semvec/pypi-packages huggingface dataset:
finetune-am 0.0.1
Additive Manufacturing related tools
Requires Python: >=3.10

# finetune-am
Additive Manufacturing related software modules

## Getting Started
1. Installation
```bash
pip install am
```

2. Create a new `Workspace`:
```bash
am create_workspace test
```

3. Navigate to workspace directory and `manage.py` file
```
python manage.py
```

4. Drop `.gcode` file in `parts` folder within workspace directory.

5. Select and parse `.gcode` file within `parts` folder with.
```
python manage.py parse_gcode
```

6. Create solver.
```
python manage.py create_solver model="eagar-tsai" name="eagar-tsai" device="cuda:0"
```

7. Create simulation .
```
python manage.py create_simulation
```

8. Run simulation .
```
python manage.py run_simulation layer_index="99"
```


## References

```bibtex
@article{
	title = {Thermal control of laser powder bed fusion using deep reinforcement learning},
	volume = {46},
	issn = {2214-8604},
	url = {https://www.sciencedirect.com/science/article/pii/S2214860421001986},
	doi = {10.1016/j.addma.2021.102033},
	journal = {Additive Manufacturing},
	author = {Ogoke, Francis and Farimani, Amir Barati},
	month = oct,
	year = {2021},
	keywords = {Additive Manufacturing, Deep Reinforcement Learning, Powder Bed Fusion},
	pages = {102033},
}
```

```bibtex
@phdthesis{
	address = {United States -- California},
	type = {Ph.{D}.},
	title = {Physics-{Based} {Surrogate} {Modeling} of {Laser} {Powder} {Bed} {Fusion} {Additive} {Manufacturing}},
	copyright = {Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.},
	url = {https://www.proquest.com/docview/2503475225/abstract/71F47FA688874C02PQ/1},
	language = {English},
	school = {University of California, Davis},
	author = {Wolfer, Alexander James},
	year = {2020},
	note = {ISBN: 9798582546023},
	keywords = {Additive Manufacturing, Bayesian statistics, Mechanical engineering, Powder bed fusion, Surrogate model, Uncertainty quantification},
}
```

```bibtex
@article{
	title = {Fast solution strategy for transient heat conduction for arbitrary scan paths in additive manufacturing},
	volume = {30},
	issn = {2214-8604},
	url = {https://www.sciencedirect.com/science/article/pii/S2214860419303446},
	doi = {10.1016/j.addma.2019.100898},
	journal = {Additive Manufacturing},
	author = {Wolfer, Alexander J. and Aires, Jeremy and Wheeler, Kevin and Delplanque, Jean-Pierre and Rubenchik, Alexander and Anderson, Andy and Khairallah, Saad},
	month = dec,
	year = {2019},
	keywords = {Additive manufacturing, Gaussian convolution, Heat conduction, Powder bed fusion, Semi-analytical model},
	pages = {100898},
}
```

```bibtex
@article{
	title = {The {Theory} of {Moving} {Sources} of {Heat} and {Its} {Application} to {Metal} {Treatments}},
	volume = {68},
	issn = {0097-6822},
	url = {https://doi.org/10.1115/1.4018624},
	doi = {10.1115/1.4018624},
	number = {8},
	journal = {Transactions of the American Society of Mechanical Engineers},
	author = {Rosenthal, D.},
	month = dec,
	year = {2022},
	pages = {849--865},
}
```
gridient 0.1.3
A Python library for writing calculations to Excel while preserving formulas.
Requires Python: >=3.7

# Gridient

A Python library designed to simplify writing complex calculations and data structures to Excel files while preserving the underlying formulas. Define your logic in Python using familiar operators and let Gridient translate it into live Excel formulas.

## Features

- Define calculations using Python operators (+, -, *, /).
- Write Pandas DataFrames and Series.
- Create parameter tables.
- Apply cell styling (bold, italics, colors, etc.).
- Use custom number formatting.
- Organize output across multiple sheets using a layout system.
- Best-effort column auto-width adjustment.

## Installation

```bash
pip install -r requirements.txt
pip install .
```

## Basic Usage (Conceptual)

```python
import gridient as gr
import pandas as pd

# Define values and parameters
initial_investment = gr.ExcelValue("Initial Investment", 1000000, format="$#,##0")
discount_rate = gr.ExcelValue("Discount Rate", 0.05, format="0.00%")
params = gr.ExcelParameterTable("Parameters", [initial_investment, discount_rate])

# Perform calculations (these become Excel formulas)
revenue = gr.ExcelSeries.from_pandas(pd.Series([100, 150, 200]), name="Revenue")
profit = revenue * 0.2
profit.name = "Profit"
profit.format = "$#,##0"

# Create layout
workbook = gr.ExcelWorkbook("report.xlsx")
layout = gr.ExcelLayout(workbook)
sheet1 = gr.ExcelSheetLayout("Dashboard")

# Add components to layout
sheet1.add(params, row=1, col=1)
sheet1.add(profit, row=5, col=1)

# Add sheet to workbook layout and write
layout.add_sheet(sheet1)
layout.write() 
``` 

## TODO

- Parameters should have an option to provide a name. Such parameter shuld than be referenced by name in formulas. There should be a unique-ness check.
- Check rules for parameter names.
- Add themes: The user would pick a theme and it would style tables and values accordingly.
- Support for directly touching the xlsxwriter API.
- Support for hooks. Such that user can override internal data structures at any step of the process.
- Bug: There is an extra parenthesis in for example `=IF((B1>0),1,0)` which should not be there.