by_astronaut(eva_data)
    # Save summary duration data by each astronaut to CSV file
    write_dataframe_to_csv(duration_by_astronaut_df, duration_by_astronaut_output_file)

    # Sort dataframe by date ready to be plotted (date values are on x-axis)
    eva_data.sort_values('date', inplace=True)

    # Plot cumulative time spent in space over years
    plot_cumulative_time_in_space(eva_data, graph_file)

    print("--END--")


def read_json_to_dataframe(input_file):
    """
    Read the data from a JSON file into a Pandas dataframe.
    Clean the data by removing any rows where the 'duration' value is missing.

    Args:
        input_file (file or str): The file object or path to the JSON file.

    Returns:
         eva_df (pd.DataFrame): The cleaned data as a dataframe structure
    """
    print(f'Reading JSON file {input_file}')
    # Read the data from a JSON file into a Pandas dataframe
    eva_df = pd.read_json(input_file, convert_dates=['date'], encoding='ascii')
    eva_df['eva'] = eva_df['eva'].astype(float)
    # Clean the data by removing any rows where duration is missing
    eva_df.dropna(axis=0, subset=['duration', 'date'], inplace=True)
    return eva_df


def write_dataframe_to_csv(df, output_file):
    """
    Write the dataframe to a CSV file.

    Args:
        df (pd.DataFrame): The input dataframe.
        output_file (file or str): The file object or path to the output CSV file.

    Returns:
        None
    """
    print(f'Saving to CSV file {output_file}')
    # Save dataframe to CSV file for later analysis
    df.to_csv(output_file, index=False, encoding='utf-8')


def plot_cumulative_time_in_space(df, graph_file):
    """
    Plot the cumulative time spent in space over years.

    Convert the duration column from strings to number of hours
    Calculate cumulative sum of durations
    Generate a plot of cumulative time spent in space over years and
    save it to the specified location

    Args:
        df (pd.DataFrame): The input dataframe.
        graph_file (file or str): The file object or path to the output graph file.

    Returns:
        None
    """
    print(f'Plotting cumulative spacewalk duration and saving to {graph_file}')
    df = add_duration_hours(df)
    df['cumulative_time'] = df['duration_hours'].cumsum()
    plt.plot(df['date'], df['cumulative_time'], 'ko-')
    plt.xlabel('Year')
    plt.ylabel('Total time spent in space to date (hours)')
    plt.tight_layout()
    plt.savefig(graph_file)
    plt.show()


def text_to_duration(duration):
    """
    Convert a text format duration "HH:MM" to duration in hours

    Args:
        duration (str): The text format duration

    Returns:
        duration_hours (float): The duration in hours
    """
    hours, minutes = duration.split(":")
    duration_hours = int(hours) + int(minutes)/60
    return duration_hours


def add_duration_hours(df):
    """
    Add duration in hours (duration_hours) variable to the dataset

    Args:
        df (pd.DataFrame): The input dataframe.

    Returns:
        df_copy (pd.DataFrame): A copy of df with the new duration_hours variable added
    """
    df_copy = df.copy()
    df_copy["duration_hours"] = df_copy["duration"].apply(
        text_to_duration
    )
    return df_copy


def calculate_crew_size(crew):
    """
    Calculate the size of the crew for a single crew entry

    Args:
        crew (str): The text entry in the crew column containing a list of crew member names

    Returns:
        (int): The crew size
    """
    if crew.split() == []:
        return None
    else:
        return len(re.split(r';', crew))-1

def add_crew_size_column(df):
    """
    Add crew_size column to the dataset containing the value of the crew size

    Args:
        df (pd.DataFrame): The input data frame.

    Returns:
        df_copy (pd.DataFrame): A copy of the dataframe df with the new crew_size variable added
    """
    print('Adding crew size variable (crew_size) to dataset')
    df_copy = df.copy()
    df_copy["crew_size"] = df_copy["crew"].apply(
        calculate_crew_size
    )
    return df_copy


def summary_duration_by_astronaut(df):
    """
    Summarise the duration data by each astronaut and saves resulting table to a CSV file

    Args: 
        df (pd.DataFrame): Input dataframe to be summarised

    
    Returns:
        sum_by_astro (pd.DataFrame): Data frame with a row for each astronaut and a summarised column 
    """
    print(f'Calculating summary of total EVA time by astronaut')
    subset = df.loc[:,['crew', 'duration']] # subset to work with only relevant columns
    subset = add_duration_hours(subset) # need duration_hours for easier calcs
    subset = subset.drop('duration', axis=1) # dropping the extra 'duration' column as it contains string values not suitable for calulations
    subset = subset.groupby('crew').sum() 
    return subset


if __name__ == "__main__":

    if len(sys.argv) < 3:
        input_file = 'data/eva-data.json'
        output_file = 'results/eva-data.csv'
        print(f'Using default input and output filenames')
    else:
        input_file = sys.argv[1]
        output_file = sys.argv[2]
        print('Using custom input and output filenames')

    graph_file = 'results/cumulative_eva_graph.png'
    duration_by_astronaut_output_file = 'results/duration_by_astronaut.csv'

    main(input_file, output_file, duration_by_astronaut_output_file, graph_file)
                         

</details>