HESTIA Utils
Install
- Install the module:
pip install hestia_earth.utils
- Add this to your environment variables:
API_URL=https://api.hestia.earth
WEB_URL=https://www.hestia.earth
Usage
- To download a file from the HESTIA API:
from hestia_earth.schema import SchemaType
from hestia_earth.utils.api import download_hestia
cycle = download_hestia('cycleId', SchemaType.CYCLE)
sandContent = download_hestia('sandContent', SchemaType.TERM)
- To search for a specific Node on HESTIA:
from hestia_earth.schema import SchemaType
from hestia_earth.utils.api import find_node_exact
source = find_node_exact(SchemaType.SOURCE, {'bibliography.title': 'My Bibliography'})
- To get a lookup table from local file system:
from hestia_earth.schema import SchemaType
from hestia_earth.utils.lookup import load_lookup
df = load_lookup('path/to/my/lookup.csv')
- To get a lookup table from HESTIA:
from hestia_earth.schema import SchemaType
from hestia_earth.utils.lookup import download_lookup
df = download_lookup('crop.csv')
LSRS Format
You can use this library to convert HESTIA data into the LSRS format.
convert() accepts Impact Assessment, Cycle, Site and Source nodes and returns one
simplified dict per node:
- Impact Assessment → the 88 LSRS indicator columns plus metadata (including land transformation and deforestation read from its indicators).
- Cycle → yield, EVS, seed, pesticide, diesel, electricity, crop residues, and (using
the nutrient/unit helpers in
lsrs/models_utils.py) fertiliser kg N / kg P2O5 / kg K2O, lime kg CaCO3 and irrigation m3. - Site → site type, country, region and current land cover.
- Source → bibliography title and year.
These JSON outputs can be rendered to Excel or stored elsewhere. lsrs/models_utils.py
holds nutrient/unit/land-cover helpers copied from hestia-earth-models so this library
has no dependency on it.
For example, this will let you download all verified aggregation on HESTIA:
- Add the env variable
API_ACCESS_TOKENand use the API Key from your account. - Create a file to store the search query:
{
"bool": {
"must": [
{ "match": { "@type": "ImpactAssessment" } },
{ "match": { "aggregatedDataValidated": true } }
]
}
}
- Run
python3 convert_to_lsrs.py --query-path test.json --data-state recalculated
This will create 2 files:
query-results-lsrs.json: contains the raw JSON data needed to generate the Excel filequery-results-lsrs.xlsx: cotains the LSRS data in Excel format.
Note: if you make some changes to the Excel generation, you can run this to use the json data and re-generate the Excel file quicker: python3 render_lsrs.py --filepath query-results-lsrs.json
Converting multiple files and rendering into a single Excel
You can use the convert script to create all the JSON files containing the raw data, then render everything together at once:
- Run all your conversion, using
--skip-renderingparameter:
python3 convert_to_lsrs.py --query-path test1.json --data-state recalculated --skip-rendering
python3 convert_to_lsrs.py --query-path test1.json --data-state recalculated --skip-rendering
- This will write multiple JSON files in the
samplesfolder. Once done, render all together:python3 render_lsrs.py --folder samples. This will create the finalsamples.xlsxfile.
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