scf_theoretical/output.py at main · lx249/scf_theoretical · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
"""
Output settings.
Author: Liming Xu
Email: lx249@cam.ac.uk
"""

import pandas as pd
import numpy as np

# The columns names and their dtypes.
column_dtypes = [
    ("timestep", int),
    ("node_idx", int),
    ("tier", int),
    ("power", int),
    ("is_bankrupt", bool),
    ("stock", int),
    ("cash", float),
    ("order_from", int),
    ("buy_amount", int),
    ("receive_amount", int),
    ("purchase_value", float),
    ("sale_value", float),
    ("cash_from", int), # Sender of payment
    ("pay_amount", float), # Amount of payment
    ("unfilled", int),  # Unfilled orders
    ("issued", int),  # Issued orders
    ("b_loan", float), # Loan from bank financing
    ("receivable", float),
    ("payable", float),
    ("debt", float)
]
columns = list(zip(*column_dtypes))[0]


class Writer(object):

    def __init__(self, sim_id, column_dtypes=column_dtypes):
        self.output_file = f"output_data/output__sim_{sim_id}.csv"
        self.output = pd.DataFrame(np.empty(0, dtype=np.dtype(column_dtypes)))


    def append(self, data_at_t):
        """
        Append data into the output dataframe.

        Parameters
        ---------
        `data_at_t`: dict
            the output data at a time step.
        """
        incoming_output = pd.DataFrame.from_dict(data_at_t)
        self.output = pd.concat([self.output, incoming_output], ignore_index=True)


    def write(self):
        self.output.to_csv(self.output_file, index=False)