rnpn

This module contains functionality to download and load data from NPN. It uses rnpn as client.

Could use https://data.usanpn.org/observations/get-started to figure out which species/phenophases combis are available.

Example:

```python
from springtime.datasets.rnpn import (
    RNPN,
    npn_species,
    npn_phenophases
)

# List IDs and names for available species, phenophases
species = npn_species()
phenophases = npn_phenophases()

# Load dataset
dataset = RNPN(
    # Syringa vulgaris / common lilac
    species_ids={'name': 'lilac', 'items': [36]},
    phenophase_ids={'name': 'Leaves', 'items': [483]},
    years=[2010, 2012],
)
dataset.download()
gdf = dataset.load()

# or with area bounds
dataset = RNPN(
    years = [2010, 2011],
    area = {'name':'some', 'bbox':[-112, 30, -108, 35.0]}
)
```

Requires rnpn R package. Install with

install.packages("rnpn")

`SpeciesByFunctionalType`

Bases: BaseModel

Species by functional type.

`PhenophasesByName`

Bases: BaseModel

Phenophases by name.

`RNPN`

Bases: Dataset

Download and load data from NPN.

Attributes:

Name	Type	Description
`years`		timerange. For example years=[2000, 2002] downloads data for three years.
`resample`		Resample the dataset to a different time resolution. If None, no resampling.
`species_ids`	`Optional[Union[NamedIdentifiers, SpeciesByFunctionalType]]`	formatted as a dictionary of the form `{"name": "myname", "items": [id1, id2, ...]}`. Alternatively, you can supply a valid functional type as a string. Use npn_species or npn_species_ids_by_functional_type to see options.
`phenophase_ids`	`Union[NamedIdentifiers, PhenophasesByName]`	formatted as a dictionary of the form `{"name": "myname", "items": [id1, id2, ...]}`. Alternatively, you can supply a valid phenophase name as a string. Use npn_phenophases or npn_phenophase_ids_by_name to see options.
`area`	`Optional[NamedArea]`	A dictionary of the form `{"name": "yourname", "bbox": [xmin, ymin, xmax, ymax]}`.
`use_first`	`bool`	When true uses first_yes columns as value, otherwise the last "_yes"-columns.
`aggregation_operator`	`Literal['min', 'max', 'mean', 'median']`	"min", "max", "mean", or "median" (default). How to aggregate in case of multiple observations per site/year

`to_recipe()`

Print out a recipe to reproduce this dataset.

`download(timeout=30)`

Download the data.

Parameters:

Name	Type	Description	Default
`timeout`	`int`	time in seconds to wait for a response of the npn server.	`30`

Raises:

Type	Description
`TimeoutError`	If requests still fails after 3 attempts.

`load()`

Load the dataset into memory.

`npn_species()`

Get available species on npn.

Returns:

Type	Description
`DataFrame`	Pandas dataframe with species_id and other species related fields.

`npn_phenophases()`

Get available phenophases on npn.

Returns:

Type	Description
`DataFrame`	Pandas dataframe with phenophase_id and other phenophase related fields.

`npn_stations()`

Get available stations on npn.

Returns:

Type	Description
`GeoDataFrame`	Dataframe with station_id and other station related fields.

`npn_species_ids_by_functional_type(functional_type)`

Lookup species ids by functional type.

`npn_phenophase_ids_by_name(phenophase_name)`

Lookup phenophase ids by name.