| """Utilities to build a `pandas.DataFrame` from the output of a VectorCube |
| based job. Usefull to collect the output of point based extraction. |
| """ |
|
|
| from pathlib import Path |
|
|
| import pandas as pd |
|
|
| VECTORCUBE_TIMESTAMP_FORMAT = "%Y-%m-%d %H:%M:%S%z" |
| TIMESTAMP_FORMAT = "%Y-%m-%d" |
|
|
|
|
| def load_json(input_file: Path, bands: list) -> pd.DataFrame: |
| """Reads a json file and outputs it as a proper pandas dataframe. |
| |
| Parameters |
| ---------- |
| input_file: PathLike |
| The path of the JSON file to read. |
| bands: list |
| The name of the bands that will be used in the columns names. The band |
| names must be the same as the vector cube that resulted into the parsed |
| JSON file. |
| Returns |
| ------- |
| df: pd.DataFrame |
| A `pandas.DataFrame` containing a combination of the band names and the |
| timestamps as column names. |
| For example, the Sentinel-2 green band on the 1st October 2020 is will |
| have the column name `S2-L2A-B02:2020-10-01` |
| """ |
|
|
| df = pd.read_json(input_file) |
|
|
| target_timestamps = list( |
| map(lambda date: date.strftime(TIMESTAMP_FORMAT), df.columns.to_pydatetime()) |
| ) |
|
|
| df = df.rename(dict(zip(df.columns, target_timestamps)), axis=1) |
|
|
| expanded_df = pd.DataFrame() |
| for col in df.columns: |
| expanded_col = pd.DataFrame( |
| df[col].to_list(), columns=[f"{feature}:{col}" for feature in bands] |
| ) |
| expanded_df = pd.concat([expanded_df, expanded_col], axis=1) |
|
|
| return expanded_df |
|
|
