semvec/pypi-packages huggingface dataset:
pythondata-cpu-cv32e40p 1.0.1.post1909
Python module containing system_verilog files for CV32E40P cpu.
Requires Python: >=3.5

# pythondata-cpu-cv32e40p

Non-Python  files needed for the cpu cv32e40p packaged
into a Python module so they can be used with Python libraries and tools.

This is useful for usage with tools like
[LiteX](https://github.com/enjoy-digital/litex.git).

The data files can be found under the Python module `pythondata_cpu_cv32e40p`. The
`pythondata_cpu_cv32e40p.data_location` value can be used to find the files on the file
system.

Example of getting the data file directly;
```python
import pythondata_cpu_cv32e40p

my_data_file = "abc.txt"

with open(os.path.join(pythondata_cpu_cv32e40p.data_location, my_data_file)) as f:
    print(f.read())
```

Example of getting the data file using `litex.data.find` API;
```python
from pythondata_cpu_cv32e40p import data_file

my_data_file = "abc.txt"

with open(data_file(my_data_file)) as f:
    print(f.read())
```


The data files come from https://github.com/openhwgroup/cv32e40p
and are imported using `git subtrees` to the directory
[pythondata_cpu_cv32e40p/system_verilog](pythondata_cpu_cv32e40p/system_verilog).



# Installing

## Directly from git repository

### Manually

You can install the package manually, however this is **not** recommended.

```
git clone https://github.com/litex-hub/pythondata-cpu-cv32e40p.git
cd pythondata-cpu-cv32e40p
sudo python setup.py install
```

### Using [pip](https://pip.pypa.io/) with git repository

You can use [pip](https://pip.pypa.io/) to install the data package directly
from github using;

```
pip install --user git+https://github.com/litex-hub/pythondata-cpu-cv32e40p.git
```

If you want to install for the whole system rather than just the current user,
you need to remove the `--user` argument and run as sudo like so;

```
sudo pip install git+https://github.com/litex-hub/pythondata-cpu-cv32e40p.git
```

You can install a specific revision of the repository using;
```
pip install --user git+https://github.com/litex-hub/pythondata-cpu-cv32e40p.git@<tag>
pip install --user git+https://github.com/litex-hub/pythondata-cpu-cv32e40p.git@<branch>
pip install --user git+https://github.com/litex-hub/pythondata-cpu-cv32e40p.git@<hash>
```

### With `requirements.txt` file

Add to your Python `requirements.txt` file using;
```
-e git+https://github.com/litex-hub/pythondata-cpu-cv32e40p.git
```

To use a specific revision of the repository, use the following;
```
-e https://github.com/litex-hub/pythondata-cpu-cv32e40p.git@<hash>
```

## Via [PyPi](https://pypi.org/project/pythondata-cpu-cv32e40p/)

### Using [pip](https://pip.pypa.io/)

```
pip install --user pythondata-cpu-cv32e40p
```

### With `requirements.txt` file

Add to your Python `requirements.txt` file using;
```
pythondata-cpu-cv32e40p
```

# Issues and Fixes

This package is autogenerated from the sources in
<https://github.com/openhwgroup/cv32e40p>
using the [pythondata-auto](https://github.com/litex-hub/pythondata-auto)
toolset. Pull requests and issues on this pythondata repo may not be monitored.
pythondata-cpu-mor1kx 5.2.post151
Python module containing verilog files for OpenRISC1000 cpu.
Requires Python: >=3.5

# pythondata-cpu-mor1kx

Non-Python  files needed for the cpu mor1kx packaged
into a Python module so they can be used with Python libraries and tools.

This is useful for usage with tools like
[LiteX](https://github.com/enjoy-digital/litex.git).

The data files can be found under the Python module `pythondata_cpu_mor1kx`. The
`pythondata_cpu_mor1kx.data_location` value can be used to find the files on the file
system.

Example of getting the data file directly;
```python
import pythondata_cpu_mor1kx

my_data_file = "abc.txt"

with open(os.path.join(pythondata_cpu_mor1kx.data_location, my_data_file)) as f:
    print(f.read())
```

Example of getting the data file using `litex.data.find` API;
```python
from pythondata_cpu_mor1kx import data_file

my_data_file = "abc.txt"

with open(data_file(my_data_file)) as f:
    print(f.read())
```


The data files come from https://github.com/openrisc/mor1kx.git
and are imported using `git subtrees` to the directory
[pythondata_cpu_mor1kx/verilog](pythondata_cpu_mor1kx/verilog).



# Installing

## Directly from git repository

### Manually

You can install the package manually, however this is **not** recommended.

```
git clone https://github.com/litex-hub/pythondata-cpu-mor1kx.git
cd pythondata-cpu-mor1kx
sudo python setup.py install
```

### Using [pip](https://pip.pypa.io/) with git repository

You can use [pip](https://pip.pypa.io/) to install the data package directly
from github using;

```
pip install --user git+https://github.com/litex-hub/pythondata-cpu-mor1kx.git
```

If you want to install for the whole system rather than just the current user,
you need to remove the `--user` argument and run as sudo like so;

```
sudo pip install git+https://github.com/litex-hub/pythondata-cpu-mor1kx.git
```

You can install a specific revision of the repository using;
```
pip install --user git+https://github.com/litex-hub/pythondata-cpu-mor1kx.git@<tag>
pip install --user git+https://github.com/litex-hub/pythondata-cpu-mor1kx.git@<branch>
pip install --user git+https://github.com/litex-hub/pythondata-cpu-mor1kx.git@<hash>
```

### With `requirements.txt` file

Add to your Python `requirements.txt` file using;
```
-e git+https://github.com/litex-hub/pythondata-cpu-mor1kx.git
```

To use a specific revision of the repository, use the following;
```
-e https://github.com/litex-hub/pythondata-cpu-mor1kx.git@<hash>
```

## Via [PyPi](https://pypi.org/project/pythondata-cpu-mor1kx/)

### Using [pip](https://pip.pypa.io/)

```
pip install --user pythondata-cpu-mor1kx
```

### With `requirements.txt` file

Add to your Python `requirements.txt` file using;
```
pythondata-cpu-mor1kx
```

# Issues and Fixes

This package is autogenerated from the sources in
<https://github.com/openrisc/mor1kx.git>
using the [pythondata-auto](https://github.com/litex-hub/pythondata-auto)
toolset. Pull requests and issues on this pythondata repo may not be monitored.
pythondata-cpu-picorv32 1.0.post218
Python module containing verilog files for PicoRV32 cpu.
Requires Python: >=3.5

# pythondata-cpu-picorv32

Non-Python  files needed for the cpu picorv32 packaged
into a Python module so they can be used with Python libraries and tools.

This is useful for usage with tools like
[LiteX](https://github.com/enjoy-digital/litex.git).

The data files can be found under the Python module `pythondata_cpu_picorv32`. The
`pythondata_cpu_picorv32.data_location` value can be used to find the files on the file
system.

Example of getting the data file directly;
```python
import pythondata_cpu_picorv32

my_data_file = "abc.txt"

with open(os.path.join(pythondata_cpu_picorv32.data_location, my_data_file)) as f:
    print(f.read())
```

Example of getting the data file using `litex.data.find` API;
```python
from pythondata_cpu_picorv32 import data_file

my_data_file = "abc.txt"

with open(data_file(my_data_file)) as f:
    print(f.read())
```


The data files come from https://github.com/YosysHQ/picorv32
and are imported using `git subtrees` to the directory
[pythondata_cpu_picorv32/verilog](pythondata_cpu_picorv32/verilog).



# Installing

## Directly from git repository

### Manually

You can install the package manually, however this is **not** recommended.

```
git clone https://github.com/litex-hub/pythondata-cpu-picorv32.git
cd pythondata-cpu-picorv32
sudo python setup.py install
```

### Using [pip](https://pip.pypa.io/) with git repository

You can use [pip](https://pip.pypa.io/) to install the data package directly
from github using;

```
pip install --user git+https://github.com/litex-hub/pythondata-cpu-picorv32.git
```

If you want to install for the whole system rather than just the current user,
you need to remove the `--user` argument and run as sudo like so;

```
sudo pip install git+https://github.com/litex-hub/pythondata-cpu-picorv32.git
```

You can install a specific revision of the repository using;
```
pip install --user git+https://github.com/litex-hub/pythondata-cpu-picorv32.git@<tag>
pip install --user git+https://github.com/litex-hub/pythondata-cpu-picorv32.git@<branch>
pip install --user git+https://github.com/litex-hub/pythondata-cpu-picorv32.git@<hash>
```

### With `requirements.txt` file

Add to your Python `requirements.txt` file using;
```
-e git+https://github.com/litex-hub/pythondata-cpu-picorv32.git
```

To use a specific revision of the repository, use the following;
```
-e https://github.com/litex-hub/pythondata-cpu-picorv32.git@<hash>
```

## Via [PyPi](https://pypi.org/project/pythondata-cpu-picorv32/)

### Using [pip](https://pip.pypa.io/)

```
pip install --user pythondata-cpu-picorv32
```

### With `requirements.txt` file

Add to your Python `requirements.txt` file using;
```
pythondata-cpu-picorv32
```

# Issues and Fixes

This package is autogenerated from the sources in
<https://github.com/YosysHQ/picorv32>
using the [pythondata-auto](https://github.com/litex-hub/pythondata-auto)
toolset. Pull requests and issues on this pythondata repo may not be monitored.
aiida-submission-controller 0.3.0
Utilities for controlling submission flow in AiiDA
Requires Python: >=3.9

# AiiDA submission controller

Some classes to help managing large number of submissions, while controlling the maximum number of submissions running at any given time.

This project is still in early phase and the API might change.

It includes an abstract base class that implements the main logic, a very simple example of an implementation
to compute a 12x12 addition table (in `examples/add_in_batches.py`), and a main script to run it (and get results and show them).

To use it, you are supposed to launch the script (e.g. in a `screen` terminal) with something like this:
```bash
cd examples
while true ; do verdi run add_in_batches.py ; sleep 5 ; done
```
where you can adapt the sleep time.
Typically, for real simulations, you might want something in the
range of 5-10 minutes, or anyway so that at every new run you have at least some new processes to submit,
but still less that the maximum number of available slots, to try to keep the 'queue' quite filled at any
given time.

There is also a second subclass that, rather than just creating new submissions from some extras, will use (input) nodes in another group as a reference for which calculations to run (e.g.: a group of crystal structures, representing the inputs to a set of workflows).