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%configure_logging

from trase.tools.sps import SupplyChain

for year in [2015, 2016, 2017, 2018, 2019]:
    supplychain = SupplyChain("argentina/soy", year)
    supplychain.preparation()
    supplychain.load()
    supplychain.run()
    supplychain.flow_report_by_attribute(
        "vol_bean", ["status", "branch"], significant_digits=10
    )
    supplychain.export_results()
    supplychain.upload_results()

import pandas as pd

supplychain.upload_results()

import pandas as pd

from trase.tools.sps import SupplyChain

df = supplychain.get("flows")

# df.head()
# df_assets.head()
# df = df.groupby(["exporter_name"]).agg(sum)
# df = df.sort_values("vol_bean", ascending=False)
df = df[df["branch"] == "2.2.1"]
df = df[
    [
        "exporter_name",
        "customs_office.customs_office",
        "country_of_destination",
        "product_type",
        "vol_bean",
        "province.name",
    ]
]
df

import pandas as pd
import os

os.getcwd()
df = pd.read_csv(
    "/usr/share/TRASE/trase/models/argentina/soy/2018/results/results.csv", sep=";"
)
# df = df[df["LVL3_TRASE_ID_PROD"]== "AR-STOCK"]
df.head()

df = df.groupby(["BRANCH"]).agg(sum)
df = df.sort_values("VOLUME_TRADED", ascending=False)
df

# destined_for_quequen = df['ZONE_DESTINATION'] == 'QUEQUEN'
# origin_not_linked_to_quequen = ~df['ZONE_ORIGIN'].isin(['QUEQUEN', 'ZONA 3', 'ZONA 4'])
# should_exclude_quequen = destined_for_quequen & origin_not_linked_to_quequen
# df = df[~should_exclude_quequen]

# df[df['destination'] == "B BLANCA"]

Sankey

from trase.tools.sps import sankey

df = supplychain.get("flows")
sankey(
    df,
    "vol_bean",
    [
        "department_of_production.province.name",
        "exporter_name",
        "country_of_destination",
    ],
)

Chloropleth

from trase.tools.sps import consolidate, rename
from trase.tools.jupyter.observable import notebook

df = supplychain.get("flows")
df = df[df["exporter.cuit"] == "3350673744"].copy()  # NIDERA ARGENTINA
df = consolidate(df, ["vol_bean"], ["department_of_production.geocode"])
df = rename(df, {"department_of_production.geocode": "id", "vol_bean": "value"})
notebook(
    "@trase/choropleth-canvas",
    ["chart"],
    {"country": "argentina", "width": 1500, "data": df.to_dict("records")},
)