semvec/pypi-packages huggingface dataset:
free-robux-codes 2.0.5
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Requires Python: >=3.6, <4
Keywords: sample,setuptools,development

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grafatko 0.2.6
An app for creating and visualizing graphs and graph-related algorithms.
Requires Python: >=3.7.1
Keywords: graph graphs pyqt5 algorithm algorithms

# Grafátko [gɾafɑtko]
An app for creating and visualizing graphs and graph-related algorithms.

![](https://raw.githubusercontent.com/xiaoxiae/Grafatko/master/example.png)

## Running Grafátko
First, install the app by running `pip install grafatko`.
Then you can simply run the `grafatko` command from a terminal of your choice.

---

Another way to run Grafátko (albeit a little more difficult) is to first clone the repository, go to the base directory and run the project as a Python module:

```console
xiaoxiae@thinkpad ~> git clone https://github.com/xiaoxiae/Grafatko.git
xiaoxiae@thinkpad ~> cd Grafatko/
xiaoxiae@thinkpad ~> pip install -r requirements.txt
xiaoxiae@thinkpad ~> python -m grafatko.__init__
```

## Controls

### Mouse
- **right button** creates new nodes/vertices
	- **+ shift** doesn't deselect the currently selected nodes:
- **left button** selects and drags nodes/vertices around the screen
	- if held, drags nodes along with the mouse
	- **+ shift** toggles selection on nodes/vertices
		- if held, moves the entire graph component
- **middle button** pans
- **mouse wheel** zooms
	- **+ shift** rotates nodes about the currently selected ones

### Keyboard
- **r** toggles 'tree mode' for smoother visualisation of trees
	- essentially applies special forces on the nodes to group them by distance from the currently selected node
	- only works if a single node is selected
- **space** centers on the currently selected nodes
- **delete** deletes the currently selected items

### Other
- `complement` -- makes the graph [complemented](https://en.wikipedia.org/wiki/Complement_graph)
- `reorinet` -- changes the directions of the vertices of the graph

## Visualizing algorithms
The app allows for visualising custom algorithms on the currently edited graph.
Examples of valid programs can be found in the `examples/` folder.

After creating a graph, you can go to `Algorithms -> Run` and select the one you want to run on the graph.
The program then calls a function with the same name as the file, the only parameter being the `DrawableGraph` object to run the algorithm on.

When the animation is running, you can use the `pause`, `resume` and `clear` to control it.

## Importing/exporting graphs
The app uses a simple text-based format for importing and exporting graph.
Each line of the file the format `n1 [direction] n2 [weight]`, where:
- `n1` and `n2` are labels of the nodes forming the vertex, containing no whitespace characters
- `[direction]` is used in directed graphs and is either `->` or `<-`
- `[weight]` is used in weighted graphs, denotes the weight of the vertex (either int or float)

Examples of valid graphs can be found in the `examples/` folder.
ndmath 0.0.5
N-dimensional complex step and finite step differentiation, and Newton's method.
Requires Python: >=3.6

# ndmath

ndmath is a Python library for N-dimensional complex step and finite step differentiation, and Newton's method.

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install ndmath.

```bash
pip install ndmath
```

## Examples

Calculating the Jacobian at a point:
```python
import ndmath

ndmath.complexGrad(lambda x : x[0]**2+x[1], [1,2]) # returns array([2., 1.])
ndmath.finiteGrad(lambda x : x[0]**2+x[1], [1,2], 10**-7) # returns array([2.0000001, 1.])
```
Using the root-finding Newton's method:
```python
def func(x):
    return [(x[1]-3)**2+x[0]-5, 2*x[1]+x[0]**3]

def fprime(x):
    return ndmath.complexGrad(func, x)

x0 = [0,0] #initial estimate

ndmath.nDimNewton(func, x0, fprime) # returns array([-1.02890183,  0.54461778])
```

## Dependencies

* [NumPy](https://numpy.org/)

## Contributing
Pull requests are welcome. Feel free to open an issue first to discuss what you would like to change.

## License
[MIT](https://choosealicense.com/licenses/mit/)

## References
* Martins, J. R. R. A., Sturdza, P., and Alonso, J. J., 2003, "The Complex-Step Derivative Approximation," ACM Trans. Math. Software, 29(3), pp. 245–262.