# Paper Per Year
A command-line tool to visualize an academic author's publications per year using data from Google Scholar.
## Note
Numbers are not everything and number of published papers is certainly no metric for academic success.
## Installation
You can run this using uvx without installation.
```bash
uvx paper-per-year [OPTIONS] AUTHOR_NAME
```
Alternatively, you can install it using pip.
```bash
[uv] pip install paper-per-year
```
## Usage
```bash
paper-per-year [OPTIONS] AUTHOR_NAME
```
For example:
```bash
# Save plot in current directory with default style
paper-per-year "Adam Dziedzic"
# Save plot in a specific directory with custom style
paper-per-year "Adam Dziedzic" -o ~/Documents/plots --style whitegrid --context poster
```
The command will:
1. Search for authors matching the provided name
2. Display a list of found authors with their affiliations
3. Prompt you to select the correct author
4. Generate a beautiful PDF plot showing the number of publications per year
5. Save the plot as `Author_Name.pdf` in the specified directory
6. Output year-count data to stdout for potential piping
### Options
- `-o, --output-dir`: Directory to save the output PDF (default: current directory)
- `--style`: Plot style (choices: darkgrid, whitegrid, dark, white, ticks; default: darkgrid)
- `--context`: Plot scaling context (choices: paper, notebook, talk, poster; default: talk)
### Plot Features
- Beautiful Seaborn-styled visualizations
- Publication counts displayed on top of each bar
- Automatically adjusted layout and spacing
- High-resolution output (300 DPI)
- Multiple style options for different use cases
### Output Format
The command outputs the year-count data to stdout in a tab-separated format:
```
YEAR COUNT
2020 5
2021 3
2022 7
...
```
This allows for easy piping to other commands, for example:
```bash
# Get total publication count
paper-per-year "Adam Dziedzic" | awk '{sum += $2} END {print sum}'
# Find the most productive year
paper-per-year "Adam Dziedzic" | sort -k2 -nr | head -n1
```
## Requirements
- Python 3.13 or higher
- scholarly
- numpy
- matplotlib
- seaborn
- click
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