semvec/pypi-packages huggingface dataset:
fertilizer 0.3.0
fertilizer helps your seeds grow
Requires Python: >=3.11

<p align="center"><img width="200" src="https://github.com/user-attachments/assets/65959d34-87f7-426a-abe3-9e5f9cd4e4fc"></p>
<h1 align="center">fertilizer (based on <a target="_blank" href="https://github.com/soranosita/crops">crops</a>)</h1>

<p align="center">fertilizer helps your seeds grow</p>

---

<p align="center">Read the <a target="_blank" href="https://github.com/moleculekayak/fertilizer/wiki">Almanac</a> for help</p>
pyparcels 1.4.2
Helper functions for simplifying parcel fabric workflows with the ArcGIS Python API
Requires Python: <4,>=3.6
Keywords: arcgis python api, parcels, development

# Parcel Fabric Python API Utils
## Helper functions for simplifying parcel fabric workflows with the ArcGIS Python API

**Install arcgis module**

`conda install -c esri arcgis`

**Install [pyparcels](https://pypi.org/project/pyparcels/)**

`pip install pyparcels`


### Utils
- Basic functions to write json to a file, get a server token and a simple timer decorator

### Features
#### features/feature_utils
- Functions for getting and querying FeatureLayer and FeatureLayerCollection objects. 
- Simplifies and shortens return values for basic feature layer info.

### Versioning
#### versioning/versioning_utils
- Functions for creating and deleting versions. 
- Start and stop edit sessions for branch versioned editing.

### Parcels
#### parcels/parcels_utils
- Functions to query parcel features 
- Returns dicts with correct format for submission to Parcel Fabric REST methods.  
- Create parcel records for record driven workflows. 
- Copy external line features into a parcel type.
rePLS 0.0.5
Residual Partial Least Squares Learning
Keywords: PLS, rePLS, PCR, LR


# rePLS


## Examples 

```python
from rePLS import rePLS, rePCR, reMLR
import numpy as np

n_samples, n_features, n_outcomes, n_confounders = 100, 10,5,2
n_components = 2
rng = np.random.RandomState(0)

Y = rng.randn(n_samples,n_outcomes)
X = rng.randn(n_samples, n_features)
Z = rng.randn(n_samples, n_confounders)

reg = rePLS(Z=Z,n_components=n_components)
reg.fit(X,Y)
Y_pred = reg.predict(X,Z)
```