hestia-earth-utils 0.17.16

HESTIA's utils library

HESTIA Utils

Install

1. Install the module:

pip install hestia_earth.utils

2. Add this to your environment variables:

API_URL=https://api.hestia.earth
WEB_URL=https://www.hestia.earth

Usage

1. 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)

2. 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'})

3. 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')

4. 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.

For example, this will let you download all verified aggregation on HESTIA:

1. Add the env variable API_ACCESS_TOKEN and use the API Key from your account.
2. Create a file to store the search query:

{
  "bool": {
    "must": [
      { "match": { "@type": "ImpactAssessment" } },
      { "match": { "aggregatedDataValidated": true } }
    ]
  }
}

3. 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 file
• query-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:

1. Run all your conversion, using --skip-rendering parameter:

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

2. This will write multiple JSON files in the samples folder. Once done, render all together: python3 render_lsrs.py --folder samples. This will create the final samples.xlsx file.