import pandas as pd

import torch

def log(fname, columns, values):

    # TODO convert to numpy for lower space requirements

    try:

        df = pd.read_csv(fname)

    except FileNotFoundError:

        df = pd.DataFrame(columns=columns)

    df = df.append(dict(zip(columns, values)), ignore_index=True)

    df.to_csv(fname, index=False)

def log_iou(fname, epoch, ious: torch.Tensor):

    try:

        df = pd.read_csv(fname)

    except FileNotFoundError:

        df = pd.DataFrame(columns=["epoch", "iou"])

    serialized_ious = ious.flatten().numpy()

    for iou in serialized_ious:

        df = df.append(dict(zip(["epoch", "iou"], [epoch, iou])), ignore_index=True)

    df.to_csv(fname, index=False)

def log_full(epoch, id, config, result_dict, type):

    data = {**config, **result_dict}

    try:

        df = pd.read_csv(f"logs/{type}/{config['model']}/{id}.csv")

    except FileNotFoundError:

        print("File not found, creating new")

        df = pd.DataFrame(columns=data)

    data["epoch"] = epoch

    # data now contains all scores for every sample, so iterate over samples

    new_df = df.append(data, ignore_index=True)

    new_df.to_csv(f"logs/{type}/{config['model']}/{id}.csv", index=False)