Source code for cowidev.grapher.files.explorer

import json
from dataclasses import dataclass
from typing import Callable

import pandas as pd
import numpy as np

from cowidev.utils.s3 import obj_from_s3




@dataclass
class Exploriser:
    location: str = "location"
    date: str = "date"
    pivot_column: str = None
    pivot_values: str = None
    function_input: Callable = lambda x: x
    function_output: Callable = lambda x: x



    def read(self, input_path: str):
        if input_path.startswith("s3://"):
            return obj_from_s3(input_path)
        return pd.read_csv(input_path)




    def pipe_pivot(self, df: pd.DataFrame) -> pd.DataFrame:
        if self.pivot_column is not None and self.pivot_values is not None:
            return df.pivot(
                index=[self.location, self.date],
                columns=self.pivot_column,
                values=self.pivot_values,
            ).reset_index()
        return df




    def pipe_nan_to_none(self, df: pd.DataFrame) -> pd.DataFrame:
        return df.replace({np.nan: None})




    def pipe_to_dict(self, df: pd.DataFrame) -> pd.DataFrame:
        return df.to_dict(orient="list")




    def pipeline(self, df: pd.DataFrame) -> dict:
        df = (
            df.pipe(self.function_input)
            .pipe(self.pipe_pivot)
            .pipe(self.pipe_nan_to_none)
            .pipe(self.function_output)
            .pipe(self.pipe_to_dict)
        )
        return df




    def to_json(self, obj):
        return json.dumps(
            obj,
            # Use separators without any trailing whitespace to minimize file size.
            # The defaults (", ", ": ") contain a trailing space.
            separators=(",", ":"),
            # The json library by default encodes NaNs in JSON, but this is invalid JSON.
            # By having this False, an error will be thrown if a NaN exists in the data.
            allow_nan=False,
        )




    def run(self, input_path: str, output_path: str):
        df = self.read(input_path)
        data = df.pipe(self.pipeline)
        with open(output_path, "w") as f:
            f.write(self.to_json(data))