Make time of moon measurement clearer

[iansaxon]

Issue: the original time of measurement was implicitly 00:00:00 (zero hundred hours, or midnight), but this was not explicitly stated in the readme, moon_data_ephem.py script, or outputs.

Proposed resolutions in this pull request:

• Make time of measurement explicit in the readme, script, and outputs. Place user settings at the top of the script to facilitate easy changes, with a practical example given.
• Name the CSV with the time (if 00:00:00 chosen then the file gets the special name 'AtZeroHundredHours', otherwise the 24 hour UTC time format is used).

Known tradeoffs:

• The time column is inefficient in terms of storage space, but gives clarity to the outputs and will facilitate creating multiple outputs and later concatenating them if the user wishes.
• Arguably the timestamp in the filename should appear at the end, to follow a general to specific naming scheme, which will be easier to work with if a user has multiple CSV outputs. However, this script includes the time statement in its original location for consistency.

Checks: I've included an Excel check zero file to compare the previous output to the new output, which is identical except for the insertion of a new time column.

[gemini-code-assist]

Code Review

This pull request introduces user-configurable measurement times and date ranges to the lunar data calculation script, updating the README documentation accordingly. Feedback on the changes suggests avoiding the introduction of pandas as a heavy dependency by reverting to Python's built-in csv and datetime modules. Additionally, the reviewer noted that the download link in the README needs to be updated to match the new default filename, and pointed out a syntax error in the configuration examples within both the README and the script comments.

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[gemini-code-assist]

Introducing pandas as a dependency solely for generating a date range and writing a CSV file adds significant overhead and a heavy external dependency. The script can easily achieve the same functionality using Python's built-in csv module, which was used in the previous version.

Suggested change

	import pandas as pd
	import csv

[gemini-code-assist]

Instead of using pandas to generate the date range and write the CSV, we can use Python's built-in datetime and csv modules. This avoids loading all rows into memory at once and removes the heavy pandas dependency entirely.

current_decade = None
total_rows = 0
with open(filename, 'w', newline='') as csvfile:
    fieldnames = ['Date', 'Time_UTC', 'Area', 'Category', 'Phase']
    writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
    writer.writeheader()

    for n in range((end_date - start_date).days + 1):
        date = start_date + datetime.timedelta(days=n)
        measurement_datetime = datetime.datetime.combine(date, measurement_time_utc)
        decade = (measurement_datetime.year // 10) * 10
        if decade != current_decade:
            current_decade = decade
            print(f"Processing decade {decade}s")

        ephem_date = ephem.Date(measurement_datetime)
        prev_new = ephem.previous_new_moon(measurement_datetime)
        next_new = ephem.next_new_moon(measurement_datetime)
        moon_phase = (ephem_date - prev_new) / (next_new - prev_new)

        writer.writerow({
            'Date': date.strftime('%Y-%m-%d'),
            'Time_UTC': measurement_datetime.strftime('%H:%M:%S'),
            'Area': round(illuminated_area(moon_phase), 4),
            'Category': calculate_moon_phase_category(moon_phase),
            'Phase': round(moon_phase, 4),
        })
        total_rows += 1

print(
    f"Run completed successfully for {filename}: "
    f"start={start_datetime:%Y-%m-%d %H:%M:%S} UTC, "
    f"end={end_datetime:%Y-%m-%d %H:%M:%S} UTC, "
    f"rows={total_rows}"
)

[iansaxon]

@isaacbernat please let me know if you're put off by the pandas approach. I was hoping to get a speed boost by getting all the data from ephem, then writing as a single dataframe to CSV. It turns out though that you have to call ephem for one date at a time, so the time savings are limited only to the writing stage.

If you prefer to eliminate pandas as a dependency, to keep this repo as lightweight as possible, no problem from my perspective.