semvec/pypi-packages huggingface dataset:
reportracingtable 1.0.0
Generate Monaco Racing report
Requires Python: >=3.8

# reportracingtable Package

#### - package for build Monaco Racing report
#### - use `report` command from command line

## Installation
### Using PIP
```bash
pip install reportracingtable
```

## Usage in scripts
`script_name.py`
```python
from reportracingtable import main

if __name__ == '__main__':
    data = main()
```

## Usage from command line
```bash
$ python script_name.py --folder "/path/to/file"
$ python script_name.py --driver "Driver Name"
$ python script_name.py --decs True
```

## Usage from command line without imports
```bash
$ report --folder "/path/to/file"
$ report --driver "Driver Name"
$ report --string --decs True
```

## License
`reportracingtable` is licensed (MIT license).
ProstateNetLoaders 0.0.1
Prostate Net loading functions

# Prostate Net Loader

Prostate Net Loader contains functions to assist the Data Mining and loading process of Patients originated from the ProCAncer-AI Horizon's 2020 project.  
The package is under construction for the time being therefore any suggestion would be appreciated.

## Installation

Install the project via pip or pull the repo

```bash
pip install PROSTATENETLOADER == 0.0.1
```
    
## Usage/Examples Series Parser tool
Detailed explanation of the series parser tool is presented at the ParquetParser_Examples.ipynb
## Usage/Examples PROSTATENETLOADER Module

Examples could be found in Module_Examples.ipynb regarding the package. An example for a single patient is presented below

### Single Patient
a) Import Libraries
```python
import pandas as pd
import SimpleITK as sitk
import ProstateNetLoaders
```
b) Set the patient folder path and the csv extracted by the sequence selector tool
```python
pth = "PCa-..."
metadata= pd.read_csv("results.csv", 
                names=["patient_id", "study_uid", 
                "series_uid", "series_type", "series_type_heuristics"])
```
c) Execute loaders and pick orientation ("AX","COR", "SAG") and sequence ("T2","ADC","DWI") and whether to be AI sequence parser (Heuristics = False) or Heuristics = True
```python
a = ProstateNetLoaders.ExecuteLoader.Execute(pth, metadata,  Heuristics = True) 
a.LoadArrays(orientation="AX", seq="T2")
```
d) Get dictionaries where keys are the series names, values are the Image numpy arrays
```python
pat,ann = a.GetItems() 
```

### Batch Loading 

The structure of the folders should be like this

```python
pth_batch = "Patients"
patients = {}
Sequence = "T2" # pick you sequence between "T2", "ADC", "DWI"
T2_absence = [] # Store the names of the failed patients
for patient in os.listdir(pth_batch):
    pat = os.path.join(pth_batch,patient)
    a = ProstateNetLoaders.ExecuteLoader.Execute(pat, metadata)
    try:
        a.LoadArrays(orientation="AX", seq=Sequence)
        pat,ann = a.GetItems()
        patients.update({patient:{Sequence:np.array(list(pat.values())[0]),"Lesion": np.array(list(ann.values())[0])}})
    except: 
        T2_absence.append(patient)
        continue
```
## Authors

- [Dimitris Zaridis](dimzaridis@gmail.com)
- [Harry Kalantzopoulos](xkalantzopoulos@gmail.com)
- [Eugenia Mylona](mylona.eugenia@gmail.com)
- [Nikolaos Tachos](ntachos@gmail.com)
- José Guilherme Almeida


## Badges

Add badges from somewhere like: [shields.io](https://shields.io/)

[![MIT License](https://img.shields.io/badge/License-MIT-green.svg)](https://choosealicense.com/licenses/mit/)
![Python](https://img.shields.io/badge/Python-3.6-green)


## License

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


![Logo](https://www.procancer-i.eu/wp-content/uploads/2020/07/logo.png)
ProstateNetLoader 0.0.1
Prostate Net loading functions

# Prostate Net Loader

Prostate Net Loader contains functions to assist the Data Mining and loading process of Patients originated from the ProCAncer-AI Horizon's 2020 project.  
The package is under construction for the time being therefore any suggestion would be appreciated.

## Installation

Install the project via pip or pull the repo

```bash
pip install PROSTATENETLOADER == 0.0.1
```
    
## Usage/Examples Series Parser tool
Detailed explanation of the series parser tool is presented at the ParquetParser_Examples.ipynb
## Usage/Examples PROSTATENETLOADER Module

Examples could be found in Module_Examples.ipynb regarding the package. An example for a single patient is presented below

### Single Patient
a) Import Libraries
```python
import pandas as pd
import SimpleITK as sitk
import ProstateNetLoaders
```
b) Set the patient folder path and the csv extracted by the sequence selector tool
```python
pth = "PCa-..."
metadata= pd.read_csv("results.csv", 
                names=["patient_id", "study_uid", 
                "series_uid", "series_type", "series_type_heuristics"])
```
c) Execute loaders and pick orientation ("AX","COR", "SAG") and sequence ("T2","ADC","DWI") and whether to be AI sequence parser (Heuristics = False) or Heuristics = True
```python
a = ProstateNetLoaders.ExecuteLoader.Execute(pth, metadata,  Heuristics = True) 
a.LoadArrays(orientation="AX", seq="T2")
```
d) Get dictionaries where keys are the series names, values are the Image numpy arrays
```python
pat,ann = a.GetItems() 
```

### Batch Loading 

The structure of the folders should be like this

```python
pth_batch = "Patients"
patients = {}
Sequence = "T2" # pick you sequence between "T2", "ADC", "DWI"
T2_absence = [] # Store the names of the failed patients
for patient in os.listdir(pth_batch):
    pat = os.path.join(pth_batch,patient)
    a = ProstateNetLoaders.ExecuteLoader.Execute(pat, metadata)
    try:
        a.LoadArrays(orientation="AX", seq=Sequence)
        pat,ann = a.GetItems()
        patients.update({patient:{Sequence:np.array(list(pat.values())[0]),"Lesion": np.array(list(ann.values())[0])}})
    except: 
        T2_absence.append(patient)
        continue
```
## Authors

- [Dimitris Zaridis](dimzaridis@gmail.com)
- [Harry Kalantzopoulos](xkalantzopoulos@gmail.com)
- [Eugenia Mylona](mylona.eugenia@gmail.com)
- [Nikolaos Tachos](ntachos@gmail.com)
- José Guilherme Almeida


## Badges

Add badges from somewhere like: [shields.io](https://shields.io/)

[![MIT License](https://img.shields.io/badge/License-MIT-green.svg)](https://choosealicense.com/licenses/mit/)
![Python](https://img.shields.io/badge/Python-3.6-green)


## License

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


![Logo](https://www.procancer-i.eu/wp-content/uploads/2020/07/logo.png)
PROSTATENETLOADER 0.0.1
Prostate Net loading functions

# Prostate Net Loader

Prostate Net Loader contains functions to assist the Data Mining and loading process of Patients originated from the ProCAncer-AI Horizon's 2020 project.  
The package is under construction for the time being therefore any suggestion would be appreciated.

## Installation

Install the project via pip or pull the repo

```bash
pip install PROSTATENETLOADER == 0.0.1
```
    
## Usage/Examples Series Parser tool
Detailed explanation of the series parser tool is presented at the ParquetParser_Examples.ipynb
## Usage/Examples PROSTATENETLOADER Module

Examples could be found in Module_Examples.ipynb regarding the package. An example for a single patient is presented below

### Single Patient
a) Import Libraries
```python
import pandas as pd
import SimpleITK as sitk
import ProstateNetLoaders
```
b) Set the patient folder path and the csv extracted by the sequence selector tool
```python
pth = "PCa-..."
metadata= pd.read_csv("results.csv", 
                names=["patient_id", "study_uid", 
                "series_uid", "series_type", "series_type_heuristics"])
```
c) Execute loaders and pick orientation ("AX","COR", "SAG") and sequence ("T2","ADC","DWI") and whether to be AI sequence parser (Heuristics = False) or Heuristics = True
```python
a = ProstateNetLoaders.ExecuteLoader.Execute(pth, metadata,  Heuristics = True) 
a.LoadArrays(orientation="AX", seq="T2")
```
d) Get dictionaries where keys are the series names, values are the Image numpy arrays
```python
pat,ann = a.GetItems() 
```

### Batch Loading 

The structure of the folders should be like this

```python
pth_batch = "Patients"
patients = {}
Sequence = "T2" # pick you sequence between "T2", "ADC", "DWI"
T2_absence = [] # Store the names of the failed patients
for patient in os.listdir(pth_batch):
    pat = os.path.join(pth_batch,patient)
    a = ProstateNetLoaders.ExecuteLoader.Execute(pat, metadata)
    try:
        a.LoadArrays(orientation="AX", seq=Sequence)
        pat,ann = a.GetItems()
        patients.update({patient:{Sequence:np.array(list(pat.values())[0]),"Lesion": np.array(list(ann.values())[0])}})
    except: 
        T2_absence.append(patient)
        continue
```
## Authors

- [Dimitris Zaridis](dimzaridis@gmail.com)
- [Harry Kalantzopoulos](xkalantzopoulos@gmail.com)
- [Eugenia Mylona](mylona.eugenia@gmail.com)
- [Nikolaos Tachos](ntachos@gmail.com)
- José Guilherme Almeida


## Badges

Add badges from somewhere like: [shields.io](https://shields.io/)

[![MIT License](https://img.shields.io/badge/License-MIT-green.svg)](https://choosealicense.com/licenses/mit/)
![Python](https://img.shields.io/badge/Python-3.6-green)


## License

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


![Logo](https://www.procancer-i.eu/wp-content/uploads/2020/07/logo.png)
monacoreportwoo 1.7
Generate Monaco Racing report
Requires Python: >=3.8

# Racingreport Package

#### - Package for build Monaco Racing report
#### - `report` for use from command line

## Installation
### Using PIP
```bash
pip install monacoreportwoo
```

## Usage in scripts
`script_name.py`
```python
from monacoreport import main

if __name__ == '__main__':
    data = main()
```

## Usage from command line
```bash
$ python script_name.py --folder "/path/to/file"
$ python script_name.py --driver "Driver Name"
$ python script_name.py --decs True
```

## Usage from command line without imports
```bash
$ report --folder "/path/to/file"
$ report --driver "Driver Name"
$ report --string --decs True
```

## License
`racingreport` is licensed (MIT license).